Persuasive Socio-Technical Systems: Practicing Social Influence Powers to Change People's Behaviors...

Persuasive Socio-Technical Systems: Practicing Social Influence Powers to Change People's Behaviors and Attitudes

Twitter Case Studies

Agnis Stibe

Doctoral Candidate and Project Researcher

Department of Information Processing Science

[email protected] 29224488

@agsti

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Agnis Stibe “Persuasive Socio-Technical Systems”

Riga Business School October 8, 2012

Persuasion is: !!

the influence !

of beliefs, attitudes, intentions, motivations, or behaviors.!

a process !aimed at changing peopleʼs attitude or behavior, by using written or spoken words to convey information, feelings, or reasoning, or a

combination of them.!

Source: http://en.wikipedia.org/wiki/Persuasion

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Source: http://www.flickr.com/photos/34557143@N07/3283901503/

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Source: BJ Fogg

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Behavior Change Support Systems!!

- PSD Model!

- O/C Matrix!


Source: Oinas-Kukkonen H.

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PSD Model


6

limited to – autogenous approaches in which people use information technology to change their own attitudes or behaviors through building upon their own motivation or goal. Beyond being a special case of a persuasive system, a BCSS also has characteristics of its own. A BCSS places more emphasis on the actual outcome than a persuasive system, which, even if its developers were interested in the outcomes as well, in most cases emphasizes more the persuader’s intent than measuring the actual outcome. Another special characteristic of BCSSs is that they request a much stronger emphasis on positive user experience and stickiness to motivate users to engage with them regularly over an extended period of time.

The intent The event The strategy

Primary task support Computer-human dialogue support

Perceived system credibility

Social influence

Persuasive software features

Persuasion context

Intended outcome/change

Use, user, and technology contexts

Message, route

Persuasion postulates

Consistency (P2)

Incrementality(P3)

Usefulness and ease of use (P5)

Unobtrusiveness(P6)

Routes (P4)

Transparency (P7)

IT is never neutral (P1)

Fig 1 The persuasive systems design process (modified from [5])

The Persuasive Systems Design model [38, 5], or more briefly the PSD, is the state of the art vehicle for designing and evaluating BCSSs [46-52]. See Fig. 1. The PSD model defines seven postulates or core issues that are common for all BCSSs. Philosophically and as already stated above, information technology is never neutral but rather it always influences its user(s) in one way or another (P1). Moreover, building BCSSs requires insight from software and information systems design as well as from psychology. Lessons learned from psychology include that (P2) people like their views about the world to be organized and consistent, (P3) persuasion is often incremental, and (P4) the direct and indirect routes are key persuasion strategies.5 Important software design requirements to be always kept in mind when developing BCSSs are that: (P5) behavior change support systems should be both useful and easy to use, (P6) persuasion through behavior change support systems must always be unobtrusive to a user’s primary tasks, and (P7) persuasion through behavior change support systems should always be transparent. Quite understandably, if a system is useless or difficult to use, or it is not well-mapped with a user’s first and foremost interests and needs, it is unlikely that it could be very persuasive. The transparency requirement emphasizes the need for revealing the designer bias behind a BCSS.

5 Psychological theories tend to differ between each other in their views to and emphasis of P2, P3 and P4.


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Outcome/Change Matrix



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PSD Model


6

limited to – autogenous approaches in which people use information technology to change their own attitudes or behaviors through building upon their own motivation or goal. Beyond being a special case of a persuasive system, a BCSS also has characteristics of its own. A BCSS places more emphasis on the actual outcome than a persuasive system, which, even if its developers were interested in the outcomes as well, in most cases emphasizes more the persuader’s intent than measuring the actual outcome. Another special characteristic of BCSSs is that they request a much stronger emphasis on positive user experience and stickiness to motivate users to engage with them regularly over an extended period of time.

