SMARTPHONE APPS AND VIRTUAL REALITY AS ROAD SAFETY I … · smartphone safe-driving app as an...
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SMARTPHONE APPS AND VIRTUAL
REALITY AS ROAD SAFETY
INTERVENTIONS: EXAMINING THEIR
REAL-WORLD EFFECTS FOR YOUNG
DRIVERS
Daniel Lyubomirov Vankov Master of Business Administration
Master of Finance Bachelor of Finance
Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy
Centre for Accident Research & Road Safety – Queensland
School of Psychology and Counselling
Faculty of Health
Queensland University of Technology
2019
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers i
Keywords
Apps, Consumer-Oriented Technology, Drink-driving, Driving Under the Influence, Drug-driving, Interventions, Risky Driving Behaviour, Road Safety, Smartphones, Speeding, Theory of Planned Behaviour, Virtual Reality, Young Drivers.
ii Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
Executive summary
1.35 million people lost their lives on the road in 2016 with young drivers being
at higher risk. Calling for innovation, there is evidence that current global road safety
efforts do not yield the desired results. In recent years, novel Consumer-Oriented
Technologies (COT) have been introduced to reduce road trauma. For the current
research, COTs are defined as technologies that answer consumer (or individual
driver) wants or needs. As such, they are not necessarily designed from a road safety
perspective. While COTs may be quickly adopted, the evidence is lacking about their
impact on safety. Thus, this research sought to answer the fundamental question of
how does a COT intervention influence young drivers' safety.
This PhD program of research evaluated the effects of two COT-based
interventions over a period of three months. One of the interventions used a
smartphone safe-driving app to reduce speeding (480 participants). The other used
Virtual Reality (VR) simulations of risky driving to influence driving under the
influence of alcohol or drugs (DUI) (329 participants). The participants were assigned
to an intervention condition or a control condition (no intervention) to allow for robust
evaluation.
The evaluation framework was underpinned by an extended Theory of Planned
Behaviour (TPB). Self-report questionnaires were administered before exposure to the
interventions. Follow-up surveys were conducted approximately three months after the
initial surveys. The findings did not provide evidence for the safety benefits of using
the two deployed COTs beyond some limited secondary effects. This suggested that
the positive impact of some COTs may be overstated. However, the findings should
be interpreted in the light of the encountered limitations, such as high drop-out rates,
lack of information on participants' pre-intervention familiarity with COT, lack of
naturalistic data for comparison or data from control participants also being potentially
impacted by the experiments. Nevertheless, this research contributed new and valuable
knowledge towards using COTs in prevention efforts, with practical considerations in
road safety interventions' design, implementation and evaluation.
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers iii
Abstract
Transport, particularly road transport, is part of people's everyday lives,
powering economies and growth around the world. However, it comes at a cost, and
in many cases, its impact has considerable negative consequences to people's health
and well-being. In 2016 alone, a record 1.35 million people lost their lives on the road.
Young drivers are globally disproportionately affected. The international community
engages a vast amount of effort to minimise road trauma with concrete targets set both
globally, through the United Nations Decade of Action for Road Safety and the
Millennium Development Goals, and locally, with countries establishing their policies
and programs to support road safety. Unfortunately, there is evidence that those global
efforts to reduce deaths and injuries caused by road crashes do not yield the expected
results.
The rapid development and integration of technology in recent years are
regarded as a potential route to reduce road trauma. There is evidence in the literature
that technology has both positive and negative impacts on drivers' performance. For
example, a class of COTs that makes its way into the lives of drivers, such as
smartphones and many of the apps delivered through them, answer consumer (or
individual driver) wants or needs. However, they do not contribute to executing the
driving task.
Some COTs predominantly introduce risks for drivers but, nevertheless, are
expected to expand their presence in the young drivers' ecosystem. The literature
highlights smartphones as a significant source of unwanted and unintended distraction,
thus a big threat in terms of safety. However, unlike for other COTs, the literature also
suggests that smartphones may have the potential to positively influence young drivers
in the context of carefully designed safe-driving apps. A different type of software
application, such as VR simulations, is making its way in prevention efforts outside
the cars. As VR simulations are not used while real driving takes place, they are seen
as a safer option to raise awareness on driving-related risks in conditions that sit
between the laboratory and the real-life experience. Both academia and business
embrace the power of such COTs and offer solutions in an attempt to address road
iv Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
safety problems in general, and young drivers' overrepresentation in road crashes, in
particular.
COTs solutions may be quickly adopted and integrated into awareness-raising
initiatives. However, limited research has been undertaken to investigate if they offer
real safety benefits. The newer the technology, the less knowledge there is around its
impact. This PhD program of research used a multidisciplinary approach to address
this research gap, sitting at the intersection of road safety, psychology and human-
computer interaction fields.
The research process drew upon a combination of data collection methods to
address the research questions appropriately (e.g., qualitative methods in systematic
reviews and a focus group, and quantitative methods in the evaluation of
interventions). Initially, the problems of young drivers were investigated, and the
behaviours of interest were identified. Then behavioural theories were examined to
determine the most suitable framework to be applied. As a result, this PhD program of
research was underpinned by an extended TPB as a theoretical framework. The
studies’ implementation of the adopted framework also addressed limitations,
observed in prior road safety studies underpinned by TPB, such as not implementing
interventions as part of the research or not collecting data before and after the
interventions to evaluate their effect. The TPB constructs of most interest were the
participants' intention and the participants' self-reported behaviour. These constructs
and their predictors were assessed before and after the two implemented interventions.
Before the two COT-based interventions were implemented, two systematic
literature reviews of COTs investigated the application of smartphone safe-driving
apps and VR simulations of risky driving in road safety research. In the first systematic
review, positive safety benefits for young drivers in naturalistic settings were reported
as a result of a deployed smartphone safe-driving-app intervention in only three out of
80 papers that had been selected for full-text review (22 papers included in the
qualitative synthesis, 13 of them involved young drivers aged 18-25). No safety
benefits for young drivers from using VR simulations of risky driving were found
during the second systematic review with a qualitative synthesis of 6 papers.
Nevertheless, the systematically-explored body of evidence informed the two
intervention studies' evaluations. The systematic reviews provided insights on how two
examples of contemporary COTs, smartphone safe-driving apps and VR, were
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers v
previously used within a road safety context, similar to the current program of
research.
Preliminary work was implemented to establish criteria and to select a
smartphone safe-driving app as an intervention tool. The off-the-shelf safe-driving app
"Flo - driving insights" (Flo) was selected as a tool for the smartphone safe-driving
app intervention. Due to a lack of the same variety and availability of apps to choose
from in the VR domain, the software "3D Tripping", for which real safety benefits had
not yet been investigated, was selected and used as an intervention tool in the VR
intervention.
The first intervention explored whether Flo as an intervention tool can positively
influence the young drivers' (aged 18 to 25) intention not to speed as well as their
subsequent self-reported behaviour of not speeding during the three months of the
intervention. Self-report questionnaires were administered before (n=480) and after
(n=210) the intervention period. A Control group (n=126 after the intervention period)
was established so that any general shifts over this period could be identified and not
assigned as a result of the intervention. A Flo leaderboard was created in which the
study participants could observe each other's driving scores, i.e. driving performance
and achievements. Periodically those driving scores were recorded so that it was
possible for the research team to follow individual driver's progress (n=62).
The collected data were assessed at several levels. Pre-intervention data were
analysed through a 3-step hierarchical multiple regression analysis on intention not to
speed. It was found that a significant variation in intention not to speed was explained
by demographic variables (gender, age and driving license) (6%), by TPB constructs
(instrumental attitude, affective attitude, subjective norm, descriptive norm, self-
efficacy and perceived controllability) over and above the demographics (48%) and by
additional predictors (past behaviour of not speeding, perceived risk, moral norm,
peers' norm, and impulsivity) over and above TPB (19%). Past behaviour of not
speeding was both the strongest individual (69%) and the strongest unique predictor
(18%). Including sensitivity to punishment and sensitivity to reward at the third step of
the model increased the explained variance and decreased the individual and the
unique contribution of past behaviour of not speeding by 2% in each case.
The effect of the intervention was analysed through a series of one-way and two-
way analysis of covariance (ANCOVA) tests. No statistically significant effects of the
vi Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
intervention were observed. No consistent effects were revealed by exploring the
leaderboard data, either.
Post-intervention data were analysed by a 3-step hierarchical multiple regression
to investigate whether behaviour of not speeding during the three months of the
intervention could have been predicted by the participants’ baseline data. A significant
variation was explained by demographic variables (gender, age and driving license)
(10%), by TPB constructs (intention not to DUI, self-efficacy and perceived
controllability) over and above the demographics (40%) and by additional predictors
(past speeding behaviour, perceived risk, moral norm, peers' norm, impulsivity,
sensitivity to reward and sensitivity to punishment) over and above TPB (14%). Once
again, past behaviour of not speeding was both the strongest individual (55%) and the
strongest unique predictor (10%).
Finally, one-way and two-way ANCOVA tests were performed to investigate
for any potential negative side effects, namely an increased smartphone engagement
amongst the study participants as a result of the intervention. Effects with statistical
significance were found only in the interaction between condition and gender, which
suggested that the intervention facilitated a decreased level of smartphone engagement
amongst the Intervention group male participants.
The second intervention aimed to examine the effect of VR simulations of risky
driving on DUI intention and self-reported behaviour amongst young people (aged 18
to 25) with "3D Tripping" as an intervention tool. Similar to the first intervention
study, self-report questionnaires were administered before (n=329) and three months
after (n=138) the VR intervention was implemented. A convenience Control group
(n=39 three months after the intervention) was established to help identify general
shifts over time.
The collected data was assessed at three levels. Initially, in a 3-step logistic
regression analysis of pre-intervention data, the demographic variables (gender, age
and driving experience) explained a significant 6.3% to 11.1% of the variance in
intention not to DUI. Adding the TPB constructs (instrumental attitude, affective
attitude, subjective norm, descriptive norm, self-efficacy and perceived controllability)
explained 21.8% to 38.3% of the variance in intention not to DUI. Adding additional
predictors (past behaviour of not DUI, perceived risk, moral norm, peers' norm, and
impulsivity) explained 30.1% to 52.9% of the variance in intention not to DUI (36.5%
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers vii
to 73.3% when sensitivity to punishment and sensitivity to reward were also added).
Past behaviour of not DUI was the strongest statistically significant unique predictor
(p = .006, odds ratio = 43.08).
A series of chi-square tests for independence, McNemar's tests and Wilcoxon
Signed Ranks tests were performed to evaluate the effect of the intervention. No
statistically significant effects were found.
A second 3-step logistic regression analysis was performed after the intervention
to investigate whether participants’ behaviour of not DUI during the three months after
the intervention could have been predicted with their baseline data. The demographic
variables (gender, age and driving experience) explained between 7.7% and 13.1% of
the variance in behaviour of not DUI during the three months after the intervention.
Adding the TPB constructs (intention not to DUI, self-efficacy and perceived
controllability) explained between 11.3% and 19.3% of the variance in behaviour of
not DUI during the three months after the intervention. At the third step, the model,
including additional predictors (past behaviour of not DUI, perceived risk, moral
norm, peers' norm, impulsivity, sensitivity to punishment and sensitivity to reward),
explained between 24.3% and 41.5% of the variance in behaviour of not DUI during
the three months after the intervention. The strongest statistically significant unique
contributor was peers' norm (p = .005, odds ratio = 1.63).
Besides the evidence for both the predictive power of TPB and the positive
effects in regards to smartphone engagement, i.e. distraction, a behaviour of secondary
interest in the smartphone safe-driving app study, findings from both intervention
studies did not provide statistically significant evidence of safety benefits in regards to
the main behaviours of interest, speeding and DUI. These findings suggest that the
positive impact of some technologies, in general, and of the two deployed COTs, in
particular, may be overstated. They also point to the need for robust evaluations to be
undertaken before technology applications roll-out to the general public. Overall, this
program of research highlighted the usefulness of theoretically-grounded evaluation
concerning emerging solutions offered by COTs. Although bringing to market is what
developers are usually interested in, the premature release of COTs may lead to
unsustained claims.
This research contributed new and valuable knowledge towards using COT in
prevention efforts targeting the public, in general, and young drivers, in particular.
viii Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
Such information may provide an evidence-based foundation for road safety
stakeholders, such as road safety social entrepreneurs and researchers, in their search
for new and better instruments to address existing risks on the road. The PhD thesis
describes practical considerations in the design, implementation and evaluation of
interventions, including involvement of a large number of young people, using social
media for recruitment, evaluation of self-reports and using off-the-shelf technology
applications for prevention purposes. Furthermore, by examining not only young
drivers' cognitive determinants but also their demographic and personality
characteristics, this research acknowledged that people are different by nature and
there is no "one size fits all" solution when it comes to promoting safe driving
behaviour on the road.
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers ix
Table of contents
Keywords .................................................................................................................................. i
Executive summary .................................................................................................................. ii
Abstract ................................................................................................................................... iii
Table of contents ..................................................................................................................... ix
List of figures ........................................................................................................................ xiii
List of tables ............................................................................................................................ xv
Glossary of terms ................................................................................................................ xviii
List of abbreviations ............................................................................................................... xx
Funding and awards ............................................................................................................. xxii
Statement of original authorship ......................................................................................... xxiii
Acknowledgement .............................................................................................................. xxiv
Chapter 1: Introduction ...................................................................................... 1
1.1 Research problem ........................................................................................................... 3
1.2 Research aim and objectives ........................................................................................... 5
1.3 Significance of the research ............................................................................................ 6
1.4 Outcomes ........................................................................................................................ 7
1.5 Document outline ........................................................................................................... 8
Chapter 2: Literature review .............................................................................. 9
2.1 Young drivers' risky driving behaviours ......................................................................... 9
2.2 Contributing factors ...................................................................................................... 11 2.2.1 Driving experience ............................................................................................. 11 2.2.2 Optimism bias ..................................................................................................... 13 2.2.3 Gender ................................................................................................................ 13
2.3 Interventions ................................................................................................................. 14 2.3.1 Driver training .................................................................................................... 14 2.3.2 Media campaigns ................................................................................................ 16 2.3.3 Law enforcement ................................................................................................ 17
2.4 Consumer-oriented technologies .................................................................................. 17 2.4.1 Context ............................................................................................................... 18 2.4.2 Safe-driving apps ................................................................................................ 19 2.4.3 Virtual reality ..................................................................................................... 21
2.5 Conclusion and identification of a research gap ........................................................... 22
Chapter 3: Theoretical considerations informing intervention evaluation framework .......................................................................................................... 25
3.1 Introduction .................................................................................................................. 25
3.2 Transtheoretical Model of Health Behavior Change .................................................... 26
3.3 Health Belief Model ..................................................................................................... 29
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3.4 Social Cognitive Theory .............................................................................................. 31
3.5 Theory of Planned Behaviour ...................................................................................... 34 3.5.1 Criticisms and limitations of TPB ..................................................................... 36
3.6 Extending TPB ............................................................................................................. 36 3.6.1 Additional normative influences ........................................................................ 37 3.6.2 Risk perception .................................................................................................. 38 3.6.3 Personality characteristics .................................................................................. 39
3.7 Conclusion ................................................................................................................... 40
Chapter 4: Research design ............................................................................... 42
4.1 Research questions ....................................................................................................... 42
4.2 The extended TPB and selecting intervention tools ..................................................... 45
4.3 Methodology ................................................................................................................ 48
4.4 Intervention studies’ designs ........................................................................................ 50 4.4.1 Participants ........................................................................................................ 50 4.4.2 Surveys .............................................................................................................. 51 4.4.3 Variables ............................................................................................................ 55 4.4.4 Analyses ............................................................................................................. 58 4.4.5 Ethics ................................................................................................................. 60
Chapter 5: Study 1 - Systematic review of safe-driving apps ........................ 61
5.1 Rationale for conducting a systematic review ............................................................. 61
5.2 Method ......................................................................................................................... 62 5.2.1 Search databases ................................................................................................ 62 5.2.2 Literature search criteria .................................................................................... 62 5.2.3 Search term ........................................................................................................ 63
5.3 Search and screening results ........................................................................................ 63
5.4 Findings ........................................................................................................................ 65 5.4.1 Studies’ designs and samples ............................................................................. 75 5.4.2 Sensors and measures ........................................................................................ 76 5.4.3 Benefits .............................................................................................................. 77
5.5 Summary ...................................................................................................................... 78
5.6 Discussion .................................................................................................................... 79
Chapter 6: Selecting a safe-driving app ........................................................... 82
6.1 Focus group design ...................................................................................................... 82 6.1.1 Participants ........................................................................................................ 82 6.1.2 Procedure and materials ..................................................................................... 83 6.1.3 Data analysis ...................................................................................................... 84
6.2 Findings from the Focus group .................................................................................... 84 6.2.1 Vision on young people's use of technologies in the car ................................... 85 6.2.2 Discussing smartphone safe-driving apps .......................................................... 86
6.3 Synthesis of Focus group’s findings ............................................................................ 88
6.4 Selecting a safe-driving app for an evaluation ............................................................. 90
6.5 Conclusion ................................................................................................................... 96
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app .......................................................................................................... 98
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers xi
7.1 Introduction .................................................................................................................. 98
7.2 Method ........................................................................................................................ 101 7.2.1 Study design ..................................................................................................... 101 7.2.2 Recruitment ...................................................................................................... 101 7.2.3 Intervention tool ............................................................................................... 102 7.2.4 Procedure .......................................................................................................... 103 7.2.5 Participants ....................................................................................................... 105 7.2.6 Intervention ...................................................................................................... 106
7.3 Hypotheses .................................................................................................................. 107
7.4 Preliminary analysis ................................................................................................... 109 7.4.1 Missing data ..................................................................................................... 110 7.4.2 Data transformation .......................................................................................... 110 7.4.3 Dropouts ........................................................................................................... 112 7.4.4 Assumptions checks ......................................................................................... 112 7.4.5 Personality characteristics ................................................................................ 114
7.5 Results ........................................................................................................................ 114 7.5.1 Participants' intention not to speed before the intervention (RQ2.1, H.1 -
H.3) 114 7.5.2 Changes in salient beliefs (RQ2.2, H.4 - H.7) .................................................. 119 7.5.3 Predictors of behaviour of not speeding during the intervention (RQ 2.3,
H.8 - H.10)........................................................................................................ 124 7.5.4 Potential negative effects: Self-reported smartphone engagement (RQ2.4,
H.11) ................................................................................................................. 126
7.6 Discussion ................................................................................................................... 131 7.6.1 Findings ............................................................................................................ 131 7.6.2 Strengths ........................................................................................................... 135 7.6.3 Limitations........................................................................................................ 136
7.7 Conclusion .................................................................................................................. 138
Chapter 8: Study 3 - Systematic review of VR simulations of risky driving .... 140
8.1 Rationale for conducting a systematic review ............................................................ 140
8.2 Method ........................................................................................................................ 141 8.2.1 Search databases ............................................................................................... 141 8.2.2 Literature search criteria ................................................................................... 141 8.2.3 Search term ....................................................................................................... 141
8.3 Search and screening results ....................................................................................... 142
8.4 Findings ...................................................................................................................... 143 8.4.1 Studies’ samples ............................................................................................... 146 8.4.2 Measures ........................................................................................................... 146 8.4.3 Benefits ............................................................................................................. 146
8.5 Summary ..................................................................................................................... 147
8.6 Discussion ................................................................................................................... 147
Chapter 9: Study 4 - Intervention with VR simulations of risky driving ... 149
9.1 Introduction ................................................................................................................ 149
9.2 Method ........................................................................................................................ 150 9.2.1 VR tool and intervention .................................................................................. 150 9.2.2 Recruitment ...................................................................................................... 155
xii Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
9.2.3 Data collection procedure ................................................................................ 157 9.2.4 Participants ...................................................................................................... 158
9.3 Hypotheses ................................................................................................................. 158
9.4 Preliminary analysis ................................................................................................... 160 9.4.1 Missing data ..................................................................................................... 160 9.4.2 Dropouts .......................................................................................................... 160 9.4.3 Assumption checks and data transformation ................................................... 161 9.4.4 Personality characteristics ................................................................................ 163
9.5 Results ........................................................................................................................ 163 9.5.1 Participants' intention not to DUI before the intervention (RQ4.1, H.12 -
H.14) ................................................................................................................ 163 9.5.2 Changes in salient beliefs (RQ4.2, H.15 - H.17) ............................................. 168 9.5.3 Predictors of behaviour of not driving under the influence of drugs or
alcohol after the intervention (RQ4.3, H.18 - H.20) ........................................ 172
9.6 Discussion .................................................................................................................. 175 9.6.1 Findings ........................................................................................................... 175 9.6.2 Strengths .......................................................................................................... 177 9.6.3 Limitations ....................................................................................................... 178
9.7 Conclusion ................................................................................................................. 180
Chapter 10: General discussion ........................................................................ 182
10.1 Overall contribution ................................................................................................... 182
10.2 Integration of findings, strengths and limitations ...................................................... 185 10.2.1 Theoretical considerations ............................................................................... 185 10.2.2 Practical considerations ................................................................................... 187 10.2.3 Methodological considerations ........................................................................ 189
10.3 Future research directions .......................................................................................... 190 10.3.1 A researcher's wish list to COT developers ..................................................... 194
10.4 Chapter summary ....................................................................................................... 197
References ............................................................................................................... 201
Appendices .............................................................................................................. 219
Appendix A Smartphone safe-driving apps on Google Play and iTunes .......... 221
Appendix B Study 2 Questionnaire ...................................................................... 226
Appendix C Study 4 Questionnaire ...................................................................... 233
Appendix D Additional Study 2 Effects of the intervention models .................. 246
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers xiii
List of figures
Figure 1.1. Number and rate of road traffic death per 100,000 population: 2000–2016 (WHO, 2018) ............................................................................... 1
Figure 1.2. Widening gap between the actual and desired progress towards the EU 2020 target (Adminaite et al., 2018) ........................................................ 2
Figure 1.3. Australian progress until 2017 towards fatality target (BITRE, 2018) .............................................................................................................. 3
Figure 2.1. Percentage of South Australia drivers involved in a crash five years after licensing (Austroads, 2008). ................................................................ 15
Figure 3.1. TTM stages of change ............................................................................. 27
Figure 3.2. The Health Belief Model ......................................................................... 30
Figure 3.3. Social Cognitive Theory Model ............................................................... 32
Figure 3.4. Theory of Planned Behaviour (Ajzen, 1991) ........................................... 34
Figure 3.5. Extension of the Theory of Planned Behaviour in the current program of research. ................................................................................... 41
Figure 4.1. Outline of the overall thesis methodology ............................................... 49
Figure 5.1. Data extraction flowchart based on the PRISMA statement. .................. 65
Figure 6.1. Focus group visual brainstorming tools ................................................. 84
Figure 7.1. Time on screen as reported by Flo for each trip ................................... 100
Figure 7.2. Smartphone with Flo, providing real-time feedback while driving. ..... 102
Figure 7.3. Safe-driving app intervention design .................................................... 104
Figure 7.4. Example screenshot of Flo GoOz leaderboard ..................................... 104
Figure 8.1. Data extraction flowchart based on the PRISMA statement. ................ 142
Figure 9.1. A user is getting used to managing the VR driving simulator. .............. 151
Figure 9.2. The VR software visualises parking of the vehicle before entering the night club. ............................................................................................. 152
Figure 9.3. A choice to experience impaired driving as a result of ecstasy, cannabis or magic mushrooms influence is given to users. ....................... 152
Figure 9.4. A user driving under the VR-simulated influence of magic mushrooms. ................................................................................................ 153
Figure 9.5. A participant, operating the VR driving simulator in front of their peers. .......................................................................................................... 155
Figure 9.6. A participant is operating the VR software on a fully adjusted VR driving simulator. ....................................................................................... 155
Figure 10.1. Outline of the thesis studies and findings ............................................ 184
Figure 10.2. Example of future safe-driving app intervention design ..................... 193
xiv Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
Figure 10.3. Example of a future DUI VR intervention design ................................ 194
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers xv
List of tables
Table 1.1. Definitions of key terms ......................................................................... xviii
Table 4.1. COTs' selection criteria, derived from the extended TPB framework. ..... 47
Table 4.2. Constructs and time of measurement. ....................................................... 54
Table 4.3. Items, adapted from Elliott and Thomson (2010). .................................... 56
Table 4.4. Items, adapted from Gannon et al. (2014). ............................................... 58
Table 5.1. Impact and effect of apps, games and gamification on young drivers' road safety. ................................................................................................... 66
Table 6.1. Country of origin and gender of participants in the Focus group. ........... 83
Table 6.2. Criteria for smartphone safe-driving apps, synthesised from Focus group’s findings. .......................................................................................... 89
Table 6.3. Smartphone safe-driving apps (yes=1, no=0, double points for SC3). ..... 91
Table 7.1. Means, standard deviations and bivariate correlations for the TPB variables at Time 1 (n=480). ..................................................................... 115
Table 7.2. 3-step hierarchical multiple regression analysis, predicting intention not to speed for all participants at Time 1, with demographic factors, TPB variables and additional variables as predictors (n=480). ............... 116
Table 7.3. Linear multiple regression analysis predicting Intention not to speed at Time 1 with demographic factors, TPB variables and additional variables, including sensitivity, as predictors (n=210). ............................ 118
Table 7.4. Means, standard deviations and bivariate correlations for the standard TPB variables at Time 2 (n=157). .............................................. 119
Table 7.5. Means and standard deviations for the Control and the Intervention groups' intention not to speed at Time 1 and Time 2 (n=157). .................. 120
Table 7.6. Interaction effects between Condition and personality characteristics, intention not to speed, adjusted for Time 1 values (n=157). ..................................................................................................... 120
Table 7.7. Means and standard deviations for the Control and the Intervention groups' behaviour of not speeding at Time 1 and Time 2 (n=157). .......... 121
Table 7.8. Interaction effects between Condition and personality characteristics, past behaviour of not speeding during the three months of the intervention, adjusted for Time 1 values (n=157). .............. 122
Table 7.9. Effect of the intervention on TPB variables, adjusted for Time 1 values (n=157). .......................................................................................... 122
Table 7.10. 3-step hierarchical multiple regression analysis, predicting behaviour of not speeding during the three months of the intervention for all participants at Time 2, with demographic factors, TPB variables and additional variables as predictors (n=210). ....................... 125
xvi Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
Table 7.11. Mean, standard deviation and bivariate correlations for the phone interaction variables at Time 1 (n=157). ................................................... 126
Table 7.12. Self-reported phone interaction at Time 1 and Time 2 (n=157). .......... 127
Table 7.13. Mean, standard deviation and bivariate correlations for the phone interaction variables at Time 2 (n=157). ................................................... 128
Table 7.14. Phone interactions' means and standard deviations for the Control (n=126) and the Intervention group (n=31) at Time 1 and Time 2. .......... 128
Table 7.15. Effect of the intervention on phone interaction variables, adjusted for Time 1 values, with Condition as a fixed factor (n=157). .................... 128
Table 7.16. Interaction effects between Condition and personality characteristics, phone interaction variables, adjusted for Time 1 values (n=157). .......................................................................................... 129
Table 8.1. Virtual reality in the road safety literature ............................................. 144
Table 9.1. Frequencies, means, standard deviations and bivariate correlations for the TPB variables at Time 1 (n=329). .................................................. 164
Table 9.2. Logistic regression analysis, predicting Intention not to DUI for all participants at Time 1, with demographic factors as predictors (n=329). ..................................................................................................... 165
Table 9.3. Logistic regression analysis predicting Intention not to DUI for all participants at Time 1 (n=329) with demographic factors and TPB variables as predictors. .............................................................................. 165
Table 9.4. Logistic regression analysis predicting Intention not to DUI for all participants at Time 1 (n=329) with demographic factors, TPB variables and additional variables as predictors. ..................................... 166
Table 9.5. Logistic regression analysis, predicting Intention not to DUI for all participants at Time 1, with demographic factors, TPB variables and all additional variables as predictors (n=138). ......................................... 167
Table 9.6. Frequencies, means, standard deviations and bivariate Spearman correlations for the TPB variables at Time 2 (n=138). ............................. 168
Table 9.7. Dichotomised DVs' frequencies per group condition (n=138) ............... 169
Table 9.8. Logistic regression analysis predicting behaviour of not DUI during the three months after the intervention for participants at Time 2 (n=138) with demographic factors as predictors. ..................................... 173
Table 9.9. Logistic regression analysis, predicting Behaviour of not DUI during the three months after intervention for participants at Time 2, with demographic factors and TPB from Time 1 as predictors (n=138). ..................................................................................................... 173
Table 9.10. Logistic regression analysis, predicting Behaviour of not DUI during the three months after intervention for participants at Time 2, with demographic factors, TPB and additional predictors from Time 1 as predictors (n=138). ............................................................................... 174
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers xvii
Table 10.1. Interaction effects between Condition and personality characteristics, intention not to speed, adjusted for Time 1 values (n=144). ..................................................................................................... 246
Table 10.2. Interaction effects between Condition and personality characteristics, past behaviour of not speeding during the three months of the intervention, adjusted for Time 1 values (n=144). .............. 247
Table 10.3. Effect of the intervention on TPB variables, adjusted for Time 1 values (n=144). .......................................................................................... 248
10.4. Interaction effects between Condition and personality characteristics, intention not to speed, adjusted for Time 1 values (n=210). ..................... 249
Table 10.5. Interaction effects between Condition and personality characteristics, past behaviour of not speeding during the three months of the intervention, adjusted for Time 1 values (n=210). .............. 250
Table 10.6. Effect of the intervention on TPB variables, adjusted for Time 1 values (n=210). .......................................................................................... 250
Table 10.7. Effect of the intervention on TPB variables, adjusted for Time 1 values (n=62). ............................................................................................ 252
xviii Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
Glossary of terms
The following Table 1.1 provides definitions of key terms that are used in this
document.
Table 1.1. Definitions of key terms
Consumer-
oriented
Relating to the needs and interests of individual consumers,
rather than businesses (Cambridge Business English Dictionary).
Distraction Anything that diverts the driver’s attention away from the
primary tasks of navigating the vehicle and responding to critical
events (NHTSA, 2010).
Driving under the
influence of
alcohol
Illegal behaviour in which the driver operates a vehicle with
blood alcohol concentration (BAC) level above the limits set by
the law.
Driving under the
influence of drugs
Illegal behaviour in which the driver operates a vehicle after
consuming illegal drugs.
Gamification Using game design in a non-game context in an attempt to
boost drivers' motivation and commitment to use a new in-
vehicle system (Diewald, Möller, Roalter, Stockinger, & Kranz,
2013).
Immersive (for
VR)
Keeps the immersion alive and engaging, rather than being
merely impressive, by enabling interaction with nearly
everything in the virtual world. It also offers good content or
gameplay that’s independent of technology, making VR
interactions core to the experience, and easing the user quickly
and smoothly into the virtual world (Intel)1.
Incentives A thing that motivates or encourages someone to do something
(Oxford Dictionary of English).
Leaderboard A leaderboard is a rank list of the people involved. Its purpose is
to show them where they rank in a gamified system. Those at the
1 https://software.intel.com/en-us/articles/guidelines-for-immersive-virtual-reality-experiences - Accessed on 04/03/2019
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers xix
top enjoy the fame of being seen by all. To the rest, the
leaderboard shows where they stand relative to their peers
(Duggan & Shoup, 2013).
Persuade To make someone do or believe something by giving them a
good reason to do it or by talking to that person and making
them believe it (Cambridge Dictionary of English).
Realistic (for VR) Makes the virtual world seem real by providing smooth 3D
video, realistic sound, intuitive controls for manipulating the
environment, and natural responses to the user’s actions in the
virtual world (Intel)1.
Risky Driving
Behaviour (in the
case of young
drivers)
Any risky driving undertaken by the young driver which
increases the likelihood of the young driver being involved in a
car crash and may harm or fatally injure the young driver
themselves, their passenger(s), and other road users such as
pedestrians, cyclists, drivers and passengers in other vehicles
(Scott-Parker, 2012).
Simulation An imitation over time of real-world processes or system
operations (Banks, 1998).
Speeding Illegal behaviour in which the driver operates a vehicle at a
speed which is above the set speed limit for the respective
section of the road.
Virtual reality or
VR
A medium composed of interactive computer simulations that
sense the participant's position and actions and replace or
augment the feedback to one or more senses, giving the feeling
of being mentally immersed or present in the simulation (a
virtual world) (Sherman & Craig, 2018).
xx Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
List of abbreviations
ADAS Advanced Driver Assistance Systems
AIHW Australian Institute of Health and Welfare
AONSW Audit Office of New South Wales
ATC Australian Transport Council
BAC Blood alcohol concentration
BIS-11 Barratt Impulsiveness Scale Version 11
BITRE Bureau of Infrastructure Transport and Regional Economics
CDCP Centers for Disease Control and Prevention
COT Consumer-oriented technologies
DUI Driving under the influence of alcohol or drugs
EC European Commission
GDL Graduated Driver Licensing
GPS Global Positioning System
HBM Health Belief Model
ICT Information and Communication Technology
ITF International Transport Forum
NHTSA National Highway Traffic Safety Administration
OECD Organisation for Economic Co-operation and Development
PBC Perceived Behavioural Control
PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses
SET Self-Efficacy Theory
SCT Social Cognitive Theory
TPB Theory of Planned Behaviour
TRA Theory of Reasoned Action
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers xxi
TTM Transtheoretical model of health behavior change
VR Virtual reality
SPSRQ Sensitivity to Punishment and Sensitivity to Reward Questionnaire
WHO World Health Organisation
xxii Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
Funding and awards
The program of research constituted a PhD project with a focus on young
drivers, funded by the Australian Government Department of Education through a
2015 Endeavour Postgraduate Scholarship. The project sought to build on Daniel
Vankov's already established prior experience as a road safety social entrepreneur in
relation to the implementation of road safety education programs for young drivers (18
to 25 years). These programs had incorporated COT tools (e.g. mobile driving
simulators and VR) and communication strategies to reach out to young drivers in
Europe, Asia and Latin America.
The Queensland University of Technology awarded Daniel Vankov with a
2018 Student Leadership Award for his work and contributions.
Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers xxiii
Statement of original authorship
The work contained in this thesis has not been previously submitted to meet
requirements for an award at this or any other higher education institution. To the best
of my knowledge and belief, the thesis contains no material previously published or
written by another person except where due reference is made.
Signature:
Date:
QUT Verified Signature
xxiv Smartphone Apps and Virtual Reality as Road Safety Interventions: Examining their Real-World Effects for Young Drivers
Acknowledgement
I wish to thank my supervisory team. Without them, this thesis would not have
happened. To my principal supervisor, Dr Ronald Schroeter – thank you for your
patience, support and guidance, which ultimately made it possible to complete this
program of research. To my associate supervisor, Professor Andry Rakotonirainy –
thank you for making time in your busy schedule to review. To my second associate
supervisor, Dr Melanie White - thank you for introducing me to the depths of
psychology, a field I did not know so much about before embarking on my PhD
journey. It was both a privilege and an honour to work with all of you. I am grateful
for all I have learned as a consequence of that opportunity.
I would also like to express my special gratitude to Professor Divera Twisk. Your
support came when I most needed it. This support made it possible for me to
understand the full value of the quality work we were doing.
I wish to thank the Australian Government Department of Education for
awarding me a 2015 Endeavour Postgraduate Scholarship. This scholarship enabled
me to embark on my four-year-long research journey with the Centre for Accident
Research and Road Safety – Queensland (CARRS-Q), Queensland University of
Technology. I cannot imagine a more supportive and welcoming environment for both
my research project and my extracurricular activities. Thus, I would like to thank
explicitly the CARRS-Q Directors, Professor Narelle Haworth and Professor Andry
Rakotonirainy, who supported my ideas for open-access participants' recruitment,
open public interventions, vocational visits, invited talks, and fundraisers.
"The hardest part of any journey is taking the first step". Thank you for helping
me take mine!
Chapter 1: Introduction 1
Chapter 1: Introduction
Global efforts to reduce deaths and injuries caused by road crashes are starting
to wear off. The Global Status Report on Road Safety (WHO, 2018) shows that despite
all the efforts of the international community to address the problem, road fatalities
continue to climb (see Figure 1.1). 1.35 million people lost their lives on the roads in
2016 (WHO, 2018). In different parts of the world, initiatives, such as the Decade of
Action for Road Safety 2011-2020, bring different results with the latest statistics
calling for novel complementary approaches in the prevention efforts. A report,
exploring road safety from a slightly different perspective, suggests that two-thirds of
the reduction in road fatalities in the years between 2008 and 2014 were not due to
targeted efforts but were a result of the 2008 Global Financial Crisis (ITF, 2015). The
same report points out that unemployment and the accompanying less affordable
travel, especially when it comes to young drivers, as well as generally fewer kilometres
driven, were amongst the contributors for improved road safety within this period. The
established causality suggests an explanation of the recent upward trend in road
fatalities, i.e. global economic recovery, may have reversed the positive trends of
trauma reduction in road safety. Statistics from developed economies around the world
support the suggestion.
Figure 1.1. Number and rate of road traffic death per 100,000 population: 2000–2016 (WHO, 2018)
The U.S. National Highway Traffic Safety Administration (NHTSA) recorded
8.4% increase of fatalities in 2015 in comparison with 2014, the highest rate on U.S.
2 Chapter 1: Introduction
roads since 1964, followed by a 5.4% increase in 2016 (NHTSA, 2017) before a
reduction of 1.8% in 2017 (NHTSA, 2018b). The population was affected in all
segments (male/female, day/night, drivers/pedestrians).
The European Union (EU), which has been one of the best examples of achieving
results with targeted actions, also shows mixed results with a widening gap between
actual and desired progress towards targets (Adminaite, Calinescu, Jost, Stipdonk, &
Ward, 2018). Figure 1.2 visualises the recorded reduction in road fatalities between
2010 and 2017 (light blue line) and the desired trend projected target line from 2010
onwards (deep blue line). The European Transport Safety Council recorded an increase
of fatalities in 2014 and 2015 before reverses in 2016 and 2017 (Adminaite et al.,
2018). Thus, the achieved average annual reduction in road deaths since 2010 in EU
equals 3.1% while a 6.7% average was needed to reach the EU 2020 targets (Adminaite
et al., 2018).
Figure 1.2. Widening gap between the actual and desired progress towards the EU 2020 target (Adminaite et al., 2018)
Australia is globally regarded as a high-achiever in road safety, but recent
statistics reveal negative trends. The number of fatalities increased by 5% from 1,150
in 2014 to 1205 in 2015 and by further 7.3% to 1293 in 2016 before a reduction of
5.2% to 1226 was recorded in 2017 (BITRE, 2018). The figures are a serious concern,
given that Australia was consistently ahead of its fatalities' reduction targets. The
achievements may further deteriorate if the economy continues to do well. Figure 1.3
Chapter 1: Introduction 3
illustrates the Australian fatalities trends since 2008. The solid blue line shows the
targeted number of fatalities; the dotted grey line shows the current trend.
Figure 1.3. Australian progress until 2017 towards fatality target (BITRE, 2018)
1.1 RESEARCH PROBLEM
The target population of this PhD program of research is young drivers, aged 18
to 25 with a valid driver's license, who drive a car. Research findings suggest that
young drivers have a 2 to 3 times higher crash risk than experienced drivers (SafetyNet,
2009). However, such an estimate does not reveal the gravity of the full picture. Young
drivers drive much less than their experienced colleagues. If miles driven is taken as a
basis, their risk factor to be involved in crashes is estimated at ten times higher than
the risk factor of experienced drivers (McKnight & McKnight, 2003).
Statistics across jurisdictions evidences overrepresentation of young drivers in
road crashes. In Australia, the 17-25 age group accounts for 19% of the fatalities with
the rate remaining above the national average (BITRE, 2018). In the US, in 2016, the
15 to 20-year-old age group represented only 5.4% of all licensed drivers but was
involved in 9% of the fatal crashes (NHTSA, 2018a). In 2017, in the EU, the 18 to 24-
year-old represented 8% of the population but 14% of the road fatalities (EC, 2018).
In the 30 member states of the Organisation for Economic Co-operation and
Development (OECD), the proportion of the young drivers under the age of 25 in the
population is 10.1% while in the fatalities it is 26.7% (OECD, 2006). Not surprisingly
4 Chapter 1: Introduction
globally young people, aged 15 to 24 years, are overrepresented in road crashes with
traffic injury being the leading cause of death in the 5-29 age group (WHO, 2018).
There are numerous reasons for young drivers to be exposed to increased crash
risk. Major reasons are poor control of the vehicle (Patten et al., 2006), inability to
identify hazards on the road (Pollatsek, Fisher, & Pradhan, 2006) and sensation
seeking (Hatfield, Fernandes, & Job, 2014; Schroeter, Oxtoby, & Johnson, 2014;
Scott-Parker, Watson, King, & Hyde, 2013). Young drivers are also more likely to
crash as a result of distraction (McEvoy, Stevenson, & Woodward, 2006). Helping
them to understand the consequences of reckless behaviour as well as the impact on
safety of each decision behind the wheel is a serious multidisciplinary challenge to
which COTs may be an answer.
Young drivers are early technology adopters (Lee, 2007). Current research
explores COTs in road safety aiming not only to transform driving into a constantly
engaging and fun activity but also to cultivate constructive driving behaviour
(Schroeter, Rakotonirainy, & Foth, 2012; Steinberger, Schroeter, Foth, & Johnson,
2017). Some COT applications, such as Fiat eco:drive or Nokia's "Routine Driving"
and "Driving Miss Daisy", have attracted attention and have already been reviewed
(Bellotti, Berta, & De Gloria, 2014). Such COT applications were made available to
the general public and explored serious gaming concepts, i.e. they were designed with
a primary goal different than mere entertainment. However, there is evidence showing
that the positive effects of using them are not guaranteed and can be compromised by
undesired driving behaviour (Ecker, Holzer, Broy, & Butz, 2011). COTs are not
necessarily designed from a road safety perspective. COTs may be responding to
consumers' (or individual drivers') wants or needs, unrelated to the primary driving
task.
While COTs are often seen as a potential threat in road safety research, they offer
the unique characteristic of being able to provide more acceptable suggestions from
the perspective of young drivers (Lee, 2007). Therefore, COTs (see Section 2.4)
represent opportunities, which the current research leveraged, to positively influence
young drivers. An extended TPB (Ajzen, 1988) was operationalised to evaluate the
effects of these opportunities (see Section 3.7).
Leveraging evidence-based opportunities to deliver safety benefits for young
drivers, such as the ones the current program of research evaluated, may be
Chapter 1: Introduction 5
mainstreamed through the young drivers’ ecosystem. The young drivers’ behaviour
and safety on the road are influenced by their ecosystem. This ecosystem is complex
and involves a multitude of stakeholders (Scott-Parker, Goode, & Salmon, 2015).
These stakeholders have different types of relationships with the young drivers, e.g.
authoritarian (government, police), commercial (car manufacturers, insurance
companies, driving instructors), personal (parents, peers) or not-for-profit (design
researchers, not-for-profit organisations (NFPs)). Tackling major social problems,
such as young drivers' road trauma, requires joint efforts. Yet, in practice, they are
typically assigned to governments or NFPs (Porter & Kramer, 2011).
NFPs are unique in that they are managed by visionary social entrepreneurs
(SEs). The commercial side of their endeavours is a necessary means to deliver good
(Kotler, Hessekiel, & Lee, 2012). SEs are believed to be able to mainstream research
findings into practice. Their contributions have been researched in multiple fields such
as civic engagement (Levinson, 2012), social transformation (Mair & Noboa, 2006),
sustainable development (Seelos & Mair, 2004) as well as in health promotion
(Catford, 1998). SEs are often carriers of social innovation (Lettice & Parekh, 2010),
having unique skills to a) take a new perspective on the problems, b) create new
ecosystems, and c) appeal to the customer base.
In the context of this PhD thesis, it should be noted that the author has established
considerable background experience as a SE in relation to the implementation of road
safety education programs for young drivers. These skills contributed towards this PhD
project to be supported by a 2015 Endeavour Postgraduate Scholarship (see Funding
and awards). As such, the author's personal drive to contribute towards reducing road
trauma amongst young drivers by using NFPs as a vehicle forms the backdrop of this
thesis.
1.2 RESEARCH AIM AND OBJECTIVES
The PhD aimed to examine the effects of two COT-based interventions to reduce
risky driving behaviour among young drivers. The first one transformed an existing
risk source (smartphones) into one for motivating safe driving behaviour. The second
one deployed VR for the same purpose. Although it is unlikely that any chosen
example of COTs would represent their full and true potential, it would have been out
of scope for the current PhD program of research to deploy the designed evaluation
6 Chapter 1: Introduction
framework (see Section 3.7) to investigate several different smartphone safe-driving
apps and VR simulations of risky driving. Therefore findings are reported about the
two used COTs only, and shall not be generalised to COTs in general.
The evaluations of both interventions were grounded in TPB. As such, both of
them assessed the impact of novel technology-based practice-oriented approaches to
raise awareness on driving-related risks outside the laboratory environment, i.e. in the
real world, without incurring substantial development and deployment costs as part of
the studies themselves. The following three key objectives were pursued as part of the
research investigation:
1. Understand to what extent the use of smartphone safe-driving apps and VR
simulations of risky driving are associated with safety benefits for young
drivers in the literature;
2. Identify smartphone safe-driving apps and VR simulations of risky driving
that could potentially persuade young drivers to adopt safer on-road
behaviour as a result of road safety interventions; and
3. Investigate the extent to which the use of an example safe-driving app and a
VR simulation of risky driving was associated with positive changes in
participating young drivers' self-reported intentions and self-reported
behaviour on the road over three months.
1.3 SIGNIFICANCE OF THE RESEARCH
International efforts continue to focus on road crash prevention, but their
potential to sustain the positive reduction trends is undermined by the global economic
recovery (ITF, 2015). Existing strategies have their place, but new ones are needed to
support further international efforts to reduce road trauma. In-vehicle COTs, and
particularly the smartphone, are very often regarded as a major cause for crashes,
especially in relation to young drivers (WHO, 2011). At the same time, a number of
road safety stakeholders embrace their positive potential in an effort to reduce existing
risks and help young drivers improve their behaviour. Outside the vehicles, VR
applications attempt to create environments close to the real world while eliminating
the negative consequences of reckless decisions (in the case of road safety). As such,
road safety stakeholders have started to embrace this opportunity towards raising
Chapter 1: Introduction 7
awareness through VR simulations of risky driving (Lang, Liang, Xu, Zhao, & Yu,
2018). However, the safety benefits of such initiatives have not been researched.
The significance of the current research is that it followed a practice-oriented
methodology that used psychological and social constructs applied to COT
applications that aim to motivate safer driving behaviour to reduce road trauma
amongst young drivers in the real world. The PhD program of research used two
currently available COT applications, a smartphone safe-driving app and VR
simulations of risky driving, to facilitate the acquisition of safer driving practices and
the cessation of risky driving behaviours amongst the target group, focusing on:
- Operationalising of two COT-based interventions which did not appear to
increase risks, such as distraction, for the participating drivers;
- Conducting studies in the participants' free-living environment;
- Quantifying the changes in driving intention and behaviour through the
evaluation of self-reported data.
1.4 OUTCOMES
The research delivered new knowledge and insights about the effects on young
drivers' intention and behaviour from the rapidly expanding use of 1) smartphone safe-
driving apps and 2) VR simulations of risky driving.
The outcomes of this research are twofold:
1. Contributing to a better understanding of the real safety benefits from using
two examples of COT applications, a smartphone safe-driving app (see
Chapter 7) and VR simulations of risky driving (see Chapter 9), for risk
prevention purposes in a road safety context; and
2. Informing the future evaluation of interventions, supported by COTs and
more specifically by smartphone safe-driving apps and VR simulations of
risky driving, that aim to reduce risky driving behaviours in young drivers
(see Subsection 10.3).
In the broader context of the current program of research, the author of the
current thesis believes that those research outcomes stand a higher chance to be later
operationalised in the real world through the involvement of SEs. Given SEs’
experience in ameliorating a diverse set of problems, they were consulted along with
8 Chapter 1: Introduction
road safety researchers as an expert reference group within the framework of the
current program of research (see Chapter 6). Thus, the current project pursued not
simply innovation but rather innovation that might possibly be applied in the real world
on a larger scale using SEs as a vehicle. This innovation was pursued in regards to
addressing:
1. A knowledge gap by investigating what might be the actual safety benefits
in the real world of the growing quantity of COTs, available to drivers, by
assessing the impact of two COT examples, a safe-driving app and a VR
simulation of risky driving; and
2. A number of limitations, identified in the literature, such as examining self-
reported scores over time to inform more robust conclusions.
1.5 DOCUMENT OUTLINE
Following this Introduction in Chapter 1, a Literature review is presented in
Chapter 2. It reviews and discusses the young drivers' problem and what COTs may
influence drivers’ behaviour. Applicable theories are reviewed in detail in Chapter 3.
Chapter 3 also focuses on how the TPB was operationalised in the Research design,
details on which are given as Chapter 4. Chapter 5 reports on Study 1, systematic
review, and explores in depth the application and utility of smartphone apps in road
safety research with a focus on young drivers. Chapter 6 establishes criteria for
selecting a safe-driving app as an intervention tool and searches and evaluates the
available apps on the app stores to inform the final selection. Chapter 7 (Study 2)
evaluates an operationalised intervention with an off-the-shelf smartphone safe-
driving app, which was selected following a systematic selection process that is
outlined in the previous Chapter 6. It examines the intervention impact on the young
drivers’ self-reported intention not to speed and their behaviour of not speeding during
the 3-month intervention period in their free-living environment. Chapter 8 (Study 3,
systematic review) investigates the actual and potential application and utility of VR
simulations of risky driving in road safety research and practice, with a focus on young
drivers. Chapter 9 reports on Study 4, which evaluates an operationalised intervention
with a VR simulation of risky driving in which participants were driving a virtual car,
simulating the DUI of their choice (alcohol or drugs). It provides information on the
effect of the intervention on the young drivers’ self-reported intention not to DUI and
behaviour of not DUI in the three months after the intervention. Chapter 10 concludes
the document with an overall discussion.
Chapter 2: Literature review 9
Chapter 2: Literature review
Chapter 2 reviews the young drivers' problem in the road safety domain. It begins
with exploring the fatal five risky behaviours that are associated with the
overrepresentation of young people in road crashes. It continues with a review of
contributing factors to those crashes that were considered when designing the current
program of research. An examination of interventions deployed as countermeasures
follows. The Chapter continues to describe evidence in the literature on addressing
young drivers' risky road behaviour through different types and implementations of
COT-based road safety interventions. COTs use in road safety is discussed, resulting
in an overview of the additional risks they introduce to the drivers. The chapter
concludes with the main considerations, which this PhD program of research
subsequently focuses on and the articulation of the Research Gap.
2.1 YOUNG DRIVERS' RISKY DRIVING BEHAVIOURS
The global problem of young drivers’ safety remains a focus of risk prevention
interventions due to unsatisfactory results of past implementations (Scott-Parker,
King, & Watson, 2015). Traffic crashes do not only lead to young people being
overrepresented in road fatalities (BITRE, 2018), but they are also the leading external
cause for hospitalisation due to injury (AIHW, 2008). Several risky behaviours,
classified as the fatal five (speeding, DUI, not wearing a seatbelt, fatigue and
distraction), are often reported by young drivers (Scott-Parker & Oviedo-Trespalacios,
2017).
Speeding is ruled to be the cause of 43% of young drivers' fatal crashes in
comparison to 23% for older drivers involved in fatal crashes (AONSW, 2011).
Almost 50% of young people admit speeding at least once by 10 to 25 km/h during
their last ten trips (D. Smart et al., 2005). Despite the consequences of speeding being
widely-known, young drivers are very likely to engage in such behaviour, without
being ashamed by the fact, and are more likely to report it (Fleiter, Watson, Lennon,
& Lewis, 2006; Horvath, Lewis, & Watson, 2012).
DUI impairs driving performance and, as a result, increases the risk of crashes
(Hingson, Heeren, Levenson, Jamanka, & Voas, 2002). The risk increases when the
10 Chapter 2: Literature review
driver is young (Peck, Gebers, Voas, & Romano, 2008), which is why Graduated
Driver Licensing (GDL) systems adopt zero alcohol tolerance (see Subsection 2.3.1).
Still, a recent US survey shows staggering statistics in youth drink driving. In the 30
days before the survey, 7.8% of the young driver respondents had driven after drinking
alcohol, while 20% rode with a driver who had been drinking (CDCP, 2016). In
Australia, drink-driving is identified as a primary contributor to 30% of the fatal and
9% of the non-fatal injuries for all drivers (ATC, 2011). Drug-driving is a main
behavioural factor in 7% of the fatal and 2% of the non-fatal crashes for all drivers in
Australia (ATC, 2011) with younger drivers (18–29 years) reporting higher than
expected engagement in such behaviour (Ward, Schell, Kelley-Baker, Otto, & Finley,
2018). ATC (2011) does not provide the respective crash statistics stratified by age
groups. While numbers by age groups in the case of DUI cannot be found in reports
such as the one of ATC (2011), there is evidence that DUI-related risks increase five
times for young drivers, aged under 21, in comparison with older drivers (Peck et al.,
2008).
Fatigue is a primary contributor to 6% of all crashes and 15% of the fatal crashes
(Legislative Assembly of Queensland: Parliamentary Travelsafe Committee, 2005).
Yet, 80% of drivers report driving while fatigued (Obst, Armstrong, Smith, & Banks,
2011). Research identifies fatigue as the psychological state to most commonly impair
young drivers (Wundersitz, 2012). Similar to other risky behaviours on the road, young
people are more likely to engage in driving when fatigued than their more experienced
colleagues (McGwin Jr & Brown, 1999; Rhodes & Pivik, 2011).
Not wearing a seatbelt is a contributing behavioural factor in 20% of the fatal
and 4% of the non-fatal crashes for all Australian drivers (ATC, 2011). There is
evidence that the use of seatbelts reduces the number of fatalities on the road (Dinh-
Zarr et al., 2001). In recent years seatbelt use by young drivers reached up to 85% and
is comparable with the trends in the general population (Pickrell & Liu, 2015).
Nevertheless, restraint non-use during short trips is still reported by 20% of young
Australian drivers (Scott-Parker & Oviedo-Trespalacios, 2017), which may further
contribute to their overrepresentation in fatal crashes.
Last, but not least, distraction is the most commonly observed risky behaviour
in young people with 41.5% admitting sending an SMS or an e-mail at least once in
the past month (CDCP, 2016). Currently, in the US, 10% of fatal crashes and 18% of
Chapter 2: Literature review 11
injury crashes are reported as a result of distraction (NHTSA, 2015). More than 50%
of young drivers are identified as distraction-prone (Schroeder, Meyers, & Kostyniuk,
2013). As it was the case with the other fatal four behaviours on the road, as a result
of being distracted young drivers are more vulnerable while driving than their older
colleagues (McEvoy et al., 2006). The problem is likely to deepen as a result of the
increase in the number of electronic devices in the cars (Parliament of Victoria Road
Safety Committee, 2006; WHO, 2011), a forecast largely confirmed by both reviews
of the literature (Oviedo-Trespalacios, Haque, King, & Washington, 2016) and crash
statistics (NHTSA, 2017).
The reviewed literature highlighted speeding and DUI as the deadliest of the fatal
five risky behaviours on the road with young drivers being at a higher risk of crash
involvement as a result of both of them. As a consequence, the potential safety benefits
of reducing speeding and DUI amongst young drivers can be perceived as
comparatively higher than the potential safety benefits, related to the other three risky
behaviours. This makes speeding and DUI reduction a suitable target of road safety
efforts. It is worth acknowledging that examples in the literature can be found with
speeding defined as "driving at an illegal speed over the limit" or "driving at an
inappropriate speed" or both. Not all sources define their understanding of "speeding"
and make the distinction explicit. For the current PhD program of research, speeding
is defined as "driving at an illegal speed over the legal limit".
2.2 CONTRIBUTING FACTORS
Young drivers’ crash involvement is influenced by a multitude of characteristics
(road, vehicle, personality, etc.) as well as broader social factors (family, friends)
(Scott-Parker, 2012). Variables related to those factors and characteristics are likely to
interact and influence each other at any given point of time, ultimately influencing the
young people’s behaviour on the road. Following is a review of the literature exploring
some of those factors that were considered relevant for the current PhD program of
research.
2.2.1 Driving experience
Driving experience, or more likely the lack of it, can be considered an objective
constraint for young drivers to fully understand the potential implications of their
behaviour on the road. Limited driving experience results in a diminished capability
12 Chapter 2: Literature review
to both recognise and respond to road hazards (Deery, 2000; Scialfa et al., 2011). In
contrast to their own beliefs of being better than experienced drivers (Gosselin,
Gagnon, Stinchcombe, & Joanisse, 2010), there is evidence that young drivers
recognise fewer risks than their experienced colleagues (Fisher, Pradhan, Pollatsek, &
Knodler Jr, 2007). Failure to comprehensively understand the situation on the road in
any given moment may put young drivers at a disadvantage when it comes to
anticipating risks and adequately responding to them when they materialise.
Anticipating and responding to risks is likely to improve with driving practice.
So more practice is recommended for the risk of crash involvement to be reduced, but
the more young drivers drive, the higher their crash risk (Prato, Toledo, Lotan, &
Taubman - Ben-Ari, 2010). GDL programs are designed as a response. They require
experienced drivers to monitor novice drivers, thus reducing the risks of exposure
while the much-needed practice is being acquired. However, such supervision is
usually limited during the first year of driver licensing. Extending it may not be
practical or possible. For example, when behaviour is illegal and by definition should
not take place on the road, extended supervision might not have the chance to tackle
them due to their rare occurrence. Nevertheless, such behaviours contribute to causing
crashes and acquiring experience in relation to them in a safe (laboratory) environment
may save lives in the real world.
The present PhD program of research made an effort to compensate for the
young drivers' inexperience by providing them with an opportunity to gain experience
in two separate interventions:
1) In a rational and understandable way, a smartphone safe-driving app, able to
interpret driving events and communicate feedback, simulated constant supervision in
the young drivers' free-living environment while they were operating their vehicles in
their daily routine. The feedback was coupled with impartial and unemotional advice
on how safety could have been improved.
2) In safe experimental conditions, a VR simulation of risky driving allowed
young drivers to let their emotions off-leash and experience the dangers of DUI. The
participants were able to see how their driving abilities were affected as a result of the
simulated intoxication in a safe environment.
Chapter 2: Literature review 13
2.2.2 Optimism bias
Lack of driving experience may not be a standalone contributing factor to young
drivers’ increased driving risks, particularly in critical situations, although it is
associated with increased risk of crash involvement (McCartt, Mayhew, Braitman,
Ferguson, & Simpson, 2009). "This will not happen to me." often flashes through
people's mind when they are exposed to negative information. This kind of optimism
bias makes people believe that negative things are more likely to happen to others than
to themselves (Weinstein, 1980). Young drivers are prone to optimism bias related to
their driving (Gosselin et al., 2010; Harré, Foster, & O'Neill, 2005; Horswill, Waylen,
& Tofield, 2004; White, Cunningham, & Titchener, 2011). Researchers report that
young drivers see themselves as being better drivers than their peers and, thus, they
think they are less likely to be involved in crashes (Harré et al., 2005; Horswill et al.,
2004; White et al., 2011). Other research reports that young drivers have the same self-
perception when compared with more experienced drivers, too (Gosselin et al., 2010).
This, as a consequence, may lead to increased risk-taking and subsequent crash
involvement.
Young drivers' overestimation of their driving skills, combined with their
tendency to underestimate potential risks, increases their overall crash risk. Revealing
an accurate picture of one’s driving performance may help address young drivers’
optimism bias and subsequently improve their driving behaviour. The current PhD
program of research explored that assumption through COTs interventions that
provided immediate personalised feedback to the involved participants.
2.2.3 Gender
Males are consistently overrepresented in crash fatalities in comparison to
females (BITRE, 2018). Young males are more likely to engage in risky behaviour on
the road (Rhodes & Pivik, 2011) which is why the gender effect is often assessed as
part of road safety studies, e.g. investigating speeding (Fernandes, Hatfield, & Soames
Job, 2010; Horvath et al., 2012) or DUI (Fernandes et al., 2010; Peck et al., 2008).
Thus, gender differences are at least controlled for (Horvath et al., 2012; Parr et al.,
2016) or investigated in detail (Obst et al., 2011; Rhodes & Pivik, 2011).
There is evidence that males and females are impacted differently by road safety
interventions (Lewis, Watson, White, & Elliott, 2013). In general, males are more
14 Chapter 2: Literature review
tolerant than females to take risks while driving (Redshaw, 2006). Males also report
violating traffic regulations more often than females (Castellà & Pérez, 2004;
Constantinou, Panayiotou, Konstantinou, Loutsiou-Ladd, & Kapardis, 2011). Lewis et
al. (2013) found that it was important for male drivers to feel in control while, at the
same time, they perceived to have little control over their speeding. Lewis et al. (2013)
suggested that mass media message content should be developed with young males as
a primary target as speeding-related beliefs were particularly relevant for them.
