From Health to E-Health990645/FULLTEXT01.pdf · neglected the perspectives of health-care...

DOCTORA L T H E S I S

Luleå University of TechnologyDepartment of Business Administration and Social Sciences Division of Industrial Marketing, e-Commerce and Logistics

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2008:68

From Health to E-Health: Understanding Citizens’ Acceptance of

Online Health Care

Marie-Louise Jung

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08:68

Universitetstryckeriet, Luleå

Marie-Louise Jung

From Health to E-Health:Understanding Citizens’ Acceptance of

Online Health Care

Marie-Louise Jung

Luleå University of Technology Department of Business Administration and Social Sciences Division of Industrial Marketing, e-Commerce and Logistics

2008

To my nearest and dearest: My parents Valerie and Reinhard

and my sister Kristina, with all my love.

Acknowledgements

I believe that we can learn something from everyone, who comes into our lives, in a professional or private sense. Some “teachers” are a little more obvious to us, such as our actual instructors, bosses, supervisors, senior colleagues, and of course, our parents and other family members. Others might be a little less obvious, but they are equally significant. Many people were involved in the production of this thesis, all of whom I thank deeply for their help, support, and companionship along the long PhD student road, which sometimes was very rewarding and other times quite frustrating.

First and foremost, I would like express my deepest gratitude to Professor Esmail Salehi-Sangari, head of the Division of Industrial Marketing, e-Commerce and Logistics at Luleå University of Technology, for taking me into the program and giving me this opportunity, for his faith in me, and for his immense support throughout. I have truly learned a great deal from him in the last couple of years. Also, I am very grateful for being lucky enough to have had Professor Pierre Berthon, Clifford F. Youse Chair of Marketing, McCallum School of Business, Bentley College, as my supervisor. Professor Berthon is recognized as a brilliant academic, and his feedback, questions, and suggestions have helped me tremendously to structure my ideas, solve problems, and improve my work overall. At the same time, he has been an amazing mentor, motivating me in ways that have driven me to exceed my own expectations and building me up whenever frustration seemed to become unbearable. Another person who has helped me greatly with his invaluable comments, questions, and suggestions for improvement is Professor Arthur Money, who agreed to be my opponent in my internal seminar—thank you!

Furthermore, I would like to thank Karina Tellinger from Apoteket AB and Agneta Granström from Norbottens Läns Lansting, who devoted their valuable time for in-depth interviews during the exploratory stage of this research project. They were indeed vital in helping me spot industry problems so that I could design the research accordingly. Additionally, I am very thankful to the 10 individuals in the exploratory study who shared their views, attitudes, perceptions and expectations of e-health with me, the 5 judges who contributed extensively by sitting in front of the items pools, patiently studying, organizing, categorizing and adapting more than 100 items, and the experts who provided crucial feedback on the questionnaire. Also, my thanks go to the 50 students at Luleå University of Technology and the over 800 Swedish citizens that have put their time into completing the questionnaire, many of them providing comments to share their hopes and worries.

At this point, I would also like to thank my wonderful colleagues (past and present) at the Division of Industrial Marketing, e-Commerce and Logistics. Thank you all for giving me valuable feedback through all stages of this project in official research meetings as well as during our a little less official coffee breaks (and after-works). Thank you for being such a great team and for making me feel so comfortable and at home in the office. I would particularly like to thank Magnus Hultman, Maria Styvén, Lena Goldkuhl, Åsa Wallström, Tim Foster, Lars Bäckström, and Anne Engström, as they have engaged in discussing my ideas with me countless times, helped me solve problems, and on more than one occasion also discovered problems (to then help me solve them again).

Apart from academia and my professional life, I owe a very big THANK YOU to my friends. I feel very fortunate to have so many wonderful people in my life, both in Sweden and abroad, who inspire me and who I so much enjoy being around. Thank you for still being there—even though I wasn’t in the last several months. There are six people in particular, who I have grown very close to over the last years. They are Karoline Thumm and Stephan Herz, and my four fabulous flatmates, Anna-Mia Brulin, Petra Viklund, Katerina Elfversson and Anders Pettersson. You are some of the most wonderful people I have ever met. Thank you for the seemingly endless contemplations and discussions about life, thank you for listening, for sharing, and for providing different perspectives and points of view. Thank you for being true friends and for always putting up with me, which I am sure, was not always an easy task, especially towards the end of the PhD.

Finally, I would like to express my deepest gratitude to my parents, Valerie Ann Jung and Reinhard Paul Jung, as well as my sister, Kristina Jung. The three of you are role models in many different ways. I look up to you, and I have learned—and continue to learn—so much from you. I am very lucky to have you, since all of you, even from that far away, have always been there for me, supported me in every possible way, and even in crazy times (and there weren’t few of them) always provided a safe haven I could fall back on. DANKE!

Luleå in September 2008,

Marie-Louise Jung

Abstract

In light of the challenges arising from an ageing population and runaway health-care costs, e-health offers tremendous opportunities for public and private health-care providers worldwide to optimize service delivery and enhance the quality of care being provided to patients. Even though the potential of e-health is acknowledged in academia as well as among practitioners, its application has proven to be remarkably difficult. In order for e-health to be successful, it is imperative that it offers services which address the needs of citizens and patients. Academia has recognized the importance of research on e-health adoption, but it focused mainly on organizational adoption and technological advances while it neglected the perspectives of health-care consumers. The purpose of this study was to investigate citizens’ acceptance of e-health services by identifying the determinants of their intention to use e-health in the future. This knowledge of citizens’ attitudes and needs is crucial to health-care managers who wish to pursue successful e-health initiatives.

Through an extensive review of the literature and some initial exploratory fieldwork, factors relevant to citizens’ acceptance of e-health were identified. Based on those determinants, a citizen survey was developed that collected the perceptions and attitudes of Swedish citizens towards the use of two Swedish e-health services: the online health guide and the ask-the-doctor online service. This investigation leads to the proposal of the e-Health Acceptance Model (eHAM), a theoretical framework that helps to understand and predict citizens’ acceptance of online health care.

This study demonstrates that awareness of the existence of such e-health services among Swedish citizens remains rather low despite the fact that the general attitude towards using e-health is quite positive. This emphasizes the importance of making citizens aware of the e-health alternatives available to them. The study finds that citizens’ intention to use e-health is determined primarily by their attitude towards using e-health, which in turn results from the overall compatibility of e-health with citizens’ needs, its perceived usefulness, and the risks associated with e-health usage. Offering clarity to the discrepancy and doubt about the role of the attitude construct that persisted in previous research, attitude here constitutes a strong mediator, capturing the majority of the effects of behavioral beliefs on usage intentions. Along with attitude, the perceived accessibility of e-health affects directly citizens’ intention to use e-health in the future. In contrast to earlier assumptions and empirical findings, the key TAM determinant perceived ease of use,as well as the social influence construct subjective norm that is regarded as crucial in predicting social behavior, did not demonstrate any major effect on citizens’ intention to use e-health.

The technological advances required for e-health are available today, and citizens are generally positive about such developments in health-care service delivery. However, the adoption of innovations, new technologies, and services takes time, since changing people’s habits is not an easy task. Thus, for the time being, it is important to view e-health as a powerful complement rather than a substitute for traditional channels. Not everyone is e-ready at this point, but the potential is there. The findings of this study highlight the critical role of communication. Citizens must be made aware of the service alternatives available to them as well as the advantages their use can bring. Uncertainty and hesitancy can be mitigated and adoption propelled by implementing risk-reducing measures and design features, and communicating those actions to citizens. The overall effort will be enhanced by ensuring information quality and strengthening provider-citizen relationships.

Table of Contents

1 Chapter One: Introduction............................................ 1

1.1 Introduction................................................................................................ 1 1.1.1 Challenges in Current Health Care and the Role of e-Health ........... 11.1.2 E-Health defined .............................................................................. 21.1.3 E-Health Services ............................................................................. 31.1.4 Benefits of e-Health.......................................................................... 41.1.5 Drawbacks of e-Health ..................................................................... 7

1.2 This Study ................................................................................................ 101.2.1 Problem Discussion and Purpose of this Study ................................ 101.2.2 Delimitation ................................................................................... 121.2.3 Organization of Chapters................................................................ 12

2 Chapter Two: Literature Review ................................. 13

2.1 User Acceptance and Adoption Behavior .................................................. 132.1.1 The TRA and the Evolution of the TAM ...................................... 142.1.2 Alternative Models of User Adoption ............................................. 16

2.1.2.1 TPB - Perceived Behavioral Control ...................................... 172.1.2.2 Model of Utilization of PC ..................................................... 172.1.2.3 Motivational model................................................................. 182.1.2.4 The Innovations Diffusion Theory.......................................... 19

2.1.3 Extensions of TAM ........................................................................ 202.1.3.1 Antecedents of Perceived Usefulness – the TAM 2 ................. 212.1.3.2 Antecedents of Perceive Ease of Use ....................................... 222.1.3.3 Moderators ............................................................................. 24

2.2 Technology Adoption in the Consumer Context ..................................... 262.2.1 Need for Interaction....................................................................... 262.2.2 Risk ............................................................................................... 262.2.3 Perceived Playfulness/Shopping Enjoyment.................................... 272.2.4 Compatibility ................................................................................. 272.2.5 Quality Criteria .............................................................................. 282.2.6 Technology Readiness.................................................................... 28

2.3 User Adoption of e-Health........................................................................ 282.3.1 Barriers to e-Government and e-Health Services ............................ 31

2.4 Summary of the Literature Review ........................................................... 32

3 Chapter Three: Developing the Research Model ........... 33

3.1 The Technology Acceptance Model.......................................................... 333.1.1 Main assumptions ........................................................................... 333.1.2 Perceived Usefulness and Ease of Use ............................................. 34

3.1.2.1 Perceived Usefulness ............................................................... 343.1.2.2 Perceived Ease of Use ............................................................. 35

3.2 Extending TAM........................................................................................ 363.2.1 Exploratory study ........................................................................... 37

3.2.1.1 Method................................................................................... 373.2.1.2 Delimitations .......................................................................... 393.2.1.3 Results.................................................................................... 39

3.2.2 Additional Hypotheses.................................................................... 403.2.2.1 Access and Perceived Accessibility........................................... 403.2.2.2 Trust and the Perceived Credibility of the HP ........................ 413.2.2.3 Output Quality....................................................................... 433.2.2.4 Result Demonstrability ........................................................... 433.2.2.5 Subjective Norm..................................................................... 433.2.2.6 Compatibility.......................................................................... 443.2.2.7 Perceived Risk........................................................................ 45

3.2.3 The Shifting Effect of Moderators................................................... 463.2.3.1 Gender and Age...................................................................... 463.2.3.2 Experience with the Internet and Previous Use of e-Health .... 47

3.3 The Proposed Research Model ................................................................. 483.3.1 The a Priori e-Health Acceptance Model (eHAM) .......................... 483.3.2 Summary of the Research Model.................................................... 50

4 Chapter Four: Methodology ........................................ 53

4.1 Research Design ....................................................................................... 534.2 Research Approach ................................................................................... 534.3 Research Strategy...................................................................................... 544.4 Sampling ................................................................................................... 54

4.4.1 The Case of Sweden....................................................................... 544.4.2 Sampling Frame.............................................................................. 554.4.3 Sampling Method and Sample Size ................................................. 56

4.5 Data Collection......................................................................................... 574.5.1 The e-Health Services under Investigation...................................... 574.5.2 Scale Development ......................................................................... 584.5.3 Data Collection Process .................................................................. 65

4.5.3.1 Responses ............................................................................... 664.5.3.2 Non-response bias................................................................... 67

4.6 Data Analysis............................................................................................. 68

4.6.1 Data examination............................................................................ 684.6.1.1 Normality ............................................................................... 684.6.1.2 Outliers .................................................................................. 694.6.1.3 Missing Data ........................................................................... 69

4.6.2 Measure Validation and Structural Equation Modeling.................... 704.6.2.1 Overall Model Fit ................................................................... 704.6.2.2 Measurement Model (CFA) .................................................... 714.6.2.3 Path Model............................................................................. 71

4.7 Quality of the Study.................................................................................. 724.7.1 Reliability ...................................................................................... 724.7.2 Validity........................................................................................... 724.7.3 Common Method Bias ................................................................... 73

5 Chapter Five: Analysis and Discussion.......................... 75

5.1 Descriptive Statistics and Data Examination............................................... 755.1.1 Awareness and Previous Use of e-Health ........................................ 755.1.2 Users versus Nonusers .................................................................... 765.1.3 Assessment of Normality................................................................. 775.1.4 Missing Data................................................................................... 805.1.5 Outliers .......................................................................................... 81

5.2 Validation of Measurements ...................................................................... 815.2.1 Individual Constructs...................................................................... 825.2.2 Metric Invariance across the two samples ........................................ 855.2.3 Measurement Model....................................................................... 865.2.4 Validity........................................................................................... 86

5.2.4.1 Content Validity ..................................................................... 875.2.4.2 Construct Validity................................................................... 875.2.4.3 Criterion Validity.................................................................... 88

5.2.5 Common Method Bias ................................................................... 885.3 Testing the e-Health Acceptance Model (eHAM) ..................................... 89

5.3.1 Parceling ........................................................................................ 895.3.2 Structural Equation Modeling......................................................... 90

5.3.2.1 HG Sample............................................................................. 915.3.2.2 Ask-the-Doctor Online Sample .............................................. 93

5.4 eHAM and the Impact of Moderating Variables ........................................ 955.4.1 Users vs. Nonusers.......................................................................... 955.4.2 Male vs. Female.............................................................................. 985.4.3 Internet Experience .......................................................................1005.4.4 Age ...............................................................................................1025.4.5 Summary of Moderating Effects.....................................................105

5.5 Cross-Validation and Summary of Hypotheses Tests.................................1055.6 Summary .................................................................................................108

6 Chapter Six: Conclusions and Implications ..................111

6.1 Introduction.............................................................................................1116.2 Determinants of Citizens’ Intention to Use e-Health ................................112

6.2.1 Determinants of Attitude toward Using e-Health...........................1136.2.2 Determinants of Perceived Usefulness ............................................1156.2.3 Determinants of Perceived Risk.....................................................116

6.3 Theoretical Contributions ........................................................................1176.3.1 The eHAM and its Power .............................................................1186.3.2 The eHAM Propositions ...............................................................1206.3.3 Ease of Use and Subjective Norm..................................................124

6.4 Managerial Implications............................................................................1256.5 Limitations ...............................................................................................1276.6 Suggestions for Future Research...............................................................128

References .....................................................................131

Appendices ....................................................................... IAppendix A: Scale Development .......................................................................... IAppendix B1: Questionnaire (Swedish Version – HG) ......................................VIIAppendix B2: Questionnaire (English Version – HG) ..................................... XIVAppendix C1: Cover Letter (Swedish Version)................................................. XXAppendix C2: Cover Letter (English Version)................................................. XXIAppendix C3: Reminder Postcard (Swedish Version).....................................XXIIAppendix C4: Reminder Postcard (English Version) .....................................XXIIIAppendix D: Testing for Non-Response Bias ...............................................XXIVAppendix E: Individual Construct Measure Validation and Purification..........XXVAppendix F: Metric Invariance – Results of the Multigroup Analysis ........... XXXIAppendix G: Overview of eHAM Fit Indices .............................................XXXII

LIST OF TABLES

Table 1.1: e-Health Categories ............................................................................ 4Table 2.1: Significant Determinants of PC Utilization .........................................18Table 2.2: Motivational Model Constructs ..........................................................19Table 2.3: Perceived Attributes of Innovations ....................................................20Table 2.4: TAM2 – Antecedents of Perceived Usefulness....................................22Table 2.5: Antecedents of Perceived Ease of Use.................................................23Table 2.6: Summary on the Role of Moderators in Existing Models ...................24Table 2.7: Antecedents of e-Health Acceptance ..................................................29Table 3.1: Additional Variables Identified in Previous Research ..........................38Table 3.2: Hypothesized Moderating Effects .......................................................48Table 3.3: The eHAM Constructs, Definitions and Hypotheses ..........................51Table 4.1: Age Distribution of the Swedish Population and the Sample Frames...56Table 4.2: Overview of this Study’s Measurement...............................................62Table 4.3: Response Rate by Type of Service and Sample Frame........................66Table 4.4: Gender distribution among the Responses and Sample Frames ...........67Table 4.5: Overview of Most Common Fit Indices and Rules of Thumb............71Table 5.1: Previous Use of e-Health among the Respondents .............................76Table 5.2: Cross-tabulation and Chi-square test on Use and Gender ...................76Table 5.3: Cross-tabulation and Chi-square test on Use and Age.........................77Table 5.4: Cross-tabulation and Chi-square test on Use and Level of Education..77Table 5.5: Descriptive Statistics on all Metric Items.............................................78Table 5.6: Overview of the Final Measurement ..................................................83Table 5.7: Correlations of the Constructs in the Measurement Model .................86Table 5.8: AVE and Squared Correlations among Constructs ..............................88Table 5.9: Means and Correlations of the Parceled Constructs.............................90Table 5.10: Rules of Thumb on the Strenght of Correlation Coefficients............90Table 5.11: Statistical Reasoning for First Modification .......................................92Table 5.12: Statistical Reasoning for Second Modification ..................................94Table 5.13: T-test – Differences in Group Means, Use........................................96Table 5.14: Comparison of Standardized Path Coefficients, Use..........................96Table 5.15: T-test – Differences in Group Means, Gender ..................................98Table 5.16: Comparison of Standardized Path Coefficients, Gender ....................99Table 5.17: T-test – Differences in Group Means, Experience...........................101Table 5.18: Comparison of Standardized Path Coefficients, Experience.............102Table 5.19: T-test – Differences in Group Means, Age......................................103Table 5.20: Comparison of Standardized Path Coefficients, Age........................104Table 5.21: Summary of Moderating Effects......................................................105Table 5.22: T-test – Difference in Sample Means between HG and ATD .........106Table 5.23: GOF-Indices of the A Priori and the Modified eHAM...................106Table 5.24: Comparison of the Standardized Path Coefficients ..........................107

Table 5.25: Overview of Hypothesis Testing ....................................................109Table 6.1: Overview of Propositions vs. Initially Stated Hypotheses ..................120Table D.1: Independent Sample t-Test to test for Response Bias...................XXIVTable E.2: GOF-Indices for the five-item Perceived Usefulness Scale.............XXVTable E.3: CFA on Result Demonstrability and Output Quality ...................XXVITable E.4: GOF-Indices of the Resulting Credibility Construct ................. XXVIITable E.5: GOF-indices of the Perceived Risk Construct .......................... XXVIII

LIST OF FIGURES

Figure 1.1: Spectrum of Care............................................................................. 5Figure 1.2: Organization of Chapters ............................................................... 12Figure 2.1: Theory of Reased Action............................................................... 14Figure 2.2: Technology Acceptance Model (TAM).......................................... 15Figure 2.3: Unified Theory of Acceptance and Use of Technology .................. 25Figure 2.4: ICTAM......................................................................................... 30Figure 3.1: The A Prioi e-Health Acceptance Model (eHAM) ......................... 49Figure 5.1: Modified eHAM on the HG sample (simplified image) .................. 93Figure 5.2: Modified eHAM on the ATD sample (simplified image) ................ 95Figure 6.1: Empirically derived eHAM (e-Health Acceptance Model).............120Figure E.1: 2-dimensional Credibility Construct (HG sample) .................. XXVIIFigure E.2: 2-dimensional Risk Construct (HG sample)...........................XXVIII

“The first wealth is health.”

Ralph Waldo Emerson

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1 Chapter One: Introduction This first chapter in this dissertation offers an introduction to the managerial problem and

objectives of this study. It provides a background for the research area and introduces the main concepts and ideas. This is followed by a problem discussion presenting the motivations for

conducting such research resulting in the formulation of the problem statement. At the end of this chapter, delimitations are discussed and an overview of the structure of the dissertation is

provided.

1.1 Introduction E-health (i.e., electronic health-care service delivery) is generally seen as a strategic tool for overcoming the challenges faced by health-care sectors worldwide (Chismar and Wiley-Patton, 2003). The huge potential that is attributed to e-health to help balance an enormous and consistently growing health-care demand with limited resources, has already led to an increased use of the Internet as a source for health information and service delivery. Additionally, the market for information technology in health care is expected to grow even further (Cline and Haynes, 2001; Kerwin, 2002; Powell et al., 2003; Tarre, 2003; Jai Ganesh, 2004; An, 2005). According to the Commission of the European Communities (2004) four out of five doctors in Europe had access to the Internet in 2004. A fourth of the European population used the Internet to collect health information and around 40% considered the Internet to be a good medium for collecting such information (Commission of the European Communities, 2004). Yet, the full potential of e-health is far from being tapped, which makes understanding citizens’ needs and expectations—and taking them in to consideration—essential and a precedent for moving toward more sophisticated and high quality health-care services.

1.1.1 Challenges in Current Health Care and the Role of e-Health

The challenges faced by public health care in Europe during the next several years stem mainly from the demographic development of our population (Lanseng and Andreassen, 2007). The average age of the population continues to increase steadily (Karlsson et al., 2004), thus posing a real problem to health care, as it is the elderly that represent the biggest consumers of health care (Tarre, 2003). This steadily increasing demand must be met. At the same time, public providers are under enormous pressure to become more efficient and cost effective, as public financial resources are limited, and health care constitutes one of the biggest targets for government expenditure (Tarre, 2003). As well, the number of children being born is decreasing (Cabrera et al., 2004), which aggravates the financial situation even more, since fewer people are paying in to the public fund. Another major

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issue in health care is access, as it is crucial to be able to offer health-care access to all citizens, including those living in remote areas, where it is often quite difficult to obtain care, particular among specialists (Tarre, 2003). Compounding this is the fact that within the next several years, the people born in the 1940s will begin to retire resulting in an immediate increase in the number of retirees (stemming from the baby boom in those years). This will lead to a smaller productive workforce that will result in a lack of human resources, and a deficit that will grow larger over the next decades (Cabrera et al., 2004). Now, the health-care sector as a workplace already has an increased rate of personnel turnover, and a lack of specialist doctors and nurses is already evident today (Tarre, 2003).

Applying information and communication technology (ICT) enables providers to deliver more citizen-centered health care faster and more efficiently (Duffy et al.,2003; Commission of European Communities, 2004; González et al., 2006). With its reach, the Internet offers the possibility to deliver health care on both a global as well as a local level (Duffy et al., 2003). The Internet can serve as a tool to improve access to services to geographically dispersed populations, support information exchange, increase revenue, reduce costs and improve the quality of care to patients (Kerwin, 2002; Commission of the European Communities, 2004; Jai Ganesh, 2004; González et al., 2006). Higher income levels, higher levels of education and computer literacy within the population as well as the desire by many people to make informed choices, enable citizens nowadays to be much more comfortable doing things electronically (Williams et al., 2002; Commission of the European Communities, 2004). The opportunity to link patients to health-care professionals via ICT—and health-care professionals on different levels to one another—is extremely attractive to policy makers around the world (May et al.,2001).

In light of the challenge of providing access to an increasingly ageing society, and in view of limited financial and human resources, it becomes crucial to investigate and understand how ICT can be utilized to improve health-care service delivery tocitizens.

1.1.2 E-Health defined As mentioned earlier, e-health constitutes the application of ICT across the whole range of functions that affect the health sector (Commission of the European Communities, 2004). Also other terms are often used interchangeably; these include e-healthcare, medical informatics, health informatics, consumer health informatics,telemedicine, telecare or telehealth. Most, however, refer to different aspects of technology in health care, which results in confusion about what really is meant by e-health (Pagliari, 2005).

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Pagliari (2005) thus attended to this problem and suggested a common definition of e-health based on the work of Eng (2001), which, according to Pagliari (2005), aptly represents the phenomenon. We regard this definition being the most suitable as it highlights the particular role played by the Internet in e-health. Accordingly, e-health is defined as:

“The use of emerging information and communication technology, especially the Internet, to improve or enable health and health care” (Pagliari, 2005).

There are two main objectives of e-health. The first one is to give more responsibility, power and information to the patient so that he/she can be an active part in his/her own health care (Baldwin et al., 2002a; Guillén et al., 2002; Nicholas et al., 2003). The second aim is to utilize ICT in the most effective and efficient ways to support the interaction of patient and health-care provider in primary as well as in secondary care (Baldwin et al., 2002a). E-health is about improving access to and the quality of health-care services to citizens (Mukherjee and McGinnis, 2007). As stated by Mukherjee and McGinnis (2007) the future of e-health envisions patients who are empowered by current health information relative to diagnosis and treatment to make their own decisions on their health care without having to leave the house.

In the following section, we will further explain what e-health comprises and how it can benefit providers and consumers. Also, the disadvantages and challenges posed by e-health will be discussed leading to the problem discussion and the problem statement of this research.

1.1.3 E-Health Services E-health services include everything from very basic health information services to the more advanced interactive services (Löfstedt, 2007). The most common e-health services and applications discussed are electronic prescriptions, telemedical applications (such as teleconsulation and telemonitoring), evidence-based medicine, electronically supported administrative functions, electronic patient records, specialist-oriented and patient-oriented information provision, virtual health teams and other services connecting stakeholders in health care as well as distance learning and provider education (Kassirer, 2000; Ligtvoet, 2003; Sharma et al.,2005; Wen and Tan, 2005; González, 2006).

Such e-health services can be broadly categorized into three main groups; namely (1) consumer information services, (2) telemedical care, and (3) health business support services (Bauer, 2002; Wilson, et al., 2004) (See Table 1.1).

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Table 1.1: e-Health Categories, source: Bauer, 2002

Category Example - Biomedical Web sites Consumer Information

Service - Online support groups - Tele-consultation - Tele-monitoring

Telemedical Care

- Decision Support Systems - Management needs in Health Care Health Business

Support Systems - Electronic Health Record

In general, there are three ways for consumers to access health information online: (a) by searching directly, (b) by attending online support groups, and (c) by consulting a health professional online (Cline and Haynes, 2001). As indicated in Table 1.1 above, typical consumer information services in e-health are biomedical Web sites and online support groups (some of which even include a health professional who moderates the discussion in the forum) (Bauer, 2002). These can be managed by either non-profit governmental or private sector organizations, or commercial for-profit companies that provide information and services for a fee (Bauer, 2002). Wilson and Lankton (2004) differentiate between sites that offer health information to the public and provider-delivered e-health, including services such as online formularies, e-prescriptions, test results, and doctor-patient communication. Telemedical care addresses direct patient care services such as tele-consultation via videoconferencing or simple email, helpbots (computer-based decision support software), and tele-monitoring (Bauer, 2002). In practical as well as academic literature, this is often referred to as telemedicine or telecare. Health Business Support Services is the fastest growing application area of e-health and covers Web sites as well as networks targeting the business and management needs of health-care providers. An application in this area that is frequently discussed and resides on almost all government agendas is the electronic health record (EHR) or the electronic patient record as it is also known.

1.1.4 Benefits of e-Health There are different levels of health care from self-directed care, to primary care, secondary care, tertiary care, and finally, to long-term care. Long-term care is directed towards chronic illnesses and disabilities because of which a hospital stay is no longer appropriate (Goldstein, 2000). As was explained previously, two main trends are apparent in health care at present. One trend is to embrace more patient-focused care that aims at improving the quality of care (Ligtvoet, 2003). A second trend lies in trying to shift from institutional care to home-based care in order to save costs. As can be seen in Figure 1.1 below, the more the trend heads towards institutional care, logically, the more expensive the service becomes. E-health can support both of these trends, shifting health care more towards the

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patient and to provide a base for self-care to citizens, as well as to provide a more citizen-centered and high-quality health care.

Figure 1.1: Spectrum of Care, source: Goldstein, 2000

The Potential to Reduce Costs Goldstein (2000) refers to a General Accounting Office study conducted in 1996, which found that less than half of the patients who come to the emergency department at a hospital actually need to see a physician right away. Also, other studies have found that many times patients actually do not need to see a doctor, as the information required could also be provided by other means than a face-to-face visit (Goldstein, 2000, p. 203). This affords an enormous potential for cost savings, constituting one of the major advantages of e-health. Several other studies have also demonstrated the potential of the Internet to lower costs in health care (Sharma et al., 2005).

Yet, e-health promises to deliver more benefits to health-care consumers, organizations, doctors, nurses and others involved in the provision of health care. In summary, these include:

the empowerment of citizens and promotion of self-care; improved access to health-care services; increased quality of more citizen-centered health-care services; and a facilitated communication flow and information exchange.

Empowerment of Citizens and Promotion of Self-care The application of the Internet gives more power to the consumer (i.e. the patient) (Chin, 2000), as it enables patients to collect health information themselves and in this way, educate themselves and become less dependent on the health-care professional (Potts and Wyatt, 2002; Bauer, 2002; Miller, 2002; Commission of the European Communities, 2004). A much wider spectrum of health information is made available to citizens via the Internet, supporting self-help and widening patient choice, activating and allowing them to assume responsibility for their own health (Gueler and Uebeyli, 2002; Thompson et al., 2002; Duffy et al., 2003;

Lower Costs Higher Costs

Self- directed Primary Secondary Tertiary Long-term

- Patient homecare - Prevention

- Primary care- Physician / allied health - Professional

- SpecialistOutpatient care

- Hospital - Institution

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Powell et al., 2003). This shift in power impacts the traditional doctor-patient relationship, thus challenging professionals to offer new types of services (Duffy etal., 2003).

Improved Access to Health-care Services The Internet also enables providers of public as well as private health care to improve access to health-care services (Cline and Haynes, 2001; Commission of the European Communities, 2004). Providing access to information, services as well as psychosocial support is crucial in the health-care sector (Bauer, 2002; Williams et al., 2002b). Yet, doctors, specialists, and other health-care providers usually are concentrated in populated regions (Miller, 2001). Providing health care electronically, therefore, offers the opportunity to improve rural access to health-care services, since the Internet’s reach is far greater than traditional avenues. Furthermore, online services are easily updateable, they are available 24/7, and they facilitate self-directed learning, thus making it more convenient to the citizen (Powell et al., 2003). Besides, citizens may value the anonymity of e-health, as there are certain health-related issues one might be interested in learning about but may not feel comfortable talking about with someone face-to-face (Cline and Haynes, 2001; Powell et al., 2003; Flicker et al., 2004; Burke and Weill, 2005).

Facilitated Communication and Information Exchange The health-care industry is a knowledge-based, information intensive industry involving plenty of data from hospitals, general practitioners, clinics, laboratories, and insurances, just to mention a few, (Grimson et al., 2000; Commission of the European Communities, 2004). Central to an information intensive industry is the ability to share this information effectively (Baldwin et al., 2002b). The interactivity offered by the Internet is likely the characteristic that has made the most profound impact on health care (Powell et al., 2003). This includes the possibility of communicating via email, which can fundamentally change the way things work, especially in health care, as it is steadily becoming an important part of medical practice (Powell et al., 2003). The Internet facilitates communication between health-care professionals and allows faster identification, organization, and transmission of health-related information (Bauer, 2002; Saritas and Keenan, 2004). The ability to access a complete sheet of information on the patients’ medical history brings enormous advantages to health-care professionals, as the duplication of medical steps can be avoided, efficiency increased and service quality improved simultaneously (Snyder et al., 2005).

The application of the Internet in health care has a strong influence on the relationship between the patient and the health-care provider. The shift of power to the patient can help create stronger partnerships between patients and doctors (Bauer, 2002; Sharma et al., 2005) since the Internet not only offers patients the ability to search for and collect health information, but it can also enable them to

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communicate directly with health-care professionals (Miller, 2002). Another area that becomes more tangible through applying the Internet is the area of C2C (citizen-to-citizen) or P2P (patient-to-patient), yet another important issue in health care. These forms of communication change the traditional ways of preventive health care and health promotion, offering both interpersonal interaction and social support (Eysenbach et al., 1999; Cline and Haynes, 2001). Support groups are available online for almost every disease and condition, supplemented by near endless numbers of discussion topics.

Increased Quality through More Citizen-centered Health-care Services Finally, the Internet enables health-care providers to increase the quality of health care by being able to provide more citizen-focused and tailored health care to their patients (Cline and Haynes, 2001). Kerwin (2002) identifies six facets that are key in improving health care, namely patient safety, effectiveness, patient-centered delivery that reflects patient preferences, timely service delivery, efficient resource use and equitable delivery of care. She argues that there are Internet applications capable of addressing those aspects, thus providing the opportunity to improve the quality of health service. Patients want to be informed and value the power offered by the Internet to be able to participate to a greater degree in their own health care (Sharma et al., 2005). Also, Finkelstein et al. (1999) recognizes that e-health services provide greater convenience to patients, since this makes it possible for them to access information and care from home, rather than having to travel to the doctor and stand in line (Anaert and Delesie, 2001; Baldwin et al., 2002a). Service quality for patients is further improved as the information exchange and flow between doctor and patient, and especially between doctors and other health professionals, is both facilitated and supported. Professionals increasingly recognize the potential of using the Internet to improve the quality of their services while simultaneously reducing costs (Mullner, 2002).

1.1.5 Drawbacks of e-Health There is no doubt about the various advantages offered by e-health. Yet, there are disadvantages and challenges that must be realized and understood to ensure that measures can be taken to overcome them. Drawbacks and challenges include:

replaced direct social and physical contact; time insensitivity; low quality health information online; privacy and security; misuse and abuse; malpractice;physician reimbursement; and access.

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Replaced Direct Social and Physical Contact One major difference between e-health services and traditional services is that direct social and physical contact is replaced. There is doubt about whether high quality health care can be provided via electronically mediated communication without including physical contact (Bauer, 2002; Jai Ganesh, 2004). Furthermore, health care online is relatively anonymous and lacks formality (Jai Ganesh, 2004), which might make consumers feel that health care becomes cold and lacks feeling (Kerwin, 2002), thus making them feel uncomfortable. This is commonly referred to as the dehumanization of care. Also, doctors may abstain from using the Internet for consultations due to the increased risk of misunderstanding (Burke and Weill, 2005; Westelius and Edenius, 2006).