The intent The event The strategy

Primary task support Computer-human dialogue support


Social influence

Persuasive software features

Persuasion context

Intended outcome/change

Use, user, and technology contexts

Message, route

Persuasion postulates

Consistency (P2)

Incrementality(P3)

Usefulness and ease of use (P5)

Unobtrusiveness(P6)

Routes (P4)

Transparency (P7)

IT is never neutral (P1)

Fig 1 The persuasive systems design process (modified from [5])

The Persuasive Systems Design model [38, 5], or more briefly the PSD, is the state of the art vehicle for designing and evaluating BCSSs [46-52]. See Fig. 1. The PSD model defines seven postulates or core issues that are common for all BCSSs. Philosophically and as already stated above, information technology is never neutral but rather it always influences its user(s) in one way or another (P1). Moreover, building BCSSs requires insight from software and information systems design as well as from psychology. Lessons learned from psychology include that (P2) people like their views about the world to be organized and consistent, (P3) persuasion is often incremental, and (P4) the direct and indirect routes are key persuasion strategies.5 Important software design requirements to be always kept in mind when developing BCSSs are that: (P5) behavior change support systems should be both useful and easy to use, (P6) persuasion through behavior change support systems must always be unobtrusive to a user’s primary tasks, and (P7) persuasion through behavior change support systems should always be transparent. Quite understandably, if a system is useless or difficult to use, or it is not well-mapped with a user’s first and foremost interests and needs, it is unlikely that it could be very persuasive. The transparency requirement emphasizes the need for revealing the designer bias behind a BCSS.

5 Psychological theories tend to differ between each other in their views to and emphasis of P2, P3 and P4.


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Categories of Persuasive Features



Behavior Change Support Systems: A Research Model and Agenda 9

to human-computer dialogue help move towards achieving the goal set for using the BCSS. The perceived system credibility design principles relate to how to design a system so that it is more believable and thereby more persuasive. The design principles in the social influence category describe how to design the system so that it motivates users by leveraging social influence.

Other users

User Social influence


Human-computer dialogue

Primary task support !

Fig. 1. Four categories of design principles for BCSSs

Tørning and Oinas-Kukkonen [25] have analyzed the scientific research publications in the PERSUASIVE conferences during 2006-2008 as regards the software system features and the abovementioned categories. According to their study, the most utilized features have been tailoring, tunneling, reduction, and self-monitoring (representing the primary task category), suggestion (for supporting human-computer dialogue), surface credibility (in support of perceived system credibility), and social comparison, normative influence, and social learning (relating to social influence).

Many types of research on software system features have been conducted. For instance, Harper et al. [26] studied the roles that social influence and social comparison may play in online communities for motivating members rather than editors to contribute and moderate content. Andrew et al. [27] studied the challenges in implementing suggestion and how it differs from and overlaps with other techniques, in particular tunneling, reduction, and self-monitoring. Räisänen et al. [28] studied the right-time suggestions of messages. Cugelman et al. [29] demonstrated that system credibility, in particular the system’s trustworthiness, affects a user’s behavioral intent. Gamberini et al. [30] showed that in some situations a persuasive strategy based on reciprocity is more effective than one based on reward, as well as that the presence of social proof features seems counterproductive when using a reciprocity strategy, whereas it seems to improve compliance with a request when using a reward strategy. At a more general level, Zhu [8] conducted a meta-study of persuasive techniques in BCSSs motivating for regular physical activity. The results of this study suggest that very few previous studies resulted in achieving the intended goal. Only a few studies took advantage of any persuasive techniques, and

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Categories of Persuasive Features



4.2 Design of system features: Categories and principles

The ideas mentioned above in the previous chapters already cover a multitude of aspects that need to be recognized when designing BCSSs. However, more precise software requirements are needed to communicate the ideas from the management and/or conceptual design to software designers and programmers. For this reason, the PSD model suggests a set of design principles under four categories, namely primary task, dialogue, system credibility, and social support [41]. The design principles of the primary task category focus on supporting the carrying out of the user’s primary task. Design principles related to computer-human dialogue help move towards achieving the goal set for using the BCSS. The system credibility design principles relate to how to design a system so that it is more believable and thereby more persuasive. The design principles in the social support category describe how to design the system so that it motivates users by leveraging social influence. For the categories and design principles belonging to them, see Figure 1. Tørning and Oinas-Kukkonen [57] have studied the scientific research of system features in these categories. According to their study, the most utilized features are tailoring, tunneling, reduction, and self-monitoring (representing the primary task category), suggestion (for supporting dialogue), surface credibility (in support of system credibility), and as social comparison, normative influence, and social learning (relating to social support). A wide variety of BCSSs have been developed using these kinds of design principles. For instance, Forget et al. [21] developed an easy-to-use password creation mechanism to get users to create stronger passwords (many people choose weak passwords that are vulnerable for attackers who guess the passwords within the most probable password spaces). Obermair et al. [40] unobtrusively integrated into a physical working environment an interactive picture frame which contains a moving portrait of a person the user likes, and provides affective feedback for the user to adopt better sitting habits while working at the computer. Chi et al. [9] developed a smart kitchen application for improving home cooking by providing calorie awareness regarding the food ingredients used in the meals prepared during the cooking process. This was based on ubiquitous sensors for tracking the number of calories in different ingredients, and then providing