Nevertheless, evidence suggests that such campaigns had less effect on male than on
female drivers (Lewis, Watson, & White, 2010; Lewis, Watson, & Tay, 2007; Lewis,
Watson, Tay, & White, 2007).
In the current program of research, gender, together with driving experience and
age, was controlled for as a demographic characteristic in the regression models to
allow for a more accurate assessment on the predictive contribution made by
theoretical constructs in regards to the dependent variables (DVs) of interest. When
allowed by the specific statistical test and by the respective sample size, gender was
also investigated as a moderator, which allowed for a more in-depth exploration of the
intervention effects.
2.3 INTERVENTIONS
The literature reviewed above provides insights and evidence on why young
drivers may be taking unnecessary risks on the roads and what are the potential
consequences for them. Another set of knowledge can be utilised in road safety
intervention design, implementation and evaluation that originates in previous
prevention efforts. Road safety stakeholders address young drivers’ vulnerability on
the road at multiple levels through a variety of interventions and initiatives, some of
which are further discussed in turn.
2.3.1 Driver training
Driver training aims to provide young people with the necessary minimum
experience for them to be able to be licenced as drivers. It is regulated by the
government and delivered to learner drivers by more experienced drivers, driving
instructors and, to a lesser extent, by road-safety-related businesses. Countries, such
as Australia and the USA, have introduced GDL systems, which vary in structure and
complexity. It should be noted that the current program of research involved
Chapter 2: Literature review 15
participants, who were licenced drivers under the Australian GDL system (Walker,
2014). In Australia, novice drivers start with a learner's (L) license. After passing a
learner driver’s test, drivers are issued with a provisional driver’s licence, initially a
provisional one (P1) license, followed by a provisional two (P2) license. Finally, after
holding a provisional licence for between two to four years, depending on the state or
territory, novice drivers are eligible for an open driver’s licence. The process of going
through the different license stages is characterised with decreasing limitations. For
example, learner drivers can drive only under the supervision of an openly-licensed
driver. This requirement ceases when the driver obtains a provisional license.
However, at that time, a restriction on driving with multiple passengers starts applying.
The most relevant license limitations for the current research are the ban on using
mobile phones and the zero BAC requirement during the entire provisional period
(Walker, 2014), which are also recommended by the Australian GDL policy
framework.
In an effort to improve driver training there has been a trend towards more
supervision hours and delayed licensing, as well as more diverse supervision,
involving as many stakeholders (e.g. parents and schools) as much as possible
(Senserrick, 2007). As a result, learner drivers under supervision are the safest in the
world, but this fact changes after their provisional license is granted (see Figure 2.1)
(Mayhew, Simpson, & Pak, 2003).
Figure 2.1. Percentage of South Australia drivers involved in a crash five years after licensing (Austroads, 2008).
16 Chapter 2: Literature review
The tenets that better safety on the road is achieved by more intensive driver
training is widely accepted (Watson, 1997). However, the literature does not suggest
that the conventional driver training necessarily leads to safer driving habits (Vernick
et al., 1999; Watson, 1997). Neither Vernick et al. (1999) nor Watson (1997) provides
an explicit answer as to “why” increased driver training does not result in a reduced
crash rate. Both studies found no evidence across jurisdictions in support of instilling
more driver training upon young drivers. However, neither of them challenges the fact
that driver training does improve driving skills. The problem is that this improvement
is not translated into improved safety levels. A more recent review of the literature
confirms that training improves skills, but it also confirms that evidence for crash
reduction, as a result, remains questionable (Beanland, Goode, Salmon, & Lenné,
2013).
2.3.2 Media campaigns
While it remains ambiguous that drivers’ training improves road safety, other
educational tools are put to work with that purpose. Fear-based media campaigns, such
as, for example, the Northern Ireland Department of Education Road Safety Anti Drink
Driving Ad2, try to reveal the full scope of possible consequences of reckless driving
in order to change driver attitudes and in turn facilitate safer driving practices. Such
campaigns are usually initiated by governments or road-safety-related businesses. At
the same time, a growing body of evidence suggests that they have little effect on the
target group, especially on the riskier male drivers (Lewis, Watson, & White, 2010;
Lewis, Watson, & Tay, 2007; Lewis, Watson, Tay, & White, 2007).
Other studies provide mixed results. For example, Tay (2005) provides evidence
of the anti-speeding media campaigns in Victoria independent effect on male drivers'
crash rates. However, merely visualising risks may not be sufficient to change
behaviour (Shope & Bingham, 2008). As discussed earlier (see Subsection 2.2.2)
young people are susceptible to optimism bias. They underestimate the likelihood of
being involved in a crash as a consequence of the risks they are taking, compared to
the likelihood of it happening to others (Gosselin et al., 2010; Harré et al., 2005;
Horswill et al., 2004; White et al., 2011). Thus, the overall unsatisfactory results of
2 https://youtu.be/0x4Qrjyf4lQ
Chapter 2: Literature review 17
media campaigns may be rooted in the inherently optimistic way the young people
think.
2.3.3 Law enforcement
Other measures take place in cases where mass education produces
unsatisfactory results. Law enforcement is the major government tool, channelled
through the Police, to tackle unlawful driving behaviour. It is believed to have
complementary effects with media campaigns (Tay, 2005). The author provides
evidence of its independent effect on drink-driving crashes and interactive effect with
the respective media campaign on speeding-related crashes. Its effectiveness builds on
people’s perceptions about the risk of apprehension in combination with the
probability of sanctions (Tay, 2005). Tay (2005) sees its success as a function of the
police presence level and the hit rate of the respective intervention.
Law enforcement increasingly utilises innovative technologies to improve
success. For example, alcohol ignition interlocks do not allow the engine to start if the
driver is above the legal blood alcohol concentration (BAC) limit. There is evidence
that alcohol ignition interlocks devices are feasible in both commercial and public
settings (Silverans, Alvarez, Assum, Evers, & Mathijssen, 2007). They are an example
of a context-based personalised enforcement when the right of decision, i.e. starting
the vehicle, is taken away from the driver and delegated to the technology to avoid the
possibility for an unfavourable outcome, i.e. crash. Other technologies systematically
provide similar decision support to drivers in the context of other detected potential
risks such as distraction or drowsiness (Kashevnik & Lashkov, 2018), which may
indirectly support law enforcement.
2.4 CONSUMER-ORIENTED TECHNOLOGIES
The use of technologies in the road safety domain is regarded as posing a mix of
potential benefits and threats to the drivers, in general, and young drivers, in particular.
There is evidence that using contemporary technologies in the car can make safe
driving an engaging, challenging and enjoyable task (Schroeter et al., 2012;
Steinberger et al., 2015). However, not all evidence provides support for their positive
impact on safety.
The presence of multiple technologies competing for the drivers' attention, such
as connecting smartphones, in-car video, audio and other electronic devices, is very
18 Chapter 2: Literature review
likely to deepen existing problems, such as distraction (Parliament of Victoria Road
Safety Committee, 2006). On the one hand, there is evidence that the use of technology
devices while driving distracts young drivers from their primary task: driving (Blanco,
Biever, Gallagher, & Dingus, 2006; Ferguson, 2003; Lee, 2007; WHO, 2011), which
may lead to tragic consequences. On the other hand, more and larger screens, in
combination with innovative interface concepts and possibly large-scale augmented
reality experiences, will be introduced in the cars to maximise car manufacturers'
revenue (McKinsey&Company, 2014). These are often guided by marketing and
consumer demands, rather than by safety considerations. The general sentiment is that
the problem is likely to both increase and evolve together with the number and
complexity of the available technologies (WHO, 2011). The way drivers use and
interact with COTs should be holistically examined, leaving no room for
underestimation of potential unintended safety counter-effects. Ignoring safety
considerations when COTs' design takes place may lead to unexpected consequences.
2.4.1 Context
COTs can provide additional experiences that may or may not be related to the
driving tasks and are not necessarily designed considering the safety of the drivers. For
example, they allow drivers and passengers to listen to music, watch movies, make or
receive calls, send and read text messages, browse the Internet, etc. In the literature,
COTs are often regarded as a source of distraction, which increases driving associated
risks (Parliament of Victoria Road Safety Committee, 2006). With the evolution of the
smartphone, currently, most COTs, including GPS (Global Positioning System), can
be found in a single device in the car (Regan, Williamson, Friswell, Hatfield, &
Grzebieta, 2012). This makes smartphones perhaps the most widely recognised
example of a COT that has infiltrated the car. However, this does not change the nature
of their adverse effects on driving (Rowden & Watson, 2013), and they continue to
cause major road safety concerns by distracting drivers. Klauer, Dingus, Neale,
Sudweeks, and Ramsey (2006) found that 65% of near-crashes and 78% of all crashes
are due to reduced drivers’ attention to their primary task.
COTs have been shown to drain important cognitive and operational capabilities
of the driver (Kircher, Ahlström, & Patten, 2011). A body of evidence in the literature
reveals a number of common behaviours observed in drivers when distracted by using
COTs. These include reduction of speed, lower control of the vehicle speed, often
Chapter 2: Literature review 19
changing position on the road, reduction of the ability to detect hazards, and an overall
decline in reaction time (Regan, Lee, & Young, 2009).
For example, looking away from the road, which is often a requirement to
operate a COT, reduces the drivers' abilities to brake in a case of an event or to keep
the vehicle in the lane (Lamble, Laakso, & Summala, 1999). The authors exposed 12
drivers, aged 19 to 27 years, to attention-demanding tasks in the car while approaching
a decelerating vehicle on the road. Haque and Washington (2013) provide further
evidence that young drivers' reaction time increased in general in the presence of a
distraction. In a simulator study in three conditions of phone conversations (with a
handheld device, with hands-free and no conversation), Haque and Washington (2013)
recorded slower reactions when a hands-free device was used, and there was no
looking away from the road involved to operate it. Recarte and Nunes (2000) provide
clues about why that might be happening. The authors found changed scanning
patterns in mobile phones users while driving. By observing eye movements while
participants performed verbal and scanning tasks, Recarte and Nunes (2000) recorded
less attention paid to the mirrors, the dashboard or the road, which are vital to detecting
and responding to hazards. Using a phone while driving introduces a second cognitive
task, competing for the driver's attention that is unrelated to the core one of operating
the vehicle. As a result, dual-task drivers make more errors and detect fewer hazards
(Briggs, Hole, & Land, 2016).
The reviewed literature suggests that technology applications that are not
directly related to the driving tasks, which is the case of COTs, increase the risk of
distraction. However, COTs can potentially persuade, i.e. by giving user drivers a good
reason to drive safer, and may influence their behaviours as a result (Fogg, 2009).
When it is designed to persuade drivers positively, COTs can foster safer driving
behaviour (Schroeter et al., 2012; Steinberger et al., 2015), an implication assessed by
the current PhD program of research.
2.4.2 Safe-driving apps
Mobile phone use while driving increases the chances of crash four times (White,
Hyde, Walsh, & Watson, 2010). The drivers themselves are well aware of the problem,
with 14% of them confirming that distraction contributed to their crash (McEvoy &
Stevenson, 2007). Still, a very large number report using their mobile phone in the car:
20 Chapter 2: Literature review
43% receive calls, 36% make calls, 27% read and 18% send text messages (White et
al., 2010).
Mobile phones, including smartphones, although identified as a major source of
distraction while driving (WHO, 2011), also offer various opportunities in terms of
novel technology-driven road safety approaches. Apps coming out of academia, such
as the CarSafe app (You et al., 2013), have been developed to monitor driver behaviour
and warn drivers about identified risks. However, they have only had limited uptake
to date. Insurance companies (e.g. AAMI in Australia, State Farm in the US,
Telefonica in Spain, and AVIVA in the UK) also embrace the opportunity through
proprietary smartphone safe-driving apps in an effort to promote safer driving
practices (also sell insurance and collect driving data). Other companies and start-ups
have created driving apps such as Rookie Dongle, Flo or Automatic. Some of those
apps not only monitor speed through GPS but can also block incoming calls and
messages on the mobile phone. Others essentially serve the purpose of a driving coach
with feedback, aiming to help drivers to learn and apply the road rules.
Safe-driving apps often leverage gameful designs or gamification (Diewald et
al., 2013) to boost motivation and commitment to use. The gamification of driving
may lead to an increased drivers’ engagement with their driving tasks (Steinberger et
al., 2017). Safe-driving apps use a number of tools to trigger such a positive outcome,
such as challenges, feedback, social approval, and rewards (Markey, 2014). For
example, while using such apps, the drivers may earn points for driving "safely". Those
points can then potentially be redeemed for rewards or shared as achievements on
social media.
There is a variety of safe-driving apps that claim to be able to improve the
drivers’ skills and performance. For example, You et al. (2013) developed and tested
a smartphone app (CarSafe) to alert drowsy and distracted drivers. They tested the app
on the road with 12 drivers aged 23-53. Six of them drove in a controlled environment
with a co-pilot that gave them instructions to perform dangerous manoeuvres when the
situation was safe. The other six were monitored while driving in their usual daily
routine. While there was real driving performance being observed, the data collection
was related to assessing the CarSafe accuracy rather than to evaluate its influence on
the drivers. No data was collected about changes in the drivers' behaviour as a result
of using the app, which is the focus of the current thesis.
Chapter 2: Literature review 21
Another study evaluating a smartphone safe-driving app intervention focused on
the usage of smartphone apps rather than on safety benefits. Musicant and Lotan
(2015) deployed a smartphone app (RefuelMe) that at the point of the intervention was
available for iPhone users for free. Their target group was composed of 21 soldiers
aged 18-19 and their friends, also 21 people. The authors collected data through three
sources: the app (naturalistic: 29,335 recorded trips, scored based on recorded G-force
events, such as speeding, hard acceleration, hard braking and fast cornering),
surveys/phone interviews (self-reports) and the soldiers’ Facebook page (qualitative
data). A limitation of the study was that it did not establish a self-reported baseline for
comparison and could not evaluate behavioural changes. The authors inferred driving
behaviour from the recorded trip scores and observed a decrease in scores with the
progress of the study. Similar to the intervention implemented in the current research,
the app was monitoring and recording the real driving behaviour as well as providing
feedback (real-time, at the end of the trip and weekly). Although driving behaviour
was observed, the focus of the study was on app usage and what could motivate
adoption. Since the study was focused on adoption and usage, driving performance
feedback was very forgiving.
Musicant and Lotan (2015) found that once all incentives were obtained by the
participants, they stopped using the app. They attributed this to the fact that obtaining
the rewards was more related to mere participation than to performing safer. Besides
the incentives, another possible explanation of Musicant and Lotan (2015) result was
that the app had to be manually started and stopped. While the current program of
research also utilised incentives, all participants were eligible to receive the same
incentive at the end of their participation, allowing for an investigation of the influence
of a safe-driving app on participants’ driving behaviour, regardless of the reasons for
using it. Long-term adoption was not a targeted result. A self-starting capability of the
smartphone safe-driving app was a desired feature when selecting an intervention tool.
2.4.3 Virtual reality
Moving away from the possibility to introduce additional risks in the young
drivers’ experience on the road, a second opportunity to use technology for improving
young drivers’ safety was utilised as a part of this PhD program of research. Young
drivers' use VR in their daily lives (Lang et al., 2018). An increasing number of
immersive virtual experiences is currently becoming available for households as
22 Chapter 2: Literature review
consumer electronics. HTC Vive, Oculus Rift, PlayStation VR, Google Daydream
View and Samsung Gear are commercially available consumer-grade devices that
bring the experience on demand.
Designated tasks, delivered through VR and experienced by users, were found
to potentially lead to prosocial behaviour (van Loon, Bailenson, Zaki, Bostick, &
Willer, 2018). Blascovich et al. (2002) argue that VR is a well-suited methodological
tool for experimental social psychology that helps overcome limitations such as lack
of replication or control-mundane realism trade-off. Its main advantage is the
possibility for individual perspective-taking, which is arguably more successful than
traditional role-playing exercises (van Loon et al., 2018). As a result, there is evidence
that VR can increase empathy ((Garner, 2017; Ingram et al., 2019; van Loon et al.,
2018). Nevertheless, Ahn, Bailenson, and Park (2014), Morina, Ijntema, Meyerbröker,
and Emmelkamp (2015), Schwebel, McClure, and Porter (2017), Theng, Lee,
Patinadan, and Foo (2015) and van Loon et al. (2018) provide mixed evidence for
behavioural change success in their studies. Thus, whether VR interventions ultimately
lead to a behavioural change is not certain.
The situation in road safety research is not different, signifying that VR is just
making its way into it. There is a limited number of road safety studies leveraging this
technology, despite it providing an opportunity to simulate life-threatening situations
in safety. Most of the VR research has been in the domain of pedestrian safety
(Bhagavathula, Williams, Owens, & Gibbons, 2018; Morrongiello, Corbett, Foster, &
Koutsoulianos, 2018; Schwebel, Combs, Rodriguez, Severson, & Sisiopiku, 2016;
Schwebel et al., 2017) but little evidence was found on the VR potential to deliver
safety benefits. This may or may not be due to the novelty of the technology but makes
a systematic investigation of their real effect, both as evidenced in the literature and as
part of a real-world intervention, a gap that needed to be addressed.
2.5 CONCLUSION AND IDENTIFICATION OF A RESEARCH GAP
The presented literature review showed that existing efforts might not have the
potential to reduce crashes further. Existing strategies, delivered through driver
training, media and law enforcement, are working, but their effect seems to plateau.
Although many of those strategies are focused on young drivers, young drivers
continue to be overrepresented in crashes (BITRE, 2018; NHTSA, 2018a; EC, 2018;
Chapter 2: Literature review 23
WHO, 2018), with evidence of their increased risk exposure. The overall situation calls
for novel complementary approaches to persuade young drivers to reduce their risk-
taking, which, in turn, could help lead reversing the crashes' trend, bringing numbers
below the plateau.
The current program of research utilised novel interventions as complementary
approaches. It focused those interventions on risky behaviours where arguably the
potential benefit is highest, i.e. behaviours, which are significant contributors to both
the number and the severity of road crashes. As evidenced by the literature, the risky
behaviours of speeding and DUI bear high potential severity of consequences that may
follow if young drivers engage in them. Thus those were targeted by the two
intervention studies, evaluated in Chapter 7 and Chapter 9.
As a contemporary response to the call for novel complementary approaches,
instead of trying to train the young drivers out of their bad habits, the current research
evaluated the potential of technology to deliver additional experience to the young
drivers and, as a consequence, to improve their driving behaviour in regards to the two
targeted behaviours, speeding and DUI. Fogg (2009) suggested that technologies may
change users' behaviours through persuasion. Schroeter et al. (2012) and Steinberger
et al. (2015) supported that view further suggesting that COTs, designed to persuade
drivers, could help young drivers adopt safer driving behaviour.
Available COTs, namely smartphone safe-driving apps and VR simulations of
risky driving, provide opportunities to help address speeding and DUI as risky
behaviours. These opportunities are currently being explored by both academia and
businesses. However, the reviewed literature did not provide extensive evidence for
safety benefits for the young drivers, stemming from smartphone safe-driving apps
and VR simulations of risky driving. Such safety benefits seemed to have been
predominantly measured in conditions that do not resemble the use of the two example
COTs in the real world, e.g. through simulator tests with small samples and lack of
investigation of long-term effects. Thus, little is known about those two COTs' safety
impact in real-world road safety interventions, although both are readily available for
the general public and, more so, they are targeting young drivers.
At the same time, it is acknowledged that traditional literature reviews may
suffer from bias, originating in the reviewer's impressions (Mulrow, 1994). Systematic
literature reviews are regarded as a means to address such bias, which shall deliver
24 Chapter 2: Literature review
improved reliability of findings as well as an increased confidence in the conclusion
(Fineout-Overholt, Melnyk, Stillwell, & Williamson, 2010). As systematic reviews
represent a scientific investigation on their own (Mulrow, 1994), the two systematic
reviews, part of the current program of research, are presented as separate Chapter 5,
focusing on smartphone safe-driving apps, and Chapter 8, focusing on VR simulations
of risky driving.
Overall, the missing evidence of real-world effects from the use of smartphone
safe-driving apps and VR simulations of risky driving represented a significant
research gap, which the current program of research aims to address. This gap
represents the basis for the current project to systematically evaluate the effect of
smartphone safe-driving apps and VR simulations of risky driving. Thus, the
investigation looks at those effects both as evidenced in the literature and as part of
intervention studies. The two implemented intervention studies focus on behavioural
change the two COTs can trigger in the ordinary young driver, in the longer term, in
their free-living environment. Their findings are presented in Chapter 7, for an
example smartphone safe-driving app COT, and in Chapter 9, for an example VR
simulations of risky driving COT.
Chapter 3: Theoretical considerations informing intervention evaluation framework 25
Chapter 3: Theoretical considerations informing intervention evaluation framework
Chapter 3 reviews theories that were applied in domains similar to the ones
explored by the current PhD program of research: health risks prevention, road safety,
human-computer interaction and young people. It presents the considerations taken
into account when choosing the most suitable framework to guide the evaluations of
the implemented interventions. Those considerations include whether the theory was
considered to be well suited:
1) to evaluate behaviour and its underlying constructs;
2) for the nature of the targeted behaviours (speeding and DUI);
3) to be applied at an interpersonal level, as the implemented interventions
regard young drivers as part of a dynamic system involving other people;
and
4) to account for the interrelation between its constructs.
The final choice was made depending on how well the respective theory fit the
needs of the planned interventions.
3.1 INTRODUCTION
Behaviour change is considered achieved when there is evidence for new
behaviour adoption on the basis of new knowledge acquisition (Bandura, 1986). Thus,
for the current PhD project's interventions to be considered successful, their evaluation
has to find significant evidence that a desired behavioural change on the individual
level or, at least, change in the intention to perform the behaviour of interest, was
achieved, as a result of the respective intervention. Appropriate theoretical grounding
is not only a sound basis for evaluating the intervention achievements, but it also
provides insights on how behavioural change could be motivated amongst the
participants (Kohler, Grimley, & Reynolds, 1999).
26 Chapter 3: Theoretical considerations informing intervention evaluation framework
Researchers are extensively using behaviour change theories to underpin
interventions' design, development and evaluation. The current PhD program of
research targeted behavioural change in regards to achieving positive changes in young
people's intention and behaviour on the road. Both personal and social factors are
found to influence the behaviour of young drivers (Shope & Bingham, 2008). Keeping
in mind that young drivers' behaviour is influenced by many factors, this chapter
discusses the choice of a theory that informed this PhD project, both theoretically and
methodologically.
Theories with a history of being applied in fields relevant for the current research
such as health promotion, human-computer interaction, young people, road safety and
combinations of them were considered when choosing an appropriate one to guide the
current research evaluation framework. In their systematic review covering 256
articles (out of 8680 articles initially retrieved), Davis, Campbell, Hildon, Hobbs, and
Michie (2015) identified 82 theories in the social and behavioural sciences literature.
Three of them accounted for 165 (60%) of the reviewed articles across behaviour
change: Transtheoretical model of health behavior change (TTM) (Prochaska &
Velicer, 1997) (33%), TPB (Ajzen, 1988) (13%), Social Cognitive Theory (SCT)
(Bandura, 1986) (11%) and Health Belief Model (HBM) (Hochbaum, Rosenstock, &
Kegels, 1952) (3%). The Theory of Reasoned Action (TRA) (Ajzen & Fishbein, 1980)
and the Self-Efficacy Theory (SET) (Bandura, 1977) were also included in the analysis
as they are the antecedents of TPB and SCT respectively. These were the theories
considered, and following the detailed considerations are provided.
3.2 TRANSTHEORETICAL MODEL OF HEALTH BEHAVIOR CHANGE
TTM (Prochaska & Velicer, 1997) argues that each individual is in a different
stage of behavioural change in relation to a specific health-related behaviour. The
theory is widely used to explain the way people engage in activities to change their
behaviour, how they progress through the changes, and what efforts are put in place to
maintain new and better behaviour. According to the model, there are six stages of
change (Precontemplation (Not Ready); Contemplation (Getting Ready); Preparation
(Ready); Action; Maintenance and Termination) with the first five being measurable
(see Figure 3.1). TTM suggests strategies, based on identified individual readiness to
change, which can help a person move from one stage to the next one. The opposite
process (relapse) is also possible at any stage.
Chapter 3: Theoretical considerations informing intervention evaluation framework 27
Figure 3.1. TTM stages of change
TTM is applied in various health-related interventions, such as stress
management (Prochaska et al., 2012), reduction of smoking (Prochaska, DiClemente,
Velicer, & Rossi, 1993), improving energy balance (Van Duyn et al., 1998; Velicer et
al., 2013) or obesity reduction (Mauriello et al., 2010). In many cases, TTM is used to
assess interventions' impact on more than one aspect (e.g. technology, young people
and risky behaviour), relevant to the current PhD program of research. For example,
Prochaska et al. (2012) used a telephone and an online program coaching to improve
wellbeing in terms of improved life evaluation, healthy behaviour, emotional and
physical health. Aveyard et al. (1999) used three TTM-based computer sessions and
three class lessons over a year to reduce smoking in young people and their peers.
Velicer et al. (2013) implemented a computer-based intervention to prevent substance
abuse. Thus TTM is identified as a useful model in multidisciplinary research
including human-computer interaction (Aveyard et al., 1999; Di Noia, Contento, &
Prochaska, 2008; Gold et al., 2016) and persuasive technologies that change users'
behaviours and attitudes through social influence and persuasion (Fogg, 2009).
In contrast with other health domains, the literature review did not reveal many
TTM-based applications in the road safety field. Five studies in three fields were
identified in which the model is applied: recidivist drink drivers (Freeman et al., 2005;
Polacsek et al., 2001), work-related safety (Banks, 2008; Murray, White, & Ison, 2012)
and more recently, senior drivers (Kowalski, Jeznach, & Tuokko, 2014).
Polacsek et al. (2001) implemented an intervention for drink drivers, attending a
“Driving while intoxicated” school, to investigate if the Victim Impact Panels (VIPs)
of Mothers Against Drunk Driving had an additional effect on recidivism or on
progressing individuals through the stages of change towards not drinking while
driving. VIPs are designed to reveal the full scope of the negative consequences of
28 Chapter 3: Theoretical considerations informing intervention evaluation framework
substance-impaired driving, helping offenders recognize and internalize the offence's
long-term effects. In the same field, Freeman et al. (2005) investigated the TTM
constructs of stages of change and self-efficacy in recidivist drink drivers. The authors
used TTM-based scales to measure self-efficacy levels (Drinking/Driving Efficacy
Scale (Wells-Parker, Burnett, Dill, & Williams, 1997)) and motivation to change drink
driving behaviour (Readiness to Change Questionnaire (Rollnick, Heather, Gold, &
Hall, 1992)), Stages of Change for Drink Driving Questionnaire (Wells-Parker,
Williams, Dill, & Kenne, 1998)). This shows that available scales can be successfully
combined in a comprehensive questionnaire in an effort to build a more holistic picture
of an intervention's effect.
Kowalski et al. (2014) used the TTM framework to develop specific questions
addressing constructs such as stages of change, decisional balance, change processes
and self-efficacy. Their study demonstrated how the TTM helps to understand at what
stages of change the participants were, and whether participants were aware of the
need to change their behaviour. The latter is a prerequisite of going through
behavioural change.
Linking TTM to the more narrow settings of occupational road safety, Banks
(2008) explored the correlation to determine relations between the individual stages of
change and crash involvement, fatigue and distraction. The author concluded that the
stage of change was a significant independent predictor of crash involvement.
Predicting potential crash involvement through the participants' stage of change can
be useful for the current research. Such link suggests that young drivers' crash
involvement can potentially be reduced if an intervention manages to help young
people progress from one stage of change to another.
Like most models, TTM has attracted criticism (Littell & Girvin, 2002; West,
2005). Littell and Girvin (2002) see the biggest problem in trying to oversimplify
complex behavioural change processes into stages. Other researchers found instability
of the stages themselves (De Nooijer, Van Assema, De Vet, & Brug, 2005; Hughes,
Keely, Fagerstrom, & Callas, 2005), challenging the model presumption that people
make coherent and stable plans. The model also neglects the fact that many health
problems arise from semi-automated unhealthy habits, which are not easy to change
(West, 2005). For example, Velicer et al. (2013) reported limited results in directly
addressing smoking and alcohol. The authors suggested that reactivity and
Chapter 3: Theoretical considerations informing intervention evaluation framework 29
defensiveness towards behavioural change may have been due to the behaviours’
addictive nature. So, for some behaviours, the model can propose an incorrect
intervention strategy (West, 2005).
TTM could have been considered relevant to the current research. This PhD
program of research aimed to improve young drivers’ behaviour on the road, i.e. push
them from one stage to another in respect of their risky behaviour. TTM explores five
different stages. The advantage of TTM is that aids understanding at which stage of
change the study participants are, in particular when dealing with groups that are not
homogenous. Dividing a participant pool into subgroups, based on their stage, further
allows understanding how effective a respective intervention is in the case of each
different stage, i.e. by assessing whether and how far the participants from the
respective stage of change progressed, as a result of the intervention. Thus the model
could provide valuable information for what type of participants such interventions
could deliver maximum benefit.
In relation to the current program of research, a critical aspect in determining the
TTM as a suitable model to address the research gap is that the two interventions to be
evaluated (a smartphone safe-driving app, see Subsection 7.2.6, and VR simulations
of risky driving, see Subsection 9.2.1) were not stage-tailored. Tailoring them to the
needs of a group of participants carried the risk of the chosen target stage not being
identified correctly due to oversimplification of the behavioural processes in driving.
The current program of research also looked into problematic driving habits, which in
many cases may be semi-automated and, thus, might not be accounted for by the
model. In addition, TTM focuses on the individual and not on the interpersonal level.
Accounting for interpersonal relations was a needed model's characteristic so that
normative influences for achieving a positive change can be assessed. Thus, TTM was
considered not fully suiting the needs of the current project.
3.3 HEALTH BELIEF MODEL
HBM (Hochbaum et al., 1952) addresses the TTM limitation of dividing the
process of behavioural change into stages by looking at the process as a whole. HBM
also does not assume that people make plans, and necessarily follow them. The model
argues that a person has to perceive a direct threat, with serious and potentially fatal
outcome, to consider changing behaviour. Additionally, such change has to be directly
30 Chapter 3: Theoretical considerations informing intervention evaluation framework
related to a benefit that is within reach, i.e. the effort to obtain the benefit is less than
the benefit itself (see Figure 3.2).
Figure 3.2. The Health Belief Model
The HBM predictive power finds varying support in the literature. Janz and
Becker (1984) investigated 46 studies and found the HBM construct to be statistically
significant in predicting behaviour, with ratios of 81% for susceptibility, 65% for
seriousness, 78% for benefits, and 89% for barriers. However, other authors raise the
question of whether, despite good results, other theories are a better fit. For example,
Quine, Rutter and Arnold (1998) examined the predictive power of HBM through path
analysis in a longitudinal study. They looked at its ability to offer an explanation of
the factors that determine helmet use by school-aged cyclists. The study compared the
HBM results with other results, coming from comparable studies underpinned by TPB.
Quine, Rutter and Arnold (1998) found that TPB offered greater predictive ability.
Şimşekoğlu and Lajunen (2008) reported similar results. They compared the predictive
power of HBM and TPB as well as the fit of the two theories to collected self-reported
data on seatbelt use among students, front-seat passengers. Şimşekoğlu and Lajunen
(2008) reported that HBM was not a good fit for the data, while TPB was.
HBM is known to suffer from conceptual difficulties. For example, there is no
indication of how the different beliefs influence each other or how they influence the
behaviour when combined (Quine, Rutter, & Arnold, 2000). Furthermore, the model
does not consider tailoring strategies to encourage healthier behaviour as well as the
accuracy of the information about the behaviour (Mackenzie, 2016); it does not
Chapter 3: Theoretical considerations informing intervention evaluation framework 31
address the TTM limitation of focusing mainly on the individual; and it does not
consider other factors that may have an influence on one's health behaviour, such as
norms.
HBM has been applied in various fields, relevant to the current project, such as
health prevention (Janz & Becker, 1984), young people (Gillibrand & Stevenson,
2006) and human-computer interaction (Ng, Kankanhalli, & Xu, 2009). With evidence
of being successfully used in road safety studies, investigating young people's safe
behaviours, HBM could be considered relevant for the current research, which
investigated safety benefits from using two examples of COT on the young drivers'
speeding and DUI behaviour. For example, Ghavami, Harandy and Kabir (2016) used
HBM constructs in a questionnaire to assess the effect an HBM-designed intervention
had on primary school students in regards to obeying traffic rules. HBM could inform
what the likelihood is that each young driver would cease risky driving, which of the
constructs is the most relevant in predicting the behaviour, and what health promotion
advice is best suited for an intervention. However, the HBM's primary focus on the
individual makes it difficult to integrate interpersonal factors. Thus, HBM was
considered as not being the most suitable to inform the current research interventions'
evaluation framework.
3.4 SOCIAL COGNITIVE THEORY
With a history of being applied for evaluation purposes, the Bandura (1977) Self-
Efficacy Theory (SET) was considered as an overarching framework. SET aims to
explain and predict behavioural changes as a result of interventions. As the name
suggests, SET is focused on self-efficacy, i.e. on how much an individual believes they
are able to achieve specific goals. However, similar to TTM and HBM, SET alone
does not account for interpersonal factors (e.g. social norms).
SCT (Bandura, 1986) extends SET with many of its constructs being similar to
HBM constructs (Donovan, 2011). SCT suggests that humans function as a result of
interactions between their environment, personality and behaviour (see Figure 3.3).
SCT argues that personality is developed through observational learning and social
experience. The model goes beyond the SET's limitation of not accounting for
interpersonal factors, which are accounted for by the “environment” construct in SCT.
32 Chapter 3: Theoretical considerations informing intervention evaluation framework
The environment constitutes the external influences on one's behaviour while the
personality is one's own motivational factors to perform the behaviour.
The model's personality dimension includes key constructs which influence
performing the desired behaviour: 1) self-efficacy or how much an individual believes
they are able to achieve specific goals, outcome expectations or the individual's
expectations, in case they perform a behaviour; 2) self-control or how much an
individual is able to autonomously regulate their own intentions and behaviours,
reinforcements or internal or external responses to an individual's behaviour, affecting
their likelihood to continue it; and 3) observational learning or the ability of an
individual to reproduce a behaviour after observing it in others. Within these
constructs, SCT considers both self-reflection, i.e. whether an individual is able to
analyse their own behaviour critically, and potential influences of personality
characteristics.
Figure 3.3. Social Cognitive Theory Model
SCT has a long track record of being applied in fields relevant to the current
project. It was used to inform studies, concerned with health risks prevention (Miller,
Shoda, & Hurley, 1996; Schwarzer & Renner, 2000; Wallace, Buckworth, Kirby, &
Sherman, 2000), human-computer interaction (Compeau & Higgins, 1995; Ifinedo,
2016), young people (Ifinedo, 2016; Wallace et al., 2000) and their peers (Compeau
& Higgins, 1995; Ifinedo, 2016; Rana & Dwivedi, 2015; Wallace et al., 2000).
The theory has also been used to inform road safety research. For example,
Tranter and Warn (2008) used an SCT-based questionnaire to investigate the attitudes
of mature drivers towards speeding and traffic rules violations. The study found
support that personality characteristics, such as higher interest in motorsports,
associate with a higher propensity to engage in speeding behaviour and can predict
Chapter 3: Theoretical considerations informing intervention evaluation framework 33
speeding violations. Providing further evidence around speeding, Yıldırım-Yenier,
Vingilis, Wiesenthal, Mann, and Seeley (2016) used SCT to explore the relationships
between thrill-seeking, attitudes towards speeding and driving violations. Their study
found that driving offences can be directly predicted by thrill-seeking, a personality
influence in SCT. Investigating into the nature and mechanisms of influence between
SCT core constructs (personality, behaviour and environment), Scott-Parker (2012)
used the model as an overarching framework to explore young people’s risky driving
behaviour. The author found that all three factors (personality, behaviour and
environment) were associated with young drivers’ risky behaviour on the road.
While recognising its relative utility, some researchers see SCT as a collection
of logical statements that are difficult to empirically test (Smedslund, 1978). Other
researchers see its constructs as based on variables that are not well defined, and that
cannot be observed and assessed (Lee, 1989). For example, the instruments to measure
self-efficacy may not be carefully developed and validated (Frei, Svarin, Steurer-Stey,
& Puhan, 2009). In addition, Mackenzie (2016) questioned SCT's ability to account
for motivation at the moment of executing the behaviour.
The SCT could have offered a suitable theoretical grounding for this PhD
program of research, e.g. its use to explain risky behaviours in young drivers. The
model can inform interventions' evaluation design and can provide insights into the
participants' self-efficacy (How to support personal confidence in achieving behaviour
change results? What information to be provided to increase self-reflection?), and
normative influences (How to shape the intervention environment to encourage safe
behaviour on the road?). However, the lack of carefully developed and validated
measurement instruments is a notable limitation. Developing and validating
questionnaires was outside of the scope of this PhD program of research. In addition,
through its real-time feedback feature (see Subsection 7.2.3), the smartphone safe-
driving app in the Study 2 intervention could influence participants when they perform
risky driving. The SCT's questioned ability to account for motivation at the moment
of executing the behaviour would challenge its suitability to evaluate the effects of
such interactions, making the limitation a valid concern for the current program of
research. As a result, SCT was not considered a good fit for the present research
evaluation purpose.
34 Chapter 3: Theoretical considerations informing intervention evaluation framework
3.5 THEORY OF PLANNED BEHAVIOUR
The current PhD program of research envisaged interventions that can
potentially not only improve driving behaviour but also change young people's
intention to perform specific behaviours. Ajzen and Fishbein's (1980) TRA makes an
effort to predict behaviour and regards intention as the best predictor. According to
TRA, intention, in turn, is influenced by the individual’s attitudes and norms.
However, a recognised limitation of the theory is that TRA does not account for how
the individual sees the effort (easy or difficult) that has to be put in place to achieve
the desired behaviour, or, at least, to form an intention to perform it. As a response for
the need of improving the theory, Ajzen (1988) added perceived behavioural control
(PBC) to TRA, as an additional third factor, influencing both intention and behaviour,
which saw TRA evolve into TPB (see Figure 3.4).
Figure 3.4. Theory of Planned Behaviour (Ajzen, 1991)
According to TPB (Ajzen, 1991), intention to perform a behaviour is the best
predictor of future behaviour, as its immediate antecedent. In turn, intention is
predicted by three interrelated factors: 1) how favourable, or unfavourable, the
behaviour is perceived to be (attitude), 2) whether important others are perceived as
approving or disapproving the behaviour of interest (subjective norm), and 3) how
easy, or difficult, performing the behaviour is perceived to be (perceived behavioural
control, PBC) (Ajzen, 1991). PBC is also considered as a direct predictor of behaviour.
The TPB components have been used to explain various risk-related behaviours in the
health domain, e.g. safer sex, uptake of vitamin C or cycle helmet use (Rutter & Quine,
2002). Rutter and Quine (2002) provided evidence that around 40%, on average, of the
variance in both health behaviour and intention can be explained through TPB.
Chapter 3: Theoretical considerations informing intervention evaluation framework 35
Supporting the evidence for TPB's predictive power in the health domain in
general (Rutter & Quine, 2002), a body of literature reports on the theory's suitability
to evaluate constructs in relation to risky behaviours on the road, such as speeding and
DUI, which are of primary interest for this program of research.
Complex behaviours such as speeding and DUI can be difficult to evaluate.
However, the TPB has been shown to be useful in this context, e.g. by Stead, Tagg,
MacKintosh, and Eadie (2005). The authors found that the TPB constructs predicted
between 47% and 53% of the variance in the participants' speeding intention and
between 33% and 40% of the variance in the participants' speeding behaviour. Others,
such as Warner and Åberg (2008) found that TPB constructs account for up to 73% of
the variance in intention to speed; and Elliott and Thomson (2010) found that TPB
constructs predicted 55% of the variance in the participants' speeding intention and
47% of the variance in the participants' speeding behaviour. In a study focused on
young adults' DUI, Chan, Wu, and Hung (2010) found that the TPB explained 79% of
the variance in intention to drink and drive. In another study, Potard, Kubiszewski,
Camus, Courtois, and Gaymard (2018) found that the standard TPB (attitude,
subjective norm, and PBC) explained 44%, while the extended TPB (including past
behaviour with the standard constructs) explained 52% of the variance in DUI
intention. Although speeding and DUI are complex behaviours to investigate, these
studies demonstrate the TBP's suitability as an evaluation framework.
In addition, TPB was also found suitable to predict distraction in drivers, the
behaviour of secondary interest for the current program of research. Chen, Donmez,
Hoekstra-Atwood and Marulanda (2016) used the TPB framework to assess attitudes,
social norms, and PBC in drivers, engaging in distracting tasks that were not relevant
to the driving task. The authors found TPB to explain 45.2% of the variance in
distraction. Comparable findings were reported by Bazargan-Hejazi et al. (2017), who
found that TPB explained 47% of the variance in intention to engage in phone
distraction while driving.
TPB addresses some of the suitability issues identified for the TTM, HBM and
SCT in the context of this program of research. TPB is not stage-tailored and accounts
for interpersonal relations through its subjective norm, addressing the discussed TTM
limitations of bring stage-tailored and focusing on the individual. Despite not being
stage-tailored, TPB allows for tailoring strategies to encourage specific behaviour, for
36 Chapter 3: Theoretical considerations informing intervention evaluation framework
example, through interventions as in Quine, Rutter, and Arnold (2001). It also explores
different beliefs, and their influences through its attitude construct, addressing the
discussed HBM limitation of not accounting how different beliefs interact and
influence behaviour together. There are also validated questionnaires in the TPB
toolset, as in Lennon, Oviedo-Trespalacios, and Matthews (2017), Chen et al. (2016),
Haque et al. (2012), Elliott and Thomson (2010) and Quine et al. (2001), addressing
the discussed SCT limitation of lack of carefully developed and validated instruments.
3.5.1 Criticisms and limitations of TPB
A criticised TPB assumption is that it sees the person as having all the resources
and skills to enact the behaviour of interest (Mackenzie, 2016). Unconscious
influences on behaviour (Sheeran, Gollwitzer, & Bargh, 2013) are also pointed out as
a major limitation, as TPB exclusively focuses on rational reasoning. Other researchers
underline the TPB static explanatory nature as a limitation (Sniehotta, Presseau, &
Araújo-Soares, 2014). Sniehotta et al. (2014) also suggest that the main problem of
TPB is in the validity of its predictions, as the sequence of influences, it explores, is in
conflict with the available evidence. For example, shifts in behaviour, as a
consequence of intervention, are not always moderated directly through the TPB
constructs and, sometimes, when the behaviour is driven by a habit, reverse causation
is possible (Webb & Sheeran, 2006). In such cases, the intention has little influence on
the behaviour and past behaviour is a much stronger predictor to both intention and
future behaviour.
Some of those limitations can be addressed through research design. For
example, the static nature limitation could be addressed by a longitudinal design of the
studies, exploring shifts in the TPB constructs over time, as in Quine et al. (2001) and
in Stead et al. (2005). The assumption that people have the needed resources and skills
to enact a behaviour could be addressed by providing research participants with
additional resources and skills that could enable them to perform the behaviour of
interest. Other limitations require an extension of TPB, as discussed in turn.
3.6 EXTENDING TPB
Conner (2015) suggests that a more constructive approach of capitalising on the
vast body of knowledge surrounding TPB shall be adopted by extending the theory,
instead of following Sniehotta et al. (2014) proposal to retire it. For example, to
Chapter 3: Theoretical considerations informing intervention evaluation framework 37
establish causality effects, an intervention's evaluation could compare self-reported
results against shifts in the underlying TPB constructs as a result of the intervention,
as in Stead et al. (2005), or could explore past behaviour in the analysis, as in Elliott
and Thomson (2010). A deeper investigation of causality effects within TPB could
also be achieved by splitting the original TPB constructs into components and
assessing them separately (Conner & Sparks, 2005; Elliott & Thomson, 2010).
Unconscious influences can be addressed by exploring personality characteristics in
implemented studies (Sheeran et al., 2013) or demographic factors, as in Horvath et al.
(2012) (see Section 2.2. for a discussion on gender and driving experience). Conner
(2015) suggests in addition to personality characteristics, other potential predictors can
also be explored to provide further explanation on why an intervention may have
triggered a behavioural change if such is found in the first place.
The literature offered evidence for potentially useful constructs that can be used
to extend TPB to account for additional influences, i.e. 1) the normative influences,
not accounted for by TPB, of moral norm and peers' norm, 2) risk perception, and 3)
the personality characteristics impulsivity, sensitivity to punishment and sensitivity to
reward. Those are discussed in the following sections.
3.6.1 Additional normative influences
Intention is, in general, weakly predicted by the TPB subjective norm (Armitage
& Conner, 2001). At the same time, conformism influences the behaviour of young
drivers in their early stages of learning to drive (Falk et al., 2014). Acquiring the
driving license usually happens when young people are trying to find their own
identity, pursuing independence from their parents (Engström, Gregersen,
Hernetkoski, Keskinen, & Nyberg, 2003; Laapotti, Keskinen, Hatakka, & Katila,
2001) while being influenced by social norms or influencing each other through
establishing norms within their inner circle of friends. Forming driving habits, that not
only lead to behaviour in compliance with the traffic legislation but also lead to safe
traffic participation, may be critical at this time. Such an effort can potentially reduce
future efforts and costs. To extend the TPB predictive validity, Armitage and Conner
(2001) suggest the expansion of its normative component.
Friends or peers may reinforce the perception of a behaviour being right or
wrong (Conner & Sparks, 2005). Drivers may be much more influenced by their peer
drivers than they realise (Chen et al., 2016). Young drivers are very susceptible to peer
38 Chapter 3: Theoretical considerations informing intervention evaluation framework
pressure, especially when COT, such as a mobile phone (Chen et al., 2016), is involved
(Lee, 2007).
Peers have a significant impact, usually directed towards risky, unsafe and illegal
driving behaviours (Engström et al., 2003; Falk et al., 2014), and are considered as a
major source of increased crash risk for young adults (Falk et al., 2014). For example,
young drivers are more likely to engage in speeding when approved by their friends
(Fleiter et al., 2006; Horvath et al., 2012). Similarly, in the case of DUI, Sela‐Shayovitz
(2008) showed that perceived peer pressure had a significant impact on young drivers,
as well as on their involvement in DUI-related road crashes. Other researchers suggest
that the potential of peer influence can also have a positive effect, i.e. protect against
risky behaviour (Kaye, 2014; Otto, Ward, Swinford, & Linkenbach, 2014; Weston &
Hellier, 2018).
Thus, as part of interventions' evaluation, the present PhD program of research
could assess the contribution of peers' norm., i.e. whether the participants' friends are
seen as disapproving or approving of the respective participant engaging in the
behaviour of interest. For the purpose of the current PhD program of research, peers
were defined as other young people, at the same, or nearly the same age as the
respective participants, who may, or may not, be their friends, and may, or may not,
be participating as participants in the current program of research.
Regardless of the nature of or the reason for the risky behaviour, it may also be
negatively influenced due to the fact that behaviour is perceived as "normal" in the
first place (Ward et al., 2017). The predictive role of moral norm is argued to be distinct
from the standard TPB constructs (Conner & Sparks, 2005; Manstead, 2000). The
individual's moral norm would see engaging in a behaviour perceived as correct or
incorrect from a personal perspective and, thus, assessing it may complement
evaluations of other norms such as the subjective norm (Ajzen, 1991). Building on
that, as part of the interventions' evaluation, the present PhD program of research could
assess the contribution of moral norm.
3.6.2 Risk perception
Tay (2005) established that avoiding negative consequences is the motivation
behind specific behaviour. He indicated the importance not only of the probability of
punishment but also the individual risk tolerance (of getting punished, for example).
Chapter 3: Theoretical considerations informing intervention evaluation framework 39
People decide whether taking a risk is justified based on a number of factors, including
the perception of vulnerability, the severity of the threat and potential benefits
(Hochbaum et al., 1952). A better understanding of risk perception in a specific
population may provide additional insights when assessing the impact of an
implemented intervention on the involved participants. Risk perception was shown to
have a strong influence on risky driving intention (Ward et al., 2017) and behaviour
(Rhodes & Pivik, 2011), including speeding and DUI (Fernandes et al., 2010), the
behaviours of current inquiry. Thus, the participants’ perceived risk of being involved
in a crash or of being caught by the police while performing specific behaviour could
be assessed within the current PhD program of research.
3.6.3 Personality characteristics
Personality characteristics are influential and, as such, have been explored
widely in previous research in an effort to explain young people’s risk-taking (Scott-
Parker, 2012). Young drivers’ personality characteristics have been studied in relation
to each of the fatal five risky behaviours on the road: speeding (Tao, Zhang, & Qu,
2017), DUI (Fernandes et al., 2010; Luk et al., 2017), not wearing a seatbelt (Fernandes
et al., 2010), fatigue (Fernandes et al., 2010) and distraction (Parr et al., 2016).
The impact of personality on young drivers’ behaviour such as speeding and DUI
can, therefore, not be dismissed. In regards to those behaviours, the literature provides
evidence for the influence of the following personality characteristics: impulsivity,
sensitivity to punishment and sensitivity to reward.
Impulsivity was previously studied in relation to young drivers (Scott-Parker,
2012). It is seen as the young drivers' risky driving most robust predictor (Luk et al.,
2017). A number of studies have shown a positive relationship between self-reported
risky driving and impulsivity (Constantinou et al., 2011; Pearson, Murphy, & Doane,
2013). Thus, it may be critical to assess its contribution when evaluating the impact of
an intervention.
Sensitivity to punishment and sensitivity to reward were also found to predict
risky driving (Constantinou et al., 2011). Castellà and Pérez (2004) established that
sensitivity to reward was positively correlated, while sensitivity to punishment was
negatively correlated with traffic rules violations. In a recent literature review,
Sensitivity to reward was further shown to have a high negative impact on the young
40 Chapter 3: Theoretical considerations informing intervention evaluation framework
drivers' risky driving behaviours (Scott-Parker & Weston, 2017). With evidence
whether young drivers are more influenced by the perspective of being punished or by
the chance of being rewarded, an intervention can take completely different forms. The
intervention designers can choose whether to stress more on its negative (punishment)
or on its positive (rewards) aspects, depending on their expectations for the target
group composition.
In this PhD program, impulsivity, sensitivity to punishment and sensitivity to
reward were therefore considered to provide an understanding regarding the effect of
the implemented interventions.
3.7 CONCLUSION
Evidence for new behaviour adoption is needed to confirm successful behaviour
change (Bandura, 1986). In the case of interventions, evaluating their achievements
through an appropriate theoretical framework would provide the necessary insights
(Kohler et al., 1999). Researchers use different behavioural theories to underpin
evaluations, depending on the specifics of the interventions, they investigate. The
presented review explored some of the most widely applied theories in the social and
behavioural sciences literature. However, given the characteristics of the current PhD
program of research, not all of them were found suitable to guide the present research
evaluation framework.
Building on the wealth of existing TPB literature, an extended TPB (Ajzen,
1988) framework was considered most suitable to inform the evaluation of the two
interventions (see Figure 3.5), part of this PhD program of research, where the
influence of two COTs on young drivers' intention and behaviour was investigated in
regards to reducing speeding and DUI. Both interventions provided an opportunity to
collect, analyse and compare self-report (through surveys) and limited observational
(through a chosen smartphone app leaderboard) data. Analysing the collected data
through the extended TPB framework could both address known TPB limitations and
provide insights on changes in the participants' driving intention and behaviour.
Examining TPB constructs and additional predictors could build a complete picture of
which construct accounted for changes in the participants’ intentions and self-reported
behaviours, and where the intervention had a greater effect.
Chapter 3: Theoretical considerations informing intervention evaluation framework 41
Figure 3.5. Extension of the Theory of Planned Behaviour in the current program of research.
Overall, the extended TPB (Figure 3.5) was considered a potential good fit for
the present PhD program of research with respect to a better understanding of young
drivers' salient beliefs towards speeding and DUI. The following research design
chapter provides details on how this extended TPB was operationalised in terms of
surveys, variables and applied analysis.
42 Chapter 4: Research design
Chapter 4: Research design
Chapter 4 describes the research design methodology that was adopted to
achieve the aim and objectives of this program of research. The current thesis aimed
to close the identified research gap (see Section 2.5) by examining the effects of two
examples of COTs, a smartphone safe-driving app and VR simulations of risky
driving. As discussed earlier, the research investigation pursued three key objectives
to accomplish the aim:
1. Understand to what extent the use of those two COTs is associated in the
literature with safety benefits for young drivers;
2. Identify an example of each of two COTs that could potentially persuade
young drivers to adopt safer on-road behaviour; and
3. Investigate the extent to which such behavioural change happened, as a result
of two real-world interventions with the two example COTs as intervention tools.
First, the Chapter defines the research questions, answered later in the thesis to
address those three objectives. Then, it provides details on establishing theory-based
selection criteria. Finally, a general overview of the adopted research methodology
within the current program of research is presented before discussing in more detail
the two intervention studies’ design.
4.1 RESEARCH QUESTIONS
The identified research gap (see Section 2.5) called for an investigation of the
real-world impact of COTs that are readily available to young drivers to experience,
such as smartphones safe-driving apps and VR simulations of risky driving. The
limited knowledge about their safety benefits required a systematic approach to start
closing the gap.
Building on the generated knowledge from the literature, focusing on using safe-
driving apps in research (see Subsection 2.4.2), a need for a more in-depth systematic
review was identified to explore research question one:
RQ1. What is the state of the art evidence of the safety benefits of smartphone
safe-driving apps for young drivers?
Chapter 4: Research design 43
A large number of smartphone safe-driving apps is available online today. A
larger number of smartphone safe-driving apps exist when taking into consideration
those available outside app stores, e.g. when developed for research purposes only.
The wide availability of smartphone safe-driving apps is contrasted against limited
scientific evidence for a positive impact on their users in real life. Unfortunately,
despite this lack of evidence, smartphone safe-driving apps’ developers may be
tempted to claim that such positive impact exists as part of their marketing efforts, e.g.
based on monitoring driving behaviour through the app itself, which can include
detection of risky events, such as speeding. Overall, the fact that speeding plays such
a significant role in young drivers' safety, the rapid increase in the number of available
safe driving apps, and the limited evidence about these apps' safety impact in a free-
living environment, led to research question two:
RQ2. How do young drivers’ self-reported behaviour of not speeding and
intention not to speed alter in their free-living environment, as a result of exposure to
a smartphone safe-driving app intervention?
Additionally, the adopted extended TPB framework (see Section 3.7) allowed
exploring in-depth the two constructs of interest, i.e. self-reported behaviour of not
speeding and intention not to speed. Such a focus could potentially supply more
detailed information on the effect the smartphone safe-driving app intervention had,
e.g. the influence of which constructs could be explored further to increase the impact.
Assessing self-reported behaviour of not speeding and intention not to speed would
allow determining 1) what the young drivers planned to do in the absence of an
intervention, 2) how the intervention impacted them, and 3) was it possible to predict,
with the information, available for the drivers before they were subjected to an
intervention, how they actually behaved after receiving the intervention. Thus, the
following secondary questions were addressed in the case of the smartphone safe-
driving app intervention:
RQ2.1. What did we know about the participants before the intervention, and
to what extent could the extended TPB framework predict their intention not
to speed?
RQ2.2. Did the intervention change the participants' salient beliefs, as
depicted by the TPB constructs?
44 Chapter 4: Research design
RQ2.3. Using the extended TPB framework, to what extent could the data
available before the intervention predict the participants' behaviour of not
speeding during the intervention?
RQ2.4. How did the intervention influence the participants’ engagement with
their smartphones?
Investigating the above research questions further our understanding to what
extent the use of one smartphone safe-driving app may be associated with safety
benefits for young drivers. This understanding can potentially provide support for
using such apps in the framework of novel technology-based approaches to reduce
road trauma.
As part of this research's aim to examine COTs more broadly, another COT was
examined, Virtual Reality (VR). VR is less widely spread, compared to smartphones,
but technology advancements and sinking prices are leading to continued proliferation.
In the road safety context, VR (see Subsection 2.4.3) provides a unique opportunity to
simulate potentially life-threatening situations in a safe environment. While
experiencing VR, users can perform designated tasks, which may assist them in taking
a different perspective through the simulated experience. As discussed earlier, such
shifts in the individual perspective-taking may lead to behavioural change. Due to the
novelty of the technology, however, there is limited knowledge about VR's potential
to deliver safety benefits in parallel with such behavioural change, which led to
research question three:
RQ3. How is VR applied in road safety research to motivate behavioural change
in young drivers?
In the light of the limited knowledge about safety benefits from using VR
technology applications, ongoing global interventions utilise VR to raise awareness on
the risks of DUI, another behaviour with a high negative impact on young drivers (see
Section 2.1). At the same time, the added value of such VR simulations of risky driving
as a tool in those real-world interventions had not yet been evaluated. Therefore,
research question four was:
RQ4. How do young drivers’ self-reported behaviour of not DUI and intention
not to DUI alter in their free-living environment as a result of a VR intervention?
Chapter 4: Research design 45
Similar to the case with the smartphone safe-driving app intervention, the
adopted extended TPB framework allowed exploring in-depth self-reported behaviour
of not DUI and intention not to DUI through the following secondary questions:
RQ4.1. What did we know about the participants before the intervention, and
to what extent could the extended TPB framework predict their intention not
to DUI?
RQ4.2. Did the intervention change the participants' salient beliefs, as
depicted by the TPB constructs?
RQ4.3. Using the extended TPB framework, to what extent could the data
available before the intervention predict the participants' behaviour of not
DUI after the intervention?
Overall, it was argued that, grounded in the extended TPB evaluation
framework, the two COTs-based interventions, leveraging a safe-driving app and VR
simulations of risky driving, could reduce young drivers’ speeding or DUI,
respectively. Therefore, by separately answering the research questions formulated
above, the following overarching research question was assessed:
How do COTs-based interventions influence young drivers?
This overarching research question is aligned with the overall aim of this PhD
thesis. The research questions, defined above, align with individual studies and the
overall adopted methodology, as discussed in the following sections.
4.2 THE EXTENDED TPB AND SELECTING INTERVENTION TOOLS
To be able to influence people, i.e. change behaviour, Ajzen (2006) suggests that
interventions be designed towards influencing one or more of its predictors. However,
Ajzen (2006) does not specify what interventions (e.g. face-to-face, media campaign
or others) may deliver that influence. Fife‐Schaw, Sheeran, and Norman (2007) agree
that TPB is silent in guiding appropriate strategies to influence its basic constructs.
The intervention intended to influence the young participating drivers by using
two COTs as tools, a smartphone safe-driving app in Study 2 and a VR simulation of
DUI in Study 4. Even though there is a lack of specific strategic guidance, the adopted
extended TPB evaluation framework (see Section 3.7) can still serve as a starting point
in establishing criteria for selecting examples of such COTs. Such criteria will
46 Chapter 4: Research design
explicitly identify which of the extended TPB underlying constructs could potentially
be influenced by the two implemented COTs-based interventions.
An intervention cannot change all extended TPB constructs. Thus, the current
section focuses on 1) what variables in the extended TPB model can be influenced, 2)
how a decision can be made whether the respective COT can influence them, and 3)
which of them have been shown to be most influential in predicting the targeted driver
behaviours.
Regarding the first point (what), it is noted that no intervention can change
demographic variables. Furthermore, Personality characteristics, such as impulsivity
and sensitivity, are perceived as relatively stable (Scott-Parker, 2012). Thus, they are
not seen as potentially changeable in the short term, either. Ajzen (2006) suggests that
interventions should focus on salient beliefs as they are readily accessible and might
be influenced. Thus, the 6 potentially changeable constructs (attitude, norms, PBC,
moral norm, peers' norm and perceived risk) can be used to provide guidance on the
selection of COTs within the current extended TPB framework.
Regarding the second point (how), the lack of TPB insights on what could
potentially be successful in influencing its constructs (Ajzen, 2006; Fife‐Schaw et al.,
2007) leave the adopted constructs' definitions themselves (see Section 3.5 and Section
3.6) as a starting point of establishing selection criteria for COTs (see Table 4.1). Those
selection criteria were formed as binary questions, directly pointing at the respective
construct's definition. A positive (yes) answer carries 1 point. A negative (no) answer
does not carry points. The number of positive answers determines the relevant COT
ranking. The selection criteria were applied in Chapter 6 and Chapter 9.
Elaborating on the third point (most influential), the selection criteria might
consider not only the constructs but also their potential to mediate the desired changes.