Time Insensitivity Another disadvantage of health care via the Internet is its time insensitivity. Even though the 24/7 accessibility of the Internet poses an advantage for both provider and consumer, it can at the same time present a major drawback. Due to the lack of personal, real-time contact, it might be difficult or even impossible to respond to a health-care request immediately. An email for instance, might not be read immediately, an eventuality that could be dangerous or even fatal in an urgent situation (Bauer, 2002).

Low Quality Health Information Online Another major concern frequently identified in academic and practical literature is the quality of online health information (Eysenbach et al., 1999; Cline and Haynes, 2001; Kerwin, 2002; Duffy et al., 2003; Burke and Weill, 2005). Cline and Haynes (2001) explain that a lack of peer review or regulation on the net can lead to poor quality and inaccurate or misleading health information, which could have dangerous consequences. The problem is that anyone can provide information online and the source is not always clear. It can be very difficult for individuals to distinguish between good, questionable and patently incorrect information (Cline and Haynes, 2001; Kerwin, 2002; Burke and Weill, 2005). Often, Web sites providing health information already state that even though they attempt to provide only accurate information, they do not want to be held responsible for the reliability of the information posted or for the result of a treatment that was incorrect (Burke and Weill, 2005). Many sites emphasize that a health professional should be consulted before relying solely on self-treatment based on the information and advice provided (Burke and Weill, 2005).

Privacy and Security Privacy has been highlighted as one of the biggest concerns people have regarding health care on the Internet (Duffy et al., 2003; Burke and Weill, 2005). One’s health status or treatment interests are very private and sensitive issues. Yet,

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tracking makes it possible to save searching behavior without the individual’s knowledge or consent. Security remains an issue, since far from all electronic transmissions are secured by means of encryption technology (Bauer, 2002). There is an urgent need for standards for maintaining the privacy and confidentiality of an individual’s medical records (Jai Ganesh, 2004).

Misuse and Abuse There is a need for clear-cut rules for the online provision of prescriptions and drugs to avoid misuse (Burke and Weill, 2005). While in Sweden, e-prescriptions are still limited to renewals of previously filled prescriptions, other countries, such as the US, have difficulty controlling the distribution of drugs online, as there are different rules in different states (Burke and Weill, 2005). The Internet opens markets to people worldwide. Additionally, the question of how an individual can be certain that the person at the other end really is a health professional must still be tackled (Bauer, 2002).

MalpracticeA thin line already exists between formal and informal consultations in traditional health care, but this is even more pronounced in cyberspace, thus making the allocation of responsibilities, regulations and laws difficult (Cline and Haynes, 2001). Doctors may be reluctant to apply online means for consultation since it already is difficult enough to diagnose a patient without examining him/her. Recording a diagnosis that is based solely on electronic communication (which is the case in email consultations in contrast to, for instance, a telephone call) may indeed scare doctors (Kassirer, 2000). This issue of liability might also deter doctors from utilizing the Internet for consulting patients (Burke and Weill, 2005). The urgent need for standards and regulation in this area again becomes apparent (Jai Ganesh, 2004).

Physician Reimbursement In a study on doctors’ attitudes towards using email for consultations, Kittler et al.(2004) found that even though doctors generally thought positively about the option of consulting via email, they were concerned about not being compensated. This is an issue that must also be dealt with in order to make the Internet an attractive forum for health professionals as well (Jai Ganesh, 2004; Burke and Weill, 2005).

AccessFinally, even though improved access through online health care is emphasized as a main advantage and a significant opportunity for health-care providers, it also poses a distinct challenge. The challenge is represented by the fact that not everyone has access to the Internet. In fact, those in need of health care, who would benefit the most from home delivered health-care services, are also the ones who do not have

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access to it. In third world countries for instance, where improving access to health care is a major issue, online health care would obviously not have any impact on a population with low computer literacy and an inadequate infrastructure. Yet, also in the Western world, an insufficient infrastructure and computer illiteracy, especially among the elderly, can pose problems (Eysenbach et al., 1999). This issue of access for parts of the population, or the digital divide as it often is called, is a key topic in e-health that needs to be addressed (Kerwin, 2002; Duffy et al., 2003, Powell et al., 2003).

1.2 This Study

1.2.1 Problem Discussion and Purpose of this StudyBased on the preceding introductory discussion, we can summarize, that the demographic development of our society, exploding health-care costs, ever more limited budgets in the public sector, and the shrinking productive workforce, placed public health-care providers under enormous pressure to act. In light of those challenges, e-health has emerged as a potential savior and has gained increasing amounts of attention (Chismar and Wiley-Patton, 2003; Gueler and Uebeyli, 2002). The ability of e-health to improve access to health care is acknowledged, while at the same time, it is recognized that e-health provides the opportunity to empower citizens and to make the doctor-patient relationship more equal. Obviously, e-health is not just a buzzword; in reality, it can actually deliver immense benefits, improve the service quality of health care and provide greater efficiency (Hsu et al., 2005; Wen and Tan, 2005).

Yet, even though the potential of e-health has been recognized in practice as well as in academia, and in view of the fact that the first papers on e-health were published more than a decade ago, its application in the health-care sector has still proven to be remarkably difficult (Leonard, 2004; Shortliffe, 2005). Despite the fact that it is the biggest service industry, the health-care sector still lags behind other industries, and the full potential of e-health is far from being tapped (Parente, 2000; Orr et al., 2001; Kerwin, 2002; Chismar and Wiley-Patton, 2003; Fonkych and Taylor, 2005; Wickramasinghe et al., 2005; Shortliffe, 2005; Westelius and Edenius, 2006). Hsu et al. (2005) conducted research on the acceptance of e-health services over time and found that access to e-health services is growing rapidly. At the same time their results also show that most people, even though access is given, still do not use such services. This low level of use highlights the importance of continued research in the area before these new services can achieve their promise to improve the quality and efficiency of health care (Hsu et al., 2005).

Health-care services are quite different from ordinary services, which may be the reason for the slow overall response (Lanseng and Andreassen, 2007). Health-care

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services differ from other consumer services as individuals typically demand health care while under considerable stress, since their mind or body is involved in the process (Lanseng and Andreassen, 2007). Also, credibility is crucial in health care, as it really can be a matter of life and death. Therefore, learning what it takes to be able to leverage the Internet in health care as it has been possible in other industries, becomes imperative (Moehr et al., 2006).

Due to the slow adoption, research in the field is limited to exploratory reports and project descriptions that have a heavy focus on technology (Cline and Haynes, 2001; An, 2005). However, there is an urgent need for research into methods and conceptual frameworks that will help investigate the capabilities of the Internet in the context of health care (Cline and Haynes, 2001; Hsu et al., 2005). Cline and Haynes (2001) call for research on the “optimum use of the web for communicating about health and medicine.”

Löfstedt (2007) found that public health-care providers in Sweden believe that citizens lack interest in using e-health. Others argue that it is still unclear what types of services citizens value and how best to provide them (Howitt et al., 2002; Ross et al., 2003). Many e-health projects have been rejected and later failed as the services implemented simply did not correspond to what citizens wanted to use (Wilson and Lankton, 2004). Thus, as has been previously indicated by Vimarlund and Olve (2005), there is an urgent need to investigate the factors that influence citizens’ acceptance of e-health services. Knowing what determines, what hinders, and what drives individuals’ acceptance of e-services is crucial to health-care providers (Hsu et al., 2005; Wilson and Lankton, 2004; Shortliffe, 2005; Lanseng and Andreassen, 2007; Löfstedt, 2007). This discussion builds the base for this study’s research problem, which is formulated as follows:

Research Problem:

Consequently, the purpose of this research is to investigate citizens’ acceptance of e-health by identifying the factors that explain and predict their intention to use e-health services. The approach taken to serve this research purpose is to first uncover the factors that are relevant to citizens’ acceptance of e-health based on an extensive review of the literature and some exploratory field work. Then, in the scope of the main field study, the collection of quantitative data on citizens’ attitudes with respect to the previously identified factors will help describe the way in which the different factors influence each other, and in turn affect citizens’ intentions to use e-health.

What are the factors that influence citizens’ acceptance of e-health services?

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1.2.2 Delimitation As mentioned previously, e-health is defined as the use of emerging information and communication technology, especially the Internet, to improve or enable health and health care. Even though this definition emphasizes the critical role of the Internet, e-health includes a range of technologies and technological applications that can be used to process and exchange information. Until now, highly advanced tele-monitoring and teleconferencing technologies are used primarily by professionals to support their practices. These services are not yet developed to the stage at which individuals can choose to use them instead of traditional services. Consequently, as this study aims at investigating citizens’ acceptance of e-services in health care, it concentrates on the Internet (actually only the World Wide Web as part of the Internet) as the e-health medium, and examines services that health-care providers offer directly online and from which citizens are free to choose.

1.2.3 Organization of Chapters In the first chapter the reader was introduced to the managerial problem and the challenges posed by health care today, as well as, the concept of e-health as a solution. This has led to the problem discussion and the statement of the purpose for this research. The following chapter constitutes a summary of the literature review that has been conducted to identify relevant theories and models for this study’s theoretical foundation. This is then followed by Chapter Three which captures the development of the research model. Chapter Four provides an overview of the methodological approach taken, including a section on scale development and a discussion on the quality of the study. In Chapter Five the results and the analysis of the data are presented and discussed. In the final chapter of this dissertation, the conclusions are drawn, implications for managers and theory outlined, and limitations and suggestions for future research put forward.

Figure 1.2: Organization of Chapters

Chapter One: Introduction

Chapter Two:Literature Review

Chapter Three: Research Model

Chapter Five: Analysis and Discussion

Chapter Four: Methodology

Chapter Six: Conclusionsand Implications

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2 Chapter Two: Literature Review This chapter provides a summary of the literature. Primarily the Information Systems (IS)

domain has focused on the acceptance of new IT products. Perhaps the most successful theoretical framework in this area is that of the Technology Acceptance Model (TAM), which

will form the theoretical basis for this study. Thus, in the first section of this review, the TAM and its alternatives will be discussed. In the second section, more specific literature about the adoption of e-services will be reviewed, and the chapter will end with a brief

summary of research efforts on the adoption of e-health and more generally, barriers to e-government services.

In consumer behavior and services marketing research, several frameworks are put forward that capture the decision making process of consumers. These consist of steps including need recognition, pre-purchase search, evaluation of alternatives, the actual purchase, and post-purchase evaluation (e.g., Hoffman and Bateson, 2001). Such frameworks are useful for the investigation of an individual’s decision process when selecting among alternative services and evaluating a particular service. Yet, those frameworks are rather abstract and do not consider the medium (van der Hejden, 2003) which does, however, play a central role in the provision of e-health.

With a focus on the medium, the issue of technology acceptance has been studied quite extensively in information systems (IS) research. Within IS, there are various streams of research, with one focusing on the organization as an adopter, the second one investigating task-technology fit, and the third one concentrating on the individual as adopter of a new system or technology (Venkatesh et al., 2003). For investigating individuals’ technology acceptance, IS literature has made use of theories from social psychology and adapted them to the information systems context. This line of IS research provides the starting point for this study’s literature review.

2.1 User Acceptance and Adoption Behavior The underlying hypothesis in technology adoption literature is the assumption that an individual’s behavior (adoption of a system/IT) is determined by the individual’s intention to perform this behavior (intention to use the system/IT). This assumption is anchored in one of the most influential models in social psychology, in which most adoption theory, including that from other fields, find its foundation: the Theory of Reasoned Action (TRA).

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2.1.1 The TRA and the Evolution of the TAM The Theory of Reasoned Action (TRA) developed by Fishbein and Ajzen in 1975 is one of the most well established theories promising to predict and explain human behavior. It constitutes a general model not specific to any particular behavior (Ajzen and Fishbein, 1980). This conceptual framework is applicable to any context and can be used to explain why someone might purchase a new car just as well as why someone might vote for Obama versus McCain. TRA suggests that a particular behavior is determined by the intention to perform this behavior, as individuals generally behave as they intend. As stated above, the construct of intention is key and constitutes a well established construct in IS literature as well as in other fields (Venkatesh et al., 2003). The intention construct is believed to be a strong mediator of any other effects on actual behavior (Davis et al., 1989).

The TRA suggests that the intention to perform is determined mainly by two factors, the attitude toward the behavior and subjective norm. According to Ajzen (1988, p.4), an attitude is “a disposition to respond favorably to an object, person, institution, or event.”The determinant attitude toward the behavior reflects personal influences, how the individual evaluates performing this behavior. It is determined by the beliefs of the individual about the outcome of the activity and the perceived value of this outcome (Ajzen and Fishbein, 1980). Subjective norm catches the social impact, reflecting, “the person’s perception of the social pressures put on him to perform or not perform the behavior” (Ajzen and Fishbein, 1980). Then, TRA posits that individuals will perform a particular behavior when they have a positive attitude towards performing this behavior and when they believe that others important to them think they should perform it. Underlying the TRA is the assumption that the attitude stems from beliefs, meaning that what underlies an individual’s attitude towards behavior is the individual’s behavioral beliefs. The beliefs that constitute subjective norm are referred to by Ajzen and Fishbein (1980) as normative beliefs (an individual’s beliefs of whether the people close to him/her want him/her to perform or not perform the behavior). As proposed by Ajzen and Fishbein (1975), the TRA is visualized in Figure 2.1 below.

Figure 2.1: Theory of Reased Action (TRA)

Behavioral Beliefs

Normative Beliefs

Attitude

Subjective Norm

Behavioral Intention

ActualBehavior


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As mentioned before, the TRA is a very general model which is not aimed at explaining any specific behavior in a particular context. Thus, Davis (1986) proposed a model based on TRA to predict and explain an individual’s acceptance of a particular IT system. This model is called the Technology Acceptance Model (TAM) and was developed in the context of employees’ adoption of a particular system in their work environment. It became one of the most promising and influential models in IS literature (Agarwal and Prasad, 1999; Chen et al., 2002). Relying on TRA, behavioral intention (intention to use a system) is assumed the main determinant of the actual behavior (actual use of the system). TAM, also in line with TRA, further hypothesizes an individual’s intention to use to be determined by the individual’s attitude (attitude towards using the system). However, one of the main differences between TAM and TRA is that TAM does not include the social influence, the construct of subjective norm. Davis (1986), however, takes what was called behavioral beliefs by Fishbein and Ajzen (1975) and, applied to the context of IT adoption, breaks those down into two key constructs that should mediate all external effects on the attitude. Davis (1986) argues that the attitudinal beliefs that form an individual’s attitude towards using the technology relate to the perceivedusefulness and the perceived ease of use of the technology. The TAM is outlined in Figure 2.2 below.

Figure 2.2: Technology Acceptance Model (TAM)

Perceived usefulness is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989 p. 320) and is hypothesized to also influence behavioral intention directly. This is because it is assumed that employees might intend to use the system, as they believe it is useful and they are able to do their job better, but it does not necessitate a positive attitude by the individual towards the service (Davis et al., 1989). Perceived ease of use is defined as “the degree to which a person believes that using a particular system is free of effort” (Davis, 1989 p. 320). Perceived ease of use holds two main mechanisms by which it influences attitude and behavior, namely through self-efficacy and instrumentality (Davis et al., 1989). The self-efficacy mechanism is reflected in the hypothesized impact of perceived ease of use on attitude and the instrumental impact

PerceivedUsefulness

PerceivedEase of Use

External Variables

Attitudetoward Use

Intention to Use

ActualSystem Use

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on perceived usefulness (Davis et al., 1989), suggesting basically “the easier it is to use, the more useful it can be” (Venkatesh and Davis, 2000 p. 187). Even though both constructs are found to be significant determinants of the intention to use, perceivedusefulness emerges as a stronger determinant than perceived ease of use (Venkatesh and Davis, 2000). This finding has been confirmed repeatedly by later research. Davis (1989) argues that perceived ease of use might even be an antecedent to perceivedusefulness, as opposed to directly impacting on attitude toward system usage.

Davis et al. (1989) compare the two theoretical models TAM and TRA and their power to predict and explain user’s technology acceptance at two different points in time, finding both models to perform fairly well. Yet, where TRA explained around 32% of the variance in behavioral intention, TAM explained up to 50%. Also, subjective norm did not demonstrate any effect in either period. Davis et al.(1989) emphasize the strength of the construct of behavioral intention as the major determinant of usage behavior in both models, and explain that any other variable that impacts on usage behavior does this via behavioral intention. This is supported by Venkatesh and Davis (2000), who found the correlation of intention and actualuse in all tests to be between 0.44 and 0.57, with intentions being a significant mediator of all other impacts on use. Another major finding of the study by Davis et al. (1989) is the difference in the results depending on the period in time when the individual’s perceptions of the technology use were measured.

Yet, Davis et al. (1989) found that the attitude construct did not perform as well as hypothesized as the mediator of all effects on intention. In fact, the role of attitude as a mediator has been questioned earlier in social psychology research, with some scientists arguing that attitude, instead of being a strong predictor of behavior and a mediator of behavioral beliefs, poses one of many factors that influence behavior (Ajzen and Fishbein, 1980). For this reason, in some later research using TAM, the attitude variable has been omitted.

2.1.2 Alternative Models of User Adoption The TAM framework has been extensively tested and refined over the last several years. Indeed, it is now considered perhaps the most robust model for explaining an individual’s acceptance of a new technology. This motivates us to use the TAM as a theoretical foundation for this study. However, there are alternative approaches to modeling user’s adoption, which are briefly presented in this section. The discussion of additional perspectives helps identify additional constructs that may be pertinent to the adoption of e-health.


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Alternative models include:

- the Theory of Planned Behavior (TPB),- the Model of PC Utilization,- the Motivational Model, and - the Innovations Diffusion Theory (IDT)

2.1.2.1 TPB and the Role of Perceived Behavioral Control

The Theory of Planned Behavior is also based on TRA and extends it with the aim of better explaining an individual’s behavior in specific situations. The major difference between TRA and TPB is the addition of the concept of “control,” which is not considered in TRA and has been acknowledged as a major limitation of the model (Ajzen, 1991). The basic line of thinking is the same as in TRA, with the intention to use determining behavior and attitude toward the behavior and subjective norm as the key independent constructs. Yet, TPB hypothesizes that this only occurs when the behavior is under volitional control by the individual. Ajzen (1991) argues that this often is not the case. The additional construct of perceivedbehavioral control reflects the individuals “perception of the ease or difficulty of performing the behavior of interest” (Ajzen, 1991 p. 183). This concept corresponds to the perceived ease of use concept in TAM, but is more context-independent. Difficulty might be perceived due to limitations in time, money, skills, or cooperation of others, etc. (Ajzen, 1991).

Hence, according to TPB an individual’s behavior is determined by a joint function of intention and perceived behavioral control. TPB posits that the behavioral intention in turn is determined by a function of the attitude toward the behavior, subjective norm and again, perceived behavioral control. The antecedents of the constructs in the TPB are the same as in the TRA, namely beliefs. In line with TRA, behavioral beliefs are assumed to determine the attitude, normative beliefs to determine subjective norm, and control beliefs to be preceding perceived behavioral control (Ajzen, 1991).

In a much later study, Lee and Allaway (2002) use this concept of perceived control to investigate consumer’s adoption of self-service technology. Lee and Allaway (2002) propose personal control, which is a construct consisting of three distinct dimensions: predictability, controllability, and outcome desirability. Their results provide that personal control not only impacts on technology adoption in general, but also on perceived risk and the perceived value by the individual.

2.1.2.2 Model of Utilization of PC

Another model is proposed by Thompson et al. (1991), and was developed for the particular context of PC utilization. This model is based on the Theory of Human

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Behavior by Triandis (1977), which poses a comparative view to TRA and TPB. Thompson et al. (1991) predict utilization of personal computers by using the following constructs that are hypothesized to impact directly on PC utilization: job-fit, complexity, long-term consequences, affect towards use, social factors, and facilitating conditions. This was tested in an organizational context and the results suggest that social norms as well as complexity, job fit, and long-term consequences have a significant impact on usage. The definitions of these constructs are provided in Table 2.1 below.

Table 2.1: Significant Determinants of PC Utilization and their Definitions

Construct Definition Social Factors The individual’s internalization of the reference groups’ subjective culture, and specific

interpersonal agreements that the individual has made with others, in specific social situations (Triandis1980, p. 210; adapted by Thompson et al., 1991)

Complexity The degree to which an innovation is perceived as relatively difficult to understand and use (Rogers and Shoemaker, 1971, p. 154; adapted by Thompson et al.,1991)

Job-Fit The extent to which an individual believes that using a PC can enhance the performance of his or her job (Thompson, et al. 1991, p. 129)

Long-term Consequences

Outcomes that have a pay-off in the future, such as increasing the flexibility to change jobs or increasing the opportunities for more meaningful work (Thompson et al.,1991, p. 129)

The constructs Thompson et al. (1991) put forward in their model are similar to the TAM constructs, as job-fit and long-term consequences correspond to perceivedusefulness and complexity to perceived ease of use. In contrast to TAM, Thompson et al. (1991) include social influences, as part of TRA (subjective norm), and found support for it. No support was found, however, for affect, a construct similar to the attitudeconstruct in TAM, the role of which, as explained earlier, has already been questioned in TAM. Facilitating conditions, which are related to issues that Ajzen (1991) covers in perceived behavioral control, did not play a significant role in Thompson et al.’s (1991) model of PC utilization either.

2.1.2.3 Motivational model

Davis et al. (1992) have made one more attempt to model an individual’s technology adoption and propose the Motivational Model, also developed with the aim to explain what motivates employees to use computers in their work. The purpose of Davis et al. (1992) is to compare the impact of perceived usefulness on intentions versus the impact of enjoyment. They assume that perceived usefulness would account for a major part of the variance in intentions, but expected enjoyment to explain significant variance beyond that. Making use of motivation theory in psychology, the main constructs of the motivational model are extrinsic and intrinsic motivation. Davis et al. (1992) explain that perceived usefulness is reflected in extrinsicmotivation while enjoyment is reflected in intrinsic motivation. Two more variables,


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studied previously in the context of employees’ adoption of computers, were included as well, namely perceived ease of use (as put forward in TAM) as well as perceived output quality, both of which were hypothesized to be antecedents of both perceived usefulness and enjoyment. Finally, the moderator task performance was considered as moderating the impact of ease of use and quality on usefulness. The original definitions of the constructs are summarized in Table 2.2 below.

Table 2.2: Motivational Model Constructs and their Definitions

Construct Definition Extrinsic Motivation: Perceived Usefulness

The performance of an activity because it is perceived to be instrumental in achieving valued outcomes that are distinct from the activity itself, such as improved job performance, pay, or promotions (Davis et al., 1992, p. 1112)

Intrinsic Motivation: Enjoyment

The performance of an activity for no apparent reinforcement other than the process of performing the activity per se (Davis et al., 1992, p. 1112)

Perceived Ease of Use

The effort one experiences in the process of carrying out tasks using a given system (Davis et al., 1992, p. 1115)

Output Quality Judged by observing intermediate or end products of using the system, such as documents, graphs, calculations, and the like (Davis et al., 1992, 1115)

Task importance A measure of how important the computer-supported task is to a person’s job(Davis et al., 1992, p. 1115)

When tested, the motivational model performed well, strengthening the previous research which found the significance of perceived usefulness to usage intentions and pointing out the secondary but still significant role of enjoyment. Furthermore, the two constructs were found to mediate the impact of perceived ease of use and outputquality on usage intentions.

2.1.2.4 The Innovations Diffusion Theory

Derived from sociology, the innovations diffusion theory (IDT) by Rogers (1995) is another well-established and influential model of adoption behavior. Rogers (1995) argues that there are four main elements critical to the diffusion of innovations, namely the innovation itself, communication channels, time, and the socialsystem. In terms of the innovation itself, he claims that there are certain attributes that directly influence the rate of adoption of innovations. Those variables include the following: relative advantage, compatibility, complexity, triability, and observability,and influence an individual’s attitude towards the innovation and ultimately its adoption (Rogers 1995). One important difference between the IDT as proposed by Rogers and other IT user acceptance models is that Rogers measures the attitude of an individual towards an innovation itself and not the attitude towards using that innovation (Moore and Benbasat, 1996). Definitions of the perceived attributes of innovations are given in Table 2.3.

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Table 2.3: Perceived Attributes of Innovations and their Definitions

Construct Definition Relativeadvantage

The degree to which an innovation is perceived as being better that the idea it supersedes (Rogers, 1995, p. 212)

Complexity The degree to which an innovation is perceived as relatively difficult to understand and use (Rogers, 1995, p. 242)

Compatibility The degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters (Rogers, 1995, p. 224)

Triability The degree to which an innovation may be experienced with on a limited basis(Rogers, 1995, p. 243)

Observability The degree to which the results of an innovation are visible to others (Rogers, 1995, p. 244)

Herlitzer et al. (2003) apply this theory to the context of a rural telehealth program in New Mexico with the aim of assessing and predicting its adoption. They found IDT to be a suitable tool to understand technology adoption also in the context of e-health projects.

Moore and Benbasat (1996) propose an instrument for measuring an individual’s perceived innovation adoption based on IDT. They further develop and adapt its five main concepts and propose eight constructs with 34 items altogether. Apart from relative advantage, compatibility and triability, which were taken directly from IDT, Moore and Benbasat (1996) propose ease of use (related to IDT’s complexity construct) as well as demonstrability and visibility (which are both related to IDT’s observability). Moreover, voluntariness and image were included. The additional construct of image was considered relevant since social status is widely regarded as a strong motivational factor for an individual. Moore and Benbasat (1996) explain that Rogers actually regards image as part of relative advantage, but that they separate the two constructs, since previous research established a rather significant difference between them.

2.1.3 Extensions of TAM Since it was first proposed in 1986, TAM has been widely tested, extended, compared, and combined with alternative theories and models. Even though its power is widely acknowledged, current research still calls for further investigation to increase its capability to explain and predict technology acceptance by individuals (An, 2005). In the original model by Davis (1986), as in TRA, the impact of external variables is acknowledged. However, because those external variables are assumed to be fully mediated via perceived usefulness and perceived ease of use, they are not further specified. Since then, efforts have been made to identify antecedents of both perceived usefulness and perceived ease of use, and to integrate those


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into the TAM (e.g., Venkatesh and Davis, 1996; Agarwal and Prasad, 1999; Karahanna and Straub, 1999; Venkatesh, 2000; Venkatesh and Davis, 2000).Karahanna and Straub (1999) propose two antecedents to perceived usefulnesscovering the social aspects that have been reported missing in TAM, social influenceand social presence, as well as two antecedents to ease of use, namely perceivedaccessibility and user training and support. Their empirical test provided support for the influencing role of all antecedents apart from social training and support. This variable did not show significant impact on any of the TAM variables.

Agarwal and Prasad (1999) argue that individual differences play an important role in technology acceptance as individuals differ with respect to demographical variables and attitudes, which will in turn influence their acceptance of technology. Agarwal and Prasad (1999) propose five variables as antecedents to perceivedusefulness and ease of use, namely the role with regard to technology (provider or user),tenure in workforce, the level of education, prior/similar experiences, and participation in training. Empirical testing of the variables demonstrated that only the participation in training events has an effect on individuals’ perceptions of usefulness, which contradicts Karahanna and Straub’s (1999) findings as discussed above. The level of education, prior or similar experiences, and the role of the individual with regard to technology demonstrated an effect on perceptions of ease of use. The amount of time an employee spent within the organization did not show any effect on perceptions of usefulness or ease of use.

2.1.3.1 Antecedents of Perceived Usefulness – the TAM 2

The social impact reflected in the variable of subjective norm in TRA, is not considered in TAM. Davis et al. (1989) explain that it is not included since subjective norm is “one of the least understood variables” and has “uncertain theoretical and psychometric status.” Yet, this lack of social effects has led to repeated criticism of TAM (Chen et al., 2002). Venkatesh and Davis (2000) extend TAM by integrating antecedents to perceived usefulness, including the social influence variables (subjective norm and image) that have been reported missing in the original TAM, as well as cognitive instrumental processes variables (job relevance, output quality, and resultdemonstrability). Furthermore, they consider the moderating influences of experienceand voluntariness. The resulting model is called TAM2, and its constructs and their definitions are outlined in Table 2.4.

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Table 2.4: TAM2 – Antecedents of Perceived Usefulness and their Definitions

Antecedent Construct Definition Subjective Norm A person’s perception that most people who are important to him

think he should or should not perform the behavior in question(Fishbein and Ajzen, 1975, p. 302; adapted by Venkatesh and Davis, 2000)

Social Influences

Image The degree to which use of an innovation is perceived to enhance one’s … status in one’s social system (Moore and Benbasat, 1991 p. 195; adapted by Venkatesh and Davis, 2000)

Job Relevance The individual’s perception regarding the degree to which the target system is applicable to his or her job (Venkatesh and Davis, 2000, p. 191)

Output Quality The perceptions of people on how well the system performs its tasks (Venkatesh and Davis, 2000 p. 191)

Cognitive Instrumental Processes

ResultDemonstrability

Tangibility of the results of using the innovation (Moore and Benbasat, 1991, p. 203; adapted by Venkatesh and Davis, 2000).

Voluntariness The extent to which potential adopters perceive the adoption decision to be non-mandatory (Venkatesh and Davis, 2000, p. 188)

Moderators

Experience The level of experience with using a target system (Venkatesh and Davis, 2000)

Both image and voluntariness were previously proposed by Moore and Benbasat (1996) as part of their IDT-based scale (see page 20). Image is regarded as relevant since people often are influenced by social normative impacts with the aim of gaining a favorable image in a group (Venkatesh and Davis, 2000). Thus, TAM 2 posits that subjective norm exerts an impact on image, meaning that the opinion of people in a group perceived as relevant will influence an individual’s behavior, which will in turn have an impact on the individual’s status in the group (Venkatesh and Davis, 2000). Voluntariness as well as the additional variable experience function as moderators of the impact of subjective norm on perceived usefulness and intention to use, and will be discussed later (section 2.1.3.3 on moderators). The cognitive instrumental process variables are suggested as individuals, to some extent, base their judgments of perceived usefulness on what the technology or system offers them with respect to what they need to perform in their job (Venkatesh and Davis, 2000). TAM2 was tested in a longitudinal study on four different computer systems in four organizations. TAM2 was strongly supported with 40% to 60% of the variance in perceived usefulness perceptions being explained as well as 34% to 52% of the variance in usage intentions.

2.1.3.2 Antecedents of Perceive Ease of Use

It is argued that while the determinants of perceived usefulness have been more frequently investigated, the determinants of perceived ease of use have somewhat


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been overlooked (Venkatesh, 2000; Venkatesh and Davis, 2000). Venkatesh (2000) thus focuses on perceived ease of use and proposes antecedents to this construct. He believes these antecedents to be general beliefs about technology as well as beliefs that result from direct experience with the technology (Venkatesh, 2000). Venkatesh (2000) proposes constructs related to perceived behavioral control (as discussed in TPB), intrinsic motivation (from the motivational model), and emotion,namely computer self-efficacy, perceptions of external control, computer playfulness and computer anxiety. These are considered anchors, meaning they are system-independent. Individuals are expected to anchor their beliefs about how easy a system is for them to the general beliefs they have about computers (Venkatesh, 2000). After having gained experiences with the system perceived ease of use is expected to change or to be adjusted as Venkatesh (2000) explains. Hence, another two constructs representing adjustments (beliefs shaped due to direct experience) are also considered, namely perceived enjoyment and objective usability. Their definitions are presented in Table 2.5 below.

Table 2.5: Antecedents of Perceived Ease of Use and their Definitions

Antecedent Construct Definition ComputerSelf-efficacy

Perceptions of internal control: represents one’s belief about her/his ability to perform a specific task (following Compeau and Higgins 1995a; Venkatesh, 2000, p. 347)

Facilitatingconditions

Perceptions of external control: specific issues include the availability of support staff, which is an organizational response to help users overcome barriers and hurdles to technology use, especially during the early stages of learning and use(Venkatesh, 2000, p. 247)

ComputerPlayfulness

Intrinsic motivation: the degree of cognitive spontaneity in microcomputer interactions (following Webster and Martocchio, 1992; Venkatesh, 2000, p. 348)

Anchors

Computeranxiety

Emotion: an individual’s apprehension, or even fear, when she/he is faced with the possibility of using computers (following Simonson et al., 1987; Venkatesh, 2000, p. 349)

PerceivedEnjoyment

The extent to which the activity of using a specific system is perceived to be enjoyable in it’s own right, aside from any performance consequences resulting from system use (following Davis, 1992; Venkatesh, 2000, p. 351) Adjustments

Objective Usability

A construct that allows for a comparison of systems based on the actual level (rather than perceptions) of effort required to complete specific tasks (Venkatesh, 2000, p. 350)

These antecedents of perceived ease of use were integrated into TAM and their impact was investigated. When tested, up to 60% of the variance in perceived ease of use could be explained, which according to Venkatesh (2000) is twice as much as previous research can demonstrate. Venkatesh (2000) found that even after

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having gained experience with a particular system, what the individual generally thinks about the system still has the strongest impact on perceived ease of use.

2.1.3.3 Moderators

Moderators, such as demographical characteristics as well as other situational variables, can have a profound impact on user adoption and shift the dynamics in user acceptance models. As discussed earlier, Venkatesh and Davis (2000) suggest two moderators in TAM2, voluntariness and experience (see Table 2.2). Voluntarinessis suggested as moderating the impact of subjective norm on the individual’s intention to use, as only in mandatory situations, subjective norm was correlated with usage intentions (Venktaesh and Davis, 200). The construct experience is included since it is expected to moderate the impact of subjective norm on the perceivedusefulness as well as of subjective norm on intention to use. Experience constituted a significant moderator, as those individuals studied relied less on social influences as they gained experience with the system (Venkatesh and Davis, 2000).