real-time feedback on these through a display. Parmar et al. [44] presented a personal health information system, which was designed to influence the health behaviors of rural women in India through offering them information for increasing their awareness about menses and maternal health. Their design was based on the Theory of Planned Behavior and employed social cues to increase the perceived behavior control. Many interesting BCSS research contributions can be found directly on system features. Harper et al. [26] have studied the roles that social influence and social comparison may play in online communities for motivating members rather than editors to contribute and moderate content in them. Web information systems may display social comparisons to show members how they compare to others in the system in a manner similar to how Amazon shows a list of its top reviewers. The findings suggest that these techniques are a powerful way to redirect the users’ attention and increase their input into the system. Andrew et al. [2] have studied the challenges in implementing suggestion and how it differs from and overlaps with other techniques, in particular tunneling, reduction, and self-monitoring. Räisänen et al. [49] have studied the right-time suggestions of messages through smoking cessation as an example, and their findings seem to confirm that delivering messages does affect people more at certain moments than it does at other moments. Cugelman et al. [11] have demonstrated that system credibility, in particular the system’s trustworthiness, affects a user’s behavioral intent. Gamberini et al. [23] have shown that in some situations a strategy based on reciprocity is more effective than one based on reward. Quite interestingly, the presence of social proof features seems counterproductive when using a reciprocity strategy, whereas it seems to improve compliance with a request when using a reward strategy. At a more general level, Zhu [60] conducted a meta-study of persuasive techniques in BCSSs motivating for regular physical activity. The results of this study suggest that very few previous studies resulted in achieving the intended goal. Only a few studies took advantage of any persuasive techniques, and none of these interventions were conceptually designed through persuasive design frameworks. The conclusion of this study was that designing a new generation of BCSSs should be based on such frameworks.

Primary task support

Tailoring Tunneling Reduction Self-monitoring Simulation Personalization Rehearsal

Dialogue support

Suggestion Praise Liking Reminders Rewards Similarity Social role

Persuasive systems design techniques

System credibility

Surface credibility Authority Trustworthiness Expertise Real-world feel

3rd party endorsements Verifiability

Social support

Social comparison Normative influence Social learning Recognition Cooperation Social facilitation Competition

Figure 1. Persuasive systems design techniques.

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Expected Contribution

Feedback

Participation

Behavior Change

Incrementality?!

Cognitive!Dissonance?!

Social Learning

Social Comparison

Normative Influence

Social Facilitation

Cooperation

Competition

Recognition


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Socio-Technical Context

Social Influence

Social Web

Individuals Persuasion


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Behavior Change Related Theories


2

support system (BCSS) as a key construct for research into persuasion, influence, nudge and coercion, suggesting the O/C matrix as a means for analyzing the intent of a BCSS. Section 4 will position persuasion as the central approach for achieving behavior change via BCSS, suggesting the PSD model as a means for analyzing the persuasive potential of the system. Section 5 will exemplify research into BCSS through the domain of health interventions. Section 6 will discuss the implications and future research directions of the suggested approach. In general, this article lays ground for the new frontier of research on BCSSs.