The literature agrees that intention is a stronger predictor of behaviour than PBC
(Armitage & Conner, 2001; Fife‐Schaw et al., 2007; Hagger & Chatzisarantis, 2005).
However, the evidence around predicting intention is mixed. Norms are generally seen
as having lower predictive power in TPB than attitude and PBC (Armitage & Conner,
2001; Fife‐Schaw et al., 2007; Hagger & Chatzisarantis, 2005). Fife‐Schaw et al.
(2007) point out attitude as typically having the strongest predictive value. However,
assumptions on predictive value should better be made in regards to the specific
Chapter 4: Research design 47
behaviour of interest. Those behaviours in the case of the current program of research
are speeding and DUI.
Table 4.1. COTs' selection criteria, derived from the extended TPB framework.
Selection
criteria (SC) N Construct Definition Question
SC1 Attitude
How the driver sees the
behaviour, favourable or
unfavourable.
Does the COT help the driver better
see whether their behaviour on the
road is favourable or unfavourable?
SC2 Norms
Whether the driver's important
referents would approve or
disapprove their engagement in a
particular behaviour.
Does the COT provide information on
how the driver's important referents
see their behaviour?
SC3 PBC
How easy, or difficult, the driver
perceives performing the
behaviour.
Does the COT improve the driver's
ability to perform the behaviour?
SC4 Moral norm Whether the driver perceives the
behaviour as "normal".
Does the COT help the driver
understand the morality of their
behaviour?
SC5 Peers' norm
Whether the driver's peers are
seen as disapproving or approving
of the respective participant
engaging in the behaviour.
Does the COT provide information on
how the driver's peers perform the
behaviour?
SC6 Perceived
risk
Whether the driver perceives a
risk of being involved in a crash
or being caught by the police
while performing a specific
behaviour.
Does the COT provide information on
the possibility the driver to be
involved in a crash or to be caught by
the police while performing the
behaviour?
A review of TPB studies focused on speeding indicates that PBC, and not
attitude, is the strongest predictor of intention (Elliott and Thomson, 2010; Stead et
al., 2005; Warner & Åberg, 2008). While Stead et al. (2005) and Elliott and Thomson
(2010) provide support for attitude having the second strongest value and confirm
norms as having the lowest contribution, Warner and Åberg (2008) found the reverse.
In DUI studies, PBC was found as the strongest predictor by Moan and Rise (2011)
and Potard et al. (2018), followed by norms and then attitude. Chan, Wu, and Hung
(2010) provide evidence that attitude has the strongest influence on intention, followed
by PBC and norms. Given that typically PBC is the strongest predictor of intention in
48 Chapter 4: Research design
the case of speeding and DUI, interventions may focus on using COTs that are
expected to influence PBC. Thus, a positive answer to the PBC-related question should
be weighted more highly when using the questions as criteria for selecting the most
promising COT.
4.3 METHODOLOGY
This PhD program of research used a mixed-methods design, consisting of both
qualitative and quantitative approaches (Figure 4.1). Similar research tracks were used
to expand the knowledge around the real-world effects of using both COTs,
smartphone safe-driving apps and VR simulations of risky driving, as intervention
tools. They were structured as follows:
As the first step within each track, Study 1 and Study 3 followed PRISMA,
evidence-based guidelines on a minimum set of items for systematic reviews reporting.
The two studies investigated the two COTs’ characteristics and their effects on young
drivers' behaviour and safety, as evidenced by the available literature. Both systematic
reviews confirmed the research gap, identified in Section 2.5. The systematic reviews
identified limited knowledge about safety benefits in the real world, delivered to young
drivers by smartphone safe-driving apps in respect of reducing speeding, and by VR
simulations of risky driving in respect of reducing DUI.
The second step focused on selecting suitable examples of the two COTs, a
smartphone safe-driving app and VR simulations of risky driving, to be used as
intervention tools. The established theory-based selection criteria (see Section 4.2)
were used to evaluate potential candidates. Chapter 6 enriched the knowledge,
generated by Study 1, with insights from a Focus group and a systematic review of
smartphones apps stores, to inform the choice of a safe-driving app. As a result, Flo
was selected as a good fit to be assessed within this specific program of research. At
the same time, VR apps stores are not as richly stocked as the smartphones apps stores.
Road safety VR software for ordinary consumers is still not available. Thus, selecting
VR software as an intervention tool was not performed systematically. Rather, based
on convenience, the software "3D Tripping" was obtained from its developers to be
deployed as an intervention tool in the framework of the current program of research.
Chapter 4: Research design 49
RESEARCH TRACKS
SAFE-DRIVING APPS VIRTUAL REALITY
Step 1 Study 1 (Chapter 5) Study 3 (Chapter 8)
Systematic Review of Safe-driving Apps Systematic Review of VR
RQ1: What is the state of the art evidence of
the safety benefits of smartphone safe-driving
apps for young drivers?
RQ3: How is VR applied in road safety
research to motivate behavioural change in
young drivers?
METHOD: Systematic Review (adhering to
the PRISMA guidelines)
METHOD: Systematic Review (adhering to
the PRISMA guidelines)
ANALYSIS: Narrative ANALYSIS: Narrative
Step 2 Chapter 6
Selecting a safe-driving app
Step 3 Study 2 (Chapter 7) Study 4 (Chapter 9)
Intervention with an off-the-shelf smartphone
safe-driving app
Intervention with VR simulations of risky
driving
RQ2: How do young drivers’ self-reported
behaviour of not speeding and intention not to
speed alter in their free-living environment, as
a result of exposure to a smartphone safe-
driving app intervention?
RQ4: How do young drivers’ self-reported
behaviour of not DUI and intention not to DUI
alter in their free-living environment as a result
of a VR intervention?
METHOD: Intervention with cross-sectional
pre- and post-surveys, complemented by
driving scores from safe-driving app
leaderboard.
METHOD: Intervention with cross-sectional
pre- and post-surveys.
ANALYSIS: Hierarchical multiple regression,
Analysis of covariance.
ANALYSIS: Logistic regression, McNemar's
test, Chi-square test for independence,
Wilcoxon Signed Ranks Test.
Figure 4.1. Outline of the overall thesis methodology
The third and final step was to investigate how the selected two examples of
COTs influenced young drivers in their free-living environment as part of two
interventions. Each intervention leveraged one of the two COTs. Surveys were
administered before the interventions took place to establish a baseline on all repeated
measures. A second self-reported set of data was collected from the participants
50 Chapter 4: Research design
approximately three months after the first survey. Collecting data at two time points
allowed assessing the long-term effects of the two interventions, as depicted by those
repeated measures. As a result, the two intervention studies were designed to follow a
similar process. The section below provides details on this design, which was
developed before the actual interventions took place, i.e. at the point of seeking ethical
clearance.
4.4 INTERVENTION STUDIES’ DESIGNS
The common features of the studies at step three of the two research tracks are
described in the following subsections of this chapter. Details on their actual
implementation as well as on the obtained results that are specific for each separate
study, such as design, participants and procedures, are later described in separate
sections in the respective chapters. This separated reporting avoids repetition in the
respective chapters due to the common features.
4.4.1 Participants
Study 2 and Study 4 involved participants. A short overview is provided below
with more details included in Chapter 7 and Chapter 9.
Study 2 and Study 4 tested the predictability of the TPB constructs and the
additional variables through regression. A higher number than advised by statistical
texts was targeted in anticipation for possible dropout during the second data
collection. Tabachnick and Fidell (2007) specify that n=>104 + m, where m is the
number of predictors, is needed so that separate regressions can be conducted in each
setting.
In order to participate in Study 2 or in Study 4, a participant had to be aged 18
to 25 with a valid driver's license. In Study 2, an additional criterion was that the young
drivers had to drive a car a minimum of 100 kilometres per month, and use a car as the
only means of transport (to avoid collecting data that originates in travelling by means
other than a car). The target number of participants in Study 2 was 140. However, as
the study methodology allowed for a larger number of participants, which would have
increased the statistical significance of the obtained results, more participants were
expected to be recruited. Thus the potential number of participants was capped at
1,000. In Study 4, the additional criteria "Have no history of seizures or epilepsy" was
used when recruiting the Intervention group due to a number of potential risks
Chapter 4: Research design 51
associated with the use of the VR headset while seated in a static position. Initially,
the target number in Study 4 was set for a minimum of 200 participants per condition
to anticipate potential dropouts.
Participants in both Study 2 and Study 4 were expected to perform self-screening
if they meet the inclusion criteria to participate before they consent and complete a
survey. Consent was required for all participants. It was specific to the respective study
and was implied, i.e. such was considered obtained after a participant went through the
study information sheet, generated their anonymous identifier, according to a
predefined formula, and started completing the survey. An anonymous identifier was
generated by the respective participants themselves, as per a predefined formula, and
included: day of birth, first letter of first name, first letter of family name and last two
digits of mobile number (example 24DL08). Those anonymous identifiers were used
in both interventions to connect data sets for the same participants from the two times
they completed the respective surveys.
The participants' e-mail addresses were kept so that they can be contacted to
complete the second survey, approximately three months after the first one. However,
the records linking their anonymous identifier with their e-mails were destroyed after
the first time they completed a survey. After destroying the link, the anonymous
identifiers were the only means to connect datasets that originated from the same
person. Only through those datasets, upon destruction of the link, it was impossible for
the person to be identified.
Participants in Study 2 and in the Control group of Study 4 were recruited
through Facebook. Participants in the Intervention group of Study 4 needed to be
present in person to experience the VR simulation of DUI. Thus, convenience
sampling was used for their recruitment through live events. Two tablets with Internet
connection were available for them to complete the survey before they were admitted
to operate the VR driving simulator. A detailed description of the Study 2 recruitment
process is provided in Section 7.2. Section 9.2 provides details on the Study 4
recruitment process.
4.4.2 Surveys
The current project interventions were evaluated through an extended TPB
theoretical framework (see Figure 3.5) to overcome some TPB limitations (see Section
52 Chapter 4: Research design
3.5) relevant to the current program of research. Following Elliott and Thomson's
(2010) work, the current PhD program of research assessed potential separate effects
of dichotomised standard TPB constructs (attitude, subjective norm, and PBC, which
were defined in Section 3.5). For the purpose of the current PhD program of research,
attitude was dichotomised into instrumental attitude (cognitive), determined by how
the driver sees the behaviour, e.g. good or bad, and affective attitude (emotional),
determined by how they feel about the behaviour, e.g. will they enjoy the behaviour or
choose to not perform the behaviour, etc. Subjective norm, referring to whether
individual's important referents would improve or disapprove their engagement in a
particular behaviour, and descriptive norm, or whether those important referents are
thought to perform the behaviour themselves, were explored separately. Self-efficacy,
i.e. the individual's ability to perform the behaviour, and perceived controllability, i.e.
whether the environment would constraint or provide an opportunity for the behaviour
to be performed, were assessed as separate predictors, too.
A review of the literature provided support for such a decision. For example,
affective attitude (Lawton, Parker, Manstead, & Stradling, 1997; Rhodes & Pivik,
2011) and instrumental attitude (Elliott & Thomson, 2010) can be distinguished as
separate predictors of intention. A meta-analysis of twenty-one hypotheses (total
sample N = 8097) showed both descriptive norm and subjective norm as statistically
significant independent predictors, too (Rivis & Sheeran, 2003). Self-efficacy and
perceived controllability are empirically separable (Ajzen, 2002), and they are used in
such a manner in road safety research, e.g. in Horvath et al. (2012). These reports
corroborate the approach of using split components of the standard TPB measures,
which in turn allowed the establishment of readily distinguishable causal effects.
The evaluation of the interventions within this PhD program of research utilised
additional predictors in addition to TPB while assessing for a behavioural change that
might have been triggered by the intervention. Changeable in the short-term
constructs, past behaviour (Conner & Sparks, 2005; Elliott & Thomson, 2010; Gauld,
Lewis, White, & Watson, 2016; Haque et al., 2012), moral norm (Conner & Sparks,
2005; Elliott & Thomson, 2010; Manstead, 2000), peers' norm (Conner & Sparks,
2005; Fleiter et al., 2006) and perceived risk (Gannon, Rosta, Reeve, Hyde, & Lewis,
2014; Haque et al., 2012; Rhodes & Pivik, 2011), previously being utilised to extend
TPB, were explored together with more stable personality characteristics (impulsivity,
Chapter 4: Research design 53
sensitivity to punishment and sensitivity to reward), previously found to be relevant to
young drivers (Scott-Parker, 2012). Thus the rational reasoning, on which TPB is
criticised to be exclusively focused (Sniehotta et al., 2014), was complemented by
measuring potential unconscious influences (Sheeran et al., 2013), which originated in
people's distinctive personality.
Both interventions employed online surveys for the participants. The surveys
consisted of three parts (see Appendices C and D for the complete surveys) appearing
in fixed order both at Time 1 (before the intervention) and Time 2 (after the
intervention) (see Table 4.2). The surveys at Time 1 and Time 2 of each intervention
were offered approximately three months apart in both interventions. Three months
were considered enough for the participants to have had the opportunity to perform the
risky behaviour of interest (Bingham et al., 2011). Each time the survey was expected
to take approximately 10 minutes to complete.
Part 1 of the surveys, demographic data, contained 4 items in Study 2 and 3 items
in Study 4: age, gender, type of driver's license (in Study 2) or driving experience (in
years, in Study 4) and state of residence (measured in Study 2 only). Demographic
data in Study 4 were collected at Time 1 only. At Time 2 in Study 4, the Intervention
group participants, only, were asked what did they choose to experience when driving
the driving simulator with "3D Tripping" VR software: alcohol, ecstasy, magic
mushrooms or cannabis.
Part 2 of the survey contained 16 repeated-measure items in Study 2 and 13 in
Study 4. Single items were used to measure theory components to maximize response
rate and minimise fatigue biases (Hart, Rennison, & Gibson, 2005) in the domains of
speeding (Study 2) and DUI (Study 4). 11 items were adapted from Elliott and
Thomson (2010) to measure TPB variables as well as the additional predictors: past
behaviour, moral norm and peers' norm. Detailed information on the level of
adaptation is provided in Table 4.3 (see Subsection 4.4.3). Elliott and Thomson (2010)
developed and validated their scales through a study involving 1403 participants. The
participants were English drivers, within the age range 18 to 91, caught for speeding,
up to four months before the study. Internal consistency (Cronbach’s α) ranged from
0.84 to 0.97 for the different scales. The other additional predictors were two items
adapted from Gannon et al. (2014) to measure perceived risk and three items borrowed
from Gauld et al. (2016), and used only in Study 2, to measure smartphone use to
54 Chapter 4: Research design
examine whether the smartphone safe-driving app did not increase distraction amongst
the participants.
Table 4.2. Constructs and time of measurement.
Construct No. of
items
Study 2
Measured at...
Study 4
Measured at...
Source
Time
1
Time
2
Time
1
Time
2
Demographic data 4 / 3 ✓ ✓ ✓
N.A. VR experience 1 ✓
Intention 2 ✓ ✓ ✓ ✓
Adapted from Elliott and
Thomson (2010)
Attitudes 2 ✓ ✓ ✓ ✓
Norms 4 ✓ ✓ ✓ ✓
PBC 2 ✓ ✓ ✓ ✓
Past behaviour 1 ✓ ✓ ✓ ✓
Risk perception 2 ✓ ✓ ✓ ✓ Adapted from Gannon et al.
(2014)
Smartphone use 3 ✓ ✓ Gauld et al. (2016)
Impulsivity 30 ✓ ✓ Patton and Stanford (1995)
Sensitivity to
reward
24 ✓ ✓
Torrubia, Ávila, Moltó, and
Caseras (2001) Sensitivity to
punishment
24 ✓ ✓
Part 3 of the survey was different at Time 1 and Time 2. It explored the
participants' personality characteristics. As those were not expected to change in the
short term, the related questionnaires were administered only once. At Time 1, data
was collected on the participants' impulsivity, using the 30-item Barratt Impulsiveness
Scale Version 11 (BIS-11) on 4-point Likert scales (Patton & Stanford, 1995). Internal
Chapter 4: Research design 55
consistency (Cronbach's α) of BIS-11 varies between .79 and .83 (Patton & Stanford,
1995). At Time 2, data was collected on the participants' sensitivity to punishment and
sensitivity to reward, using the 48-item Sensitivity to Punishment and Sensitivity to
Reward Questionnaire (SPSRQ) in yes/no format (Torrubia et al., 2001). SPSRQ
returns two scores for each individual as a result. One represents their sensitivity to
punishment and the other their sensitivity to reward. The higher the score, the more
sensitive the person is. Torrubia et al. (2001) reported a sensitivity to punishment
Cronbach's α of .82 for females and .83 for males, and a sensitivity to reward
Cronbach's α of .75 for females and .78 for males.
4.4.3 Variables
Participants' speeding (Study 2) or DUI (Study 4) intention and past behaviour
were used both as outcome variables (dependent variables, DVs) and as predictors
(independent variables, IVs). Thus, they are included in the following description of
the predictor variables.
TPB items, adapted from Elliott and Thomson (2010) (see Table 4.3 for the
adaptations with the changes being underlined), were used to measure the TPB
constructs using 9-point scales (scored 1–9). Intention to perform the behaviour of
interest, speeding or DUI, (2 items) focused on 1) what the driver believes will happen
and 2) what the driver believes is possible to happen. Attitudes towards the behaviour
of interest (2 items) focused on drivers' 1) cognitive (instrumental) attitude and 2)
emotional (affective) attitude. Norms (2 items) explored 1) what would people, who
are important to the driver, think of them violating the behaviour of interest (subjective
norm) and 2) what those people will do themselves according to the driver (descriptive
norm). PBC (2 items) explored 1) the drivers' own view of their abilities (internal
factors, self-efficacy) and 2) whether it would be possible for them to use those abilities
in real life (external factors, perceived controllability). Example question: To what
extent do you intend to drive faster than the speed limit over the next 3 months?
(Possible answers: No extent at all (1) to A great extent (9)).
Some of the additional predictors used in the surveys were also adapted from
Elliott and Thomson (2010) (see Table 4.3 for the adaptations with the changes being
underlined) and measured on 9-point Likert scales (scored 1–9). Past behaviour,
speeding (in Study 2) or DUI (in Study 4), explored the drivers' recall of their actual
behaviour in the recent three months. Two items were used to measure additional
56 Chapter 4: Research design
norms: 1) what would drivers' peers, in particular, think of the driver behaviour (peers'
norm); and 2) the drivers' understanding towards their own behaviour (moral norm).
Example question: Would your friends disapprove or approve of you driving under the
influence of alcohol or drugs over the next 3 months? (Possible answers: Definitely
disapprove (1) to Definitely approve (9)).
Table 4.3. Items, adapted from Elliott and Thomson (2010).
Construct Original item Item used in Study 2 Item used in Study 4
Intention
To what extent do you
intend to drive faster than
the speed limit over the next
6 months?
To what extent do you
intend to drive faster than
the speed limit over the next
3 months?
To what extent do you
intend to drive under the
influence of alcohol or drugs
over the next 3 months?
Intention
How often do you think you
will drive faster than the
speed limit in the next 6
months?
How often do you think you
will drive faster than the
speed limit in the next 3
months?
How often do you think you
will drive under the
influence of alcohol or drugs
in the next 3 months?
Instrumental
attitude
How bad or good would it
be for you personally if you
drove faster than the speed
limit over the next 6
months?
How bad or good would it
be for you personally if you
drove faster than the speed
limit over the next 3
months?
How bad or good would it
be for you personally if you
drove under the influence of
alcohol or drugs over the
next 3 months?
Affective
attitude
How unenjoyable or
enjoyable would it be for
you personally if you drove
faster than the speed limit
over the next 6 months?
How unenjoyable or
enjoyable would it be for
you personally if you drove
faster than the speed limit
over the next 3 months?
How unenjoyable or
enjoyable would it be for
you personally if you drove
under the influence of
alcohol or drugs over the
next 3 months?
Subjective
norm
Would the people who are
important to you disapprove
or approve of you driving
faster than the speed limit
over the next 6 months?
Would the people who are
important to you disapprove
or approve of you driving
faster than the speed limit
over the next 3 months?
Would the people who are
important to you disapprove
or approve of you driving
under the influence of
alcohol or drugs over the
next 3 months?
Descriptive
norm
How often do you think the
people who are important to
you will drive faster than
the speed limit over the next
6 months?
How often do you think the
people who are important to
you will drive faster than
the speed limit over the next
3 months?
How often do you think the
people who are important to
you will drive under the
influence of alcohol or drugs
over the next 3 months?
Self-efficacy How confident are you that
you will be able to avoid
How confident are you that
you will be able to avoid
How confident are you that
you will be able to avoid
Chapter 4: Research design 57
driving faster than the speed
limit over the next 6
months?
driving faster than the speed
limit over the next 3
months?
driving under the influence
of alcohol or drugs over the
next 3 months?
Perceived
controllability
Over the next 6 months,
how much do you feel that
avoiding driving faster than
the speed limit is under
your control?
Over the next 3 months,
how much do you feel that
avoiding driving faster than
the speed limit is under
your control?
Over the next 3 months, how
much do you feel that
avoiding driving under the
influence of alcohol or drugs
is under your control?
Moral norm
How wrong do you think it
would be for you to drive
faster than the speed limit
over the next 6 months?
How wrong do you think it
would be for you to drive
faster than the speed limit
over the next 3 months?
How wrong do you think it
would be for you to drive
under the influence of
alcohol or drugs over the
next 3 months?
Past speeding
behaviour
How often did you drive
faster than the speed limit
over the last 6 months?
How often did you drive
faster than the speed limit
over the last 3 months?
How often did you drive
under the influence of
alcohol or drugs over the last
3 months?
Peers' norm
Would the people who are
important to you disapprove
or approve of you driving
faster than the speed limit
over the next 6 months?
Would your friends
disapprove or approve of
you driving over the speed
limit over the next 3
months?
Would your friends
disapprove or approve of
you driving under the
influence of alcohol or drugs
over the next 3 months?
Perceived risk (2 items), adapted from Gannon et al. (2014) (see Table 4.4 for
the adaptations with the changes being underlined), was measured on 9-point scales
(scored 1–9) focusing on 1) how the drivers' perceived the risk of being involved in a
road crash and 2) whether they worry about being caught by the Police. Example
question: If you were to drive over the speed limit over the next 3 months, how much
would you worry about being involved in a road crash? (Not at all worried to worried
very much).
Smartphone use (3 items from Gauld et al. (2016) used only in Study 2) was
explored with a focus on the drivers' behaviour with respect to 1) initiating (less)
communication; 2) monitoring/reading (less) communication; and 3) responding (less)
to communication, on a 7-point scale where each score was related to a certain
frequency of use (more than once per day; daily; 1–2 times per week; 1–2 times per
month; 1–2 times per 3 months; once a year; never). Example question: How often do
you do the following on your smartphone while driving: Initiate communication on
58 Chapter 4: Research design
social interactive technology? (Starting a communication) (Possible answers: More
than once per day; Daily; 1–2 times per week; 1–2 times per month; 1–2 times per 3
months; Once a year; Never).
Table 4.4. Items, adapted from Gannon et al. (2014).
Construct Original item Item used in Study 2 Item used in Study 4
Perceived
risk
If you were to drink
walk, how much
would you worry
about being involved
in a road crash?
If you were to drive over the
speed limit over the next 3
months, how much would
you worry about being
involved in a road crash?
If you were to drive over the next
3 months under the influence of
alcohol or drugs, how much
would you worry about being
involved in a road crash?
If you were to drink
walk, how much
would you worry
about being involved
in a road crash?
If you were to drive over the
speed limit over the next 3
months, how much would
you worry about being
caught by the Police?
If you were to drive over the next
3 months under the influence of
alcohol or drugs, how much
would you worry about being
caught by the Police?
All items described above were repeated-measures, thus used both at Time 1 and
Time 2. Data on personality characteristics, used as additional predictors in the
analysis, were collected only one time per intervention.
BIS-11 (Patton & Stanford, 1995) was used at Time 1 of the interventions to
measure impulsivity as a construct. BIS-11 is a 30 item self-report tool using 4-point
Likert scales (1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost
Always/Always)). Example question: I plan tasks carefully. (Possible answers: 1
(Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)).
SPSRQ (Torrubia et al., 2001) replaced BIS-11 at Time 2 of the interventions as
part 3 of the survey to measure the drivers' sensitivity to punishment and sensitivity to
reward. SPSRQ is a 48 item self-report tool, using a "yes/no" format, with 24 items
focusing on each of the two constructs. Example question: Do you often refrain from
doing something because you are afraid of it being illegal? (Possible answers:
Yes/No).
4.4.4 Analyses
Data from study participants was collected through Google forms in Study 2 and
QUT Key Survey software in Study 4. The data from the driver surveys were checked,
Chapter 4: Research design 59
coded and entered into Statistical Package for the Social Sciences (SPSS Statistics 25).
A record was kept on data coding and scales recoding. Due to the predominant use of
closed questions, there was limited missing or invalid data. Descriptive statistics for
all variables were examined. The data were checked for outliers. Outliers did not
appear to have an overly influential impact on the analyses.
The data were checked to test for assumptions of parametric tests, including
normality, linearity, homoscedasticity, homogeneity of variance and multicollinearity.
Inspection of standardised residual scores, scatterplots, skewness and kurtosis values,
95% trimmed means, visual inspections of histograms and Shapiro-Wilk statistics
revealed that assumptions were sufficiently met in Study 2. Thus, parametric tests were
used (one-way between-groups multivariate analysis of variance (MANOVA), 3-step
hierarchical multiple linear regression, one-way and two-way analyses of covariance
(ANCOVA)) to analyse the data in Study 2. Initially, the analysis of Study 4 was
intended to follow the same process as Study 2. One-way between-groups MANOVA
was used to preliminary assess the data. The Study 4 DVs' data had significant
deviations from normality and was, therefore, dichotomised. Thus, non-parametric
tests were used to analyse the data (3-step multiple logistic regression, Chi-square test
for independence, McNemar's test, Wilcoxon Signed Ranks Test). Further details
about the performed analyses are provided in the relevant sections of Chapter 7 and
Chapter 9.
Despite the different set of tests, used in Study 2 (parametric) and Study 4 (non-
parametric), regression analysis was used to analyse the data from both studies. Since
data collection used measures, developed by Elliott and Thomson (2010), data analysis
was guided by their methodology, too. Elliott and Thomson (2010) not only
reconceptualised the standard TPB by dichotomizing the original constructs but also
retained their separately defined components when assessing their predictive strength.
Thus instrumental attitude and affective attitude were used in the models instead of a
single attitude measure. Separate subjective norm and descriptive norm were used
instead of a subjective norm only. Finally, self-efficacy and perceived controllability
were explored separately as part of PBC. More details on the undertaken approach are
provided in Section 3.7 above.
The difference between the current analysis and the approach of Elliott and
Thomson (2010) is that demographic factors were included in the analysis before TPB
60 Chapter 4: Research design
at Step 1. That was done to account for the influences of gender, age and driving
experience (denoted by driving license in Study 2), an approach guided by Horvath et
al. (2012). This allowed for a more accurate reflection of the standard TPB constructs'
predictive ability, assessed at Step 2, over and above the demographics. The predictive
ability of the additional predictors (past behaviour, perceived risk, moral norm, peers'
norm, impulsivity, sensitivity to reward and sensitivity to punishment), over and above
the TPB variables, was investigated at Step 3.
4.4.5 Ethics
Study 2 and Study 4 involved the participation of consenting adult participants.
Ethical clearance was obtained from the QUT Human Research Ethics Committee. In
Study 2, the participants completed two online surveys and used a safe-driving app
while driving (QUT Ethics Approval Number 1700000846). In Study 4, the
participants completed two online surveys and drove a VR driving simulator (QUT
Ethics Approval Number 1800000214).
Chapter 5: Study 1 - Systematic review of safe-driving apps 61
Chapter 5: Study 1 - Systematic review of safe-driving apps
This Chapter 5 presents the first study of the program of research. The study
investigated to what extent smartphone safe-driving apps were previously explored in
research and whether any safety benefits were reported. It addressed RQ1:
What is the state of the art evidence of the safety benefits of smartphone safe-
driving apps for young drivers?
Following a PRISMA design (Liberati et al., 2009), Study 1 reviewed the
available literature, in terms of safe-driving apps' characteristics and effects on young
drivers' behaviour and safety. First, the search results from the two used databases are
presented, followed by key findings and a discussion.
5.1 RATIONALE FOR CONDUCTING A SYSTEMATIC REVIEW
The risk of road crashes appears to be higher for young drivers than the risk for
older drivers (McKnight & McKnight, 2003; SafetyNet, 2009). The increased risk of
crash involvement manifests itself into young drivers being overrepresented in road
fatalities. For example, young Australian drivers represent 12% of the population, but
19% of the driver fatalities (BITRE, 2018). Five risky behaviours (speeding, DUI, not
wearing a seatbelt, fatigue and distraction) as pointed out as main contributors to that
statistics (Scott-Parker & Oviedo-Trespalacios, 2017).
At the same time, young drivers embrace technology, and researchers explore
paths to use COTs to reduce the young drivers' risky behaviours (Lee, 2007; Schroeter
et al., 2012; Steinberger et al., 2017). Safe-driving apps are an example of such COTs
that are regarded as having a potential to positively influence the young drivers
(Castignani, Derrmann, Frank, & Engel, 2017; Musicant & Lotan, 2015; You et al.,
2013). However, a small proportion of the existing literature provides knowledge
about the effects of safe-driving apps in the real world. There are also differences
between how safe-driving apps are operationalised for research purposes, i.e. different
study designs, different measurement, and different outcomes. These inconsistencies
made it difficult to make conclusions about the effects, stemming from using safe-
62 Chapter 5: Study 1 - Systematic review of safe-driving apps
driving apps to reduce young drivers' risky behaviours. Furthermore, no other
systematic review looks systematically into the literature for evidence for such effects.
5.2 METHOD
The PRISMA guidelines were used as a framework for the current systematic
review. A review protocol was developed following the guidelines with the following
steps:
1. Development of the research question.
2. Identification of search databases.
3. Definition of scope, inclusion, and exclusion criteria.
4. Definition of a search term.
5. A systematic search for information.
6. Screening and selection of studies (see PRISMA flowchart as Figure 5.1).
7. Review of selected articles.
8. Summarising of findings.
5.2.1 Search databases
Relevant papers were identified through searches in Transport Research
International Documentation (TRID, https://trid.trb.org) and Scopus
(https://www.scopus.com). Both databases are widely used for PRISMA-guided
systematic reviews in the context of road safety (Oviedo-Trespalacios, Truelove,
Watson, & Hinton, 2019; Shekari Soleimanloo, 2016; Staton et al., 2016).
5.2.2 Literature search criteria
Papers published from 2010 onwards, and written in English, were considered
for inclusion in the review. The search results were limited to the year 2010 as a
starting point to cover the time contemporary smartphone apps started to appear on the
consumer market. This trend is indicated by the sharp increase of public interest in
apps (see Google Trends) and the word "app" being named "Word of 2010"3. The
actual and potential application and utility of smartphone apps, games and
gamification in (a) road safety research more generally, (b) road safety practice more
generally, (c) young drivers road safety research specifically, and (d) young drivers
3 https://www.americandialect.org/app-voted-2010-word-of-the-year-by-the-american-dialect-society-updated
Chapter 5: Study 1 - Systematic review of safe-driving apps 63
road safety practice specifically, was put in focus. Games and gamification are
regarded in the literature as potential routes to boost drivers’ engagement (see
Subsection 2.4.2). Thus, the two terms were used as complementary search terms to
smartphone apps to avoid potential omission of studies that focus on the games and
gamification aspects, rather than on the fact that those aspects are delivered through a
smartphone app. As a result, papers exploring active use of apps, games and
gamification on the driver's smartphone, with or without feedback influencing their
driving behaviour, were explored. Papers not relevant to driving, mobile phone use
literature reviews, evaluation of traffic data, phone use surveys, medical, technical
solutions, detection and assessment of driver distraction or fatigue, traffic modelling,
crash prediction, road condition detection, theoretical discussions, and ones with focus
on vulnerable road users with no connection to drivers were excluded.
5.2.3 Search term
The search term deployed in TRID was (road OR driver) safety (app OR apps
OR "smartphone application" OR game OR games OR gamification) ("mobile phone"
OR smartphone). The search term deployed in Scopus was ( ALL ( road OR driver )
AND ALL ( safety ) AND ALL ( app OR apps OR "smartphone application" OR
game OR games OR gamification ) AND ALL ( "mobile phone" OR smartphone
) ).
5.3 SEARCH AND SCREENING RESULTS
The search in TRID, executed on September 12, 2017, returned 864 records.
Those results were limited to the years 2010 – 2017, which reduced the records to 377
records. By excluding languages other than English, the number of records became
365. The titles of those 365 records were screened for relevance. After the ones
considered to be potentially relevant were marked, 80 remained and were downloaded
for abstract screening. After screening of the abstracts, 35 records were marked for
further processing.
The search in Scopus returned 1246 records. Similar to above, those results were
limited to the years 2010 – 2017, which reduced the number of records to 1178. By
excluding languages other than English the number became 1164. Screening for
relevance in this specific case was initially performed by groups of publications by the
author. Scopus has functions on the left side of the screen that allows for quick search
64 Chapter 5: Study 1 - Systematic review of safe-driving apps
within the current search results based on different criteria, such as the authors. Using
this function, authors were ranked by the number of identified papers and explored
top-down. Within the authors' work, their field of interest could then be more easily
recognised by screening a group of titles as a whole. This allowed the exclusion of
several papers at the same time as not being relevant, such as medical research or
crowdsensing, also reducing the number of authors to be looked at subsequently. This
approach of grouping papers was applied to authors with three or more listed papers.
25 authors were excluded using this approach, which reduced the result to 1088
records. The remaining authors had 2 or fewer publications. Those and all other
remaining titles were individually screened for relevance irrespective of the authors'
other title. After screening titles, 120 selected records were downloaded to screen their
abstract. Out of those, 55 were marked for further processing after the abstracts'
screening.
All papers marked for further processing after abstracts' screening from both
databases were imported into an Excel sheet to allow removing of duplicates. 80 papers
remained after removing duplicate records. 68 full texts were downloaded. 12 could
not be found online and needed to be found elsewhere. Nevertheless, one referred to
commercialising the outcome of a paper that was already found. Another record was a
presentation. Two referred to projects that had recently started, but there were no
results published, yet. This left eight documents to be found. Their authors were
emailed and asked to provide the respective article. Five of the contacted authors sent
their articles. Three remained missing from the initial selection of 80. One of those
was recognised as already existing in the current selection. After working through the
full texts of the documents, 31 articles were included in the detailed full-text analysis.
Four main themes were identified across the 31 papers: usage (3 studies); eco-driving
(6 studies), safety in addition to eco-driving (4 studies) and safety-only (18 studies).
As the current PhD program of research focused on evidence for safety benefits as a
result delivered by smartphone safe-driving apps, the 3 studies that focused on usage
and the 6 studies that focused on eco-driving were excluded as irrelevant. Thus, 22
papers were retained to be included in the qualitative synthesis (see Figure 5.1).
Chapter 5: Study 1 - Systematic review of safe-driving apps 65
Figure 5.1. Data extraction flowchart based on the PRISMA statement.
5.4 FINDINGS
Once the final selection of articles to be analysed in detail was completed, the
elements of interest to the current study were summarised. Type of study, type and
number of participants, sensors used, measures taken, and focus of the studies, as well
as their findings, were considered in the analysis of the 22 papers, included in the
qualitative synthesis (see Table 5.1).
66 Chapter 5: Study 1 - Systematic review of safe-driving apps
Table 5.1. Impact and effect of apps, games and gamification on young drivers' road safety.
Authors Intervention Type of study
(design details)
Type of
participants
Number of
participants
Sensors used Measures
taken
Focus Summary of findings
Zhang et al.
(2014)
The app warned drivers before an
accident blackspot. It also advised
them to take a break from driving
after a certain time. Both were
provided in real-time while driving
on expressways through an icon and
voice. No interaction was necessary.
The app is started and stopped
manually.
The app assessed the driving, based
on smoothness, speed, acceleration
and deceleration, as well as drivers'
recall on their driving safety. It
provided scores, driving history and
ranking based on the assessment.
Naturalistic
(each driver
driving on five
different routes
of expressways)
University
students
5 GPS Speeding,
acceleration,
deceleration,
driving
smoothness
Safety Participants obeyed
speed limits
represented by very
high scores on
speeding (over 80).
Birrell,
Fowkes, and
Jennings
(2013)
The app provided driving safety
and fuel efficiency feedback to the
drivers in real-time. Warnings were
issued in relation to lane departure
and headway distance. Advice was
Naturalistic (a
50 minute
mixed route
driving
scenario)
30 males
(mean age
42.33) and
10 females
40 LDW camera,
OBD2,
accelerometer
GPS
Time
headway, lane
changes,
glance
Safety
and
eco
Three times
reduction of
tailgating and
improvement of
Chapter 5: Study 1 - Systematic review of safe-driving apps 67
given in relation to gear changes,
acceleration and braking. All advice
was identical and followed a
predefined script as the driving
route was predetermined.
(mean age
40.6)
frequencies
and duration
4.1% in fuel
efficiency.
Creaser et al.
(2015)
The app was recording phone use
while driving of a control and two
intervention groups. The app was
starting automatically, and there
was no need for participants to
interact with it. However, the
participants were reminded to
mount the phone before driving. In
one of the intervention groups, the
phone usage was blocked. In the
second intervention group, the app
additionally was sending
notifications to parents when risky
behaviour was detected.
Naturalistic (12
months free
driving)
Novice
teen drivers
(mean age
16.03, 130
males, 144
females)
274 Phone blocking
app
Phone use Safety Compared to the
control group, the
intervention groups
used their
smartphones
significantly less for
calling and texting
while driving.
Rodrigues,
Macedo,
Serpa, and
Serpa (2015)
The 3D game was introducing
traffic regulations to promote safe
driving through. While driving in
the game, participants were
Simulator
(educational 3D
traffic rules
game)
13 males
and 2
females (19
15 N/A N/A Safety The game stimulated
motivation and
learning while being
fun.
68 Chapter 5: Study 1 - Systematic review of safe-driving apps
provided with feedback about their
traffic rules violations, when
detected.
to 36
years).
Steinberger,
Proppe,
Schroeter, and
Alt (2016)
In a driving game environment, the
app was re-engaging the drivers in
the driving task when speed changes
were necessary due to speed limits. It
was also providing feedback on the
participants’ driving behaviour while
driving.
Simulator (90
minutes
sessions)
19 male
drivers (18
to 25), 5
researchers
(26 to 36)
24 N/A Speed, eye
glances
Safety Improved driver
engagement,
decrease in speed
violations, increased
visual distraction.
Riener and
Reder (2014)
The app was gathering speed, gear-
shifting and braking force data. It
was providing visual and auditory
recommendations to the drivers in
real-time. The drivers were driving
an equipped car on a predefined
route. Their driving performance
was ranked.
Naturalistic
(19.4 km long
straight
commuter track
with no sharp
bends)
Males (23
to 26 years)
9 OBD2, GPS,
accelerometer,
Open street map
Speed, gear-
shifting, brake
force.
Safety
and
eco
No evidence of
improvement due to
the app steering
recommendations.
Rahman,
Qiao, Li, and
Yu (2016)
The app was alerting the
participants while driving for traffic
hazards (headway, speed, and
acceleration) through sound, visual,
and voice warnings.
Simulator (20
minutes
sessions)
12 males,
12 females
24 N/A Headway
distance,
headway time,
speed, and
Safety Both worker fatalities
and vehicle collisions
were reduced.
Chapter 5: Study 1 - Systematic review of safe-driving apps 69
acceleration/d
eceleration
He et al.
(2017)
The app was issuing curve-speed
warnings in real-time to enable the
driver to improve the longitudinal
control of the vehicle.
Naturalistic (a
selected road
with curves)
N/A N/A GPS, compass Digital maps,
vehicle speed,
vehicle height
and curve
radius
Safety Decreased lateral
acceleration on a
dangerous curve with
the warning system
enabled.
Fitz-Walter,
Johnson,
Wyeth,
Tjondronegor
o, and Scott-
Parker (2017)
The app was collecting trip data to
allow easier and more accurate
recording of learner drivers’ driving
practices.
The app was starting automatically.
However, it had to be stopped
manually.
Naturalistic (1-
month free
driving)
Learner
drivers
25 N/A Weather, start
and end time
and location.
Safety No significant
change in the
behaviour despite the
gamified version
being seen as more
enjoyable and
motivating.
Botzer,
Musicant, and
Perry (2017)
The app was issuing collision
warnings in real-time. The app had
to be turned on and off manually.
Naturalistic (one
to two weeks)
Age from
24 to 60
26 Smartphone
camera, GPS,
motion sensors
Time-stamped
acceleration
and warnings
Safety Warnings of
imminent collisions
triggered
decelerating, thus,
safer driving. Fewer
warnings were issued
with time. However,
21/26 drivers stopped
using the app after
70 Chapter 5: Study 1 - Systematic review of safe-driving apps
the experiment
ended.
Louveton,
Mccall,
Koenig,
Avanesov, and
Engel (2016)
The app was giving different tasks
to the drivers while they were
driving on a straight road after a
lead vehicle. The tasks were timed
according to speed change events of
the lead vehicle. The tasks were
both voiced and visualised.
Simulator 15 female
and 14
male (22 to
49)
29 N/A Task
completion
time and error
rate
Safety Increased cognitive
load and poorer
performance,
generated visual
distraction.
Birrell,
Fowkes, and
Jennings
(2014)
The app provided driving safety
and fuel efficiency feedback to the
drivers in real-time. Warnings were
issued in relation to lane departure
and headway distance. Advice was
given in relation to gear changes,
acceleration and braking. All advice
was identical and followed a
predefined script as the driving
route was predetermined.
Naturalistic (50-
minute mixed
route driving
scenario)
30 males
(mean age
42.33) and
10 females
(mean age
40.6)
40 LDW camera,
OBD2,
accelerometer
GPS
Time headway Safety
and
eco
Three times
reduction of
tailgating and
improvement of
4.1% in fuel
efficiency.
Jiang, Zhang,
Chikaraishi,
Seya, and
The app was introducing different
information to the drivers during the
intervention period at 5 stages:
1. Only collected data.
Naturalistic (3
months free
driving)
Drivers,
who used
expressway
for at least
100 GPS Speeding,
acceleration,
deceleration,
Safety The app influences
driving safety
significantly when
underpinned by a
Chapter 5: Study 1 - Systematic review of safe-driving apps 71
Fujiwara
(2017)
2. Information on rest areas and
blackspots was introduced.
3. Service and parking area
information was added.
4. Drivers were given the opportunity
to self-evaluate their driving. On that
basis, scoring was introduced. This
was followed by introducing driving
advice.
5. The last function of the app was
the “Drive & Love” safety
education campaign.
4 times per
month
driving
smoothness
careful combination
of provided
information
depending on the
driver's stage of
change.
Schartmüller
and Riener
(2015)
The app was measuring speed, lane
position and distance to the front
vehicle. It was issuing visual and
auditory warnings to the drivers in
real-time. The system was
preconfigured and run fully
automated.
Naturalistic
(every
participant had
to drive 10,4 km
long track
twice)
Age range
18 to 60
17 Build-in camera Vehicle
detection and
tracking, lane
detection and
tracking,
vehicle
distance
estimation
Safety Enhanced perception
of minimal distance
required.
Birrell and
Fowkes
(2014)
The app provided driving safety
and fuel efficiency feedback to the
drivers in real-time. Warnings were
Naturalistic
(fixed driving
route)
10 males
and 5
females
15 (out of
40)
Adapted LDW
camera, OBD2,
Headway, lane
departure, gear
change,
Safety
and
eco
No visual distraction
caused by the in-
vehicle smart driving
72 Chapter 5: Study 1 - Systematic review of safe-driving apps
issued in relation to lane departure
and headway distance. Advice was
given in relation to gear changes,
acceleration and braking. All advice
was identical and followed a
predefined script as the driving
route was predetermined.
(over 21
years)
accelerometer,
GPS
acceleration
and braking
system providing
feedback.
Li, Qiao,
Qiao, and Yu
(2016)
The app was calculating headway
distance and time to collision in a
pre-equipped vehicle. It was
providing a real-time warning
message to the driver when
predetermined thresholds were met.
The warning message was
encouraging the driver to keep a
safer distance.
Naturalistic
(limited local
community
route)
N/A N/A OBD2, GPS Braking
distances,
deceleration
and speed.
Safety Improved speed
compliance and
deceleration
performance, keeping
safer distances to
intersections and
other vehicles.
Ryder, Gahr,
Egolf,
Dahlinger, and
Wortmann
(2017)
The app was providing visual
hotspots warning to the drivers in
real-time. The warnings issued,
based on analysing historical
accident data.
Naturalistic
(four weeks)
Professiona
l drivers
57 OBD2 Location,
driver’s
personality,
dangerous
braking
events, vehicle
speed
Safety Driver behaviour
improvement through
in-vehicle accident
hotspots warnings
influenced by the
individual’s
personality. No
Chapter 5: Study 1 - Systematic review of safe-driving apps 73
immediate effect on
driver behaviour
outside lab
experiments.
Hu et al.
(2015)
The app was calculating a mood-
fatigue profile of the driver in real-
time. Based on the specific
calculation, it was proposing
suitable mood-calcified music.
Simulator 32 males
and 16
females
48 Front camera Mood, fatigue Safety Decreased fatigue
and negative mood
compared to a
traditional
smartphone-based
music player.
Creaser et al.
(2015)
As a result of in-vehicle
monitoring, the app was providing
real-time warnings when an unsafe
driving behaviour was detected, e.g.
missing seatbelt, speeding or phone
use. It was sending messages to the
parents of some of the drivers if
they did not comply with the
warning.
Naturalistic (12
months free
driving)
Newly
licensed
teens
300 Accelerometers,
GPS, in-vehicle
Arduino
microprocessor,
seatbelt sensor,
passenger
sensors
Speeding, hard
turning,
braking,
accelerations,
seatbelt
Safety Reduced risky
driving behaviours.
Musicant and
Botzer (2016)
The app was issuing sound and
visual collision warnings in real-
time.
Naturalistic (2-3
weeks)
8 females
and 18
males (24
to 60 years)
26 GPS, camera
and smartphone
dynamic
sensors.
Time-stamped
warnings and
speed.
Safety Lower speed when
issued warnings,
safer distance.
74 Chapter 5: Study 1 - Systematic review of safe-driving apps
Williams,
Peters, and
Breazeal
(2013)
The app was using voice and facial
expressions to communicate with
the driver while they were
travelling in a simulated
environment. It was detecting
phone communication events (e.g.
getting late for a calendar
appointment, sending or receiving
messages), it was suggesting
solutions on how to respond.
Simulator 20 males
and 24
females
(mean age
28.6 years)
44 N/A Navigation,
collision
warnings,
Internet,
entertainment
systems and
messaging
services
Safety Less interaction
stress, more often
safety precautions
and increased
companionship with
the assistant in
comparison to
smartphone users.
Birrell,
Young,
Stanton, and
Jennings
(2017)
The app provided driving safety
and fuel efficiency feedback to the
drivers in real-time. During high
driver cognitive load, e.g. driving in
a city, the presented information
was limited. More information was
presented during driving with lower
cognitive demands, e.g. on a
highway.
Simulator (5-min
simulated
scenarios) and
Naturalistic (a
mixed 0.1 miles
driving route)
N/A 25
(simulator),
40
(naturalistic)
OBD2, GPS,
camera
Headway, lane
departures,
acceleration,
gear changing
Safety Modulated driving
workload towards
manageable levels
depending on current
driving task
demands.
Chapter 5: Study 1 - Systematic review of safe-driving apps 75
5.4.1 Studies’ designs and samples
The analysis of the 22 papers revealed that the studies were implemented in
two types of settings, naturalistic (16 papers) and simulator (7 papers). One study was
implemented both in naturalistic and simulator settings (Birrell et al., 2017), hence the
sum of studies implemented in one or the other setting is more than the number of
analysed papers.
While simulator studies tend to be similar in that they are implemented in
laboratory conditions, the analysed naturalistic studies offered greater design
variability. Nine of the naturalistic studies were characterised by predefined routes (He
et al., 2017; Riener & Reder, 2014; Zhang et al., 2014). The other seven studies focused
on the drivers' behaviour in their free-living environment (Botzer et al., 2017; Jiang et
al., 2017; Ryder et al., 2017). The free-living driving studies were characterised by
time limits, ranging from one week (Botzer et al., 2017) to 12 months (Creaser et al.,
2015).
The samples' design offered a similar variability. Two naturalistic studies ((He
et al., 2017; Li et al., 2016) did not provide any information for their participants.
Birrell et al. (2017) supplied information about the number of participants without
other details. The other 19 studies provided more comprehensive information. For
example, the number of reported involved participants in the different studies ranged
from 5 (Zhang et al., 2014) to 300 (Creaser et al., 2015). Some studies reported the
participants' occupation, e.g. university students (Zhang et al., 2014) or professional
drivers (Ryder et al., 2017), without providing information on gender for example.
Other studies were interested in other characteristics, such as frequency of using a
certain road (Jiang et al., 2017) or driving experience (Fitz-Walter et al., 2017). Some
studies focused only on male drivers (Riener & Reder, 2014; Steinberger et al., 2016),
while a majority had both males and females as participants (Birrell et al., 2013;
Creaser et al., 2015; Rahman et al., 2016). The reported age range of the participant
had a large spread, from a mean age of 16.03 (Creaser et al., 2015) to an upper limit
of 60 years (Botzer et al., 2017; Musicant & Bolzer, 2016; Schartmüller & Riener,
2015).
76 Chapter 5: Study 1 - Systematic review of safe-driving apps
5.4.2 Sensors and measures
The contemporary smartphones possess a number of sensors that safe-driving
apps can leverage. Those are the internal clock, cameras, GPS, accelerometer,
gyroscope and magnetometer. Additional information can be generated by monitoring
the software, operated on the smartphone. Such information may include what apps
are being used, whether phone calls are being initiated or answered, or whether
messages are being read or written.
One study reported on the use of a phone-blocking app to monitor the
participants' smartphone use (Creaser et al., 2015). Six studies did not provide
information on any sensors used, five of which reported on simulator studies
(Louveton et al., 2016; Rahman et al., 2016; Rodrigues et al., 2015; Steinberger et al.,
2016; Williams et al., 2013) and one on a naturalistic study (Fitz-Walter et al., 2017).
Smartphone sensors were used in 14 of the analysed studies.
The most widely used smartphone sensor was the GPS, 12 studies, e.g. Zhang
et al. (2014), Birrell, Fowkes, and Jennings (2014), and Riener and Reder (2014).
Another sensor, used in the analysed studies, was the accelerometer, 7 studies, e.g.
Creaser et al. (2015), Birrell and Fowkes (2014) and Botzer et al. (2017). The
smartphone cameras were the third sensor used in more than half of the studies, 7
studies, e.g. Birrell et al. (2017), Musicant and Botzer (2016) and Hu et al. (2015).
The different sensors provide opportunity measures to be taken about different
risky behaviours. For example, the GPS and the smartphone accelerometer allow
assessment of speed, acceleration, deceleration and driving smoothness (Botzer et al.,
2017; Riener & Reder, 2014; Zhang et al., 2014). The smartphone cameras allow for
vehicle detection, vehicle tracking, lane detection, lane tracking, and headway distance
estimation (Birrell et al., 2013; Birrell et al., 2014; Schartmüller & Riener, 2015).
In addition to smartphone sensors, some of the studies used OBD2 to collect
additional data. The OBD2 connects directly to the car systems. Thus, the collected
data might be expected to be more accurate than the data generated by smartphone
sensors. The use of OBD2 was reported by seven of the reviewed studies. OBD2 was
most often used in conjunction with the smartphone GPS and accelerometer to collect
additional data about speed, acceleration and braking, e.g. in Li et al. (2016), Birrell et
al. (2014) and Riener and Reder (2014). Different than the smartphone GPS and
Chapter 5: Study 1 - Systematic review of safe-driving apps 77
accelerometer, the OBD2 can provide data about gear-shifting, which is useful
information when eco-driving is analysed together with safety (Birrell & Fowkes,
2014; Riener & Reder, 2014).
5.4.3 Benefits
Out of the 22 analysed papers, 18 papers focused entirely on safety, while 4
papers explored safety together with eco-driving. Three of the eco-driving papers
reported results from one study, in which a 4.1% improvement in fuel efficiency
together with 3 times reduction of tailgating was observed (Birrell et al., 2013; Birrell
et al., 2014) while no visual distraction was caused by the in-vehicle smart driving
system (S. A. Birrell & Fowkes, 2014). Those findings did not find support in the
fourth eco-driving paper, which concluded with no evidence of improvement due to
the app steering recommendations (Riener & Reder, 2014). Both eco-driving studies
were implemented in naturalistic settings with fixed driving routes.
The studies, focused only on safety, offered a greater variability of both
implementations and results. Implementations were not only in naturalistic settings (12
studies), as in the case of eco-driving but also in simulated environments (7 studies),
with one study using both.
The simulator studies reported mixed results. For example, Rodrigues et al.
(2015) found their smartphone game implementation to stimulate motivation and
learning while being fun. More directly related to safety, Steinberger et al. (2016)
found improved driver engagement and decrease in speed violations. Reduction of
both vehicle-to-vehicle crashes and worker fatalities was reported by Rahman et al.
(2016). Hu et al. (2015) found a decrease in fatigue and negative mood. Birrell et al.
(2017) reported on modulated driving workload towards manageable levels, a result
which finds support in Williams et al. (2013). Williams et al. (2013) found less
interaction stress, more often safety precautions and increased companionship.
However, not all studies found a positive impact on safety. Louveton et al. (2016)
reported on a generated visual distraction, leading to an increased cognitive load and
poorer performance. The increased visual distraction was also reported by Steinberger
et al. (2016), although it did not degrade lane-keeping performance.
The diversity of findings was greater in naturalistic settings implementations.
For example, Creaser et al. (2015) reported less distraction of their Intervention groups
78 Chapter 5: Study 1 - Systematic review of safe-driving apps
in comparison to the Control group. With regards to speeding, Zhang et al. (2014)
reported increased compliance with speed limits. Li et al. (2016) also found that
participants improved their speeding profiles. In addition, Li et al. (2016) observed
improved deceleration rates, extended braking distances to the leading vehicle or
intersection stop line. Musicant and Botzer (2016) reported on speed and headway
distance improvements, as a result of issued warnings. In-car warning systems were
used to trigger a variety of results, such as a decreased lateral acceleration on a
dangerous curve (He et al., 2017), decelerating to avoid collisions (Botzer et al., 2017),
enhanced perception of minimal distance required (Schartmüller & Riener, 2015) or
reduced frequency of risky driving behaviours in general (Creaser et al., 2015).
However, despite the evidence of effects on driver behaviour, Ryder et al.
(2017) found it challenging to trigger driving behaviour improvements in naturalistic
settings. The authors showed that the drivers’ personality influences their likelihood
to improve behaviour as a result of an intervention. In support, Jiang et al. (2017) found
a significant influence on driving safety when the information provided to the drivers
is customised to their personality. For example, safety diagnosis and blackspot
information were found beneficial for careless and irritable drivers, while feedback,
self-diagnosis and drivers' ranking was beneficial to drivers who wanted to decrease
their speeding (Jiang et al., 2017).
5.5 SUMMARY
Only 16 of the 22 reviewed papers were concerned with driving studies in the
real world; 6 referred to studies in simulated conditions only. In addition, 3 of the
papers referred to the same study, leaving 14 studies, exploring effects in naturalistic
settings, thus, being somewhat similar and relevant for the current program of research.
Out of those 14 papers, only 4 explicitly reported as being focused on young drivers.
One of those 4 papers reported no significant change in the behaviour despite the
gamified version being seen as more enjoyable and motivating (Fitz-Walter et al.,
2017). Only three studies, focused on young drivers, reported positive safety benefits
from the deployed interventions (Creaser et al., 2015; Creaser et al., 2015; Zhang et
al., 2014).
Zhang et al. (2014) focused on speeding. In a naturalistic driving study, they
used the smartphone GPS to monitor the speed of five university students. Each young
Chapter 5: Study 1 - Systematic review of safe-driving apps 79
driver drove on five different predefined routes of expressways. The authors'
conclusions were that the participants obeyed the speed limits, which was depicted by
the smartphone app awarding them very high scores on speeding (over 80).
The other two studies described results from a 12-month field operational test of
a Teen Driver Support System (TDSS) on the roads of Minnesota (Creaser et al.,
2015). The test involved 300 newly licensed teens who were provided through a
smartphone with in-vehicle real-time feedback about their risky behaviours. Those
behaviours were reported to the parents of a subset of the teens through text messages.
Additional data was collected through the vehicle outside the smartphones. Depending
on the type of feedback received, the participants were divided into three conditions:
control with no feedback, a first treatment group with TDSS only and a second
treatment group with TDSS and parental notifications. The participants were
remunerated with USD 300 upon completion and could keep the smartphone and its
accessories after the end of the study. As a result of the test, it was found that in
comparison to the control group, the two intervention groups called and texted
significantly less per mile driven (Creaser et al., 2015). Another observed result was
that the driver alerts, generated through the in-vehicle monitoring, lead to reduced
frequency of risky driving behaviours (Creaser et al., 2015).
5.6 DISCUSSION
Smartphone safe-driving apps can be used for low-cost safety interventions that
take advantage of the built-in smartphone sensors' capabilities. However, while
answering research RQ1 (What is the state of the art evidence of the safety benefits of
smartphone safe-driving apps for young drivers?), it was found that a safe-driving app
can deliver a variety of safety benefits. Benefits include increased work safety
(Rahman et al., 2016), improved drivers’ mood (Hu et al., 2015) or enhances distance
perception (Schartmüller & Riener, 2015).
Some researchers used the same smartphone apps they designed, developed or
obtained to investigate different behaviours in the involved drivers. For example, the
same app modulated driving workload towards manageable levels (Birrell et al., 2017)
and reduced tailgating (Birrell et al., 2014). Another app improved speed limit
compliance on a predefined route (Zhang et al., 2014) but also increased safety over
three months (Jiang et al., 2017). A third app improved deceleration patterns (Botzer
80 Chapter 5: Study 1 - Systematic review of safe-driving apps
et al., 2017) and promoted lower speeds in general (Musicant & Botzer, 2016). A
fourth app reduced both drivers’ phone interactions (Creaser et al., 2015) and the risky
driving behaviour frequency in general (Creaser et al., 2015). Thus, the systematic
review provided evidence for the potential of smartphone safe-driving apps to deliver
safety benefits in regards to different behaviours or several behaviours at the same
time.
Despite the evidence for safety benefits, delivered by safe-driving apps in
general, only three studies in the current systematic review reported clear safety
benefits for the involved young drivers in naturalistic settings. None of those three
studies would easily qualify as a scalable real-world intervention. One assessed only
five people, who drove on a predefined stretch of a highway (Zhang et al., 2014). The
other two were part of a multimillion-dollar investigation with substantial incentives
used to recruit and retain participants (Creaser et al., 2015; Creaser et al., 2015).
The reviewed literature provided support not only for the need for a further
investigation into the safety benefits delivered by smartphone safe-driving apps but
also for the adopted study design. The fact that a number of past studies focused on
speeding in naturalistic settings (Li et al., 2016; Musicant & Botzer, 2016; Zhang et
al., 2014) suggests that speeding is a behaviour that is likely to be influenced by a
smartphone safe-driving app intervention. Increased compliance with speed limits was
observed in both simulated (Steinberger et al., 2016) and naturalistic conditions
(Zhang et al., 2014). However, the assessed time period varied from 5 minutes (Birrell
et al., 2017) to one year (Creaser et al., 2015). Thus, drawing comparisons and
clustering benefits presents a challenge due to the differences in the settings in which
the studies were implemented.
The feedback, issued by a safe-driving app, was found useful in a number of
settings, such as lowering speed in naturalistic driving (Musicant & Botzer, 2016),
decreasing fatigue and reducing negative mood in simulator driving (He et al., 2017),
preventing collisions in naturalistic driving (Botzer et al., 2017), improving distance
perception in naturalistic driving (Schartmüller & Riener, 2015), and reducing young
drivers’ risk-taking in naturalistic driving (Creaser et al., 2015). Furthermore,
increased distraction generated by smartphone safe-driving apps (Louveton et al.,
2016; Steinberger et al., 2016) could be possible unwanted side effects of a safe-
driving app intervention. Such evidence provided support for including distraction-
Chapter 5: Study 1 - Systematic review of safe-driving apps 81
related questions in the Study 2 surveys. These questions had the objective to help
assess whether participants’ smartphone interactions while driving did not increase as
a result of the safe-driving app intervention.