Venkatesh et al. (2003) present a summary of previous research efforts that include moderators in user adoption models (pp. 433-435). These moderators are the two brought up by Venkatesh and Davis (2000), experience and voluntariness, as well as the demographics gender and age. An excerpt of the summary by Venkatesh et al.(2003) is outlined in Table 2.6.

Table 2.6: Excerpt of Summary on the Role of Moderators in Existing Models, source: Venkatesh et al. (2003, pp. 433-435)

Model Experience Voluntariness Gender AgeTRA Davis et al.

(1989),Karahanna et al.(1999a)

Hartwick and Barki (1994) impactsuggested though not included in model

N/A N/A

TAM and TAM2

Davis et al.(1989)Szajna (1996) Venkatesh and Davis (2000)

Venkatesh and Davis (2000)

Venkatesh and Morris,2000; Gefen and Straub, 1997

N/A

TPB Morris and Venkatesh (2000)

Hartwich and Barki (1994)

Venkatesh etal. (2000)

Morris and Venkatesh (2000)

Combined TAM-TPB

Taylor and Todd (1995)

N/A N/A N/A

Model of PC Utilization

Thompson et al.(1994)

N/A N/A N/A

IDT Karahanna et al.(1999a)

Not as a moderator but direct effect on intention

N/A N/A


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Venkatesh et al. (2003) attempt to combine all main user acceptance models that exist in IS research and propose an integrated model of user acceptance, which they call the Unified Theory of Acceptance and Use of Technology (UTAUT). A significant part in UTAUT is the inclusion of the above moderators and their impact on different relationships in the model. Venkatesh et al. (2003) found all four moderators to be significant for various relationships. The research model, called UTAUT, including the moderator variables, is depicted in Figure 2.3 below:

Figure 2.3: Unified Theory of Acceptance and Use of Technology

Age, in particular, has been found to moderate several relationships, as it repeatedly has been found to influence individuals’ ability to use new technological advances (Gilbert et al., 2004).

Gefen and Straub (1997) have taken a closer look at gender and technology adoption, and integrated the gender variable into TAM. Female behavior is often stated to be more tactful, gentle, emotional, and aware of other people’s feelings whereas men’s behavior is stereotyped to be more aggressive, independent, unemotional, logical, and competitive (Gefen and Straub, 1997). Gefen and Straub (1997) explain that previous research shows that differences in the sexes occur in computer-related circumstances and suggest that this might affect the acceptance of IT in organizations. Gefen and Straub (1997) found that there were differences in the initial expectations of men and women; yet, these did not have an effect on the actual use of IT.

Performance Expectancy

EffortExpectancy

SocialInfluence

Facilitating Conditions

Gender Age Experience Voluntariness of Use

Behavioral Intention

UseBehavior

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2.2 Technology Adoption in the Consumer Context – Self-Help Technology and Online Services

In general, IS research focuses on employees in an organization when investigating individuals’ adoption of IT systems. In contrast, it is much more challenging to convince consumers of adopting a technology or a system than it is to do so with employees in an organizational context (Curran and Meuter, 2005). It is thus very likely that different factors come into play. TAM has been applied to predict adoption behavior in the consumer context before and has been adapted and extended accordingly (e.g., Parthasarathy and Bhattacherjee, 1998; Moon and Kim, 2001; Chen et al., 2002; Koufaris, 2002; van der Heijden, 2003; Curran and Meuter, 2005; Lin et al., 2007). Below, a brief summary of the main findings of this research stream is provided, discussing those constructs that are considered most relevant to technology adoption in the consumer context.

2.2.1 Need for Interaction One of the most apparent differences between offline and online services is the missing personal interaction in online services. In their attempt to model the uptake of self-service technology by consumers, Curran and Meuter (2005) thus bring forward a construct called need for interaction. Curran and Meuter (2005) regard individuals’ need for interaction to be imperative to e-service adoption and define the construct, based on Dabholkar (1992), as “a desire to retain personal contact with others during a service encounter.” Data was collected on consumers’ attitudes regarding three technologies in the banking context (an ATM, online banking, and telephone banking). However, in none of the three banking technologies investigated did consumers’ need for interaction play a significant role in its acceptance.

2.2.2 Risk Apart from need for interaction, Curran and Meuter (2005) include another variable, namely consumers’ perceptions of risk. Risk is a situational variable and addresses the likelihood of a particular outcome given a behavior, and the threat and severity of negative consequences from performing this behavior. Risk was frequently touted as playing a decisive role in e-service adoption and has been investigated empirically by several other researchers as well (e.g., Forsythe and Shi, 2003; Pavlou, 2003; Featherman and Pavlou, 2003; Van der Hejen et al., 2003b; Bauer etal., 2005). Still, the nature of this construct remaines rather unexplored (Curran and Meuter, 2005; Featherman et al., 2006). The Internet provides a very open and global, yet distant and impersonal environment for transactions of any kind, which creates uncertainty for consumers (Pavlou, 2003). According to Bauer et al. (2005),


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consumers may even prefer to minimize risk than to maximize value, which implies that a risk perceived as too high can lead to the rejection of a service.

According to Pavlou (2003), risk perceptions in the online context mainly apply to two forms of uncertainty: environmental (related to the technology, the Internet, and the infrastructure) and behavioral (relational; related to the provider and service/product). Environmental uncertainty leads to economic and privacy risks. Behavioral uncertainty is connected to economic, personal, and seller performance. Several researchers discuss different risk dimensions with the most common dimensions being financial risk, performance risk, psychological risk, physical risk, personal risk, the risk of time/convenience loss, social risk, source risk, and privacy risk (Forsythe and Shi, 2003; Featherman and Pavlou, 2003; Lim, 2003). Still, Featherman and Pavlou (2003) put forward an overall risk dimension evaluating all criteria together as well.

In all studies, empirical evidence offers support for the influential role of the riskconstruct on consumers’ use of e-services (Featherman and Pavlou, 2003; Forsythe and Shi, 2003; van der Heijden et al., 2003; Bauer et al., 2005; Curran and Meuter, 2005). Curran and Meuter (2005) even found risk to be the most significant determinant, even though its impact varied depending on the technology investigated.

2.2.3 Perceived Playfulness/Shopping Enjoyment Perceived playfulness or shopping enjoyment as termed by others, is also considered as a relevant part of e-service adoption (e.g., Moon and Kim, 2001; Koufaris, 2002; van der Heijden, 2003). Perceived playfulness is based on intrinsic enjoyment (Koufaris, 2002) and defined as “the strength of one’s belief that interacting with a WWW will fulfill his or her intrinsic motives” (Moon and Kim, 2001, p. 224). Koufaris (2002) and van der Heijden (2003) found shopping enjoyment together with perceivedusefulness to have a positive impact on intention to return. Moon and Kim (2001) hypothesize perceived playfulness to have an effect on both the attitude towards, but also directly on the behavioral intention. When tested in the TAM, perceivedplayfulness performed well and demonstrated significant effects on the TAM variables in all three studies.

2.2.4 Compatibility Compatibility is one of the innovation characteristics key to the innovation’s adoption put forward in Roger’s (1995) IDT, and it has been incorporated repeatedly into e-service acceptance models. In fact, its importance has already been highlighted by Ajzen and Fishbein (1980). The construct measures the innovation’s compatibility with existing values and beliefs, previously introduced ideas, and potential adopters’ needs (Chen et al., 2002). Chen et al. (2002) integrate compatibility

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as an additional independent into TAM, influencing both attitude towards using as well as perceived usefulness. Wu and Wang (2005) propose compatibility as a direct antecedent to usefulness and usage intentions. Both Chen et al.’s (2002) as well as Wu and Wang’s (2005) findings suggest that compatibility is the strongest determinant of usage intentions.

2.2.5 Quality Criteria Lin and Lu (2000) have attempted to combine further IS theory with the TAM and propose the IS quality variables (information quality, response time, and systemaccessibility) as more technically oriented antecedents to perceived usefulness and ease of use. As presented earlier in this chapter, Venkatesh and Davis (2000) introduced a construct similar to information quality, namely output quality, as an antecedent to usefulness already in TAM2. Lin and Lu’s (2000) empirical investigation on a Web site provided support for the significance of the three constructs, accounting for almost 60% of the variance in perceived usefulness and almost 50% of the variance in perceived ease of use.

2.2.6 Technology Readiness Finally, in a rather recent study, Lin et al. (2007) argue that TAM for an online context lacks the inclusion of more individual-specific variables and integrate an individual’s technology readiness factor into TAM. Lin et al. (2007) postulate that an individual’s personal technology readiness affects all constructs in the TAM. Empirical results of a Web survey provide support for this proposition. Connected to this notion is the inclusion of a Web skills construct, as suggested by Koufaris (2002). Koufaris (2002) argues that the Web skills construct is similar to computer self-efficacy, which has been found to affect usage intentions through the emotional state of the user (e.g., by reducing anxiety). The empirical investigation confirmed an indirect effect of Web skills on usage intentions.

Other variables put forward by various researchers in the e-service adoption context include product involvement, value-added search mechanisms, and challenges(Koufaris, 2002) as well as visual attractiveness (van der Heijden, 2003). Parthasarathy and Bhattacherjee (1998) conclude that adopters can, apart from the perceived service attributes (such as usefulness and compatibility), be distinguished from non-adopters based on their sources of influence (external and interpersonal), serviceutilization, and network externality (complementary product usage), during their time of initial adoption.

2.3 User Adoption of e-Health As highlighted in the introduction of this thesis, research that relates to an individual’s adoption of e-health services is very limited. Even though literature shows quite some interest in e-health adoption, it often lacks theoretical grounding.


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Most papers found on e-health are generally descriptive in nature with the aim of simply testing the acceptance of a particular system empirically (Cline and Haynes, 2001). In the search for relevant literature for this research four studies on the issue of e-health adoption were identified (Wilson and Lankton, 2004; and An, 2005; Lanseng and Andreassen, 2007; Klein, 2007). These are presented in more detail in the following section. Finally, in order to explore further factors that may play a role in the very particular context of e-health services, a brief summary of literature on barriers to e-government in general is then presented.

Wilson and Lankton (2004) investigate models for explaining and predicting patient’s acceptance of provider-delivered e-health. Here, a study was conducted of patients who had recently signed up for e-services of a particular health-care provider and their use of the services offered. Wilson and Lankton (2004) test five antecedents of e-health acceptance that were identified in health-care literature and, so they argue, are of conceptual importance: namely individuals’ satisfaction with medical care, health-care knowledge, Internet dependence, information-seeking preference, andhealth-care need. The hypotheses Wilson and Lankton (2004) pose in terms of antecedents are outlined in Table 2.7.

Table 2.7: Antecedents of e-Health Acceptance, source: Wilson and Lankton (2004)

Proposed antecedent Hypothesis Satisfaction with medical care

Patients who are more satisfied with their medical care will have higher acceptance of e-health

Healthcare knowledge Patients with lower perceived health knowledge will have higher acceptance of e-health

Internet dependence Patients with higher Internet dependence will have higher acceptance of e-health

Information-seeking preference

Patients who have a higher information-seeking preference will have higher acceptance of e-health

Healthcare need Patients with higher health care need will have higher acceptance of e-health

The survey was distributed online with the questionnaire measuring the perceptual constructs intrinsic motivation/perceived ease of use, perceived usefulness/extrinsic motivation, and behavioral intention to use e-health from adoption literature in combination with the five hypothesized antecedents. Their results demonstrate that all five antecedents proposed perform well. Yet, Wilson and Lankton (2004) point to the need for investigation of additional antecedents, such as the patient’s health-care involvement, normative influences, and the socioeconomic status. Moreover, it is important to investigate the issue of continuing use (Wilson and Lankton, 2004). By including only patients who have signed up earlier for e-services, Wilon and Lankton (2004) may have biased their sample, since in this way, possibly only those individuals familiar with the Internet and e-services are sampled. This has been

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recognized by Wilson and Lankton (2004) as well, who thus call for continued research on the issue.

An (2005) proposes a model to explain and predict consumers’ health information and service usage behavior on the Internet. An (2005) uses the extended version of the TAM, the TAM2 as proposed by Venkatesh and Davis (2000), as the basis for her model, which she calls Information and Communication Technology Acceptance Model (ICTAM). As independent variables, An (2005) uses all cognitive instrumental processes of TAM2 apart from job relevance. Job relevance was excluded since the attitudes of individuals using e-health services are considered not to be task-oriented. Furthermore, job relevance is regarded as part of compatibility(Davis and Venkatesh, 2000). As originally suggested in IDT, An (2005) therefore considers compatibility relevant and incorporates it in to the ICTAM. The mediator variables in the ICTAM are perceived ease of use, perceived usefulness, and perceivedplayfulness and behavioral intention to use, as proposed by Moon and Kim (2001). The outcome variables of the ICTAM are Web site usage, leading to the self-reported measure of actual use and the amount of time spent using the technology in previous TAM models, as well as Web site loyalty. Web site loyalty, a construct adapted from marketing research, was not considered previously in TAM research. The ICTAM is pictured in Figure 2.4. An (2005) found that ICTAM explains between 47% and 74% of the variance in the behavioral intention to use e-health, which is more than TAM or TAM2 usually explain. She found most variables in ICTAM to be significant. The only construct that was not found to be significant is perceived ease of use, which supports previous findings. This might imply that health-care consumers regard perceived ease of use as part of usefulness or playfulnesson the Internet (An, 2005). An (2005) also investigates gender differences in connection with the behavioral intentions and found this demographic variable to be significant.

Figure 2.4: Information and Communication Technology Acceptance Model (ICTAM)

SubjectiveNorm

Compatibility


Output Quality

ImagePerceivedUsefulness


PerceivedPlayfulness

WebsiteUsage

WebsiteLoyality

SubjectiveNorm

Compatibility


Output Quality

ImagePerceivedUsefulness


PerceivedPlayfulness

WebsiteUsage

WebsiteLoyality

BehavioralIntentionto Use


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One of the two most recent studies is that conducted by Lanseng and Andreassen (2007), who investigate people’s readiness and attitude toward performing self-diagnosis by making use of the TAM assumptions. Yet, Lanseng and Andreassen (2007) saw difficulties in measuring intentions based on expectations, as most individuals have not used the technology before. Thus, they introduce another measure, the technology readiness index (TRI). Furthermore, trust in the service provider is integrated as Lanseng and Anderssen (2007) see it as a key issue in the health-care context, where credence is crucial but difficult to verify, and the quality of the service is difficult to assess (Lanseng and Andreassen, 2007). Here, TAM was also found to be a robust model that predicts acceptance sufficiently even in an e-health context. Surprisingly, Lanseng and Andreassen (2007) could not find support for the inclusion of trust in the service provider in TAM to increase predictive power, and therefore conclude that trust might not have a major impact in their e-health context, as the sample included can be regarded highly technology ready. However, we want to underscore that this non-significance of the trust construct should not be misinterpreted. As trust in their study is modeled as an antecedent to perceived usefulness and ease of use, it may, relative to model parsimony, not contribute substantially to the predictive power of usage intentions. Yet, this does not imply that the concept is of no importance to usage intentions.

The second example of recent research into e-health acceptance is put forward by Klein (2007), who uses a similar approach and integrates the issue of trust into TAM. Klein (2007) bases the study on the example of a third-party health-care portal and differentiates between trust in the health-care provider and trust in the vendorof the site. In contrast to Lanseng and Anderssen (2007), Klein (2007) finds support for the inclusion of trust in TAM in total explaining around 47% of the variance in actual use. However, Klein (2007) does not discuss further the single contribution of trust.

2.3.1 Barriers to e-Government and e-Health Services In the search for additional variables that become relevant to the issue of adoption in the specific context of e-health services, literature on barriers to health-care services as well as public services on a more general level has been reviewed as well. Gilbert et al. (2004) identify eight factors that are correlated to an individual’s willingness to use public services online. Gilbert et al. (2004) did not measure individual’s perceptions but identified those factors individuals consider important when deciding, whether to use a public service online. These factors are cost, time,visual appeal, experience, financial security, information quality, low stress, and trust.

Gilbert et al.’s (2004) results do not fully comply with previous work, which likely stems from the fact that, as explained earlier, technology acceptance models are developed in the context of employees rather than online consumer behavior.

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Two-thirds of the factors identified constitute barriers, which emphasizes the importance of dealing with the current challenges. Another convincing finding evidenced by Gilbert et al. (2004) is the significance of the difference in age and its correlation to the willingness to adopt public e-services.

Smith (2006) has investigated barriers to the uptake of e-prescriptions in the USA and tested the importance of several constructs on individuals’ willingness to purchase prescriptions online. Smith (2006) found that a lack of technology trust as well as associated system cost pose barriers as well. Moreover, according to Smith (2006) the risk of unsecured patient health and medical information hinders the acceptance of e-prescriptions in the USA.

2.4 Summary of the Literature Review In order to investigate citizens’ acceptance of e-health services, an extensive literature review was conducted. Since the Internet as the medium plays a crucial role in e-health service delivery, the research stream considered most relevant to the purpose of this study is the research stream on user adoption from the IS field. It builds on the assumption that behavior can be determined by the individual’s intention to perform this behavior, an assumption rooted in social psychology. Several models are advanced, among them the Technology Acceptance Model (TAM). The TAM is known for its power, robustness, and applicability to different contexts, and has thus been widely used.

In the first part of this literature review, the evolution of the TAM was outlined, starting with the Theory of Reasoned Action. Also, alternative models to the TAM were presented, including the Theory of Planned Behavior, the Model of PC Utilization, the Motivational Model, and the Innovations Diffusion Theory, in order to explore additional variables that are not considered in TAM. The second section of this literature review summarized literature that deals with the extension of TAM by proposing antecedents to perceived usefulness and perceived ease of use as well as moderating effects. In the third section then, research that applies the TAM to the consumer context was outlined, identifying further variables that become relevant when moving to the online context. These include need for interaction, risk, perceived playfulness, compatibility, quality criteria, and technology readiness. Finally, in the last section of this chapter, previous research on e-health adoption as well as on a more general level, barriers to public e-services, was presented. As it was highlighted in the introductory chapter of this thesis, literature on e-health adoption is still very limited and most often focuses on technical aspects. Four studies on consumer’s acceptance of e-health could be identified, which particularly emphasize the crucial role of trust in this context.

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3 Chapter Three: Developing the Research Model This chapter guides the reader through the development of this study’s research model. In the first section of the chapter, the main assumptions based on TAM are summarized, and the first hypotheses are developed. The second section covers the process of how the model was

extended. Based on the results of an exploratory study, a series of additional variables relevant to the e-health context were added to the model. The research model is then proposed in the final section, all constructs are defined, and an overview of the hypotheses to be tested

is provided.

3.1 The Technology Acceptance Model: the Foundation of the Research Model

The purpose of this research is to investigate citizens’ acceptance of e-health by identifying factors that explain and predict their intention to use e-health services. The literature review presented in the previous chapter provides that the Technology Acceptance Model (TAM) as set forth by Davis in 1986 is a well-established and powerful model explaining and predicting individuals’ acceptance of technology. As argued by Venkatesh (2000), TAM is rather robust and parsimonious and demonstrates high predictive power, which makes it easy to apply to different contexts. TAM provides a basis for tracing the impact of external factors on internal beliefs, attitudes, and intentions (Davis et al., 1989, p. 985). To this end, theoretical insights from TAM will be employed as the basis for the research model in this investigation.

3.1.1 Main assumptions: Actual Use, Intention to Use and Attitude toward Use

The main assumptions underlying the TAM form the base of the research model for this study (see Figure 2.2 on page 15). Actual use is proposed as the final dependent variable in the original TAM, representing the measurement of acceptance. All effects on actual use are hypothesized to be mediated by the construct intention to use. The concept of intention to use constitutes “a measure of the strength of one’s intention to perform a specified behavior” (Davis et al., 1989 p.984) and has in later research frequently been used as the dependent construct and measure of acceptance instead of actual use. Intention to use builds a suitable measure for acceptance, especially in situations where the technology is still at a very early stage and actual use is thus difficult to measure (Lanseng and Andreassen, 2007). The notion of behavioral intentions has thus gained vast attention throughout various research fields. Empirical support for the reliability of the causal relationship between intention and actual behavior could be found in both attitudinal and IS research (Davis et al., 1989; Ajzen, 1991; Lanseng and Andreassen, 2007). Due to the early stage of e-health and an expected low level of use, difficulty with the

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actual use construct as the dependent variable also arises in this study. Consequently, and since the causal impact of intention to use on actual use can be regarded as rather established, usage intentions will instead serve as the main dependent variable and represent acceptance in the final model to be tested.

TAM comprises attitude toward use as the mediating construct, a construct similar to intrinsic motivation from motivational theory and the affect construct proposed by Thompson et al. (1991). It is defined by Davis et al. (1989 p.984) based on the original established by Fishbein and Ajzen (1975) as “an individual’s positive or negative feelings about performing the target behavior.” However, there has been doubt about the significance of attitude as the mediating construct, expressed even by Davis himself, as direct effects of the beliefs (perceived usefulness and ease of use) on intention to use have been observed as well. The notion behind the attitude construct, however, is that it captures the influence of the beliefs. If it there are direct effects of the beliefs on intention, the attitude construct would lose its value as the mediator (Davis et al., 1989). Davis and colleagues (1989), for instance, found TAM to explain equally well even without the attitude construct, yet being more parsimonious.

According to Legris et al. (2003), who compared 22 studies that applied TAM to various contexts, found that only seven of these studies investigated both attitudeand intention to use. Only three studies included attitude, and eight intention to use.Four studies ignored both constructs and focused solely on direct effects on actual use. Despite of the doubt about the role and significance of the attitude construct, it will be considered and included in the research model. Testing the impact of this construct will not only provide additional empirical evidence, which may help clarify the inconsistencies on the role and dynamics of the attitude construct as the mediator, but might also lead to some insight on the existence of possible barriers. In line with previous work, we pose the following hypothesis:

H1: Attitude toward using e-health will have a significant positive effect on intention to use e-health.

3.1.2 Perceived Usefulness and Ease of Use TAM posits that all external variables that influence an individual’s acceptance of a particular system are mediated by the two key constructs perceived usefulness andperceived ease of use.

3.1.2.1 Perceived Usefulness

Perceived usefulness is defined by Davis (1989) as “the degree of which a person believes that using a particular system would enhance his or her job performance (Davis et al., 1989, p. 985). It is similar to the construct relative advantage from IDT, extrinsic motivation


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(MM), job-fit (MPCU), and outcome expectations (SCT). Perceived usefulness consistently appears as the strongest determinant of attitude toward use (Venkatesh etal., 2003), with standardized regression coefficients typically around 0.6 (Venkatesh and Davis, 2000).

As explained by Moore and Benbasat (1991), an innovation or a new system is meant in a certain way to be better than its predecessor, which is evident in the term relative advantage Rogers (1995) uses. Even though Davis’ perceived usefulnessconstruct does not include the term “relative,” the definition of the construct is in relative terms (Moore and Benbasat, 1991). Davis (1989) defines perceived usefulnessbased on the definition of the word useful, being “capable of being used advantageously” (Davis, 1989, p. 320). It provides that the innovation used is perceived as useful if it delivers advantages over an alternative system or product. In the organizational context for which TAM was originally developed, perceivedusefulness was connected to job performance. However, when placed into a non-organizational context, the definition of perceived usefulness must be adjusted. Therefore, it will be redefined to fit the context of this study. In this study, perceived usefulness is understood as the degree to which an individual believes he or she will gain from using e-health in a health-related matter.

Previous work on TAM and related literature suggests that perceived usefulness has an impact on intention to use, both via the individual’s attitude towards using as well as directly, leading to the following hypotheses:

H2: Perceived usefulness will have a significant positive effect on attitude towards using e-health.

H3: Perceived usefulness will have a significant positive effect on intention to use e-health.

The direct impact of perceived usefulness reflects the situation in which the individual does not necessarily have a positive attitude towards the use of an innovation, but due to the advantages expected of its use, intends to use it anyway.

3.1.2.2 Perceived Ease of Use

Perceived ease of use is defined by Davis (1989) as “the degree of which a person believes that using a particular system would be free of effort” (Davis et al., 1989, p. 985). The definition is based on the definition of the word “ease,” being “freedom from difficulty or great effort,” (Davis, 1989) and is used in this study. According to Venkatesh et al. (2003), the ease of use construct is related to complexity (MPCU), and ease of use from IDT as well as the effort expectancy construct they suggest in their study. Furthermore, it is related to, yet more specific as, the behavioral control

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concept proposed in TPB. In comparison to perceived usefulness, perceived ease of usehas demonstrated a much less consistent impact on usage intentions throughout previous research (Venkatesh and Davis, 2000).

Davis (1989) explains that ease of use comprises essentially two dimensions, self-efficacy and instrumentality, which impact on usage intentions in different ways. An individual’s self-efficacy will increase the easier the system is to use. Self-efficacy is seen as one of the main drivers of intrinsic motivation and its impact is reflected in the hypothesized direct effect of perceived ease of use on attitude (Davis, 1989). The instrumental dimension of perceived ease of use grasps the instrumental impact of an easier to use system on performance, being reflected in the hypothesized relationship on perceived usefulness (Davis, 1989). This implies that the easier a service is to use, the more useful it will be perceived. The following hypotheses are posed:

H4: Perceived ease of use will have a significant positive effect on attitude toward using e-health.

H5: Perceived ease of use will have a significant positive effect on perceived usefulness.

It is suggested that its influence is greater in the beginning and becomes non-significant over periods of extended and sustained use (Venkatesh et al., 2003). In the context of this study, it will likely be connected to an individual’s computer skills and level of Internet experience. Also, depending on whether it is studied in the pre- or postadoption stage, its impact may differ. In a preimplementation phase, perceived ease of use is suggested to impact directly on intentions, and in the postimplementation situation, only indirectly via perceived usefulness (Szajna, 1996).

3.2 Extending TAM The parsimony of TAM, which on one hand makes the model so powerful, on the other represents one of its major limitations (Venkatesh, 2000). According to Venkatesh (2000), TAM does not explain behavior sufficiently to be able to draw conclusions about how systems need to be designed in order to be accepted by potential users. Also, it has been argued that TAM lacks task focus (Moon and Kim, 2001). TAM contains external variables, emphasizing that, if these have an impact on use, will do so via the mediating constructs perceived ease of use and perceived usefulness. These external variables represent various individual, contextual and system specific differences, as well as managerially controllable factors that influence the internal beliefs, attitudes and intentions represented in TAM (Davis, 1989). However, TAM does not specify these external variables further. Previous research thus recommends enhancing TAM by extending the model through identifying


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and integrating particular external variables that significantly affect the key constructs in TAM (O’Cass and Fenech, 2003). Davis himself highlights that future research should investigate how other variables impact on perceived usefulness,perceived ease of use and intention to use. Identifying controllable external variables and being able to trace the impact of those on perceived usefulness, attitude and intentions, enables managers to develop strategies to influence user adoption (Davis, 1989; Legris et al., 2003).

TAM has been designed to fit the context of employees’ use of IT in job-related tasks. Yet, employees are only users of a technology or system; they are not evaluators or purchasers of that technology (Lanseng and Andreassen, 2007). As consumers’ acceptance of a technology in a service context can be quite different from employees, further exploration and investigation is necessary. In consumer services, the customer is highly involved in the production of the service, which according to Lin et al. (2007), might further limit the explanatory power of TAM. For the purpose of this study, TAM will be extended with constructs that have been recognized as being missing in previous research, as well as constructs that become relevant in a service context and in the particular context of e-health.

3.2.1 Exploratory study Different factors might contribute to the acceptance of an innovation depending on the technology, target user and the context (Moon and Kim, 2001). As it is impossible to consider all variables that play into an individual’s decision, we will focus on those that are critical to e-health adoption. Therefore, the relevance of the additional variables put forward in previous research must be assessed carefully. A small, exploratory study was conducted with the aim of identifying which of the additional factors proposed in previous literature are the most relevant in the context of a citizen’s adoption of e-health, or more precisely, to identify those constructs that are not relevant and can thus be excluded from further investigation.

3.2.1.1 Method

Ten individuals (five male, five female), between 22 and 57 years of age, with different educational backgrounds (two students, two engineers, two administrators, two researchers, one professor in nursing and one manager in early retirement), and varying levels of experience with e-health were interviewed. The interviews were held face-to face (apart from one which was conducted on the telephone) and divided in two parts. First, e-health was discussed on a general level to familiarize the subjects with the concept and some example services (as most of them were not aware of the existence of such e-health services), and to explore the individuals’ general attitude. Also, particular focus was given to issues, concerns, or comments that were made by the individuals, as they may be indicators of what influences their acceptance.

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In the second part of the interview, three common e-health services were presented to the individuals: (1) an online health dictionary with general information on diseases, symptoms and treatments (hereafter referred to as online health guide), (2) online prescription renewal, and (3) an ask-the-doctor online service. Those three services are the three most common e-health services that are readily available to citizens in Sweden today. A list of variables brought up in connection to the theories and models discussed in the previous chapter was created (see Table 3.1 for the list of additional variables). During the interviews, the individuals were told to put those variables in order, following the importance of the variables to their decision on whether to use an e-health service. Each individual did this twice, once for the e-health service listed above they considered themselves most likely to use, and for the one they considered themselves least likely to use. For each construct, a definition was provided to make it easier for the individuals to understand its meaning. Yet, the researcher was present the entire time and further explained the meaning of the variables if the individuals experienced problems. After having placed the variables in order, the researcher asked each interviewee what each variable meant to him/her in the context of the service they evaluated and in what way it was or was not important to them. This information would also be beneficial later when developing measurements for the variables. The interviewees were also asked to think about possible additional factors that influence their decision but have not been included in the ranking.

Table 3.1: Additional Variables Identified in Previous Research

Construct Type Model Source Subjective Norm Independent,

antecedent to usefulness

TRA; TAM 2; ICTAM

Ajzen & Fishbein 1980; Venkatesh & Davis 2000; Chau & Hu 2001; An 2005; Bauer et al. 2005

Image Independent, antecedent to usefulness

TAM 2; ICTAM

Moore & Benbasat 1991; Karahanna et al., 1999a; Venkatesh et al. 2000; An 2005

Output Quality Independent, antecedent to usefulness

TAM 2; ICTAM; TAM online

Venkatesh & Davis 2000; An 2005; Lin & Lu 2000

Result Demonstrability

Independent, antecedent to usefulness

TAM 2; ICTAM

Venkatesh & Davis 2000; An 2005

Compatibility Independent, affecting attitude

IDT;ICTAM

Rogers 1995; Parthasarathy & Bhattacherjee 1998; Chau & Hu 2001; An 2005; Chen et al., 2001; Wu & Wang 2005


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Construct Type Model Source Playfulness/Enjoyment

Independent, affecting attitude

TAM in an online and self-servicecontext;attribute-based model

Davis et al. 1992; Dabholkar 1996; Moon & Kim 2001; Koufaris 2002; van der Heijden 2003

Risk Independent, affecting attitude

TAM in an online and self-servicecontext

De Ruyter et al. 2001; Featherman & Pavlou 2003; Pavlou 2003; van der Heijden 2003b; Bauer et al. 2005; Curran & Meuter 2005; Wu & Wang 2005

3.2.1.2 Delimitations

With the intention to maximize the proposed model’s predictive and explanatory power while simultaneously keeping its complexity and dependence on one specific context to a minimum, trade-offs were required. Table 3.1 outlines a summary of antecedents to perceived usefulness and other independent variables that have been proposed in previous research. Also, antecedents to perceived ease of usehave been proposed (see Venkatesh, 1996/2000) and found to have rather high predictive power of perceived ease of use. Yet, the impact of perceived ease of use on attitude towards using and in turn intention to use has been found to be relatively low compared to perceived usefulness (Venkatesh and Davis, 2000). Its impact has even been found to be indirect via perceived usefulness and in some studies even trivial or non-significant. Even though we acknowledge the existence and relevance of antecedents to perceived ease of use, considering the above argument, it has been decided not to include antecedents of perceived ease of use in the research model for this study.

Also, voluntariness and job relevance, as originally developed in and for the context of system adoption by employees in an organization, were not considered further in this investigation, as they do not apply to the consumer service context.

3.2.1.3 Results

The ranking of the constructs by the ten individuals demonstrated consistency in terms of two constructs that were regarded as not relevant by all interviewees. These two constructs are image and playfulness/enjoyment, and were consistantly given the lowest ranking points by all individuals. The unimportance of those two constructs is due to the change of context from organizational to consumer health services, and thus provides sufficient motivation for the exclusion of those two constructs in further investigation. All other constructs were considered relevant by the individuals to differing degrees, yet with output quality, result demonstrability and risk ranked highest in all interviews. This motivates us to include all other variables

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to investigate further their relevance to individuals’ intention to use e-health and their impact on other constructs in the model.

Apart from assessing the relative importance of the constructs suggested by previous researchers, another aim of the exploratory study was to ascertain whether any other factors might exist, that influence individuals’ acceptance of e-health. Two additional variables that were not considered in the ranking were identified: firstly, trust was highlighted as one of the most pivotal factors for the individuals, and secondly, a lack of access to the service was brought up as a possible barrier to adoption.

The following section comprises a continuation of the hypothesis development based on the additional variables that were considered relevant in the exploratory study. This includes an additional but brief literature review on the two variables that had not been covered in the preceding literature chapter, access and trust.

3.2.2 Additional Hypotheses

3.2.2.1 Access and Perceived Accessibility

Inaccessibility is a likely reason for rejection (Culnan, 1984) and can become an issue when moving from the employee context to the online consumer context. In most previous studies on TAM, this issue is controlled by presuming access is given, meaning the respondents are asked to answer the questions given they had access to the service. This makes perfect sense in an organizational context, where employees are provided with a particular system by the organization. The exploratory study demonstrated that access can, however, be critical in the e-service/e-health context, as a lack of access to the Internet, for instance, might go as far as contributing to the so-called digital divide. In IS literature, some earlier efforts have integrated accessibility to acceptance models, such as O’Reilly (1982), Culnan (1984), Rice and Shook (1988), Davis et al. (1992), and most recently, Karahanna and Straub (1999). Rice and Shook (1988) use an objective measure of access, such as actual, physical access to an information source. On the other hand, O’Reilly (1982), Culnan (1984) and Karahanna and Straub (1999) employ an attitudinal variable called perceived accessibility, which constitutes a better measure for this construct when investigating the early phase of an innovation and thus poses a somewhat more hypothetical setting.