2 Theoretical background

The study of users’ attitudes and behavior has a long history in computer science [15]. Prominent theories related to user attitudes and behaviors include, for instance, the Theory of Reasoned Action [6] and the Theory of Planned Behavior [7]. Lessons have been drawn especially from social psychology and cognitive psychology, and even some original computer science theories have been developed, such as the Technology Acceptance Model [16] and the Unified Theory of Use and Acceptance of Technology [17]. These theories are useful for understanding behavioral intentions and control related to information systems and their use, and some of them are relatively well-known among computer scientists. Besides these attitude and behavior-related theories, there are some theories that specifically discuss the change of attitudes or behaviors, such as the Self-Efficacy Theory [8], the Social Cognitive Theory [9], the Elaboration Likelihood Model [10], the Cognitive Dissonance Theory [18], and the Goal Setting Theory [19]; even a few extensions of these theories into the computer science field do exist such as the Computer Self-Efficacy [20]. These change-specific theories are not very well-known among computer science researchers, however. For a summary of behavior change related theories, see Table 1.

Table 1 Behavior change related theories

Theory of Reasoned Action Individual behavior is determined by behavioral intentions, i.e., an individual's attitude toward the behavior and subjective norms about the behavior [6]

Theory of Planned Behavior Individual's perception of the ease with which the behavior can be performed, i.e., behavioral control, influences individual’s behaviors [7]

Technology Acceptance Model Perceived usefulness and perceived ease of use determine an individual's intention to use a system, which leads into actual system use; perceived ease of use impacts perceived usefulness; assumes that actors are free to act without limitations when they just have an intention to act; based on Theory of Reasoned Action [16]

Unified Theory of Acceptance and Use of Technology

Performance expectancy, effort expectancy, social influence, and facilitating conditions determine the usage intention and usage behavior, whereas gender, age, experience, and voluntariness of use moderate this impact; extended from Technology Acceptance Model [17]

Self-Efficacy Theory Individuals who perceive themselves as capable of taking action also do take action; strengthening the sense of efficacy happens through vicarious experiences, social models, social persuasion, and reducing people's stress reactions and altering their negative emotional proclivities and misinterpretations of their physical states [8, 21]

Social Cognitive Theory Observing others performing a behavior influences the perceptions of individual’s own ability to perform the behavior, i.e. self-efficacy, and the perceived expected outcomes [9]

Elaboration Likelihood Model Central and peripheral routes are key routes for persuasion; central route is used when information processing is based upon critical thinking; peripheral route is based on rules of thumb; change via central route is more enduring, resistant and predictive of behavior [10]

Cognitive Dissonance Theory Individuals seek consistency among their cognitions such as beliefs and opinions; inconsistency between attitudes or behaviors creates dissonance that needs to be eliminated [18]

Goal Setting Theory Goals affect performance through directing attention and effort, energizing, persistence, and by leading to arousal and/or use of task-relevant knowledge and strategies; the highest goals produce the highest levels of effort and performance; specific, difficult goals consistently lead to higher performance than urging people to do their best; when goals are self-set, people with high self-efficacy set higher goals than people with lower self-efficacy; people with high self-efficacy are more committed to the assigned goals and and to responding more positively to negative feedback [19]

Computer Self-Efficacy Computer self-efficacy means individual’s judgment of one’s capabilities to use computers for both task performance and computer performance; anxiety, innovativeness, task characteristics, prior performance, and perceived effort play a role; based on Self-Efficacy Theory [20]


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CASE STUDY : 1!!

Comparative Analysis of Recognition and Competition!as Features of Social Influence Using Twitter!

!


Source: Stibe A. and Oinas-Kukkonen H.

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Research Context

Recognition Competition

PSD model : Social Influence

Social Cognitive Theory : Self-Regulation



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Research Question

How and to what extent social influence design principles!

can persuade people !

to participate in sharing feedback?!

Recognition Competition



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Research Framework


CT Competition

CR Cooperation

RE Recognition

SF Social Facilitation

SL Social Learning

H1 Cooperation

Competition

Recognition

Social Facilitation

Malone and Lepper, 1987 Interpersonal Motivators

Zajonc, 1965

Social Learning Theory

Bandura, 1991 Social Cognitive Theory

Bandura, 1976

Self-Regulation

Observation

Oinas-Kukkonen and Harjumaa, 2009, PSD

User Behavior Targeted to

Feedback Sharing

H2

H3

H4

H5

SOFTWARE FEATURES ENVIRONMENTAL

USER FACTORS PERSONAL

USER BEHAVIOR BEHAVIORAL

Judgment

Self-Response

Vicarious Learning

!Source: Stibe A. and Oinas-Kukkonen H.