From a more theoretical perspective in safe-driving app interventions, evidence
was found on the impact of drivers' personality on their behaviour (Jiang et al., 2017;
Ryder et al., 2017). This evidence supported an earlier decision the adopted TPB
theoretical framework to be extended with constructs that measure personality
characteristics (see Subsection 3.6.3).
Additionally, despite a large number of safe-driving smartphone apps readily
available online, and more developed for research purposes, the literature provided
limited information on the considerations made when choosing one as an intervention
tool. Thus, the next chapter explores the topic of selecting a safe-driving app from
these currently available, which can motivate safe driving behaviour among young
drivers as part of an intervention.
82 Chapter 6: Selecting a safe-driving app
Chapter 6: Selecting a safe-driving app
This Chapter 6 provides details on the work done in the process of investigating
available safe-driving apps and selecting one for deployment as part of an intervention.
It begins with describing the implemented Focus group design, participants, findings
and limitations. The Focus group is followed by a description of the investigation of
the Google Play and iTunes app stores to identify available safe-driving apps.
Subsequently, the pros and cons, as per the Focus group recommendation, of a
selection of some more popular apps are investigated. Three apps are tested in the real
world before finally selecting one app to be deployed as an intervention tool.
6.1 FOCUS GROUP DESIGN
The first step in choosing the most appropriate app was to consult an expert
reference group, which was implemented via a Focus group. Substantial previous
experience in road safety at an executive level or in road safety research, an ongoing
commitment for cooperation, and a professional record of successful impact in the
field were considered when selecting the participants. The design of the Focus group
purposefully involved road safety entrepreneurs (SEs). This involvement was guided
by the author's underlying personal interest and experience working as a SE, as well
as the motivation to establish grounds for using the research outcomes in the real world
through NFPs (see Section 1.1).
The Focus group with the road safety experts took place as part of their existing
international meeting in Shanghai, China. The Focus group lasted for two hours. It was
fully audio- and video-recorded, to capture all visual elements of the discussion, and
subsequently transcribed. This structure of the process helped to capture how the
discussion evolved over the course of the Focus Group while the participants interacted
with the visuals and changed them.
6.1.1 Participants
The participants in the Focus group represented a convenience sample of
academia (3 people), applied research (1 person) and project leaders (6 people) in the
domain of awareness-raising road safety interventions for young drivers. The Focus
group sample was stratified to be both gender (6 males and 4 females) and culturally
Chapter 6: Selecting a safe-driving app 83
(6 countries) balanced (see Table 6.1). All invited participants had experience in risk
prevention programs for young drivers and were involved in road safety projects on
an international level.
Table 6.1. Country of origin and gender of participants in the Focus group.
Country of origin Gender
Male Female Argentina 1Austria 2 1Belgium 1China 3Hungary 1Romania 1
Total 6 4
A QUT ethical clearance (Approval Number 1600000340) was obtained before
participants were involved in the Focus group. Participants were anonymised by
assigning an identifier to each of them. Identifiers were assigned randomly depending
on the sequence the participants' consent forms were collected and stored. Identifiers
range from P1 to P10. Those identifiers are referred to in the analysis when a quote is
cited.
6.1.2 Procedure and materials
The Focus group procedure followed a two-step approach, discussing the
following:
Step 1. Personal views about how young people use in-car information and
communication technologies (ICT) and what innovative ICT can
influence them positively.
Step 2. Discussing smartphone safe-driving apps.
Participants were encouraged to share their experience in ICT and in methods of
ICT implementation with high potential to positively influence young drivers’
behaviour. During the discussion, the road safety experts' attitudes towards ICT, its
evolution, and what challenges it brings in the vehicles, were explored. They were able
to elaborate by supporting their statements with information about what worked, how
it worked, and why did they think it worked in their cultural context. Furthermore, they
discussed what could work better, and why did they think it could work better. Visual
brainstorming tools (flipchart drawings and post-it notes), coordinate plane and SWOT
84 Chapter 6: Selecting a safe-driving app
analysis (see Figure 6.1) and a short video of the DriveScribe app review4 further
facilitated the discussion.
Figure 6.1. Focus group visual brainstorming tools
6.1.3 Data analysis
The Focus group audio- and video-recorded session was transcribed and coded
in NVivo 11. A thematic analysis was performed to identify patterns in the participants'
responses (Braun & Clarke, 2006). The subsequent interpretation of the data facilitated
the generation of codes, which were aggregated into themes. The main identified
themes were 1) vision on young people's use of technologies in the car, and 2)
smartphone safe-driving apps. Three subthemes, describing considerations to facilitate
the adoption of smartphone safe-driving apps, were identified under theme 2. Those
were 2.a. young drivers' needs and their safety, 2.b. young drivers' ecosystem, and 2.c.
road safety stakeholders' needs. Selected quotes, relevant to each theme and subtheme,
are integrated into and discussed as part of the following Section 6.2.
6.2 FINDINGS FROM THE FOCUS GROUP
The sample of 10 participants produced 110 statements (mean rate of 11
statements per participant, ranging from 1 statement to 21 statements per participant).
A statement is defined as a participant's input, from the moment they start speaking to
the moment they stop. Those statements represented the respective participant's
opinion, perception or attitude in relation to one or more identified theme or subtheme.
4 https://www.youtube.com/watch?v=pbqqUpO6qXI
Chapter 6: Selecting a safe-driving app 85
As discussed above, two main themes and three subthemes emerged from the focus
group discussion. Details in relation to each theme and subtheme are presented in the
subsequent subsections.
6.2.1 Vision on young people's use of technologies in the car
The first theme in the Focus group revolved around the use of technologies in
the car and how young people interact with them. The participants shared that,
according to them, young people use technology in cars because it is there and they
like it. The discussion quickly centred on ICTs in the car that have no added value to
the driving tasks, yet appear attractive and engaging to young drivers. Those
technologies are usually not “safety-enhancing equipment" [P10]. Distracting devices
came more into focus, and a new division of technology users appeared: the ones who
have money and the ones who do not have money. "Those who can afford to have all
of the equipment they want all of it." [P6] The ones that cannot afford to have the
expensive equipment were referred to by P6 as "the normal guys, [who] are driving
carefully." However, after a short reflection, P6 added "But actually the mobile phone
is still there. It's really a bad habit, and they are using it everywhere." This reflection
was supported by the most pessimistic participant in relation to ICT, [P3], who stated
"According to me using the new technology in car in ... is not so popular", but later
added, "besides the mobile and social media." Thus, the Focus group participants
viewed technology as being out there and widely available to the drivers. In their
opinion, drivers with financial means are likely to possess more sophisticated
technology. However, according to them, the drivers’ financial means have no effect
on the level of smartphone penetration in cars.
The mobile phone emerged as a ubiquitous problem, carrying a low benefit and
a high risk. Participants highlighted that this was irrespective of the drivers’ social
status. A discussion on why drivers use mobile phones, although they know it is
dangerous and illegal, emerged. The moderator raised the question of what
technologies could influence young people positively, given that the ones they like are
usually related to the increase of risks, associated with driving. The participants agreed
that mobile phones, driving under the influence of alcohol and speed (the behaviours
of interest for the current program of research) are the three most common reasons for
increased risks among young drivers. They also agreed that they have to be tackled
differently and that there is available ICT to prevent risks, related to each of the three.
86 Chapter 6: Selecting a safe-driving app
For example, Alcolock was identified as most likely to prevent driving under the
influence of alcohol, Intelligent Speed Adaptation - to control the speed, and
"everything that prevents mobile devices to be used, provided they are in the car, is
also good for reducing accidents and getting safer roads" [P7]. The participants shared
a common view that, since young people are attracted to risk-enhancing devices
through their characteristics, a similar approach should be used when designing
prevention tools.
6.2.2 Discussing smartphone safe-driving apps
The initial Focus group discussion around young people and in-car ICT was
summarised by P6:
"If it is to make it attractive to the young [drivers], it has to be in their own
language. I mean, yeah, if it is smartphones and social media, you have to be on there,
and try to get their attraction there. [sic]"
P6 pointed a direction to follow if young drivers’ are to be reached. Regardless
of the technology, it has to be attractive and to speak to the young drivers in a way to
be easily understood. Since smartphones and social media are believed to be both
attractive and understandable than they should be used to reach out to young drivers.
To visualise an implemented potential transformation of a smartphone from a
risk-enhancing to a safety-enhancing device, the DriveScribe review video was shown
to the participants. DriveScribe leverages smartphones and social media in the form of
a well-intentioned safe driving app, which might be perceived as speaking in the young
drivers’ own language. The video review triggered the question if safe-driving apps,
in general, have an added value to the driver's tasks, or they simply increase the
distraction. P2 shared that, from what was seen, the app is a little different than a GPS
with a speed warning. The only difference was earning points. P4 supported the view
that the app shows what is already known by the driver with the only difference that it
is supported by numbers. P4 wondered if this could change the attitude. P4 argued that
the data can still be useful for third parties such as insurance companies, for example,
when they calculate premiums. P7 generalised the situation with new technology, and
narrowed down how helpful application should be framed:
Chapter 6: Selecting a safe-driving app 87
"We have to move forward with the technology as the technology moves forward.
So, I think, it's the important step when a new problem arises when you look for
solutions to overcome the new problems."
The subsequent discussion uncovered the features that may make young drivers
embrace safe-driving apps. The Focus group participants provided their considerations
for research design efforts that aim at deploying safe-driving apps for research
purposes. As the Focus group included SEs, accounting for such consideration may
provide benefit in the long term, beyond the scope of the current program of research
(see Section 1.1). For example, using SEs’ insights as guidelines here may help
mainstream adoption of safe-driving apps in future implementations, outside road
safety research (see Section 1.4). Thus, as per the summarised opinion of the Focus
group participants, a safe-driving app intervention is likely to succeed when the
following is met:
1. Young drivers' needs and safety (subtheme 2.a) should be accounted for by
designing an attractive solution ("if it is not mandatory by law, you have to be
attractive.” [P8]) that is interesting, “sexy” and speaks to young people in their
own language so that it does not “scare” them ("Connected to insurance, that
would scare and nobody will use it." [P8]). The solution should embed
incentives (“You have to have incentives." [P8]), incorporate gamification or
social network sharing ("share it in their social networks" [P8]), to support
long-term adoption. It should also be fixed, hands-free to reduce any potential
for increasing driving risks (“It should be fixed in the car so that you cannot
use it with your hands while driving." [P10]). Optional blocking of incoming
calls, texts, notifications or social media, can be a good function to enhance
safety, too ("Other functions should be blocked." [P10]). Audio feedback can
help keep eyes and attention on the road, which is essential in reducing road
hazards ("The feedback should be auditory as well." [P10]).
2. Dynamics and interrelations in the young drivers' ecosystem (subtheme 2.b)
should be considered when aiming for widespread use. The involvement of as
many stakeholders as possible can support the joint efforts of road safety
researchers. Possible connection with GDL can provide a whole new level of
motivation for using a safe-driving app (“driving schools to start to use it [...]”
[P6]; “connect it [apps] to [...] driving license probation” [P1]).
88 Chapter 6: Selecting a safe-driving app
3. Road safety stakeholders' needs (subtheme 2.c) should also be accounted for
when designing implementations to be used in both laboratory and in real-
world conditions. Thus their experience and support can be used when to
understand how driving behaviour can be influenced more efficiently (“You
can use [apps...] like a kind of simulator when you do an education campaign”
[P4]; “[apps...] be used in real life or real driving.” [P7]). The opportunity to
replace expensive equipment with other quality means is important in every
endeavour (“If it can replace the very high-cost simulators. [That means] Low
cost!” [P4]). Low cost will undoubtedly appeal and, thus, boost adoption. Last,
but not least, the implementation should trigger discussions, especially in the
open public ("the data should be available to the public." [P10]), for example
through a leaderboard. This will make the researchers' efforts more visible and
will open the door for more feedback that could help adoption and
improvements.
6.3 SYNTHESIS OF FOCUS GROUP’S FINDINGS
The purpose of the implemented Focus group was to provide understanding
about the road safety experts' attitudes towards ICT, its evolution, and what challenges
it brings in the vehicles. More specifically, the Focus group sought to gain a
comprehensive understanding of potential real-world limitations that might discourage
safe-driving apps' adoption. The young drivers' specific needs and motivations were
explored in the process of identifying such limitations. The Focus group was
exploratory in nature and added to the present program of research a better
understanding of potential criteria which may boost safe-driving apps adoption if met.
Those criteria aligned with some of the theory-based criteria for selecting COTs.
Alignment was found with the criteria, relevant for influencing norms and PBC, the
expected strongest predictor of intention (see Section 4.2). More specifically, SC2
(relevant for norms) is consistent with two focus group's recommendations. First,
involving as many stakeholders as possible in supporting the joint efforts of road safety
researchers would potentially include important referents of the participants. Those
important referents can both express their attitude towards the participants' behaviour
or share information about their own behaviour. And second, such exchange of
information can serve as a trigger for open public discussions together with the
interventions, which was the other SC2 relevant recommendation of the focus group.
Chapter 6: Selecting a safe-driving app 89
Discussions in public can additionally reinforce the establishment of norms. SC3
(relevant for PBC) is somewhat consistent with the recommendations not to distract
drivers. Not distracting the drivers would keep them engaged in their primary driving
task, which, in turn, would enable them to perform the behaviour of interest better. To
achieve this SC3-relevant outcome, the focus group recommended 1) devices to be
fixed and hands-free, 2), incoming communication to be blocked, and 3) the app to
provide audio feedback. Finally, SC5 (relevant for peers' norm) aligns with
recommendations such as to include leaderboards and enable social network sharing.
A leaderboard would allow the young drivers to see how their peer young drivers rank.
This information would potentially motivate them to improve their behaviour to enjoy
the fame of being at the top (Duggan & Shoup, 2013). Social network sharing would
allow them to reinforce the fame, thus, reinforcing the peer pressure.
The focus-group-based criteria, which are complementary to the theory-based
ones, are summarised in Table 6.2. Those criteria should be carefully applied in other
research projects and should be considered together with the Focus group limitations.
The main limitation of the Focus group was related to the DriveScribe app. Its review
facilitated the discussion by providing a real-life example. However, it also biased the
discussion as the Focus group participants kept referring to it while the objective was
to discuss smartphone safe-driving apps in general. This potentially reduced the scope
of the findings. Having a more diverse number of safe-driving apps reviews could
potentially add value to future discussions. Thus results should not be generalised.
Table 6.2. Criteria for smartphone safe-driving apps, synthesised from Focus group’s findings.
Criteria Findings
Low-cost In order to boost adoption, it would be best if the app is free for users and
does not require hardware, such as a dongle, in addition to the smartphone
itself.
Safety The app should be able to provide live feedback to the driver as well as run
invisibly in the background, thus, not causing additional distraction. It also
shall have a self-starting capability.
Availability The app shall not be geographically restricted.
Information The app shall provide various types of information. It shall have a web
interface. It shall provide detailed after-trip feedback on a Google map. It
90 Chapter 6: Selecting a safe-driving app
should also feature user groups (i.e. proprietary leaderboards) with the
achievements being possible to share on social media.
6.4 SELECTING A SAFE-DRIVING APP FOR AN EVALUATION
The next step, following the Focus group, was to browse the app stores (Google
Play and iTunes) to identify safe-driving apps. Based on findings from the Focus
group, apps were searched and studied. The used terms were "road app", "smart
driving", "safe driving" and "OBD game". Sixty-six apps from the search results in
Google Play and 20 from iTunes were selected for detailed investigation (see
Appendix B). The established list of safe-driving apps was narrowed down to three
apps, shortlisted for user testing. Finally, one was selected to be evaluated as an
intervention tool.
The online reviews, left by users in the smartphone safe-driving apps’ marketing
profiles, were used to narrow down the number of apps to be investigated in more
detail. The Focus group recommendations, discussed in the previous section, were
compared against the pros and cons of a selection of some more popular apps. In Table
6.3, those apps were scored using the established theoretical selection criteria (see
Section 4.2). Double points were assigned to a positive answer on the PBC-related
question because PBC is seen as typically the strongest predictor of speeding.
Chapter 6: Selecting a safe-driving app 91
Table 6.3. Smartphone safe-driving apps (yes=1, no=0, double points for SC3).
Name Pros Cons Scoring
Flo - driving
insights
1. Can provide live feedback to the driver or can run invisibly in the
background. The life feedback can help the driver understand their real-
time behaviour and potentially inform conclusions whether it is
favourable or unfavourable (SC1).
2. Provides detailed after-trip feedback on a Google map. This detailed
information can potentially help the driver understand the implications of
any changes in their behaviour, thus enabling them to improve it (SC3).
3. Offers leaderboard. The leaderboard shows to the driver how their peers
are doing (SC5). Comparing with their peers can help the driver assign a
moral value to their behaviour (SC4).
4. Free for users.
5. No geographic restriction.
6. Has a web interface.
7. Does not need a dongle.
1. Has problems with
synchronising with the GPS
signal when on autostart.
2. Drivers with short trips may
in general score lower than
drivers with long ones.
3. Does not have all car
manufacturers and models.
SC1 SC2 SC3 SC4 SC5 SC6
Yes No Yes Yes Yes No
1 0 2 1 1 0
Total points: 5
92 Chapter 6: Selecting a safe-driving app
AAMI Safe Driver
1. Monitors for exact speed limits, not the general ones. The speed limits
feedback can help the driver understand whether it is favourable or
unfavourable (SC1).
2. Provides detailed after-trip feedback on a Google map. This detailed
information can potentially help the driver understand the implications of
any changes in their behaviour, thus enabling them to improve it (SC3).
3. Good mix of gamified elements (scores, badges). Receiving scores and
badges can represent a higher morality of the behaviour (SC4).
4. Runs in the background.
5. Does not need a dongle.
6. Free for users.
1. Cannot provide real-time
feedback.
2. Fails to record and analyse
long journeys.
3. Has problems with
synchronising with the GPS
signal when on autostart.
4. Very wide thresholds are
set for recording an offence.
5. Designed to sell an
insurance product.
6. Has problems with
calculating the overall
score.
SC1 SC2 SC3 SC4 SC5 SC6
Yes No Yes Yes No No
1 0 2 1 0 0
Total points: 4
Chapter 6: Selecting a safe-driving app 93
Hellas Direct (one
of the DriveWell
implementations)
1. Provides detailed after-trip feedback on a Google map. This detailed
information can potentially help the driver understand the implications of
any changes in their behaviour, thus enabling them to improve it (SC3).
2. Uses all common gamification elements (scores, leaderboards, badges,
etc.). Receiving scores and badges can represent a higher morality of the
behaviour (SC4).
3. Offers a leaderboard. The leaderboard shows to the driver how their peers
are doing (SC5). Comparing with their peers can help the driver assign a
moral value to their behaviour (SC4).
4. Does not need a dongle.
5. Runs in the background.
6. Free for users.
1. Available only in selected
jurisdictions, i.e. not being
tested in Australia.
2. Always attached to
insurance products.
3. Looks like over-gamified –
may benefit from a "lite"
version.
4. Difficult to advise when the
user is not the driver.
5. Does not have a user-start
option.
SC1 SC2 SC3 SC4 SC5 SC6
No No Yes Yes Yes No
0 0 2 1 1 0
Total points: 4
94 Chapter 6: Selecting a safe-driving app
SafeDrive
1. Blocks calls and texts. Thus, it helps the driver perform the behaviour
(SC3).
2. Free and not geographically restricted.
3. Tries to connect gamification (earning points) with the real world
(receiving rewards). Received points can represent a level of morality of
the behaviour (SC4).
4. Works on auto-start.
1. Does not monitor other data
than mobile phone usage
data.
2. No real rewards besides
discounts.
3. Has problems with setting
user info.
4. Has problems with
synchronising with the GPS
signal.
SC1 SC2 SC3 SC4 SC5 SC6
No No Yes Yes No No
0 0 2 1 0 0
Total points: 3
Automatic 1. Uses accurate vehicle data. The accurate vehicle data can help the driver
understand whether it is favourable or unfavourable (SC1).
2. Provides detailed feedback. This detailed information can potentially help
the driver understand the implications of any changes in their behaviour,
thus enabling them to improve it (SC3).
3. Assist with crash alert (SC6).
4. Diagnoses the car.
1. Costs 99.99 AUD.
2. Requires a dongle.
3. Restricted to the US.
4. Does not support all cars.
SC1 SC2 SC3 SC4 SC5 SC6
Yes No Yes No No Yes
1 0 2 0 0 1
Total points: 4
Chapter 6: Selecting a safe-driving app 95
Rookie Dongle
1. Uses accurate vehicle data. The accurate vehicle data can help the driver
understand whether it is favourable or unfavourable (SC1).
2. Notifies parents or guardians about reckless driving. Thus, it may trigger
information on how driver's important referents see their behaviour
(SC2).
3. Provides detailed feedback. This detailed information can potentially help
the driver understand the implications of any changes in their behaviour,
thus enabling them to improve it (SC3).
4. Does not require a smartphone as it has a built-in GPS and mobile
connection.
1. Costs 338.99 EUR with a
one-year subscription.
2. May discourage adoption
because of notification to
third parties.
SC1 SC2 SC3 SC4 SC5 SC6
Yes Yes Yes No No No
1 1 2 0 0 0
Total points: 4
96 Chapter 6: Selecting a safe-driving app
The final step was to install three apps on the candidate's smartphone for real
driving testing. The initial criteria, used for that specific purpose, were derived from
the focus group's recommendations. Those were:
- Availability. Not all apps had permissions to be used in Australia, i.e. their
usage was geographically restricted.
- Affordability. The apps had to be free of charge to use, which, if deployed
in an intervention, might increase the likelihood of a widespread adoption
and, thus, the potential for recruiting a larger sample.
As per the focus group's recommendations, the compliant apps were Flo, "AAMI
Safe Driver" and "SafeDrive". All three apps were available in Australia and were free
to use. Data were collected for more than 2,000 kilometres of driving. It is worth noting
that common problems were encountered in all three apps during the tests. For
example, there were problems with the apps synchronising with GPS signal, when on
autostart, i.e. when the apps were not started manually by pressing a designated button.
There were also app-specific problems. For example, failure to record and analyse long
journeys and problems with calculating the overall score were encountered in "AAMI
Safe Driver". "SafeDrive" had problems with registering user information. Although
no major problems were encountered with Flo, shortcomings were not missing. For
example, short trips, in general, generated lower scores than long trips, which can
potentially put at disadvantage drivers that drive on shorter distances. Shortcomings
with "AAMI Safe Driver" and "SafeDrive" were encountered, too. For example
"AAMI Safe Driver" did not provide real-time feedback, while "SafeDrive" was
focusing on mobile phone usage.
6.5 CONCLUSION
The Focus group participants seemed willing to explore the positive potential
of technologies in their work. This presented an opportunity for the COTs evaluated
as part of the current thesis to be potentially applied on a larger scale in the real world.
With the current program of research providing evidence around using COTs in
interventions that go into the research participants’ free-living environment, the
findings discussed in the thesis may well add value beyond the scope of the thesis
itself.
Chapter 6: Selecting a safe-driving app 97
The undertaken real driving review explored existing apps, i.e. whether they
are suitable to be deployed as an intervention tool in the framework of the current PhD
program of research. Real-time feedback and collecting driving data capabilities of the
smartphone safe-driving apps were essential for the current program of research. Thus,
Flo was better suited than "AAMI Safe Driver" and "SafeDrive". This decision was
supported by 1) the real driving review, 2) the smartphone safe-driving apps systematic
literature review (see Chapter 5), 3) the Focus group participants’ opinions and
recommendations (see Section 6.3), and 4) the theoretically-grounded apps' scores (see
Table 6.3), where Flo scored 5, the highest of the three tested apps. These
considerations lead to the selection of Flo to be evaluated as an intervention tool going
forward.
98 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
Chapter 7 presents the second study of this program of research. Study 2 assessed
whether a smartphone safe-driving app intervention could influence young drivers'
self-reported behaviour of not speeding and intention not to speed. First, a brief
introduction of the study is presented, before outlining its key aims, method and
hypotheses, followed by the study results, and a discussion.
7.1 INTRODUCTION
Risk prevention efforts yield unsatisfactory results, leaving the safety of young
drivers in focus (Scott-Parker et al., 2015). Young people continue to be
overrepresented in road fatalities (BITRE, 2018), with speeding causing 43% of them
(AONSW, 2011). At the same time, available in-car COTs, such as infotainment
systems or smartphones, compete with the primary driving task for drivers' attention,
threatening for existing road safety problems, e.g. driver distraction, to further
deteriorate (Parliament of Victoria Road Safety Committee, 2006). In parallel with
increasing concerns that problems related to technologies are likely to both increase
and evolve (WHO, 2011), both academia and businesses suggest that the power of
COTs, in general, and smartphones, in particular, provide untapped opportunities to
reduce risks, and, more importantly, to reduce risks amongst young drivers (see
Chapter 5 and Chapter 6).
Study 2 examined the effects of a safe-driving app intervention to reduce risky
driving behaviour, more specifically speeding, amongst young drivers. It evaluated an
intervention that deployed a safe-driving app, Flo (see Chapter 6 for the app selection
process), to transform existing risk sources (smartphones) into ones for motivating safe
driving behaviour. The study operationalised an approach, suggested by Schroeter et
al. (2012), to persuade young drivers to behave safer on the road without the need to
reduce the fun from driving or to incur substantial deployment costs.
Even though smartphones allow the monitoring and assessment of other risky
driving manoeuvres beyond speeding, such as hard acceleration, hard braking and fast
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 99
cornering, those were not targeted. The focus of the current research was to understand
whether, and to what extent, the intervention with an off-the-shelf safe-driving app as
a tool positively influenced the young drivers' intention not to speed as well as their
subsequent self-reported behaviour of not speeding within the TPB evaluation
framework (see Figure 3.5 in Section 3.7). In Study 2, a quantitative evaluation of the
implemented safe-driving app intervention was conducted based on surveys-collected
data. The choice of TPB as an overarching framework to guide this quantitative
evaluation was carefully determined after a review of the most widely used theories in
the social and behavioural sciences (see Chapter 3).
Study 2 aimed to answer RQ2: "How do young drivers’ self-reported behaviour
of not speeding and intention not to speed alter in their free-living environment, as a
result of exposure to a smartphone safe-driving app intervention?"
The intervention had a span of three months. The variables identified as being of
most interest were:
1. Intention not to speed, measured before the intervention, as it related to
what the drivers planned to do during the following three months without
being influenced;
2. Behaviour of not speeding during the three months of the intervention,
measured after the intervention, as it reflected what the drivers had been
doing during the intervention.
In the literature, speeding is defined as "driving at an illegal speed over the limit"
or "driving at an inappropriate speed" or both. However, "driving at an inappropriate
speed" can be regarded as a less specific definition. Thus, for the current purpose,
speeding is defined as illegal behaviour, which sets a defined threshold for a behaviour
to be considered as speeding.
It was expected that as a result of the intervention participants in the Intervention
group would report significantly greater behaviour of not speeding during the three
months of the intervention as well as greater intention not to speed in comparison with
the Control group. To achieve the expected result, the smartphone safe-driving app Flo
was deployed to be used by the Intervention group participants over three months. It
served the purpose of a driving coach with feedback on the targeted behaviour in the
framework of this project (speeding), as well as other risky manoeuvres (hard
100 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
acceleration, hard braking and fast cornering). A driving coach with feedback was
expected to potentially influence the intervention participants' perceived behavioural
control (PBC, see Figure 3.5 in Section 3.7) by showing them that whatever they do
on the road had a consequence that could be measured and reported. Such an
experience may show the participants they are in control of what is reported through
their behaviour. If successful, through the established relations within the TPB, such
an influence would impact a) the dichotomised TPB constructs (see Subsection 4.4.2),
and b) the young drivers’ self-reported behaviour of not speeding and intention not to
speed.
Notwithstanding, the app of choice was not expected to increase distraction
significantly, thus, to negatively influence the participants' behaviour in regards to
initiating, monitoring/reading, and responding to social interactive technology on their
smartphones while driving. As a potential clue to the participant, the app provided
insights into the time each participant had their phone screen active while driving (see
Figure 7.1).
Figure 7.1. Time on screen as reported by Flo for each trip
Study 2 was analysed in four parts to address the overarching RQ2. The first part
looked at the whole sample of participants before the intervention was applied to part
of them (RQ2.1). It explored their intention not to speed and its predictors. The second
part focused on the changes the intervention might or might not have triggered in
regards to speeding (RQ2.2). The third part assessed how much of the self-reported
behaviour of not speeding during the three months of the intervention could have been
predicted with the data available before the intervention took place (RQ2.3). Finally,
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 101
the fourth part assessed whether an additional risk, namely increased distraction, was
introduced with the intervention (RQ2.4).
7.2 METHOD
7.2.1 Study design
Study 2 was designed as a randomised controlled experiment in the
participants' free-living environment. The intervention was implemented as part of
their normal daily routine with no intention that routine to be impacted by the
intervention. All participants completed the same questionnaire at the point of
recruitment. After recruitment, the list of participants was split by gender, to ensure
gender balance in both subsequent conditions. Without any other consideration, half
of the male participants and half of the female participants were randomly assigned to
the Intervention group. The remaining halves were assigned to the Control group. The
gender stratification was performed to ensure that both conditions reflect the original
gender balance of the recruited sample.
The Intervention group was asked to install and use for three months the
intervention tool, the Flo smartphone safe-driving app. The Control group was
instructed that they would be contacted after three months and that they are not
expected to do anything else before that. After the three-month intervention period
expired, both groups were contacted with a request to complete the identical second
questionnaire (see Appendix B). The design of the questionnaire is discussed in
Subsection 4.4.2 of this thesis.
7.2.2 Recruitment
Recruitment was conducted between the 01st and the 30th of April 2018.
Otherwise, potential participants were not time-constrained to consider participation.
As personal presence was not required, participants were allowed to complete the
survey at any time of the day and at any device, when and where it was convenient for
them.
Participants were initially approached online through QUT students' e-mail lists
and social media. A Facebook ad campaign was implemented as part of the social
media outreach. The campaign was set to target people who were aged 18 to 25;
resided in Australia; spoke English; had interests in smartphones, driving or motor
vehicles. The ad showed a static image of hands on a wheel and a look over a car
102 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
dashboard with a clear highway as a perspective. The accompanying text was
communicating to the viewer the additional requirement of driving 100 kilometres per
month, which was impossible to set as a targeting parameter of the ad campaign, and
was inviting them to complete the online survey. The text was also informing them
that the survey would take 10 minutes to complete. The campaign reached more than
50,000 people. The click-through rate was approximately 5%.
There was no cost related to the participants' involvement in the study. However,
the safe-driving app uses smartphone sensors. Continued use of those sensors, such as
GPS for example, running in the background can decrease battery life. The driving
app, we used, was built to use minimal power, but power consumption in the
smartphone was considered an indirect cost to be borne by the participants.
Participation in a random draw of gift vouchers was offered as an incentive to
participate. A participant was eligible for incentive after completing both surveys (at
Time 1 and at Time 2). The study had a total incentive fund of 1,500 AUD divided
into 10 Coles/Myer vouchers of 150 AUD each.
7.2.3 Intervention tool
The smartphone safe-driving app, Flo (see Figure 7.2 and Chapter 6 for
information about the selection process of Flo) was the intervention tool in Study 2.
Flo essentially served the purpose of a driving coach with feedback, aiming to help the
participating drivers learn to drive safer. It was installed on the intervention group
participants' smartphones.
Figure 7.2. Smartphone with Flo, providing real-time feedback while driving5.
5 Screenshot source: https://www.youtube.com/watch?v=A9GLHohkqbo
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 103
In general, Flo provides insights to the driver in real-time by tracking
movements, using GPS, and calculating values for cornering, acceleration and braking.
It can also silently work in the background, without providing real-time feedback, to
minimise distraction. Based on the gathered data, it assigns scores for every trip. The
app saves all trip data, including driving behaviour, which the driver can review after
driving either through a web profile or through the app itself. For users that join a
leaderboard, a score of their driving behaviour ranks them with respect to other users.
Driving scores and rankings were additional data that were collected through Flo for
some of the participants.
Flo automatically detects and records the driving trips, i.e. drivers do not need
to remember to start the app before every trip. However, it should be noted that during
a trip, Flo cannot distinguish between being a driver or just being a passenger. This
means that if the smartphone owner is just the passenger, the app will self-start and log
data as if they were the driver. This limitation led to the inclusion requirement of
"Drive a car as the only means of transport" for a participant to be eligible (as noted in
the participant information sheet).
7.2.4 Procedure
A participants' information sheet was provided to the participants online, as a
cover sheet of the first survey, before they started completing the survey. At Time 1
(April 2018), the self-completion questionnaires were used to collect data on
demographics, TPB constructs (self-efficacy, perceived controllability, instrumental
attitude, affective attitude, subjective norm, descriptive norm, intention not to speed
and behaviour of not speeding) and additional predictors (past speeding behaviour,
perceived risk, moral norm, peers' norm, impulsivity) (see Figure 3.5). Detailed
information on the items is provided in Subsection 4.4.3.
Upon completion of the Time 1 survey, the participants were randomly divided,
nevertheless gender-stratified as noted earlier, into a Control group with no
intervention (n=241, 117 females) and an Intervention group with app deployment
(n=243, 123 males) (see Figure 7.3). The participants in the Control group were not
required to do anything in relation to the study during the three-month intervention
period.
104 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
Figure 7.3. Safe-driving app intervention design
The Intervention group was instructed to install Flo from either Google Play or
iTunes, depending on the operating system of their smartphone. Subsequently,
invitations to join the in-app GoOz leaderboard group (see Figure 7.4) were issued
through the app web interface. The group was specifically created for the current study.
As some participants did not receive those invitations, a separate e-mail was sent to
them with instructions on how to initiate joining the group from their end. The group
allowed the research team to observe and periodically record how many trips each
participant made during the last 30 days, and what their current driving score was.
Figure 7.4. Example screenshot of Flo GoOz leaderboard
At Time 2 (three months after Time 1, August 2018), an invitation to complete
the second survey was sent to all participants. Two reminders were sent to participants
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 105
who did not complete the survey. The ones that completed it were excluded from the
reminders by matching their anonymous identifiers. The self-generated participants'
anonymous identifiers were used to link datasets from Time 1 and Time 2, related to
the same person.
7.2.5 Participants
Initially, 504 young drivers completed the first survey. Partially completed
surveys were not considered. After removing 23 duplicates, as well as one entry of
participants who explicitly requested in writing that they wanted to opt-out of the study
after they had completed the first survey, 480 cases (245 male; Mage = 20.88 years,
SD = 2.10) were retained for analysis. This number was well above the initially
targeted minimum desired number of 140 participants (see Subsection 4.4.1).
Of the 480 participants, 217 (45.2%) reported to have an open driver's licence,
193 (40.2%) reported having a provisional (Year 2) licence, 67 (14.0%) – a provisional
(Year 1) licence, and 3 (.6%) – a learner licence (see Subsection 2.3.1 for details on
the Australian GDL framework).
The biggest group of participants reported living in Victoria (141, 29.4%),
followed by New South Wales (132, 27.5%), Queensland (99, 20.6%), Western
Australia (42, 8.8%), South Australia (39, 8.1%), Tasmania (18, 3.8%), Australian
Capital Territory (8, 1.7%) and Northern Territory (1, 0.2%). The sample distribution
was aligned with the general distribution of the population in Australia6. The
Australian Bureau of Statistics reports 32.0% of the population as living in New South
Wales, 25.8% in Victoria, 20.0% in Queensland, 10.4% in Western Australia, 7.0% in
South Australia, 2.1% in Tasmania, 1.7% in the Australian Capital Territory and 1%
in the Northern Territory. Data by states and territories was not explored further in the
analysis due to low numbers in some of the cases and because no evidence could be
found in the literature that pointed towards a potential effect of the participants'
geographic location within Australia.
At Time 2, 210 young drivers (109 male; Mage = 21.01 years, SD = 2.12)
completed the second questionnaire. The dropout rate of 56.25% exceeded the initially
6 http://www.abs.gov.au/ausstats/[email protected]/latestProducts/3101.0Media%20Release1Mar%202018 (Accessed on 18/12/18)
106 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
expected 14.29%, as encountered by Musicant and Lotan (2015). Nevertheless, due to
the larger initial sample, the collected 210 cases were more than the 120 initially
sought. The Intervention group remained with 84 participants, while the Control group
remained with 126. Each of the two groups had more than the initially targeted min
n=60, surpassing two and three times, respectively, the n=42 reported by Musicant and
Lotan (2015).
A look at the Flo leaderboard revealed that only 62 participants joined it. Thus a
decision was taken to revise the number of participants, which were considered an
Intervention group, to make sure that the analysed data belonged to people who
actively participated in the intervention. Twelve of the 62 participants in the
leaderboard never generated a score, suggesting that they did not use the app after
signup, and were therefore removed. As a result, 50 participants remained as being
considered part of the Intervention group. The data of 19 of those 50 participants could
not be reliably linked between the leaderboard and the survey data through the
anonymous identifiers. Thus 31 entries remained for the main analysis in this thesis.
It has to be acknowledged that for the purpose of analysis, the Intervention group
can be constituted differently, depending on the analyst’s preferences. For example, it
can be argued that all Intervention group participants, who completed the second
questionnaire, are part of the Intervention group as there is no evidence that they did
not use the app. Another option would be to analyse data from participants who are
considered highly engaged in the study, e.g. generated a score during more than half
of the intervention period. A third option could be to have a demographic match
between the Intervention group and the Control group. Those additional options might
potentially provide evidence for different impacts of the implemented intervention.
Should the reader prefer the analysis to be based on one of the three presented
variations of Intervention group constitution, these alternative analyses are presented
in Appendix D. However, in this chapter, a participant is considered to be part of the
Intervention group only when data could be reliably linked across the two surveys, and
the Flo leaderboard as this is sufficient evidence that they used the smartphone app
and therefore underwent the intervention as defined in this thesis.
7.2.6 Intervention
Between completing the survey at Time 1 (April 2018, before the intervention)
and Time 2 (three months after Time 1, August 2018), the participants from the
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 107
Intervention group were subjected to Flo, the off-the-shelf safe-driving app. Only those
participants were expected to use Flo for three months, while they were driving. They
were not required to complete any tasks outside their normal daily routine so that their
free-living environment (see RQ2) remains intact by the current study. Using the app
was expected to persuade them to adopt a safer driving behaviour as evidenced by their
self-reports at Time 2 with self-reports at Time 1 as a baseline. The participants from
the Control group were not expected to do anything during that same period.
7.3 HYPOTHESES
The deployed extended TPB framework (Ajzen, 1988) (see Section 3.7)
constitutes a powerful model for evaluating interventions. It identifies determinants of
behaviour that can potentially be influenced, thus, modified. It further identifies other
determinants that cannot be influenced but still determine behaviour. In that respect,
initially looking at the predictors of intention not to speed at baseline to address RQ2.1,
it was hypothesised that:
H.1. Demographic variables (gender, age and driving license) would account
for a significant variation in intention not to speed. Speed is a major contributor
to crashes (AONSW, 2011), and the increased crash risk is shown to associate
with both inexperience and age (McCartt et al., 2009). Also, gender is shown
to play a role in the young drivers' risky behaviours (Scott-Parker, 2012).
H.2. TPB constructs (instrumental attitude, affective attitude, subjective norm,
descriptive norm, self-efficacy and perceived controllability) would account
for a significant variation in intention not to speed, over and above the
demographic variables. According to Sniehotta et al. (2014), TPB may lack
sufficient predictive power in longitudinal studies, or in studies with samples,
coming outside the university campuses, a view not shared by Conner (2015),
and not supported by findings of studies the current one leverages on, e.g.
Elliott and Thomson (2010).
As suggested by Conner (2015), an extended theoretical framework may address
widely noted criticisms and limitations of the TPB, as well as provide additional
insights on the effect of the deployed intervention. Additional predictors may also
explain additional variation in the DVs, after controlling for TPB variables. A detailed
108 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
discussion about the additional predictors, used to extend TPB, is presented in Section
3.6. Thus, it was hypothesised that:
H.3. Additional predictors (past behaviour of not speeding, perceived risk,
moral norm, peers' norm, impulsivity, sensitivity to reward and sensitivity to
punishment) would account for a significant variation in intention not to speed,
over and above the TPB variables.
New technology designed to persuade drivers can help young drivers adopt safer
driving behaviour in naturalistic settings as a result of safe-driving apps interventions,
focused on speeding (Li et al., 2016; Musicant & Botzer, 2016; Zhang et al., 2014)
(see Chapter 5). Thus, the implemented intervention provided a basis to investigate
RQ2.2 or the actual safety benefits of a safe-driving app. For the effects of the
intervention, it was hypothesised that after the intervention:
H.4. Participants in the Intervention group would report significantly greater
intention not to speed in the future than the Control group participants.
H.5. Participants in the Intervention group would report significantly greater
behaviour of not speeding during the three months of the intervention than the
Control group participants.
H.6. Due to the expectation that the app can influence attitudes, PBC, moral
norm and peers' norm (see Section 6.4), the safe-driving app intervention
would have positively influenced the Intervention group participants'
instrumental attitude, affective attitude, self-efficacy and perceived
controllability, moral norm and peers' norm directly. Through the correlations
of those constructs within the framework, we also expected subjective norm,
descriptive norm and perceived risk to be influenced indirectly.
H.7. The intervention would improve the participants' driving, as represented
by the observed driving scores in the safe-driving app leaderboard.
In intervention design, knowing what your participants would do in the future
could be very useful information. Such knowledge could help intervention designers
calibrate interventions better, and in advance, to address potential risks. Thus, to
address RQ2.3, predictors of behaviour of not speeding during the three months of the
intervention were investigated to assess how much of the behaviour during the
intervention, reported at Time 2, could have been predicted with the information
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 109
available before the intervention, as reported at Time 1. In that respect, it was
hypothesised that:
H.8. Demographic variables (gender, age and driving license) would account
for a significant variation in behaviour of not speeding during the three months
of the intervention.
H.9. TPB constructs (intention not to speed, self-efficacy and perceived
controllability) would account for a significant variation in behaviour of not
speeding during the three months of the intervention, over and above the
demographic variables.
H.10. Additional predictors (past behaviour of not speeding, perceived risk,
moral norm, peers' norm, impulsivity, sensitivity to reward and sensitivity to
punishment) would account for a significant variation in behaviour of not
speeding during the three months of the intervention, over and above the TPB
variables.
Mobile phones are identified as a major source of distraction while driving
(WHO, 2011). Thus, the current intervention provided an opportunity to investigate
RQ2.4, or if despite the good intentions of implementing the intervention, the safe-
driving app did not increase the drivers' distraction. In that respect, potential negative
effects were investigated. For the self-reported smartphone interaction behavioural
measures, it was hypothesised that:
H.11. The intervention would not significantly increase the Intervention group
drivers' distraction in terms of initiating (less), monitoring/reading (less) or
responding (less) to communication in comparison to the Control group
participants.
7.4 PRELIMINARY ANALYSIS
Before analysing the results in detail, a preliminary data analysis was performed,
to deal with missing data, to transform data, to decide how to deal with dropouts, and
to establish the participants' profiles in regards to their personality characteristics.
These preliminary analyses are discussed in turn.
110 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
7.4.1 Missing data
There was no missing data. The setup of the data collection required all questions
to be compulsorily answered with a limited number of answer options to choose from,
i.e. successful submission of the online questionnaire could only occur if once all
questions were completed.
7.4.2 Data transformation
Following data collection, measures for intentions, attitudes, norms (with the
exclusion of moral norm) and behaviour were recoded (transformed), so that higher
scores indicated greater agreement with the construct (perceived negatively-geared
answer to the left of the scale, smaller value, and perceived positively-geared answer
to the right of the scale, higher value). The reported Boolean answers on smartphone
use (initiated (less) communication, monitored/read (less) communication, and
responded (less) to communication) were assigned numerical values and, thus, were
converted into a scale from 1 (More than once per day) to 7 (Never), i.e. again,
perceived negatively-geared answer to the left of the scale, smaller value, and
perceived positively-geared answer to the right of the scale, higher value.
The two separate intention questions "To what extent do you intend to drive
faster than the speed limit over the next 3 months?" (A great extent to no extent at all
after recoding) and "How often do you think you will drive faster than the speed limit
in the next 3 months?" (All the time to never after recoding) for the construct intention
were strongly and significantly correlated (Pearson's r = .79, p < .001). Thus, they were
combined (through finding an average) into a single measure intention not to speed.
The other TPB measures were retained as separate in the analysis in accordance with
the Elliott and Thomson's (2010) model, an approach discussed in further detail in
Subsection 4.4.2.
The questions "If you were to drive over the speed limit over the next 3 months,
how much would you worry about being involved in a road crash?" (Not at all worried
to worried very much) and "If you were to drive over the speed limit over the next 3
months, how much would you worry about being caught by the Police?" (Not at all
worried to worried very much) for the construct perceived risk were strongly and
significantly correlated (Pearson's r = .53, p < .001). Thus, they were combined
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 111
(through finding an average) into a single measure perceived risk, to be used as an
additional predictor.
Gender was recoded, from a string, into a numeric variable, with "0" denoting
males and "1" denoting females.
For the regression analysis, driving licence was recoded from a string into a
numeric variable with "0" denoting a learner license, "1" denoting a provisional (year
1) license, "2" denoting a provisional (year 2) license, and "3" denoting an open
license.
For the ANCOVA analysis, driving licence was recoded from a string into a
numeric variable with "1" denoting both a provisional (year 1) license and a
provisional (year 2) license, and "2" denoting an open license. The different recoding
was implemented because none of the learner drivers and only a low number of
provisional (year 1) drivers completed the second survey.
For the ANCOVA analysis, participants were grouped according to the
following scores:
- BIS-11 scores for impulsivity, as per Stanford et al. (2009):
o Denoted with "1", participants with scores lower than 52 were
part of the low impulsivity group.
o Denoted with "2", participants with scores between 52 and 71
were part of the "normal" impulsivity group.
o Denoted with "3", participants with scores above 72 were part of
the high impulsivity group.
- Sensitivity to punishment score, with a cut-off the mean scores of the
distribution, or 13.51 (SD=5.60):
o Denoted with "1", participants with scores lower than 13,
inclusive, were part of the low sensitivity to punishment group.
o Denoted with "2", participants with scores above 14, inclusive,
were part of the high sensitivity to punishment group.
- Sensitivity to reward score, with a cut-off the mean scores of the
distribution, or 11.50 (SD=4.60):
o Denoted with "1", participants with scores lower than 11,
inclusive, were part of the low sensitivity to reward group.
112 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
o Denoted with "2", participants with scores above 12, inclusive,
were part of the high sensitivity to reward group.
7.4.3 Dropouts
A preliminary one-way MANOVA was performed in regards to the provided
answers on the extended TPB variables (11 DVs) at Time 1. The IV was whether Time
2 questionnaire was completed or not. Homogeneity assumption was met, with a Box's
M p = .34. No significant difference was found for the DVs (Wilks' Lambda = .99, F
(11, 468) = .63, p = .80, ηp2 = .015) between the participants who completed only the
Time 1 questionnaire and those who completed both questionnaires. A further look
into each DV with a Bonferroni-adjusted α level benchmark set at .004 (standard value
of .05 divided by 11, the number of DVs), did not reveal any significant difference
between the two groups on any of the measures. Thus, the data collected from the
participants at Time 2 could be considered representative for the participants of the
whole sample. Nevertheless, the full data set was retained in the analysis at Time 1.
7.4.4 Assumptions checks
To determine whether random assignment to the Intervention and the Control
group was successful, a preliminary one-way between-groups MANOVA was
performed on answers about demographics (so variables gender, age and driving
experience as depicted by driving license) at Time 1. The IV was the condition, so
Intervention or Control. No significant difference between the two groups of
participants was found (Wilks' Lambda = 1.00, F (3, 476) = .09, p = .97, ηp2 = .001).
Thus, the random assignment to the two conditions was considered successful.
Normality for the DVs, intention not to speed, measured at Time 1, and
behaviour of not speeding during the three months of the intervention, measured at
Time 2, was assessed statistically, via skewness and kurtosis, and visually, via
histograms and Q-Q plots. Intention not to speed was negatively skewed at Time 1 (-
.68, std. error = .17, z = -6.09). The value for kurtosis was -.65 (std. error = .22, z = -
2.91). Although the absolute values fell within the generally accepted range of -2:2,
the calculated z-value for skewness suggested a departure from normality (Kim, 2013).
However, Kim (2013) suggests that z-values be ignored in samples of more than 300
participants. Histograms, with imposed normal curves, and Q-Q plots examination
suggested normal distribution. The Shapiro-Wilk test suggested a non-normal
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 113
distribution (p < .001). However, it is considered very sensitive and potentially
unreliable in samples > 50 (Elliott & Woodward, 2007).
Behaviour of not speeding during the intervention was positively skewed (.04,
std. error = .17, z = 0.24) with negative kurtosis (-1.21, std. error = .33, z = 3.63). Both
values fell within the generally accepted range of -2:2. Similar to the case of intention
not to speed, the calculated z-values suggested a departure from normality, but could
safely be ignored (Kim, 2013). The histogram, with an imposed normal curve, and a
Q-Q plot examination, suggested normal distribution while Shapiro-Wilk did not
suggest one (p < .001).
An examination of the Boxplots in each of the two DVs' case did not reveal
outliers, and also suggested normal distribution. Given that despite negative kurtosis
underestimation of variance disappears in samples > 200 (Tabachnick & Fidell, 2007),
the present study assumed normality was sufficient to explore the data with parametric
tests.
Additional normality checks were performed together with investigating each
regression model. Visual inspections of the regressions standardised residual P-P plots
and Scatterplots suggested no major deviations from normality. Results showed a
Mahalanobis distance above the suggested values (Tabachnick & Fidell, 2007).
However, subsequent inspections of the data files revealed only one or two cases
exceeding the suggested values for different tests. The maximum Cook’s distances
were negligible, indicating that the identified outliers did not have a major influence
on the data analysis.
Multicollinearity was investigated as part of the regression analysis due to high
correlation coefficients between some variables. This was most noticeable between
past behaviour of not speeding and intention not to speed (r=0.83, p < .001), which
were both entered as IVs at Step 3 of the linear regression model to predict behaviour
of not speeding during the three months of the intervention. All variance inflation
factors had a value lower than four, which in those specific cases reflected the strong
correlations. As such, there was no need to remove those variables from the analysis.
Lastly, the assumptions were met in the one-way ANCOVA tests. In some of the
two-way ANCOVA tests, the assumption of equality of variance was not met. No α
adjustments were necessary when there was no significant interaction effect. In one
114 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
case with a significant interaction effect, a lower, more conservative α (.025) was
adopted when exploring the data (Wickens & Keppel, 2004).
7.4.5 Personality characteristics
Consistent with Patton and Stanford (1995), the internal reliability analysis of
BIS-11 data, collected at Time 1, revealed high internal consistency with a Cronbach's
α of 0.84. The generally acceptable limit is 0.7 (DeVellis, 2016). The participants
(n=480) had a mean score of 58.62 (SD=10.33).
The internal reliability check of SPSRQ data, collected at Time 2, revealed high
internal consistency in both components, sensitivity to punishment (Cronbach's α of
0.84) and sensitivity to reward (Cronbach's α of 0.81). The participants (n=210) had a
mean score of 13.51 (SD=5.60) on the sensitivity to punishment scale and 11.50
(SD=4.60) on the sensitivity to reward scale.
7.5 RESULTS
7.5.1 Participants' intention not to speed before the intervention (RQ2.1, H.1 - H.3)
The following analysis of data, collected at baseline, was guided by RQ2.1 (What
did we know about the participants before the intervention, and to what extent could
the extended TPB framework predict their intention not to speed?), and by H.1, H.2
and H.3 respectively (see Section 7.3).
7.5.1.1 Means, standard deviations and bivariate correlations.
Table 7.1 below presents the means, standard deviations and Pearson's r
correlations for the TPB variables. Although on average participants considered that
they were in control of their not speeding (mean perceived controllability of 7.41 on a
9-point scale), they were not very certain they will be able to perform not speeding
(mean self-efficacy of 5.70 on a 9-point scale). Such uncertainty may partially explain
their comparatively low (close to the scale mid-point) reported past behaviour of not
speeding score (5.54 on a 9-point scale). The lowest mean score (5.14 on a 9-point
scale) was given for descriptive norm, which can be interpreted that everyone thought
their important others are speeding. On all measures, a reasonably-wide variability of
the scores was observed with the full range of possible responses being used.
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 115
Table 7.1. Means, standard deviations and bivariate correlations for the TPB variables at Time 1 (n=480).
Mean SD 1 2 3 4 5 6 7 8
1. Past behaviour of not speeding 5.54 2.38 - .83** .58** .56** .32** .39** .56** .16**
2. Intention not to speed 6.27 2.31 - .62** .58** .39** .37** .55** .18**
3. Instrumental attitude 6.26 2.21 - .66** .50** .35** .43** .15**
4. Affective attitude 5.44 2.47 - .40** .26** .41** .14**
5. Subjective norm 7.03 2.26 - .34** .29** .14**
6. Descriptive norm 5.14 2.15 - .33* .06
7. Self-efficacy 5.70 2.69 - .37**
8. Perceived controllability 7.41 2.11 -** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
Table 7.1 shows consistency with TPB in that past behaviour of not speeding is
highly correlated with intention not to speed (r=0.83, p < .001). Although it meant that
whoever speeded in the past would do that again in the future, it confirmed a strong
link to be explored. The table also shows that both past behaviour of not speeding and
intention not to speed were significantly correlated with all of their underlying
constructs. However, subjective norm and descriptive norm were moderately
correlated with them, while the correlations with instrumental attitude and affective
attitude were strong. Self-efficacy was strongly correlated with both past behaviour of
not speeding and intention not to speed, while perceived controllability exhibited a
weak correlation with the two constructs.
7.5.1.2 Predictors of intention not to speed
Following the order of entry described in Section 4.4.4, a 3-step hierarchical
multiple regression was conducted to assess which measures (demographics, TPB and
additional predictors), and to what extent, account for the variance in the participants’
self-reported intention not to speed for the whole sample (n=480) at Time 1.
As shown in Table 7.2, at Time 1, the demographic variables explained a
significant 6% (adj. R2 = .06, p < .001) of the variance in intention not to speed.
Nevertheless, the explained variance was very small, and age did not emerge as a
significant predictor. However, gender (β=.20, p < .001) and driving license (β=-.17,
p = .006) were statistically significant independent predictors.
These results were consistent with H.1, which predicted that demographic
variables would account for a significant variation in intention not to speed.
116 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
Table 7.2. 3-step hierarchical multiple regression analysis, predicting intention not to speed for all participants at Time 1, with demographic factors, TPB variables and additional variables as predictors
(n=480).
Step Variables R2 R2change Fchange Step 1 β Step 2 β Step 3 β Step 3
sr2
Bivariate
R2
1 Gender 0.06** 0.06 10.28** 0.204** 0.005 0.047 0.002 0.04**
Age 0.063 0.067 -0.006 <.001 <.01
Driving license -0.166* -0.111* -0.008 <.001 0.02*
2 Instrumental
attitude
0.54** 0.48 80.88** 0.284** 0.143** 0.008 0.39**
Affective
attitude
0.212** 0.054 0.001 0.33**
Subjective norm 0.019 0.051 0.002 0.15**
Descriptive
norm
0.141** 0.026 <.001 0.14**
Self-efficacy 0.309** 0.103* 0.006 0.31**
Perceived
controllability
-0.020 0.003 <.001 0.03**
3 Past behaviour
of not speeding
0.73** 0.19 66.82** 0.638** 0.183 0.69**
Impulsivity -0.008 <.001 0.05**
Perceived risk -0.061* 0.002 0.11**
Moral norm -0.004 <.001 0.19**
Peers' norm 0.015 <.001 0.17**
All beta weights are standardised. * p < .05 ** p < .001
Adding the TPB variables, at Step 2, significantly increased the explained
variance (ΔR2 = .48, p < .001). Thus, the explained variance exceeded 50%. Four TPB
variables emerged as significant predictors: instrumental attitude (β=.28, p < .001),
affective attitude (β=.21, p < .001), descriptive norm (β=.14, p < .001) and self-efficacy
(β=.31, p < .001), as well as driving license (β=-.11, p = .009).
The results were consistent with H.2, which predicted that TPB constructs would
account for a significant variation in intention not to speed, over and above the
demographic variables.
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 117
Adding the additional predictors, at Step 3, significantly increased the explained
variance, over and above TPB (ΔR2 = .19, p < .001). The statistically significant
independent predictors in the final regression equation were past behaviour of not
speeding (β=.64, p < .001), instrumental attitude (β=.14, p < .001), self-efficacy
(β=.10, p = .001) and perceived risk (β=-.06, p = .046).
Exploring the individual bivariate relations between the DV and the IVs (final
column in Table 7.2) showed that, if considered separately, all IVs, except age, were
statistically significant predictors of intention not to speed. The three strongest
individual predictors were past behaviour of not speeding, instrumental attitude and
affective attitude, which explained 69%, 39% or 33% of the variance, respectively.
However, when all IVs were considered in an overall model, past behaviour of not
speeding uniquely explained the highest percentage of the variance, 18% in this case.
Assessing the contribution of sensitivity to punishment and sensitivity to reward
as additional predictors required the regression test to be run only for the 210
participants who completed the second survey (see Table 7.3), as SPSRQ was
administered only at Time 2. With sensitivity to punishment and sensitivity to reward
added as additional predictors at the multiple hierarchical regression Step 3, the
explained variance over and above TPB was a significant 75% (adj. R2 = .73, p < .001),
2% more than in the equation assessing the full sample. The statistically significant
independent predictors in the final regression equation were past behaviour of not
speeding (β=.58, p < .001), instrumental attitude (β=.13, p = .038) and self-efficacy
(β=.18, p < .001).
Exploring the individual bivariate relations showed that, while sensitivity to
reward was a statistically significant predictor of intention not to speed, sensitivity to
punishment was not. Past behaviour of not speeding remained the strongest individual
predictor of intention not to speed, explaining 67% of the variance. Once again, when
all IVs were considered in an overall model, past behaviour of not speeding uniquely
explained the most variance, 16%. Self-efficacy also emerged as a noticeable unique
predictor, explaining 1.7% of the variance.
118 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
Table 7.3. Linear multiple regression analysis predicting Intention not to speed at Time 1 with demographic factors, TPB variables and additional variables, including sensitivity, as predictors
(n=210).
Step Variables R2 R2change Fchange Step 1 β Step 2 β Step 3 β Step 3
sr2
Bivariate
R2
1 Gender 0.07* 0.07 5.08* 0.188* 0.017 0.028 0.001 0.04*
Age 0.101 0.082 0.007 <.001 <.01
Driving license -0.236* -0.107 -0.019 <.001 0.03*
2 Instrumental
attitude
0.58** 0.51 39.90** 0.307** 0.128* 0.006 0.39**
Affective
attitude
0.191* 0.076 0.003 0.34**
Subjective norm 0.009 0.081 0.004 0.16**
Descriptive
norm
0.111* 0.009 <.001 0.10**
Self-efficacy 0.385** 0.178** 0.017 0.35**
Perceived
controllability
-0.067 -0.030 0.001 0.03*
3 Past behaviour
of not speeding
0.75** 0.17 19.43** 0.577** 0.159 0.67**
Impulsivity 0.006 <.001 0.04*
Perceived risk -0.004 <.001 0.12**
Moral norm -0.036 0.001 0.20**
Peers' norm 0.026 <.001 0.18**
Sensitivity to
punishment
-0.018 <.001 <.01
Sensitivity to
reward
-0.053 0.002 0.11**
All beta weights are standardised. * p < .05 ** p < .001
The results provide support for H.3, which predicted that additional predictors
(past speeding behaviour, perceived risk, moral norm, peers' norm, impulsivity,
sensitivity to reward and sensitivity to punishment) would account for a significant
variation in intention not to speed, over and above the TPB variables.
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 119
7.5.2 Changes in salient beliefs (RQ2.2, H.4 - H.7)
As discussed in Section 7.2.5, a total of 210 completed questionnaires were
collected at Time 2, 126 from Control group participants and 84 from Intervention
group participants. 31 of the 84 Intervention group participants could be linked to the
data collected in the app leaderboard. Therefore, the analysis in the current section is
based on those 31 entries. It is guided by RQ2.2. Did the intervention change the
participants' salient beliefs, as depicted by the TPB constructs?, as well as by H.4,
H.5, H.6 and H.7.
7.5.2.1 Means, standard deviations and bivariate correlations.
At Time 2, approximately three months after the intervention was deployed, the
relations between the TPB measures remained statistically significant (see Table 7.4).
The most notable differences in comparison to Time 1 were that participants reported
lower scores on average.
Table 7.4. Means, standard deviations and bivariate correlations for the standard TPB variables at Time 2 (n=157).