In the Oxford English Dictionary, the word “accessible” is defined as “able to be reached, entered, influenced, or understood.” Perceived accessibility captures an individual’s perception of the ease or difficulty to gain access to or reach something. Accordingly, we define perceived accessibility as an individual’s expectation of how easy or difficult it is for him/her to access the e-health service.


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It has even been argued that perceived accessibility of information likely is a more powerful predictor of choice of information source than the actual quality of the information (O’Reilly, 1982; Culnan, 1984; Rice and Shook, 1988). According to O’Reilly (1982), this is due to the vast amount of information available and, in an employee environment, strong pressures to produce results. Rice and Shook (1988) add that the importance of accessibility probably lies with the nature of information itself. Before accessing and receiving it one doesn’t know its quality; therefore, before accessing the information one does not know if it is worth going through whatever effort might be required to access it. This also means that perceived accessibility is likely to be most significant in the early stage of adoption, where the quality of the information has not yet been experienced much or at all. According to O’Reilly (1982) and Culnan (1984), perceived accessibility determines use constantly and independent of other variables that are significant to use.

Even though the three earlier studies (O’Reilly, 1982; Culnan, 1984; Rice and Shook, 1988) found support for the hypothesized effect of perceived accessibility on use, Davis et al. (1992) reports inconsistent results, with a significant impact found in one study but not in the other. Davis et al. (1992) argue that this may depend on whether access actually is an issue in the respective context of study. As, for example, a lack of access to the Internet would make it impossible to consume an e-health service, we suggest that this attitudinal variable has a positive and direct impact on an individual’s intention to use e-health. A direct impact rather than a mediated impact by attitude is assumed, since an individual may have a positive attitude toward using the service but does not intend to use the service in the future, simply due to a lack of access. This had been pointed out during the exploratory study as well. Accordingly, we posit that:

H6: Perceived accessibility of the e-health service will have a significant positive effect on intention to use the e-health service.

3.2.2.2 Trust and the Perceived Credibility of the Health-Care Provider (HP)

In the exploratory study, the most important factor that kept coming up was the issue of trust. In e-commerce research, trust has been found to have a direct, positive effect on usage intentions (e.g., Gefen, 2000; Gefen and Straub, 2003; Pavlou, 2003). In previous literature, a lack of trust in the provider is actually touted one of the main reasons why consumers hesitate to engage in e-commerce (Pavlou, 2003; Lee and Turban, 2001; Grabner-Kraeuter and Kaluscha, 2003; Lanseng and Andreassen, 2007; Klein, 2007).

An enormous body of research exists on trust, causing notable confusion about how it should be defined and what it really includes. Various researchers look at different forms and facets of trust (e.g., trust in the service, in the service provider,

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in the offline service provider, trust in the technicality of the Web site, or trust in the Web site itself).

In health care, the issue of source credibility is imperative (Mukherjee and McGinnis, 2007), as credence properties are crucial but can normally not be verified easily, thus making it difficult to assess the quality of the service (Lanseng and Andreassen, 2007). In the online context, this issue is aggravated, since there even more uncertainty exists. The biggest concern of the individuals in the exploratory study was doubt about whether one can rely on the information provided. Is the person providing information really a professional? Has he/she really, carefully, read my question? Is the information provided correct? Again, the degree of uncertainty in an online environment is much larger than in traditional settings (Grabner-Kraeuter and Kaluscha, 2003; Pavlou, 2003) and as put by Mukherjee and McGinnis (2007), most people are either unable or unwilling to assess the value of the information provided online. There is greater distance between the individual and the service provider, as well as a lack of face-to-face contact (Gummerus et al., 2004). Trust in this context thus describes the individuals’ perceptions of the credibility of the health-care service provider as the source of the health information that is obtained. Highly credible sources are known to generate more positive attitudes than less credible sources (Ohanian, 1991). Perceiving the provider as credible in turn, means that the individual can express trust so that the uncertainty involved in using the e-health service and the possible risks involved can be overcome. Following the definition of the word credible in the Concise Oxford Dictionary, a credible source is a “believable” and “convincing”source. According to Lanseng and Andreassen (2007), credibility refers to “the extent to which one partner believes that the other has the required expertise to perform effectively and reliably” (p. 402). In line with the other constructs in the research model, the perceived credibility of the health-care provider is measured. Accordingly, perceived credibility is defined as the extent to which an individual perceives the e-health provider to have the required expertise to perform effectively and reliably.

Based on the results of the exploratory study, we believe that the perceived credibility of the health-care provider (HP) will have an indirect effect on usage intentionsvia perceived usefulness, which has been found previously by, for instance, Pavlou (2003), and Lanseng and Andreassen (2007). This relationship is reflected in statements during the interviews such as: “If the provider is not credible and I can’t trust the service, it is not useful to me.” It is thus hypothesized that:

H7: Perceived credibility of the HP will have a significant positive effect on perceived usefulness.


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3.2.2.3 Output Quality

Put forward by Venkatesh and Davis (2000) as an antecedent of perceived usefulnessin TAM2, output quality of e-health was regarded as most important by the individuals in the exploratory study. Originally defined as “the perceptions of people on how well the system performs its tasks” (Venkatesh and Davis, 2000) output quality in the consumer service context captures an individual’s perception of the quality of the outcome of using the service. In the case of e-health this is the quality of the health-care response given to the request; the health information obtained. Also, Song et al. (2006) state, that the quality of the information is the single most important attribute for users of health information. Venkatesh and Davis (2000) argue that output quality is distinct from perceived usefulness, as a different judgmental process is involved. We agree with Venkatesh and Davis (2000), and assume that the output quality of e-health will positively influence perceived usefulness. We thus hypothesize that:

H8: Output quality will have a significant positive effect on perceived usefulness.

3.2.2.4 Result Demonstrability

According to Venkatesh and Davis (2000), there is an obvious connection between positive results and usage. Yet Venkatesh and Davis (2000) argue that if a system delivers relevant and positive outcomes, these need to be obvious to the individual. This is captured in the construct result demonstrability, defined as “the tangibility of the results of using the innovation” (Moore and Benbasat, 1991, p. 302). An (2005) examined result demonstrability in an e-health context and could provide evidence for its role as an antecedent of perceived usefulness. Also, the individuals in the exploratory study regarded result demonstrability of e-health as crucial. Thus, we hypothesize that:

H9: Result demonstrability will have a significant positive effect on perceived usefulness.

3.2.2.5 Subjective Norm

TAM has repeatedly been criticized for not considering social influences (Chen etal., 2002) as they were in TRA, TPB, and later in TAM2, represented by the construct of subjective norm. Subjective norm, or social factors, as it is called in MPCU, was proposed to grasp the influence produced by the social surrounding on an individual’s decision making. Venkatesh and Davis (2000) define it as the “influence to accept information from another as evidence about reality.” The idea behind the social influence construct is that even though individuals may not have a favorable attitude towards using an innovation, they intend to use it anyway, as they believe it is expected of them by their social surrounding (Venkatesh and Davis, 2000). This implies a direct relationship between subjective norm and intention to use, and reflects

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compliance in a typical mandatory employee-executive situation. In a voluntary setting though, this relationship does not persist (Venkatesh and Davis, 2000).

Yet Venkatesh and Davis (2000) differentiate between compliance and internalization, where internalization instead constitutes a direct effect of subjectivenorm on perceived usefulness (Venkatesh and Davis, 2000). Internalization reflects the influence of others’ opinions on one’s own perceptions of usefulness. Since e-health services are not mandatory and situations of compliance do not normally occur, the hypothesized direct relationship between subjective norm and usageintentions is not applicable to this context. The impact of internalization, on the other hand, is expected to be relevant in the e-health setting. Even though the exploratory study provided rather inconsistent results, some individuals found others’ opinions to be very important to them. Therefore, in line with Venkatesh and Davis (2000), we propose that:

H10: Subjective norm will have a positive direct effect on perceived usefulness.

3.2.2.6 Compatibility

Compatibility, defined as “the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters” (Rogers, 1995, p. 224), has been integrated into TAM in the context of e-service acceptance before (e.g. Chen et al., 2001; An, 2005; Wu and Wang, 2005) and was found to have a significant direct impact on attitude towards use. Chen et al. (2001) and Wu and Wang (2005) even found compatibility to be a stronger determinant than perceivedusefulness, and view it as a primary driver in the online environment. An (2005), who did not include the mediating attitude construct, still found that compatibilityhad a significant, direct impact on usage intentions (An, 2005). It comprises the ability to reconcile using e-health with the style (originally working style in the employee context; life style in the consumer context respectively) of the individual. If an innovation is compatible with the individual’s way of doing things, it is more familiar and in turn becomes less uncertain in its use (Rogers, 1995). In line with Chen et al. (2001), we hypothesize that the compatibility of using e-health with an individual’s beliefs, values, and needs will positively affect an individual’s attitudetowards using e-health. Hence:

H11: Compatibility will have a significant positive effect on attitude toward using e-health.

According to Rogers (1995, p. 224) “an innovation can be compatible or incompatible with (1) sociocultural values and beliefs, (2) previously introduced ideas, or (3) client needs for the innovation.” Moore and Benbasat (1991) argue that the notion “being compatible with needs” overlaps with the construct relative advantage (i.e. perceived


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usefulness) and consequently simply exclude the term “needs” from the original definition of compatibility. Perceived usefulness, however, is more of a service/technology-oriented construct, while compatibility (even though it still represents the service evaluated) represents the match between using e-health and the individual’s experiences, beliefs and needs. Even though we acknowledge the existence of a relationship between the two constructs, in this study, instead, compatibility is hypothesized to affect perceived usefulness. A person who perceives using e-health to be compatible with him/her is more likely to regard the service as being useful as well. An effect of compatibility on perceived usefulness has been investigated before and was found significant (e.g. Chau and Hu, 2001; Chen et al.,2002; Wu and Wang, 2005). Thus:

H12: Compatibility will have a significant positive effect on perceived usefulness.

3.2.2.7 Perceived Risk

The exploratory study showed that individuals have concerns about certain risks that come with the use of e-health, and which influence their decision on whether to use e-health. Risks that were brought up include the risk of misunderstanding or misinterpreting the information provided, which can have severe implications. Also, as discussed in the introductory chapter, it is difficult in cyberspace to assess the quality of the information that is provided, which poses a risk. Moreover, privacy and security concerns were brought up by the individuals in the interviews as well as in previous literature.

Perceived risk has been investigated by Pavlou (2003) and van der Heijden et al.(2003) in online transactions, by Bauer et al. (2005) in the context of mobile marketing acceptance, and by Curran and Meuter (2005) in the context of self-service adoption. Curran and Meuter (2005, p. 105) define perceived risk as “theprobability of certain outcomes given a behavior, and the danger and severity of negative consequences from engaging in those behaviors.” Risk perceptions in the online context go back essentially to two forms of uncertainty, environmental (related to the technology - the Internet) and behavioral uncertainty (related to the provider and service/product) (Pavlou, 2003). Curran and Meuter (2005) found this construct to be the only predictor of attitude towards the use of the e-service they investigated (self-service banking). Yet this might be very specific to the particular context. Due to the obvious risks in using an e-health service (e.g. misinterpretation of information, wrong information, privacy and security violations, etc.), we hypothesize that:

H13: Perceived risk will have a significant negative effect on attitude towards using e-health.

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Moreover, as was explained earlier, perceiving the health-care provider as credible is crucial in e-health as it helps to overcome uncertainty. In other words, credibility reduces risk (Mayer et al, 1995; Pavlou, 2003; van der Heijden et al.,2003; Gummerus et al., 2004). Accordingly, we pose a final hypothesis:

H14: Perceived credibility of the HP will have a significant negative effect on perceived risk.

3.2.3 The Shifting Effect of Moderators Moderating effects due, for instance, to demographical differences or external circumstances can shift the dynamics in a model. In the original TAM, such moderating effects are not considered, but later efforts (e.g. Venkatesh et al., 2003) suggest that moderators in technology adoption models include an individual’s experience (with the system), age, gender and voluntariness. As discussed on page 39, citizens may always choose traditional health-care services instead, which makes voluntariness irrelevant in this context. Yet, experience, age and gender, all three of which have been found to be important moderators (e.g., Gefen and Straub, 1997; Venkatesh and Davis, 2000; Koufaris, 2002; Pavlou, 2003; O’Cass and Fenech, 2003; Venkatesh et al., 2003; Gilbert et al., 2004; Salam et al., 2005; Featherman etal., 2006), are considered relevant to the context of e-health and must be taken into consideration.

3.2.3.1 Gender and Age

Venkatesh et al. (2003) explain that there are theoretical grounds for assuming that the impact of perceived usefulness on usage intentions will be influenced by both genderand age. Men have always been viewed as more interested in technology while women have been regarded as users who are somewhat more passive (van Slyke et al., 2002). According to van Slyke et al. (2002), even though women visit the Internet just as frequently as men do, men remain more likely to purchase via the Internet. According to Gefen and Straub (1997), there are differences in the sexes in computer-related circumstances which may impact on the role of determinants in technology adoption. Venkatesh and Morris (2000), for instance, found perceived usefulness to be more salient for men, while constructs that relate to technical abilities (such as perceived ease of use) appeared to be more salient for women (e.g., Venkatesh and Morris, 2000). Additionally, Venkatesh et al. (2003) demonstrate that social influences are stronger for women. Gefen and Straub (1997) confirm differences in the sexes in their initial expectations, but no support for differences in adoption behavior was found. In line with previous work, we thus hypothesize gender to moderate the effects of perceived usefulness and perceived ease of use on attitude (see Table 3.2 for a summary of the hypothesized moderating effects).


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Also, the common notion that older generations experience greater difficulty in processing complex stimuli and particularly in handling new technology persists. In the particular context of e-services, Gilbert et al. (2004) found a significant difference in usage intentions depending on the age of the individuals. Compared to their younger counterparts, individuals over the age of 55 were found to be less likely to adopt e-services. Based on those findings, we hypothesize that age will moderate the effect of perceived usefulness and perceived ease of use on attitude towarduse, as well as of accessibility on usage intentions.

3.2.3.2 Experience with the Internet and Previous Use of e-Health

Venkatesh et al. (2003) point out that future research should include moderating variables beyond age and gender. Prior experience with computers and the Internet is an important determinant of e-service acceptance (Karahanna et al. 1999; Koufaris, 2002; Pavlou, 2003; Yoh et al., 2003; Salam et al., 2005). First and foremost, the overall computer literacy of individuals and their experience with the Internet will most likely affect the individual’s beliefs, attitudes, and intentions to use e-health (Venkatesh et al., 2003; Yoh et al., 2003). Both Yoh et al. (2003), and O’Cass and Fenech (2003) found support for integrating Web experience as a construct in the TAM rather than only as a moderating effect.

Moreover, it is suggested that perceptions change over time while the service is being used/experienced (Davis et al., 1989; Karahanna et al., 1999a; Gilbert et al, 2004). For instance, Karahanna et al. (1999) and Venkatesh and Davis (2000) suggest that experience with the system/service in question will weaken, for instance, the impact of social influences (subjective norm) on the individual’s behavior. This transpires because the individual becomes more confident using the system as he/she gains experience with it. In the context of e-services, Gilbert et al.(2004) also found support for the significance of the experience component.

Following previous research, the level of an individual’s Internet experience is hypothesized to moderate the relationships of perceived ease of use and perceivedusefulness on attitude toward use, as well as the impact of subjective norm on perceivedusefulness. Beyond that, we believe that the level of Internet experience will moderate the role of accessibility, as those individuals with a high level of Internet experience are likely to have access to the Internet (thus diminishing the issue of lack of access).

Also, we suggest that the previous use of e-health will moderate the impact of perceived accessibility on intention to use. Rice and Shook (1988) found that perceivedaccessibility is more strongly associated with usage for the most recent adopters than for those who have been using the system for longer periods.

__ Chapter Three _____________________________________

48

Table 3.2: Hypothesized Moderating Effects

Path Moderator Hypothesis Explanation Reference Gender H2a Stronger for men Gefen & Straub 1997;

Venkatesh & Morris 2000; Venkatesh et al. 2003

Age H2b Stronger for younger individuals

Venkatesh et al. 2003; Gilbert et al. 2004

U => ATT

Internet Experience

H2c Stronger for the more experienced

O’Cass & Fenech 2003

Gender H4a Stronger for women

Gefen & Straub 1997; Venkatesh & Morris 2000

Age H4b Stronger for older individuals

Gilbert et al. 2004

EU => ATT

Internet Experience

H4c Stronger for the less experienced

O’Cass & Fenech 2003

Age H6a Stronger for older individuals

Facilitating conditions: Venkatesh et al. 2003

Internet Experience

H6b Only for the less experienced

---

ACC => ITU

Previous Use H6c Only for nonusers Rice & Shook 1988 Previous Use H10a Only for nonusers Karahanna et al. 1999a;

Venkatesh & Davis 2000 SN => U

Gender H10b Stronger for women

Venkatesh et al. 2003

Table 3.2 summarizes the moderating effects that have been tested previously and for which support was found. Previous research, however, has only investigated moderating effects on the key TAM variables (i.e., perceived usefulness, perceived ease of use, subjective norm). As this study’s research model includes variables and paths that have not been tested for moderating effects before, all paths will be examined in terms of moderating effects by any of the four proposed moderators age, gender, Internet experience, and previous use.

3.3 The Proposed Research Model

3.3.1 The a Priori e-Health Acceptance Model (eHAM) By assembling the pieces and integrating the additional variables and hypotheses proposed in the previous sections into the original TAM, this study’s research model emerges. We call it the a priori e-Health Acceptance Model (eHAM). The apriori eHAM is illustrated in Figure 3.1. As explained above, even though several hypotheses that address the moderating effects previously identified are suggested, all paths in the eHAM will be examined in terms of moderating effects of previous use, Internet experience, age and gender. Please note that, for the sake of clarity, those moderating effects are not pictured in Figure 3.1.


49

Figure 3.1: The A Prioi e-Health Acceptance Model (eHAM)

The upper right part of the eHAM surrounded by the blue dashed box represents the TAM which builds the model’s foundation with actual use as the dependent variable. In TAM, actual use constitutes the measure of acceptance and the final dependent variable. However, the impact of intention to use on actual use is a causal relationship, which means that, as visualized in Figure 3.1, the two events occur at two different points in time. Yet in respect to the early stage of e-health and the still very low numbers of usage, this study concerns time period one only. The predictive power of intention to use has been repeatedly tested in previous research and the construct is commonly accepted as a reliable predictor of use which motivates this decision.

In an attempt to explain citizens’ intention to use e-health more adequately, TAM was extended with several variables relevant to the e-health setting. First, in addition to attitude towards use, the perceived accessibility of e-health is hypothesized to influence usage intentions both directly and positively. Also, apart from the two key variables in TAM, perceived usefulness and perceived ease of use, the eHAM posits that compatibility and perceived risk play a major role in explaining citizens’ attitude towards using e-health. Moreover, the eHAM suggests several antecedents to the key variable perceived usefulness, namely output quality, result demonstrability, and subjective norm, as previously put forward in TAM2, as well as the perceived credibility of the HP. Moreover, ease of use and compatibility are hypothesized to affect perceived usefulness.

Time 2 Time 1

ActualUse

Intention to Use

Attitudetoward Use

Compati-bility

PerceivedRisk

PerceivedAccessibility


PerceivedUsefulness

Credibility of HP

Subjective Norm

ResultDemonstr.

Output Quality

H1 +

H2 +

H3 +

H4 +

H5 +

H12+ H11 +

H14 -

H7 +

H10 +

H9 +

H8 +

H13 -

H6 +

__ Chapter Three _____________________________________

50

Finally, the perceived credibility of the HP is assumed to have a direct, negative impact on perceived risk.

3.3.2 Summary of the Research Model In order to recall the meaning of all eHAM constructs, Table 3.5, provides an overview of the conceptual and the corresponding e-HAM definitions of all constructs. These definitions are important to ensure that measurements are correct later, and that what is intended to be measured is in fact what is measured.

Tab

le 3

.3: T

he

eHA

M C

onst

ruct

s, D

efin

itio

ns

and H

ypoth

eses

Const

ruct

O

rigi

nal

TA

M D

efin

itio

n

eHA

M d

efin

itio

n

Hyp

oth

eses

A

ctual

Use

T

he d

egre

e to

whi

ch a

n in

divi

dual

use

s th

e sy

stem

. T

he d

egre

e to

whi

ch a

n in

divi

dual

use

s th

e e-

heal

th s

ervi

ce in

que

stion

. In

tention t

o

Use

A

mea

sure

of t

he s

treng

th o

f one

’s

inte

ntio

n to

per

form

a s

pecif

ied

beha

vior

. (D

avis

et a

l., 1

989,

p. 9

84)

A m

easu

re o

f the

stre

ngth

of a

n in

divi

dual

’s in

tent

ion

to u

se th

e e-

heal

th

serv

ice in

que

stion

. A

ttitude

tow

ard U

sing

An

indi

vidu

al’s

pos

itive

or n

egat

ive

feel

ings

abo

ut p

erfo

rmin

g th

e ta

rget

be

havi

or.

(Dav

is et

al.,

198

9, p

. 984

)

An

indi

vidu

al’s

pos

itive

or n

egat

ive

feel

ings

abo

ut u

sing

the

e-he

alth

ser

vice

in

ques

tion.

H1:

Att

itude

tow

ard

usin

g e-

heal

th w

ill h

ave

a sig

nific

ant

posit

ive

effe

ct o

n in

tent

ion

to u

se e

-he

alth

.Per

ceiv

ed

Use

fuln

ess

The

deg

ree

to w

hich

a p

erso

n be

lieve

s th

at u

sing

a pa

rticu

lar s

yste

m w

ould

en

hanc

e hi

s or

her

job

perfo

rman

ce.

(Dav

iset

al.

1989

, p. 9

85)

The

deg

ree

to w

hich

an

indi

vidu

al b

elie

ves

he/s

he w

ill g

ain

out o

f usin

g th

e e-

heal

th

serv

ice in

que

stion

in a

hea

lth-r

elat

ed

mat

ter.

H2:

Per

ceiv

ed u

sefu

lnes

s w

ill h

ave

a sig

nific

ant

posit

ive

effe

ct o

n at

titud

e to

war

ds u

sing

e-he

alth

. (m

oder

ated

by

gend

er,

age,

Int

erne

t ex

perie

nce)

H3:

Per

ceiv

ed u

sefu

lnes

s w

ill h

ave

a sig

nific

ant

posit

ive

effe

ct o

n in

tent

ion

to u

se e

-hea

lth.

Per

ceiv

ed E

ase

of U

se

The

deg

ree

to w

hich

a p

erso

n be

lieve

s th

at u

sing

a pa

rticu

lar s

yste

m w

ould

be

free

of e

ffort.

(D

avis

et a

l., 1

989,

p.

985)

The

deg

ree

to w

hich

an

indi

vidu

al b

elie

ves

that

usin

g th

e e-

heal

th s

ervi

ce in

que

stion

w

ould

be

free

of e

ffort.

H4:

Per

ceiv

ed e

ase

of u

se w

ill h

ave

a sig

nific

ant

posit

ive

effe

ct o

n at

titud

e to

war

ds

usin

g e-

heal

th. (

mod

erat

ed b

y ge

nder

, ag

e, I

nter

net

expe

rienc

e)H

5: P

erce

ived

eas

e of

use

will

hav

e a

signi

fican

t po

sitiv

e ef

fect

on

perc

eive

d us

eful

ness

.Per

ceiv

ed

Acc

essi

bility

Abi

lity

to b

e re

ache

d, e

nter

ed,

influ

ence

d, o

r und

ersto

od.

(The

C

onci

se O

xfor

d D

ictio

nary

, 198

2,

p. 6

)

An

indi

vidu

al’s

per

cept

ion

of h

ow e

asy

or

diffi

cult

it is

for h

im/h

er to

acce

ss th

e e-

heal

th s

ervi

ce in

que

stion

.

H6:

Per

ceiv

ed a

cces

sibili

ty w

ill h

ave

a sig

nific

ant

posit

ive

effe

ct o

n in

tent

ion

to u

se

the

e-he

alth

ser

vice

. (m

oder

ated

by

age,

Int

erne

t E

xper

ienc

e an

d pr

evio

us u

se)

Const

ruct

O

rigi

nal

TA

M D

efin

itio

n

eHA

M d

efin

itio

n

Hyp

oth

eses

Per

ceiv

ed

Cre

dib

ility

of

the

HP

The

ext

ent t

o w

hich

one

par

tner

be

lieve

s th

at th

e ot

her h

as th

e re

quire

d ex

perti

se to

per

form

effe

ctive

ly a

nd

relia

bly.

(La

nsen

g an

d A

ndre

asse

n,

2007

p. 4

02)

The

ext

ent t

o w

hich

an

indi

vidu

al b

elie

ves

that

the

e-he

alth

pro

vide

r has

the

requ

ired

expe

rtise

to p

erfo

rm e

ffecti

vely

and

re

liabl

y.

H7:

Per

ceiv

ed c

redi

bilit

y in

the

HP

will

hav

e a

signi

fican

t po

sitiv

e ef

fect

on

perc

eive

d us

eful

ness

.H

14: P

erce

ived

cre

dibi

lity

in t

he H

P w

ill h

ave

a sig

nific

ant

nega

tive

effe

ct o

n pe

rcei

ved

risk

. O

utp

ut

Qual

ity

The

per

cept

ions

of p

eopl

e on

how

wel

l th

e sy

stem

per

form

s its

task

s.(V

enka

tesh

and

Dav

is, 2

000,

p.

191)

An

indi

vidu

al’s

per

cept

ion

of h

ow w

ell t

he

e-he

alth

ser

vice

in q

uesti

on p

erfo

rms.

H

8: O

utpu

t qu

ality

will

hav

e a

signi

fican

t po

sitiv

e ef

fect

on

perc

eive

d us

eful

ness

.

Res

ult

Dem

onst

ra-

bility

Tan

gibi

lity

of th

e re

sults

of u

sing

the

inno

vatio

n. (

Moo

re a

nd B

enba

sat,

1991

, p. 2

03)

An

indi

vidu

al’s

per

cept

ion

of th

e ta

ngib

ility

of t

he re

sult

of th

e e-

heal

th

serv

ice in

que

stion

.

H9:

Res

ult

dem

onst

rabi

lity

will

hav

e a

signi

fican

t po

sitiv

e ef

fect

on

perc

eive

d us

eful

ness

.Subje

ctiv

e N

orm

A

per

son’

s pe

rcept

ion

that

mos

t peo

ple

who

are

impo

rtant

to h

im/h

er th

ink

he/s

he s

houl

d or

sho

uld

not p

erfo

rm th

e be

havi

or in

que

stion

. (F

ishbe

in a

nd

Ajz

en, 1

975,

p. 3

02)

An

indi

vidu

al’s

bel

ief t

hat m

ost p

eopl

e w

ho a

re im

porta

nt to

him

/her

wou

ld

thin

k th

at h

e/sh

e sh

ould

or s

houl

d no

t use

th

e e-

heal

th s

ervi

ce in

que

stion

.

H10

: Sub

ject

ive

norm

will

hav

e a

signi

fican

t po

sitiv

e ef

fect

on

perc

eive

d us

eful

ness

. (m

oder

ated

by

prev

ious

use

and

gen

der)

Com

pat

ibility

The

deg

ree

to w

hich

an

inno

vatio

n is

perce

ived

as

cons

isten

t with

the

exist

ing

valu

es, p

ast e

xper

ienc

es, a

nd n

eeds

of

pote

ntia

l ado

pter

s. (

Rog

ers,

1995

, p.

224)

The

deg

ree

to w

hich

an

indi

vidu

al

perce

ives

the

e-he

alth

ser

vice

in q

uesti

on to

be

cons

isten

t with

his/

her e

xisti

ng v

alue

s,

past

expe

rienc

e, a

nd n

eeds

.

H11

: Com

patib

ility

will

hav

e a

signi

fican

t po

sitiv

e ef

fect

on

attit

ude

tow

ard

usin

g e-

heal

th.

H12

: C

ompa

tibili

ty w

ill h

ave

a sig

nific

ant

posit

ive

effe

ct o

n pe

rcei

ved

usef

ulne

ss.

Per

ceiv

ed R

isk

The

pro

babi

lity

of ce

rtain

out

com

es

give

n a

beha

vior

, an

d th

e da

nger

and

se

verit

y of

neg

ativ

e co

nseq

uenc

es fr

om

enga

ging

in th

ose

beha

vior

s. (

Cur

ran

and

Meu

ter,

200

5, p

. 105

)

The

de

gree

to

w

hich

an

in

divi

dual

pe

rceiv

es c

erta

in n

egat

ive

outco

mes

and

the

da

nger

an

d se

verit

y of

ne

gativ

e co

nseq

uenc

es

from

us

ing

the

e-he

alth

se

rvice

in q

uesti

on.

H13

: Per

ceiv

ed r

isk w

ill h

ave

a sig

nific

ant

nega

tive

effe

ct o

n at

titud

e to

war

ds u

sing

e-he

alth

.

_________________________________________________ Methodology __

53

4 Chapter Four: Methodology This Chapter provides an overview and justification of the methodology used to gather the

research data and the techniques used to analyze the data.

4.1 Research Design A research design is the blueprint of a research initiative for collecting, measuring, and analyzing data, and it depends on the purpose of the research (Cooper and Schindler, 2006). The question of whether a research project should be of exploratory, descriptive or causal nature is not an either/or question; in fact, the designs can overlap as the purpose of the research might differ depending on the stage of the research process (Cooper and Schindler, 2006). The purpose of this research is to identify factors that are central to citizens’ acceptance of e-health and to explain how those factors influence an individual’s intention to use e-health. In the scope of this investigation, a model is advanced that describes citizens’ beliefs and attitudes. Yet particularly during the development stage of the model, exploratory techniques are employed as well, which is often the case in business research (Hair et al., 2007). Thus, this research is characterized by an exploratory and descriptive design.

4.2 Research Approach Another distinction of research types is made between qualitative and quantitative approaches. Quantitative research is based on numbers, counts and measures of constructs used to represent the characteristics of an event or activity, whereas qualitative research is concerned with words, pictures, descriptions, and narratives (Sullivan, 2006). As quantitative data are measurements and counts of numbers, statistics are used to analyze the data (Hair et al., 2007). Qualitative data, because it is not based on numbers or counts, are mainly collected through unstructured interviews or observation (Hair et al., 2007). Some researchers regard one approach as superior. This is near-sighted though, as both approaches have their strengths and weaknesses (Hair et al., 2007). The strengths of a quantitative approach, for instance, are its structure and the representativeness of the data (Hair et al., 2007). A strength of the qualitative approach is that more in-depth and detailed information can be sought, thus helping to find an answer to why things are a certain way. In order to make informed decisions, both qualitative and quantitative data are often required in business research, as the two approaches complement each other rather well (Hair et al., 2007). We strive for representative data that can be used to test the proposed hypotheses, which requires a quantitative approach in the formal study. In the exploratory stage, however, qualitative data were collected primarily through interviews (i.e., the exploratory study as presented in Chapter 3).

__ Chapter Four ______________________________________

54

In the following sections, research strategy, sampling, data collection, and analysis of the formal study are presented and discussed.

4.3 Research Strategy The research strategy determines how empirical data are collected and analyzed (Yin, 1994), and builds a general plan for the research regarding how to proceed in order to fulfill its purpose (Saunders et al., 2000). The choice of research strategy is influenced by different decisions: (1) the type of research question posed; (2) the extent of control the researcher has over actual behavioral events; and (3) the degree of focus on contemporary as opposed to historical events (Yin, 1994). Different research strategies include experiments, surveys, case studies, history, and archival analysis (Yin, 1994). As we strive for representative data from individuals, seek answers to questions of who, what and how much, and since we do not need to have control over the events investigated, survey research is considered the most suitable strategy for the formal study. Also, when data are collected through questionnaires, standardized comparisons are made easy. Survey research is one of the most popular strategies in business and management as it allows for collecting data from a large sample in a rather economical way (Saunders et al., 2000).

4.4 Sampling Ideally, one would like to collect data from the entire population to be investigated. Obviously, this is not feasible in most instances since, especially in consumer studies, the population is very large (Hair et al., 2007). For this reason, a representative sample of the population must be drawn.

4.4.1 The Case of Sweden The target population of a research is the total of all individuals relevant to the research project that share some common set of characteristics (Hair et al., 2007). This research is conducted in Sweden and is thus aligned to fit the Swedish context. The main reason for choosing Sweden as the context of this study is that the researcher is situated in Sweden, which facilitates access and data collection. However, the Swedish context is also regarded as very suitable for several other reasons. First, compared to other countries, Sweden is among the leaders in terms of Internet access and Internet use by its population (WII, 2007). Also, with respect to the broad field of e-government, Sweden is ranked among the leading countries worldwide (Wiklung and Lindh, 2005). Sweden has been touted as one of the pioneering nations in telemedicine, and many examples of local and regional e-health projects and programs are already in place (Olsson and Jarlman, 2004). Several online health-care services could be identified that would serve as capable units of analysis for this study.