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Research Setting

•  A system developed on top of Twitter

•  A pilot study conducted in class setting with master students

–  37 participants in two computer rooms

•  18 in recognition room

•  19 in competition room

–  30 minutes hands-on use of the system

–  6 questions in total displayed to the participants

–  Participants responded to questions using Twitter

•  Online questionnaire about perceptions (47 questions, mainly Likert-7)



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Persuasive 2012 Linköping, Sweden: June 7, 2012

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Item Recognition Competition t-value df p Twitter is a powerful tool to call for action outside the virtual world.

5.50 4.32 2.937 35 .006**

I believe that the system would work well in a real airport. 5.56 4.47 2.775 35 .009**

I think that the system is effective for encouraging users to participate.

6.11 5.11 2.570 35 .015*

Findings: Recognition vs. Competition

More encouraging to participate

Independent sample t-test



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Item Yes No t-value df p

Displaying public recognition or the top responders helped me to monitor my performance.

All 5.44 3.25 4.512 33 .000** Recognition 5.54 3.50 3.427 15 .004** Competition 5.36 3.00 2.977 16 .009**

Tweets provided by others on the big display encouraged me to come up with my tweets.

All Non-significant difference Recognition 5.69 5.00 3.323 12 .006** Competition Non-significant difference

Displaying public recognition or the top responders motivated me to produce more tweets.

All 5.00 3.75 2.352 33 .025* Recognition 5.38 3.50 2.409 15 .029* Competition Non-significant difference

Findings: Had vs. Had Not (seen themselves on the screen)

More encouraging and motivating to tweet



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Conclusions

•  Contributions: –  Scientific:

An empirical analysis of persuasive software features from the PSD model;

–  For business:

A persuasive and operational system to engage customers in feedback sharing.

•  Limitations: –  Class setting;

–  Sample: education and age;

–  Missing the control group.

•  Further research: –  Field-testing - actual use;

–  Other social influence features.



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CASE STUDY : 2!!

Social Influence on Customer Engagement: !The Effects of Social Learning, Social Comparison, and Normative Influence !


Source: Stibe A., Oinas-Kukkonen H., and Lehto T.

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Social Cognitive Model

BEHAVIORAL!!

BEHAVIORAL INTENTION:!-  To engage in feedback

sharing (using information system)!

PERSONAL!!

USER FACTORS:!-  Vicarious learning!-  Self-regulation!

ENVIRONMENTAL!!

SOFTWARE FEATURES:!-  Social learning!-  Social comparison!-  Normative influence!



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Research Model


PP!Perceived Persuasiveness!

NI!Normative Influence!

BI!Behavioral Intention!

SC!Social Comparison!

SL!Social Learning!

H3!

H4c!

H4d!

H2!

H1!

H4b!

H4a!

Persuasive Software Features!


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Ongoing Studies: Social Comparison


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Ongoing Studies: Normative Influence


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Results


PP!Perceived Persuasiveness!

45%!

NI!Normative Influence!

36%!

BI!Behavioral Intention!

24%!

SC!Social Comparison!

34%!

SL!Social Learning!

β=0.20*"

β=0.47**"

β=0.59**"

β=0.53**"

β=0.28*"

β=0.21*"

β=0.28*"

Persuasive Software Features!


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CASE STUDY : 3!!

Incremental Persuasion through Microblogging:! A Survey of Twitter Users in Latvia!


Source: Stibe A., Oinas-Kukkonen H., Berzina i., and Pahnila S.

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Research question

What kinds of inherent persuasion patterns do exist in Twitter that can !

change usersʼ behaviors and/or attitudes? !



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Research settings July 19-28, 2010 Latvia

Quantitative survey online: -  37 questions

-  403 valid responses

Invitations for users: -  7 tweets by authors

-  1 author’s blog entry in

-  http://ilzeberzina.wordpress.com/

-  Several authors’ messages in other social networks

-  37 retweets by other Twitter users

-  1 reference in technology blogger article



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Profile of the respondents


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Age


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Number of followees and followers you have in Twitter?