Mean SD 1 2 3 4 5 6 7 8
1. Behaviour of not speeding during
the three months of the intervention5.24 2.21
-.82**.55**.63**.29**.35**.61**.21**
2. Intention not to speed 5.85 2.20 - .52**.67**.27**.34**.60**.23**
3. Instrumental attitude 6.22 1.83 - .67**.40**.29**.42**.30**
4. Affective attitude 5.34 2.21 - .33**.28**.43**.21**
5. Subjective norm 6.69 2.27 - .42**.23**.25**
6. Descriptive norm 5.05 1.97 - .22** .13
7. Self-efficacy 5.34 2.54 - .29**
8. Perceived controllability 7.33 1.95 -** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
7.5.2.2 Impact of the intervention
A series of one-way ANCOVA tests was performed to evaluate the effect of the
intervention on the DVs, so a) intention not to speed (measured at Time 2) and b)
behaviour of not speeding during the three months of the intervention. The condition,
so Control group and Intervention group, was the fixed factor IV. The covariates to
control for pre-existing conditions within the two groups were a) intention not to speed
(measured at Time 1) and b) past behaviour of not speeding (measured at Time 1).
A series of two-way ANCOVAs assessed whether a number of individual
differences and personal characteristics, previously discussed as potential influencers
120 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
on the way young people behave on the road (see Section 3.6), moderated the effect of
the participants' condition. Those were the mentioned-earlier demographic variables
(gender and driving experience, denoted by driving license) and additional predictors
(impulsivity, sensitivity to reward and sensitivity to punishment). Those were chosen
as they are stable in time and, thus, are not expected to be influenced by an
intervention.
In the case of intention not to speed, an inspection of the mean scores revealed
that the Control group reported a greater decrease of their intention not to speed than
the Intervention group (see Table 7.5).
Table 7.5. Means and standard deviations for the Control and the Intervention groups' intention not to speed at Time 1 and Time 2 (n=157).
Condition Mean Std. Deviation NTime 1 Control 6.38 2.243 126
Intervention 6.36 2.165 31Total 6.37 2.221 157
Time 2 Control 5.78 2.256 126Intervention 6.13 1.958 31Total 5.85 2.199 157
After adjusting for the participants' self-reported intention not to speed before
the intervention, no significant difference between the Control group and the
Intervention group was found in intention not to speed at Time 2, F (1, 154) = 1.14, p
= .28, ηp2 = .007. There was a statistically significant (p < .001) strong relationship
between intention not to speed at Time 1 and intention not to speed at Time 2, as
indicated by a ηp2 = .417. After finding the non-significant effect of the intervention
between the two groups in respect to their intention not to speed, two-way ANCOVAs
found no significant effects on the result with personality characteristics as moderators
either (see Table 7.6).
Table 7.6. Interaction effects between Condition and personality characteristics, intention not to speed, adjusted for Time 1 values (n=157).
Moderator F (1, 152) p ηp2
Gender .161 .689 .001
Driving experience 2.140 .146 .014
Impulsivity .042 .959 .001
Sensitivity to punishment 2.280 .133 .015
Sensitivity to reward .369 .544 .002
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 121
These results did not support H.4, which predicted that participants in the
Intervention group would report significantly greater intention not to speed in the
future than the Control group participants.
Similar to intention not to speed, for behaviour of not speeding (self-reported
frequency of not speeding before and during the intervention), an inspection of the
mean scores revealed that the Control group reported a greater reduction of its score
than the Intervention group (see Table 7.7).
Table 7.7. Means and standard deviations for the Control and the Intervention groups' behaviour of not speeding at Time 1 and Time 2 (n=157).
Condition Mean Std. Deviation N Time 1 Control 5.54 2.26 126
Intervention 5.84 2.44 31 Total 5.60 2.29 157
Time 2 Control 5.15 2.23 126 Intervention 5.61 2.14 31 Total 5.24 2.21 157
After adjusting for the participants' self-reported past behaviour of not speeding
before the intervention, no significant difference between the Control group and the
Intervention group was found in past behaviour of not speeding during the three
months of the intervention, F (1, 154) = .67, p = .41, ηp2 = .004. There was a statistically
significant (p < .001) strong relationship between past behaviour of not speeding
before the intervention and past behaviour of not speeding during the three months of
the intervention, as indicated by a ηp2 = .510. After finding the non-significant effect
of the intervention between the two groups in respect to their past behaviour of not
speeding during the three months of the intervention, two-way ANCOVAs found no
significant effects on the result, with personality characteristics as moderators, either
(see Table 7.8). The assumption of equality of variance was not met when investigating
the interaction effect between the group condition and driving experience, and
impulsivity. Despite that this created a bias in the obtained result, given that there was
no significant interaction effect, no α adjustments were necessary.
These results did not support H.5, which predicted that participants in the
Intervention group would report significantly greater behaviour of not speeding during
the three months of the intervention than the Control group participants.
122 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
Table 7.8. Interaction effects between Condition and personality characteristics, past behaviour of not speeding during the three months of the intervention, adjusted for Time 1 values (n=157).
Moderator F (1, 152) p ηp2
Gender .586 .445 .004
Driving experience 1.954 .164 .013
Impulsivity .378 .686 .005
Sensitivity to punishment .264 .608 .002
Sensitivity to reward .424 .516 .003
Thus, the intervention did not have any significant effect on either of the DVs,
intention not to speed and past behaviour of not speeding during the three months of
the intervention. No significant effect was found on any of the other potentially-
modifiable Time 2 extended TPB variables, after adjusting for Time 1 values, either
(see Table 7.9).
Table 7.9. Effect of the intervention on TPB variables, adjusted for Time 1 values (n=157).
Variable F (1, 154) p ηp2
Instrumental attitude .094 .759 .001
Affective attitude .007 .935 < .001
Subjective norm .616 .434 .004
Descriptive norm .080 .778 .001
Self-efficacy 1.093 .297 .007
Perceived controllability 1.392 .240 .009
Moral norm .718 .398 .005
Peers' norm .348 .556 .002
Perceived risk 1.488 .224 .010
These results did not support H.6, which predicted that the safe-driving app
intervention would have positively influenced the Intervention group participants'
instrumental attitude, affective attitude, self-efficacy and perceived controllability,
moral norm and peers' norm directly as well as subjective norm, descriptive norm and
perceived risk indirectly.
Three more variations of the Intervention group were investigated, to build a
more comprehensive picture of the potential effects of the intervention: 1) a subgroup
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 123
of 18 highly engaged Intervention participants, ones that had a score generated in more
than half of the intervention period, were compared to all 126 entries in the Control
group; 2) all 126 entries in the Control group were compared with all 84 entries in the
Intervention group; and 3) a sub-sample of 31 participants from the Control group was
selected to match, as close as possible, the sample of 31 active Intervention group
participants. The selection in the third case was made by looking at the participants'
demographics in the following order: state of origin, gender, age, and driving licence.
When a complete match was not possible, a close one was selected, e.g. the same
gender, age and driving licence but in a different state, or the same state of origin,
gender and driving licence but age, plus or minus 1. Those results are provided as
Appendix D.
7.5.2.3 Leaderboard driving data
Out of 243 invited participants, 62 successfully joined the GoOz group in the
safe-driving app. Thus, the overall installation success rate was 26%. After three
months of intervention, only 18 out of the 62 participants in the in-app leaderboard
maintained an active status, i.e. had a generated score in their profile. Fifty of the
participants had a score at one or another time during the intervention, which suggested
that 12 could not get the app to collect their driving data at all. Only 15 of the
participants had scores generated in each of the 13 intervention weeks.
Given that score generation depends on route, driving style, car, etc., comparing
scores between participants would not provide a lot of insight; neither would
comparing the absolute change in scores for the respective participant. Thus the
relative change in scores was computed for each of the participants, that had more than
one generated score, by subtracting their first score value from their last one and
dividing the result by their first score. The average result was an improvement of 2%,
with values ranging from -92% to 380%. Fifteen out of 50 participants with scores
registered improvement in their driving score, 22 showed deterioration, and for 13
there was no change. Potential improvement seemed not to play a role in whether a
participant remained active until the end of the study. Out of the 18 participants that
were active at week 13, 10 registered an overall increase in their score, while 8 had a
decrease.
These results provided limited support for H.7, which predicted that the
intervention would improve the participants' driving, as represented by the observed
124 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
driving scores in the safe-driving app leaderboard. The hypothesis was valid for 30%
of the observed cases and not valid for the other 70%, i.e. the majority of the sample.
7.5.3 Predictors of behaviour of not speeding during the intervention (RQ 2.3, H.8 - H.10)
The analysis of the data collected after the intervention was guided by RQ2.3
(Using the extended TPB framework, to what extent could the data available before
the intervention predict the participants' behaviour of not speeding during the
intervention?), and by H.8, H.9 and H.10.
In this analysis, data from all 210 participants who completed the second
questionnaire was assessed, irrespective of their condition, Control or Intervention,
whether they managed to join the app leaderboard, or how active they were, as no
evidence for any statistically significant effect from the intervention was found in the
previous analysis. A 3-step hierarchical multiple regression analysis was performed to
identify which measures (demographics, TPB and additional predictors), and to what
extent, account for the variance in the participants’ self-reported behaviour of not
speeding during the three months of the intervention. The order of entering IVs was
guided by the model, described in Section 4.4.4.
As shown in Table 7.10, the demographic variables from Time 1 explained a
significant 10% (adj. R2 = .08, p < .001) of the variance in behaviour of not speeding
during the three months of the intervention. Gender (β=.17, p = .011) and driving
license (β=-.34, p < .000) were statistically significant independent predictors. Age did
not emerge as a significant predictor.
The results were consistent with H.8, which predicted that demographic
variables would account for a significant variation in behaviour of not speeding during
the three months of the intervention.
Adding the TPB variables, at Step 2, significantly increased the explained
variance, over and above demographics (ΔR2 = .40, p < .001). Thus, the explained
variance reached 50%. Two TPB variables emerged as significant predictors. The TPB
variables, intention not to speed (β=.51, p < .001) and self-efficacy (β=.16, p = .014),
were statistically significant independent predictors, as well as driving license (β=-.20,
p = .007).
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 125
The results were consistent with H.9, which predicted that TPB constructs would
account for a significant variation in behaviour of not speeding during the three months
of the intervention, over and above the demographic variables.
Table 7.10. 3-step hierarchical multiple regression analysis, predicting behaviour of not speeding during the three months of the intervention for all participants at Time 2, with demographic factors,
TPB variables and additional variables as predictors (n=210).
Step Variables R2 R2change Fchange Step 1 β Step 2 β Step 3 β Step 3
sr2
Bivariate
R2
1 Gender 0.10** 0.10 7.25** 0.169* 0.046 0.025 <.001 0.03*
Age 0.149 0.086 0.031 <.001 <.01
Driving license -0.338** -0.196* -0.156* 0.011 0.05*
2 Intention not to
speed
0.50** 0.40 53.90** 0.513** -0.001 <.001 0.44**
Self-efficacy 0.164* 0.086 0.004 0.27**
Perceived
controllability
0.098 0.092 0.007 0.06**
3 Past behaviour
of not speeding
0.64** 0.14 11.33** 0.578** 0.103 0.55**
Impulsivity 0.010 <.001 0.05*
Perceived risk 0.082 0.004 0.12**
Moral norm -0.024 <.001 0.16**
Peers' norm 0.043 0.001 0.13**
Sensitivity to
punishment
-0.038 0.001 <.01
Sensitivity to
reward
-0.188** 0.026 0.15**
All beta weights are standardised. * p < .05 ** p < .001
Adding the additional predictors, at Step 3, significantly increased the explained
variance, over and above TPB (ΔR2 = .14, p < .001). The statistically significant
independent predictors in the final regression equation were past behaviour of not
speeding (β=.58, p < .001), sensitivity to reward (β=-.19, p < .001) and driving license
(β=-.16, p = .013). Investigating the individual bivariate relations between the DV and
the IVs showed that, if considered separately, all IVs, except for age and sensitivity to
punishment, were statistically significant predictors of behaviour of not speeding
126 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
during the three months of the intervention. The three strongest individual predictors
were past behaviour of not speeding, intention not to speed and self-efficacy, which
explained 55%, 44% or 27% of the variance, respectively. However, when all IVs were
considered in an overall model, past behaviour of not speeding, sensitivity to reward
and driving licence uniquely explained the most variance in behaviour of not speeding
during the three months of the intervention, 10.3%, 2.6% and 1.1%, respectively.
The results provided support for H.10, which predicted that the additional
predictors (past speeding behaviour, perceived risk, moral norm, peers' norm,
impulsivity, sensitivity to reward and sensitivity to punishment) would account for a
significant variation in behaviour of not speeding during the three months of the
intervention, over and above the TPB variables.
7.5.4 Potential negative effects: Self-reported smartphone engagement (RQ2.4, H.11)
This analysis investigated whether the intervention did not introduce additional
risks, i.e. increased distraction, despite the good intentions behind its implementation.
It was guided by RQ2.4 (How did the intervention influence the participants’
engagement with their smartphones?) and by H.11.
7.5.4.1 Internal reliability, means, standard deviations, bivariate correlations and frequencies
Three repeated measures were used to assess the participants' interaction with
their smartphones before and after the intervention, focusing on if they initiated (less)
communication, monitored/read (less) communication, or responded (less) to
communication. The scale had a high internal consistency with a Cronbach's α of .91.
Table 7.11. Mean, standard deviation and bivariate correlations for the phone interaction variables at Time 1 (n=157).
Mean SD 1 2 3 Initiate (less) communication 5.27 2.05 - .72** .80** Monitor/read (less) communication 4.84 2.14 - .78** Respond (less) to communication 5.00 2.12 -
** Correlation is significant at the 0.01 level (2-tailed).
The three items were highly correlated between each other, with averages close
to the value of 5, meaning that people on average reported one to two interactions for
the past three months (see Table 7.11). This, however, was shaped by the large
proportion of people reporting no interaction at all (see Table 7.12). Despite that Q-Q
plots examination suggested normal distribution, the items were negatively skewed. If
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 127
"no interaction" answers were removed, the skewness would become close to zero,
and the histograms would suggest normal distribution, too.
Table 7.12. Self-reported phone interaction at Time 1 and Time 2 (n=157).
How often do you do the following on your smartphone while driving:
Initiate communication
on social interactive
technology?
Monitor/read social
interactive
technology?
Respond to
communication on social
interactive technology?
Time 1 Time 2 Time 1 Time 2 Time 1 Time 2
More than once per
day
6.4% 7.6% 8.3% 12.7% 7.0% 8.9%
Daily 6.4% 7.0% 10.2% 8.9% 8.9% 12.1%
1–2 times per week 12.1% 15.9% 14.6% 20.4% 14.6% 14.6%
1–2 times per month 10.8% 9.6% 7.0% 8.9% 10.2% 13.4%
1–2 times per 3
months
8.3% 10.2% 16.6% 9.6% 10.2% 14.0%
Once a year 5.7% 6.4% 2.5% 5.7% 3.8% 7.0%
Never 50.3% 43.3% 40.8% 33.8% 45.2% 29.9%
Table 7.12 shows how often (in %) participants (n=157) reported engaging in
initiating, monitoring/reading, and responding to social interactive technology on their
smartphone while driving at Time 1. For example, 40.8% of the participants, in the
current study, reported never monitoring/reading, 45.2% reported never responding,
and 50.3% reported never initiating communication while driving. On the other hand,
12.8% of the participants initiated communication at least once per day, 33.1%
monitored/read communication at least once per week, and 40.7% responded to
communication at least once per month.
At Time 2, there was a noticeable difference in the reported frequencies. While
initiating communication remained the least common behaviour, the most commonly
observed one was responding. The proportion of people who reported never engaging
reduced to 33.8% in monitoring/reading communication, to 29.9% in responding to
communication, and to 43.3% in initiating communication while driving. The
reduction in scores was confirmed by the means at Time 2 (see Table 7.13).
128 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
Nevertheless, the mean scores still indicated that people reported, on average, one to
two interactions for the past three months. The correlation coefficients, at Time 2, did
not visually differ much from the observed coefficients at Time 1.
Table 7.13. Mean, standard deviation and bivariate correlations for the phone interaction variables at Time 2 (n=157).
Mean SD 1 2 3 Initiate (less) communication 5.00 2.10 - .71** .82** Monitor/read (less) communication 4.46 2.21 - .76** Respond (less) to communication 4.52 2.07 -
** Correlation is significant at the 0.01 level (2-tailed).
A deeper inspection of the mean scores revealed that both groups reported a
reduction in their scores, meaning a greater smartphone use while driving during the
intervention period of three months (see Table 7.14).
Table 7.14. Phone interactions' means and standard deviations for the Control (n=126) and the Intervention group (n=31) at Time 1 and Time 2.
Condition
Time 1 Time 2 Change in means (T1-T2) Mean SD Mean SD
Initiate (less) communication
Control 5.17 2.05 4.91 2.09 -0.26 Intervention 5.65 2.04 5.36 2.13 -0.29
Monitor/read (less) communication
Control 4.77 2.13 4.39 2.13 -0.38 Intervention 5.13 2.16 4.71 2.56 -0.42
Respond (less) to communication
Control 4.94 2.11 4.43 2.03 -0.51 Intervention 5.23 2.17 4.90 2.24 -0.33
7.5.4.2 Impact of the intervention
To further assess if the intervention did not, indeed, have a negative influence
on the participants' smartphone use, three one-way ANCOVA tests were performed on
the DVs (initiating (less) communication, monitoring/reading (less) communication,
and responding (less) to communication at Time 2) with condition (Control and
Intervention) as a fixed factor and IVs (initiating (less) communication,
monitoring/reading (less) communication, and responding (less) to communication at
Time 1). No significant differences between the Control group and the Intervention
group were found on any of the DVs (See Table 7.15).
Table 7.15. Effect of the intervention on phone interaction variables, adjusted for Time 1 values, with Condition as a fixed factor (n=157).
Variable F (1, 154) p ηp2
Initiate (less) communication .343 .559 .002
Monitor/read (less) communication .107 .744 .001
Respond (less) to communication .868 .353 .006
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 129
After finding the non-significant effect of the intervention on the two groups in
respect to any of the DVs, two-way ANCOVAs were performed to investigate whether
any personality characteristics did not moderate that results. The assumption of
equality of variance was not met when investigating the interaction effect between the
group condition and gender in the case of monitor/read (less) communication. Despite
that it created a bias in the obtained result, given that there was no significant
interaction effect, no adjustments were necessary. Significant interaction effects, with
a low to medium effect sizes, were found between the group condition and gender and
sensitivity to punishment for the participants' initiating (less) communication and
between the group condition and gender for the participants' responding (less) to
communication (see Table 7.16).
Table 7.16. Interaction effects between Condition and personality characteristics, phone interaction variables, adjusted for Time 1 values (n=157).
Variable Moderator F (1, 152) p ηp2
Initiate (less) communication
Gender 4.935 .028* .031
Driving experience .243 .623 .002
Impulsivity .560 .572 .007
Sensitivity to punishment 4.306 .040* .028
Sensitivity to reward .133 .716 .001
Monitor/read (less) communication
Gender 1.994 .160 .013
Driving experience 1.438 .232 .009
Impulsivity .831 .438 .011
Sensitivity to punishment .269 .605 .002
Sensitivity to reward .401 .528 .003
Respond (less) to communication
Gender 5.816 .017* 0.37
Driving experience .015 .901 < .001
Impulsivity .874 .419 .012
Sensitivity to punishment 2.873 .092 .019
Sensitivity to reward .244 .622 .002
* p < .05
130 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
Further analysis was undertaken to follow-up on the observed significant
interaction effects between the group condition and gender. No statistically significant
main effects were found in neither the case of initiating (less) communication
(condition (F (1, 152) = .28, p =. 60, ηp2 = .002); gender (F (1, 152) = 2.15, p = .14,
ηp2 = .014)) nor in the case of responding (less) to communication (condition (F (1,
152) = .78, p =. 38, ηp2 = .005); gender (F (1, 152) = 1.98, p = .16, ηp2 = .013)). The
lack of main effects suggested that males and females behaved differently, depending
on their condition. Two-way gender-split ANCOVAs did not show a statistically
significant effect for the female participants' initiating (less) communication (F (1, 73)
= 1.22, p = .27, ηp2 = .016) or responding (less) to communication (F (1, 73) = .98, p
= .33, ηp2 = .013). However, the effect for the male participants was statistically
significant both when initiating (less) communication (F (1, 78) = 4.34, p = .04, ηp2 =
.053) and when responding (less) to communication (F (1, 78) = 6.50, p = .01, ηp2 =
.077). Investigating the mean scores revealed that the males in the Intervention group
reported higher initiating (less) communication mean score (5.84), i.e. lower rate of
initiating communication, than the males in the Control group (4.81), as well as higher
responding (less) to communication mean score (5.45), i.e. lower rate of responding to
communication, than the males in the Control group (4.27).
In a follow-up analysis on the significant interaction effect between the group
condition and the participants' sensitivity to punishment in the case of initiating (less)
communication was performed, no statistically significant main effect of condition (F
(1, 152) = .42, p =. 52, ηp2 = .003) was found. However, there was a main effect of
sensitivity to punishment (F (1, 152) = 4.27, p =.040, ηp2 = .027). In a two-way
ANCOVA, split by sensitivity to punishment, the assumption of equality of variance
was not met in the case of low-sensitive participants. Thus a lower, more conservative,
α (.025) was adopted when assessing the result (Wickens & Keppel, 2004). The
obtained result did not show a statistically significant effect for the participants neither
with low (F (1, 70) = 4.05, p = .048, ηp2 = .055) nor with high sensitivity to punishment
(F (1, 81) = .76, p = .39, ηp2 = .009).
The results were consistent with H.11, which predicted that the intervention
would not significantly increase the Intervention group drivers' distraction in terms of
initiating, monitoring/reading or responding to communication, in comparison to the
Control group participants.
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 131
7.6 DISCUSSION
The study was split and analysed in four parts: First, to establish a baseline for
the implemented smartphone safe-driving app intervention, it sought to understand
more about the young drivers that took part in the intervention (RQ2.1). Second, to
investigate if driving for three months with a safe-driving app, installed on a
participant's smartphone, produced statistically significant effects (RQ2.2). Third, to
help understand, irrespective of condition, how much of the participants' self-reported
behaviour during the three months of the intervention could have been predicted before
the intervention happened, with the data collected at Time 1 (RQ2.3). And forth, to
investigate whether, along with the good intentions, the intervention did not, indeed,
have a negative influence on the participants' smartphone use (RQ2.4).
7.6.1 Findings
Demographic variables play an important role in planning a road safety
intervention. Thus, their predictive contributions were investigated first, in relation to
the participants’ intention not to speed before the intervention (RQ2.1) and their
behaviour of not speeding during the three months of the intervention (RQ2.3). In line
with previous research (Horvath et al., 2012; Scott-Parker, 2012), gender was found
to be a significant contributor in explaining both DVs of interest, intention not to speed
and behaviour of not speeding during the three months of the intervention. In the
obtained results, females were generally associated with higher scores, i.e. they were
drivers who reported less risky behaviour. Driving experience, as depicted by driving
licence, was also found to be a significant contributor. Greater risky behaviour was
reported by drivers with more driving experience (with an open drivers' licence). Age
did not emerge as a significant contributor.
After the demographic variables were explored, RQ2.1 and RQ2.3 guided the
investigation of the contribution of the extended TPB framework in the regression
equations. Consistent with the literature (Elliott & Thomson, 2010), the extended TPB
framework was a good fit for the study. It was predicted that the TPB constructs would
account for a significant variation in intention not to speed, over and above the
demographics, before the intervention took place. Consistent with Horvath et al.
(2012), TPB explained large amounts of significant variance, over and above the
demographic variables. A regression test was run on data from all 480 participants to
identify which of the constructs played a role within the sample. Instrumental attitude,
132 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
affective attitude, descriptive norm and self-efficacy, from the TPB variables, emerged
as significant contributors. Thus the study provided support for the predictive validity
of TPB, with 48% additional variance in intention not to speed being explained. It was
also predicted that the standard TPB constructs would account for a significant
variation in behaviour of not speeding during the three months of the intervention, over
and above the demographics. Intention not to speed and self-efficacy emerged as
significant contributors in the performed linear regression test. TPB added a significant
40% of additional explained variance, over and above the demographics.
As a third and final step in the regression equations, additional predictors were
assessed in relation to RQ2.1 and RQ2.3. Several additional predictors (past behaviour
of not speeding, perceived risk, moral norm, peers' norm, impulsivity, sensitivity to
punishment and sensitivity to reward) were examined to investigate whether they
contributed to explaining additional variance in either intention not to speed or
behaviour of not speeding during the three months of the intervention as DVs, over
and above TPB. Consistent with Elliott and Thomson (2010), past behaviour of not
speeding was a strong predictor in both regression models. Past behaviour of not
speeding was also the strongest individual predictor, as well as the predictor,
explaining the most unique variance, in both DVs.
The observed predictive power of past behaviour of not speeding suggested that
offenders might be a good fit for a smartphone safe-driving app intervention.
Nevertheless, the value of awareness-raising might be higher in prevention efforts,
which suggests that prevention shall target all drivers, irrespective of their past
behaviour. Still, designing different interventions for drivers with different behaviour
might be worth considering.
Contrary to Gannon et al. (2014), perceived risk was a significant independent
predictor of intention not to speed. However, that was true only until sensitivity to
reward and sensitivity to punishment were added, which, this time, confirmed Gannon
et al. (2014) findings. The two sensitivity measures were added when analysing the
smaller sample of 210 participants, as those two constructs were measured only at
Time 2. The observed difference might be due to the two sensitivity measures
accounting for the predictive power of perceived risk and diluting its predictive
validity. Perceived risk was not a significant independent predictor of behaviour of not
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 133
speeding during the three months of the intervention, which agreed with Gannon et al.
(2014) findings.
Consistent with Constantinou et al. (2011) and Pearson et al. (2013), impulsivity
did not explain additional variance over and above TPB in intention not to speed or in
behaviour of not speeding during the three months of the intervention. In the case of
intention not to speed, neither did sensitivity to reward and sensitivity to punishment,
nor the additional normative influences of moral norm and peers' norm, leaving past
behaviour of not speeding as the only additional predictor with significance, over and
above the TPB constructs. The picture was almost the same for behaviour of not
speeding during the three months of the intervention as a DV. The difference was that,
consistent with Castellà and Pérez (2004) and Scott-Parker and Weston (2017),
sensitivity to reward emerged as a second significant predictor in the final model.
Participants, who reported greater sensitivity to reward, reported more speeding.
Given that personality characteristics are considered stable and hard to change,
the findings suggested that focusing on them is of little use when TPB is measured.
Consistent with Ajzen's (2006) recommendations, targeting salient beliefs, generally
accepted as modifiable, is what interventions might better focus on. Nevertheless, if
TPB measures are not taken, and there is information on personality, it might be
considered useful, as the respective bivariate models were statistically significant. For
example, in the case of the current sample, designing an intervention, focusing on
participants with high sensitivity to reward, might be beneficial.
Despite the predictive power of the model, the intervention itself was not found
to be able to influence any of the TPB constructs over time (RQ2.2). Although mean
scores for measures, including both intention not to speed and behaviour of not
speeding, decreased for both the Control group and the Intervention group in the three
months of the intervention, that change was not explained by the intervention. A more
in-depth exploration, through two-way ANCOVAs, investigating whether any stable
characteristic, i.e. gender, driving experience, impulsivity, sensitivity to reward or
sensitivity to punishment, moderated that result, did not find anything significant,
either. Separate ANCOVA tests did not find significant differences over time as a
result of the intervention between the Control group and the Intervention group on any
of the participants' salient beliefs.
134 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
As a result of applying the COTs' selection criteria (see Section 4.2), there was
an initial expectation of the participants' salient belief to change. Flo was expected to
be able to influence the extended TPB framework dichotomised attitude (instrumental
attitude and affective attitude), dichotomised PBC (self-efficacy and perceived
controllability), moral norm and peers' norm (see Section 6.4). The objective of the
current program of research was to find evidence for that influence in a safe-driving
app intervention implemented as close to the real world as possible. The collected data,
however, did not provide evidence to support this initial expectation.
The collected data on driving scores from the app’s leaderboard supported the
notion of the intervention having no effect. For the participants for which driving
scores were calculated, the results were mixed, with marginal average improvement.
Many participants had no improvement at all. Some registered a score deterioration.
An additional objective of the study was to investigate whether the implemented
intervention, which intended to reduce speeding, did not produce any side effects, and
more particularly did not encourage smartphone distraction (RQ2.4). The study
specifically focused on three previously investigated associated behaviours (initiating
(less) communication, monitoring/reading (less) communication, and responding
(less) to communication) (Gauld et al., 2016). The findings showed that a substantial
number of young drivers did not engage at all with their smartphones while driving.
The number of those, that never interacted was higher than the number, reported by
Gauld et al. (2016), which might be attributed to the more diverse sample of the current
study. Gauld et al. (2016) sample comprised 79% of university students.
Consistent with previous research (Gauld et al., 2016), initiating (less)
communication was the least common behaviour, while monitoring/reading (less)
communication was the most common one. No evidence was found that the
intervention significantly changed the self-reported phone use of the participants when
condition was the only IV in the implemented ANCOVAs. However, when separate
two-way ANCOVAs tests were performed, with gender, driving experience,
impulsivity, sensitivity to reward or sensitivity to punishment as a second IV, evidence
was found that the intervention might have impacted distraction. Male participants in
the Intervention group reported significantly higher mean scores in both initiating
(less) communication and responding (less) to communication. This suggested a
potential positive side effect of the intervention in relation to the generally-perceived-
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 135
as-riskier male group. Despite the intervention having no effect on the males' intention
and self-reported behaviour, it might have made them more focused on the driving
task, and less prone to be distracted by their smartphones.
7.6.2 Strengths
Study 2 deployed a novel low-cost intervention approach, which can be
potentially easily replicated without substantial funds and technological upgrades,
which is the case in Creaser et al. (2015). Nevertheless, an effort was made to involve
a comparatively large sample to improve generalisability of the findings in comparison
with other lower-cost research interventions, focused on young drivers (Fitz-Walter et
al., 2017; Zhang et al., 2014). The intervention was designed to be as far from the
laboratory conditions as possible, trying to mimic a casual release of a safe-driving
app to the general public, where no expectations are imposed on adoption and usage.
The driving was intentionally unsupervised, and potential rewards to the participants
were not tied to anything related to the app.
Study 2 addressed several limitations reported in previous studies. Musicant and
Lotan (2015) considered the app they used as forgiving, i.e. the app was not providing
too much critical feedback on the participants’ behaviour. Since improving driving
behaviour was the focus in the current study, rather than adoption and usage, Flo was
perceived as providing critical feedback. There was no interest in it being forgiving.
The app was providing real-time alerts or post-trip analysis, depending on the
preference of the participant. It also had a self-starting capability, thus, reducing the
effort required from the participant, another limitation, reported by Musicant and
Lotan (2015). Different from Musicant and Lotan (2015), but similar to Creaser et al.
(2015), a baseline for evaluating behavioural change was established. Creaser et al.
(2015) used parental control to motivate behavioural change. Parental control, and
external influence, in general, is less likely to exist than not in reality, when the
decision to use the app is taken by the driver and is not coordinated with other parties.
In the current study, such external influence was not deployed.
Another strength of Study 2 was the diverse information, collected for the
participants, on top of the sample’s demographic representativeness. Diversity was
achieved in terms of gender, geographical coverage (in Australia), age (as long as it is
within the predefined frame of 18 to 25) and driving experience (despite the low
number of learner drivers, which may be due to 18 being the minimum required age to
136 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
participate). Recruitment reached out beyond the university campuses, which are a
common source for study participants' recruitment, and, thus, usually reported as a
limitation of findings. The study also allowed a Control group to be established to
control for any general influence, which might have been experienced by the study
participants. Personality characteristics (impulsivity, sensitivity to punishment and
sensitivity to reward) were explored together with other additional predictors (past
behaviour, perceived risk, moral norm and peers' norm), over and above the TPB
constructs. The diversity of the collected variables allowed a more complete effects’
picture to be established.
Another particular strength of the current study was its span across three months.
Few studies focus on smartphone use changes over time within a particular sample. A
more traditional approach is to focus on a time snapshot when distraction is analysed
and to explore the underlying causes. The current study adopted a more
unconventional approach, and did not look at distraction as separate risky driving
behaviour, but rather observed it as part of more complex research design, focused on
speeding. Although not in the primary focus, from a systematic perspective, the
smartphone use results provided insights into the effects of the intervention as a
potential disruptor. An increased smartphone interaction, as denoted by lower mean
scores for all participants, was observed between Time 1 and Time 2. The change in
the self-reported scores was not found to be significantly influenced by the
intervention. However, a further exploration found a significant effect of the
intervention on the Intervention group male participants' smartphone interaction. They
increased their self-reported mean score, i.e. interacted less with their smartphones, as
a result of the intervention.
7.6.3 Limitations
The surveys’ design provided no room for committing unintentional errors.
Nevertheless, the data was collected through online questionnaires and might have
been susceptible to bias, which the technique inherits. The anonymous nature of the
data collection, the impossibility of consequences for reporting speeding, as well as
the fact that rewards were offered irrespective of provided data, should have minimised
bias.
To increase the diversity of information collected for the Intervention group
participants, an attempt was made to incorporate real driving data by observing their
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 137
scores in the Flo leaderboard. Unfortunately, this effort had a limited added value due
to the scarce collected data, which emerged as a limitation. In addition to the scarcity,
there was also uncertainty about the correctness of the collected data. That uncertainty
stemmed from the inability of Flo to distinguish when a participant is a driver and
when they are not (see Subsection 7.2.3). The leaderboard did not provide additional
information for each participant other than the driving score, the ranking and the
number of trips. Through the leaderboard, it could not be inferred whether the
participants knew each other, either. All participants observed the same information in
the leaderboard. However, it cannot be inferred whether they saw it as representative
of their driving behaviour, or whether it was well correlated with their driving. The
research team had no information on the algorithms used to generate the leaderboard
information.
Another limitation was that no specific information was systematically collected
on the potential difficulties the participants might have experienced in working with
the app. Individual participants reported difficulties in installing the app, getting the
app to collect data, or in joining the app leaderboard group. The interest in using the
app was observed as fading away for participants that did not experience major
problems. Very few remained an active status by the end of the study. Ultimately, most
of them dropped from using the app. This might have been partially because using the
app was not inherently required by the study, and was not a prerequisite to enter the
prize draw. The reason for not listing such a requirement was that the situation should
be as close to reality as possible, where rewards, beyond the promise to improve one's
driving skills, hardly exist.
It also has to be acknowledged that, because the intervention was intended to
resemble real-world conditions, no checks were conducted to determine the extent of
participants' prior familiarity with COTs, as such are not likely to be performed in the
real world. The participants’ COTs familiarity might be another explanation for the
large drop-out rate. It is possible that participants, more familiar with COTs and, more
specifically, with apps and safe-driving apps, were more likely to download and use
Flo, than those without such an experience.
Data on how many of the Intervention group participants were interested in using
the safe-driving app, in general, was not collected, which emerged as a limitation
during analysis. For many participants, who completed the second survey, there was
138 Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app
no evidence that the app was actually used. Thus, the only reference number of true
Intervention group participants was provided by the number of users that joined the
safe-driving app group, which was much less than the total number of participants.
Problems with installation and data collection in the app could be inferred from
the observed leaderboard scores, too. Not all participants joined the leaderboard. For
the ones that joined, score generation did not happen all the time during the
intervention. However, information was not gained as to why some participants were
not able, and others were, to download and install the app, as well as to subsequently
join the leaderboard. This information would have been useful, as using the app was a
condition for the Intervention group. Such problems might help explain the large drop-
out rate.
Overall, Study 2 had a significant drop-out rate, greater than initially expected.
56.25% of the participants did not complete the second survey. The Intervention group
was additionally reduced, as a result of the encountered difficulty in confirming actual
participation in the intervention. Not all leaderboard scores could be linked with
specific self-reports due to data coding. Although the collected 157 cases were more
than the initially planned 120, this fact reduced the predictive potential of the study
and limited the validity of the findings.
An additional limitation of the study was that the underlying salient beliefs of
distraction were not explored. As the main focus was on speeding, attempts to collect
additional data would have increased the amount of input required from the
participants, thus, further reducing the potential to retain them. However, the collected
data revealed changes in the reported smartphone use behaviours over time, which
pointed in a positive direction, i.e. reduced distraction. Such results might be due to
participants, becoming more vigilant to their behaviour, but robust conclusions cannot
be made with the currently available data.
7.7 CONCLUSION
Study 2 was implemented as close to a free-living environment as possible and
was the first to examine potential real-world safety benefits from an intervention in
which the general public adopts a free non-obligation off-the-shelf safe-driving app.
With safe-driving apps coming out of both academia and businesses (see Chapter 5
and Chapter 6) and the natural propensity of their developers to stress their perceived
Chapter 7: Study 2 - Intervention with an off-the-shelf smartphone safe-driving app 139
advantages, there was a need to investigate whether a safe-driving app makes a
significant contribution in terms of real-world safety benefits. An additional question,
with no less importance, was whether it introduced additional risks while being used.
This was a valid concern, given that mobile phones, including smartphones, are
identified as a major source of distraction (WHO, 2011), and using them, while
driving, increases four times the chances of a crash occurring (White et al., 2011).
Study 2 was implemented to consider a multitude of psychological influences
(Scott-Parker, 2012) as well as demographic parameters (Horvath et al., 2012). Such
design was expected to provide more in-depth insights on the subsequent intervention
results.
Study 2 exhibited several strengths such as real-world replicable nature, a
comparatively large and diverse sample, a control for any general influence, diverse
collected information, and a focus on long-term effects. Nevertheless, the study had
its limitations, e.g. potential for self-report bias, poor real driving data, lack of
information about the participants’ familiarity with COT and what problems they
experienced with Flo, and large drop-out rate.
Overall, the findings of the current study offered support for the use of an
extended TPB framework to explain young drivers' speeding intention and behaviour.
Personality characteristics did not play the expected role, as suggested by the literature,
indicating that a clear picture of the salient beliefs might be sufficient to design an
intervention. Nevertheless, while investigating RQ2 (How do young drivers’ self-
reported behaviour of not speeding and intention not to speed alter in their free-living
environment, as a result of exposure to a smartphone safe-driving app intervention?),
it was found that the implemented intervention did not succeed in positively
influencing neither the participants’ self-reported behaviour of not speeding nor their
intention not to speed or underlying constructs. However, there was evidence that it
significantly decreased smartphone engagement while driving, i.e. distraction,
amongst the male participants. Thus, future research should be focused on
investigating additional possible routes for influence, preferably with larger samples,
and with the possibility to analyse both self-reports and naturalistic driving data.
140 Chapter 8: Study 3 - Systematic review of VR simulations of risky driving
Chapter 8: Study 3 - Systematic review of VR simulations of risky driving
Chapter 8 describes the third study of the present program of research. Study 3
investigated the extent to which VR was previously explored in road safety research
and whether any safety benefits were reported. It addressed RQ3:
How is VR applied in road safety research to motivate behavioural change in
young drivers?
Following a PRISMA design, Study 3 reviewed the available literature in terms
of VR deployment and effects on young drivers' behaviour and safety. First, the search
results from the two used databases are presented, before outlining key findings,
followed by a discussion.
8.1 RATIONALE FOR CONDUCTING A SYSTEMATIC REVIEW
The young drivers' propensity to adopt and explore technology (Lee, 2007)
offers an opportunity that shall not be limited to using for road safety already
ubiquitous technologies, such as smartphones (see Chapter 5 and Chapter 6). A
potentially higher added value to reduce road trauma amongst young drivers may be
hidden in emerging technologies, such as VR. Furthermore, VR, unlike smartphones,
is less likely to generate unwanted additional risks, such as an increase in distraction.
VR has the capability to simulate life-threatening situations in safety. Thus, it may
potentially improve behaviour (van Loon et al., 2018). Whether VR can facilitate the
adoption of safer behaviour on the road is yet to be seen.
Little research has been done in the domain of VR. Although there is already
evidence of VR's potential to increase empathy (Garner, 2017; Ingram et al., 2019; van
Loon et al., 2018), the evidence for successful behavioural change is mixed (Ahn et
al., 2014; Morina et al., 2015; Schwebel et al., 2017; Theng et al., 2015; van Loon et
al., 2018). This called for a systematic investigation of the available literature. To the
author's best knowledge, to date, there is no systematic review exploring the available
evidence for VR effects in road safety.
Chapter 8: Study 3 - Systematic review of VR simulations of risky driving 141
8.2 METHOD
Study 3 mirrored the Study 1 methodology and used the PRISMA guidelines as
a systematic review framework. The review protocol followed the same steps:
1. Development of the research question.
2. Identification of search databases.
3. Definition of scope, inclusion, and exclusion criteria.
4. Definition of a search term.
5. A systematic search for information.
6. Screening and selection of studies (see PRISMA flowchart as Figure 8.1).
7. Review of selected articles.
8. Summarising of findings.
8.2.1 Search databases
Similar to Study 1, relevant papers were identified through searches in TRID
(https://trid.trb.org) and Scopus (https://www.scopus.com).
8.2.2 Literature search criteria
Papers published from 2010 onwards, and written in English, were considered
for inclusion in the review. The investigation focused on the actual and potential
application and utility of VR in (a) road safety research more generally, (b) road safety
practice more generally, (c) young drivers road safety research specifically, and (d)
young drivers road safety practice specifically. Papers were excluded in the cases when
there was no connection to drivers, when they were not relevant to driving or when
they referred to augmented reality, literature reviews, evaluation of traffic data,
medical, technical solutions, traffic modelling, theoretical discussions, and
pedestrians.
8.2.3 Search term
The search term deployed in TRID was (road OR driver) safety "virtual reality"
( headset OR "HTC Vive" OR "Oculus Rift" OR "PlayStation VR" OR "Google
Daydream View" OR "Samsung Gear"). The search term deployed in Scopus was (
ALL ( road OR driver ) AND ALL ( safety ) AND ALL ( "virtual reality" ) AND
ALL ( headset OR "HTC Vive" OR "Oculus Rift" OR "PlayStation VR" OR
"Google Daydream View" OR "Samsung Gear" ) ). Initially, the brands of the most
common headsets were not part of the search term. However, without specifying those,
142 Chapter 8: Study 3 - Systematic review of VR simulations of risky driving
the returned search result contained a large number of regular driving simulator
studies, referring to themselves as VR ones, which is technically correct but does not
reflect the notion of VR as a consumer-oriented technology (COT). Thus a decision
was made to specify in the search term itself what hardware was considered to be
relevant to VR as a COT in the context of this thesis.
8.3 SEARCH AND SCREENING RESULTS
The search in TRID, executed on October 08, 2018, returned one record. The
article was in the domain of pedestrian safety and, thus, not relevant to the current
study.
The search in Scopus covered the years 2010 to 2017. The output was limited to
documents in English. The search returned 50 records. The titles of all records were
screened for relevance. Ten articles were selected for abstract review. After the
abstract screening, seven were retained for full-text review and were downloaded for
the purpose. One of the studies was subsequently excluded as it deployed a dynamic
driving simulator while the headset was for recording electroencephalographic
changes, not VR experience. Thus, 6 papers were retained to be included in the
qualitative synthesis (see Figure 8.1).
Figure 8.1. Data extraction flowchart based on the PRISMA statement.
Chapter 8: Study 3 - Systematic review of VR simulations of risky driving 143
8.4 FINDINGS
Table 8.1 shows the final selection of 6 papers to be analysed in detail. It includes
the elements of interest to the current study, such as the type and number of
participants, the measures taken, and summaries of the studies’ findings. Although the
level of relevance of the shortlisted studies to the current thesis is debatable, a decision
was taken to include them in the synthesis. Some of the studies did not evaluate VR as
a real-world intervention, similar to the one that is implemented in the current program
of research. For example, Ropelato, Zünd, Magnenat, Menozzi, and Sumner (2017)
focused on simulator sickness only, Gaibler, Faber, Edenhofer, and von Mammen
(2015) used a keyboard to control the software. The design that is closest to the Study
4 (Chapter 9) is the design in Orfila et al. (2015). The authors focused on eco-driving
in an event for the general public. Despite the challenges of finding similarities
between the selected studies, all six were included in the analysis because, given their
low number, they can still provide useful information on how VR is used in road safety.
In contrast with Study 1, in Study 3, fewer studies’ characteristics were included
in the qualitative analysis. The studies’ design was not considered separately as all VR
studies happen in simulated settings, i.e. they cannot be implemented in naturalistic
driving settings. VR also does not offer the diversity of sensors, present in
smartphones, which made the topic irrelevant for Study 3.
144 Chapter 8: Study 3 - Systematic review of VR simulations of risky driving
Table 8.1. Virtual reality in the road safety literature
Authors VR use Type of participants Number of participants Measures taken Summary of findings
Gonzalez et al.
(2017)
The VR simulated the risk of
tractor overturning while being
driven on a farm road.
Aged 16 to 56, 93
male and 34 female
127 Self-reported perception
of the risk and safety
Participants reported positive
experience from using the tractor
driving simulator that can
potentially help them drive safer,
but they would need more training.
Ropelato et al.
(2017)
The VR simulated driving
through a virtual city with
streets, 519 buildings, and 40
other cars.
5 female and 12 male,
mean age 29.5 years
17 Self-reported simulator
sickness
Driving the simulator slightly
increased discomfort. The test ran
for under 15 minutes.
Nevertheless, 4 participants
aborted the simulation.
Agrawal, Knodler,
Fisher, and Samuel
(2017)
The VR simulated latent
hazards. An example is given
about a truck blocking the view
immediately before a pedestrian
crossing.
Young novice drivers,
aged 18 to 25 years
24 Eye movements, hazards
anticipation
The young drivers improved their
ability to detect threats. Compared
to the control groups, V-RAPT
users anticipated significantly
more latent hazards.
Gaibler et al. (2015) The VR simulated DUI by
introducing lags in the responses
of the software to steering and
acceleration commands.
Students 40 User-friendliness Players were engaged in the drink-
driving game and reported
enjoying it although it was not set
to achieve great realism.
Chapter 8: Study 3 - Systematic review of VR simulations of risky driving 145
Authors VR use Type of participants Number of participants Measures taken Summary of findings
Chen, Xu, Lin, and
Radwin (2015)
The VR simulated driving in
which the driver had to check
their blind spots before changing
lanes. The drivers had to step on
a pedal if they detected a white
truck in the blind spot.
14 between 18 and 35
years and 12 between
65 to 75 years, 15
female and 11 male
26 Target detection Younger drivers were twice more
successful in target detection in
less time than older ones. They
also rotated their trunks on
average in two-times greater
radius than the older drivers.
Orfila et al. (2015) The VR simulated driving of an
automatic car with a petrol
engine.
Random visitors 1900 Fuel consumption Moderate acceleration and
constant speed improve fuel
consumption. No immersion
realism of the simulation.
146 Chapter 8: Study 3 - Systematic review of VR simulations of risky driving
8.4.1 Studies’ samples
The common impression from all six studies in regards to characterising their
samples is that the provided information seems scarce. Three studies provided
information about both their participants’ age and gender (Chen et al., 2015; Gonzalez
et al., 2017; Ropelato et al., 2017). One study specified the age of the participants but
not the gender while stressing on their experience, i.e. young novice drivers (Agrawal
et al., 2017). Gaibler et al. (2015) also implied the importance of experience by
recruiting students. However, they do not provide any further information about their
participants. Orfila et al. (2015) were also vague, only reporting that their participants
were random visitors to their intervention stand. The reported age range is between 16
(Gonzalez et al., 2017) and 75 (Chen et al., 2015). The reported numbers of involved
participants also vary greatly between the studies, ranging from 17 (Ropelato et al.,
2017) to 1,900 (Orfila et al., 2015).
8.4.2 Measures
The variability of the studies in respect to their samples continues in what was
measured within them and, thus, their focus. Three of the studies focused on self-
reports, collecting data on risk and safety perception (Gonzalez et al., 2017), simulator
sickness (Ropelato et al., 2017) and VR simulation user-friendliness (Gaibler et al.,
2015). The other three studies focused on the participants driving, looking into their
hazards anticipation (Agrawal et al., 2017), target detection (Chen et al., 2015) and
fuel consumption (Orfila et al., 2015).
8.4.3 Benefits
The full texts revealed that research on VR was at its very early stages, which
makes it hard to draw an overall conclusion or to find support for findings across
studies. For example, Ropelato et al. (2017) focused on VR-triggered simulator
sickness, rather than on driving-related measures. Gaibler et al. (2015) focused on the
VR user-friendliness rather than on the risky behaviour the VR simulated. If the VR
experience is not set to achieve great realism, assigning a positive result to it might be
questionable, as in the reportedly-enjoyable Gaibler et al. (2015) drunk-driving game.
Orfila et al. (2015) could not achieve great VR realism either. The authors reported no
immersion realism of the simulation. However, they found that moderate acceleration
and constant speed improved fuel consumption.
Chapter 8: Study 3 - Systematic review of VR simulations of risky driving 147
Focused on driving behaviour, Agrawal et al. (2017) measured eye movements
and hazards anticipation. The authors found that the VR improved the VR-trained
participants’ ability to detect threats in comparison with the control participants. Chen
et al. (2015) also focused on detection. However, in their study, the VR was used as a
measurement tool to compare younger and older participants, rather than to produce
any effect on them. Gonzalez et al. (2017) concluded that VR driving simulations
could potentially help participants drive safer. The authors exposed their participants
to virtual tractor driving simulations. However, they also concluded that the
participants would need more training to achieve such a positive result.
8.5 SUMMARY
Although all analysed studies reported, or implied, the involvement of young
drivers, the target group of the current program of research, only Agrawal et al. (2017)
specifically targeted them. Two of the studies targeted specific driving behaviour,
drink-driving (Gaibler et al., 2015) and eco-driving (Orfila et al., 2015). However, both
studies reported that the deployed VR technology as a game (Gaibler et al., 2015) or
in an intervention with free public access (Orfila et al., 2015) was far from realistic
and immersive. Only Agrawal et al. (2017) reported safety benefits as a result of using
VR to train participants in latent hazards anticipation. However, the authors did not
investigate whether these beneficial effects are sustained in the long-term. Thus, while
addressing RQ3 (How is VR applied in road safety research to motivate behavioural
change in young drivers?), this systematic review further confirmed the conclusion,
made in Chapter 2, i.e. that little is known about the use of VR in road safety. Very
little is known about its potential for safety impact in real-world interventions, too,
especially when young drivers are involved and specific driving behaviours, such as
DUI, are targeted.
8.6 DISCUSSION
As discussed in Chapter 2, the literature provides evidence on the negative
impact of DUI on young drivers. For example, the young drivers' crash risk increases
five times as a result of engaging in DUI (Peck et al., 2008), making DUI a target for
much-needed prevention efforts. As a response, current prevention campaigns utilise
VR technology to raise awareness on the risks of DUI. Such use of VR takes place in
the light of limited knowledge around its potential to deliver road safety benefits.
148 Chapter 8: Study 3 - Systematic review of VR simulations of risky driving
Given that limited evidence, the current PhD project had an opportunity to expand the
available knowledge in the identified underexplored research space.
VR, not only in road safety research but in any other field, currently cannot be
delivered on anything else than through a simulation. As such, in road safety, it is
delivered through a simulator. Simulators are known for causing simulator sickness.
Ropelato et al. (2017) found that driving the VR simulator increased discomfort. Their
test run for under 15 minutes, and despite it being comparatively short, 4 participants
aborted the simulation. The work of Ropelato et al. (2017) in investigating self-
reported simulator sickness provided support strict exclusion criteria to be used when
recruiting participants. Thus, the requirement "Have no history of seizures or epilepsy"
was used when recruiting the Intervention group in Study 4.
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 149
Chapter 9: Study 4 - Intervention with VR simulations of risky driving
Chapter 9 presents the fourth, and final, study of the current program of research.
Study 4 assessed if a VR intervention influenced young drivers' self-reported DUI
behaviour and intention.
This chapter 9 first provides a brief introduction of Study 4, which is followed
by an outline of the study key aims, method and hypotheses. The chapter then explores
the study results and, finally, is concluded with a discussion.
9.1 INTRODUCTION
Drink-driving is identified as a main contributor to 30% of the fatal and 9% of
the non-fatal injuries (ATC, 2011). Still, research shows that 7.8% of the young driver
respondents had driven after drinking alcohol, while 20% rode with a driver who had
been drinking (CDCP, 2016). Drug-driving is a main behavioural factor in 7% of the
fatal and 2% of the non-fatal crashes (ATC, 2011). DUI impairs driving performance,
increasing the risk of crashes (Hingson et al., 2002) five times for young drivers (Peck
et al., 2008) who, in turn, report high engagement in DUI (Ward et al., 2018).
Study 4 examined the effects of a VR intervention to influence self-reported DUI
behaviour and intention among young drivers. It assessed the impact of a novel COT-
based practice-oriented approach, implemented as part of real-world awareness-raising
intervention, to persuade young drivers to adopt safer and more responsible driving
behaviour. The intervention enabled participants to choose their "high" and step behind
the wheel of a virtual car. The intervention's effect was measured through two
questionnaires, one before the intervention, and another, three months after it, to
answer RQ4 (How do young drivers’ self-reported behaviour of not DUI and intention
not to DUI alter in their free-living environment as a result of a VR intervention?).
Similar to Study 2, the variables identified of most interest were intention not to
DUI and behaviour of not DUI during the three months after the intervention. Intention
not to DUI was assessed before the intervention. The construct reflected what the
drivers planned to do during the following three months without being influenced.
150 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
Behaviour of not DUI during the three months after the intervention was assessed after
the intervention. The construct reflected what the drivers had actually been doing after
the intervention took place. It was expected that, as a result of the intervention,
participants in the Intervention group would report significantly greater past behaviour
of not DUI during the three months after the intervention, DUI being defined as driving
above the legal BAC limit for alcohol or under the influence of illegal drugs, as well
as significantly greater intention not to DUI, in comparison with the Control group.
The evaluation of Study 4 was grounded in an extended TPB (see Section 3.7),
consistent with the evaluation undertaken as part of Study 2. It was analysed in three
parts to address the overarching RQ4. The first part looked at the sample of participants
before some of them were exposed to the intervention (RQ4.1). The analysis focused
on the participants’ intention not to DUI and its predictors. The second part focused
on the changes the intervention might or might not have triggered in regards to the
TPB constructs (RQ4.2). Finally, the third part answered the question of how much of
the self-reported behaviour of not DUI during the three months after the intervention
could have been predicted with data, available before the intervention (RQ4.3).
9.2 METHOD
Study 4 was designed as a controlled experiment with an Intervention group and
a Control group. This section provides details on the VR tool and how it was used in
the implemented intervention. Subsequently, it discusses the involved participants,
how they were recruited for the intervention, and the subsequent data collection
procedure. Details are presented, in turn, in separate subsections.
9.2.1 VR tool and intervention
In contrast to smartphone safe-driving apps (see Section 6.4), there was less
choice of VR apps available to select from. Due to the novelty of the VR technology,
the VR apps stores were not as readily accessible and as richly stocked. As a result,
road safety VR software for the ordinary consumer was not available. For that reason,
the current program of research did not incorporate a separate process for choosing
appropriate VR software as the case was with the safe-driving apps.
At the time, the VR software "3D Tripping" was deployed in road safety
interventions in Europe, Asia and South America – interventions which, arguably, did
not undergo theory-based evaluation. For the purpose of evaluation within the current
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 151
program of research, "3D Tripping" was sourced free of charge from its developers.
Thus, in Study 4, "3D Tripping" became the VR-based intervention tool.
Initial criteria supporting using "3D Tripping" as a tool in a VR intervention
were derived from the literature (see Chapter 8). Lack of realism (Gaibler et al., 2015)
and immersion (Orfila et al., 2015) were reported limitations in VR studies. VR realism
would include realistic sound and intuitive controls for manipulating the vehicle in the
virtual world. VR immersion would enable driving interaction in the virtual world of
the simulation. "3D Tripping" addressed those two limitations convincingly enough
for the purpose of this program of research.
"3D Tripping" immerses users into VR-simulated DUI. At first, they enter the
car to drive on a straight stretch of a road, without any other traffic participants, and
without any DUI impairment. This experience allows users time to get used to
managing the VR driving simulator. It also serves as a beginning to an overarching
story of the VR experience, in which it is suggested that users drive completely sober
to a night club (see Figure 9.1).
Figure 9.1. A user is getting used to managing the VR driving simulator.
After this initial familiarisation drive, the story continues at the night club, where
the vehicle is parked, and the premise is entered (see Figure 9.2).
152 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
Figure 9.2. The VR software visualises parking of the vehicle before entering the night club.
Once inside the night club, users are given a choice between alcohol and drugs,
and they need to make a selection. If drugs are selected, then an additional choice,
between ecstasy, cannabis and magic mushrooms, is given (see Figure 9.3).
Figure 9.3. A choice to experience impaired driving as a result of ecstasy, cannabis or magic mushrooms influence is given to users.
Once a selection is made, a picture with people on a dance floor continues the
narrative before users would find themselves back in the car and ready to drive back
home (see Figure 9.4) under the influence of the selected alcohol or drug. Until that
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 153
moment, all users have the same experience using this tool. However, from that
moment on the different choices lead to different road trips, events and DUI VR
experiences.
Figure 9.4. A user driving under the VR-simulated influence of magic mushrooms.
The VR alters the DUI experience in the following ways:
- Alcohol, the vision area is reduced. There is a delay between the vehicle's
response to a given command.
- Ecstasy, everything moves at an increased pace. Sensors are sharpened.
Everything is very colourful and flashy, also getting blurry at intervals.
- Cannabis, everything is very slow. Colours are calm. Vision does not stretch
very far, very much the opposite of ecstasy.
- Magic mushrooms, the world is unreal, with imaginary sceneries and
characters. The vehicle behaves opposite to the commands it receives.
The chosen DUI experience is additionally reinforced by different types of
music. Each experience is composed of several parts, happening on different types of
roads, e.g. motorway or rural. If the user crashes, they are taken back to the beginning
of the respective part, thus, being given a chance to correct their behaviour. The
simulation is over when a point is reached where a Police car appears and pulls the
user over.
154 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
In summary, using "3D Tripping" as part of the intervention allowed the young
participants of this study (see Subsection 9.2.4 Participants) to "drive stoned" in a safe
virtual environment, and to learn about the dangers of mixing substance abuse and
driving. The VR simulated experiences revealed to them precisely how their
perception of reality and, therefore, driving competence was impaired.
Overall, “3D Tripping” offered features that seemed worth investigating in terms
of using VR as a COT road safety intervention. The lack of choice when selecting the
VR intervention tool did not allow its expected influence on the drivers to be compared
to the influence of other VR software packages. Nevertheless, the theory-based COTs
selection criteria (see Section 4.2) were applied to "3D Tripping" to generate a better
understanding of its potential to influence the participants' salient beliefs.
During an intervention, the software negatively affects the participants' driving
abilities while they are sober. Thus, the participants can make a conclusion on whether
such behaviour is favourable or unfavourable (SC1, see Table 4.1). The software
significantly alters their experience when DUI. Thus, they can see that they are able to
execute full control over the behaviour only by choosing not to DUI (SC3). "3D
Tripping" is designed to be used during interventions with free access. As a
consequence, the VR simulation is typically performed in front of public (SC2),
including participant's friends and peers (SC5), which allows the participants' norms
to be influenced. Those spectators are welcome to comment and discuss the behaviour
which can serve as a moral benchmark (SC4). The VR simulation ends by either a car
crash or by the participant being caught by the Police. Both outcomes can potentially
increase the perceived risk associated with DUI (SC6). As a result, "3D Tripping",
when used as part of a public installation, scores highly on the theory-derived selection
criteria (see Section 4.2). Scoring high raised an expectation that the VR would be able
to influence the participants' extended TPB salient beliefs successfully within the
framework of the current study.
For the purpose of the study, "3D Tripping" was operated on a driving simulator
console, consisting of Oculus Rift goggles, driving seat, driving wheel Logitech G29
and a computer (see Figure 9.5). The simulator was installed at venues with free public
access to encourage the drivers' interactions with the public and their peers. The
scenarios, happening inside the virtual environment, were visualised for the spectators
on a large TV.
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 155
Figure 9.5. A participant, operating the VR driving simulator in front of their peers.
To achieve the maximum possible effect, each VR simulation was preceded with
making sure the respective participant was as comfortable as possible. First, the
participant was invited to take a seat. Second, the distance between the seat and the
simulator pedals was adjusted to the participant’s preference. Third, the VR headset
was adjusted to fit the participant (see Figure 9.6). After the VR driving simulator was
adjusted for a participant’s comfort, the VR software was started.
Figure 9.6. A participant is operating the VR software on a fully adjusted VR driving simulator.