_________________________________________________ Methodology __

55

4.4.2 Sampling Frame A major limitation of most previous research into technology acceptance is that the sample is drawn from a group of individuals that at some point already have used the service in question (e.g. Shook and Rice, 1988; Davis, 1989; Davis et al. 1989; Adams et al., 1992; Szajna, 1996; Gefen and Straub, 1997; Parthasarathy and Bhattacherjee, 1998; Venkatesh and Davis, 2000; Moon and Kim, 2001; Koufaris, 2002; Pavlou, 2003; Venkatesh et al., 2003; Chen et al., 2002; Wilson and Lankton, 2004; An, 2005). When investigating the issue of acceptance we believe it is crucial to include those individuals that have not previously used the service under investigation to be able to identify what may have kept them from becoming users. Health care is a topic that concerns everyone, which would make the entire Swedish population a target population of this study. We can, however, limit the target population with respect to age. A lower age limit was set at 16, as health issues and health care for children under 16 is taken care of primarily by their parents. Furthermore, children under 16 in Sweden are protected by law from direct marketing, which makes including this age group in the survey impossible. Also, an upper age limit was set. According to the WII (2007), 89% of the Swedish population in the age range of 55 to 64 uses the Internet. In the age group from 65 to 74, a significant drop in Internet usage to just under 50% can be seen. The older generation is simply not accustomed to computers and the Internet, and they are not very likely to become involved over time. However, we preferred to establish a higher age limit and run the risk of having low Internet usage rates in the sample rather than setting the limit too low and excluding the largest pool of health-care consumers, the elderly, simply because they tend to use the Internet to a smaller extent. Therefore, an upper age limit of 69 years was set.

A sampling frame is a list of the elements from which the sample can be drawn (Hair et al., 2007). A consumer database called POSTIAD consisting of 96,000 Swedish consumers that have indicated that they are interested in “health and health foods” was used as the main sampling frame. We believe that individuals who have indicated their interest in health and health foods will show greater interest in the health-care services offered to them and thus might be more interested in participating in this investigation. Of the 96,000 individuals in the health and health foods interest group, a total of 82,784 individuals belong to the age group 16 to 69 as set for the sample in this study. Yet more than 96% of those 82,784 potential subjects are older than 30, leaving the age group from 16 to 29 significantly underrepresented. While this reflects the assumption that health and health care becomes more relevant with increasing age, we regard it as essential not to exclude the younger generation. Thus, the sample was complemented by a sample population of individuals in the age group 16 to 29 which was drawn from a consumer database called SPAR, which is based on the Swedish telephone register. As all individuals in the POSTIAD database also are included in the bigger register

__ Chapter Four ______________________________________

56

SPAR, the two sample frames differ only in terms of the indication of interest in health by the POSTIAD sample.

4.4.3 Sampling Method and Sample Size In general, there are two broad categories of sampling methods: probability sampling,which is typically used in quantitative research and non-probability sampling, which is typically used in qualitative research. Probability sampling means that a random procedure is used to draw the sample from the population, giving every element in the target population a known and non-zero chance of being selected (Hair et al.,2007). The goal of probability sampling is to ensure objectivity and draw a sample representative of the target population. There are different approaches to probability sampling. Simple random sampling is a straightforward method of random sampling, which gives each element in the target population the same chance of being selected. In systematic random sampling, a certain number from the list of elements is used as the starting point; thereafter, every nth element from the list is selected. Using the stratified random sampling technique, the target population is split up in non-overlapping groups (called strata) based on particular characteristics. Then, the total sample size as well as the size of the sample is determined for each of the strata. This technique increases the accuracy of the sample information (Hair et al., 2007). Finally, in cluster sampling, the target population consists of heterogeneous groups called clusters, from which the sample is drawn.

As explained earlier, two sample frames are used based on the age of the individuals in each sample frame. Thus, a stratified random sampling technique is used. With the aim of increasing the representativeness of the sample, the proportion of the addresses acquired from the two sample frames, POSTIAD and SPAR, follows the age structure of the Swedish population (which can be referred to as the strata). As outlined in Table 4.1 around 25% of the Swedish population in the age of 16 to 69 is below 30. Thus, 25% of the sample will be retrieved from SPAR and 75% from POSTIAD.

Table 4.1: Age Distribution of the Swedish Population and the Sample Frames, source: SCB (2006)

Swedish population Age

Total PercentSample Frame

16-19 490,271 7.80%20-29 1,089,311 17.33%

SPAR

30-39 1,233,492 19.62%40-49 1,240,707 19.74%50-59 1,194,152 19.00%60-69 1,037,403 16.51%

POSTIAD

TOTAL 6,285,336 100

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57

Determining the appropriate sample size is an important step in both quantitative and qualitative research. In quantitative research, sample size can impact statistical tests in the way that small samples make tests insensitive and large samples overly sensitive (Hair et al., 1998). For structural equation modeling (SEM) techniques, which is the main technique used to analyze the data and test the hypotheses (as will be explained later), Hair et al. (1998, p. 605) suggest a sample size of 200. If the model is overly large or complex, however, a larger sample should be used.

The sample size of similar studies (consumer studies that investigate some form of system/e-service use or acceptance) ranges from around 150 (Moon and Kim, 2001) to just over 600 (Curran and Meuter, 2005). We could see a pattern of most studies using a sample of around 200 responses for each service or technology investigated, following what is suggested by Hair et al. (1998). In this study, two e-health services will be investigated (further discussion on the number and type of services will follow in the data collection section). In line with previous research, we aimed at a sample size of 200 individuals for each service, leading to an aspired total sample of 400 responses. With customer surveys, one can typically expect a response rate of around 20%. Considering this, in order to achieve the aspired sample of 200 responses for each service, in total, 2,000 questionnaires were sent out, 1,000 for each service.

4.5 Data Collection

4.5.1 The e-Health Services under Investigation In Sweden, health care is mainly provided by public institutions and lies in the responsibility of the 21 county councils. The most common services public health-care providers offer directly to citizens online are ask-the-doctor services, health guides, appointment booking and the possibility to renew prescriptions online. To be able to compare the different types of services while keeping time and financial constrains in mind, it was decided to investigate citizens’ perceptions on two of those services, namely the online health guide and the ask-the-doctor online service.

An online health guide is a Web site, on which general health-care information is provided in the form of a dictionary. Here, one can search for information on diseases, symptoms, and forms of treatment. Symptoms and diseases are explained, and tips and advice on how to treat smaller complaints is given. The ask-the-doctoronline service is a service available on a Web site through which one can anonymously send a message to a doctor about a health question or problem, and the user is guaranteed to receive an answer within one week. The personal answer can then be read by logging on to the site with a code that is provided when the message was sent. This service is anonymous, and the only personal information included is age, gender and the county in which the individual lives. The services

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58

are not meant to replace a visit to a doctor, but to serve as an additional tool for citizens to take better care of their own health and that of their family.

It was decided to investigate both of those services for two reasons. First, the two services fulfill the same need: the request for additional health information. This makes it possible to compare citizens’ opinions about the two services. If the services were too different in their aims, it would not be possible to measure the constructs in the same way (using the same items). Secondly, online health guides are generally regarded as useful and a good idea whereas individuals have more doubt concerning the ask-the-doctor online service. In the exploratory study, in seven out of nine cases, the respondents picked the online health guide as the service they are most likely to use and the ask-the-doctor online service as the service they are least likely to use. Even though we cannot state with certainty that this will also be the case in the formal study, it would deliver interesting results that may enable us to pinpoint service-specific factors that are key to acceptance.

In Sweden, health guides and ask-the-doctor services are provided by the public health-care providers but also by other private companies or non-profit organizations. Since health-care delivery in Sweden, however, is the responsibility of the public sector, the focus of this investigation is on those e-services provided by the county councils.

4.5.2 Scale Development This section covers the development process of the scales used to measure the constructs in the research model. The aim was to rely on previously established scales as much as possible. Due to the change of context from an organizational perspective for which the scales were initially developed, to e-health, the content validity of the scales and the relevance of single items still required careful assessment.

In line with previous research, all constructs in the model constitute beliefs, attitudes, and intentions, and are measured using multiple-items on a seven-point Likert-type scale. Seven-point Likert-scales are most common in business research when measuring perceptions or attitudes. More points on the scale bring higher precision; yet, too many points can make it difficult for the respondent to differentiate between the categories (Hair et al., 2007). Interval scales are the highest level of measurement and allow for more sophisticated calculations beyond those that can be accomplished using nominal and ordinal scales (Hair et al., 2007).

Scales were developed following the procedure for developing better measures by Churchill (1979) and include the following steps: (1) specification of domains of each construct, (2) generation of item pools, (3) categorization and evaluation of all

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59

items by a panel of judges, (4) pretest 1 with experts, (5) larger scale pretest 2 with 50 students, (6) assessment of scale reliability and scale purification.

Specification of Domains The measurement of a concept needs to be based on its conceptual definition (Nunnally, 1994). It is crucial to identify the domains of a construct based on its conceptual definition to ensure that the scale measures all aspects of the constructs (content validity). Each item in a scale should correspond to a domain of the construct one is interested in measuring (Davis, 1989).

Generation of Item Pools Once each concept is defined theoretically and the domains are identified based on these definitions, an item pool is created. All items used to measure a particular construct in previous research were assessed in terms of their applicability to e-health and if applicable, the wording was adapted to fit the context. Moreover, the qualitative data from the exploratory study was used to identify further items that measure aspects of the constructs that become relevant in the new context.

Categorization and Evaluation After having created item pools for all constructs, five judges (faculty from the Industrial Marketing and e-Commerce Research Group at Luleå University of Technology) were, in separate rounds, given index cards with the definitions of all independent constructs and all respective items from the item pool. The task was to categorize the items as the judges felt they belonged and then assign one of the construct index cards to each of the categories. The judges categorized rather consistently, with 90% of the items ending up in the right categories. There was some confusion about two of the constructs that were regarded as being too similar (output quality and result demonstrability), but since the scales of those two antecedents are taken from previous research (i.e. Venkatesh and Davis, 2000), no actions were taken concerning the confusion of the items. Instead, the distinctness of the two constructs will be assessed later in the field study. The grouping of the items by the judges also helped to explore the possible multidimensionality of the constructs. Since dimensions appeared to exist for some of the constructs (judges created sub-categories), when theoretical justification for those dimensions could be found, additional items were created to better access all of those dimensions. This was the case for the perceived risk construct. A few items were regarded as irrelevant or not applicable to the context of e-health by the judges and were dropped as a result. The judges were furthermore encouraged to provide feedback on the wording of items which resulted in additional adjustment. Finally, the judges were asked if they felt any aspects of a construct (based on the definition they were given) were missing, but none of the judges believed that was the case.

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Pretest 1 After the round of judges and a first round of scale purification, a first draft of the questionnaire was developed with respect to the online health guide. Thereafter, the questionnaire was pre-tested by six individuals (four experts being one professor, one assistant professor and one faculty member in marketing, and one faculty member from IS as well as two students) who were asked to give their detailed feedback on this questionnaire. Six individuals for questionnaire pre-testing is considered an appropriate number of cases; not too many to provide substantial incremental information and not too few (at least five is suggested by Hair et al., 2007, p. 279). The pre-testing led to further rewording in some of the items, changes in the scale anchors, and some restructuring of the questionnaire. The revised version of the questionnaire was then translated (and back-translated) into the Swedish language.

Pretest 2The next step in the scale development process was to conduct another larger scale pretest to assess the questionnaire and purify the scales. During pretest 1, the similarity of many of the items was pointed out several times, as this similarity makes it difficult for the respondent to distinguish between the items and answer the questions. Furthermore, the questionnaire was considered very long. As a result, the aim of pretest 2 was to identify and retain those items in each scale that together best measure the respective constructs and to eliminate all other items so that the number of questions in the scale could be substantially reduced.

The questionnaire was distributed to two classes of business students at the Division of Business Administration and Management at Luleå University of Technology which together represented 58 students, resulting in 50 complete and usable returned questionnaires. The students participating in the pretest were asked to comment on items or the questionnaire in general if questions arose or they experienced problems. Overall comments were provided by 22 students, primarily on the length of the questionnaire and the similarity of many of the questions, but also on single items. Those comments were considered and further changes in formulation were made where necessary. Items that were found redundant were dropped.

Assessing Scale Reliability and Construct ValidityThe main intention of pretest 2 was to purify the scales. This was based on the results of a combination of factor analyses and scale reliability tests using Cronbach’s alpha. Item-item and item-scale correlations as well as the effects on alpha when item deleted were used to determine which items should be considered for elimination. Before any items were dropped, however, content validity of the remaining items on the scale was assessed carefully. In some cases, items were retained even though they slightly reduced the overall scale alpha.

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A description of the scale development and reduction process for each construct individually can be found in Appendix A. An overview of the resulting scales used for collecting the data in the main field study is presented in Table 4.2. Please note that the items outlined below represent the English translation of the Swedish items used for the purpose of data collection (see Appendix B1 and B2 for a copy of the health guide questionnaire in Swedish and English).

Tab

le 4

.2: O

verv

iew

of th

is S

tudy’

s M

easu

rem

ent

Const

ruct

It

emSourc

e U

1 –

Hav

e yo

u us

ed t

he c

ount

y co

unci

l’s X

bef

ore?

U

2 –

If y

es, h

ow o

ften

do y

ou u

se it

?

U3

– H

ave

you

used

the

X o

ffere

d by

ano

ther

org

aniz

atio

n be

fore

?

Act

ual

Use

U4

– If

yes

, how

ofte

n do

you

use

it?

Dav

iset

al.

1989

; M

oon

& K

im 2

001;

Che

n et

al.

2002

ITU

1 –

I w

ill u

se X

on

regu

lar

basis

IT

U2

– I

pred

ict

I w

ill u

se X

IT

U3

– I

inte

nd t

o us

e X

Inte

ntion

to

Use

ITU

4 –

I w

ill s

tron

gly

reco

mm

end

X t

o ot

hers

Ven

kate

sh 2

000;

Ven

kate

sh &

Dav

is (2

000)

; C

hen

et a

l. (2

002)

; M

oon

& K

im (

2001

); W

ilson

& L

ankt

on (

2005

)

AT

T1

– I

wou

ld li

ke u

sing

X

AT

T2

– I

wou

ld fe

el g

ood

abou

t us

ing

X

Att

itude

tow

ard U

se

AT

T3

– U

sing

X is

wise

Dav

is 19

89;

Dab

holk

ar 1

996;

Ven

kate

sh &

D

avis

2000

; M

oon

& K

im 2

001;

Ven

kate

sh

et a

l. 20

03; C

urra

n &

Meu

ter

2005

U

1 –

X w

ould

mak

e ob

tain

ing

heal

th in

form

atio

n m

ore

conv

enie

nt

U2

– X

wou

ld m

ake

heal

th in

form

atio

n m

ore

acce

ssib

le

U3

– X

wou

ld e

nabl

e m

e to

find

ans

wer

s to

my

heal

th q

uest

ions

mor

e qu

ickl

y

U4

– X

wou

ld e

nhan

ce m

y ef

fect

iven

ess

in m

anag

ing

heal

th c

are

U5

– X

wou

ld b

e us

eful

for

man

agin

g m

y he

alth

car

e U

6 –

X w

ould

mak

e it

easie

r fo

r m

e to

gai

n th

e in

form

atio

n I

wan

t

U7

– X

wou

ld o

ffer

addi

tiona

l hea

lth in

form

atio

n

Per

ceiv

ed

Use

fuln

ess

U8

– T

he a

dvan

tage

s of

X fa

r ou

twei

gh t

he d

isadv

anta

ges

Dav

is

1989

; M

oore

&

B

enba

sat

1991

; A

dam

set

al

. 19

92;

Dav

is et

al

. 19

92;

Ven

kate

sh &

Dav

is 20

00;

Ven

kate

sh 2

000;

M

oon

& K

im 2

001;

Kou

fari

s 20

02;

Che

n et

al.

2002

; V

enka

tesh

et

al.

2003

; W

ilson

&

Lank

ton

2004

; Cur

ran

& M

eute

r 20

05

expl

orat

ory

inte

rvie

ws

EU

1 -

Lear

ning

to

oper

ate

X w

ould

be

easy

for

me

EU

2 –

My

inte

ract

ion

with

X w

ould

be

simpl

e E

U3

– It

wou

ld b

e ea

sy fo

r m

e to

bec

ome

skilf

ul a

t us

ing

X

EU

4 –

I w

ould

find

X e

asy

to u

se

EU

5 –

It w

ould

be

easy

to

rem

embe

r ho

w t

o us

e X

Per

ceiv

ed

Eas

e of U

se

EU

6 –

X w

ould

be

diffi

cult

to u

se

Dav

iset

al

. 19

89;

Dav

is et

al

. 19

92;

Ven

kate

sh 2

000:

Ven

kate

sh &

Dav

is 20

00;

Moo

n &

K

im

2001

; C

hen

et

al.

2002

; K

oufa

ris

2002

; V

enka

tesh

et

al

. 20

03;

Wils

on

& L

ankt

on 2

004;

Ven

kate

sh e

t al

.,20

03; C

urra

n &

Meu

ter,

200

5

Const

ruct

It

emSourc

e SN

1 -

Peop

le w

ho in

fluen

ce m

e w

ould

thi

nk I

sho

uld

use

X

SN2

– Pe

ople

who

are

impo

rtan

t to

me

wou

ld t

hink

I s

houl

d us

e X

SN

3 –

Peop

le w

ho a

re im

port

ant

to m

e w

ould

enc

oura

ge m

e to

use

X

Subje

ctiv

e N

orm

SN4

– Pe

ople

who

influ

ence

me

wou

ld t

hink

usin

g X

is a

goo

d id

ea

Ajz

en 1

991;

Dav

is et

al.

1989

; Fi

shbe

in &

A

jzen

197

5; V

enka

tesh

& D

avis

2000

; A

n 20

05

OQ

1 –

The

qua

lity

of t

he in

form

atio

n I

obta

in fr

om X

wou

ld b

e hi

gh

OQ

2 –

X w

ould

per

form

wel

l O

Q3

– X

wou

ld o

ffer

good

info

rmat

ion

OQ

4 –

X w

ould

offe

r ac

cura

te in

form

atio

n O

Q5

– X

wou

ld o

ffer

up-t

o-da

te in

form

atio

n

Outp

ut

Qual

ity

OQ

6 –

X w

ould

offe

r re

leva

nt in

form

atio

n

Bai

ley

& P

ears

on 1

983;

Dav

is et

al.

1989

; V

enka

tesh

& D

avis

2000

; Son

g et

al.

2006

RD

1 –

I w

ould

hav

e no

diff

icul

ty t

ellin

g ot

hers

abo

ut t

he a

dvan

tage

s of

usin

g X

R

D2

– I

wou

ld b

e ab

le t

o co

mm

unic

ate

to o

ther

s th

e re

sults

of u

sing

X

Res

ult

Dem

onst

ra-

bility

RD

3 –

The

adv

anta

ges

of u

sing

X a

re a

ppar

ent

to m

e

Moo

re

&

Ben

basa

t 19

92;

Ven

kate

sh

&

Dav

is 20

00; A

n 20

05

T1

– A

s pr

ovid

er o

f X t

he c

ount

y co

unci

l is

depe

ndab

le

T2

- A

s pr

ovid

er o

f X t

he c

ount

y co

unci

l is

trus

twor

thy

T

3 -

As

prov

ider

of X

the

cou

nty

coun

cil i

s cr

edib

le

T4

- A

s pr

ovid

er o

f X t

he c

ount

y co

unci

l is

since

re

Per

ceiv

ed

Cre

dib

ility

of

the

HP

T5

- A

s pr

ovid

er o

f X t

he c

ount

y co

unci

l is

hone

st

Lich

tens

tein

& B

eard

en 1

989;

Pav

lou

2003

AC

C1

– I

expe

ct it

to

be e

asy

for

me

to a

cces

s X

A

CC

2 –

I do

not

fore

see

any

prob

lem

s ge

ttin

g ac

cess

to

X

Per

ceiv

ed

Acc

essi

bility

AC

C3

– X

wou

ld b

e ve

ry a

cces

sible

for

me

Dav

iset

al.

1989

; O’R

eilly

198

2

Const

ruct

It

emSourc

e R

1 –

Usin

g X

wou

ld b

e hi

ghly

risk

y R

2 –

I w

ould

be

conc

erne

d to

bec

ome

a hy

poch

ondr

iac

whe

n us

ing

X

R3

– I

wou

ld b

e co

ncer

ned

abou

t m

y pr

ivac

y w

hen

usin

g X

R

4 –

I w

ould

feel

sec

ure

usin

g X

R

5 –

I w

ould

feel

saf

e us

ing

X

R6

- T

here

is li

ttle

dan

ger

that

any

thin

g w

ould

go

wro

ng w

hen

usin

g X

R

7 –

I w

ould

be

conc

erne

d ab

out

misi

nter

pret

ing

the

info

rmat

ion

rece

ived

fr

om X

R

8 –

I w

ould

be

conc

erne

d ab

out

the

qual

ity o

f th

e in

form

atio

n re

ceiv

ed

from

X

R9

– I

wou

ld b

e co

ncer

ned

that

wro

ng in

form

atio

n w

ould

be

prov

ided

via

X

R10

– I

wou

ld b

e co

ncer

ned

asse

ssin

g th

e qu

ality

of

the

info

rmat

ion

that

is

prov

ided

Per

ceiv

ed R

isk

R11

- W

rong

tre

atm

ent

of a

hea

lth m

atte

r ca

n be

an

outc

ome

whe

n I

use

X

Dab

holk

ar

1996

; Fe

athe

rman

&

Pa

vlou

20

03;

O’C

ass

& F

enec

h 20

03;

Cur

ran

&

Meu

ter

2005

ex

plor

ator

y in

terv

iew

s

CB

1 –

Usin

g X

wou

ld fi

t w

ell w

ith t

he w

ay I

like

to

do t

hing

s C

B2

– U

sing

X w

ould

fit

into

my

life

styl

e C

om

pat

ibility

CB

3 –

Usin

g X

wou

ld b

e co

mpa

tible

with

the

way

I li

ke to

do

thin

gs

Moo

re &

Ben

basa

t 19

91; C

hen

et a

l. 20

02

_______________________________________Methodology __

65

4.5.3 Data Collection Process There are three types of surveys: self-administered surveys, telephone surveys, and surveys via personal interviews (Cooper and Schindler, 2006). Self-administered surveys can be mail surveys, electronic surveys, drop-off/pick-up surveys or similar approaches (Hair et al., 2003).

Compared to telephone surveys, the advantages that accompany traditional mail surveys include a wider access and better coverage, their ability to handle large samples, the possibility of anonymity, their relative affordability, and their inherent ability to give the respondents the opportunity to complete the questionnaire at their own pace (Hair et al., 2007). The disadvantages, however, are that the questionnaire needs to be simple and no opportunity exists for the respondents to ask follow-up questions. Furthermore, response rates are rather low and non-response cannot be followed up (Hair et al., 2007). Web surveys simplify data collection and analysis, and have become more popular in recent years, due to their reach and relative inexpensiveness. Previous research on e-service acceptance and related topics in an online context have worked with online surveys (e.g., Chen et al., 2002; O’Cass and Fenech, 2003; Pavlou, 2003; Wilson and Lankton, 2004; An, 2005; Klein, 2007). However, we regard the use of a Web survey in this research as inappropriate because a major limitation of using a Web survey is that only individuals with a certain level of Internet experience would constitute the sample, and those citizens who are not Internet and/or computer-savvy would thus be excluded. This, we believe would introduce bias. Also, Web surveys are known for their low response rates. Therefore, data is collected taking the traditional mail survey approach, also targeting individuals with few or no computer skills.

Two questionnaires were developed, one for each service. The two questionnaires share the same items (formulated to address the respective service) and include an introductory section, in which the service is briefly explained and instructions are given about how to complete the questionnaire. As there is an enormous number of health-related Web sites, and since this study’s interest is in the e-services that is provided by public health-care providers (as the biggest health-care provider in Sweden), it is highlighted in the introductory section of the questionnaire that even though the focus is not on a specific Web site, the services investigated are those provided by the public health-care provider (the Swedish county councils). A cover letter was sent out with the questionnaire to explain to the recipient what the survey is about, why it is important for him/her to respond, and what will happen with his/her answers (see Appendix C1 and C2 for a copy of the cover letter in Swedish and English).

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There are a few tips and tricks on how to increase response rates. As suggested by Hair et al. (2007), it helps to give the cover letter a personal touch by writing personally and by actually signing the correspondence. Furthermore, the letter should convince the individual that his/her response is particularly important and has some social or other relevance. Also, the length of the questionnaire is very important and should not exceed four pages (printed on both sides). In order for the respondent not to incur any cost, a postage-paid and addressed envelope needs to be enclosed.

These suggestions have been considered in the design of the questionnaire and the cover letter. As another means to increase the response rate, a follow-up reminder postcard was sent out 10 days after the survey (see Appendix C3 and C4 for a copy of the reminder postcard in English and Swedish).

4.5.3.1 Responses

After a period of 7 weeks, 821 questionnaires were returned. Of those 821, 50 questionnaires were excluded as (a) respondents had sent them back empty and indicated that they did not want to participate, (b) as they were returned incomplete or (c) as they were considered questionable (e.g., the respondents had marked a single number for all questions). This leaves 771 completed and usable questionnaires resulting in an effective response rate of 38.6%. The distribution of the responses among the two services is relatively equal, though slightly more responses were received regarding the ask-the-doctor service. The two samples will from this point onwards be referred to as the HG (health guide) and ATD (ask-the-doctor) samples. Among the two different sample frames used (POSTIAD: Swedish citizens above 30 who indicated interest in health and health foods; SPAR: Swedish citizens between 16 and 29) a much higher response rate was achieved from the POSTIAD database (44.6%) as compared to SPAR (17.4%). Table 4.3 below provides an overview of the responses.

Table 4.3: Response Rate by Type of Service and Sample Frame

Total ATD HGSent Resp. % Sent Resp. % Sent Resp. %

POSTIAD (30-69)

1,500 669 44.6 750 341 45.5 750 328 43.7

SPAR(16-29)

500 87 17.4 250 49 19.6 250 38 15.2

Missing Values 15 10 5Total 2,000 771 38.6 1,000 400 40.0 1,000 371 37.1

It is not possible to establish whether the response rate from the POSTIAD database was higher due to the indicated interest in health and health foods of the

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individuals or due to the fact that older individuals in general are more likely to respond to a mail survey as such.

4.5.3.2 Non-response bias

Non-response bias taps the issue of a possible difference between those that have responded and those that have not, which would not directly allow one to generalize to the entire sample and in turn the entire population (Armstrong and Overton, 1977). Three primary methods address the estimation of non-response bias. First, one can compare the characteristics of the sample with known values for the population: e.g., in terms of age and gender. Second, subjective estimates can be used, such as socioeconomic differences or differences in the respondents’ interest in the study. Finally, extrapolation is a method that can be used for characteristics of the sample for which no information about the population is available (Armstrong and Overton, 1977).

Table 4.4 provides an overview of the gender and age structure of the sample compared to the sample frames used. Regarding the younger individuals between 16 and 29 that were retrieved from the telephone-based register SPAR, the response rate was substantially higher among women. More than 64% of the respondents are women, even though the distribution in the sample frame (which almost exactly represents the Swedish population) is rather equal. This strengthens the belief that women in general are more interested in health related topics. As the intention when deciding on a sample frame was to involve individuals that are interested in health-related topics, but we were not able to identify individuals between 16 and 29 who exhibited a particular interest in health beforehand (as possible in the other age group), the higher response rate by women is regarded more welcoming than problematic. In the older age group, over 75% of the respondents are women, which is only just above the women quota in the sample frame (73.4%).

Table 4.4: Gender distribution among the Responses and Sample Frames

Gender Respondents Sample Frame

Male 35.6% 51.5%16 – 29 (SPAR) Female 64.4% 48.5%

Male 24.7% 26.6%30-69 (POSTIAD) Female 75.3% 73.4%

In terms of demographics, we can thus say that there is only a slight bias in the responses in terms of gender and age. In the younger age group (16 – 29), more women responded even though the gender distribution in the sample frame was rather equal. We attribute this to the fact that the sample frame SPAR is a general

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telephone-based consumer register, where no particular interest in health-related matters was indicated (in contrast to POSTIAD). Yet, because the POSTIAD category health and health-foods is comprised of more than 73% women, this demonstrates that women are more likely to show interest in health-related matters. Furthermore, due to the lower response rate of the SPAR individuals (which could also stem from interest in health-related matters that increases with age), individuals between 16 and 29 are slightly underrepresented in the sample. Yet again, as the intention was to include individuals that are interested in health-related topics, this bias in gender and age is not considered problematic.

In order to establish whether there is non-response bias in terms of other characteristics of the sample, the extrapolation method is used, since no values are known for the population. Extrapolation is based on the assumption that late responders are more like non-responders and thus, in order to test for non-response bias, those who answered early are compared with those who answered late. The first quartile of responses is compared to the last quartile of responses by conducting an independent sample two-tailed t-test. The t-test was performed on a series of attitudinal variables that were considered to be rather representative of the main constructs and that could be used to characterize respondents (please see Appendix D for a summary of the analysis output). None of the tested attitudinal variables reflected a significant difference between early and late responders.

4.6 Data Analysis Multivariate techniques are employed to analyze the data and to test the proposed hypotheses and the research model using two common statistical software packages (SPSS and AMOS). Multivariate analyses are analytical techniques where multiple measurements (more than two variables) are analyzed simultaneously on each individual or object under investigation (Hair et al., 1998). There are three stages in this study’s data analysis process. First, the data needs to be prepared for analysis and the descriptives examined in order to deal with issues such as normality, outliers, and missing data. Thereafter, measures are validated building the basis for the actual testing of the proposed hypotheses and the research model in stage three.

4.6.1 Data examination

4.6.1.1 Normality

Normality is one of the most fundamental assumptions in multivariate analysis and describes the degree to which a distribution of the sample data corresponds to a normal distribution (Hair et al., 1998). While one cannot assume that data measured on an ordinal or nominal scale is normally distributed (Hair et al., 2007), the assumption that the data measured on interval scales is normally distributed is one of the main premises for many statistical tools and multivariate analyses.

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Indicators for normality are the skewness and kurtosis values that can be calculated for each variable. Hair et al. (2007) suggests that data can be considered to be normally distributed when the skewness values lay within a threshold of +/- 1 and kurtosis of +/- 3. Also, the range and the standard deviation provide an indication of how the data is distributed.

4.6.1.2 Outliers

Another careful assessment that needs to be made is the identification and examination of outliers, as outliers can substantially distort the findings of the statistical tests. First, all variables will be examined individually by looking at the extreme values. Variables with extreme values are then further investigated by reviewing the scatter plots. Finally, as suggested by De Vaus (2002), the standardized residuals will be considered to identify cases with strange combinations on independent and dependent variables. Cases with high standardized residuals (those that are “far away from the mean of all responses”) can be considered outliers and should be further examined and perhaps even dropped from analysis.

The outliers and the normality of the data are examined for each variable individually and will be presented in the beginning of the following analysis chapter along with some other descriptive data analyses.

4.6.1.3 Missing Data

As in the case of most survey data, there is missing data. As missing data can impact the validity of the findings, it must be addressed (Hair et al., 2007). Data can be missing for different reasons that can be related to data collection or data entry problems. There are different ways of dealing with missing data. One is simply to eliminate those respondents that have missing data. Yet if the sample size is small, this might leave the researcher with a sample too small to permit further analysis (Hair et al., 2003). There is a general rule of thumb that the elimination of the entire questionnaire should be considered when more than 10% of the answers are missing (Hair et al., 2007). Of the 771 usable questionnaires, three cases were identified that had more than 10% of data missing. Consequently, those three cases were eliminated.

When metric data is used, one approach is simply to replace the values still missing with the mean (Hair et al., 2003). Yet one problem with that method is that it gives all missing cases of one variable the same number (the mean) which reduces variability and is thus not recommended to be used (de Vaus, 2002). Regression analysis or Expectation-Maximization (EM) methods are the most sophisticated methods for imputing a value for missing data and are generally recommended (de Vaus, 2002). Analytical tests to examine missing data patterns are crucial before any

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actions are taken. The results of those tests and the measures taken to address the missing data are presented in the analysis chapter.

4.6.2 Measure Validation and Structural Equation Modeling Structural equation modeling (SEM) is a set of analytical techniques that enable the researcher to analyze a series of dependence relationships simultaneously, to measure unobserved variables, and to account for measurement error when estimating (Hair et al., 1998; Byrne, 2001). SEM techniques are of confirmatory nature and will be employed for validating the measures used and to test the proposed research model. The AMOS 7.0 software package is used for the SEM analyses.

4.6.2.1 Overall Model Fit

There are three types of measures with which model fit can be assessed, namely absolute, incremental, and parsimonious goodness-of-fit measures (Hair et al., 1998). Goodness-of-fit means the degree of correspondence of the observed input (correlation matrix) with what is predicted by the model. Absolute-fit measures are measures of the overall goodness-of-fit for both the structural and the measurement model together. Incremental fit measures are goodness-of-fit measures of the structural model in comparison to a specified null model (the simplest model that can be theoretically justified) to determine the degree of improvement. A researcher should strive for parsimony and avoid an “overfitting” of the model. Parsimonious fit measures are goodness-of-fit measures that evaluate parsimony and adjust for the number of parameters in the estimated model. As SEM evolved over time, many goodness-of-fit measures have been proposed. Hair et al. (1998) encourage researchers to employ one or more measures of each type. In Table 4.5 an overview is provided of the most common indices and rules of thumb which will be employed in this research.

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Table 4.5: Overview of Most Common Fit Indices and Rules of Thumb; source: Hair etal., (2006); Kline (2005); Byrne (2001); Chau and Hu (2001); Hu and Bentler (1999); Adams et al., (1992)

Type of Measure Index Description Rule of Thumb for acceptable fit

GFI Goodness of Fit > .90 AGFI Adjusted Goodness of Fit > .80 RMSEA Root Mean Square Error

of Approximation < .10Absolute Fit

Measures

X² Likelihood Ratio Chi-square statistic

> .05

Parsimonious Fit Measures

CMIN/df Normed Chi-Square < 5.0

CFI Comparative Fit Index > .90 Incremental Fit Measures NFI Normed Fit Index > .90

4.6.2.2 Measurement Model (CFA)

According to Hair et al. (1998), confirmatory factor analysis (CFA) is a SEM technique that is particularly useful for validating measures. In contrast to exploratory factor analysis, in confirmatory analysis, the researcher has total control over the specification of the indicators for each latent construct and can acquire goodness-of-fit measures for each specification. CFA further provides a good tool for assessing the multidimensionality of a construct (Gerbin and Anderson, 1988).