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#",1"$"2*.3+" $"2*.3+"./4"013*"

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516617*3+"

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How often do you tweet?

χ2(6)=18.059, p=0.006 The amount of tweeting increases over time.



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%#6.27/#238#457.#

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D3E.#B3#/.:.72<#45301/#

F5#350#0?..0#

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Regarding content in Twitter you consider yourself as?

χ2(9)=29.789, p=0.000 Experienced users generate more content than new users.



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:.28.7#

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What is the level of credibility in Twitter?

χ2(9)=21.130, p=0.012



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)1:#

The longer one has used the Twitter the higher trust the user has for it.

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Are there unwritten behavioral rules in Twitter?

χ2(6)=19.064, p=0.004 Twitter users learn over time unwritten communication and/or behavioral rules in Twitter.



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;5#

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Is Twitter a powerful tool to call to action outside the virtual world?

χ2(6)=18.551, p=0.005



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6.34#,1#+.2#

71#

Twitter is powerful tool to call for action offline, i.e. outside the virtual world, and experienced users are more ready to take action based on their communication via Twitter.

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Summary of findings

Content generators

Trust information

Recognize unwritten

communication rules

Powerful tool to call to action outside the

virtual world

Number of followers and

followees Intensity of tweeting



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4th postulate of Persuasive Systems Design framework

I N C R E M E N T A L S T E P S



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!#$"

!#%"

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!#'"

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D.12=@572:"7B:./"

>2::"05"2?E53"

CHANGE

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CASE STUDY : 4 (ongoing)!!

A Longitudinal Study of Behaviors and Attitudes !of Twitter users in Latvia!


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A

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0 20 40 60 80 100 120 140 160 180 200

Pilnībā nepiekrītu

Nepiekrītu

Daļēji nepiekrītu

Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

Twitter influences my thoughts.

A

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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0 20 40 60 80 100 120 140 160


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

In Twitter, there are norms that should be followed by users, including me. (Normative Influence)

A

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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0 20 40 60 80 100 120 140 160 180 200


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

Twitter allows me to compare myself with others. (Social Comparison)

A

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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0 20 40 60 80 100 120 140 160 180 200 220


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

In Twitter, I can observe the behavior of other users and learn from it. (Social Learning)

A

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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0 20 40 60 80 100 120 140 160 180 200


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

Twitter is an influential tool to call for actions outside the virtual world.

A

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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0 20 40 60 80 100 120 140 160 180 200 220


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

In Twitter, there is an observable tendency of followers to stratify in the groups of interests.

A

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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B

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0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

Twitter influences my behavior.

B

B

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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0 10 20 30 40 50 60 70 80 90 100 110 120 130 140


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

In Twitter, I can compete with other users. (Competition)

B

B

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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0 10 20 30 40 50 60 70 80 90 100 110 120 130 140


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

In Twitter, users receive recognition for special merit. (Recognition)

B

B

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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0 10 20 30 40 50 60 70 80 90 100 110 120 130 140


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

There are “unwritten” communication and behavioral rules in Twitter, which users need to follow.

B

B

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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C

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0 20 40 60 80 100 120 140 160 180 200


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

In Twitter, I can observe other current active users. (Social Facilitation)

C

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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0 20 40 60 80 100 120 140 160 180 200 220 240 260


Nepiekrītu


Neesmu izlēmis

Daļēji piekrītu

Piekrītu

Pilnībā piekrītu

In Twitter, I have an opportunity to cooperate with others. (Cooperation)

C

Disagree completely

Disagree

Somewhat disagree

Undecided

Somewhat agree

Agree

Agree completely

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Summary!


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Summary of Current Findings

Feedback

Participation

Behavior Change

Social Comparison Normative Influence

Social Facilitation Cooperation

Competition

Recognition


[email protected]

@agsti

29224488

Thanks to:

the Foundation of Nokia Corporation the Finnish Funding Agency for Technology and Innovation the Doctoral Program on Software and Systems Engineering

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