9.2.2 Recruitment
The intervention group participants were recruited face-to-face at events where
the VR driving simulator with the "3D Tripping" was installed. The simulator was
156 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
installed at venues with free public access, similar to the way this type of simulator
would be used in the real world as a road safety intervention. A pilot test was carried
out at the Brisbane Queen Street Mall on the 28th of May as part of a larger Rotary
Club of Brisbane event. This allowed testing the equipment and the recruitment
procedure.
As a result of the pilot test, an option for age "more than 25" was added as a
possible selection under "age", to avoid both disappointments in the public, and
misunderstandings on behalf of some potential recruits. Data from entries with "more
than 25" selected were not included in the analysis. Furthermore, to aid recruitment,
only spots with high student traffic at the QUT campuses were considered for the VR
driving simulator setup, such as the QUT library lobbies or the QUT Cube.
The QUT Garden Point Campus HiQ reception space, level 3 of P block, was
eventually chosen as the most appropriate location that was also available at the time.
It is an open access area with a constant flow of people. It also has security cameras
and is locked outside business hours. The main recruitment took place from 16th to the
27th of July 2018, from 10:00 a.m. till 4 p.m., Monday to Friday.
A social media campaign was implemented on Facebook to recruit Control group
participants. The objective was to collect data from the general public in parallel with
collect data for the Intervention group participants. The Control group recruitment ran
from 19th to 31st of July 2018. The Facebook campaign was set to target people who
were aged 18 to 25, resided in Australia, spoke English and possessed a driving license.
A critical criterion that was considered when designing the Facebook campaign
was to produce as little impact on the people seeing it as possible, while still informing
them adequately of the purpose of the study to be able to obtain their informed consent
for participation. The purpose of the Facebook campaign was not to influence the
potential participants, rather to preserve the quality of the recruited group as a Control
condition. The sole purpose was to invite Facebook users to complete the online survey
after they provide their informed consent to participate in the study.
The Facebook campaign showed an ad with text and a static image of a white
car, taking a turn while driving on a night city road. The image background had blurry
city lights and reflections. The researcher regarded those lights and reflections as
potentially suggestive of a DUI theme. The text was encouraging the viewer to help
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 157
investigate whether VR influences young people's behaviour on the road. Lastly, it
clarified that the viewer was not expected to take part in the VR intervention itself and
that the survey was going to take 7 minutes of their time.
Overall, the Facebook campaign reached slightly less than 20,000 people. The
click-through rate was approximately 2%, more than two times lower than the
campaign in Study 2. Extrinsic incentives (vouchers) were offered to all participants.
All participants (both Control and Intervention) were offered to enter into a pool for a
random draw. The pool comprised 10 Amazon vouchers of 50 AUD in Survey 1, and
10 Amazon vouchers of 100 AUD in Survey 2. An additional $10 voucher was offered
only to the Intervention group participants. These vouchers were offered for the
additional effort of driving the VR driving simulator with "3D Tripping" software. The
$10 vouchers were offered until the minimum required number of 200 participants was
reached. Due to financial restrictions, the $10 vouchers were not offered to participants
above that minimum number. Nevertheless, young people were welcome to take part
in the intervention without that extrinsic incentive. Not offering the extrinsic incentive
did not reduce the number of interested potential participants (see Subsection 4.4.5 for
details on the obtained ethical clearance).
9.2.3 Data collection procedure
"3D Tripping" did not allow for any data to be collected with respect to the
participants' driving performance. Data were collected only through self-completion
questionnaires.
At Time 1 (July 2018), self-completion questionnaires were used to collect data
on demographics, TPB constructs and additional predictors. A participants'
information sheet was provided online to the participants, as a cover sheet, before they
started completing the survey. The Intervention group was required to complete a
driving scenario in the "3D Tripping" VR environment after completing the survey.
The same survey was completed by the Control group. The Control group had no task
other than filling the surveys at Time 1 and Time 2.
At Time 2 (approximately three months after Time 1, November 2018), an
invitation to complete the second survey was sent to all participants. Following the
invitation, two reminders were sent to the participants. The self-generated participants'
158 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
anonymous identifiers were used to link datasets from Survey 1 and Survey 2,
originating from the same participant.
9.2.4 Participants
Initially, 282 participants took part in the VR intervention. Twenty-two entries
were subsequently removed, as the age option "more than 25" was selected. One
duplicate case was removed. Seventy people completed the Control group survey.
Partially completed surveys were not considered. No people requested in writing that
they wanted to opt-out of the study after they had completed the first survey. In the
end, 329 cases (237 male; Mage = 20.92 years, SD = 2.16) were retained for analysis.
The average driving experience of the sample was 3.25 years (SD = 2.07).
At Time 2, 138 young drivers (91 male; Mage = 20.93 years, SD = 2.22)
completed the second survey, 99 Intervention group participants and 39 Control group
participants. Their driving experience ranged from 0 to 9 years (M = 3.38 years, SD =
2.07). The dropout rate of 58.10% exceeded initial expectations. Both groups had less
than the initially required number of participants (see Section 4.4.1). From the
Intervention group, 52 participants reported that they had chosen to experience alcohol
in the VR simulator; 20 reported choosing magic mushrooms; 15 – cannabis; and 12 –
ecstasy.
9.3 HYPOTHESES
To investigate the predictors of DUI at baseline (RQ4.1), it was hypothesised
that:
H.12. Demographic variables (gender, age and driving experience) would
account for a significant variation in intention not to DUI. DUI is a major
contributor to crashes (ATC, 2011), and increased crash risk is shown to
associate with both inexperience and age (McCartt et al., 2009). Gender is
often identified as playing a role in the young drivers' risky behaviours, too
(Scott-Parker, 2012).
H.13. TPB constructs (instrumental attitude, affective attitude, subjective
norm, descriptive norm, self-efficacy and perceived controllability) would
account for a significant variation in intention not to DUI, over and above the
demographic variables.
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 159
According to Sniehotta et al. (2014), TPB may lack sufficient predictive power
in longitudinal studies, or in ones with samples coming outside the university
campuses. Other researchers, such as Conner (2015), do not share that view. Away
from the theoretical discussion, findings from longitudinal studies, such as Elliott and
Thomson (2010), a study the current one leverages on (see Subsection 4.4.2), show the
TPB as having sufficient predictive power. As suggested by Conner (2015), an
extended theoretical framework may answer a great deal of the criticism, and address
the TPB limitations, as well as provide additional insights on the effect of the deployed
intervention. Additional predictors may also explain additional variation in the DVs
after controlling for TPB variables. Thus, it was hypothesised that:
H.14. Additional predictors (past behaviour of not DUI, perceived risk, moral
norm, peers' norm, impulsivity, sensitivity to reward and sensitivity to
punishment) would account for a significant variation in intention not to DUI,
over and above the TPB variables.
To explore the effects of the intervention (RQ4.2), it was hypothesised that, after the
intervention:
H.15. Participants in the Intervention group would report significantly greater
intention not to DUI than the Control group participants.
H.16. Participants in the Intervention group would report significantly greater
behaviour of not DUI than the Control group participants.
H.17. Due to the expectation that the VR software can influence all salient
beliefs (see Subsection 9.2.1), the VR intervention would have positively
influenced the Intervention group participants' instrumental attitude, affective
attitude, subjective norm, descriptive norm, self-efficacy, perceived
controllability, moral norm, peers' norm and perceived risk.
Finally, looking at the predictors of behaviour of not DUI during the three
months after the intervention, it was assessed how much of the behaviour after the
intervention, reported at Time 2, could have been predicted with the information
available before the intervention, at Time 1 (RQ4.3). In that respect, it was
hypothesised that:
160 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
H.18. Demographic variables (gender, age and driving experience) would
account for a significant variation in behaviour of not DUI during the three
months after the intervention.
H.19. TPB constructs (intention not to DUI, self-efficacy and perceived
controllability) would account for a significant variation in behaviour of not
DUI during the three months after the intervention, over and above the
demographic variables.
H.20. Additional predictors (past behaviour of not DUI, perceived risk, moral
norm, peers' norm, impulsivity, sensitivity to reward and sensitivity to
punishment) would account for a significant variation in behaviour of not DUI
during the three months after the intervention, over and above the TPB
variables.
9.4 PRELIMINARY ANALYSIS
Preliminary data analysis was performed before studying the results from the
different studies in detail. It dealt with missing data, transforming data, deciding how
to deal with dropouts, and establishing the participants' profile in regards to their
personality characteristics.
9.4.1 Missing data
There was no missing data. The setup of the data collection required all questions
to be compulsorily answered with a limited number of answer options to choose from,
except for the driving experience measure, which required an integer to be entered.
Thus successful submission of a questionnaire could happen only if it contained all
questions completed.
9.4.2 Dropouts
A preliminary one-way between-groups MANOVA was performed in regards to
the provided answers on the extended TPB variables (11 DVs) at Time 1. The IV was
whether Time 2 questionnaire was completed or not. Homogeneity assumption was
not met with a Box's M p < .001. The equality of variance assumption was violated for
a number of constructs (intention, subjective norm, self-efficacy, perceived
controllability, moral norm, peers' norm and past DUI behaviour). The obtained
Wilks' Lambda value was .93 with a p = .018 and a ηp2 = .069, F (11, 317) = 2.13.
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 161
Thus, due to the assumptions' violations, it could not be concluded whether there was
a significant difference amongst the people who completed both surveys, and those
that completed only Survey 1. Nevertheless, the descriptive statistics revealed that the
participants who completed only Survey 1 scored on average lower on all constructs.
This meant that the data from comparatively riskier participants were not available at
Time 2. Thus, a decision was made to retain the data from participants who did not
complete the Survey 2, for the purpose of the subsequent analysis where possible, e.g.
at Time 1.
9.4.3 Assumption checks and data transformation
The design of the survey provided little room for committing unintentional
errors. Errors were possible only when answering the question around driving
experience, and as a result, errors were observed. In three cases, a discrepancy between
the reported age and driving experience was identified. The reported driving
experience was considered to be a too big number when compared to expected, also
possible and legal, values of age. All three cases did not complete the second survey.
Given that it could not be concluded whether the mismatch was due to malicious intent
or to a data entry error, a decision was made to replace those scores with the median
score for driving experience.
Following data collection, measures for intention, attitudes, norms (with the
exclusion of moral norm) and past DUI behaviour were recoded (transformed) so that
higher scores indicated greater agreement with the construct (perceived negatively-
geared answer to the left of the scale, smaller value, and perceived positively-geared
answer to the right of the scale, higher value).
The two separate questions "To what extent do you intend to drive under the
influence of alcohol or drugs over the next 3 months?" (A great extent to no extent at
all after recoding) and "How often do you think you will drive under the influence of
alcohol or drugs in the next 3 months?" (All the time to never after recoding) for the
construct intention yielded very strong significantly correlated results (Pearson's r =
0.73, p < .001), thus, they were combined (through finding an average) into a single
measure intention not to DUI.
The questions "If you were to drive under the influence of alcohol or drugs over
the next 3 months, how much would you worry about being involved in a road crash?"
162 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
(Not at all worried to worried very much) and "If you were to drive under the influence
of alcohol or drugs over the next 3 months, how much would you worry about being
caught by the Police?" (Not at all worried to worried very much) for the construct
perceived risk yielded very strong significantly correlated results (Pearson's r = 0.72,
p < .001), thus, they were combined (through finding an average) into a single measure
perceived risk.
Gender was recoded, from a string, into a numeric variable, with "0" denoting
males and "1" denoting females.
Normality of the DVs, intention not to DUI at Time 1 and behaviour of not DUI
during the three months after the intervention (past behaviour of not DUI, measured
at Time 2), was assessed statistically, via skewness and kurtosis, and visually, via
histograms and Q-Q plots. Intention not to DUI was negatively skewed at Time 1 (-
4.94, std. error = .13). The respective values for kurtosis was 28.88 (std. error = .27).
Behaviour of not DUI during the three months after the intervention was also
negatively skewed (-5.52, std. error = .21) with a positive kurtosis (38.13, std. error =
.41). The values suggested a departure from normality, which was confirmed by a
visual examination of the histograms and the Q-Q plots. Boxplots also revealed
outliers and supported the suggestion for lack of normal distribution. However, such
distribution was hardly surprising given the behaviour that was being examined, and
the questions asked. The recommended choice, in such situations, is that
nonparametric tests are used when analysing the data. Those tests rely on medians
rather than on means.
The medians of intention not to DUI at Time 1 and behaviour of not DUI during
the three months after the intervention were examined. The results showed that the
medians were at the maximum of the scale, 9. If nonparametric tests were applied to
the data with medians at the maximum of the scale, the result would be that the
intervention did not produce any effect. An investigation of the frequencies of the two
variables showed that never (the maximum of the scale, 9) was selected in 85.1% of
the intention not to DUI cases, and in 84.1% of the behaviour of not DUI during the
three months after the intervention cases. Efforts to normalise the data, through
transformations (Lg10, Sqrt), did not satisfactorily improve the parameters. Thus, a
decision was made to recode the two variables into categorical scales with two values,
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 163
"0", denoting answers other than never, and "1", denoting a selection of the value 9
(never) as an answer, and to use non-parametric tests (see Subsection 4.4.4).
In some cases, the minimum expected cell frequency assumption for a chi-square
test for independence was violated. In those cases, the results of Fisher's exact tests
were reported.
9.4.4 Personality characteristics
Consistent with Patton and Stanford (1995), the internal reliability check of the
BIS-11, impulsivity, revealed a high internal consistency with a Cronbach's α of 0.81.
The value was above the generally accepted limit of 0.7 (DeVellis, 2016). The
participants (n=329) had a mean score of 60.95 (SD=9.22).
The internal reliability check of the SPSRQ revealed a high internal consistency
in both components, sensitivity to punishment (Cronbach's α of 0.87) and sensitivity to
reward (Cronbach's α of 0.75). The participants (n=138) had a mean score of 12.97
(SD=5.62) on the sensitivity to punishment scale, and of 12.04 (SD=4.31) on the
sensitivity to reward scale.
9.5 RESULTS
9.5.1 Participants' intention not to DUI before the intervention (RQ4.1, H.12 - H.14)
The analysis of collected baseline data was guided by RQ4.1 (What did we know
about the participants before the intervention, and to what extent the extended TPB
framework could predict their intention not to DUI?), and by H.12, H.13 and H.14
(see Section 9.3).
9.5.1.1 Frequencies, means, standard deviations and bivariate correlations.
Table 9.1 below presents the means, standard deviations and Spearman's r
correlations for the TPB constructs. On average participants scored very high on all
measures, above 7 on a 9-point scale. As discussed earlier, the scores on all measures
were negatively skewed.
Table 9.1 shows consistency with TPB. Past behaviour of not DUI is highly
correlated with intention not to DUI (r=0.66, p < .001). Although it means that whoever
DUI in the past will DUI in the future, it provided support for both choosing TPB as a
framework, and extending the TPB framework with the construct of past behaviour
(see Chapter 3). Another strong link was the correlation within the PBC construct, i.e.
164 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
between self-efficacy and perceived controllability (r=0.65, p < .001). The low and
non-significant correlation coefficients of self-efficacy and perceived controllability
with descriptive norm came as a surprise, as well as their low and comparatively less
significant correlations with instrumental attitude and affective attitude.
Table 9.1. Frequencies, means, standard deviations and bivariate correlations for the TPB variables at Time 1 (n=329).
Frequency of
max score (9)Mean SD 1 2 3 4 5 6 7 8
1. Past behaviour of not DUI 81.8% 8.60 1.14 - .66**.28**.44**.44**.27**.37**.34**
2. Intention not to DUI 85.1% 8.71 .98 - .34**.37**.40**.25**.41**.35**
3. Instrumental attitude 68.7% 8.25 1.45 - .59**.30**.28**.15**.19**
4. Affective attitude 68.1% 8.20 1.53 - .37**.25**.19**.18**
5. Subjective norm 85.4% 8.55 1.41 - .35**.25**.20**
6. Descriptive norm 41.6% 7.54 1.78 - -.05 .07
7. Self-efficacy 63.8% 7.43 2.69 - .65**
8. Perceived controllability 67.5% 7.60 2.57 - ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
9.5.1.2 Predictors of intention not to DUI
Following the methodology described in Section 4.4.4, a 3-step multiple logistic
regression was conducted to assess the strength of the intention not to DUI predictors
at Time 1 for the whole sample (n=329).
Step 1 of the model, which contained the demographic factors (gender, age and
driving experience), was statistically significant, χ2 (3, N = 329) = 21.40, p < .001,
indicating that the model was able to distinguish between participants who never
intended to DUI and participants who intended to do so. The model explained between
6.3% (Cox and Snell R squared) and 11.1% (Nagelkerke R squared) of the variance in
intention not to DUI, and correctly classified 84.5% of the cases. Nevertheless, the
explained variance is very small. As shown in Table 9.2, only Gender made a unique,
statistically significant contribution to the model (p = .020), with an odds ratio of .34,
i.e. female participants were more likely to indicate an intention not to DUI. Driving
experience and age did not emerge as significant predictors.
The results were consistent with H.12, which predicted that demographic
variables would account for a significant variation in intention not to DUI.
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 165
Table 9.2. Logistic regression analysis, predicting Intention not to DUI for all participants at Time 1, with demographic factors as predictors (n=329).
B S.E. Wald df p Exp(B)
95% C.I.for EXP(B)
Lower Upper
Gender -1.070 .462 5.372 1 .020 .343 .139 .848
Age -.144 .089 2.590 1 .108 .866 .727 1.032
Driving experience -.156 .087 3.225 1 .073 .855 .721 1.014
Constant 6.229 1.825 11.648 1 .001 507.231
Step 2 of the model contained the TPB variables (instrumental attitude, affective
attitude, subjective norm, descriptive norm, self-efficacy and perceived
controllability), together with the demographics (gender, age and driving experience).
It was statistically significant, χ2 (9, N = 329) = 80.91, p < .001, indicating that the
model was able to distinguish between participants, who never intended to DUI and
such that did intend to DUI, too. The model explained between 21.8% (Cox and Snell
R squared) and 38.3% (Nagelkerke R squared) of the variance in intention not to DUI,
and correctly classified 89.4% of the cases. Instrumental attitude, descriptive norm
and self-efficacy from the TPB variables emerged as significant predictors. As shown
in Table 9.3, the strongest predictor of intention not to DUI was gender (p = .042),
with an odds ratio of 2.94, i.e. once again, female participants were more likely to
indicate an intention not to DUI. The other statistically significant unique contributors
were instrumental attitude (p = .030, odds ratio = 1.31), descriptive norm (p = .003,
odds ratio = 1.35), self-efficacy (p = .013, odds ratio = 1.25) and driving experience (p
= .028, odds ratio = .80).
Table 9.3. Logistic regression analysis predicting Intention not to DUI for all participants at Time 1 (n=329) with demographic factors and TPB variables as predictors.
B S.E. Wald df p Exp (B)
95% C.I.for EXP(B)
Lower Upper
Gender 1.080 .531 4.142 1 .042 2.944 1.041 8.331
Age -.095 .107 .788 1 .375 .909 .737 1.122
Driving experience -.224 .102 4.843 1 .028 .799 .654 .976
Instrumental attitude .271 .125 4.722 1 .030 1.311 1.027 1.674
Affective attitude .161 .113 2.013 1 .156 1.175 .940 1.467
Subjective norm .214 .110 3.814 1 .051 1.239 .999 1.536
Descriptive norm .299 .101 8.795 1 .003 1.348 1.107 1.643
Self-efficacy .223 .090 6.106 1 .013 1.250 1.047 1.491
Perceived controllability .010 .092 .011 1 .918 1.010 .843 1.210
Constant -4.556 2.546 3.203 1 .074 .011
166 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
The results were consistent with H.13, which predicted that TPB constructs
would account for a significant variation in intention not to DUI, over and above the
demographic variables.
Adding the additional predictors (past behaviour of not DUI, perceived risk,
moral norm, peers' norm, impulsivity) to the model with demographic factors (gender,
age and driving experience) and the TPB variables (instrumental attitude, affective
attitude, subjective norm, descriptive norm, self-efficacy and perceived
controllability), at Step 3, produced a statistically significant result, χ2 (14, N = 329) =
117.81, p < .001. The model explained between 30.1% (Cox and Snell R squared) and
52.9% (Nagelkerke R squared) of the variance in intention not to DUI, and correctly
classified 90.3% of the cases. As shown in Table 9.4, the strongest predictor of
intention not to DUI was past behaviour of not DUI (p < .001), with an odds ratio of
3.90, i.e. participants who DUI in the past were reporting a significantly greater
intention to DUI in the future. The other statistically significant unique contributors
were instrumental attitude (p = .045, odds ratio = 1.33) and descriptive norm (p = .017,
odds ratio = 1.35).
Table 9.4. Logistic regression analysis predicting Intention not to DUI for all participants at Time 1 (n=329) with demographic factors, TPB variables and additional variables as predictors.
B S.E. Wald df p Exp (B)
95% C.I.for EXP(B)
Lower Upper
Gender 1.139 .660 2.984 1 .084 3.125 .858 11.386
Age -.034 .127 .070 1 .791 .967 .753 1.241
Driving experience -.246 .127 3.727 1 .054 .782 .609 1.004
Instrumental attitude .288 .144 4.007 1 .045 1.334 1.006 1.770
Affective attitude -.012 .146 .007 1 .932 .988 .742 1.314
Subjective norm .007 .153 .002 1 .962 1.007 .747 1.359
Descriptive norm .300 .125 5.729 1 .017 1.349 1.056 1.724
Self-efficacy .145 .117 1.532 1 .216 1.156 .919 1.453
Perceived controllability .035 .122 .081 1 .777 1.035 .815 1.315
Past behaviour of not DUI 1.362 .317 18.469 1 .000 3.903 2.097 7.263
Perceived risk -.122 .137 .802 1 .370 .885 .677 1.157
Moral norm .098 .137 .514 1 .473 1.103 .843 1.443
Peers' norm .082 .126 .421 1 .516 1.085 .848 1.388
Impulsivity .035 .025 1.959 1 .162 1.036 .986 1.088
Constant -16.612 4.623 12.915 1 .000 .000
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 167
Assessing the contribution of sensitivity to punishment and sensitivity to reward
as additional predictors required the regression test to be run only for the 138
participants who completed the second survey, as SPSRQ was administered only at
Time 2. Adding sensitivity to punishment and sensitivity to reward as additional
predictors to the model produced a statistically significant result, χ2 (16, N = 138) =
62.60, p < .001. The model explained between 36.5% (Cox and Snell R squared) and
73.3% (Nagelkerke R squared) of the variance in intention not to DUI, and correctly
classified 93.5% of the cases. As shown in Table 9.5, the strongest predictor of
intention not to DUI was still past behaviour of not DUI (p = .006), with an odds ratio
of 43.08. The other statistically significant unique contributors were instrumental
attitude (p = .044, odds ratio = 2.66) and impulsivity (p = .047, odds ratio = 1.18).
Thus, from the additional predictors, past behaviour not to DUI was the strongest
unique predictor. Impulsivity was the other statistically significant unique additional
contributor in the full equation.
Table 9.5. Logistic regression analysis, predicting Intention not to DUI for all participants at Time 1, with demographic factors, TPB variables and all additional variables as predictors (n=138).
B S.E. Wald df p Exp (B)
95% C.I.for EXP(B)
Lower Upper
Gender -.154 1.553 .010 1 .921 .857 .041 17.974
Age -.291 .384 .573 1 .449 .748 .352 1.588
Driving experience -.741 .490 2.291 1 .130 .477 .183 1.244
Instrumental attitude .980 .486 4.059 1 .044 2.664 1.027 6.910
Affective attitude -.478 .425 1.264 1 .261 .620 .269 1.427
Subjective norm .277 .742 .140 1 .709 1.320 .308 5.653
Descriptive norm .342 .446 .590 1 .442 1.408 .588 3.372
Self-efficacy .031 .437 .005 1 .943 1.032 .438 2.428
Perceived controllability .520 .502 1.070 1 .301 1.681 .628 4.501
Past behaviour of not DUI 3.763 1.373 7.509 1 .006 43.084 2.920 635.669
Perceived risk -.187 .652 .082 1 .774 .829 .231 2.978
Moral norm -.265 .302 .770 1 .380 .767 .425 1.386
Peers' norm .011 .345 .001 1 .975 1.011 .514 1.988
Impulsivity .164 .083 3.950 1 .047 1.178 1.002 1.385
Sensitivity to punishment .225 .121 3.492 1 .062 1.253 .989 1.587
Sensitivity to reward -.308 .226 1.870 1 .171 .735 .472 1.143
Constant -37.731 16.730 5.086 1 .024 .000
The results provided support for H.14, which predicted that additional predictors
(past behaviour of not DUI, perceived risk, moral norm, peers' norm, impulsivity,
168 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
sensitivity to reward and sensitivity to punishment) would account for a significant
variation in intention not to DUI, over and above the TPB variables.
9.5.2 Changes in salient beliefs (RQ4.2, H.15 - H.17)
The analysis in this section was based on 138 survey entries collected at Time 2.
It was guided by RQ4.2 (Did the intervention change the participants' salient beliefs,
as depicted by the TPB constructs?), and by H.15, H.16 and H.17.
9.5.2.1 Frequencies, means, standard deviations and bivariate correlations
At Time 2, approximately three months after the intervention, the relations
between the TPB variables remained statistically significant (see Table 9.6). The most
notable differences in comparison to Time 1 were: 1) the mean score for participants'
perceived controllability increased above 8, on the 9-point scale; 2) the previously
weak and non-significant correlation of self-efficacy and perceived controllability with
descriptive norm became moderate and significant; and 3) both subjective norm and
descriptive norm decreased and weakened their correlations with most variables, other
than with self-efficacy and perceived controllability.
Table 9.6. Frequencies, means, standard deviations and bivariate Spearman correlations for the TPB variables at Time 2 (n=138).
Frequency of
max score (9)Mean SD 1 2 3 4 5 6 7 8
1. Past behaviour of not DUI 84.1% 8.72 .93 - .62**.20**.30** .11 .18*.36**.29**
2. Intention not to DUI 79.0% 8.76 .60 - .34**.38** .21*.36**.51**.45**
3. Instrumental attitude 71.0% 8.41 1.19 - .52**.32** .21*.29** .12
4. Affective attitude 64.5% 8.10 1.62 - .26** .13 .25** .21*
5. Subjective norm 86.2% 8.80 .58 - .16 .19* .17*
6. Descriptive norm 32.6% 7.64 1.42 - .25**.27**
7. Self-efficacy 62.3% 7.80 2.31 - .55**
8. Perceived controllability 75.4% 8.30 1.81 - ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
9.5.2.2 Impact of the intervention
From the Intervention group, 47 participants reported that they had chosen to
experience one of the drug simulations. Fifty-two participants reported that they had
chosen to experience alcohol in the VR simulator. As the effects of using drugs or
alcohol might differ, the following analysis regarded them as two separate Intervention
groups. The frequencies for each variable of interest (intention not to DUI at Time 1,
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 169
intention not to DUI at Time 2, past behaviour of not DUI at Time 1, and behaviour of
not DUI during the three months after the intervention) are presented in Table 9.7.
Table 9.7. Dichotomised DVs' frequencies per group condition (n=138)
Variable Dichotomised
value
Alcohol
intervention group
Drugs intervention
group Control group
Intention not to
DUI at Time 1
Never 47 40 36
Other than never 5 7 3
Intention not to
DUI at Time 2
Never 46 33 30
Other than never 6 14 9
Past behaviour of
not DUI at Time 1
Never 44 40 35
Other than never 8 7 4
Behaviour of not
DUI during the
three months after
the intervention
Never 47 39 30
Other than never 5 8 9
A series of chi-square tests for independence were run for each group (Alcohol
or Drugs Intervention group) on each variable (intention not to DUI at Time 1,
intention not to DUI at Time 2, past behaviour of not DUI at Time 1, and behaviour of
not DUI during the three months after intervention) to investigate whether the
frequencies of the answers never and other than never for each of those four variables
were significantly different than the ones given by the Control group (see Table 9.7).
A series of McNemar's tests were performed to evaluate the effect of the intervention
on the Intervention groups' intention not to DUI and behaviour of not DUI (past
behaviour of not DUI at Time 1 and behaviour of not DUI during the three months
after the intervention) to understand whether there was a change in the proportion of
the answers never and other than never in the samples. A series of Wilcoxon Signed
Ranks Test were performed to evaluate for any changes in other TPB constructs
(instrumental attitude, affective attitude, subjective norm, descriptive norm, self-
efficacy, and perceived controllability) within the Intervention groups.
9.5.2.2.1 Alcohol Intervention group
Between-group analysis
When investigating intention not to DUI at Time 1, the minimum expected cell
frequency assumption for a Chi-square test for independence was violated. Fisher's
170 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
Exact Test returned p = 1.00, indicating no significant association between group
condition and intention not to DUI at Time 1. A Chi-square test for independence (with
Yates Continuity Correction) indicated no significant association between group
condition and intention not to DUI at Time 2 for the Alcohol Intervention group, either,
χ2 (1, N = 91) = 1.40, p = .24, phi =.15.
These results did not support H.15, which predicted that participants in the
Intervention group would report significantly greater intention not to DUI than the
Control group participants.
A Chi-square test for independence (with Yates Continuity Correction) indicated
no significant association between group condition, neither for past behaviour of not
DUI at Time 1 (χ2 (1, N = 91) = .16, p = .69, phi = -.08) nor for behaviour of not DUI
during the three months after the intervention (χ2 (1, N = 91) = 2.15, p = .14, phi =
.19).
Overall, these results did not support H.16, which predicted that participants in
the Intervention group report significantly greater behaviour of not DUI during the
three months after the intervention than the Control group participants.
Within-group analysis
For the Alcohol Intervention group, the McNemar's test did not show a
statistically significant difference in intention not to DUI (N = 52, Exact Sig. = 1.00).
At Time 2, one more person selected other than never as their intention not to DUI, in
comparison to Time 1 (see Table 9.7). The difference in behaviour of not DUI (N =
52, Exact Sig. = .45), before and three months after the intervention, was not
statistically significant, either. Three people less reported other than never as a recall
of their behaviour of not DUI, in comparison to Time 1 (see Table 9.7).
The results of a Wilcoxon Signed Ranks Test indicated that there were no
significant differences for the Alcohol Intervention group (n=52) before the
intervention and three months after the intervention, in none of the potentially-
modifiable extended TPB variables: instrumental attitude (z=-.72, p = .47), affective
attitude (z=-.19, p = .85), subjective norm (z=-.06, p = .95), descriptive norm (z=-1.32,
p = .19), self-efficacy (z=-.79, p = .43), perceived controllability (z=-.72, p = .47),
moral norm (z=-.96, p = .34), peers' norm (z=-1.94, p = .05) and perceived risk (z=-
.23, p = .82).
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 171
These results did not support H.17, which predicted that the VR intervention
would have positively influenced the Intervention group participants' instrumental
attitude, affective attitude, subjective norm, descriptive norm, self-efficacy, perceived
controllability, moral norm, peers' norm and perceived risk.
9.6.2.2.2 Drugs Intervention group
Between-group analysis
When investigating intention not to DUI at Time 1, the minimum expected cell
frequency assumption for a Chi-square test for independence was violated. Fisher's
Exact Test returned p = .34, indicating no significant association between group
condition and intention not to DUI at Time 1. A Chi-square test for independence (with
Yates Continuity Correction) indicated no significant association between group
condition and intention not to DUI at Time 2 for the Drugs Intervention group, χ2 (1,
N = 86) = .21, p = .65, phi =-.08.
These results did not support H.15, which predicted that participants in the
Intervention group would report significantly greater intention not to DUI in the future
than the Control group participants.
When investigating past behaviour of not DUI at Time 1 for the Drugs
Intervention group, the minimum expected cell frequency assumption for a Chi-square
test for independence was violated. Fisher's Exact Test returned p = .75, indicating no
significant association between group condition and past behaviour of not DUI at Time
1. A Chi-square test for independence (with Yates Continuity Correction) indicated no
significant association between group condition and behaviour of not DUI during the
three months after the intervention for the Drugs Intervention group, χ2 (1, N = 86) =
.19, p = .67, phi = .08.
These results did not support H.16, which predicted that participants in the
Intervention group would report significantly greater behaviour of not DUI during the
three months after the intervention than the Control group participants.
Within-group analysis
For the Drugs Intervention group, the McNemar's test did not show a statistically
significant difference in intention not to DUI (N = 47, Exact Sig. = .09). At Time 2,
seven more people selected other than never as their intention not to DUI, in
comparison to Time 1 (see Table 9.7). The difference in behaviour of not DUI (N =
172 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
47, Exact Sig. = 1.00), before and three months after the intervention was not
statistically significant either. One person less reported other than never as a recall of
their behaviour of not DUI, in comparison to Time 1 (see Table 9.7).
The results of a Wilcoxon Signed Ranks Test indicated that there were no
significant differences for the Drugs Intervention group (n=47), before the intervention
and three months after the intervention, in none of the potentially-modifiable extended
TPB variables: instrumental attitude (z=-.81, p = .42), affective attitude (z=-1.05, p =
.29), subjective norm (z=-1.03, p = .31), descriptive norm (z=-.04, p = .97), self-
efficacy (z=-.87, p = .40), perceived controllability (z=-.19, p = .85), moral norm (z=-
.68, p = .50), peers' norm (z=-.27, p = .79) and perceived risk (z=-1.71, p = .09).
These results did not support H.17, which predicted that the VR intervention
would have positively influenced the Intervention group participants' instrumental
attitude, affective attitude, subjective norm, descriptive norm, self-efficacy, perceived
controllability, moral norm, peers' norm and perceived risk.
9.5.3 Predictors of behaviour of not driving under the influence of drugs or alcohol after the intervention (RQ4.3, H.18 - H.20)
The analysis of data, collected after the intervention, was guided by RQ4.3
(Using the extended TPB framework, to what extent could the data available before
the intervention predict the participants' behaviour of not DUI after the intervention?),
and by H.18, H.19 and H.20.
In this analysis, data from 138 participants who completed the second
questionnaire was assessed, irrespective of their condition, Control or Intervention, as
no evidence for any statistically significant effect from the intervention was found in
the previous analysis. A 3-step direct logistic regression was conducted to assess the
strength of the predictors of behaviour of not DUI during the three months after the
intervention, following the order of entry described in Section 4.4.4 and already
applied in Study 2.
Step 1 of the model contained demographic factors (gender, age and driving
experience) and was statistically significant, χ2 (3, N = 138) = 11.02 p = .012,
indicating that the model was able to distinguish between participants who never
performed DUI during the three months after the intervention and such that did. The
model explained between 7.7% (Cox and Snell R squared) and 13.1% (Nagelkerke R
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 173
squared) of the variance in behaviour of not DUI during the three months after the
intervention, and correctly classified 84.8% of the cases. Nevertheless, the explained
variance is small. As shown in Table 9.8, only driving experience made a unique,
statistically significant contribution to the model (p = .004), with an odds ratio of .61.
Gender and age did not emerge as significant predictors.
The results were consistent with H.18, which predicted that demographic
variables would account for a significant variation in behaviour of not DUI during the
three months after the intervention.
Table 9.8. Logistic regression analysis predicting behaviour of not DUI during the three months after the intervention for participants at Time 2 (n=138) with demographic factors as predictors.
B S.E. Wald df p Exp(B)
95% C.I.for EXP(B)
Lower Upper
Gender -.320 .539 .352 1 .553 .726 .253 2.088
Age .264 .166 2.538 1 .111 1.302 .941 1.801
Driving experience -.492 .173 8.132 1 .004 .611 .436 .857
Constant -1.760 2.992 .346 1 .556 .172
At Step 2, the model contained the TPB variables (intention, self-efficacy and
perceived controllability), together with the demographics. It was statistically
significant, χ2 (6, N = 138) = 16.54, p = .011. The model explained between 11.3%
(Cox and Snell R squared) and 19.3% (Nagelkerke R squared) of the variance in
behaviour of not DUI during the three months after the intervention, and correctly
classified 85.5% of the cases. However, only intention not to DUI emerged as a
significant predictor from the TPB constructs. As shown in Table 9.9, the two
statistically significant unique contributors were intention not to DUI (p = .032, odds
ratio = 4.54), and driving experience (p = .007, odds ratio = .61).
Table 9.9. Logistic regression analysis, predicting Behaviour of not DUI during the three months after intervention for participants at Time 2, with demographic factors and TPB from Time 1 as predictors
(n=138).
B S.E. Wald df p Exp (B)
95% C.I.for EXP(B)
Lower Upper
Gender .244 .549 .197 1 .657 1.276 .435 3.739
Age .300 .178 2.846 1 .092 1.350 .953 1.914
Driving experience -.496 .185 7.160 1 .007 .609 .423 .876
Intention not to DUI 1.513 .707 4.586 1 .032 4.541 1.137 18.136
Self-efficacy -.211 .257 .671 1 .413 .810 .490 1.341
Perceived controllability .001 .264 .000 1 .997 1.001 .596 1.680
Constant -14.471 7.201 4.038 1 .044 .000
174 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
The results were consistent with H.19, which predicted that TPB constructs
would account for a significant variation in behaviour of not DUI during the three
months after the intervention, over and above the demographic variables.
At Step 3 of the model, the contribution over and above the TPB variables of
additional predictors (past behaviour of not DUI, perceived risk, moral norm, peers'
norm, impulsivity, sensitivity to reward and sensitivity to punishment) was
investigated. The model was statistically significant, χ2 (13, N = 138) = 38.36,
p < .001. It explained between 24.3% (Cox and Snell R squared) and 41.5%
(Nagelkerke R squared) of the variance in behaviour of not DUI during the three
months after the intervention, and correctly classified 88.4% of the cases. Peers' norm
and sensitivity to punishment emerged as statistically significant unique additional
predictors. As shown in Table 9.10, the three statistically significant unique
contributors were peers' norm (p = .005, odds ratio = 1.63), sensitivity to punishment
(p = .021, odds ratio = 1.16) and driving experience (p = .020, odds ratio = .59).
Table 9.10. Logistic regression analysis, predicting Behaviour of not DUI during the three months after intervention for participants at Time 2, with demographic factors, TPB and additional predictors
from Time 1 as predictors (n=138).
B S.E. Wald df p Exp (B)
95% C.I.for EXP(B)
Lower Upper
Gender -.787 .757 1.079 1 .299 .455 .103 2.009
Age .187 .198 .896 1 .344 1.206 .819 1.776
Driving experience -.522 .224 5.425 1 .020 .593 .382 .921
Intention not to DUI .776 1.137 .466 1 .495 2.173 .234 20.187
Self-efficacy -.287 .322 .792 1 .374 .751 .399 1.411
Perceived controllability .004 .302 .000 1 .991 1.004 .555 1.815
Past behaviour of not DUI -.101 .911 .012 1 .911 .903 .152 5.386
Perceived risk .257 .247 1.079 1 .299 1.293 .796 2.099
Moral norm -.352 .432 .663 1 .416 .703 .302 1.641
Peers' norm .491 .174 7.936 1 .005 1.634 1.161 2.300
Impulsivity -.044 .034 1.665 1 .197 .957 .896 1.023
Sensitivity to punishment .149 .065 5.296 1 .021 1.161 1.022 1.318
Sensitivity to reward -.122 .080 2.327 1 .127 .885 .756 1.035
Constant -4.076 9.047 .203 1 .652 .017
The results provided support for H.20, which predicted that the additional
predictors (past behaviour of not DUI, perceived risk, moral norm, peers' norm,
impulsivity, sensitivity to reward and sensitivity to punishment) would account for a
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 175
significant variation in behaviour of not DUI during the three months after
intervention, over and above the TPB variables.
9.6 DISCUSSION
Study 4 was analysed in three parts. The purpose of the first part was to establish
a baseline, understanding more about the young drivers as well as how much of their
intention not to DUI before they took part in the VR intervention (RQ4.1). The purpose
of the second part was to investigate if the intervention with the VR software "3D
Tripping" produced any statistically significant changes in regards to the assessed TPB
constructs (RQ4.2). The purpose of the third part was to help understand how much of
the participants' future DUI could have been predicted before the intervention
happened, with the data collected at Time 1 (RQ4.3).
9.6.1 Findings
The predictive contributions of demographic factors (gender, age and driving
experience) were investigated first, before the TPB predictive validity in relation to
the participants’ intention not to DUI before the intervention (RQ4.1) and behaviour
of not DUI during the three months after the intervention (RQ4.3). Consistent with
previous research (Scott-Parker, 2012), gender was statistically significant in
predicting intention not to DUI. However, gender did not have significant predictive
power when predicting behaviour of not DUI during the three months after the
intervention, which disagreed with Scott-Parker (2012). In the case of behaviour of not
DUI during the three months after the intervention, driving experience was the
strongest demographic predictor. The observed difference with existing literature
might be due to the nature of the investigated behaviour, DUI, while Scott-Parker
(2012) looked into young drivers' risky driving behaviour in more general terms.
Another reason might be the systematic differences between the two samples, before
and after the intervention, used in the regression models for intention not to DUI and
for behaviour of not DUI during the three months after the intervention. Many
participants who reported riskier behaviour at Time 1, did not complete Survey 2,
which collected the data used to assess behaviour of not DUI during the three months
after the intervention.
After the demographic factors were explored, RQ4.1 and RQ4.3 guided the
investigation of the contribution of the extended TPB framework in the regression
176 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
equations. Consistent with Study 2, and more broadly with the literature (Elliott &
Thomson, 2010; Horvath et al., 2012), the extended TPB framework predicted
additional variation, over and above the demographic factors. Instrumental attitude,
descriptive norm and self-efficacy were significant predictors of intention not to DUI.
Intention not to DUI was a significant predictor of behaviour of not DUI during the
three months after the intervention.
As a third and final step in the regression equations, additional predictors were
assessed in relation to RQ4.1 and RQ4.3. Consistent with Elliott and Thomson (2010),
past behaviour of not DUI before the intervention was a strong, unique predictor of
intention not to DUI, over and above TPB. The other additional predictor that
contributed significantly to the final regression equation was impulsivity, a finding
inconsistent with Constantinou et al. (2011) and Pearson et al. (2013). The
inconsistency might be due to the very specific behaviour, targeted by Study 4, DUI,
while Constantinou et al. (2011) and Pearson et al. (2013) looked into risky driving
behaviour, in general.
The contributions of additional predictors were different when predicting
behaviour of not DUI during the three months after the intervention. Past behaviour
of not DUI did not emerge as a statistically significant contributor, which was
inconsistent with Elliott and Thomson (2010). This might be due to, once again, the
different examined behaviours. Elliott and Thomson (2010) focused on speeding rather
than on DUI. However, it might also be due to the participants' characteristics. Elliott
and Thomson (2010) studied offenders, while in Study 4, many participants that
reported DUI at Time 1, i.e. likely offenders, did not complete Survey 2, and thus, their
data could not be assessed.
Consistent with Sela‐Shayovitz (2008), peers' norm emerged as a statistically
significant contributor in the final regression equation with behaviour of not DUI
during the three months after the intervention as a DV. Also, consistent with the
literature (Elliott & Thomson, 2010), moral norm did not emerge as a statistically
significant contributor in that regression equation. Inconsistency with the reviewed
literature (Constantinou et al., 2011) was observed in the predictive validity of
sensitivity to punishment and sensitivity to reward. Sensitivity to punishment emerged
as a statistically significant contributor in the final regression equation with behaviour
of not DUI during the three months after the intervention as a DV, while sensitivity to
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 177
reward did not, which does not offer support for the findings in Constantinou et al.
(2011). Given that Constantinou et al. (2011) studied young drivers, too, the difference
in the results might be coming from the different behaviours being investigated.
Constantinou et al. (2011) explored risky and aggressive driving through DBQ, while
Study 4 focused only on DUI.
Despite the found evidence for significant correlations between the variables, the
VR intervention was not found to be able to influence any of the extended TPB
constructs (RQ4.2). Separate tests did not find significant differences between the
Control group and the Intervention group neither on self-reported behaviour nor on
intention, over time. The results were the same on any of the potentially-modifiable
constructs within the Intervention groups, too. Similar to the safe-driving app findings,
such results came at a surprise because "3D Tripping" was initially seen as potentially
capable of influencing all salient beliefs in the adopted theoretical model (see
Subsection 9.2.1).
Such results might be rooted in the inherent social unacceptability of DUI
behaviour. In line with general expectations, participants reported that they neither did
DUI in the past nor intended to do so in the future. Positively changing behaviour that
is already positive is inherently challenging. In the future, it is suggested to look into
the effect of the intervention for offenders. Alternatively, other constructs might be
explored about a more general target group. Detailed recommendations about how
future research can build on the Study 4 findings are presented in Section 10.3.
9.6.2 Strengths
Study 4 was designed as a real-world intervention, focused on evaluating the
long-term impact of example VR simulations of risky driving software. It was
implemented similar to interventions undertaken by road safety advocacy groups in
their regular activities. Such interventions are easily replicable once the initial
investment of purchasing the necessary hardware and software is made. However,
many such interventions are not are robustly evaluated. Thus, the collected self-
reported data provided information that was not readily available from other sources.
Initially, an effort was made to involve a comparatively large sample (n=282) as
an Intervention group, to improve generalisability of the findings in comparison with
other lower-cost studies, focused on young drivers (Fitz-Walter et al., 2017; Zhang et
178 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
al., 2014). A particular strength of the study was that a Control group was established
to control for any general influence, which might have been experienced by the
Intervention group participants. Another strength was the sample’s demographic
diversity in terms of gender, age (as long as it is within the predefined frame of 18 to
25) and driving experience. Personality characteristics (impulsivity, sensitivity to
punishment and sensitivity to reward) were explored together with other additional
predictors (perceived risk, moral norm, peers' norm and past behaviour of not DUI)
on top of the TPB constructs. This allowed a more complete picture to be established
for each participant, both at a given moment and over time.
9.6.3 Limitations
Data was collected through online questionnaires, and self-reports are known to
be inherently susceptible to bias. It has to be acknowledged that the investigated
behaviour of DUI is much less socially acceptable than the investigated speeding
behaviour in Study 2, for example. Thus, higher pressure to report socially acceptable
answers may be expected. The anonymous nature of the data collection, the
impossibility of consequences for reporting DUI, as well as the fact that rewards were
offered, irrespective of provided answers, should have minimised bias.
Study 4 data were collected twice, before and three months after the intervention.
Thus, the evaluation of the long-term intervention effect did not incorporate the
assessment of changes in the constructs of interest immediately after the intervention.
It is acknowledged that with the participants being still present at the intervention
venue, additional data could have been collected. However, at the time of evaluation
design, this additional data collection was considered as imposing on the participants
a time-consuming effort, misaligned with the overall research focus. In such a
situation, and to limit the potential dropout rates, a decision was taken not to
overburden the Intervention group participants. Rather, the study focused on the
possible long-term effects only, which are much less often explored in the literature.
Another limitation of the study was the initial sample gender distribution (91
females, 236 males). Such distribution might not be a fair representation of the
Australian young drivers' population. An additional limitation of the sample was that
the Intervention group was recruited predominantly at the QUT campus. A negligible
number of 5 participants was recruited during the pilot intervention. University
campuses are a common source for study participants' recruitment, as in Zhang et al.
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 179
(2014) or Scott-Parker (2012), but it limits the generalisability of findings. Students in
psychology represent a significant proportion of the subjects in psychological research,
which introduces a known bias in findings (Smart, 1966). Although Study 4 involved
QUT students primarily, due to the choice of the intervention venue, it can be assumed
that the participants had diverse backgrounds and were pursuing different degrees in
the university.
The study was limited by the number of participants who completed both
surveys. Although the collected data from 138 participants at Time 2 was a
considerable sample, it was still insufficient for robust conclusions. Furthermore, the
participants who completed both surveys were, on average, less risky than the
participant who dropped out. The challenges associated with evaluating the effect of
the intervention on a riskier population represented a significant limitation. The
systematic bias in the retained data might be a partial explanation for the lack of
significant effects of the intervention.
Another explanation for the lack of significant effects of the intervention could
be the participants were allowed to choose from four different scenarios. The
participants were given the opportunity to make their own choice in regards to the DUI
they wanted to experience to maintain the real-world nature of the intervention. Using
different scenarios in combination with the large drop-out might have reduced the
experimental control over the mechanisms causing shifts in the participants' salient
beliefs before and after the intervention. As a result, the within-group variability might
have been potentially increased, which, in turn, is likely to result in a decreased
likability to detect differences. To increase the future studies potential to detect
differences, Section 10.3 discusses a proposal for analysing data collected through
interventions with different scenarios as a condition.
Violations of assumptions were another observed data-related issue. The
violations forced the application of non-parametric tests, limiting the validity of the
findings. Similar to Study 2, case-targeted measures to reduce dropout rate may prove
useful in future research, although this may reduce the real-world resemblance of the
study.
Another limitation was that the perceived ease or difficulty of driving under the
different conditions was not assessed with respect to the individual participants. It is
acknowledged that a participant might have found driving in the impaired VR
180 Chapter 9: Study 4 - Intervention with VR simulations of risky driving
simulation easy, or potentially more challenging and fun. In such a case, the
intervention could have promoted the DUI, despite the good intention behind its
implementation. However, the intervention was delivered "as-is" in the real world and
was evaluated as such. Furthermore, the data did not provide evidence for increased
DUI three months after the intervention.
9.7 CONCLUSION
Raising awareness of driving-related risks is not new, and education programs
have been studied in the past (Lewis, Fleiter, & Smith, 2015). In such cases,
researchers were able to provide essential insights into understanding the full
intervention implications, and subsequently, suggest strategies for addressing gaps in
their implementation. For example, researchers suggest that COTs are potentially
persuasive instruments that might help young drivers adopt safer driving behaviour
when used as intervention tools (Schroeter et al., 2012; Steinberger et al., 2015). VR
is one such COT. However, VR is a new tool. With the VR technology becoming
increasingly available and finding its way into prevention efforts, the question of how
VR can potentially improve road safety becomes necessary to investigate. At the same
time, there is a very limited number of VR studies in road safety (see Chapter 8).
Study 4 assessed the impact of a real-world VR intervention with "3D Tripping"
as an intervention tool. By doing so, it exhibited several strengths besides its real-world
nature, such as a comparatively large and diverse sample, a control for any general
influence, and a focus on long-term effects. Nevertheless, the study had its limitations,
e.g. potential for self-report bias, large drop-out rate, and problematic data. Despite
those limitations, Study 4 not only expanded the knowledge around using VR
simulations of risky driving in road safety but extended the knowledge into the context
of DUI. To achieve that, Study 4 utilised self-report items, drawn directly from the
literature and subsequently adapted to fit the study (see Subsection 4.4.3). Their
number was intentionally kept to a minimum to maximise response rate, as suggested
by Hart et al. (2005).
While answering RQ4 (How do young drivers’ self-reported behaviour of not
DUI and intention not to DUI alter in their free-living environment as a result of a VR
intervention?), it was found that the implemented intervention did not produce a
statistically significant effect on the participants’ self-reported behaviour of not DUI
and intention not to DUI. The observed results might be due to the overly positive self-
Chapter 9: Study 4 - Intervention with VR simulations of risky driving 181
reported behaviour of the participants in general. Nevertheless, the findings of the
current study supported the predictive validity of the extended TPB framework to
explore young drivers' DUI when a VR intervention is deployed. Study 4 also assessed
additional predictors, over and above TPB. The assessment of those additional
predictors revealed mixed results. Such findings provided support salient beliefs to be
considered as potentially better targets of VR interventions than personality
characteristics.
Overall, Study 4 provided insights into the complexity of DUI as a behaviour. It
showed that positively influencing DUI might be neither easy nor straightforward. The
obtained results from the "3D Tripping" intervention showed that targeting DUI
intention and DUI behaviour is not one and the same thing. Different constructs shall
be considered leading, depending on whether the objective is to influence intention or
behaviour. Specific insights on understanding the difference, stemming from the Study
4 VR simulations of risky driving, are presented in Subsection 9.6.1 above.
Suggestions on how to build on the obtained results are discussed in Section 10.3.
182 Chapter 10: General discussion
Chapter 10: General discussion
Chapter 10 begins with a summary of the rationale for this PhD research
program's focus on young drivers' safety and its overall contribution. The chapter
continues with integrating research findings, strengths and limitations, followed by a
discussion of directions for further research.
10.1 OVERALL CONTRIBUTION
Despite the consistent efforts of the international community to reduce road
trauma, fatalities continue to rise (WHO, 2018). Young people are reported across
jurisdictions as overrepresented in crashes (BITRE, 2018; NHTSA, 2018a; EC, 2018;
WHO, 2018). COTs are regarded as one of the contributors to these statistics, while,
at the same time, they do not contribute to the driving tasks (Parliament of Victoria
Road Safety Committee, 2006). This is particularly valid for young drivers, who are
recognised as early technology adopters (Lee, 2007). Nevertheless, researchers
suggest that COTs could become part of the efforts to reduce road trauma (Schroeter
et al., 2012; Steinberger et al., 2015).
Both academia and businesses promise and subsequently offer technological
solutions to increase safety. To potentially access those solutions, COTs are
increasingly available to the general consumer. Thus, COTs carry a potential for
ubiquitous low-cost outreach, but there is limited knowledge about the safety benefits
they deliver.
The current PhD program of research examined the effects of two examples of
COT-based interventions to reduce risky driving behaviour among young drivers. The
first intervention used a smartphone safe-driving app that aimed to transform the
smartphone, from an existing risk source, into a tool for reducing speeding. The second
intervention deployed VR simulations of risky driving under the influence to reduce
DUI. This was one of the first programs of research to comprehensively explore the
safety benefits on young drivers, of these two examples of off-the-shelf readily-
available COTs, in the participants' free-living environment. This was achieved across
four studies (see Figure 10.1).
Chapter 10: General discussion 183
SAFE-DRIVING APPS
Study 1 (Chapter 5)
Systematic Review of Safe-driving Apps
RQ: What is the state of the art evidence of the safety benefits of smartphone safe-driving apps for young
drivers?
METHOD: Systematic Review (adhering to the PRISMA guidelines)
ANALYSIS: Narrative
FINDINGS: Of 80 papers, selected for full-text review, 22 papers were found to be relevant for the
current program of research, with only 4 of them being explicitly focused on young drivers in naturalistic
settings. Only three articles of the four reported safety benefits for the participating young drivers,
stemming from using a smartphone safe-driving app. The interventions, reported in the three studies,
were considered to be not representative of real-world adoption and usage of smartphones safe-driving
apps.
Chapter 6
Selecting a safe-driving app
METHOD: Focus group (n=10), Systematic Review
ANALYSIS: Narrative, user testing
FINDINGS: To be considered useful as an intervention tool with persuasive potential, a safe-driving
app should be free for users, not requiring additional hardware, safe (not distracting), self-starting, not
geographically restricted and informative, i.e. provide detailed feedback. At the time, Flo was identified
as the most suitable candidate to be integrated into a real-world intervention.
Study 2 (Chapter 7)
Intervention with an off-the-shelf smartphone safe-driving app
RQ: How do young drivers’ self-reported behaviour of not speeding and intention not to speed alter in
their free-living environment, as a result of exposure to a smartphone safe-driving app intervention?
METHOD: A randomised controlled intervention with cross-sectional pre- (n=480) and post-surveys
(n=157, 126 controls), complemented by participants' (n=31) driving scores from safe-driving app
leaderboard.
ANALYSIS: Hierarchical multiple regression, Analysis of covariance.
FINDINGS: As a result of the smartphone safe-driving app intervention with Flo as an intervention tool,
no statistically significant effect was found on any of the TPB measures, including the participants' self-
reported behaviour of not speeding during the three months of the intervention and their intention not to
speed in the future. No statistically significant increased distraction was found, either. The only
statistically significant effect found was a decrease in distraction for the Intervention group male
participants.
184 Chapter 10: General discussion
VIRTUAL REALITY
Study 3 (Chapter 8)
Systematic Review of VR
RQ: How is VR applied in road safety research to motivate behavioural change in young drivers?
METHOD: Systematic Review (adhering to the PRISMA guidelines)
ANALYSIS: Narrative
FINDINGS: VR is still underused in road safety. Limited evidence on positive effects on young drivers
was found from using VR in laboratory conditions in small-scale studies. Of 7 papers selected for full-
text review, 6 were found to be partially relevant for the current program of research, with only 1
reporting safety benefits. VR simulations of risky driving were not evaluated as part of real-world
interventions.
Study 4 (Chapter 9)
Intervention with VR simulations of risky driving
RQ: How do young drivers’ self-reported behaviour of not DUI and intention not to DUI alter in their
free-living environment as a result of a VR intervention?
METHOD: A controlled intervention, with convenience-based assignment to groups, with cross-
sectional pre- (n=329) and post-surveys (n=138, 39 controls).
ANALYSIS: Logistic regression, McNemar's test, Chi-square test for independence, Wilcoxon Signed
Ranks Test.
FINDINGS: No statistically significant effect as a result of a VR intervention, with "3D Tripping" as an
intervention tool, was found on any of the TPB measures, including the participants' self-reported
behaviour of not DUI during the three months after the intervention and their intention not to DUI in the
future.
Figure 10.1. Outline of the thesis studies and findings
The four implemented studies were expected to deliver two main outcomes
within the framework of the current program of research. The first foreseen outcome
was a contribution to a better understanding of the safety benefits from using two
examples of COT applications, a smartphone safe-driving app and VR simulations of
risky driving, for risk prevention purposes in a road safety context. This first outcome
is synthesised in the following Section 10.2. The second outcome was informing the
future evaluations of COTs-based interventions, leveraging smartphone safe-driving
apps and VR simulations of risky driving, that aim to reduce risky driving behaviours
in young drivers. This second outcome is discussed in the subsequent Section 10.3.
Chapter 10: General discussion 185
10.2 INTEGRATION OF FINDINGS, STRENGTHS AND LIMITATIONS
10.2.1 Theoretical considerations
This PhD program of research investigated the effects of two COTs, a
smartphone safe-driving app and VR simulations of risky driving under the influence.
The evaluations of the two interventions were grounded in TPB (Ajzen, 1988). While
criticism of TPB and calls for its retirement have emerged (Sniehotta et al., 2014), the
theory is still widely applied across health domains with researchers suggesting paths
for its improvement (Conner, 2015). The findings from within the current program of
research added to the conversation around the TPB relevance.
Contributing to the theoretical discussion, the current program of research
followed Conner's (2015) suggestion to extend the TPB. Additional constructs,
previously being utilised to extend the theory, were drawn directly from the literature.
The current program of research utilised past behaviour (Conner & Sparks, 2005;
Elliott & Thomson, 2010; Gauld et al., 2016; Haque et al., 2012), moral norm (Conner
& Sparks, 2005; Elliott & Thomson, 2010; Manstead, 2000), peers' norm (Conner &
Sparks, 2005; Fleiter et al., 2006) and perceived risk (Gannon et al., 2014; Haque et
al., 2012; Rhodes & Pivik, 2011). As mentioned earlier, personality characteristics
(impulsivity, sensitivity to punishment and sensitivity to reward), previously found to
be relevant to young drivers (Scott-Parker, 2012), were also included in the extended
TPB framework. Including those constructs explained additional variance in the
regression models, investigating each of the DVs of primary interest (intention not to
speed, behaviour of not speeding during the three months of the intervention, intention
not to DUI and behaviour of not DUI during the three months after the intervention).
However, none of the additional predictors emerged consistently as a significant
predictor for all four DVs. An exception was past behaviour of not speeding, which
was found to uniquely explain the most variance in both intention not to speed and
behaviour of not speeding during the three months of the intervention. The past
behaviour of not DUI was the strongest predictor of intention not to DUI. Nevertheless,
the additional predictors' contribution cannot be generalised and should be analysed
carefully, in relation to each behaviour of interest, as done in Chapter 7 and in Chapter
9.
The overall research design (see Chapter 4) focused on addressing some
observed TPB limitations (see Section 3.5). For example, both interventions
186 Chapter 10: General discussion
implemented as part of the current program of research took a form with an
experimental and practice-oriented nature, thus, making the criticism that the theory
has a static explanatory nature (Sniehotta et al., 2014) less relevant. Sheeran et al.
(2013) argued that TPB focuses on people's rational reasoning and does not account
for unconscious influences. This argument was the reason the constructs of impulsivity,
sensitivity to punishment and sensitivity to reward, were added to the model.
Mackenzie (2016) criticised the TPB for the assumption that it sees the person
as having the resources and skills to enact the behaviour of interest. Safer behaviour
on the road is believed to be a result of driver training (Watson, 1997). However, the
conventional driver training experience does not necessarily lead to safer driving
behaviour (Vernick et al., 1999; Watson, 1997). In the two implemented COTs-based
interventions, the two example COTs were perceived as providing unconventional
driving experience. Thus, the participants were given opportunities to acquire new
skills or experiences and, in turn, to perform the behaviour of interest. For example,
Flo was providing critical feedback to the young drivers, which was based on
measuring their driving performance. In turn, that critical feedback was expected to
enable the study participants to manage their speed better. "3D Tripping" was putting
the participants in driving simulations, revealing the challenges of DUI. Thus, the
participants were equipped with new personalised knowledge of how their driving
performance was influenced by simulated drugs or alcohol. In turn, this new
knowledge should help the participants make a safe decision should they have to
choose between DUI and other alternatives. As a result, the criticised TPB assumption
of people having the resources and skills to enact the behaviour of interest was less
relevant.