A confirmatory factor analysis is run by specifying the measurement model, which is the research model that consists of all its constructs and their measurements as well as inter-correlations between all constructs, but excludes the hypothesized relationships (paths). The overall model fit in a confirmatory factor analysis is an indicator of the extent to which the specified indicators (items) represent the hypothesized constructs (factors) (Hair et al., 1998). Re-specifications can then be made based on the goodness-of-fit measures (as outlined in Table 4.3 above) to adjust the measurement model (i.e. the scales). Both for the measurement model as well as for the structural (path) model later on, the maximum likelihood estimation (MLE) is used, which is the default method for parameter estimation in AMOS.

4.6.2.3 Path Model

Once the measurement model is validated, a structural model is specified, which is the measurement model that incorporates the proposed hypotheses. We employ a model-development strategy, which means that the proposed research model serves as a starting point, but in line with what can be theoretically justified, we engage in a series of re-specifications (adding or deleting estimated parameters from the original model) to improve model fit. Decisions for respecifications are based on the residuals of the predicted correlation matrix, which represent the differences

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between the observed and the estimated correlation matrix as well as modification indices. However, re-specification is only made when theoretically justifiable. The researcher strives for achieving an acceptable model fit with the largest number of degrees of freedom possible (difference between the number of correlations and the number of coefficients in the model) as this increases the model’s generalizability (Hair et al., 1998).

4.7 Quality of the Study The quality of a study and the credibility of its findings are a crucial issue in any research. As expressed by de Vaus (2002), a study can only be as good as the measures it employs, which emphasizes how important it is to use good measures. Good measures mean reliable and valid measures. One thus needs to assess carefully the measures used in terms of their reliability and validity. In this final section of the chapter, issues related to the reliability and validity of this study are brought up and efforts made to avoid possible bias and increase the credibility of the findings are discussed.

4.7.1 Reliability The reliability of a measure mainly concerns two aspects, the repeatability (how consistent are the results when data is collected in the same way at another point in time) and the internal consistency (how stable is the measurement across its items) (Zikmund 1994). In order to test for the repeatability of a measurement, additional data would have to be collected; however, doing so is not feasible in the scope of this study due to constrains in time and money. Reliability will thus be assessed in terms of the measurements internal consistency. One of the most well established measures for internal consistency is the Cronbach alpha ( ), which is also used in this study to assess reliability. Cronbach’s alpha is calculated based on the correlations of the individual scale items with each other and is the most commonly used indicator of scale reliability. This value should ideally be above .7 (Hair et al., 2007). Furthermore, the average variance extracted (AVE) is calculated, which represents the average amount of variance explained among the indicators of the latent construct (Hair et al., 1998). A common threshold for the AVE is .5. The results are reported in the following analysis chapter.

4.7.2 Validity Particularly in social science research where we need to measure rather abstract concepts with relatively concrete measures, it can be difficult to establish whether the instruments measure what they are supposed to measure. This is referred to as the issue of validity. There are several ways to test for the validity of an instrument, but there is no real way to prove validity - it can only be argued (de Vaus, 2002).

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There are three primary types of validity: (1) content validity, (2) construct validity, and (3) criterion validity (Hair et al., 2007). Content (or face) validity refers to the extent to which a measure taps the different aspects/dimensions of a construct (de Vaus, 2002). Content validity is ensured by having experts go through and assess the items to ensure that each construct is well reflected. In terms of construct validity, it can be differentiated between convergent and discriminantvalidity. Convergent validity exists when the items of a measure are highly correlated. Discriminant validity addresses the notion whether two different constructs in the model really are distinct from one another (de Vaus, 2002). Finally, criterion validity can be assessed by comparing the outcome of a measure with the outcome of a well established other measure of that construct and determining whether they are correlated (de Vaus, 2002). A problem with this, however, is that it is difficult to know whether the established measure can be considered valid (de Vaus, 2002). Yet, if the construct in combination with other constructs performs as expected, one can say it has criterion validity (Hair et al.,2007, p. 247). Several analytical tests are conducted once the measurement is established. The tests and test results are presented and discussed in the measurement validation section in the next chapter.

4.7.3 Common Method Bias Common method bias is one of the main sources of measurement error and appears when the variance in the data is due to the method rather than the constructs in the model (Podsakoff et al., 2003). Common method bias can arise from having a common rater, a common measurement context, a common item context or from the characteristics of the items themselves (Podsakoff et al., 2003). Any of those types of common method bias is obviously present in most research, yet particularly where the same rater and the same method is used to measure both independent and dependent variables. This is mostly the case in behavioral research (Podsakoff et al., 2003).

Podsakoff et al. (2003) discuss several techniques that can reduce common method bias in a study. One such technique is to maintain the anonymity of the respondents. Also, counterbalancing the order of the questions in the questionnaire can help reduce common method bias. The questions in the survey were structured by constructs; however, the order of the constructs in the questionnaire was revised to ensure they did not follow the sequence as in the model, which is believed to help neutralize some of the method biases that affect the retrieval stage. Finally, careful development and assessment of the items themselves to keep them simple, specific and concise can help reduce common method bias.

Once the data is collected, some techniques have been suggested to test for the impact of common method bias. One of the most widely used techniques is

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Harman’s single factor test. Traditionally, this test is conducted by loading all variables into one exploratory factor analysis and examining the unrotated factor solution. The idea is that if common method bias is present, either one single factor will appear, or one factor will explain the majority of the variance in the data. Recently, researchers have used confirmatory factor analysis as a more sophisticated approach for Harman’s single factor tests. In the analysis chapter of this thesis, the results of a Harman’s single factor test using confirmatory factor analysis are provided. Yet it is important to emphasize that irrespective of which technique is used, a Harman’s single factor test is very insensitive and is unable to provide evidence that measures are free from common method bias (Podsakoff etal., 2003).

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5 Chapter Five: Analysis and Discussion In this chapter, the results of the data analysis are presented and discussed. It is divided into

five parts: (1) descriptive statistics are provided to give an overview of the data, its distribution, and some respondent characteristics; (2) the measurement model is validated; (3) the

structural model is specified, tested on the health guide sample (HG), and validated on the ask-the-doctor sample (ATD); (4) several multigroup analyses are run to examine the impact

of moderating effects; and finally, (5) the results are summarized, the proposed hypothesis tested, and the empirically derived eHAM is presented.

5.1 Descriptive Statistics and Data Examination As discussed in the previous chapter, before any analytical techniques can be applied, the data must first be examined to gain an understanding of its characteristics. In the following section, first an overview of the respondents’ previous level of awareness and use of e-health is provided and users are compared with nonusers with respect to some demographical variables. This is then followed by descriptive statistics of the data, including an assessment of normality, an examination of potential outliers, and finally a discussion on missing data and how it was managed.

5.1.1 Awareness and Previous Use of e-Health At the beginning of the questionnaire, a short description of the e-health service was provided in order to create a common sense of awareness amongst the survey respondents. Two questions were included about respondents’ awareness of the existence of the e-health service (as offered by a public provider and similar services) before the survey, and these showed some interesting results. The level of awareness was measured on a seven-point Likert scale asking, “How aware are you of the existence of …” with the scale anchored from “very unaware” to “very aware.” Almost 39% of the respondents in the HG sample indicated they were totally unaware of the existence of HGs offered by public providers. Also, the existence of similar services offered by other providers was completely new to 43% of the respondents. ATDs appear to be even more of a novice to citizens, as more than 66% had never heard of ATDs offered by public health-care providers, and 64% indicated they were completely unaware of ATD services in general before participating in the survey.

The low numbers of general awareness reflect the still-early stage of e-health. Consequently, usage levels are rather low yet substantially higher regarding HGs (see Table 5.1). Almost 23% of the sample had previously used a public provider HG and approximately as many had used a similar service. The rate of previous use of the ATD however is far lower at around 9% for general ATDs and 4% for public provider ATDs.

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Table 5.1: Previous Use of e-Health among the Respondents

Variable Scale Health Guide Ask-the-doctor

Total %

Yes 85 23 % 15 4 % 100 13 % No 285 77 % 383 96 % 668 87 %

Previous use of this service (Use1) Total 370 100 % 398 100 % 768 100 %

Yes 88 24 % 34 9 % 122 16 % No 282 76 % 364 91 % 646 84 %

Previous use of similar services (Use2) Total 370 100 % 398 100 % 768 100 %

Regarding an individual’s experience with e-health, it does not make a difference whether the service used was offered by public or private providers. For this reason, a new variable is created integrating the two variables Use1 (public provider) and Use3 (similar services) into one nominal general use variable. In other words, this variable groups the individuals who have used either public or similar services into a group of “users,” while it categorizes those who have not used such a service at all into the group “nonusers.” This variable reveals that 125 respondents have previously used some form of HG, and that 41 have previously used some form of ATD. Table 5.5 on page 78 summarizes some descriptive statistics regarding all metric items by construct. Two metric previous use variables (Use2 and Use4) measure the extent to which services were used amongst respondents who indicated previous experience (the users group). Even amongst users, levels of use are still rather low, with means of 2.56 and 2.67, respectively.

5.1.2 Users versus Nonusers In order to examine whether users differ significantly from nonusers with respect to the demographical variables gender, age, and the level of education, a series of cross-tabulations and Chi-square significance tests were conducted. A summary of the results of the tests appear in Tables 5.2 to 5.4. A first cross-tabulation and Chi-square test on users and nonusers shows no statistically significant difference between males and females (p=.145).

Table 5.2: Cross-tabulation and Chi-square test on Use and Gender

Male Female TotalNonusers 165 436 601Users 36 129 165

Total 201 565 766

Pearson Chi-Square Value = 2.125 Asymp. Sig. (2-sided) = .145


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Particularly interesting and in contrast with previous research on technology acceptance is that no significant differences could be found between users and nonusers in terms of age. Respondents were asked to indicate their age in years. A scale based on six age categories was then created assigning a “1” for the youngest individuals between 16 and 19, a “2” for those between 20 and 29, a “3” for 30 to 39 year-olds and so forth. The results indicate that users are neither significantly older nor younger than are nonusers.

Table 5.3: Cross-tabulation and Chi-square test on Use and Age

16-19 20-29 30-39 40-49 50-59 60-69 TotalNonusers 16 54 74 124 185 137 590Users 3 14 22 37 59 28 163

Total 19 68 96 161 244 165 753

Pearson Chi-squareValue = 3.741 Asymp. Sig. (2-sided) = .587

However, a statistically significant difference appears between users and nonusers with respect to their level of education. The level of education was measured on a five-category ordinal scale from “comprehensive school” to “post-graduate degree.” Users are on average more highly educated than are nonusers.

Table 5.4: Cross-tabulation and Chi-square test on Use and Level of Education

Compr. School

2 years up. second.

3 years up. second.

Univ. degree

Post-Grad. Total

Nonusers 81 153 129 226 10 599Users 11 28 38 86 1 164

Total 92 181 167 312 11 763

Pearson Chi-squareValue = 16.825 Asymp. Sig. (2-sided) = .002

5.1.3 Assessment of Normality Normality can be assessed by looking at the skewness and kurtosis values of each variable. Values are regarded as acceptable in the range from -1 and 1 for skewness values, and from -3 and 3 for kurtosis values (Hair et al., 2007). Also, the standard deviation values provide an indication of how the data are distributed. An overview of some measures of central tendency and dispersion (median, mean, standard deviation, skewness, and kurtosis) of all metric items on the pooled data

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from both services is provided in Table 5.5 below. The items that violate one or more of the above thresholds are highlighted in gray.

Table 5.5: Descriptive Statistics on all Metric Items (pooled data)

Variable Item N Median Mean Std. Dev. Skewn. Kurtosis

Use2 106 2 2.56 1.639 .731 .049Use

Use4 126 2 2.67 1.757 .792 -.216

RD1 771 6 5.34 1.713 -.916 .016

RD2 770 6 5.39 1.683 -.988 .206


RD3 770 5 4.88 1.809 -.574 -.593

OQ1 764 5 4.78 1.481 -.402 -.218

OQ2 767 5 4.79 1.483 -.453 -.136

OQ3 768 5 5.00 1.464 -.620 .081

OQ4 767 5 4.75 1.565 -.386 -.384

OQ5 768 5 5.11 1.508 -.660 -.044

Output Quality

OQ6 768 5 4.88 1.546 -.531 -.256

SN1 765 4 3.98 1.874 -.071 -.970

SN2 764 4 4.01 1.892 -.109 -1.013

SN3 765 4 4.02 1.894 -.106 -1.019

Subjective Norm

SN4 765 4 4.15 1.875 -.184 -.965

CB1 769 5 4.67 1.911 -.512 -.849

CB2 769 5 4.65 1.944 -.493 -.908

Compatibility

CB3 768 5 4.59 1.946 -.459 -.951

C1 767 5 4.89 1.613 -.635 -.274

C2 768 5 4.95 1.610 -.709 -.158

C3 767 5 4.67 1.587 -.473 -.357

C4 767 5 4.81 1.606 -.574 -.235

Credibility of the Health-care Provider

C5 768 5 4.85 1.621 -.592 -.266

R1 767 3 2.95 1.662 .622 -.455

R2 766 1 2.03 1.578 1.555 1.500

R3 766 2 2.77 2.314 7.752 130.509

R4 768 5 4.59 1.619 -.526 -.337

R5 769 5 4.54 1.627 -.453 -.435

R6 767 4 4.38 1.645 -.294 -.629

R7 769 4 3.63 1.763 .214 -.917

Perceived Risk

R8 769 4 3.63 1.738 .183 -.903


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Variable Item N Median Mean Std. Dev. Skewn. Kurtosis

R9 769 3 3.54 1.747 .308 -.903

R10 768 4 3.71 1.752 .105 -1.006

R11 769 4 3.63 1.784 .263 -.869

ACC1 770 6 5.72 1.601 -1.319 1.010

ACC2 770 6 5.65 1.655 -1.295 .888

Perceived Accessibility

ACC3 770 6 5.59 1.694 -1.257 .706

U1 771 5 5.29 1.571 -.679 -.179

U2 771 6 5.45 1.549 -.880 .103

U3 771 5 5.11 1.672 -.696 -.295

U4 770 5 4.61 1.682 -.341 -.576

U5 771 6 5.49 1.539 -1.086 .651

U6 771 6 5.30 1.638 -.939 .270

U7 768 6 5.38 1.578 -.907 .201

Perceived Usefulness

U8 770 5 5.06 1.655 -.663 -.222

EU1 771 6 5.55 1.622 -1.150 .609

EU2 771 5 5.12 1.633 -.764 -.066

EU3 771 6 5.32 1.625 -.900 .112

EU4 768 6 5.29 1.655 -.888 .044

EU5 770 6 5.31 1.635 -.894 .088


EU6 770 2 2.27 1.620 1.384 1.162

ATT1 769 5 4.73 1.883 -.539 -.734

ATT2 770 5 4.76 1.831 -.580 -.625

Attitude toward Use

ATT3 770 5 4.73 1.732 -.511 -.476

ITU1 770 4 3.98 1.847 -.069 -1.001

ITU2 770 5 4.56 1.869 -.460 -.851

ITU3 770 5 4.40 1.943 -.320 -1.037

Intention to Use

ITU4 768 4 4.11 1.886 -.120 -1.006

Level of Internet Experience

Exp1 762 6 5.51 1.910 -1.182 .188

Among most independent constructs, no major violations appear in terms of normality, and most skewness and kurtosis values are within the above-mentioned thresholds. All three items of the perceived accessibility construct, however, are negatively skewed, which is most likely due to Sweden’s rather high Internet coverage rate. Yet, as the violation is not substantial (only about .29 under the recommended threshold of -1), it is not regarded as problematic. Also, with a

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sample as large as the present one, non-normality will most likely not create any major problems (de Vaus, 2002; Hair et al., 2006).

In constrast to perceived accessibility, two perceived risk items (R2 and R3) seem problematic, as both are highly positively skewed and peaked. R3 covers privacy concerns and R2 addresses the risk of becoming a hypochondriac. Those extreme values suggest problems with the items and could hint at a possible perceived non-relevance of those issues on a general level in the context of the services under investigation. Thus, closer attention will be paid to those two items during scale analysis.

There are no major violations of normality among the items of the main mediating variables perceived usefulness and perceived ease of use. Items U5, EU1, and EU6 are slightly skewed, yet the levels to which they exceeded the proposed threshold values are so minor that none are regarded as problematic. All attitude toward useand intention to use items demonstrate acceptable levels of skewness and kurtosis. As can be seen by looking at mean and median, respondents, on average, have a rather positive attitude toward use (mean=4.7), and despite the low levels of awareness and lack of previous use, these indicate an intention to use such services in the future on the upper end of the scale (mean=4.3).

The level of Internet experience is regarded as an important moderator in the model. The item measuring Internet experience is negatively skewed, which means that the responses cluster at the higher end of the scale. This indicates a high level of Internet experience. Again, this corresponds to the Internet usage rate of the average Swede, and is in line with the high means of the perceived accessibility items. Since the construct serves as a moderator and not as a construct included in the path model, its non-normality is not considered problematic. The scale will be used only to form groups based on the respondents’ levels of Internet experience.

Apart from normality of each item, one should also assess multivariate normality of the data, which is a prerequisite in structural equation modeling. Unfortunately, there is no direct test for multivariate normality (Hair et al., 1998). Yet, even though univariate normality is not a guarantee for multivariate normality, when the data has a normal distribution on all individual variables, even if non-normality was evident on a multivariate level, it would most likely not cause any problems (Hair et al., 2007).

5.1.4 Missing Data In general, questionnaires in which more than 10% of the values are missing should be considered for elimination (Hair et al., 2007, p. 305). Similarly, each item should be examined in terms of the percentage of missing values, as percentages


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higher than 10% may indicate that some difficulty was experienced when responding to that particular question. Following the suggestion by Hair et al.(2007), three questionnaires that had more than 10% of their data missing were dropped. The highest percentage of missing values among the individual items was 1%, which can be considered acceptable and does not require the exclusion of any item. Still, those missing data spots had to be substituted in order to meet the multivariate analysis requirements. There are several techniques that can be used to estimate missing values, such as for instance substituting with the mean (Hair et al.,2007). Simply replacing missing values with the mean does however decrease the variance in the data and can be problematic if data are not missing completely at random. Thus, the missing values of all metric variables are imputed using Expectation-Maximation (EM), a method based on regression techniques that according to Hair et al. (1998) introduces the least bias into structural equation models.

5.1.5 Outliers Outliers were identified by considering the extreme values of all variables individually. By doing so, data entry error could be identified and was corrected in four cases. Apart from those data entry mistakes, no further values were identified that were too extreme. Also, the standard scores for all variables were calculated to determine if any of those values were above or below the suggested threshold of +/-3 for sample sizes bigger than 80 (Hair et al., 1998). Apart from one item, the standard scores of all items fell within this range. Yet, in nine cases, the standard values of the risk item R2 (concerned with becoming a hypochondriac) exceed the threshold (standard value between 3 and 3.13). In any event, this item stood out already during the assessment of normality, showing a negatively skewed distribution. This skewed distribution of R2 has most likely lead to the appearance of the outliers. The impact of this item will thus be carefully assessed during the scale analysis and validation process. In order to test for multivariate outliers, a Mahalanobis D² test was conducted on a sample of key metric variables. Two cases scored a significant D² value, but since the D² in those cases was very low, they were not considered as true outliers and were retained.

5.2 Validation of Measurements Before testing and validating the measures an independent samples t-test was run on all 58 indicators of model constructs to test for differences between the HG and the ATD sample. As differences significant at the .05 level appeared in 28 of those variables, the scales were assessed separately on the two samples. Still, in order to establish valid scales, the aim was to make the resulting scales work for both samples. First, all scales were assessed individually in terms of validity and reliability on both samples. Thereafter, the measurement model was specified and a confirmatory factor analysis (CFA) was performed on the data pooled from both

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samples. The motivation behind this was that using the entire sample allows all constructs to be run at once (due to the large sample size that is needed to run the model with full information), which considers the interplay of the constructs and helps to further validate the measures used. The CFAs are run using the AMOS 7.0 software package with maximum likelihood estimation (MLE).

5.2.1 Individual Constructs When inspecting the constructs individually, a confirmatory factor analysis (CFA) was run for those constructs with three or more indicators; this also enabled the unidimensionality of each scale to be assessed. Constructs with less than three items cannot be run separately due to the problem of under- or just identification (Hair et al., 2007, p. 609). Therefore, those scales are initially assessed in terms of reliability by calculating Cronbach’s alpha, which should be above .7, and the average variance extracted (AVE), which should be above .5. Once the constructs are integrated into the measurement model, overall model-fit measures can then be obtained to confirm validity.

The primary criterion for the elimination of indicators from a scale is the indicator’s statistical significance. Also, all indicator reliabilities must be above .5. Again, however, before any item is eliminated, its importance to the constructs’ content validity is carefully assessed. A detailed discussion on the scale validation and purification process of each construct can be found in Appendix E.

The analyses of the individual scales led to two major modifications. Firstly, the results of the CFA on the credibility construct suggest that it is not unidimensional. A solution the CFA provides that makes sense—also from a theoretical standpoint—is a second-order credibility construct consisting of the two dimensions trustworthiness and honesty. Also, the multidimensional perceived risk scale that was proposed based on the exploratory study did not achieve valid results. As even after trying to form different dimensions and/or removing individual items did not lead to better results, it was finally decided to, in line with some previous work (e.g. Featherman and Pavlou, 2003; Pavlou, 2003; Curran and Meuter, 2005), measure perceived risk on a one-dimensional scale, instead, reflecting overall risk.

The outcome of this process, the final measurement of all constructs, their average variance extracted (AVE), and the Cronbach’s alpha compared to previous research is presented in Table 5.6.

Tab

le 5

.6: O

verv

iew

of th

e Fin

al M

easu

rem

ent

(poole

d d

ata)

incl

udin

g A

VE, an

d C

ronbac

h’s

Alp

ha

in C

om

par

ison t

o P

revi

ous

Res

earc

h

Cronbach’s

Const

ruct

In

dic

ator

AV

EPre

vious

Res

earc

h

ITU

1 –

I w

ill u

se X

on

a re

gula

r ba

sis in

the

futu

re

ITU

2 –

I pr

edic

t I

will

use

X

Inte

ntion

to

Use

(IT

U)

ITU

3 –

I in

tend

to

use

X in

the

futu

re

.87

.94

.82

- .9

7 V

enka

tesh

&

Dav

is, 2

000;

.96

Wils

on &

Lan

kton

, 20

04A

TT

1 –

I w

ould

like

usin

g X

A

ttitude

tow

ard

Use

(A

TT

) A

TT

2 –

I w

ould

feel

goo

d ab

out

usin

g X

.9

5.9

7.9

0 -

.98

O’C

ass

&

Fene

ch,

2003

; M

oon

&

Kim

, 20

01;

Cur

ran

&

Meu

ter,

200

5 U

2 –

X w

ould

mak

e he

alth

info

rmat

ion

mor

e ac

cess

ible

U

3 –

X w

ould

ena

ble

me

to fi

nd a

nsw

ers

to m

y he

alth

que

stio

ns m

ore

quic

kly

U5

– X

wou

ld b

e us

eful

for

man

agin

g m

y he

alth

car

e

Per

ceiv

ed

Use

fuln

ess

(U)

U7

– X

wou

ld o

ffer

addi

tiona

l hea

lth in

form

atio

n

.73

.91

.87

- .9

8 V

enka

tesh

&

Dav

is, 2

000;

Moo

n &

K

im, 2

001,

Kou

fari

s, 20

02

EU

3 –

It w

ould

be

easy

for

me

to b

ecom

e sk

illfu

l at

usin

g X

E

U4

– I

wou

ld fi

nd X

eas

y to

use

Per

ceiv

ed

Eas

e of U

se (

EU

)

EU

5 –

It w

ould

be

easy

to

rem

embe

r ho

w t

o us

e X

.86

.95

.86

- .9

8 V

enka

tesh

&

Dav

is, 2

000

RD

1 –

I w

ould

hav

e no

diff

icul

ty t

ellin

g ot

hers

abo

ut t

he a

dvan

tage

s of

usin

g X

Res

ult

Dem

onst

rability

RD

2 –

I w

ould

be

able

to

com

mun

icat

e to

oth

ers

the

resu

lts o

f usin

g X

.8

1.8

9.8

0 -

.97

Ven

kate

sh &

D

avis,

200

0

OQ

4 –

X w

ould

offe

r ac

cura

te in

form

atio

n

OQ

5 –

X w

ould

offe

r up

-to-

date

info

rmat

ion

Outp

ut

Qual

ity

(OQ

)

OQ

6 –

X w

ould

offe

r re

leva

nt in

form

atio

n

.82

.93

.82

- .9

8 V

enka

tesh

&

Dav

is, 2

000

Cronbach’s

Const

ruct

In

dic

ator

AV

EPre

vious

Res

earc

h

SN1

– Pe

ople

who

influ

ence

me

wou

ld t

hink

tha

t I

shou

ld u

se X

SN

3 –

Peop

le w

ho a

re im

port

ant

to m

e w

ould

enc

oura

ge m

e to

use

X

Subje

ctiv

e N

orm

(S

N)

SN4

- P

eopl

e w

ho in

fluen

ce m

e th

ink

that

usin

g X

is a

goo

d id

ea

.89

.96

.81

-.94

Ven

kate

sh

&

Dav

is,

2000

CB

1 –

Usin

g X

wou

ld fi

t w

ell w

ith t

he w

ay I

like

to

do t

hing

s C

B2

– U

sing

X w

ould

fit

into

my

lifes

tyle

C

om

pat

ibility

(CB

)

CB

3 –

Usin

g X

wou

ld b

e co

mpa

tible

with

the

way

I li

ke to

do

thin

gs

.93

.97

.86

Moo

re

&

Ben

basa

t, 19

91

AC

C1

– I

expe

ct it

to

be e

asy

for

me

to a

cces

s X

A

CC

2 –

I do

not

fore

see

any

prob

lem

s ge

ttin

g ac

cess

to

X

Per

ceiv

ed

Acc

essi

bility

(AC

C)

AC

C3

– X

wou

ld b

e ve

ry a

cces

sible

for

me

.88

.95

.94

O’R

eilly

R4

– I

wou

ld fe

el s

ecur

e us

ing

X (

reve

rse

code

d)

Per

ceiv

ed

Ris

k (R

)R

5 –

I w

ould

feel

saf

e us

ing

X (

reve

rse

code

d)

.88

.93

.82

- .8

9O

’Cas

s &

Fe

nech

, 20

03;

Cur

ran

&

Meu

ter,

200

5

Tru

st: T

1 –

As

prov

ider

of X

the

cou

nty

coun

cil i

s de

pend

able

T

rust

: T2

– A

s pr

ovid

er o

f X t

he c

ount

y co

unci

l is

trus

twor

thy

.9

7

Hones

ty: T

4 –

As

prov

ider

of X

the

cou

nty

coun

cil i

s sin

cere

Cre

dib

ility

of

the

Hea

lth-c

are

Pro

vider

(C

HP)

Hones

ty: T

5 –

As

prov

ider

of X

the

cou

nty

coun

cil i

s ho

nest

.93

.97

.78

Lich

tens

tein

& B

eard

en,

1989

(on

e-di

m s

cale

)


85

For a scale to be considered reliable, Cronbach’s alpha should exceed .7. In general, one can say that the higher Cronbach’s alpha, the more reliable the scale. As pointed out by Hair et al. (2007), however, once Cronbach’s alpha exceeds .95, the high alpha might indicate that the items actually ask the same question. As can be seen in Table 5.6, in the case of four constructs (attitude toward use, compatibility,subjective norm, and the two credibility dimensions), Cronbach’s alpha exceeds .95. Yet, experts in the field of marketing and e-commerce were consulted when establishing the scales and a criterion for elimination was excessively high similarity between items. Moreover, Cronbach’s alpha of TAM constructs throughout previous research generally exceeds .90 (Yousafzai et al., 2007) and often even .95. The scales that have such high Cronbach’s alpha in this study also had high values in previous research, from where the scales were taken.

5.2.2 Metric Invariance across the two samples To be able to compare the two samples (HG and ATD), metric invariance of the scales needs to be achieved. Also, since the scales must be summated to test the model and the hypotheses, we must ensure that there are no significant differences in the factor loadings of the indicators. Metric invariance of a scale means that the respondents from different groups (in this case two different samples: HG and ATD) respond to the scales in the same way. Ideally, full metric invariance is sought when comparing relationships among groups. Yet, full metric invariance in most contexts is a very rigorous test while partial metric invariance (at least two of the factor loadings per construct are invariant) is regarded as sufficient (Hair et al.,2006).

Since the measurement model including all constructs and full information is very complex (121 parameters to be estimated), a very large sample is necessary to run multigroup analyses. As the sample sizes of the two individual samples (370 and 398) are too small to run the entire measurement model with full information, the model was split in two: Model 1, including all exogenous constructs (subjectivenorm, perceived ease of use, compatibility, credibility of the service provider, resultdemonstrability, output quality and accessibility), and Model 2, including all endogenous variables (perceived usefulness, perceived risk, attitude toward use andintention to use).

Starting with the exogenous constructs (Model 1) and running a multigroup analysis on the two samples (HG and ATD) with all measurement weights constrained to be equal, the difference in the X² statistic between the constrained and the unconstrained model is not significant at the .01 level (X²=23.884, p=.032). Also, the constrained Model 2 with all endogenous constructs did notsignificantly differ from the unconstrained Model 2 (X²=4.872, p=.676). Still, in order to be able to establish whether full or at least partial metric invariance exists

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in the measurement, each factor loading one by one was constrained separately to check if this would trigger a significant change. By doing so, possible noninvariance in the measurement can be detected (Byrne, 2001).

The results of those tests are summarized in Table F1 in Appendix F. At the .001 level, all measurements of both exogenous and endogenous constructs demonstrate full metric invariance across the two samples. If the level of confidence is lowered to .05 among the exogenous constructs, output quality and compatibility only hold partial metric invariance. Yet, as full metric invariance only breaks for two constructs at the less stringent level of significance and since partial metric invariance is regarded generally as sufficient, we conclude that all measurements are sufficiently invariant to allow comparison across the two samples.

5.2.3 Measurement Model In the next step, all constructs and their purified measurements are integrated into one model and the so-called measurement model is specified. Again, since partial metric invariance of the measurement was established, and as running the entire model requires a sufficiently big sample size, the measurement model is run on the pooled data from both samples. The CFA demonstrates that the measurement model can be regarded as fitting the data well, since all indices are far above (below) the suggested thresholds of rules of thumb (CMIN/df=2.08; GFI=.94; AGFI=.92; CFI=.99; RMSEA=.038). Table 5.7 outlines the correlations among the constructs in the measurement model.

Table 5.7: Correlations of the Constructs in the Measurement Model (pooled data)

RD SN OQ R ACC CB EU U ATT ITU CHP

RD 1.00 SN .56 1.00 OQ .67 .55 1.00 R -.57 -.50 -.61 1.00 ACC .47 .32 .40 -.51 1.00 CB .64 .55 .61 -.62 .52 1.00 EU .52 .37 .52 -.56 .68 .61 1.00 U .75 .54 .71 -.61 .45 .71 .52 1.00 ATT .67 .57 .66 -.73 .54 .80 .59 .76 1.00 ITU .64 .58 .63 -.66 .57 .75 .57 .71 .87 1.00 CHP .59 .47 .69 -.64 .47 .56 .51 .59 .61 .60 1.00

5.2.4 Validity Validity is the extent to which a construct measures what it is supposed to measure (Hair et al., 2007). There are different aspects of validity for which there are different tests and assessment routines proposed in literature. There is content (or


87

face) validity, construct (convergent and discriminant) validity, and criterion validity, which are discussed in more detail in the following sections.

5.2.4.1 Content Validity

Content validity often requires a subjective assessment of whether a scale really measures what it is supposed to measure. It is very important to establish content validity before conducting any theoretical testing (Hair et al., 2007). All constructs in this research are measured based on existing scales for which validity has already been established. Furthermore, several pretests with citizens and experts in the field were conducted during scale development, helping to increase the measurement’s content validity. Also during scale purification, before any item was dropped from the scales, its individual contribution to content validity was carefully assessed.

5.2.4.2 Construct Validity

Construct validity is reflected in the convergent and the discriminant validity of a construct. Convergent validity says that the indicators of a construct should share a high proportion of variance (Hair et al., 2006). There are several ways of assessing whether a scale has convergent validity. First, all factor loadings should be significant and ideally higher than .7 (Hair et al., 2007). Secondly, also coefficient alpha representing the reliability of a scale is a measure of convergent validity. A Cronbach’s alpha of more than .7 is recommended. Thirdly, if the average variance extracted (AVE) exceeds the threshold level of .5, then one can say that convergent validity is achieved (Shook et al., 2004; Hair et al., 2006). A criterion during scale purification and validation was that factor loadings of the indicators are above .7, and as outlined in Table 5.6, all Cronbach’s alphas have values above .8 and all indicators have an AVE higher than .5.

Discriminant validity concerns the extent to which a construct is different from other constructs in the model (Hair et al., 2006). Table 5.8 below, shows the covariances among all constructs in the model, as well as each construct’s average variance extracted (top value in each column with bold letters). In order to achieve discriminant validity the average variance extracted of a construct must be higher than the squared correlation of this construct with any other construct in the model (no other construct should explain it better than it does itself). The non-bold values for all constructs are far smaller than the respective average variance extracted for each construct, which implies discriminant validity (Shook et al.,2004).

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Table 5.8: AVE and Squared Correlations among Constructs (pooled data)

OQ RD SN CB CHP R U EU ATT ITU ACCOQ .82 RD .45 .80 SN .31 .31 .89 CB .37 .40 .31 .92 CHP .47 .35 .22 .31 .93 R .38 .32 .25 .38 .41 .87 U .51 .57 .30 .50 .35 .37 .72 EU .27 .27 .13 .38 .26 .32 .27 .86 ATT .43 .46 .33 .65 .37 .54 .59 .31 .94ITU .39 .41 .34 .56 .36 .44 .51 .32 .76 .86ACC .16 .22 .10 .27 .22 .26 .20 .46 .29 .33 .88

5.2.4.3 Criterion Validity

Criterion validity (or nomological validity) postulates that the constructs behave as expected. This, for instance, implies that correlations among different constructs reflect what is suggested in theory. All correlations in the measurement model are as expected in terms of direction and size, and make sense from a theoretical standpoint (see Table 5.7 for an overview of the correlations among the constructs).

5.2.5 Common Method Bias One remedy that can be used to test the impact of common method bias is to integrate a latent “common source” construct in the model, reflecting the same source for all indicators in the model (Podsakoff et al., 2003). The unconstrained common-source model is then compared to a constrained common-source model, where the regression weights from the “common-source” to the indicators are constrained to zero (Netemeyer et al., 1997). It was, however, impossible to specify a common source model and perform such a test, as the complexity of the model led to identification problems. Another technique widely used to test for common method bias is Harman’s single factor test, where all indicators are loaded into a single factor to determine the extent to which this one factor accounts for the variance in the indicators (Podskakoff et al., 2003). Although exploratory factor analysis has been widely used in the past, confirmatory factor analysis has been used recently as a more sophisticated technique. Having all 32 indicators load on one factor, the confirmatory analysis resulted in a normed Chi-square of 34.05 and insufficient scores on the other fit indices (e.g., GFI=.41; CFI=.52; RMSEA=.208). Compared to the normed Chi-square of the proposed measurement model (CMIN/df=2.08), this represents a significant difference of 31.97. Even though the results of the single factor test hold that no single factor accounts for the variance in the data, this test is known for being insensitive and insufficient for providing evidence of the non-existence of common method bias (Podsakoff et al., 2003). Yet, discriminant validity of all constructs was established (see section 5.2.4.2), which demonstrates are distict from one another and offers


89

some confidence that common method variance is not a substantial concern in this study.

5.3 Testing the e-Health Acceptance Model (eHAM) The a priori eHAM (as proposed in Chapter Three) is now tested on both samples individually.

5.3.1 Parceling In order to rely on model fit indices, a sample size of at least five observations per estimated parameter is required in SEM (Hair et al., 1998). With a model as complex as the a priori eHAM this means that each sample would have to have at least 600 responses. A method of dealing with a sample too small for a rather complex model is parceling. Parceling means that a set of measures (indicators) is combined into one variable by either summing or averaging those indicators (Hair et al., 2006). Each “parcel” is also called a composite indicator. For parceling to be appropriate, the scale to be summarized needs to be unidimensional, and all items need to load highly on the construct, ideally with a reliability of more than .9 (Hair et al., 2006). Also, it is important that information is not lost through parceling, which means that the parceled constructs should relate to other constructs in the same way as when reflected by the individual indicators (Hair etal., 2006).

As outlined previously in Table 5.6, all scales have a coefficient alpha higher than .9, with the only exception being result demonstrability, which is just under .9 ( = .89). After having established a good fit of the measurement model on the entire sample as well as metric invariance of the measurements across the two samples, the scales were parceled. The measurement model was then run again with summated (parceled) scales, and the model achieved a very good fit (CMIN/df=1.34; GFI=.99; AGFI=.98; CFI=1.00; RMSEA=.021). Table 5.9 displays the means and correlations of the parceled constructs among the constructs on the pooled data. When comparing the correlations of the parceled constructs (Table 5.9) to the correlations of constructs measured with the full information scales (Table 5.7), one can see that the constructs still relate to each other in the same way, though the correlations among the parceled constructs are slightly lower. Hence, parceling can be considered appropriate.

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Table 5.9: Means and Correlations of the Parceled Constructs (pooled data)

RD SN OQ R ACC CB EU U ATT ITU CHP

Mean 5.36 4.05 4.92 3.44 5.65 4.63 5.30 5.35 4.74 4.31 4.87 RD 1.00 SN .52 1.00 OQ .61 .53 1.00 R -.52 -.48 -.58 1.00 ACC .45 .32 .39 -.49 1.00 CB .60 .55 .58 -.59 .51 1.00 EU .48 .36 .49 -.53 .66 .59 1.00 U .69 .52 .66 -.57 .43 .68 .48 1.00 ATT .63 .56 .63 -.70 .53 .79 .57 .73 1.00 ITU .60 .57 .60 -.64 .55 .74 .55 .68 .85 1.00 CHP .56 .47 .67 -.62 .47 .56 .50 .57 .60 .59 1.00

5.3.2 Structural Equation Modeling The hypotheses proposed in chapter three are tested by examining the parameter estimates. These need to be statistically significant and in the predicted direction, and they must be nontrivial (Hair et al., 2006). Some rules of thumb on how to assess the strength of a correlation are outlined in Table 5.10. Chin (1998) argues that standardized structural paths should at least be at .20 and ideally above .30 in order to be considered meaningful. Also, the variance explained for the endogenous constructs, essentially the R², will help assess the validity of the structural model. Problems in fit can be identified by examining the pattern and size of the standardized residuals as well as the modification indices.

Table 5.10: Rules of Thumb on the Strenght of Correlation Coefficients, source: De Vaus (2002)

A model development approach is taken, which means that the proposed model serves as a starting point but may, as long as the modifications are in line with theory, be further developed to achieve a better fit in post-hoc analyses. These post-hoc analyses are after-the-fact tests that might lead to the test of relationships for which no hypotheses were posed. However, it is very important to only make

Coefficient Range Strength of Association .90 - .99 Near perfect .70 - .89 Very strong .50 - .69 Substantial to very strong .30 - .49 Moderate to substantial .10 – .29 Low to moderate .01 - .09 Trivial


91

modifications that can be justified theoretically. The ability to test the emerging model on two samples, as is the case here, helps to validate any modification.

5.3.2.1 HG Sample

The a priori eHAM proposed in Chapter Three did not achieve sufficient fit to the HG sample (CMIN/df=5.26; RMSEA=.11). First, neither subjective norm nor perceived ease of use showed a statistically significant effect on any of the mediating or dependent variables. Also, path estimates of the hypothesized effects of those two constructs were trivial. In order to be sure that those two constructs in fact do not have any impact, tests on the second sample (the ATD sample) as well as on several subgroups were performed to assess the issue further. The following eight subgroups were formed: users/nonusers, male/female, younger group/older group, and low/high level of Internet experience. As perceived ease of use did not demonstrate any significant effects on the ATD sample or in any of the subgroups, it was dropped from the model and from further analysis. Consequently, the two hypotheses concerning perceived ease of use, H4 and H5 are rejected.

Subjective norm demonstrated a statistically significant effect on the ATD sample, yet only at the .01 level and with a low path estimate (SN => U: .120 p=.003). In the eight subgroups, it did not show any statistical significance at p<.001. As subjective norm has also previously failed to demonstrate a significant effect (e.g., Davis et al.1989), and since it accounts for less than 1% of the variance in perceived usefulness and does not add to the variance in attitude at all, it was dropped from the model and from any further analysis. Consequently, also hypothesis H11 is rejected.

After the two insignificant constructs were dropped, the fit indices still implied some level of misfit, and the modification indices in the AMOS output as well as an examination of the standardized residual covariances, point to another modification: the addition of a path from compatibility to perceived risk (see Table 5.11). Returning to theory, we can see that already Rogers (1995, p.224), who in his Innovation Diffusion Theory put forward the compatibility construct, claims that “compatibility reduces uncertainty.” Risk, in turn is grounded in uncertainty (Pavlou, 2003). Individuals who view the use of the service as being compatible with their methods of doing things are most likely more familiar with doing things online. Consequently, they might not be deterred by Internet-related risks. A path from compatibility to perceived risk was thus added. This relationship is negative and demonstrates quite a substantial impact (-.372).

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Table 5.11: Statistical Reasoning for First Modification

Step PathStand.

ResidualCovariance

M.I.Par

Change Change in CMIN/df

1) Add: CB => R -4.575 38.531 -.219 -2.552

In the modified eHAM, compatibility affects attitude in three ways: directly as well as indirectly via perceived usefulness and perceived risk. The goal in structural equation modeling should always be to establish a parsimonious model. The mediating role of perceived usefulness and perceived risk thus needs to be assessed carefully. In the case of full mediation, the path directly from compatibility on attitude would not contribute substantially to the variance in attitude, and it should be eliminated. Eliminating the direct path to attitude, however, results in a 9% loss of the variance explained in attitude. One can therefore derive from partial mediation, with compatibility having both a significant direct effect on attitude as well as a significant indirect effect via perceived usefulness and perceived risk.

After adding the path from compatibility to perceived risk, the eHAM demonstrates good fit on the HG data (CMIN/df=3.86; GFI=.96; CFI=.98; RMSEA=.088). Other than one, all relationships are statistically significant at the p < .001 level. The direct effect of perceived usefulness on intention to use is significant at the p < .01 level, but still rather low (.106). However, its elimination would lead to a worse fit in all indices, which is why it was retained. The validation on the ATD and the moderator tests that are to follow will provide further insight on the significance of this path.

The modified eHAM explains 76% of the variance in the respondents’ intention to use a HG, 74% of their attitude toward using a HG, and 67% of perceptions about the perceived usefulness of a HG. The modified eHAM, including the added path in red, is illustrated in Figure 5.1 below. Please note that for the sake of clarity, Figure 5.1 does not map the correlations among the exogenous constructs.


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Figure 5.1: Modified eHAM on the HG sample (simplified image)

5.3.2.2 Ask-the-Doctor Online Sample

The starting point for the analyses of the second sample (ATD) is the modified eHAM, which resulted from the ad-hoc analyses on the HG sample in the previous section. The tests on the second sample help validate the eHAM. Even though the fit indices of the modified eHAM are acceptable (CMIN/df=4.04; GFI=.96; AGFI=.89; CFI=.99; RMSEA=.087) and are far better than the initially proposed a priori eHAM (CMIN/df=6.09; GFI=.93; AGFI=.83; RMSEA=.113), there is still some degree of misspecification.

One relationship is not significant, namely the impact of credibility on perceivedusefulness (CHP=>U). A cross-comparison of the modified eHAM later will establish whether the difference between these two samples is significant and meaningful. Also, later analysis on moderating effects, such as previous use, will provide further insight into the possible origin of the differences. Yet, apart from the insignificance of the CHP=>U path, the modification indices and the standardized residual covariances suggest that perceptions of output quality have an impact on perceptions of perceived risk (see Table 5.12).

.76

ITU

.67

U

CB

ACC

.46

R

OQ

RD

CHP

e1

e2

e3

.90

honeste4

.95

.89

truste5.95

.74

ATT

e6

.25

.20

.33

.66

.22

.44

-.37

.27.18

-.41-.32

.11

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Table 5.12: Statistical Reasoning for Second Modification

Step PathStand.

ResidualCovariance

M.I.Par

Change

Change in

CMIN/df 1) OQ => R -2.016 10.946 -.128 -1.062

As demonstrated in previous research, perceived risk is a multifaceted construct that integrates all sorts of risks connected to the online environment in an overall construct. The initial intention in the present study was to measure perceived risk on a multidimensional scale. This multidimensional measurement, however, did not achieve sufficient validity; therefore, it was decided instead to measure perceived riskas overall perceived risk on a one-dimensional scale. Yet, “performance risk” is often one of the facets or dimensions among others, such as financial or personal risk (Featherman and Pavlou, 2003; Forsythe and Shi, 2003). Performance risk is related to the expected quality of the outcome (Forsythe and Shi, 2003) and is a measure of the uncertainty of the outcome (product or service) not being as anticipated. This would include the outcome which, in this case, is the health information requested that may be, for example, not delivered at all, delivered different to what was promised or arriving with poor quality. Returning now to the construct of output quality and what it captures, namely the perceived quality of the information, the relationship between those two constructs becomes obvious. A low quality of the information is tantamount to a bad performance by the service. The suggested effect of output quality on perceived risk is thus regarded as logical and relevant, and the path was added. The resulting model achieves good fit on the ATD-data (CMIN/df=2.97; GFI=.97; AGFI=.92; CFI=.99; RMSEA=.071) and explains 72% of the variance in usage intentions, 74% of the variance in attitudetoward use, and 61% of the variance in perceived usefulness. The resulting empirically derived eHAM is depicted in Figure 5.2 below, including the additional path from output quality to perceived risk in red. Again, please note that for the sake of clarity, correlations between the exogenous constructs are not mapped.


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Figure 5.2: Modified eHAM on the ATD sample (simplified image)

5.4 eHAM and the Impact of Moderating Variables After having tested and modified the overall eHAM, the next step in analysis and hypothesis testing is the examination of moderating effects. A moderator is a variable that changes the effect of an independent variable on a dependent one (Hair et al., 2006). Four variables have been hypothesized to affect different relationships in the eHAM, namely previous use of the service, the level of Internet experience, age, and gender. Moderating effects can be tested by conducting multigroup SEM comparing the model in moderator-related subsamples (Hair et al.,2006). Since each subsample must be sufficiently large to run the entire model (at least 5 observations for each estimated parameter), the pooled data from the HG and the ATD sample was used for the multigroup analysis.

5.4.1 Users vs. Nonusers Moore and Benbasat (1991) argue that users of a technology should have more positive perceptions about its use than do nonusers. In the present study, previoususe is measured with a nominal variable by assigning a “0” to all individuals who have never before used the e-health service under investigation (by either public or private provider), and a “1” to those who have previously used such an e-health service (either public or private provider, or both).

When comparing the means of the parceled scales between users and nonusers, one can see that there are rather large and statistically significant (p < .001) differences on all constructs (see Table 5.13). As expected, the users group has higher usage

.72

ITU

.61

U

CB

ACC

.54

R

OQ

RD

CHP

e1

e2

e3

.92

he4

.96

.92

te5

.96

.74

ATT

e6

.28

.29

.33

.72

.09

.45

.35

-.08

-.34 -.27

.11

-.25

-.26

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intentions and generally indicates more positive perceptions/expectations and a more positive attitude toward the use of the services. Also, perceptions of risk are more than half a scale point lower among users. This supports the notion suggested earlier (e.g., More and Benbasat, 1991; Gerrard and Cunningham, 2003).

Table 5.13: T-test – Differences in Group Means among Users and Nonusers

MeanConstruct Users

(166)Nonusers

(602)

Sig.(two-tailed)

ITU 5.31 4.03 .000U 5.89 5.21 .000ACC 6.28 5.48 .000R 2.98 3.56 .000CB 5.46 4.41 .000RD 5.74 5.25 .001OQ 5.31 4.81 .000ATT 5.62 4.50 .000CHP 5.33 4.74 .000

Based on what is proposed by Rice and Shook (1988), previous use is hypothesized to moderate the impact of perceived accessibility (ACC) on usage intentions (ITU). In order to examine whether there are significant differences in the effect of ACC on ITU and possibly in the other paths suggested in the eHAM, a multigroup analysis is conducted, during which each path is constrained individually to be equal in both groups. Table 5.14 presents the standardized path coefficients and p-values for all paths in both groups. In the fifth and sixth column of the table, the results of the multigroup analysis are summarized. A significant Chi-square-statistic resulting from the constraint of a single path to be equal in the two groups implies an underlying moderating effect.

Table 5.14: Comparison of Standardized Path Coefficients of Users and Nonusers

Standardized Coefficients (1-tailed)

Multigroup Analysis Dependent Path

Users 166

NonUsers 602

p (2-tailed)

CMIN

ATT => ITU .642 (***) .706 (***) .885 0.021 U => ITU .219 (***) .090 (.002) .049 3.880

Intention to Use

ACC => ITU .139 (***) .143 (***) .566 0.321 CHP => U .241 (***) .012 (.374) .006 7.665 OQ => U .207 (.002) .261 (***) .344 0.897 RD => U .279 (***) .334 (***) .591 0.289

Usefulness

CB => U .217 (***) .315 (***) .283 1.153


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Standardized Coefficients (1-tailed)


Users 166

NonUsers 602

p (2-tailed)

CMIN

U => ATT .332 (***) .257 (***) .488 0.481 CB => ATT .428 (***) .430 (***) .764 0.090

Attitude towards Use

R => ATT -.254 (***) -.302 (***) .159 1.985 CHP => R -.448 (***) -.317 (***) .274 1.198 CB => R -.094 (.116) -.325 (***) .016 5.765

Risk

OQ => R -.134 (.055) -.193 (***) .438 0.602 *** Significant at p < .001

These multigroup analyses suggest that an individual’s previous use of the service moderates two relationships in the model. At the more stringent significance level of p > .01, the difference in the impact of compatibility (CB) on perceived risk (R) in the multigroup analysis is not significant. Yet, p is only just above .01 (.016), and the impact differs quite substantially in strength (-.094 vs. -.325). Moreover, it is significant in the nonusers group but not in the users group. Users have significantly lower perceptions of risk but simultaneously indicate a significantly higher level of compatibility (see Table 5.13). Compatibility reduces risk (Rogers, 1995), yet once the service is adopted and experience with it is accumulated, the impact of compatibility weakens. Actual experience with the service now plays a more important role in the formation of risk perceptions. Referring again to Table 5.14, one can see that all effects of compatibility are actually stronger in the nonusers sample, where perceptions are not based on previous experience.

The other relationship suggested by the multigroup analysis to be moderated by previous use is the positive effect of the credibility of the health-care provider (CHP) on perceived usefulness (U). The results imply that credibility has a significant effect on perceived usefulness as was hypothesized and determined in previous research (e.g., Gummerus et al. 2004 finding a direct impact of trust on satisfaction), yet only for users. The credibility of the health-care provider seems to become critical to perceptions about the usefulness of an e-health service, once the idea of using e-health is accepted. Before citizens have actually experienced e-health, other factors appear to be more important when deciding whether it is useful to them. This notion is supported by the higher path coefficients of the other antecedents to perceived usefulness, result demonstrability (RD), output quality (OQ), and compatibility (CB) in the nonusers group. Again, in the users group, where individuals are more compatible with using such services and generally have higher perceptions of the quality and the advantages of e-health, higher source credibility plays a more decisive role.

What is surprising, however, is that the hypothesized moderating effect of previoususe on the role of access cannot be supported, thus leading to the rejection of H6c.

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This means that even though users perceive e-health to be very accessible (mean of 6.28 on a 7-point scale), accessibility is still an issue and a determinant of their intention to use e-health. In other words, in contrast to what has been hypothesized, even though its impact is low, perceived accessibility does not seem to be less of an issue to users than to nonusers. Continued research on the role of perceivedaccessibility in e-service adoption is required to demonstrate adequately the dynamics of perceived accessibility and previous use.

5.4.2 Male vs. Female In earlier research, it was found that perceptions of men and women often differ significantly with regards to technological innovations. When comparing the means of all scales between men and women (see Table 5.15), one can see that females, on average, have a slightly higher attitude toward and intention to use e-health in the future than men. Females also perceive the services to be sightly more useful than men do, and they regard using e-health as more compatible with their lifestyle than men do. Also, men have higher perceptions of risk involved. However, none of these differences is statistically significant.

Table 5.15: T-test – Differences in Group Means of Males and Females

MeanConstruct

Male (201) Female (565)Sig.

(two-tailed)


In relation to the original TAM, gender was previously found to moderate the impact of the determinants of attitude: ease of use, subjective norm, and usefulness(Gefen and Straub, 1997; Venkateh and Morris, 2000; Venkatesh et al., 2003). To investigate the possible moderating effect of gender on the relationships in the model, the same procedure used with users/nonusers was followed: constraints on each path to be equal among males and females are set individually, in order to test for significant differences in the Chi-square. The results are outlined in Table 5.16. Again, none of the relationships in the eHAM appeared to be significantly different between males and females. Hypothesis H2a, which suggests that gender can moderate the impact of usefulness on attitude, is thus rejected.


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Table 5.16: Comparison of Standardized Path Coefficients of Males and Females

Standardized Path Coefficients (1-tailed)


Malen=201

Femalen=565

p (2-tailed)

CMIN

ATT => ITU .696 (***) .703 (***) .489 0.479 U => ITU .068 (.111) .123 (***) .390 0.739

Intention to Use

ACC => ITU .152 (***) .142 (***) .889 0.019 CHP => U .173 (.012) .027 (.228) .080 3.066 OQ => U .147 (.014) .273 (***) .111 2.546 RD => U .289 (***) .330 (***) .380 0.772

Usefulness

CB => U .326 (***) .298 (***) .817 0.053 U => ATT .251 (***) .277 (***) .856 0.033 CB => ATT .471 (***) .428 (***) .384 0.757


R => ATT -.286 (***) -.287 (***) .907 0.014 CHP => R -.287 (***) -.361 (***) .754 0.099 CB => R -.401 (***) -.247 (***) .032 4.602

Risk

OQ => R -.106 (.080) -.217 (***) .275 1.193 (***) Significant at p < .001

The difference that normally prevailed between men and women in an online context might be counterbalanced by the particular context of health care. According to van Slyke et al. (2002), such differences between male and female perceptions may go back to the overall attitude toward IT, but also may apply to shopping practices and preferences. Women are known to be more concerned about health-related issues (Rosenstock, 2005), which is reflected in the gender distribution in this study’s sample as well (more than 70% women). Furthermore, it has been established that in Sweden, relatively more women look for health information online than men (World Internet Institute, 2007). Another trend that may contribute to the insignificance of gender differences in this study is the fact women are becoming increasingly tech-savvy as compared to one or two decades ago, and there is no longer a gender gap relative to being online (Ono and Zavodny, 2003). This may particularly be true for e-services such as the ones studied here, for which are no particular technical skills are required and which in general are not considered very difficult to use. Contrary to the general notion, women do not shun computers and are, in fact, responsible for a great deal of, for instance, home email-usage (van Slyke et al., 2002). Finally, a reason for the insignificance of gender differences in this study might also be a bias in the sample, as mainly individuals that indicated an interest in health and heath foods were sampled. Again, as women generally express greater interest in health-related issues and 70% of the sample is comprised of women, the results may be distorted. We thus call for further investigation on the impact of gender on the determinants of e-health acceptance.

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5.4.3 Internet Experience The level of Internet experience is highlighted as an important variable affecting an individual’s acceptance of online services (Karahanna et al. 1999a; Koufaris, 2002; Pavlou, 2003; Yoh et al., 2003; Salam et al., 2005).

Internet experience in this study is a metric variable, which makes tests for moderation somewhat more difficult. A way to deal with the moderating effects of metric variables is to categorize them and create groups. Yet, this is only possible if the distribution of the variable is bimodal, meaning it has more than one peak around which the groups can be built (Hair et al., 2006). If this is not the case and the distribution is unimodal, groups can be created by omitting the middle part and creating two groups based on, for example, the first and last third or quarter (Hair et al., 2006). It is important that the groups remain distinct from each other and that this distinction makes sense from a practical/theoretical point of view.

Internet experience is measured on a seven-point Likert-type scale (EXP1) with a “1” indicating none or very little experience and a “7” indicating a high level of experience. As discussed in the beginning of this chapter, the distribution on the item is somewhat skewed, as almost half of the respondents indicate a very high level of Internet experience (“7” on the scale). Also, the distribution shows two peaks at the two extreme ends of the scale (1 and 7) instead of one peak, as it would with a normal distribution. Logically, two groups would be created around those two peaks. However, as almost 47% indicated a “7” on the scale whereas only 56 respondents indicated a “1,” this would create two very unequal groups. It was decided to combine the low numbers of the scale (1-4) and compare those respondents to the larger number of respondents which indicated a “7.” To ensure that this would create groups distinct from each other, two one-way ANOVAs were conducted before creating the groups, with seven groups (based on the number they indicated on the scale) and the dependent variable becoming the key variable attitude toward use, followed by intention to use. The results of the two ANOVAs establish that the respondents who indicated a “7” on the scale are significantly different from those that indicated a “1,” “2,” “3,” or “4,” as well as the individuals who indicated a “1,” “2,” “3,” or “4,” are not significantly different from each other. Thus, two groups are formed: low level of Internet experience (1, 2, 3 or 4 on the scale; n = 189) and high level of Internet experience (7 on the scale; n= 359).

When comparing means, statistically significant differences between the two groups appear on all constructs. As expected, individuals with a high level of Internet experience have a more positive attitude towards the use of e-health and are on


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average more likely to use e-health in the future. The group with high Internet experience scores higher on all constructs positively correlated with attitude and lower on perceived risk, which is negatively correlated with attitude.

Table 5.17: T-test – Differences in Group Means of Low and High Levels of Internet Experience

MeanConstruct

low (188) high (360)Sig.

(two-tailed)


The level of Internet experience is hypothesized to moderate the role of perceived accessibility (ACC) as a determinant of usage intentions (more important for less experienced) as well as the impact of perceived usefulness (U) on attitude (ATT), which was expected to be higher for more experienced users.

The multigroup analysis suggests that there are significant differences in the eHAM, particularly in the paths that affect usage intentions directly. First, as was hypothesized, Internet experience appears to moderate the impact of perceivedaccessibility on usage intentions. For individuals with a high level of Internet experience, the path becomes insignificant, since people with a high level of Internet experience are more likely to have access to the Internet (see Table 5.17: average accessibility of 6.29 on a 7-point scale). Thus, access is not perceived to be an issue among the Internet experienced and does not pose a determinant of their intention to use e-health. Consequently, hypothesis H6b is supported. Because of the insignificance of the accessibility issue, attitude toward use as well as perceivedusefulness play bigger roles in the high Internet experience group and become stronger determinants of usage intentions.

For the moderating effects to be regarded as substantial and relevant, the significance level was established at p < .01. At this level, no more differences appear in the multigroup analysis. Yet, the impact of perceived risk (R) on attitude(ATT) has a significance value just above the threshold (p=.018) and demonstrates a rather large difference in the path coefficients ( =.139), thus also providing

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support for the assumption of an underlying moderating effect of the level of Internet experience. This means that the less Internet experience an individuals has, the more impact perceived risk has on the individual’s attitude toward the services. The impact of the level of Internet experience on perceived risk is well known, and support for this relationship was determined previously (e.g., Forsythe and Shi, 2003).

In the present study, the level of Internet experience was further hypothesized to moderate the effect of perceived usefulness on attitude. The results of the multigroup analysis show a tendency to produce a moderating effect there, but this effect is only significant at the less stringent confidence level of 95%. Hypothesis H2c can thus only be supported at p < .05 and is rejected at the established level of significance of p < .01.

Table 5.18: Comparison of Standardized Path Coefficients of Individuals with low vs. high levels of Internet Experience


Multigroup Analysis

Dependent Path

Lown=188

Highn=360

p (2-tailed)

CMIN

ATT => ITU .622 (***) .724 (***) .009 6.788 U => ITU .004 (.469) .161 (***) .012 6.320

Intention to Use

ACC => ITU .381 (***) .018 (.273) .000 26.516 CHP => U .016 (.409) .090 (.027) .398 0.716 OQ => U .325 (***) .236 (***) .393 0.730 RD => U .412 (***) .240 (***) .055 3.692

Usefulness

CB => U .187 (***) .359 (***) .027 4.915 U => ATT .207 (***) .344 (***) .029 4.782 CB => ATT .416 (***) .414 (***) .851 0.036


R => ATT -.369 (***) -.230 (***) .018 5.597 CHP => R -.328 (***) -.316 (***) .996 0.000 CB => R -.306 (***) -.268 (***) .660 0.194

Risk

OQ => R -.206 (.009) -.191 (***) .995 0.003 (***) Significant at p < .001

5.4.4 Age Age is hypothesized as another important moderator in the eHAM. Age is measured numerically in the questionnaire directly asking for the respondents’ age. Therefore, the age variable had to be transformed to build groups that were distinct from each other but rather equal in size. The age of the respondents (16-69) concentrates at the upper age limit in the sample, with a mean of around 49 years. The upper and lower quartiles are at 40 and 59, respectively, meaning that 50% of the sample is between 41 and 58 years old. Thus, two distinct groups can be formed based on the quartiles with respondents ranging in age from 16 to 40 in


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the younger group, and respondents ranging in age from 59 to 69 in the older group.The two groups are equal in size and are large enough to allow the analyses to be run on the full model.

Gilbert et al. (2004), as one of few applicable studies, found a significant difference in usage intentions among younger and older consumers, as consumers over 55 years of age were found to be less likely to adopt technology. The results of the t-test in this study (see Table 5.19), however, cannot confirm this finding. The only significant differences in means between the younger and older individuals exist in the credibility construct and the three constructs related to technology-readiness: compatibility, perceived risk, and perceived accessibility. As anticipated, younger citizens appear to be more tech-savvy and perceive e-health as being more compatible with their lifestyle, they have lower perceptions of risk, and they have a higher perceived accessibility to the service. The lower risk perceptions and an overall higher compatibility of the younger group may, in turn, connect to the higher perceptions of credibility of the health-care provider. The younger individuals may express higher trust in the county council as the provider of the service (higher perceptions of source credibility), as the high compatibility and the low perceptions of online risks can decrease the worry associated with the credibility of the source. According to Pavlou (2003), credibility is simply less of an issue the “safer” the environment is perceived to be. The differences between the younger and older individuals in their technology readiness do, however, not seem to have an impact on the overall attitude of e-health and their intention to use e-health in the future.

Table 5.19: T-test – Differences in Group Means of Younger and Older Individuals

MeanConstruct

younger (194) older (194)Sig.

(two-tailed)

ITU 4.42 4.09 .073

U 5.45 5.31 .309

ACC 5.97 5.30 .000

R 3.28 3.65 .022

CB 5.02 4.47 .003

RD 5.48 5.23 .124

OQ 4.98 4.89 .545

ATT 4.87 4.68 .315

CHP 5.16 4.65 .001

The multigroup analysis reveals that differences again appear in the path coefficients related to the direct determinants of usage intentions (see Table 5.20). It was hypothesized that the impact of perceived accessibility (ACC) on usage intentions

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(ITU) would be moderated by age. The level of Internet experience already demonstrated its ability to moderate this path, and it is quite likely that the moderation of age is related to that. Internet adoption is generally higher among the younger generations (WII, 2007), and accessibility is perceived by older individuals as being substantially lower (.67 points lower than the mean of the younger group). Consequently, perceived accessibility does not show any significant impact on younger individuals’ intention to use e-health. Hypothesis H6a is therefore supported.

Furthermore, the direct impact of perceived usefulness (U) on intention to use (ITU) also differs between the two groups and is only of significant importance for the younger individuals. A stronger impact of U on ITU (via attitude) among younger individuals as compared to the more technology specific construct perceived ease of use, has been established in previous work as well (e.g., Venkatesh et al., 2003; Gilbert et al., 2004). This moderating effect was not hypothesized.

Finally, the impact of U on ATT was hypothesized to be stronger among younger individuals. The results show that is exactly the case and the path is approximately twice as strong for younger individuals. This difference, however, is only significant at the less stringent confidence level of 95%. At the established level of p <.01, hypothesis H2b must be rejected.

Table 5.20: Comparison of Standardized Path Coefficients of younger and older individuals


Multigroup Analysis

Dependent Path

Younger n=194

Oldern=194

p (2-tailed)

CMIN

ATT => ITU .622 (***) .676 (***) .405 0.693 U => ITU .282 (***) .012 (.400) .000 12.473

Intention to Use

ACC => ITU -.002 (.485) .281 (***) .000 14.663 CHP => U .159 (.007) .066 (.168) .275 1.194 OQ => U .236 (***) .270 (***) .814 0.056 RD => U .148 (.009) .381 (***) .014 5.988

Usefulness

CB => U .368 (***) .209 (***) .073 3.208 U => ATT .316 (***) .161 (***) .038 4.327 CB => ATT .433 (***) .461 (***) .450 0.571


R => ATT -.249 (***) -.382 (***) .021 5.369 CHP => R -.312 (***) .-336 (***) .886 0.020 CB => R -.332 (***) -.369 (***) .712 0.136

Risk

OQ => R -.141 (.029) -.148 (.023) .968 0.002 *** Significant at p < .001


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5.4.5 Summary of Moderating Effects As explained earlier, the confidence level is set at p < .01 for effects to be considered real moderating effects. Summing up the results of the four multi-group analyses, Table 5.21 provides an overview of the uncovered moderating effects of previous use, level of Internet experience, and age. In contrast to the findings in previous research, no moderating effect of gender could be found on any of the paths.

Table 5.21: Summary of Moderating Effects

Path Moderator Direction Status of Hypothesis (p) Age Only for the older H6a: supported (at p<.01)Internet Experience

Only for the less experienced

H6b: supported (at p<.01)ACC => ITU

Previous Use Only for nonusers H6c: not supportedU => ITU Age Only for the younger Not hypothesized (at p<.01)

Gender Stronger for men H2a: not supported Age Only for the younger H2b: not supported (at p<.01)

Supported at p<.05

U => ATT

Internet Experience

Stronger for the more experienced

H2c: not supported (at p<.01) Supported at p<.05

ATT => ITU

Internet Experience

Stronger for the more experienced

Not hypothesized (at p < .01)

CB => R Previous Use Only for nonusers Not hypothesized (at p = .016) CHP => U Previous Use Only for users Not hypothesized (at p <. 01)

5.5 Cross-Validation and Summary of Hypotheses Tests An independent samples t-test was run to assess whether attitudes and expectations differ significantly between the two services investigated (HG and ATD). The results of the t-test are presented in table 5.22 below. The most significant differences appear in perceptions of perceived usefulness (U) and intentions to use(ITU), which are significantly higher for the HG. At the .05 level, attitude towards use (ATT) differs among the two samples as well as the perceived credibility of the health-care provider, with the respondents again being more positive towards the HG. Individuals seem to be more doubtful about whether the county council as the public provider would be a credible provider of the ATD service. At the same time, the positive perceptions and attitudes toward the use of HG may explain why HGs are more widely used.