Sniehotta et al. (2014) shared concerns that TPB might not always have
sufficient predictive power on its own. While findings from both interventions
confirmed TPB as a good fit, those findings revealed mixed evidence about the level
of the predictive power of the theory. Study 2, which focused on speeding, potentially
a more common and socially acceptable behaviour, found TPB to predict more than
50% of the variance in both intention not to speed and behaviour of not speeding
during the three months of the intervention. Thus, it did not offer support for Sniehotta
et al. (2014). However, in Study 4, which focused on DUI, a potentially more extreme
behaviour with few people committing it, and much fewer feel comfortable to report
Chapter 10: General discussion 187
their engagement in it, the TPB's predictive power did not pass the 50% threshold,
which supported Sniehotta et al. (2014).
It has to be noted that the data analysis in Study 4 had limitations. For example,
the collected data was not normally distributed. Also, a substantial number of people
who reported some level of DUI before the intervention did not complete the second
survey. This missing information created a gap in the collected data. These limitations
need to be addressed in future research to make a more informed conclusion on
whether TPB is suitable as an overarching framework in interventions targeting DUI.
For example, future research may focus on recruiting a larger sample, or on DUI
offenders as a target group, which may reduce the impact of the encountered
challenges.
10.2.2 Practical considerations
Going beyond the discussion around the TPB relevance for road safety
researchers, the current findings may have implications for persuasion literature,
especially literature that explores technology. While previous research (see Chapter 5
and Chapter 8) reported some positive effects, the findings from the current research
did not provide such evidence for the two examples of COTs when used in the
participants' free-living environment. For example, findings from the smartphone safe-
driving app intervention did not find speed-related safety benefits such as those
reported by Zhang et al. (2014) for facilitating speed limits compliance. This may be
due to the small sample size, the length of the intervention period or the COT being
used. Zhang et al. (2014) examined only five people on five predefined routes. It
cannot be inferred from Zhang et al. (2014) study whether the behaviour could be
sustained for a long time, on different routes and without supervision, which was the
case in Study 2. The current findings were also different than Creaser et al. (2015)
findings. The authors reported reduced risky driving behaviours of their participants,
in general. Similar to Creaser et al. (2015), Flo was alerting the drivers of their risky
behaviour. However, Study 2 did not employ in-vehicle monitoring and parental
notifications, which might have served their deterrent purpose well in Creaser et al.
(2015) study. Thus, the results from both Study 2 and Study 4 provided insights into
how effective such COTs-based interventions are if there is no external influence on
the young drivers, i.e. when the young drivers take their own decisions in regards to
their behaviour on the road.
188 Chapter 10: General discussion
In the case of Study 4, the VR intervention with "3D Tripping" as an intervention
tool did not result in a statistically significant change in DUI. This result is different
than the result reported by Agrawal et al. (2017). This might be due to the very
different investigated behaviours, which was hazards anticipation in Agrawal et al.
(2017), while Study 4 focused on DUI. Agrawal et al. (2017) also did not examine
long-term effects. Nevertheless, Study 4 addressed limitations reported in the
systematic review (see Chapter 8), such as lack of VR realism and immersion (Gaibler
et al., 2015; Orfila et al., 2015). Furthermore, the Chapter 8 systematic review did not
identify another real-world VR intervention study. Study 4, therefore, investigated the
effects of a real-world VR intervention, making a unique contribution to the literature.
In the real world of road safety practitioners, such as social entrepreneurs, the
findings from this program of research may provide evidence that COTs need to be
investigated well in advance. Such evidence may provide the most added value before
COTs integration into road safety interventions or their release to market. The
suggestion may be increasingly valid in light of the evidence for lack of statistically
significant safety benefits, delivered through the two evaluated examples of COTs.
The observed evidence suggests that, although examples of technology may be
enjoyable and easily adopted by young drivers, COTs may have no significant positive
effect on their behaviour when used for road safety. Nevertheless, positive effects were
found by other authors (see Chapter 5 and Chapter 8). For example, Agrawal et al.
(2017) found VR simulations of risky driving to improve hazards anticipation.
Smartphone safe-driving apps intervention contributed to lowering speed (Musicant &
Botzer, 2016), decreasing fatigue and reducing negative mood (He et al., 2017;),
preventing collisions (Botzer et al., 2017), improving distance perception
(Schartmüller & Riener, 2015), and reducing drivers’ risk-taking (Creaser et al., 2015).
However, due to the differences in the focus of those studies as well as in the used
COTs’ features, it is challenging to define consistent reasons as to why similar
beneficial effects were not observed in Study 2 and Study 4. A common observation
in both systematic reviews (see Chapter 5 and Chapter 8), though, was that the
implemented COTs-based interventions were not resembling real-world interventions.
Thus, more research is required to investigate whether the claims for potential safety
benefits from particular examples of COTs are sustained in free living environment
contexts and over longer periods of time.
Chapter 10: General discussion 189
From a practical perspective, the present program of research showed the
potential synergies that could be leveraged when researchers and road safety
practitioners, such as social entrepreneurs (see Section 1.4), work together. While
theoretically-grounded evaluations of interventions are the norm amongst researchers,
they do not seem to be an equally common approach amongst road safety social
entrepreneurs. The current program of research went outside the laboratory, and into
spaces where 1) road safety social entrepreneurs usually operate, and 2) researchers do
not execute control. It generated new knowledge about the effects of the two
implemented interventions. At the same time, this less common approach to research
methodology design did not impact the quality of the research findings.
10.2.3 Methodological considerations
The two implemented interventions with COTs as intervention tools were
assessed for safety benefits for young drivers, i.e. reducing their speeding and DUI,
respectively. They were implemented in the way they are unconditionally offered to
the general public, simulating their real-world adoption. No expectations were
imposed on any facet of participants’ use of these interventions. As a result, the current
PhD program of research added to the literature evidence on two novel complementary
approaches to persuade young drivers to reduce their risk-taking in respect of speeding
and DUI. It is believed to be the first comprehensive program of research to do so.
Thus arguably, a substantial strength of the current research was that it went into the
real world of road safety interventions as far from simulated research settings and into
the young drivers' free-living environments as possible.
By going outside the laboratory, the current program of research generated new
knowledge around real-world adoption and retention rates of the particular COTs, i.e.
the safe-driving app Flo. Flo was selected, taking into consideration recommendations,
coming from road safety practitioners and researchers (see Chapter 6). The final choice
was made after comparing Flo with many other safe-driving apps. Nevertheless, the
success in getting young people to use it was lower than expected. At the same time,
it has to be acknowledged that study incentives were not tied to using the smartphone
safe-driving app in Study 2, which may be one of the reasons for the low success rate.
Yet, the lack of such incentives is a realistic aspect in free-living environments.
Although there is a body of evidence suggesting that incentives, such as rewards,
may influence driving behaviour (Musicant & Lotan, 2015; Schroeter et al., 2014),
190 Chapter 10: General discussion
exploring their effect fell outside the scope of the current project. Using performance-
based incentives would have reduced the real-world nature of the interventions, where
such are not always available. In fact, the Study 2 retention rate without incentives was
higher than the retention rate reported in Musicant and Lotan (2015) with incentives.
Musicant and Lotan's (2015) participants stopped using the studied smartphone safe-
driving app before the study was completed once they obtained all incentives. The
generated information on real-world adoption and retention rates in Study 2 may be
found useful as a comparison when reviewing existing technological solutions. Such
information can also be used to question developers’ claims when preliminarily
assessing the potential of specific safe-driving apps.
As part of the two implemented interventions' investigations, the current
program of research provided an understanding of how other COTs, namely social
media, can be used to recruit participants. The structure and the nature of research
samples are a common concern in road safety research. Social media could be explored
as an additional route to reach out to more people that can benefit from an intervention.
However, despite the initial success in recruiting larger sample sizes with the help of
social media, unexpected high dropout rates resulted in the current program of research
final data collection time point sample size requirements not being met. Therefore, the
presented findings require careful interpretation, which, nevertheless, may provide a
starting point for future research to build on.
It is important to acknowledge that the findings from the present program of
research are preliminary in nature. The intervention studies would require replication
to confirm or to disagree with the findings, preferably with other COTs as intervention
tools and with larger samples.
10.3 FUTURE RESEARCH DIRECTIONS
The TPB (Ajzen, 1988) underpinned the evaluation within the current program
of research. However, several other theories were also considered before the final
choice to use TPB was made (see Chapter 3). Given that the current TPB-based
evaluation could not find intervention effects within the Study 2 and Study 4 collected
data, future research may consider using other theories to underpin evaluation. For
example, the TTM (Prochaska & Velicer, 1997) assigns a single standard scale score
to each participant which determines their behavioural change stage. Moving through
Chapter 10: General discussion 191
those stages would signify significant changes in behaviour. Exploring the participants'
individual scores might be a simple way to identify behavioural changes. Nevertheless,
those changes will still be self-reported.
The reviewed literature showed that a large proportion of road safety research
relied on self-reports to collect data. Self-reports are likely to be easier to collect and
in larger numbers. However, it may come with the disadvantage of potential bias.
Thus, it could be argued that objective on-road behavioural measures are a better fit to
assess the impact of an intervention. Such objective data may be very difficult to
collect for DUI, and, in that case, self-reports might still be the best available tool.
However, in the case of speeding, currently available technology claims to have the
capacity to offer data-collection capabilities.
In naturalistic driving studies focused on speeding, future research that aims to
evaluate potential safety benefits delivered through smartphone safe-driving apps
could build on the knowledge presented in the current thesis through integrating the
analysis of anonymous raw data from the participants' smartphones. Available sensors
(clock, GPS, accelerometer, gyroscope and magnetometer) can provide driving
information, as well as information on interactions (e.g. using an app, answering a
phone call, writing a message). Researchers can estimate potential critical events, such
as speeding, hard acceleration, hard braking and fast cornering, if there was an
interaction with the smartphone, and the nature of the interaction. However,
researchers should consider that it is not easy to find a common definition of a critical
event threshold in the literature. Different studies used different fixed G-force levels
to identify critical events. For example, Paefgen, Kehr, Zhai, and Michahelles (2012)
used as thresholds an accelerometer output of 0.1g for acceleration and braking events,
and 0.2g for steering. Fazeen, Gozick, Dantu, Bhukhiya, and González (2012) used a
g-force of more than ±0.3g on the y-axis to determine critical events. The threshold
used by Freidlin et al. (2018) was 0.45g.
The approach of using thresholds might be considered too general to allow for
reliable comparison of driving in different conditions, on various roads and with
different cars. Adaptive algorithms that generate driver profiles for each participant
may provide a more targeted solution (Castignani et al., 2017; Saiprasert,
Thajchayapong, Pholprasit, & Tanprasert, 2014). For example, Castignani et al. (2017)
would classify an event as critical only if it deviates from the driver's normal driving
192 Chapter 10: General discussion
style, i.e. an outlier event is observed. This would allow scores, and the related changes
in the driving style, to be calculated independently for each driver, taking into
consideration their car and their route. Thus, drivers can be objectively compared,
based on the achieved change in reducing the number of critical events (number of
outliers), rather than on the triggered G-force events, which may be normal and
necessary in the specific driver's situation. Such a solution, however, may increase the
workload on the smartphone hardware and may consume additional power. Constantly
running two power-consuming apps on one smartphone may negatively affect the
device's battery life. This may result in higher participants' drop-out rates, triggered by
the need to recharge smartphones often. The challenge, of additional power
consumption, may be overcome by:
1. Collaborating with the developer of the off-the-shelf app to be deployed
as an intervention tool. However, in that case, there might be a conflict of
interest, and the researcher should consider the possibility for the data to be
filtered or manipulated before being made available.
2. Offloading some of the computation tasks by acquiring direct access to
the status of the various vehicle systems through an OBD2 reader. The OBD2
reader would help collect potentially more accurate data than the one generated
by smartphone sensors. However, in that case, the OBD2 reader’s quality, its
price and the ability of the participants to install, and properly use it, become
valid considerations. The availability of a trustworthy open-source interface to
transfer the collected data to a research server becomes another issue. If there
are means for the researcher to purchase the devices, then assisting with
installation and with getting the devices to work on the participants' vehicles will
impose constraints, both geographically and in numbers.
3. Offloading some of the computation tasks to a second smartphone with
the necessary software, installed by the researcher in advance. In that case,
delivery and installation on the participants' vehicles are still valid constraints.
They could be limited if the researcher assesses in advance constructs in relation
to participants' propensity and ease to adopt new technology. This will allow
efforts to be channelled towards people that need more help.
Once such infrastructure is established, a researcher will be able to link objective
naturalistic driving data with subjective self-report measures. In turn, this will allow
Chapter 10: General discussion 193
for a comprehensive evaluation not only of safe-driving app interventions but also of
any interventions that involve independent driving on behalf of the participants.
Apart from the described opportunities for future research from a technological
perspective, such can build on the current program of research by continuing to
examine interventions with the current technology limitations but with a different
design and larger samples. For example, further research can consider splitting the
participants into more than two groups, to control for potential influences (see Figure
10.2).
Figure 10.2. Example of future safe-driving app intervention design
Such a design, in the case of a future safe-driving apps research, can be
operationalised after recruitment, with participants randomly assigned into one of the
four groups (three intervention and one no intervention/control). Depending on the
group a participant is assigned to, they may: a) have no additional tasks; b) periodically
receive peers' feedback, e.g. a leaderboard by email, with their relative achievements
in comparison to their peers in the subgroup; c) install and use an off-the-shelf safe-
driving app while driving; or d) same as “c”, but with the additional requirement to
participate in a leaderboard group in the chosen safe-driving app.
A similar design can be implemented, to build on the results of the current Study
4, by separately exploring the effect of each of the scenarios, in which a participant
drives under the influence of drugs (Figure 10.3). This, however, would require a
larger sample to be recruited or the researcher may have to choose the "3D Tripping"
VR experience, instead of the participant.
194 Chapter 10: General discussion
Figure 10.3. Example of a future DUI VR intervention design
Both interventions could be further stratified to control for participants’ gender
or experience. Both gender and experience were shown to be significant contributors
towards explaining the variance of intentions and behaviour in the two interventions
(see Chapter 7 and Chapter 9). Males were shown to be less distracted as a result of
the smartphone safe-driving app intervention. Future research may involve measures
to leverage such influence. Those design variations will allow further, and more in-
depth research to focus on the examination of COTs-backed interventions, focusing
on more than one condition.
10.3.1 A researcher's wish list to COT developers
This section reports on reflections by the author to challenge COT engineers and
developers to create apps and technology needed to foster further behavioural research
and to propose specific design recommendations. It is noted that those
recommendations are not based on the collected data within the current program of
research. Basing them on only one example of a safe-driving app and one example of
VR simulations of risky driving would be insufficient. Furthermore, the selected
methodology was not aimed at drawing out such recommendations. It is acknowledged
that it would require a research framework similar to Vaezipour (2018), including 1)
integrating a theory that looks at user experience and technology acceptance into the
research framework, 2) implementing a qualitative inquiry into the users' experience
and preferences, and 3) comparing experience and preferences as a result of more than
one example of the same COT type used as intervention tools.
While the current research was not guided by such design-centred methodology,
there is value in reflecting on the author’s experiences conducting the behavioural
Chapter 10: General discussion 195
research using the COTs as interventions. The following paragraphs discuss a number
of challenges with respect to both VR simulations of risky driving and smartphone
safe-driving apps.
The VR simulations of risky driving did not collect any data during the current
program of research. However, driving data could potentially be collected by the VR
software while the participants are experiencing DUI driving scenarios. Comparing
experience both in "normal" and in DUI mode can be highly beneficial. To enable such
comparisons, the VR software developers can embed the same situations in both
modes. For example, a situation that would trigger a participant's braking reaction can
collect data about the time needed to react in DUI and compare it to the reaction time
in normal mode. Such driving tasks can be more complicated, e.g. lane-keeping or
overtaking, but can provide the participant with a realistic understanding of their
driving abilities (SC3, see Section 4.2). Data can also be collected with respect to
obeying traffic rules or to the number of crashes and potential victims when DUI. Such
a variety of quantitative data can serve as a basis of a much more in-depth evaluation
of the drivers' experience. By presenting the data to the respective driver, the overall
experience may become more meaningful and informative. Increasing the informative
value of the experience can potentially influence the participants DUI attitudes (SC1)
and norms (SC 2). This influence on attitudes and norms may have a higher potential
to trigger the desired behaviour change.
While the VR environment is comparatively resilient to collecting unrelated data
because all events happen as part of predetermined scenarios, the case with the
smartphone safe-driving apps is different. Thus, future safe-driving apps to be used for
research purposes will provide much more utility if they are able to:
- Always run when a participant drives. Currently, self-starting safe-driving
apps are limited in detecting then a participant actually drives (as in operates)
a vehicle (and when not). They require reaching a certain speed threshold to
start recording data. Sometimes, the smartphones' sensors do not
communicate properly delaying self-start. In other cases, the vehicle might
move slower in traffic than the threshold which would not trigger self-start
at all. A similar problem arises with the apps' self-stop. Currently, they
require a certain amount of time to pass during which the speed of the
smartphone complies with certain criteria before the app stops recording.
196 Chapter 10: General discussion
Issues arise when the vehicle is in a traffic jam for a prolonged period of time
or when the person stops the vehicle and immediately starts walking. A
related issue is that safe-driving apps do not distinguish between the
participant being a driver and not being a driver. Currently, they use their
algorithms to detect speed triggers regardless of how the speed is generated.
As a result, an app would start recording when the participant is a passenger,
rides on public transport or on a bicycle. Data in those cases should not be
collected as it does not reflect the participant's driving. In conclusion, the
ability to collect all the relevant data and only the relevant data is critical to
improve subsequent data analysis and foster future behavioural research.
- Similar trips comparison. Currently, some safe-driving apps offer detailed
trip feedback. A user is able to look at similar trips and compare them
manually. However, it would be useful if the comparison of such trips would
be supported by more sophisticated algorithms. Such algorithms could
generate nuanced reports, listing significant improvements and significant
deteriorations in different driving situations of identical, and therefore,
comparable driving trips. Such reports should be made available to both
users and researchers. Users can use the information and reflect on their
behaviour. Such reflections can influence study participants' salient beliefs
and potentially lead to improvements in their behaviour. In respect to
researchers, such reports would allow an assessment of how much, in reality,
the specific app meets the COTs selection criteria (see Section 4.2). In the
end, the researchers can make conclusions about what behavioural changes
were triggered by the provided app feedback.
- Work on all devices. The variety of available smartphones is considerable,
which can cause issues with apps' deployment and use. Currently, safe-
driving apps do not seem to work on all available smartphone devices. In the
framework of the current program of research, problems were observed with
getting Flo to work on an iPhone version. In respect to another tested app,
missing sensors in a Huawei model caused problems with collecting
comparable data. To improve usability across smartphones, app developers
might consider focusing on simpler algorithms that are built to utilise only
components, common amongst all smartphones. A challenge in such an
Chapter 10: General discussion 197
endeavour would be the competition amongst hardware developers which
generally stands in the way of hardware standards harmonisation. The
variability of those standards would require the app developers to account
for many possible platforms to host their apps. Despite those challenges and
as discussed in Section 7.6.3, reported and unreported technical problems
might be one of the reasons for the Study 2 large drop-out rate. If those
problems are overcome, two benefits might occur. The first one is that more
users will be attracted to the technology. For examples, it is not uncommon
peers to have similar devices. Thus, if one group member shows interest,
potentially others will follow. Such a scenario leads to the second potential
benefit, which is recruiting and retaining larger research samples. With larger
research samples and less technical challenges, the behavioural research
would enjoy more robust results and conclusions.
10.4 CHAPTER SUMMARY
Road safety interventions are intended to reduce road trauma. They are
considered an effective countermeasure to encourage individuals to adopt less risky
behaviours while driving. While previous research provided knowledge with respect
to road safety interventions, which may potentially influence young drivers, one area
seemed underexplored – COTs. The reason for the limited knowledge may not be that
people do not know about COTs, or are not interested in the opportunities they create.
The reason may be that COTs are novel, while well-designed research takes time.
To establish a comprehensive baseline of expanding the knowledge around using
COTs in road safety, this research used the PRISMA guidelines to systematically
review the available evidence in respect to the two deployed COTs, a smartphone safe-
driving app and VR simulations of risky driving. The findings confirmed the gap,
revealed in the traditional literature review. Previous research at the intersection of
road safety, psychology and human-computer interaction provided little evidence of
safety benefits, delivered through smartphone safe-driving apps. Such, in the domain
of VR, was practically not existent, where probably the most original contribution of
the current thesis sits.
The program of research incorporated theoretical constructs, an extended TPB
framework, into real-world road safety interventions, which used a smartphone safe-
198 Chapter 10: General discussion
driving app and VR simulations of risky driving as intervention tools. The framework
fitted well into the two interventions, helping to explain a substantial amount of
variance in regards to the DVs of interest: 75% in intention not to speed, 64% in
behaviour of not speeding during the three months of the intervention, between 36.5%
and 73.3% in intention not to DUI, and between 24.3% and 41.5% in behaviour of not
DUI during the three months after intervention.
As argued throughout this dissertation, novel technologies, such as the two
deployed COTs, are here to stay. However, using them for the right purpose requires
both an understanding of the technology applications themselves as well as an
understanding of the interventions that leverage them. This research program provided
an original contribution to knowledge by examining the effects of two COT-based
interventions to reduce risky driving behaviour among young drivers. By separately
answering each of the four research questions (see Section 10.1), it addressed the
following fundamental research question:
How do COTs-based interventions influence young drivers?
The current PhD program of research did not find evidence for statistically
significant influence on young drivers as a result of the two COTs-based interventions
with Flo and "3D Tripping" as intervention tools. Neither intention not to perform the
respective behaviour of interest nor the participants’ self-reported behaviour were
significantly different between the Intervention and Control groups of the respective
intervention.
Both Control groups were not exposed to the interventions themselves.
However, the control participants were well aware of the studies and their aims, due
to the provided participants' information before their consent to participate was
obtained, followed by responding to the baseline survey about their driving behaviour.
This fact alone might have made them more aware not only of their respective
behaviour but also of the behaviour of the people around them. Such increased
awareness might have influenced the Control groups' participants’ answers in the post-
intervention surveys. The possibility that data collected from them might have been
influenced has to be acknowledged. Such influence might have contributed to the lack
of statistically significant effects between the groups in Study 2 and Study 4.
Chapter 10: General discussion 199
None of the other TPB constructs was significantly influenced as a result of the
Study 2 and Study 4 interventions. So, within the limitations of the current program of
research, no evidence was found that either of the two implemented COT-based
interventions managed to influence the involved young drivers’ safer driving
positively. However, when Flo was used as a tool, evidence was found that the
intervention resulted in significantly decreased self-reported smartphone interactions
for male participants.
Irrespective of the lack of significant effects on speeding and DUI, significant
positive effects on the Intervention group male participants were observed in Study 2.
The positive effects were observed in regards to their smartphone interactions, i.e. self-
reported initiating (less) communication and responding (less) to communication.
Such effects were not observed for the Intervention group female participants, though.
Thus, the findings are partially consistent with Creaser et al. (2015), who reported that
both their Intervention groups interacted significantly less with their smartphones than
the Control group. It has to be noted that no pressure, e.g. parental notifications, was
used in Study 2, which might have facilitated the consistent results amongst the groups
in Creaser et al. (2015). These findings highlighted the need that, when technology is
deployed in road safety, a more complex systematic approach should be considered.
This need is equally valid when COTs were not designed for research purposes but are
readily available, which was the case with Flo and "3D Tripping".
Notwithstanding the design of an intervention or its target group, the only certain
thing is that improving countermeasures, encouraging safer driving behaviours, must
remain in focus. Given that road crashes continue to be the leading cause of death in
the 5-29 age group (WHO, 2018), there is a constant need for improvement and
innovation. The current program of research provided insights and discussed
considerations on which road safety intervention designers, both researchers and
practitioners, may draw upon in their future endeavours. Such interventions may or
may not persuade individuals to reduce their engagement in risky driving behaviours.
However, not trying is the only certain way not to contribute to road trauma reduction.
References 201
References
Adminaite, D., Calinescu, T., Jost, G., Stipdonk, H., & Ward, H. (2018). 12th Annual Road Safety Performance Index (PIN) Report.
Agrawal, R., Knodler, M., Fisher, D. L., & Samuel, S. (2017). Advanced Virtual Reality Based Training to Improve Young Drivers’ Latent Hazard Anticipation Ability. Paper presented at the Proceedings of the Human Factors and Ergonomics Society Annual Meeting.
Ahn, S. J. G., Bailenson, J. N., & Park, D. (2014). Short-and long-term effects of embodied experiences in immersive virtual environments on environmental locus of control and behavior. Computers in Human Behavior, 39, 235-245.
Ajzen, I. (1988). Attitudes, personality, and behavior. Chicago: Dorsey Press. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human
decision processes, 50(2), 179-211. Ajzen, I. (2002). Perceived behavioral control, self‐efficacy, locus of control, and the
theory of planned behavior 1. Journal of Applied Social Psychology, 32(4), 665-683.
Ajzen, I. (2006). Behavioral interventions based on the theory of planned behavior. In. Retrieved from https://people.umass.edu/aizen/pdf/tpb.intervention.pdf.
Ajzen, I., & Fishbein, M. (1980). A theory of reasoned action: Some applications and implications.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour.
Armitage, C., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta‐analytic review. British Journal of Social Psychology, 40(4), 471-499.
Audit Office of New South Wales. (2011). Improving road safety: Young drivers – Roads and traffic authority of NSW.
Australian Institute of Health and Welfare. (2008). Injury among young Australians. Bulletin 60.
Australian Transport Council. (2011). National Road Safety Strategy 2011–2020. 2011. Australian Transport Council: Canberra.
Austroads. (2008). The crash and offence experience of newly licensed young drivers in South Australia.
Aveyard, P., Cheng, K., Almond, J., Sherratt, E., Lancashire, R., Lawrence, T., . . . Evans, O. (1999). Cluster randomised controlled trial of expert system based on the transtheoretical (“stages of change”) model for smoking prevention and cessation in schools. Bmj, 319(7215), 948-953.
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory: Prentice-Hall, Inc.
Banks, J. (1998). Handbook of simulation: principles, methodology, advances, applications, and practice: John Wiley & Sons.
Banks, T. D. (2008). An investigation into how work-related road safety can be enhanced. (PhD), Retrieved from http://eprints.qut.edu.au/29683/
Bazargan-Hejazi, S., Teruya, S., Pan, D., Lin, J., Gordon, D., Krochalk, P., & Bazargan, M. (2017). The theory of planned behavior (TPB) and texting while driving behavior in college students. Traffic Injury Prevention, 18(1), 56-62.
202 References
Beanland, V., Goode, N., Salmon, P. M., & Lenné, M. G. (2013). Is there a case for driver training? A review of the efficacy of pre- and post-licence driver training. Safety Science, 51(1), 127-137. doi:https://doi.org/10.1016/j.ssci.2012.06.021
Bellotti, F., Berta, R., & De Gloria, A. (2014). A Social Serious Game Concept for Green, Fluid and Collaborative Driving. In A. De Gloria (Ed.), Applications in Electronics Pervading Industry, Environment and Society (Vol. 289, pp. 163-170): Springer International Publishing.
Bhagavathula, R., Williams, B., Owens, J., & Gibbons, R. (2018). The Reality of Virtual Reality: A Comparison of Pedestrian Behavior in Real and Virtual Environments. Paper presented at the Proceedings of the Human Factors and Ergonomics Society Annual Meeting.
Bingham, C. R., Barretto, A. I., Walton, M. A., Bryant, C. M., Shope, J. T., & Raghunathan, T. E. (2011). Efficacy of a web-based, tailored, alcohol prevention/intervention program for college students: 3-month follow-up. Journal of drug education, 41(4), 405-430.
Birrell, S., Fowkes, M., & Jennings, P. (2013). A smart driving smartphone application: Real-world effects on driving performance and glance behaviours. Paper presented at the 3rd International Conference on Driver Distraction and Inattention.
Birrell, S., Young, M., Stanton, N., & Jennings, P. (2017). Using Adaptive Interfaces to Encourage Smart Driving and Their Effect on Driver Workload. In Advances in Human Aspects of Transportation (pp. 31-43): Springer.
Birrell, S. A., & Fowkes, M. (2014). Glance behaviours when using an in-vehicle smart driving aid: A real-world, on-road driving study. Transportation research part F: traffic psychology and behaviour, 22, 113-125.
Birrell, S. A., Fowkes, M., & Jennings, P. A. (2014). Effect of using an in-vehicle smart driving aid on real-world driver performance. IEEE Transactions on Intelligent Transportation Systems, 15(4), 1801-1810.
Blanco, M., Biever, W. J., Gallagher, J. P., & Dingus, T. A. (2006). The impact of secondary task cognitive processing demand on driving performance. Accident Analysis & Prevention, 38(5), 895-906. doi:http://dx.doi.org/10.1016/j.aap.2006.02.015
Blascovich, J., Loomis, J., Beall, A. C., Swinth, K. R., Hoyt, C. L., & Bailenson, J. N. (2002). Immersive virtual environment technology as a methodological tool for social psychology. Psychological Inquiry, 13(2), 103-124.
Botzer, A., Musicant, O., & Perry, A. (2017). Driver behavior with a smartphone collision warning application–a field study. Safety Science, 91, 361-372.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101.
Briggs, G. F., Hole, G. J., & Land, M. F. (2016). Imagery-inducing distraction leads to cognitive tunnelling and deteriorated driving performance. Transportation research part F: traffic psychology and behaviour, 38, 106-117. doi:http://dx.doi.org/10.1016/j.trf.2016.01.007
Bureau of Infrastructure Transport and Regional Economics. (2018). Road trauma Australia 2017 statistical summary.
Castellà, J., & Pérez, J. (2004). Sensitivity to punishment and sensitivity to reward and traffic violations. Accident Analysis & Prevention, 36(6), 947-952. doi:https://doi.org/10.1016/j.aap.2003.10.003
References 203
Castignani, G., Derrmann, T., Frank, R., & Engel, T. (2017). Smartphone-Based Adaptive Driving Maneuver Detection: A Large-Scale Evaluation Study. IEEE Transactions on Intelligent Transportation Systems.
Catford, J. (1998). Social entrepreneurs are vital for health promotion—but they need supportive environments too. Health Promotion International, 13(2), 95-97.
Centers for Disease Control and Prevention. (2016). Youth Risk Behavior Surveillance—United States, 2015. MMWR. Surveillance Summaries, 65.
Chan, D. C. N., Wu, A. M. S., & Hung, E. P. W. (2010). Invulnerability and the intention to drink and drive: An application of the theory of planned behavior. Accident Analysis & Prevention, 42(6), 1549-1555. doi:https://doi.org/10.1016/j.aap.2010.03.011
Chen, H.-Y. W., Donmez, B., Hoekstra-Atwood, L., & Marulanda, S. (2016). Self-reported engagement in driver distraction: An application of the Theory of Planned Behaviour. Transportation research part F: traffic psychology and behaviour, 38, 151-163. doi:http://dx.doi.org/10.1016/j.trf.2016.02.003
Chen, K. B., Xu, X., Lin, J.-H., & Radwin, R. G. (2015). Evaluation of older driver head functional range of motion using portable immersive virtual reality. Experimental gerontology, 70, 150-156.
Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information systems research, 6(2), 118-143.
Conner, M. (2015). Extending not retiring the theory of planned behaviour: a commentary on Sniehotta, Presseau and Araújo-Soares. Health psychology review, 9(2), 141-145. doi:10.1080/17437199.2014.899060
Conner, M., & Sparks, P. (2005). Theory of planned behaviour and health behaviour. Predicting health behaviour, 2, 170-222.
Constantinou, E., Panayiotou, G., Konstantinou, N., Loutsiou-Ladd, A., & Kapardis, A. (2011). Risky and aggressive driving in young adults: Personality matters. Accident Analysis & Prevention, 43(4), 1323-1331. doi:https://doi.org/10.1016/j.aap.2011.02.002
Creaser, J., Morris, N., Edwards, C., Manser, M., Cooper, J., Swanson, B., & Donath, M. (2015). Teen Driver Support System (TDSS) Field Operational Test.
Creaser, J. I., Edwards, C. J., Morris, N. L., & Donath, M. (2015). Are cellular phone blocking applications effective for novice teen drivers? Journal of Safety Research, 54, 75. e29-78.
Davis, R., Campbell, R., Hildon, Z., Hobbs, L., & Michie, S. (2015). Theories of behaviour and behaviour change across the social and behavioural sciences: a scoping review. Health psychology review, 9(3), 323-344.
De Nooijer, J., Van Assema, P., De Vet, E., & Brug, J. (2005). How stable are stages of change for nutrition behaviors in the Netherlands? Health Promotion International, 20(1), 27-32.
Deery, H. A. (2000). Hazard and risk perception among young novice drivers. Journal of Safety Research, 30(4), 225-236.
DeVellis, R. F. (2016). Scale development: Theory and applications (Vol. 26): Sage publications.
Di Noia, J., Contento, I. R., & Prochaska, J. O. (2008). Computer-mediated intervention tailored on transtheoretical model stages and processes of change increases fruit and vegetable consumption among urban African-American adolescents. American journal of health promotion, 22(5), 336-341.
Diewald, S., Möller, A., Roalter, L., Stockinger, T., & Kranz, M. (2013). Gameful design in the automotive domain: review, outlook and challenges. Paper
204 References
presented at the Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.
Dinh-Zarr, T. B., Sleet, D. A., Shults, R. A., Zaza, S., Elder, R. W., Nichols, J. L., . . . Services, T. F. o. C. P. (2001). Reviews of evidence regarding interventions to increase the use of safety belts. American Journal of Preventive Medicine, 21(4), 48-65.
Donovan, R. (2011). Theoretical models of behaviour change. The SAGE handbook of social marketing, 15-31.
Duggan, K., & Shoup, K. (2013). Business gamification for dummies: John Wiley & Sons.
Ecker, R., Holzer, P., Broy, V., & Butz, A. (2011). EcoChallenge: a race for efficiency. Paper presented at the Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, Stockholm, Sweden.
Elliott, A., & Woodward, W. (2007). Statistical analysis quick reference guidebook: With SPSS examples: Sage.
Elliott, M., & Thomson, J. (2010). The social cognitive determinants of offending drivers’ speeding behaviour. Accident Analysis & Prevention, 42(6), 1595-1605. doi:http://dx.doi.org/10.1016/j.aap.2010.03.018
Engström, I., Gregersen, N. P., Hernetkoski, K., Keskinen, E., & Nyberg, A. (2003). Young novice drivers, driver education and training. Literature Review. VTI Report A, 491.
European Commission. (2018). Road safety in European Union: trends, statistics and main challenges. Report.
Falk, E. B., Cascio, C. N., O'Donnell, M. B., Carp, J., Tinney, F. J., Bingham, C. R., . . . Simons-Morton, B. G. (2014). Neural responses to exclusion predict susceptibility to social influence. JOURNAL OF ADOLESCENT HEALTH, 54(5), S22-S31.
Fazeen, M., Gozick, B., Dantu, R., Bhukhiya, M., & González, M. C. J. I. T. o. I. T. S. (2012). Safe driving using mobile phones. 13(3), 1462-1468.
Ferguson, S. A. (2003). Other high-risk factors for young drivers—how graduated licensing does, doesn't, or could address them. Journal of Safety Research, 34(1), 71-77. doi:http://dx.doi.org/10.1016/S0022-4375(02)00082-8
Fernandes, R., Hatfield, J., & Soames Job, R. F. (2010). A systematic investigation of the differential predictors for speeding, drink-driving, driving while fatigued, and not wearing a seat belt, among young drivers. Transportation research part F: traffic psychology and behaviour, 13(3), 179-196. doi:https://doi.org/10.1016/j.trf.2010.04.007
Fife‐Schaw, C., Sheeran, P., & Norman, P. (2007). Simulating behaviour change interventions based on the theory of planned behaviour: Impacts on intention and action. British Journal of Social Psychology, 46(1), 43-68.
Fineout-Overholt, E., Melnyk, B. M., Stillwell, S. B., & Williamson, K. M. (2010). Evidence-based practice step by step: critical appraisal of the evidence: part I. The American Journal of Nursing, 110(7), 47-52.
Fisher, D., Pradhan, A., Pollatsek, A., & Knodler Jr, M. (2007). Empirical evaluation of hazard anticipation behaviors in the field and on driving simulator using eye tracker. Transportation Research Record: Journal of the Transportation Research Board(2018), 80-86.
Fitz-Walter, Z., Johnson, D., Wyeth, P., Tjondronegoro, D., & Scott-Parker, B. (2017). Driven to drive? Investigating the effect of gamification on learner driver
References 205
behavior, perceived motivation and user experience. Computers in Human Behavior, 71, 586-595.
Fleiter, J. J., Watson, B. C., Lennon, A. J., & Lewis, I. M. (2006). Significant others, who are they?-Examining normative influences on speeding.
Fogg, B. (2009). Creating Persuasive Technologies: An Eight-Step Design Process. Freeman, J., Liossis, P., Schonfeld, C., Sheehan, M., Siskind, V., & Watson, B. (2005).
Self-reported motivations to change and self-efficacy levels for a group of recidivist drink drivers. Addictive Behaviors, 30(6), 1230-1235. doi:http://dx.doi.org/10.1016/j.addbeh.2004.10.007
Frei, A., Svarin, A., Steurer-Stey, C., & Puhan, M. A. (2009). Self-efficacy instruments for patients with chronic diseases suffer from methodological limitations-a systematic review. Health and quality of life outcomes, 7(1), 86.
Freidlin, R. Z., Dave, A. D., Espey, B. G., Stanley, S. T., Garmendia, M. A., Pursley, R., . . . uHealth. (2018). Measuring risky driving behavior using an mhealth smartphone app: development and evaluation of gforce. 6(4), e69.
Gaibler, F., Faber, S., Edenhofer, S., & von Mammen, S. (2015). Drink & drive: A serious but fun game on alcohol-induced impairments in road traffic. Paper presented at the Games and Virtual Worlds for Serious Applications (VS-Games), 2015, 7th International Conference.
Gannon, B., Rosta, L., Reeve, M., Hyde, M. K., & Lewis, I. (2014). Does it matter whether friends, parents, or peers drink walk? Identifying which normative influences predict young pedestrian’s decisions to walk while intoxicated. Transportation research part F: traffic psychology and behaviour, 22, 12-24.
Garner, T. A. (2017). Echoes of Other Worlds: Sound in Virtual Reality: Past, Present and Future: Springer.
Gauld, C. S., Lewis, I. M., White, K. M., & Watson, B. (2016). Young drivers’ engagement with social interactive technology on their smartphone: Critical beliefs to target in public education messages. Accident Analysis & Prevention, 96, 208-218.
Ghavami, M., Harandy, T. F., & Kabir, K. (2016). The effect of educational intervention in promoting safe behaviors in a sample of Iranian primary school students: an application of the health belief model. Global journal of health science, 8(11), 242.
Gillibrand, R., & Stevenson, J. (2006). The extended health belief model applied to the experience of diabetes in young people. British journal of health psychology, 11(1), 155-169.
Gold, M. A., Tzilos, G. K., Stein, L., Anderson, B. J., Stein, M. D., Ryan, C. M., . . . DiClemente, C. (2016). A Randomized Controlled Trial to Compare Computer-assisted Motivational Intervention with Didactic Educational Counseling to Reduce Unprotected Sex in Female Adolescents. Journal of pediatric and adolescent gynecology, 29(1), 26-32.
Gonzalez, D. O., Martin-Gorriz, B., Berrocal, I. I., Morales, A. M., Salcedo, G. A., & Hernandez, B. M. (2017). Development and assessment of a tractor driving simulator with immersive virtual reality for training to avoid occupational hazards. Computers and Electronics in Agriculture, 143, 111-118.
Gosselin, D., Gagnon, S., Stinchcombe, A., & Joanisse, M. (2010). Comparative optimism among drivers: An intergenerational portrait. Accident Analysis & Prevention, 42(2), 734-740.
206 References
Hagger, M., & Chatzisarantis, N. (2005). First‐and higher‐order models of attitudes, normative influence, and perceived behavioural control in the theory of planned behaviour. British Journal of Social Psychology, 44(4), 513-535.
Haque, M. M., & Washington, S. (2013). Effects of mobile phone distraction on drivers’ reaction times. Journal of the Australasian College of Road Safety, 24(3), 20-29.
Haque, R., Clapoudis, N., King, M., Lewis, I., Hyde, M. K., & Obst, P. (2012). Walking when intoxicated: An investigation of the factors which influence individuals’ drink walking intentions. Safety Science, 50(3), 378-384. doi:https://doi.org/10.1016/j.ssci.2011.09.017
Harré, N., Foster, S., & O'Neill, M. (2005). Self‐enhancement, crash‐risk optimism and the impact of safety advertisements on young drivers. British Journal of Psychology, 96(2), 215-230.
Hart, T. C., Rennison, C. M., & Gibson, C. (2005). Revisiting respondent “fatigue bias” in the National Crime Victimization Survey. Journal of Quantitative Criminology, 21(3), 345-363.
Hatfield, J., Fernandes, R., & Job, R. S. (2014). Thrill and adventure seeking as a modifier of the relationship of perceived risk with risky driving among young drivers. Accident Analysis & Prevention, 62, 223-229.
He, Y., Yan, X., Chu, D., Wu, C., Liu, J., & Wang, X. (2017). Development and Evaluation of a Smartphone-based Curve-speed Warning System for Heavy Duty Vehicles. Retrieved from
Hingson, R., Heeren, T., Levenson, S., Jamanka, A., & Voas, R. (2002). Age of drinking onset, driving after drinking, and involvement in alcohol related motor-vehicle crashes. Accident Analysis & Prevention, 34(1), 85-92. doi:http://dx.doi.org/10.1016/S0001-4575(01)00002-1
Hochbaum, G., Rosenstock, I., & Kegels, S. (1952). Health belief model. United States Public Health Service.
Horswill, M. S., Waylen, A. E., & Tofield, M. I. (2004). Drivers' Ratings of Different Components of Their Own Driving Skill: A Greater Illusion of Superiority for Skills That Relate to Accident Involvement1. Journal of Applied Social Psychology, 34(1), 177-195.
Horvath, C., Lewis, I., & Watson, B. (2012). Peer passenger identity and passenger pressure on young drivers’ speeding intentions. Transportation research part F: traffic psychology and behaviour, 15(1), 52-64.
Hu, X., Deng, J., Zhao, J., Hu, W., Ngai, E. C.-H., Wang, R., . . . Leung, V. (2015). SAfeDJ: a crowd-cloud codesign approach to situation-aware music delivery for drivers. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 12(1s), 21.
Hughes, J. R., Keely, J. P., Fagerstrom, K. O., & Callas, P. W. (2005). Intentions to quit smoking change over short periods of time. Addictive Behaviors, 30(4), 653-662.
Ifinedo, P. (2016). Examining students' intention to continue using blogs for learning: Perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior. doi:http://dx.doi.org/10.1016/j.chb.2016.12.049
Ingram, K. M., Espelage, D. L., Merrin, G. J., Valido, A., Heinhorst, J., & Joyce, M. (2019). Evaluation of a virtual reality enhanced bullying prevention curriculum pilot trial. Journal of adolescence, 71, 72-83.
References 207
International Transport Forum. (2015). Why Does Road Safety Improve When Economic Times Are Hard?
Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health Education & Behavior, 11(1), 1-47.
Jiang, Y., Zhang, J., Chikaraishi, M., Seya, H., & Fujiwara, A. (2017). Effects of a GPS-enabled smart phone App with functions of driving safety diagnosis and warning information provision on over-speeding violation behavior on expressways. Transportation research procedia, 25, 1815-1823.
Kashevnik, A., & Lashkov, I. (2018). Decision Support System for Drivers and Passengers: Smartphone-Based Reference Model and Evaluation. Paper presented at the Proceedings of the 23rd Conference of Open Innovations Association FRUCT.
Kaye, S.-A. (2014). Individual differences in the processing of punishment and reward cues : an application to road safety messages. (PhD), Retrieved from http://eprints.qut.edu.au/79616/
Kim, H.-Y. (2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative dentistry & endodontics, 38(1), 52-54.
Kircher, K., Ahlström, C., & Patten, C. (2011). Mobile telephones and other communication devices and their impact on traffic safety. VTI Report A, 729.
Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J. D., & Ramsey, D. J. (2006). The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data.
Kohler, C. L., Grimley, D., & Reynolds, K. (1999). Theoretical approaches guiding the development and implementation of health promotion programs. In Handbook of health promotion and disease prevention (pp. 23-49): Springer.
Kotler, P., Hessekiel, D., & Lee, N. (2012). Good Works!: Marketing and Corporate Initiatives that Build a Better World... and the Bottom Line: John Wiley & Sons.
Kowalski, K., Jeznach, A., & Tuokko, H. A. (2014). Stages of driving behavior change within the Transtheoretical Model (TM). Journal of Safety Research, 50, 17-25. doi:http://dx.doi.org/10.1016/j.jsr.2014.01.002
Laapotti, S., Keskinen, E., Hatakka, M., & Katila, A. (2001). Novice drivers' accidents and violations—a failure on higher or lower hierarchical levels of driving behaviour. Accident Analysis & Prevention, 33(6), 759-769.
Lamble, D., Laakso, M., & Summala, H. (1999). Detection thresholds in car following situations and peripheral vision: Implications for positioning of visually demanding in-car displays. Ergonomics, 42(6), 807-815.
Lang, Y., Liang, W., Xu, F., Zhao, Y., & Yu, L.-F. (2018). Synthesizing Personalized Training Programs for Improving Driving Habits via Virtual Reality.
Lawton, R., Parker, D., Manstead, A. S. R., & Stradling, S. G. (1997). The Role of Affect in Predicting Social Behaviors: The Case of Road Traffic Violations. Journal of Applied Social Psychology, 27(14), 1258-1276. doi:10.1111/j.1559-1816.1997.tb01805.x
Lee, C. (1989). Theoretical weaknesses lead to practical problems: The example of self-efficacy theory. Journal of Behavior Therapy and Experimental Psychiatry, 20(2), 115-123. doi:http://dx.doi.org/10.1016/0005-7916(89)90044-X
Lee, J. D. (2007). Technology and teen drivers. Journal of Safety Research, 38(2), 203-213. doi:http://dx.doi.org/10.1016/j.jsr.2007.02.008
208 References
Legislative Assembly of Queensland: Parliamentary Travelsafe Committee. (2005). Driving on empty: Fatigue driving in Queensland: Government Press.
Lennon, A., Oviedo-Trespalacios, O., & Matthews, S. (2017). Pedestrian self-reported use of smart phones: Positive attitudes and high exposure influence intentions to cross the road while distracted. Accident Analysis & Prevention, 98, 338-347. doi:http://dx.doi.org/10.1016/j.aap.2016.10.028
Lettice, F., & Parekh, M. (2010). The social innovation process: themes, challenges and implications for practice. International Journal of Technology Management, 51(1), 139-158.
Lewis, I., Fleiter, J., & Smith, J. (2015). Students' responses to the RACQ docudrama program.
Lewis, I., Watson, B., White, K. M., & Elliott, B. (2013). The beliefs which influence young males to speed and strategies to slow them down: informing the content of antispeeding messages. Psychology & Marketing, 30(9), 826-841.
Lewis, I. M., Watson, B., & White, K. M. (2010). Response efficacy: The key to minimizing rejection and maximizing acceptance of emotion-based anti-speeding messages. Accident Analysis & Prevention, 42(2), 459-467. doi:http://dx.doi.org/10.1016/j.aap.2009.09.008
Lewis, I. M., Watson, B. C., & Tay, R. S. (2007). Examining the effectiveness of physical threats in road safety advertising: The role of the third-person effect, gender, and age. Transportation research part F: traffic psychology and behaviour, 10(1), 48-60. doi:10.1016/j.trf.2006.05.001
Lewis, I. M., Watson, B. C., Tay, R. S., & White, K. M. (2007). The Role of Fear Appeals in Improving Driver Safety: A Review of the Effectiveness of Fear-arousing (threat) Appeals in Road Safety Advertising. International Journal of Behavioral and Consultation Therapy, 3(2), 203-222.
Li, Q., Qiao, F., Qiao, Y., & Yu, L. (2016). Implications of smartphone messages on driving performance along local streets. Bridging the East and West, 282.
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., . . . Moher, D. J. P. m. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. 6(7), e1000100.
Littell, J. H., & Girvin, H. (2002). Stages of change A critique. Behavior Modification, 26(2), 223-273.
Louveton, N., Mccall, R., Koenig, V., Avanesov, T., & Engel, T. (2016). Driving while using a smartphone-based mobility application: evaluating the impact of three multi-choice user interfaces on visual-manual distraction. Applied Ergonomics, 54, 196-204.
Luk, J. W., Trim, R. S., Karyadi, K. A., Curry, I., Hopfer, C. J., Hewitt, J. K., . . . Wall, T. L. (2017). Unique and interactive effects of impulsivity facets on reckless driving and driving under the influence in a high-risk young adult sample. Personality and Individual Differences, 114, 42-47.
Mackenzie, J. E. (2016). Mothers' sleepiness and driving in the postpartum period. (PhD), Retrieved from http://eprints.qut.edu.au/95190/
Mair, J., & Noboa, E. (2006). Social entrepreneurship and social transformation: An exploratory study. University of Navarra-IESE Business School Working Paper Series, 955.
Manstead, A. (2000). The role of moral norm in the attitude–behavior relation. In Attitudes, behavior, and social context: The role of norms and group
References 209
membership. (pp. 11-30). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.
Markey, A. R. (2014). Three essays on boredom. Mauriello, L. M., Ciavatta, M. M. H., Paiva, A. L., Sherman, K. J., Castle, P. H.,
Johnson, J. L., & Prochaska, J. M. (2010). Results of a multi-media multiple behavior obesity prevention program for adolescents. Preventive medicine, 51(6), 451-456.
Mayhew, D. R., Simpson, H. M., & Pak, A. (2003). Changes in collision rates among novice drivers during the first months of driving. Accident Analysis & Prevention, 35(5), 683-691.
McCartt, A. T., Mayhew, D. R., Braitman, K. A., Ferguson, S. A., & Simpson, H. M. (2009). Effects of age and experience on young driver crashes: review of recent literature. Traffic Injury Prevention, 10(3), 209-219.
McEvoy, S., & Stevenson, M. (2007). An exploration of the role of driver distraction in serious road crashes. Distracted driving. Sydney, Australasian College of Road Safety, 189-211.
McEvoy, S. P., Stevenson, M. R., & Woodward, M. (2006). The impact of driver distraction on road safety: results from a representative survey in two Australian states. Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention, 12(4), 242-247. doi:10.1136/ip.2006.012336
McGwin Jr, G., & Brown, D. B. (1999). Characteristics of traffic crashes among young, middle-aged, and older drivers. Accident Analysis & Prevention, 31(3), 181-198.
McKinsey&Company. (2014). Connected car, automotive value chain unbound. McKnight, A. S., & McKnight, A. J. (2003). Young novice drivers: careless or
clueless? Accident Analysis and Prevention, 35(6), 921-925. doi:10.1016/S0001-4575(02)00100-8
Miller, S. M., Shoda, Y., & Hurley, K. (1996). Applying cognitive-social theory to health-protective behavior: breast self-examination in cancer screening. Psychological bulletin, 119(1), 70.
Moan, I., & Rise, J. (2011). Predicting intentions not to “drink and drive” using an extended version of the theory of planned behaviour. Accident Analysis Prevention, 43(4), 1378-1384.
Morina, N., Ijntema, H., Meyerbröker, K., & Emmelkamp, P. M. (2015). Can virtual reality exposure therapy gains be generalized to real-life? A meta-analysis of studies applying behavioral assessments. Behaviour research and therapy, 74, 18-24.
Morrongiello, B. A., Corbett, M., Foster, A., & Koutsoulianos, S. (2018). Using Virtual Reality to Examine the Effects of Peer Modeling on Child Pedestrian Behavior. Retrieved from
Mulrow, C. (1994). Systematic reviews: rationale for systematic reviews. Bmj, 309(6954), 597-599.
Murray, W., White, J., & Ison, S. (2012). Work-related road safety: A case study of Roche Australia. Safety Science, 50(1), 129-137. doi:http://dx.doi.org/10.1016/j.ssci.2011.07.012
Musicant, O., & Botzer, A. (2016). The Safety Benefits Of Collision Warning Applications–Evidence From On-road Data. International Journal of Safety and Security Engineering, 6(2), 362-371.
210 References
Musicant, O., & Lotan, T. (2015). Can novice drivers be motivated to use a smartphone based app that monitors their behavior? Transportation research part F: traffic psychology and behaviour.
National Highway Traffic Safety Administration. (2010). Overview of the National Highway Traffic Safety Administration’s Driver Distraction Program. Report No. DOT HS, 811, 299.
National Highway Traffic Safety Administration. (2015). Traffic safety facts: Research note. Distracted driving 2013 (Report No. DOT HS 812 132). In.
National Highway Traffic Safety Administration. (2017). 2016 Fatal Motor Vehicle Crashes: Overview.
National Highway Traffic Safety Administration. (2018a). 2016 Young Drivers Traffic Safety Facts.
National Highway Traffic Safety Administration. (2018b). 2017 Fatal Motor Vehicle Crashes: Overview.
Ng, B.-Y., Kankanhalli, A., & Xu, Y. C. (2009). Studying users' computer security behavior: A health belief perspective. Decision Support Systems, 46(4), 815-825.
Obst, P., Armstrong, K., Smith, S., & Banks, T. (2011). Age and gender comparisons of driving while sleepy: Behaviours and risk perceptions. Transportation research part F: traffic psychology and behaviour, 14(6), 539-542. doi:http://dx.doi.org/10.1016/j.trf.2011.06.005
Orfila, O., Gruyer, D., Judalet, V., & Revilloud, M. (2015). Ecodriving performances of human drivers in a virtual and realistic world. Paper presented at the Intelligent Vehicles Symposium (IV), 2015 IEEE.
Organisation for Economic Co-operation and Development. (2006). Young Drivers: The Road to Safety.
Otto, J., Ward, N., Swinford, S., & Linkenbach, J. (2014). Engaging worksite bystanders to reduce risky driving. Transportation research part F: traffic psychology and behaviour, 26, 370-378. doi:https://doi.org/10.1016/j.trf.2014.02.006
Oviedo-Trespalacios, O., Haque, M. M., King, M., & Washington, S. (2016). Understanding the impacts of mobile phone distraction on driving performance: A systematic review. Transportation Research Part C: Emerging Technologies, 72, 360-380.
Oviedo-Trespalacios, O., Truelove, V., Watson, B., & Hinton, J. (2019). The impact of road advertising signs on driver behaviour and implications for road safety: A critical systematic review. Transportation Research Part A: Policy, 122, 85-98.
Paefgen, J., Kehr, F., Zhai, Y., & Michahelles, F. (2012). Driving behavior analysis with smartphones: insights from a controlled field study. Paper presented at the Proceedings of the 11th International Conference on mobile and ubiquitous multimedia.
Parliament of Victoria Road Safety Committee. (2006). Inquiry into driver distraction: final report. In: Victoria Government.
Parr, M. N., Ross, L. A., McManus, B., Bishop, H. J., Wittig, S. M. O., & Stavrinos, D. (2016). Differential impact of personality traits on distracted driving behaviors in teens and older adults. Accident Analysis & Prevention, 92, 107-112. doi:https://doi.org/10.1016/j.aap.2016.03.011
Patten, C. J. D., Kircher, A., Östlund, J., Nilsson, L., Svenson, O., Psykologiska, i., . . . Samhällsvetenskapliga, f. (2006). Driver experience and cognitive workload
References 211
in different traffic environments. Accident Analysis and Prevention, 38(5), 887-894. doi:10.1016/j.aap.2006.02.014
Patton, J. H., & Stanford, M. S. (1995). Factor structure of the Barratt impulsiveness scale. Journal of clinical psychology, 51(6), 768-774.
Pearson, M. R., Murphy, E. M., & Doane, A. N. (2013). Impulsivity-like traits and risky driving behaviors among college students. Accident Analysis & Prevention, 53, 142-148.
Peck, R. C., Gebers, M. A., Voas, R. B., & Romano, E. (2008). The relationship between blood alcohol concentration (BAC), age, and crash risk. Journal of Safety Research, 39(3), 311-319. doi:http://dx.doi.org/10.1016/j.jsr.2008.02.030
Pickrell, T. M., & Liu, C. (2015). Occupant Restraint Use in 2013: Results From the NOPUS Controlled Intersection Study. Retrieved from
Polacsek, M., Rogers, E. M., Woodall, W. G., Delaney, H., Wheeler, D., & Rao, N. (2001). MADD victim impact panels and stages-of-change in drunk-driving prevention. Journal of Studies on Alcohol, 62(3), 344-350.
Pollatsek, A., Fisher, D. L., & Pradhan, A. (2006). Identifying and Remedying Failures of Selective Attention in Younger Drivers. Current Directions in Psychological Science, 15(5), 255-259. doi:10.1111/j.1467-8721.2006.00447.x
Porter, M. E., & Kramer, M. R. (2011). The big idea: Creating shared value. Harvard Business Review, 89(1), 2.
Potard, C., Kubiszewski, V., Camus, G., Courtois, R., & Gaymard, S. (2018). Driving under the influence of alcohol and perceived invulnerability among young adults: An extension of the theory of planned behavior. Transportation research part F: traffic psychology and behaviour, 55, 38-46. doi:https://doi.org/10.1016/j.trf.2018.02.033
Prato, C. G., Toledo, T., Lotan, T., & Taubman - Ben-Ari, O. (2010). Modeling the behavior of novice young drivers during the first year after licensure. Accident Analysis & Prevention, 42(2), 480-486. doi:https://doi.org/10.1016/j.aap.2009.09.011
Prochaska, J. O., DiClemente, C. C., Velicer, W. F., & Rossi, J. S. (1993). Standardized, individualized, interactive, and personalized self-help programs for smoking cessation. Health Psychology, 12(5), 399.
Prochaska, J. O., Evers, K. E., Castle, P. H., Johnson, J. L., Prochaska, J. M., Rula, E. Y., . . . Pope, J. E. (2012). Enhancing multiple domains of well-being by decreasing multiple health risk behaviors: a randomized clinical trial. Population health management, 15(5), 276-286.
Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior change. American journal of health promotion, 12(1), 38-48.
Quine, L., Rutter, D. R., & Arnold, L. (1998). Predicting and understanding safety helmet use among schoolboy cyclists: a comparison of the theory of planned behaviour and the health belief model. Psychology and Health, 13(2), 251-269.
Quine, L., Rutter, D. R., & Arnold, L. (2000). Comparing the theory of planned behaviour and the health belief model: the example of safety helmet use among schoolboy cyclists. Understanding and changing health behaviour: From health beliefs to self-regulation, 73-98.
Quine, L., Rutter, D. R., & Arnold, L. (2001). Persuading school‐age cyclists to use safety helmets: Effectiveness of an intervention based on the Theory of Planned Behaviour. British journal of health psychology, 6(4), 327-345.
212 References
Rahman, R., Qiao, F., Li, Q., & Yu, L. (2016). Developing a smartphone based warning system application to enhance the safety at work zones. Retrieved from
Rana, N. P., & Dwivedi, Y. K. (2015). Citizen's adoption of an e-government system: Validating extended social cognitive theory (SCT). Government Information Quarterly, 32(2), 172-181. doi:http://dx.doi.org/10.1016/j.giq.2015.02.002
Recarte, M. A., & Nunes, L. M. (2000). Effects of verbal and spatial-imagery tasks on eye fixations while driving. Journal of Experimental Psychology: Applied, 6(1), 31.
Redshaw, S. (2006). Peer Reviewed Paper Dangerous Gender Performances: 'Hydraulic Masculinity' as a Norm for Young Male Drivers.
Regan, M., Williamson, A., Friswell, R., Hatfield, J., & Grzebieta, R. (2012). Submission to Staysafe Parliamentary Inquiry into Distracted Driving.
Regan, M. A., Lee, J. D., & Young, K. L. (2009). Driver distraction: theory, effects, and mitigation. Boca Raton: CRC Press.
Rhodes, N., & Pivik, K. (2011). Age and gender differences in risky driving: The roles of positive affect and risk perception. Accident Analysis & Prevention, 43(3), 923-931.
Riener, A., & Reder, J. (2014). Collective data sharing to improve on driving efficiency and safety. Paper presented at the Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.
Rivis, A., & Sheeran, P. (2003). Descriptive norms as an additional predictor in the theory of planned behaviour: A meta-analysis. 22(3), 218-233.
Rodrigues, M. A. F., Macedo, D. V., Serpa, Y. R., & Serpa, Y. R. (2015). Beyond fun: an interactive and educational 3D traffic rules game controlled by non-traditional devices. Paper presented at the Proceedings of the 30th Annual ACM Symposium on Applied Computing.
Rollnick, S., Heather, N., Gold, R., & Hall, W. (1992). Development of a short ‘readiness to change’ questionnaire for use in brief, opportunistic interventions among excessive drinkers. British Journal of Addiction, 87(5), 743-754. doi:10.1111/j.1360-0443.1992.tb02720.x
Ropelato, S., Zünd, F., Magnenat, S., Menozzi, M., & Sumner, R. (2017). Adaptive Tutoring on a Virtual Reality Driving Simulator. Paper presented at the 1st Workshop on Artificial Intelligence Meets Virtual and Augmented Worlds (AIVRAR) in conjunction with SIGGRAPH Asia 2017.
Rowden, P. J., & Watson, B. C. (2013). Mobile phone use and driving : the message is just not getting through. Paper presented at the Proceedings of the Australasian College of Road Safety Conference 2013, National Wine Centre of Australia, Adelaide, SA. http://eprints.qut.edu.au/64520/
Rutter, J., & Quine, L. (2002). Changing health behaviour (Vol. 17): Citeseer. Ryder, B., Gahr, B., Egolf, P., Dahlinger, A., & Wortmann, F. (2017). Preventing
traffic accidents with in-vehicle decision support systems-The impact of accident hotspot warnings on driver behaviour. Decision Support Systems, 99, 64-74.
SafetyNet. (2009). Novice Drivers, retrieved 18.10.2016. Saiprasert, C., Thajchayapong, S., Pholprasit, T., & Tanprasert, C. (2014, 3-7 Nov.
2014). Driver behaviour profiling using smartphone sensory data in a V2I environment. Paper presented at the 2014 International Conference on Connected Vehicles and Expo (ICCVE).
References 213
Schartmüller, C., & Riener, A. (2015). Field studies to investigate safety distance violation with a low-cost observation system. Paper presented at the Adjunct Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.
Schroeder, P., Meyers, M., & Kostyniuk, L. (2013). National survey on distracted driving attitudes and behaviors--2012. Retrieved from
Schroeter, R., Oxtoby, J., & Johnson, D. (2014). AR and gamification concepts to reduce driver boredom and risk taking behaviours. Paper presented at the Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Seattle, WA, USA. http://eprints.qut.edu.au/76134/
Schroeter, R., Rakotonirainy, A., & Foth, M. (2012). The social car : new interactive vehicular applications derived from social media and urban informatics. Paper presented at the AutomotiveUI'12, Portsmouth, New Hampshire. http://eprints.qut.edu.au/53208/
Schwarzer, R., & Renner, B. (2000). Social-cognitive predictors of health behavior:
action self-efficacy and coping self-efficacy. Health Psychology, 19(5), 487. Schwebel, D. C., Combs, T., Rodriguez, D., Severson, J., & Sisiopiku, V. (2016).
Community-based pedestrian safety training in virtual reality: A pragmatic trial. Accident Analysis & Prevention, 86, 9-15.
Schwebel, D. C., McClure, L. A., & Porter, B. E. (2017). Experiential exposure to texting and walking in virtual reality: a randomized trial to reduce distracted pedestrian behavior. Accident Analysis & Prevention, 102, 116-122.
Scialfa, C. T., Deschênes, M. C., Ference, J., Boone, J., Horswill, M. S., & Wetton, M. (2011). A hazard perception test for novice drivers. Accident Analysis & Prevention, 43(1), 204-208.
Scott-Parker, B. (2012). A compehensive investigation of the risky driving behaviour of young novice drivers. Thesis.
Scott-Parker, B., Goode, N., & Salmon, P. (2015). The driver, the road, the rules… and the rest? A systems-based approach to young driver road safety. Accident Analysis & Prevention, 74, 297-305.
Scott-Parker, B., King, M., & Watson, B. (2015). The psychosocial purpose of driving and its relationship with the risky driving behaviour of young novice drivers. Transportation research part F: traffic psychology and behaviour, 33, 16-26.
Scott-Parker, B., & Oviedo-Trespalacios, O. (2017). Young driver risky behaviour and predictors of crash risk in Australia, New Zealand and Colombia: Same but different? Accident Analysis & Prevention, 99, 30-38. doi:https://doi.org/10.1016/j.aap.2016.11.001
Scott-Parker, B., Watson, B., King, M. J., & Hyde, M. K. (2013). A further exploration of sensation seeking propensity, reward sensitivity, depression, anxiety, and the risky behaviour of young novice drivers in a structural equation model. Accident Analysis & Prevention, 50, 465-471. doi:http://dx.doi.org/10.1016/j.aap.2012.05.027
Scott-Parker, B., & Weston, L. (2017). Sensitivity to reward and risky driving, risky decision making, and risky health behaviour: A literature review. Transportation research part F: traffic psychology and behaviour, 49, 93-109. doi:https://doi.org/10.1016/j.trf.2017.05.008
Seelos, C., & Mair, J. (2004). Social entrepreneurship-The contribution of individual entrepreneurs to sustainable development.
214 References
Sela‐Shayovitz, R. (2008). Young drivers’ perceptions of peer pressure, driving under the influence of alcohol and drugs, and involvement in road accidents. Criminal Justice Studies, 21(1), 3-14.
Senserrick, T. M. (2007). Recent developments in young driver education, training and licensing in Australia. Journal of Safety Research, 38(2), 237-244. doi:http://dx.doi.org/10.1016/j.jsr.2007.03.002
Sheeran, P., Gollwitzer, P. M., & Bargh, J. A. (2013). Nonconscious processes and health. Health Psychology, 32(5), 460.
Shekari Soleimanloo, S. (2016). Effects of light and caffeine on human sleepiness and alertness: A simulated driving experiment. (PhD), Retrieved from https://eprints.qut.edu.au/95888/
Sherman, W. R., & Craig, A. B. (2018). Understanding virtual reality: Interface, application, and design: Morgan Kaufmann.
Shope, J. T., & Bingham, C. R. (2008). Teen Driving: Motor-Vehicle Crashes and Factors That Contribute. American Journal of Preventive Medicine, 35(3, Supplement), S261-S271. doi:http://dx.doi.org/10.1016/j.amepre.2008.06.022
Silverans, P., Alvarez, J., Assum, T., Evers, C., & Mathijssen, R. (2007). Alcolock programmes for professional and non-professional drivers in a European field trial. Paper presented at the The 8th International Annual Ignition Interlock Symposium, Seattle, WA.
Şimşekoğlu, Ö., & Lajunen, T. (2008). Social psychology of seat belt use: A comparison of theory of planned behavior and health belief model. Transportation research part F: traffic psychology and behaviour, 11(3), 181-191.
Smart, D., Vassallo, S., Sanson, A., Cockfield, S., Harris, A., & Harrison, W. (2005). In the driver's seat: Understanding young adults' driving behaviour.
Smart, R. G. (1966). Subject selection bias in psychological research. Canadian Psychologist/Psychologie canadienne, 7a(2), 115-121. doi:10.1037/h0083096
Smedslund, J. (1978). Bandura's theory of self‐efficacy: A set of common sense theorems. Scandinavian Journal of Psychology, 19(1), 1-14.
Sniehotta, F. F., Presseau, J., & Araújo-Soares, V. (2014). Time to retire the theory of planned behaviour. In: Taylor & Francis.
Stanford, M. S., Mathias, C. W., Dougherty, D. M., Lake, S. L., Anderson, N. E., Patton, J. H. J. P., & differences, i. (2009). Fifty years of the Barratt Impulsiveness Scale: An update and review. 47(5), 385-395.
Staton, C., Vissoci, J., Gong, E., Toomey, N., Wafula, R., Abdelgadir, J., . . . Hocker, M. (2016). Road Traffic Injury Prevention Initiatives: A Systematic Review and Metasummary of Effectiveness in Low and Middle Income Countries. PloS one, 11(1), e0144971. doi:10.1371/journal.pone.0144971
Stead, M., Tagg, S., MacKintosh, A. M., & Eadie, D. (2005). Development and evaluation of a mass media Theory of Planned Behaviour intervention to reduce speeding. Health education research, 20(1), 36-50.
Steinberger, F., Proppe, P., Schroeter, R., & Alt, F. (2016). CoastMaster: An ambient speedometer to gamify safe driving. Paper presented at the Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications.
Steinberger, F., Schroeter, R., Foth, M., & Johnson, D. (2017). Designing gamified applications that make safe driving more engaging. Paper presented at the Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems.
References 215
Steinberger, F., Schroeter, R., Lindner, V., Fitz-Walter, Z., Hall, J., & Johnson, D. M. (2015). Zombies on the road: A holistic design approach to balancing gamification and safe driving. Paper presented at the Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Nottingham, UK. http://eprints.qut.edu.au/84799/
Tabachnick, & Fidell. (2007). Using multivariate statistics: Allyn & Bacon/Pearson Education.
Tao, D., Zhang, R., & Qu, X. (2017). The role of personality traits and driving experience in self-reported risky driving behaviors and accident risk among Chinese drivers. Accident Analysis & Prevention, 99, 228-235. doi:https://doi.org/10.1016/j.aap.2016.12.009
Tay, R. (2005). The effectiveness of enforcement and publicity campaigns on serious crashes involving young male drivers: Are drink driving and speeding similar? Accident Analysis & Prevention, 37(5), 922-929. doi:http://dx.doi.org/10.1016/j.aap.2005.04.010
Theng, Y.-L., Lee, J. W., Patinadan, P. V., & Foo, S. S. (2015). The use of videogames, gamification, and virtual environments in the self-management of diabetes: a systematic review of evidence. Games for health journal, 4(5), 352-361.
Torrubia, R., Ávila, C., Moltó, J., & Caseras, X. (2001). The Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) as a measure of Gray's anxiety and impulsivity dimensions. Personality and Individual Differences, 31(6), 837-862. doi:http://dx.doi.org/10.1016/S0191-8869(00)00183-5
Tranter, P., & Warn, J. (2008). Relationships between interest in motor racing and driver attitudes and behaviour amongst mature drivers: An Australian case study. Accident Analysis & Prevention, 40(5), 1683-1689.
Vaezipour, A. (2018). Design and development of an in-vehicle human machine interface for eco-safe driving. (PhD), Retrieved from https://eprints.qut.edu.au/118058/
Van Duyn, M. A. S., Heimendinger, J., Russek-Cohen, E., DlClemente, C. C., Sims, L. S., Subar, A. F., . . . Kahle, L. L. (1998). Use of the transtheoretical model of change to successfully predict fruit and vegetable consumption. Journal of Nutrition Education, 30(6), 371-380.
van Loon, A., Bailenson, J., Zaki, J., Bostick, J., & Willer, R. (2018). Virtual reality perspective-taking increases cognitive empathy for specific others. PloS one, 13(8), e0202442.
Velicer, W. F., Redding, C. A., Paiva, A. L., Mauriello, L. M., Blissmer, B., Oatley, K., . . . Prochaska, J. O. (2013). Multiple behavior interventions to prevent substance abuse and increase energy balance behaviors in middle school students. Translational behavioral medicine, 3(1), 82-93.
Vernick, J. S., Li, G., Ogaitis, S., MacKenzie, E. J., Baker, S. P., & Gielen, A. C. (1999). Effects of high school driver education on motor vehicle crashes, violations, and licensure. American Journal of Preventive Medicine, 16(1), 40-46. doi:10.1016/S0749-3797(98)00115-9
Walker, E. (2014). Australian graduated licensing scheme - policy framework. Wallace, L. S., Buckworth, J., Kirby, T. E., & Sherman, W. M. (2000). Characteristics
of Exercise Behavior among College Students: Application of Social Cognitive Theory to Predicting Stage of Change. Preventive medicine, 31(5), 494-505. doi:http://dx.doi.org/10.1006/pmed.2000.0736
Ward, N. J., Otto, J., Schell, W., Finley, K., Kelley-Baker, T., & Lacey, J. H. (2017). Cultural predictors of future intention to drive under the influence of cannabis
216 References
(DUIC). Transportation research part F: traffic psychology and behaviour, 49, 215-225. doi:https://doi.org/10.1016/j.trf.2017.06.013
Ward, N. J., Schell, W., Kelley-Baker, T., Otto, J., & Finley, K. (2018). Developing a theoretical foundation to change road user behavior and improve traffic safety: Driving under the influence of cannabis (DUIC). Traffic Injury Prevention, 19(4), 358-363. doi:10.1080/15389588.2018.1425548
Warner, H. W., & Åberg, L. (2008). Drivers’ beliefs about exceeding the speed limits. Transportation research part F: traffic psychology and behaviour, 11(5), 376-389.
Watson, B. C. (1997). When common sense just won't do: Misconceptions about changing the behaviour of road users. Paper presented at the The 2nd International Conference on Accident Investigation, Reconstruction, Interpretation and the Law, Brisbane, Queensland. http://eprints.qut.edu.au/7295/
Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological bulletin, 132(2), 249.
Wells-Parker, E., Burnett, C., Dill, P., & Williams, M. (1997). Initial development of self-efficacy scales for controlling drinking and driving. Paper presented at the Proceedings of the 14th International Conference on Alcohol, Drugs and Traffic Safety.
Wells-Parker, E., Williams, M., Dill, P., & Kenne, D. (1998). Stages of change and self-efficacy for controlling drinking and driving: A psychometric analysis. Addictive Behaviors, 23(3), 351-363.
West, R. (2005). Time for a change: putting the Transtheoretical (Stages of Change) Model to rest. Addiction, 100(8), 1036-1039.
Weston, L., & Hellier, E. (2018). Designing road safety interventions for young drivers – The power of peer influence. Transportation research part F: traffic psychology and behaviour, 55, 262-271. doi:https://doi.org/10.1016/j.trf.2018.03.003
White, K. M., Hyde, M. K., Walsh, S. P., & Watson, B. (2010). Mobile phone use while driving: An investigation of the beliefs influencing drivers’ hands-free and hand-held mobile phone use. Transportation research part F: traffic psychology and behaviour, 13(1), 9-20.
White, M. J., Cunningham, L. C., & Titchener, K. (2011). Young drivers’ optimism bias for accident risk and driving skill: Accountability and insight experience manipulations. Accident Analysis & Prevention, 43(4), 1309-1315.
Wickens, T. D., & Keppel, G. (2004). Design and analysis: A researcher's handbook: Pearson Prentice-Hall.
Williams, K. J., Peters, J. C., & Breazeal, C. L. (2013). Towards leveraging the driver's mobile device for an intelligent, sociable in-car robotic assistant. Paper presented at the Intelligent Vehicles Symposium (IV), 2013 IEEE.
World Health Organization. (2011). Mobile Phone Use: A Growing Problem of Driver Distraction.
World Health Organization. (2018). Global status report on road safety 2018. Wundersitz, L. (2012). An analysis of young drivers involved in crashes using in-depth
crash investigation data. Decision making (Total), 62(61.0), 115. Yıldırım-Yenier, Z., Vingilis, E., Wiesenthal, D. L., Mann, R. E., & Seeley, J. (2016).
Relationships between thrill seeking, speeding attitudes, and driving violations
References 217
among a sample of motorsports spectators and drivers. Accident Analysis & Prevention, 86, 16-22. doi:http://dx.doi.org/10.1016/j.aap.2015.09.014
You, C.-W., Lane, N. D., Chen, F., Wang, R., Chen, Z., Bao, T. J., . . . Torresani, L. (2013). CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones. Paper presented at the Proceeding of the 11th annual international conference on Mobile systems, applications, and services.
Zhang, J., Jiang, Y., Sasaki, K., Tsubouchi, M., Matsushita, T., Kawai, T., & Fujiwara, A. (2014). A GPS-enabled smart phone app with simplified diagnosis functions of driving safety and warning information provision. Paper presented at the Proceedings of the 21st world congress of intelligent transport systems. Detroit, USA.
Appendices 219
Appendices
221
Appendix A
Smartphone safe-driving apps on Google Play and iTunes
Store: Google Play
Keyword Name Relevance road app Road Drivers: Legacy Road Mode DailyRoads Voyager Colorado Roads Real-time feedback Road Sidekick Lite On The Road Road Radar Speed limit alerts Easy Roads - Road Trip
Planner
Cross Road Traffic Voyage: Usa Roads CoPilot GPS - Navigationsmart driving RTA Smart Drive Speed Limit alerts while driving Smart Drive Records trips which can be shared
with friends and family. Dash - Drive Smart Provides driver score and insights to
help improve your performance. Checks engine light notifications and provides an explanation. Shows leaderboard to compare the best drivers. Provides multi-vehicle support with automatic VIN de-coding. In driving mode shows real-time data on MPG, as well as audio alerts. There are parental / alert features active for extreme deceleration, curfew, and geo-fencing.
Smart Drivers (SG) SMART DRIVING Real-time diagnostics about the car
with the following key features: Speed, RPM, coolant temperature, airflow temperature, on-board voltage, throttle position, engine load and oxygen sensors. Provides an opportunity to plan trips, calculate costs, search for attractions along the road, and others. Provides social functions to invite friends and share experience.
SmartDriver Collects driving information through the accelerometer and the GPS,
222
including distance, location, braking and acceleration, to determine driving behaviour and provide feedback.
Smart Control Free (OBD / ELM)
Shows messages about trip start and end, performance, and speed limits alert.
Driving G Monitor Uses GPS and acceleration sensors to monitor acceleration and determine driving comfort.
Auto4iBlack-DriveRecorder
Provides event alarm (shock, motion) and emergent recording. Sends 6 SOS SMS.
DriveSafe.Smart Bars incoming and outgoing calls and text messages.
Safe driving Safe driving SafeDrive Blocks calls and texts. It is free and
not geographically restricted. Tries to connect gamification (earning points) with the real world (receiving rewards). Works on auto-start.
Drive Safe Safe Driver Safety Driving Safe Driving Assistant Safe Driving + Auto SMS DashDroid - Safe Driving
App
Safe Driving Ltd. Drive Care - Safe driving Visualises odometer and distance
travelled, speeding alerts and trip details. Sends SMS alerts with details.
SEAT Safe Drive Works in the background even with the device blocked. Uses the device proximity sensor to activate the app without distractions. Visualises trips statistics only once the car has stopped.
Drive Mode Drive Safe Safe Driving Text
Machine
Drive Alive Lite iOnRoad Augmented
Driving Lite Real-time feedback
Safe Driving
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Safe Driving Flo - Driving Insights Provides live feedback to the driver
or can run invisibly in the background providing detailed after-trip feedback on a Google map. It is free for users and has a web interface. There is no geographic restriction. Does not need a dongle.
Safe Driving App Records harsh acceleration and deceleration as well as speed limit exceeded by more than 10% with location and time.
Driver Safety - Automatic SMS
Drive Now Text Later Safe Drive Enforcer Anti Texting Safe Driving
App.
Safe Driving App App4Drivers safe teen
driver Feedback
Ride Safe Way - Safe Driving
Safe Driving + Auto SMS + TTS
Drive Control. AAMI Safe Driver Embeds a good mix of gamified
elements (scores, badges). Monitors for exact speed limits, not the general ones. Runs in the background. Provides a detailed after-trip feedback on a Google map. Does not need a dongle and is free for users.
iOnRoad Augmented Driving Pro
Safe Driver Text Response DraVA Driving Coach Driving Coach GPS Safe Driving Tracker Drive Safe Text Safe Drive Safe DrivingBuddy Generates a leaderboard with
achievements and levelling. There is an opportunity for bracket competition and socialization on Facebook.
DriveSafe Mode Drive SafeOBD game Olivia Drive | OBD2 -
ELM327
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OBD2 scanner bluetooth Elm327
RealDash Android driver U-Scan Gears Pro (OBD 2 & Car) Drivemode: Driving
interface
Store: iTunes
Keyword Name Relevance road app Waze - GPS navigation,
Maps and Real-time traffic
smart driving Metromile - smart drive Provides daily driving insights. DriveSmart - Drive &
Save money Records events such as breaking, acceleration, and cornering.
ŠKODA Drive Calculates route efficiency, average speed, route distance, and saved money.
Greenlight - Safer Driving Starts Here
Generates a unique driver score based on driving data.
Driveway - Smart Driving Records events such as breaking, acceleration, and cornering.
Try and Drive Real-time data collection. T-Connect TH Smart System Monitor Start Smart: California
Teen Driver License Guide
Drive ULU Business Collects driver performance statistics (speeding, braking, driving style, eco-driving) and generates a weekly score.
Smart Drive by ÖkoTaxi Travel
Driver360 Powered by Agero Travel
Works in the background while driving to avoid driver distraction. Provides a personal driver score. Let's routes, times and trips be reviewed.
DriveProfiler Smart Lifestyle
Shows trips which can be submitted into a logbook. Keeps track of distance travelled on a graphical dashboard. Shows driver scores and provides driver behaviour feedback.
Drive Protected Travel Monitors driving speed using Apple watch.
YouDrive Travel
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Mojio Shift Lifestyle Monitors driving behaviour to increase safety and save money on gas.
Terra Drive Navigation Safe driving EverDrive™ - Safe
Driving Lifestyle Keeps past drives with detailed feedback on maps. Helps improve driving with personalized tips. Allows for competition with friends, family, and other drivers. Reduces distracted driving and encourages safe driving.
OBD game RealDash Utilities Monitors vehicle speed and current location on the map. Times laps. Gathers performance measurements (with limited accuracy).
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Appendix B
Study 2 Questionnaire
Time 1 before intervention (Sections 1, 2 & 3) Time 2 after 3 months driving with a smartphone app (Sections 1, 2 & 4) Conditions to participate:
1. Young driver, aged 18 to 25; 2. Have a valid open or provisional Australian driver's license. 3. Drive a car as the only means of transport; 4. Drive a minimum of 100 kilometres per month. N Question Possible answers
1. Demographic data 1.1 Age 18-19-20-21-22-23-
24-251.2 Gender Male / Female / Other 1.3 Type of license Learner / Provisional
(Year 1) / Provisional (Year 2) / Open
1.4 State Queensland / South Australia / New South Wales / Victoria / Australian Capital Territory / Northern Territory / Western Australia / Tasmania
2. Measuring social cognitive determinants (TPB-based) of drivers’ speeding behaviour (Elliott & Thomson, 2010). All items are measured using 9-point scales (scored 1–9).
2.1 To what extent do you intend to drive faster than the speed limit over the next 3 months? (measuring Intention to speed)
(No extent at all to A great extent)
2.2 How often do you think you will drive faster than the speed limit in the next 3 months? (measuring Intention to speed)
(Never to All the time)
2.3 How bad or good would it be for you personally if you drove faster than the speed limit over the next 3 months? (measuring Instrumental attitude)
(Extremely bad to Extremely good)
2.4 How unenjoyable or enjoyable would it be for you personally if you drove faster than the speed limit over the next 3 months? (measuring Affective attitude)
(Extremely unenjoyable to Extremely enjoyable)
2.5 Would the people who are important to you disapprove or approve of you driving faster than the speed limit over the next 3 months? (measuring Subjective norm)
(Definitely disapprove to Definitely approve)
227
2.6 How often do you think the people who are important to you will drive faster than the speed limit over the next 3 months? (measuring Descriptive norm)
(Never to All the time)
2.7 How confident are you that you will be able to avoid driving faster than the speed limit over the next 3 months? (measuring Self-efficacy)
(Not at all confident to Extremely confident)
2.8 Over the next 3 months, how much do you feel that avoiding driving faster than the speed limit is under your control? (measuring Perceived controllability)
(Not at all to Very much so)
2.9 How wrong do you think it would be for you to drive faster than the speed limit over the next 3 months? (measuring Moral norm)
(Not at all wrong to Extremely wrong)
2.10 How often did you drive faster than the speed limit over the last 3 months? (measuring Past speeding behaviour)
(Never to All the time)
2.11 Would your friends disapprove or approve of you driving over the speed limit over the next 3 months? (measuring Peers' norm)
(Definitely disapprove to Definitely approve)
Adapted from Gannon et al. (2014) to measure perceived risk.2.12 If you were to drive over the speed limit over the
next 3 months, how much would you worry about being involved in a road crash?
(Not at all worried to Worried very much)
2.13 If you were to drive over the speed limit over the next 3 months, how much would you worry about being caught by the Police?
(Not at all worried to Worried very much)
Measuring frequencies (%) of Initiating, Monitoring/reading, and Responding to Social Interactive Technology on Smartphones while Driving (Gauld et al., 2016). How often do you do the following on your smartphone while driving: 2.11 Initiate communication on social interactive
technology? (Starting a communication) More than once per day; Daily; 1–2 times per week; 1–2 times per month; 1–2 times per 3 months; Once a year; Never
2.12 Monitor/read social interactive technology? (Checking for communication)
More than once per day; Daily; 1–2 times per week; 1–2 times per month; 1–2 times per 3 months; Once a year; Never
2.13 Respond to a communication on social interactive technology? (Replying to communication)
More than once per day; Daily; 1–2 times per week; 1–2 times per month; 1–2 times
228
per 3 months; Once a year; Never
3. Measuring impulsiveness (Barratt Impulsiveness Scale Version 11) (Patton & Stanford, 1995). All items are measured using 4-point scales.
3.1 I plan tasks carefully. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.2 I do things without thinking. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.3 I make-up my mind quickly. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.4 I am happy-go-lucky. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.5 I don’t “pay attention.” 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.6 I have “racing” thoughts. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.7 I plan trips well ahead of time. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.8 I am self controlled. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.9 I concentrate easily. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.10 I save regularly. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.11 I “squirm” at plays or lectures. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.12 I am a careful thinker. 1 (Rarely/Never); 2 (Occasionally); 3
229
(Often); 4 (Almost Always/Always)
3.13 I plan for job security. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.14 I say things without thinking. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.15 I like to think about complex problems. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.16 I change jobs. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.17 I act “on impulse.” 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.18 I get easily bored when solving thought problems. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.19 I act on the spur of the moment. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.20 I am a steady thinker. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.21 I change residences. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.22 I buy things on impulse. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.23 I can only think about one thing at a time. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.24 I change hobbies. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
230
3.25 I spend or charge more than I earn. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.26 I often have extraneous thoughts when thinking. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.27 I am more interested in the present than the future. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.28 I am restless at the theater or lectures. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.29 I like puzzles. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.30 I am future oriented. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
4. Measuring reward and punishment sensitivity through Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) (Torrubia, Ávila, Moltó, & Caseras, 2001).
4.1 Do you often refrain from doing something because you are afraid of it being illegal?
Yes / No
4.2 Does the good prospect of obtaining money motivate you strongly to do some things?
Yes / No
4.3 Do you prefer not to ask for something when you are not sure you will obtain it?
Yes / No
4.4 Are you frequently encouraged to act by the possibility of being valued in your work, in your studies, with your friends or with your family?
Yes / No
4.5 Are you often afraid of new or unexpected situations?
Yes / No
4.6 Do you often meet people that you find physically attractive?
Yes / No
4.7 Is it difficult for you to telephone someone you do not know?
Yes / No
4.8 Do you like to take some drugs because of the pleasure you get from them?
Yes / No
4.9 Do you often renounce your rights when you know you can avoid a quarrel with a person or an organisation?
Yes / No
4.10 Do you often do things to be praised? Yes / No
231
4.11 As a child, were you troubled by punishments at home or in school?
Yes / No
4.12 Do you like being the centre of attention at a party or a social meeting?
Yes / No
4.13 In tasks that you are not prepared for, do you attach great importance to the possibility of failure?
Yes / No
4.14 Do you spend a lot of your time on obtaining a good image?
Yes / No
4.15 Are you easily discouraged in difficult situations? Yes / No 4.16 Do you need people to show their affection for you
all the time? Yes / No
4.17 Are you a shy person? Yes / No 4.18 When you are in a group, do you try to make your
opinions the most intelligent or the funniest?Yes / No
4.19 Whenever possible, do you avoid demonstrating your skills for fear of being embarrassed?
Yes / No
4.20 Do you often take the opportunity to pick up people you find attractive?
Yes / No
4.21 When you are with a group, do you have difficulties selecting a good topic to talk about?
Yes / No
4.22 As a child, did you do a lot of things to get people's approval?
Yes / No
4.23 Is it often difficult for you to fall asleep when you think about things you have done or must do?
Yes / No
4.24 Does the possibility of social advancement, move you to action, even if this involves not playing fair?
Yes / No
4.25 Do you think a lot before complaining in a restaurant if your meal is not well prepared?
Yes / No
4.26 Do you generally give preference to those activities that imply an immediate gain?
Yes / No
4.27 Would you be bothered if you had to return to a store when you noticed you were given the wrong change?
Yes / No
4.28 Do you often have trouble resisting the temptation of doing forbidden things?
Yes / No
4.29 Whenever you can, do you avoid going to unknown places?
Yes / No
4.30 Do you like to compete and do everything you can to win?
Yes / No
4.31 Are you often worried by things that you said or did?
Yes / No
4.32 Is it easy for you to associate tastes and smells to very pleasant events?
Yes / No
4.33 Would it be difficult for you to ask your boss for a raise (salary increase)?
Yes / No
4.34 Are there a large number of objects or sensations that remind you of pleasant events?
Yes / No
4.35 Do you generally try to avoid speaking in public? Yes / No
232
4.36 When you start to play with a slot machine, is it often difficult for you to stop?
Yes / No
4.37 Do you, on a regular basis, think that you could do more things if it was not for your insecurity or fear?
Yes / No
4.38 Do you sometimes do things for quick gains? Yes / No4.39 Comparing yourself to people you know, are you
afraid of many things? Yes / No
4.40 Does your attention easily stray from your work in the presence of an attractive stranger?
Yes / No
4.41 Do you often find yourself worrying about things to the extent that performance in intellectual abilities is impaired?
Yes / No
4.42 Are you interested in money to the point of being able to do risky jobs?
Yes / No
4.43 Do you often refrain from doing something you like in order not to be rejected or disapproved of by others?
Yes / No
4.44 Do you like to put competitive ingredients in all of your activities?
Yes / No
4.45 Generally, do you pay more attention to threats than to pleasant events?
Yes / No
4.46 Would you like to be a socially powerful person? Yes / No4.47 Do you often refrain from doing something because
of your fear of being embarrassed?Yes / No
4.48 Do you like displaying your physical abilities even though this may involve danger?
Yes / No
233
Appendix C
Study 4 Questionnaire
Eligibility to participate: 1. Young driver, aged 18 to 25; 2. Have a valid driver's license. 3. Have no history of seizures or epilepsy. (Only for the intervention group!)
Survey 1
Anonymou
s identifier
Survey 1: You are invited to participate in the research project "Driving under the influence virtual experience" information about which can be found in the Participants Information Sheet at www (link to the file). If you would like to participate in this project, please, generate your anonymous identifier below in order to proceed to Survey 1. It shall include your: day of birth, first letter of name, first letter of family name and last two digits of mobile number (example 24DL08).
(Short text, 6 characters limit)
N Question Possible answers 1. Demographic data
1.1 How old are you (in years)? (whole number) 1.2 What is your gender? Male / Female / Other1.3 How much is your driving experience (in years)? (whole number)
2. Measuring standard and extended TPB constructs on the influence of alcohol or drugs. All items are measured using 9-point scales (scored 1–9).
Adapted from Elliott & Thomson (2010) to measuring social cognitive determinants.2.1 To what extent do you intend to drive under the
influence of alcohol or drugs over the next 3 months? (measuring Intention to drive under the influence of alcohol or drugs)
(No extent at all to A great extent)
2.2 How often do you think you will drive under the influence of alcohol or drugs in the next 3 months? (measuring Intention to drive under the influence of alcohol or drugs)
(Never to All the time)
2.3 How bad or good would it be for you personally if you drove under the influence of alcohol or drugs over the next 3 months? (measuring Instrumental attitude)
(Extremely bad to Extremely good)
2.4 How unenjoyable or enjoyable would it be for you personally if you drove under the influence of alcohol or drugs over the next 3 months? (measuring Affective attitude)
(Extremely unenjoyable to Extremely enjoyable)
234
2.5 Would the people who are important to you disapprove or approve of you driving under the influence of alcohol or drugs over the next 3 months? (measuring Subjective norm)
(Definitely disapprove to Definitely approve)
2.6 How often do you think the people who are important to you will drive under the influence of alcohol or drugs over the next 3 months? (measuring Descriptive norm)
(Never to All the time)
2.7 How confident are you that you will be able to avoid driving under the influence of alcohol or drugs over the next 3 months? (measuring Self-efficacy)
(Not at all confident to Extremely confident)
2.8 Over the next 3 months, how much do you feel that avoiding driving under the influence of alcohol or drugs is under your control? (measuring Perceived controllability)
(Not at all to Very much so)
2.9 How wrong do you think it would be for you to drive under the influence of alcohol or drugs over the next 3 months? (measuring Moral norm)
(Not at all wrong to Extremely wrong)
2.10 How often did you drive under the influence of alcohol or drugs over the last 3 months? (measuring Past behaviour of not DUI)
(Never to All the time)
2.11 Would your friends disapprove or approve of you driving under the influence of alcohol or drugs over the next 3 months? (measuring Peers' norm)
(Definitely disapprove to Definitely approve)
Adapted from Gannon et al. (2014) to measure perceived risk.2.12 If you were to drive over the next 3 months under
the influence of alcohol or drugs, how much would you worry about being involved in a road crash?
(Not at all worried to Worried very much)
2.13 If you were to drive over the next 3 months under the influence of alcohol or drugs, how much would you worry about being caught by the Police?
(Not at all worried to Worried very much)
3. Measuring impulsiveness (Barratt Impulsiveness Scale Version 11) (Patton & Stanford, 1995). All items are measured using 4-point scales.
3.1 I plan tasks carefully. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.2 I do things without thinking. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.3 I make-up my mind quickly. 1 (Rarely/Never); 2 (Occasionally); 3
235
(Often); 4 (Almost Always/Always)
3.4 I am happy-go-lucky. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.5 I don’t “pay attention.” 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.6 I have “racing” thoughts. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.7 I plan trips well ahead of time. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.8 I am self controlled. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.9 I concentrate easily. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.10 I save regularly. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.11 I “squirm” at plays or lectures. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.12 I am a careful thinker. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.13 I plan for job security. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.14 I say things without thinking. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.15 I like to think about complex problems. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
236
3.16 I change jobs. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.17 I act “on impulse.” 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.18 I get easily bored when solving thought problems. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.19 I act on the spur of the moment. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.20 I am a steady thinker. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.21 I change residences. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.22 I buy things on impulse. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.23 I can only think about one thing at a time. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.24 I change hobbies. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.25 I spend or charge more than I earn. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.26 I often have extraneous thoughts when thinking. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.27 I am more interested in the present than the future.
1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.28 I am restless at the theater or lectures. 1 (Rarely/Never); 2 (Occasionally); 3
237
(Often); 4 (Almost Always/Always)
3.29 I like puzzles. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
3.30 I am future oriented. 1 (Rarely/Never); 2 (Occasionally); 3 (Often); 4 (Almost Always/Always)
Contact
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(Short text)
Survey 2 – Intervention group Eligibility to participate: Completed Survey 1 as participant in the Intervention group.
Anonymou
s identifier
You are invited to complete Survey 2 of the research project "Driving under the influence virtual experience" information about which can be found in the Participants Information Sheet at www (link to the file). If you would like to continue your participation in this project, please, enter the anonymous identifier you used in Survey 1 below in order to proceed to Survey 2. It shall include your: day of birth, first letter of name, first letter of family name and last two digits of mobile number (example 24DL08).
(Short text, 6 characters limit)
N Question Possible answers 1. Chosen experience on the driving simulator
1.1 What experience did you choose when driving the driving simulator with 3D Tripping virtual reality software?
Alcohol/Ecstasy/Magic mushrooms/Cannabis
2. Measuring standard and extended TPB constructs on the influence of alcohol or drugs. All items are measured using 9-point scales (scored 1–9).
Adapted from Elliott & Thomson (2010) to measuring social cognitive determinants.2.1 To what extent do you intend to drive under the
influence of alcohol or drugs over the next 3 months? (measuring Intention to drive under the influence of alcohol or drugs)
(No extent at all to A great extent)
238
2.2 How often do you think you will drive under the influence of alcohol or drugs in the next 3 months? (measuring Intention to drive under the influence of alcohol or drugs)
(Never to All the time)
2.3 How bad or good would it be for you personally if you drove under the influence of alcohol or drugs over the next 3 months? (measuring Instrumental attitude)
(Extremely bad to Extremely good)
2.4 How unenjoyable or enjoyable would it be for you personally if you drove under the influence of alcohol or drugs over the next 3 months? (measuring Affective attitude)
(Extremely unenjoyable to Extremely enjoyable)
2.5 Would the people who are important to you disapprove or approve of you driving under the influence of alcohol or drugs over the next 3 months? (measuring Subjective norm)
(Definitely disapprove to Definitely approve)
2.6 How often do you think the people who are important to you will drive under the influence of alcohol or drugs over the next 3 months? (measuring Descriptive norm)
(Never to All the time)
2.7 How confident are you that you will be able to avoid driving under the influence of alcohol or drugs over the next 3 months? (measuring Self-efficacy)
(Not at all confident to Extremely confident)
2.8 Over the next 3 months, how much do you feel that avoiding driving under the influence of alcohol or drugs is under your control? (measuring Perceived controllability)
(Not at all to Very much so)
2.9 How wrong do you think it would be for you to drive under the influence of alcohol or drugs over the next 3 months? (measuring Moral norm)
(Not at all wrong to Extremely wrong)
2.10 How often did you drive under the influence of alcohol or drugs over the last 3 months? (measuring Past behaviour of not DUI)
(Never to All the time)
2.11 Would your friends disapprove or approve of you driving under the influence of alcohol or drugs over the next 3 months? (measuring Peers' norm)
(Definitely disapprove to Definitely approve)
Adapted from Gannon et al. (2014) to measure perceived risk.2.12 If you were to drive over the next 3 months under
the influence of alcohol or drugs, how much would you worry about being involved in a road crash?
(Not at all worried to Worried very much)
2.13 If you were to drive over the next 3 months under the influence of alcohol or drugs, how much would you worry about being caught by the Police?
(Not at all worried to Worried very much)
239
3. Measuring reward and punishment sensitivity through Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) (Torrubia et al., 2001).
3.1 Do you often refrain from doing something because you are afraid of it being illegal?
Yes / No
3.2 Does the good prospect of obtaining money motivate you strongly to do some things?
Yes / No
3.3 Do you prefer not to ask for something when you are not sure you will obtain it?
Yes / No
3.4 Are you frequently encouraged to act by the possibility of being valued in your work, in your studies, with your friends or with your family?
Yes / No
3.5 Are you often afraid of new or unexpected situations?
Yes / No
3.6 Do you often meet people that you find physically attractive?
Yes / No
3.7 Is it difficult for you to telephone someone you do not know?
Yes / No
3.8 Do you like to take some drugs because of the pleasure you get from them?
Yes / No
3.9 Do you often renounce your rights when you know you can avoid a quarrel with a person or an organisation?
Yes / No
3.10 Do you often do things to be praised?
Yes / No
3.11 As a child, were you troubled by punishments at home or in school?
Yes / No
3.12 Do you like being the centre of attention at a party or a social meeting?
Yes / No
3.13 In tasks that you are not prepared for, do you attach great importance to the possibility of failure?
Yes / No
3.14 Do you spend a lot of your time on obtaining a good image?
Yes / No
3.15 Are you easily discouraged in difficult situations? Yes / No 3.16 Do you need people to show their affection for
you all the time?Yes / No
3.17 Are you a shy person? Yes / No 3.18 When you are in a group, do you try to make your
opinions the most intelligent or the funniest?Yes / No
3.19 Whenever possible, do you avoid demonstrating your skills for fear of being embarrassed?
Yes / No
3.20 Do you often take the opportunity to pick up people you find attractive?
Yes / No
3.21 When you are with a group, do you have difficulties selecting a good topic to talk about?
Yes / No
3.22 As a child, did you do a lot of things to get people's approval?
Yes / No
240
3.23 Is it often difficult for you to fall asleep when you think about things you have done or must do?
Yes / No
3.24 Does the possibility of social advancement, move you to action, even if this involves not playing fair?
Yes / No
3.25 Do you think a lot before complaining in a restaurant if your meal is not well prepared?
Yes / No
3.26 Do you generally give preference to those activities that imply an immediate gain?
Yes / No
3.27 Would you be bothered if you had to return to a store when you noticed you were given the wrong change?
Yes / No
3.28 Do you often have trouble resisting the temptation of doing forbidden things?
Yes / No
3.29 Whenever you can, do you avoid going to unknown places?
Yes / No
3.30 Do you like to compete and do everything you can to win?
Yes / No
3.31 Are you often worried by things that you said or did?
Yes / No
3.32 Is it easy for you to associate tastes and smells to very pleasant events?
Yes / No
3.33 Would it be difficult for you to ask your boss for a raise (salary increase)?
Yes / No
3.34 Are there a large number of objects or sensations that remind you of pleasant events?
Yes / No
3.35 Do you generally try to avoid speaking in public? Yes / No3.36 When you start to play with a slot machine, is it
often difficult for you to stop?Yes / No
3.37 Do you, on a regular basis, think that you could do more things if it was not for your insecurity or fear?
Yes / No
3.38 Do you sometimes do things for quick gains? Yes / No3.39 Comparing yourself to people you know, are you
afraid of many things? Yes / No
3.40 Does your attention easily stray from your work in the presence of an attractive stranger?
Yes / No
3.41 Do you often find yourself worrying about things to the extent that performance in intellectual abilities is impaired?
Yes / No
3.42 Are you interested in money to the point of being able to do risky jobs?
Yes / No
3.43 Do you often refrain from doing something you like in order not to be rejected or disapproved of by others?
Yes / No
3.44 Do you like to put competitive ingredients in all of your activities?
Yes / No
3.45 Generally, do you pay more attention to threats than to pleasant events?
Yes / No
241
3.46 Would you like to be a socially powerful person? Yes / No 3.47 Do you often refrain from doing something
because of your fear of being embarrassed?Yes / No
3.48 Do you like displaying your physical abilities even though this may involve danger?
Yes / No
Comments
Is there anything else you would like to share with the research team such as impressions, comments and/or suggestions?
(Short text)
Contact
Please, enter your e-mail address: /If you would like to enter into a random draw of 10 Amazon vouchers of 100 AUD each, please, enter your e-mail. We will use your e-mail for the sole purpose of communication in case you are drawn to win one of the vouchers./
(Short text)
Survey 2 – Control group Eligibility to participate: Completed Survey 1 as participant in the Control group.
Anonymou
s identifier
You are invited to complete Survey 2 of the research project "Driving under the influence virtual experience" information about which can be found in the Participants Information Sheet at www (link to the file). If you would like to continue your participation in this project, please, enter the anonymous identifier you used in Survey 1 below in order to proceed to Survey 2. It shall include your: day of birth, first letter of name, first letter of family name and last two digits of mobile number (example 24DL08).
(Short text, 6 characters limit)
N Question Possible answers 1. Measuring standard and extended TPB constructs on the influence of
alcohol or drugs. All items are measured using 9-point scales (scored 1–9).
Adapted from Elliott & Thomson (2010) to measuring social cognitive determinants.1.1 To what extent do you intend to drive under the
influence of alcohol or drugs over the next 3 months? (measuring Intention to drive under the influence of alcohol or drugs)
(No extent at all to A great extent)
1.2 How often do you think you will drive under the influence of alcohol or drugs in the next 3 months? (measuring Intention to drive under the influence of alcohol or drugs)
(Never to All the time)
1.3 How bad or good would it be for you personally if you drove under the influence of alcohol or drugs over the next 3 months? (measuring Instrumental attitude)
(Extremely bad to Extremely good)
242
1.4 How unenjoyable or enjoyable would it be for you personally if you drove under the influence of alcohol or drugs over the next 3 months? (measuring Affective attitude)
(Extremely unenjoyable to Extremely enjoyable)
1.5 Would the people who are important to you disapprove or approve of you driving under the influence of alcohol or drugs over the next 3 months? (measuring Subjective norm)
(Definitely disapprove to Definitely approve)
1.6 How often do you think the people who are important to you will drive under the influence of alcohol or drugs over the next 3 months? (measuring Descriptive norm)
(Never to All the time)
1.7 How confident are you that you will be able to avoid driving under the influence of alcohol or drugs over the next 3 months? (measuring Self-efficacy)
(Not at all confident to Extremely confident)
1.8 Over the next 3 months, how much do you feel that avoiding driving under the influence of alcohol or drugs is under your control? (measuring Perceived controllability)
(Not at all to Very much so)
1.9 How wrong do you think it would be for you to drive under the influence of alcohol or drugs over the next 3 months? (measuring Moral norm)
(Not at all wrong to Extremely wrong)
1.10 How often did you drive under the influence of alcohol or drugs over the last 3 months? (measuring Past behaviour of not DUI)
(Never to All the time)
1.11 Would your friends disapprove or approve of you driving under the influence of alcohol or drugs over the next 3 months? (measuring Peers' norm)
(Definitely disapprove to Definitely approve)
Adapted from Gannon et al. (2014) to measure perceived risk.1.12 If you were to drive over the next 3 months under
the influence of alcohol or drugs, how much would you worry about being involved in a road crash?
(Not at all worried to Worried very much)
1.13 If you were to drive over the next 3 months under the influence of alcohol or drugs, how much would you worry about being caught by the Police?
(Not at all worried to Worried very much)
2. Measuring reward and punishment sensitivity through Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) (Torrubia et al., 2001).
2.1 Do you often refrain from doing something because you are afraid of it being illegal?
Yes / No
2.2 Does the good prospect of obtaining money motivate you strongly to do some things?
Yes / No
243
2.3 Do you prefer not to ask for something when you are not sure you will obtain it?
Yes / No
2.4 Are you frequently encouraged to act by the possibility of being valued in your work, in your studies, with your friends or with your family?
Yes / No
2.5 Are you often afraid of new or unexpected situations?
Yes / No
2.6 Do you often meet people that you find physically attractive?
Yes / No
2.7 Is it difficult for you to telephone someone you do not know?
Yes / No
2.8 Do you like to take some drugs because of the pleasure you get from them?
Yes / No
2.9 Do you often renounce your rights when you know you can avoid a quarrel with a person or an organisation?
Yes / No
2.10 Do you often do things to be praised?
Yes / No
2.11 As a child, were you troubled by punishments at home or in school?
Yes / No
2.12 Do you like being the centre of attention at a party or a social meeting?
Yes / No
2.13 In tasks that you are not prepared for, do you attach great importance to the possibility of failure?
Yes / No
2.14 Do you spend a lot of your time on obtaining a good image?
Yes / No
2.15 Are you easily discouraged in difficult situations? Yes / No 2.16 Do you need people to show their affection for
you all the time?Yes / No
2.17 Are you a shy person? Yes / No 2.18 When you are in a group, do you try to make your
opinions the most intelligent or the funniest?Yes / No
2.19 Whenever possible, do you avoid demonstrating your skills for fear of being embarrassed?
Yes / No
2.20 Do you often take the opportunity to pick up people you find attractive?
Yes / No
2.21 When you are with a group, do you have difficulties selecting a good topic to talk about?
Yes / No
2.22 As a child, did you do a lot of things to get people's approval?
Yes / No
2.23 Is it often difficult for you to fall asleep when you think about things you have done or must do?
Yes / No
2.24 Does the possibility of social advancement, move you to action, even if this involves not playing fair?
Yes / No
2.25 Do you think a lot before complaining in a restaurant if your meal is not well prepared?
Yes / No
244
2.26 Do you generally give preference to those activities that imply an immediate gain?
Yes / No
2.27 Would you be bothered if you had to return to a store when you noticed you were given the wrong change?
Yes / No
2.28 Do you often have trouble resisting the temptation of doing forbidden things?
Yes / No
2.29 Whenever you can, do you avoid going to unknown places?
Yes / No
2.30 Do you like to compete and do everything you can to win?
Yes / No
2.31 Are you often worried by things that you said or did?
Yes / No
2.32 Is it easy for you to associate tastes and smells to very pleasant events?
Yes / No
2.33 Would it be difficult for you to ask your boss for a raise (salary increase)?
Yes / No
2.34 Are there a large number of objects or sensations that remind you of pleasant events?
Yes / No
2.35 Do you generally try to avoid speaking in public? Yes / No2.36 When you start to play with a slot machine, is it
often difficult for you to stop?Yes / No
2.37 Do you, on a regular basis, think that you could do more things if it was not for your insecurity or fear?
Yes / No
2.38 Do you sometimes do things for quick gains? Yes / No2.39 Comparing yourself to people you know, are you
afraid of many things? Yes / No
2.40 Does your attention easily stray from your work in the presence of an attractive stranger?
Yes / No
2.41 Do you often find yourself worrying about things to the extent that performance in intellectual abilities is impaired?
Yes / No
2.42 Are you interested in money to the point of being able to do risky jobs?
Yes / No
2.43 Do you often refrain from doing something you like in order not to be rejected or disapproved of by others?
Yes / No
2.44 Do you like to put competitive ingredients in all of your activities?
Yes / No
2.45 Generally, do you pay more attention to threats than to pleasant events?
Yes / No
2.46 Would you like to be a socially powerful person? Yes / No2.47 Do you often refrain from doing something
because of your fear of being embarrassed?Yes / No
2.48 Do you like displaying your physical abilities even though this may involve danger?
Yes / No
245
Comments
Is there anything else you would like to share with the research team such as impressions, comments and/or suggestions?
(Short text)
Contact
Please, enter your e-mail address: /If you would like to enter into a random draw of 10 Amazon vouchers of 100 AUD each, please, enter your e-mail. We will use your e-mail for the sole purpose of communication in case you are drawn to win one of the vouchers./
(Short text)
246
Appendix D
Additional Study 2 Effects of the intervention models
1. A subgroup of 18 highly engaged Intervention participants, ones that had a score
generated in more than half of the intervention period, were compared to all 126 entries
in the Control group.
A one-way ANCOVA test was performed to evaluate the effect of the
intervention of the DVs intention not to speed and past behaviour of not speeding
during the three months of the intervention, as described in Section 4.4.4. After
adjusting for the participants' self-reported intention not to speed before the
intervention, no significant difference between the Control group and the Intervention
group was found in intention not to speed, F (1, 141) = .90, p = .34, ηp2 = .006. There
was a statistically significant (p < .001) strong relationship between intention not to
speed at Time 1 and at Time 2, as indicated by a ηp2 = .416. After finding the non-
significant effect of the intervention between the two groups in respect to their
intention not to speed, two-way ANCOVAs found no significant effects with
personality characteristics as moderators of the result, either (see Table 10.1).
Table 10.1. Interaction effects between Condition and personality characteristics, intention not to speed, adjusted for Time 1 values (n=144).
Moderator F (1, 139) p ηp2
Gender .185 .667 .001
Driving experience 1.645 .202 .012
Impulsivity .933 .396 .013
Sensitivity to punishment 2.879 .092 .020
Sensitivity to reward .071 .790 .001
These results did not support H.4, which predicted that participants in the
Intervention group would report significantly greater intention not to speed in the
future than the Control group participants.
After adjusting for the participants self-reported past behaviour of not speeding
before the intervention, no significant difference between the Control group and the
Intervention group was found in past behaviour of not speeding during the three
247
months of the intervention, F (1, 141) = .55, p = .46, ηp2 = .004. There was a statistically
significant (p < .001) strong relationship between past behaviour of not speeding
before the intervention and past behaviour of not speeding during the three months of
the intervention, as indicated by a ηp2 = .488. After finding the non-significant effect
of the intervention between the two groups in respect to their past behaviour of not
speeding during the three months of the intervention, two-way ANCOVAs found no
significant effects with personality characteristics as moderators of the result, either
(see Table 10.2). The assumption of equality of variance was not met when
investigating the interaction effect between the group condition and impulsivity.
Despite the created bias in the obtained result, given that there is no significant
interaction effect, no adjustments were necessary.
Table 10.2. Interaction effects between Condition and personality characteristics, past behaviour of not speeding during the three months of the intervention, adjusted for Time 1 values (n=144).
Moderator F (1, 139) p ηp2
Gender .361 .549 .003
Driving experience .620 .432 .004
Impulsivity .269 .765 .004
Sensitivity to punishment .892 .347 .006
Sensitivity to reward < .001 .986 < .001
These results did not support H.5, which predicted that participants in the
Intervention group would report significantly greater behaviour of not speeding during
the three months of the intervention than the Control group participants.
Thus, the intervention did not have any significant effect on either of the DVs,
intention not to speed and past behaviour of not speeding during the three months of
the intervention. No such effect was found on any of the other potentially-modifiable
Time 2 extended TPB variables, after adjusting for Time 1 values, either (see Table
10.3).
These results did not support H.6, which predicted that the safe-driving app
intervention would have positively influenced the Intervention group participants'
instrumental attitude, affective attitude, self-efficacy and perceived controllability,
moral norm and peers' norm directly as well as subjective norm, descriptive norm and
perceived risk indirectly.
248
Table 10.3. Effect of the intervention on TPB variables, adjusted for Time 1 values (n=144).
Variable F (1, 141) p ηp2
Instrumental attitude .001 .975 < .001
Affective attitude .003 .960 < .001
Subjective norm .001 .972 < .001
Descriptive norm .645 .432 .005
Self-efficacy 1.344 .248 .009
Perceived controllability 2.682 .104 .019
Moral norm .468 .495 .003
Peers' norm .153 .696 .001
Perceived risk 1.537 .217 .011
2. All 126 entries in the Control group were compared with all 84 entries in the
Intervention group.
A one-way ANCOVA test was performed to evaluate the effect of the
intervention of the DVs intention not to speed and past behaviour of not speeding
during the three months of the intervention, as described in Section 4.4.4. After
adjusting for the participants' self-reported intention not to speed before the
intervention, no significant difference between the Control group and the Intervention
group was found in intention not to speed, F (1, 207) = 1.28, p = .26, ηp2 = .006. There
was a statistically significant (p < .001) strong relationship between intention not to
speed at Time 1 and at Time 2, as indicated by a ηp2 = .459. After finding the non-
significant effect of the intervention between the two groups in respect to their
intention not to speed, two-way ANCOVAs provided information about whether
personality characteristics moderated the result (see Table 10.4). The assumption of
equality of variance was not met when investigating the interaction effect between the
group condition and gender. Despite the created bias in the obtained result, given that
there is no significant interaction effect, no adjustments were necessary.
249
10.4. Interaction effects between Condition and personality characteristics, intention not to speed, adjusted for Time 1 values (n=210).
Moderator F (1, 205) p ηp2
Gender .803 .371 .004
Driving experience 4.569 .034 .022
Impulsivity .416 .660 .004
Sensitivity to punishment .110 .740 .001
Sensitivity to reward 1.247 .265 .006
As shown in Table 10.4, a significant interaction effect was found between the
group condition and driving experience, after adjusting for the participants' intention
not to speed before the intervention. Investigating further, neither of the main effects
was statistically significant: condition (F (1, 205) = 1.09, p =. 30, ηp2 = .005) or driving
experience (F (1, 205) = .22, p = .64, ηp2 = .001). The lack of main effects suggested
that provisionally and openly licenced drivers behaved differently, depending on their
condition. A two-way ANCOVA, split by driving licence, showed a statistically
significant effect for the provisionally-licenced drivers (F (1, 107) = 4.32, p = .040,
ηp2 = .039) and a non-significant effect for the open-licenced drivers (F (1, 97) = .53,
p = .47, ηp2 = .005). Investigating the mean scores revealed that the provisionally-
licenced drivers in the Intervention group reported greater intention not to speed mean
score (6.42), i.e. lower intention to speed, than the provisionally-licenced drivers in
the Control group (5.68).
These results provided partial support for H.4, which predicted that participants
in the Intervention group would report significantly greater intention not to speed in
the future than the ones in the Control group, only in the case of provisionally-licenced
drivers.
After adjusting for the participants self-reported past behaviour of not speeding
before the intervention, no significant difference between the Control group and the
Intervention group was found in past behaviour of not speeding during the three
months of the intervention, F (1, 207) = .05, p = .83, ηp2 < .001. There was a statistically
significant (p < .001) strong relationship between past behaviour of not speeding
before the intervention and past behaviour of not speeding during the three months of
the intervention, as indicated by a ηp2 = .553. After finding the non-significant effect
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of the intervention between the two groups in respect to their past behaviour of not
speeding during the three months of the intervention, two-way ANCOVAs found no
significant effects with personality characteristics as moderators of the result, either
(see Table 10.5).
Table 10.5. Interaction effects between Condition and personality characteristics, past behaviour of not speeding during the three months of the intervention, adjusted for Time 1 values (n=210).
Moderator F (1, 205) p ηp2
Gender .245 .621 .001
Driving experience .975 .325 .005
Impulsivity .672 .512 .007
Sensitivity to punishment .139 .710 .006
Sensitivity to reward 1.858 .174 .009
These results did not support H.5, which predicted that participants in the
Intervention group would report significantly greater behaviour of not speeding during
the three months of the intervention than the Control group participants.
Thus, the intervention did not have any significant effect on either of the DVs,
intention not to speed and past behaviour of not speeding during the three months of
the intervention. No such effect was found on any of the other potentially-modifiable
Time 2 extended TPB variables, after adjusting for Time 1 values, either (see Table
10.6).
Table 10.6. Effect of the intervention on TPB variables, adjusted for Time 1 values (n=210).
Variable F (1, 207) p ηp2
Instrumental attitude .128 .721 .001
Affective attitude .032 .858 < .001
Subjective norm .043 .835 < .001
Descriptive norm .031 .860 < .001
Self-efficacy .481 .489 .002
Perceived controllability .085 .771 < .001
Moral norm .097 .756 < .001
Peers' norm .137 .712 .001
Perceived risk .499 .481 .002
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These results did not support H.6, which predicted that the safe-driving app
intervention would have positively influenced the Intervention group participants'
instrumental attitude, affective attitude, self-efficacy and perceived controllability,
moral norm and peers' norm directly as well as subjective norm, descriptive norm and
perceived risk indirectly.
3. A sub-sample of 31 participants from the Control group was selected to match as
close as possible the sample of 31 active Intervention participants.
A one-way ANCOVA test was performed to evaluate the effect of the
intervention of the DVs intention not to speed and past behaviour of not speeding
during the three months of the intervention, as described in Section 4.4.4. Due to the
low number of participants considered in this sample, the moderating effects of gender,
driving experience, impulsivity, sensitivity to reward and sensitivity to punishment,
were not investigated as IVs.
After adjusting for the participants' self-reported intention not to speed before
the intervention, no significant difference between the Control group and the
Intervention group was found in intention not to speed, F (1, 59) = .44, p = .51, ηp2 =
.007. There was a statistically significant (p < .001) strong relationship between
intention not to speed at Time 1 and at Time 2, as indicated by a ηp2 = .301.
These results did not support H.4, which predicted that participants in the
Intervention group would report significantly greater intention not to speed in the
future than the ones in the Control group.
After adjusting for the participants self-reported past behaviour of not speeding
before the intervention, no significant difference between the Control group and the
Intervention group was found in past behaviour of not speeding during the three
months of the intervention, F (1, 59) < .01, p = .96, ηp2 < .001. There was a statistically
significant (p < .001) strong relationship between past behaviour of not speeding
before the intervention and past behaviour of not speeding during the three months of
the intervention, as indicated by a ηp2 = .464.
These results did not support H.5, which predicted that participants in the
Intervention group would report significantly greater behaviour of not speeding during
the three months of the intervention than the participants in the Control group.
252
Thus, the intervention did not have any significant effect on either of the DVs,
intention not to speed and past behaviour of not speeding during the three months of
the intervention. No such effect was found on any of the other potentially-modifiable
Time 2 extended TPB variables, after adjusting for Time 1 values, either (see Table
10.7).
Table 10.7. Effect of the intervention on TPB variables, adjusted for Time 1 values (n=62).
Variable F (1, 59) p ηp2
Instrumental attitude .040 .842 .001
Affective attitude .236 .610 .004
Subjective norm .301 .585 .005
Descriptive norm .498 .483 .008
Self-efficacy 3.073 .085 .050
Perceived controllability 1.218 .274 .020
Moral norm .986 .325 .016
Peers' norm .048 .828 .001
Perceived risk .014 .906 <.001
These results did not support H.6, which predicted that the safe-driving app
intervention would have positively influenced the Intervention group participants'
instrumental attitude, affective attitude, self-efficacy and perceived controllability,
moral norm and peers' norm directly as well as subjective norm, descriptive norm and
perceived risk indirectly.