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Table 5.22: T-test – Difference in Sample Means between HG and ATD

Construct HG ATD tSig.

(two-tailed)ATT 4.88 4.61 1.998 .046ITU 4.54 4.09 3.468 .001OQ 5.00 4.83 1.649 .099RD 5.33 5.39 -.497 .620R 3.49 3.39 .950 .342CHP 5.00 4.74 2.349 .019ACC 5.62 5.68 -.547 .584CB 4.74 4.53 1.531 .126U 5.53 5.19 3.369 .001

The modified eHAM, excluding any moderating effects, demonstrates a rather good fit on both samples and explains over 72% of the variance in usage intentions.The fit indices of the modified eHAM compared to the fit indices of the initially proposed a priori eHAM on both samples are provided in Table 5.23 below.

Table 5.23: GOF-Indices of the A Priori and the Modified eHAM

HG ATD Fit Indices (rule of thumb) A priori modified A priori modifiedCMIN/df (< 5) 5.260 3.890 6.091 2.976 GFI (> ,9) .940 .961 .937 .971 AGFI (> ,8) .838 .894 .830 .920 CFI (> ,9) .965 .981 .962 .988 NFI (> ,9) .958 .975 .955 .982 RMSEA (< ,1) .107 .089 .113 .071

Even though the fit indices imply good model fit, two paths (at a confidence level of .01) are significant in one sample but not in the other: the positive effect of credibility of the health-care provider on perceived usefulness (U) is very low and not significant in the ATD sample, whereas the impact of output quality (OQ) on perceived risk (R) is low and not significant in the HG sample. In order to establish whether the proposed hypotheses should be accepted or rejected, the eHAM and its propositions must be cross-validated across the two samples. A multigroup analysis will help cross-validate the eHAM and test whether the overall model and individual paths are significantly different across the two samples.

Table 5.24 outlines the standardized path coefficients and one-tailed p-values of all paths in both samples. In the fifth column, the results of the multigroup analysis are outlined, which confirm that the impact of credibility (CHP) on perceived usefulness(U) at the .01 level is statistically different in the two samples. This result suggests


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that the credibility of the source of the health information provided via a HG has an impact on the individual’s perceptions of its usefulness. This is not the case in the ATD sample, however, which may seem surprising initially. Yet, it is important to note that the HG sample contains a much higher number of users compared to the ATD sample. In the previous section on moderating effects, it was established that the impact of credibility of the health-care provider on perceived usefulness only exists for users, which is most likely the reason for the path’s insignificance on the ATD sample.

Table 5.24: Comparison of the Standardized Path Coefficients of HG and ATD

Multigroup Analysis Standardized Path Coefficients (1-tailed)

Dependent Path

HG ATD p

(2-tailed) CMIN ATT => ITU .665 (***) .724 (***) .174 1.851 U => ITU .107 (.002) .108 (.003) .891 0.019

Intention to Use

ACC => ITU .217 (***) .089 (.002) .006 7.552 U => ATT .248 (***) .278 (***) .967 0.002 CB => ATT .436 (***) .447 (***) .733 0.117


R => ATT -.315 (***) -.269 (***) .221 1.492 CHP => U .176 (***) -.077 (.053) .000 14.415 OQ => U .200 (***) .287 (***) .113 2.506 RD => U .331 (***) .334 (***) .727 0.122

Usefulness

CB => U .272 (***) .350 (***) .129 2.307 CHP => R -.354 (***) -.339 (***) .917 0.011 CB => R -.344 (***) -.248 (***) .119 2.427

Risk

OQ => R -.099 (.034) -.261 (***) .028 4.834 Bold: differences significant at p < .01

In the separate anlyses, the other path that showed significance in one sample but not in the other is the impact of output quality (OQ) on perceived risk (R), which was one of the modifications made based on the empirical testing. The multigroup analysis does not suggest this difference to be significant at the more stringent level of .01. Still, the difference in the strength of the path is quite large (=.152), with the path being trivial and only statistically significant at p<.05 relative to the use of HGs. A possible explanation may be found in the nature of the information that is provided. Via ATD, personalized information to a specific request is given, whereas general and non-personalized information is provided via HG. If the quality of the information retrieved from a HG is perceived as being low, the individual can without much difficulty look for general information on the topic elsewhere. In case of the personal response via an ATD, however, a low quality response can have more severe consequences, first because an individual may tend to rely more on the personalized answer provided by a doctor than on general information; secondly, using an ATD is more of an effort. First, a personal question must be formulated and transmitted, and then, the citizen must wait until he or she

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has received a response (which, for the time being, can still take up to seven days). Also, there is no possibility to ask a follow-up question. This notion is reflected in the higher perceptions of credibility of the health-care providers of HGs, which was discussed earlier (see Table 22).

Finally, the multigroup analysis reveals an additional difference that is statistically significant. Perceived accessibility (ACC) has a much weaker impact on usage intentions(ITU) in the ATD sample. In the previous section, the impact of perceivedaccessibility on intention to use was found to be moderated both by age and by level of Internet experience. But those moderators are not likely to be the reason for the difference in the path between the two services, as the two samples do not differ significantly in terms of age or the level of Internet experience. Therefore, this implies that unless the difference is due to another moderating effect which has not been considered in the scope of this study, perceived accessibility is not critical to the acceptance of ATDs. Yet, ATDs are far less widely accepted than HGs, which is reflected in the low usage rates presented at the beginning of this chapter. As became clear in the t-tests comparing means between ATD and HG, citizens have a significantly more positive attitude towards the use of HG than towards using ATDs. Also, the impact of attitude towards use on usage intentions turned out to be much stronger in the case of ATD. This might imply that the reason why citizens may hesitate to use ATDs most likely stems from a more negative attitude towards the use itself rather than anything else. When the attitude towards using e-health is more positive from the onset, then hinderances, such as lack of access, might play a bigger role (as in the case of HGs).

5.6 Summary Prior to the analysis, the data gathered from 768 individuals were coded and examined with regards to normality and outliers and it was dealt with missing data. Then, individual confirmatory factor analyses (CFAs) for those constructs with three or more indicators and scale analyses, based on Cronbach’s alpha for those with less than three indicators, were conducted with the aim of purifying and validating the scales. The individual scale analyses were conducted on the two service-samples separately. In order to establish whether the scales measure the same thing with respect to both services and to be able to know whether estimates in the two samples can be compared later, each indicator was then tested for metric invariance. The tests established that full metric invariance is present in all scales but two (output quality and compatibility); however, that partial metric invariance is present in all scales, which is the precondition for being able to make comparisons. The resulting scales were integrated into a measurement model that was then validated through a CFA on the pooled data from both services (n=768), where it achieved good fit.


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The research model derived from the literature study (a priori eHAM) is rather complex and would have required a very large sample for being able to test the model with full information. As several subgroup analyses with samples too small to run full information would follow, the scales of each construct were summated. A new measurement model consisting of the parceled scales was specified and tested and achieved very good fit.

The a priori eHAM was then specified in a structural equation model and run first on the HG sample. A model generation strategy was applied and one modification was made. The modified eHAM was then validated on the second sample regarding the ATD, after which one additional modification was performed. In the next stage of the analysis, the modified eHAM was then examined with respect to relevance of the hypothesized moderating effects of gender, previous use, age, and level of Internet experience. For this purpose, the data from both services was pooled and then split into four sets of subgroups with respect to the moderating effects to be examined. A series of multigroup analyses was performed to establish whether any statistically significant differences exist, which would hint at a predominating moderating effect. Finally, the eHAM was cross-validated by performing a multigroup analysis on the model with respect to the two different services.

Table 5.25: Overview of Hypothesis Testing

Path Status of Hypothesis

Moderation

H1 ATT => ITU

Supported Internet Experience: stronger for more experienced citizens

H2 U => ATT Supported --- H3 U => ITU Supported Age: only for younger citizens H6 ACC =>

ITUPartially Supported Age and Internet Experience: stronger

for older and less experienced citizens Service: only in the HG sample

H7 CHP => U Partially Supported Previous Use: only for usersH8 OQ => U Supported --- H9 RD => U Supported --- H11 CB => ATT Supported --- H12 CB => U Supported --- H13 R => ATT Supported --- H14 CHP => R Supported --- -- CB => R Not hypothesized Previous Use: only for nonusers -- OQ => R Not hypothesized --- H4 EU => ATT Not supported --- H5 EU => U Not supported --- H10 SN => U Not supported ---

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Table 25 summarizes the outcomes of the hypothesis tests. Three hypotheses are rejected, namely the two regarding the impact of perceived ease of use on attitude and perceived usefulness, and the one capturing the impact of subjective norm on perceivedusefulness. Hypothesis H7 can only be partially supported, as a significant and non-trivial impact of credibility on perceived usefulness only appeared in the HG sample. Table 25 also includes the two paths that were added based on the SEM tests, namely the effects of compatibility and output quality on perceived risk.

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6 Chapter Six: Conclusions and Implications In this final chapter, a short introduction to the state of e-health acceptance in Sweden based on the survey results is provided. After recalling the purpose of this study, the main findings are then summarized in the second section and conclusions are drawn. This is followed by an

outline of the implications of the findings to theory and management. Lastly, the research limitations are specified and discussed, and avenues for future research are suggested.

6.1 Introduction E-health offers tremendous opportunities for health-care providers to improve and enhance health-care service delivery. However, its adoption has been slow, which is why in order for e-health to be successful, it is imperative to understand citizens’ beliefs and expectations, so that e-services can be matched to their needs. In the scope of this study, an extensive citizen survey was conducted, collecting attitudes and perceptions about the use of two general e-health services that are readily available to Swedish citizens today, the online health guide (HG) and the ask-the-doctor online service (ATD).

The very early current stage of e-health and its rather slow uptake are reflected in the low level of awareness about the existence of the two e-health services among the survey’s respondents. Around 39% of the respondents in the HG survey indicated that they were totally unaware of the existence of such a service before they received the survey. In the case of the ATD, this number is even higher at around 66%.

The usage rate, on the other hand, appears to be rather high compared to the level of unawareness among the respondents. HGs (as provided by the county councils or another organization) had been used previously by 23% of the respondents, which amounts to around a third of those who indicated at least some level of awareness. ATDs were used previously only by around 9% of the respondents, which still implies that every fourth person, with at least some level of awareness, also had used one before. The results hold that attitudes towards the use of e-health in general are rather positive, even among citizens who have not used e-health previously. This is a very positive finding for health-care providers and future e-health businesses.

Comparing the two services included here, the attitudes are more positive towards the use of online HGs compared to the use of ATDs, which might explain why HGs are also more commonly used than ATDs. A comparison of users and nonusers confirms that users have a more positive attitude about the use of e-health, believe it to be more useful and less risky, and generally have more trust in the county council as the provider of public e-health. In contrast to a common belief

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and previous findings in other fields of technology acceptance, users of e-health in this study do not differ from nonusers in terms of gender or age. The difference that exists relative to the general acceptance of technology between men and women, and younger and older generations might be counterbalanced by the particular context of health care. According to van Slyke et al. (2002), differences between male and females in IT use go back to the overall attitude toward IT, but also to shopping preferences. Women have been found to consume relatively more health services (Rosentstock, 2005), which might be the outcome of their generally higher interest in health related topics. According to the World Internet Institute (2007), relatively more Swedish women look for health information online than men. This is also reflected in the gender distribution in this study’s sample, 70% of which consisted of women. Another trend that may contribute to the insignificance of gender differences in this study is the fact that women are becoming increasingly tech-savvy compared to women just one or two decades ago, and there is no longer a gender gap regarding Internet use (Ono and Zavodny, 2003). In contrast to a general notion, women do not shun computers, and they make up a sizable portion of those who, for instance, use home email (Slyke et al.,2002).

The very particular context of health care may also explain that no significant difference was found between users and nonusers of the online services in terms of age. Health often deteriorates with age, and older generations thus represent the biggest consumers of health care. Yet, even though no age-difference between users and nonusers appeared, age did show a considerable moderating effect on some of the attitudinal determinants of e-health acceptance, and thus indirectly affects acceptance. This issue will be further deliberated in section 6.2 on the determinants of intention to use e-health.

Finally, the comparison reveals that users tend to be more highly educated than nonusers. This finding is most likely connected to the correlation of the level of education with the level of Internet experience. In 2005, for instance, 94% of Swedish citizens with a high level of education used the Internet, while only 70% of the citizens with a lower level of education were Internet users (WII, 2007). Internet experience was found to be a very important moderator (this will be further discussed in the next section), which might provide an explanation for the higher level of education among users. Further investigation, however, is necessary to provide an explanation of this connection and the role played by education.

6.2 Determinants of Citizens’ Intention to Use e-Health To recall the purpose of this research, the aim was to identify the determinants of citizens’ acceptance of e-health. Based on established literature from information systems science, e-commerce and related fields, as well as a small exploratory study,


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the eHAM (i.e., e-Health Acceptance Model) was proposed. Using the data collected about the two e-health services, the eHAM was tested, modified slightly, and validated, and strong empirical support was found for the model. It is postulated that acceptance is reflected in the approval of an e-service, which consequently leads to an individual’s intention to use the service in the future. The eHAM posits several determinants of a citizen’s intention to use e-health, which are explained in more detail in the following section.

Corresponding to earlier work, the findings of this study show that citizens’intention to use e-health is mainly determined by their attitude toward using e-health. A person with a more positive attitude toward the use of e-health is more likely to use e-health in the future than would a person with a less positive or even negative attitude. As mentioned previously, HGs are much more widely accepted than ATDs, which, according to the findings of this study, result most likely from citizens’ more affirmative attitudes about using HG compared to ATDs.

Moreover, the results confirm that a person with a positive attitude toward e-health may still not intend to use the service because of a lack of access. Access was measured here as the perceived accessibility of e-health and relates to citizens’ access to the Internet as well as their perceptions of the availability of e-health services. Hence, the measure relates to e-health in general as opposed to being service specific, which is why HGs and ATDs are perceived as being equally accessible. The impact of perceived accessibility on intentions to use is small, and tests of moderation demonstrate that access is only an issue for older individuals and those with little or no Internet experience. Citizens who consider themselves experienced Internet users are likely to have the Internet readily accessible, thus explaining this finding. Age by itself is not a determinant of e-health acceptance, yet it demonstrated to moderate the impact of access. This is most likely due to the high correlation between age and the level of Internet experience (Spearman’s rho of -.301 at p<.001). Although many older citizens have become acquainted with the Internet and the usage rate in Sweden for 2005 among 55 to 64 year-olds was quite high at 89%, it is already much lower, at just under 50% for citizens in the ages of 65 to 74 (WII, 2007).

6.2.1 Determinants of Attitude toward Using e-Health Given that it is an individual’s attitude about using e-health that is the main determinant of intention to use e-health, what is it that shapes this attitude? This research reveals that there are three main determinants, namely the individual’s compatibility with using e-health, the perceived usefulness of e-health, and the perceivedrisks involved with using e-health.

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An individual’s compatibility with e-health is the degree to which he/she regards e-health to be consistent with his/her existing values and experiences. Compatibilitystands out as the strongest determinant of attitude in this research, supporting what has been found in the online context before (i.e., Chen et al., 2002). Does the use of e-health fit with the individual’s lifestyle and way of doing things? E-health can be compatible (or incompatible) with the social and cultural beliefs of the individual, with the particular needs of the individual or with beliefs and ideas that are experience based. In the online context, this construct is strongly connected to the individual’s e-readiness, as a person who enjoys doing things online and is used to doing everything else online already is also more likely to have a positive attitude towards managing health-related issues with the help of the Internet.

The perceived usefulness of e-health appeared as the second strongest determinant of an individual’s attitude towards using e-health. Perceived usefulness is the degree to which an individual believes he/she will gain from using e-health in a health-related manner. It reflects the relative advantage the individual expects to gain from using e-health compared to traditional health-care services. Perceived usefulness, put forward as one of the original TAM constructs, also has demonstrated continuously in previous research that it is one of the key factors to technology acceptance (Davis, 1989; Davis et al., 1989; Adams et al., 1992; Szajna, 1996; Lin and Lu, 2000; Venkatesh and Davis, 2000; Chau and Hu, 2001; Moon and Kim, 2001; Chen et al., 2002; Koufaris, 2002; O’Cass and Fenech, 2003; Wilson and Lankton, 2004; An, 2005; Curran and Meuter, 2005; Wu and Wang, 2005; Klein, 2007; Lanseng and Andreassen, 2007). As the qualitative exploratory study conducted in the early stages of this research reveals, the benefits citizens expect to receive from e-health include higher convenience, faster service, greater access to health care, greater effectiveness in managing health care, and overall, an additional source of health information beyond that offered through traditional channels. Comparing HGs and ATDs, one can see again that HG are perceived as being more useful than ATDs (HG: 5.53 ATD: 5.19 with t=3.369 and p=.001), thus contributing to the more positive attitude towards HGs. The qualitative exploratory study discovered that one of the major drawbacks of the ATD is the time-insensitivity, since no real-time dialog is available yet, and it can take up to a week before an answer is received.

Moreover, the results suggest that in addition to compatibility and perceived usefulness,perceived risk influences a citizen’s attitude toward, and in turn, the intention to use e-health. Perceived risk is the degree to which an individual expects certain negative outcomes and the danger and severity of negative consequences from using e-health. Again, through the qualitative investigation it could be established that the risks involved with using e-health stem mainly from the danger of receiving outdated or simply wrong information, which can have severe consequences in the health-care setting. Furthermore, the risks associated with the use of e-health


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include the fear of a loss of privacy, as health care can be a very private and sensitive issue, and the fear of a lack of security. On average, the use of HGs and ATDs is not considered overly risky, with means on the lower end of the scale for both services (HG= 3.49; ATD=3.39).

6.2.2 Determinants of Perceived Usefulness The importance of perceived usefulness to the acceptance of an innovation is widely acknowledged, which has led to the inclusion of this or similar constructs in every acceptance, adoption or diffusion model. Due to this critical role played by the perceived usefulness construct, which could again be supported by this study’s results, the eHAM holds several variables that determine citizens’ perceptions of perceivedusefulness.

The strongest determinant of perceived usefulness again turned out to be the compatibility of e-health with the individual’s social and cultural values, needs, and beliefs resulting from previous experience. Not only is a person more likely to use e-health if it matches the way he/she likes to do things, he/she will then perceive e-health as being more useful. An e-ready citizen is more likely to recognize the benefits offered to him/her by e-health. As explained previously, compatibility is more of a general construct that concerns the use of e-health as a whole. Perceptions of the compatibility of the use of HG and ATD thus do not differ significantly.

The eHAM also holds that an individual’s perception of perceived usefulness is strongly connected to the perceived tangibility of the outcome of using e-health: i.e., its result demonstrability. If the result of using e-health is obvious to citizens, they are more likely to consider the service as useful. In this study’s case of Sweden, one of the major issues associated with public health care are waiting times, with people having to wait for several months to receive certain treatment. Citizens are aware of this problematic situation, which makes the results of using e-health, in terms of what it can offer with regards to the issues in health care (e.g., “e-health reduces waiting times!”), more obvious and communicable. Hence, the result demonstrability of both the use of HGs and ATDs is perceived as quite high (HG=5.33; ATD=5.39).

Apart from the result demonstrability of e-health, another antecedent to perceivedusefulness that has been put forward already in previous research is output quality.Output quality is conceptualized in this study as an individual’s perception of how well the service performs. The outcome of using the two e-health services investigated here is health information, which means that output quality concerns the quality of the information that is provided. Is the information relevant, correct, and up-to-date? Those are the questions considered imperative to the perceived

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usefulness of the services, as any e-health service is useless if the care one receives is unreliable. The quality of the information received via HGs and ATDs is perceived to be rather high, with the ATD scoring somewhat lower than the HG (HG=5.00; ATD=4.83).

According to the findings of this study, another variable has an impact on citizens’ perceptions of perceived usefulness of e-health, namely the credibility of the health-care provider. Credibility refers to “the extent to which one partner believes that the other has the required expertise to perform effectively and reliably” (Lanseng and Andreassen, 2007). Only if the provider can be trusted can the service be perceived as useful. This relationship, however, is only true for citizens who are already using e-health. Users have already accepted the idea of using e-health, but the credibility of the health-care provider will have an influence on which e-health service to use. Yet, among nonusers who may not yet have accepted the idea of using e-health, the other variables (compatibility, result demonstrability, and output quality) are more important in determining perceived usefulness. Citizens perceive the county council, the major health-care provider in Sweden, to be more credible as the provider of HGs than ATDs (HG=5.00; ATD=4.74; t=2.349; p=.019). A higher level of expertise is required in the provision of ATDs simply because specific questions are asked and personalized responses are expected, which makes trust harder to earn.

6.2.3 Determinants of Perceived Risk Consistent with previous work (e.g. Pavlou, 2003; Curran and Meuter, 2005) in uncertain environments, such as the Internet and relative to a sensitive area, such as health care, perceived risk was found to have a rather strong negative impact on citizens’ attitude toward using e-health. Even more importantly, this study finds that perceived risk is negatively associated with three other variables in the eHAM: credibility of the health-care provider, output quality, and compatibility.

First and foremost, the credibility of the health-care provider plays a major role in citizens’ perceptions of the risks involved with the use of e-health. Being able to trust the service provider is already imperative in offline health care, as credence properties are crucial but cannot be easily verified (Lanseng and Andreassen, 2007). Moving this to cyberspace aggravates this issue even more, making provider credibility a prerequisite, as it helps to decrease uncertainty and in turn assuages perceptions of risk.

Again, the perceived quality of the information (output quality) provided is pivotal, as it captures one of the biggest risks connected with the use of e-health, the fear of receiving wrong information. Neither output quality nor perceived risk differ significantly with regards to the two services. Still, the results show that the quality of the information has a bigger effect on perceived risk in the ATD sample. As


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explained earlier, via an ATD, a more personalized and specific response is provided, whereas via HG only general health information is put online. As the response is personalized and specific to the citizen’s individual request, citizens will rely more on the information than if he/she would have retrieved the info him/herself from a HG. Thus, a low quality in the information delivered via ATD bears a much higher perceived risk.

Finally, compatibility is again negatively associated also with individuals’ perceptions of risks. A person that is accustomed to doing things online is most likely aware of the risks involved with the Internet, but chooses to accept and tolerate them while still using the services. The benefits gained from using e-health are then valued higher than the risks involved. This path was, however, only found to be significant for nonusers, mainly because once some experience with the service is gained, risk perceptions are based on this experience rather than previous experiences. This also implies that compatibility is more of a hinderance when low than is a driver when high.

6.3 Theoretical Contributions Information systems theory has been used previously to explain e-health acceptance, but primarily from the organizational or physician perspective (e.g., Kimberly and Evanisko, 1981; Hu et al., 1999; Hu et al., 2000; Chau and Hu, 2001; Chau and Hu, 2002; Herlitzer et al., 2003; Klein, 2007). Also, what has been done relative to the adoption of e-health, often within the field of medical informatics, is limited chiefly to descriptive studies, with the majority of those lacking a conceptual framework (Klein, 2007). To the knowledge of the researcher, only a very limited number of empirical studies exists that attempt to explain and predict citizens’ acceptance of e-health (i.e. Wilson and Lankton, 2004; An, 2005; Lanseng and Andreassen, 2007; Klein, 2007). The present study builds on this previous work, further develops the proposed frameworks and contributes greatly by providing evidence supporting the major assumptions, clarifying previously contradicting findings, and confirming the relevance of additional variables determining individuals’ intention to use e-health.

The theoretical propositions that result from this study’s findings not only contribute to the theoretical knowledge in an e-health context, but also to the broader field of e-commerce and services marketing. Literature on technology acceptance has focused mainly on the adoption of technology by employees in an organization for work-related tasks. This investigation extends the existing body of research by moving into the consumer service context, where the technology itself is the service. Consumers who act voluntarily and independently are most likely to behave very differently than employees in an organization, thus allowing different motivators—and barriers—to come into play.

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Also, most prior research on technology acceptance, and even many studies in e-commerce, use a student sample for data collection (van der Heijden et al., 2003a). Students, however, are quite different from the average citizen, as they normally are younger and more highly educated. Students should therefore not be regarded as being representative of the total population (van der Hejden, 2003). The present study draws its sample from a subgroup of the target population, Swedish citizens in the age of 16 to 69, thereby increasing the applicability of its results.

Furthermore, papers on e-services and three of the four studies on e-health acceptance collect data via Web surveys. The use of Web surveys automatically leads to a bias of the sample in terms of respondents’ level of Internet experience. By conducting a paper survey, the present study could even include individuals with little or no Internet experience. In addition, Web surveys allow only for the sampling of individuals who are aware and have already gained at least some experience with the service under investigation. In the e-health context, only Lanseng and Andreassen (2007) and the present study consider citizens who do not have any previous experience with e-health. This is pivotal when attempting to model determinants of acceptance, as only by including nonusers can possible barriers and hinderances to acceptance be identified. Moreover, including nonusers provides an understanding of citizens’ perceptions of e-health before they have actually experienced any e-health service. By comparing the perceptions of nonusers with the experiences of users, gaps can be identified that will yield further information about where services need to be improved.

Finally, another contribution of this study is its use of a Swedish context. Referencing Northern Europe, which has the highest range of Internet coverage and usage rates in Europe, adds to the existing research knowledge. Only very few studies are based in a European context (e.g., de Ruyter et al., 2001; Lanseng and Andreassen, 2007) with most work being done in the US or Asia.

6.3.1 The eHAM and its Power This approach has led to the proposition of the eHAM, the research model of this study which represents its main theoretical contribution. The eHAM was deduced from established IS and related theory, tested empirically and developed further. It has its roots in one of the most well established models of technology acceptance—the TAM—as established by Davis in 1986. Even though TAM is widely acknowledged as a powerful model, it has obvious limitations that repeatedly led to calls for continued research (e.g., Venkatesh, 2000; Moon and Kim, 2001; O’Cass and Fenech, 2003; Lin et al., 2007). Therefore, TAM was extended with several constructs that where noted as being missing or that became relevant to the particular context of e-health. Also, the role of four key moderators that have usually been omitted in previous research was examined.


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The outcome is the eHAM (e-Health Acceptance Model), a model that describes the dynamics of the key determinants to citizens’ acceptance of e-health. By doing so, it can predict citizens’ intention to use e-health in the future. The empirically derived eHAM is depicted in Figure 6.1. It achieves good fit on the two service samples HG and ATD as well as on the pooled dataset (GFI=.978; CFI=.989; RMSEA=.067). Model testing demonstrates that eHAM explains between 72% and 76% of the variance in citizens’ intention to use the two e-health services investigated here, which is very high compared to previous work. Alternative models in an e-health context achieve between just fewer than 40% (Song et al.,2006; Klein, 2007) to around 83% (Lanseng and Andreassen, 2007). In the consumer service context, the variance explained in usage intentions ranges from 19% to just over 62% (Dabholkar, 1996; Moon and Kim; 2001; Gefen and Straub, 2003; Curran and Meuter; 2005). In its original organizational context, TAM normally explains around 40% to 50% (Davis et al., 1989; Venkatesh and Davis, 2000).

With the three constructs of perceived usefulness, compatibility, and perceived risk, theeHAM further explains 74% of individuals’ attitude towards the use of HGs and ATDs. The variance in attitude explained by alternative acceptance models commonly lies in the range from 28% to 37% (Davis et al., 1989; Moon and Kim, 2001; O’Cass and Fenech, 2003). Again, only Lanseng and Andreassen’s (2007) model explains an equally high percentage of the variance in attitude at around 80%.

Earlier models that incorporate antecedents to perceived usefulness explain between 16% and 52% of the construct (Venkatesh and Davis, 2000; Gefen and Straub, 2003; O’Cass and Fenech, 2003; An, 2005). The eHAM, which integrates outputquality, result demonstrability, and credibility as antecedents, again lies far beyond those numbers and explains between 61% and 67% of the variance in perceived usefulness.

The eHAM, as pictured in Figure 6.1, incorporates the main findings of this study. The model holds several propositions based on those findings, which comprise the study’s specific theoretical contributions. These are presented and discussed in the following section.

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Figure 6.1: Empirically derived eHAM (e-Health Acceptance Model)

6.3.2 The eHAM Propositions Table 6.1 below provides an overview of the propositions compared to the hypotheses stated initially. Please note that due to the insignificance of two of the initially proposed constructs and the rejection of the respective hypotheses (H4, H5, H10), as well as the modifications made to the model, the numbering of the hypotheses and propositions does not match. The propositions and their individual contribution to theory are discussed in more detail in the following section.

Table 6.1: Overview of Propositions vs. Initially Stated Hypotheses

Proposition Path Moderation Initial Hypothesis and its Status P1 (+) ATT => ITU Int.Experience H1 (+) Supported P2 (+) ACC => ITU Age, Int. Experience H6 (+) Partially supported P3 (+) U => ITU Age H3 (+) Supported P4 (+) U => ATT --- H2 (+) Supported P5 (+) CB => ATT --- H11 (+) Supported P6 (-) R => ATT --- H13 (-) Supported P7 (+) OQ => U --- H8 (+) Supported P8 (+) RD => U --- H9 (+) Supported P9 (+) CB => U --- H12 (+) Supported P10 (+) CHP => U Previous Use H7 (+) Partially supported P11 (-) CB => R Previous Use Not hypothesized P12 (-) CHP => R --- H14 (-) Supported P13 (-) OQ => R --- Not hypothesized

Intention to Use

Attitudetoward Use

Compatiblity


PerceivedUsefulness

Credibility of HP

Output Quality

ResultDemonstr.

PerceivedRisk

PreviousUse

Intenet Experience

Age

P8 +

P1 +

P2 +

P3 +P4 +

P5 +

P9 +

P7 +

P10 +

P12 -

P6 -P11 -

P13 -


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This study demonstrates that the strongest determinant of citizens’ intention to use e-health is their attitude toward use, a construct that has been questioned in previous research (Davis et al., 1989). This study’s results show that attitude fully mediates the impact of perceived risk and compatibility on intention to use. Davis et al. (1989) found the TAM to explain intentions equally well even without the attitudeconstruct. Yet, the results of this study demonstrate the opposite, as (taking the pooled data) the exclusion of the attitude construct would result in a loss of explanation power in intention to use by 10%. Therefore, attitude plays an important role as a mediator and direct determinant of individuals’ intentions. Tests of moderation further reveal that this path is moderated by the individual’s level of Internet experience. Consequently, a first proposition is stated as:

P1: Attitude toward using e-health has a positive direct effect on intention to use e-health and is stronger for individuals with a higher level of Internet experience.

Apart from an individual’s attitude towards use, only perceived accessibility has a direct impact on usage intentions. Perceived accessibility has been suggested as a determinant of technology acceptance in previous work as well (e.g., O’Reilly, 1982, Culnan, 1984, Rice and Shook, 1988, Davis et al., 1992), but there is only very limited and inconclusive support for the association between the two constructs. Davis et al.(1989) argue that perceived accessibility only plays a role in situations where access is limited. This study found that the impact of perceived accessibility is also moderated by the individual’s level of Internet experience, but becomes nonsignificant for individuals that use the Internet frequently. Again, for individuals who are highly Internet experienced, attitude is the stronger determinant. Also, most likely due to the correlation between Internet experience and age (Spearman’s rho of -.301 at p<.001), the path of perceived accessibility on usage intentions is further moderated by age. Thus, perceived accessibility becomes an issue that increases with age, leading to a second proposition as follows:

P2: Perceived accessibility has a positive direct effect on intention to use e-health forolder individuals and individuals with a low level of Internet experience.

Beyond the mediated effect via attitude, a direct positive impact of perceivedusefulness on usage intentions is suggested in previous research. However, previous empirical results on this path are contradictory, since no support could be found by, for instance, Chen et al. (2002). This study’s findings show that the direct effect of perceived usefulness on usage intentions again is moderated by age. Perceived usefulnessplays a more important role for younger citizens, thus leading to a third proposition:

P3: Perceived usefulness has a positive direct impact on intention to use e-health foryounger individuals.

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Perceived usefulness consistently demonstrates that it is one of the major determinants of attitude towards use and intention to use a system, which is supported as well by the findings of this study. Previous research argues that the impact of perceived usefulnesson attitude is moderated by age, suggesting the path is stronger for younger individuals (e.g., Venkatesh et al., 2003; Gilbert et al., 2004). At the less stringent confidence level, this assumption holds in present tests as well, but the difference in the path coefficient between young and old is small and is thus not regarded as a true moderating effect. Another proposition is thereby formulated:

P4: Perceived usefulness has a positive direct effect on attitude towards using e-health.

An individual’s compatibility with the use of e-health turned out to be the strongest determinant of an individuals’ attitude towards use, which is in line findings of Chen et al. (2002), and Wu and Wang (2005), in an online context. The fifth proposition is stated accordingly:

P5: Compatibility has a positive direct impact on attitude towards using e-health.

In general, but particularly in a health care context, the Internet is an uncertain environment, which is why another construct was included in the eHAM, that of perceived risk. The results of this study provide support for the hypothesized negative impact of perceived risk on attitude and correspond with findings of previous works (e.g., Pavlou, 2003; Curran and Meuter, 2005). Hence, proposition six:

P6: Perceived risk has a negative direct impact on attitude towards using e-health.

Even though parsimony is acknowledged and sought after, in order to be able to provide recommendations to managers, it is not only important to explain usageintentions and attitude, but also their antecedents. The original TAM does not specify external variables or antecedents to perceived usefulness, and the only construct that is assumed to affect perceived usefulness is perceived ease of use. Thus, the R² of perceived usefulness in previous works is typically very low (e.g. 1% in Davis etal., 1989).

Output quality and result demonstrability are proposed as antecedents to perceivedusefulness in Venkatesh and Davis’ (2000) TAM2. This research finds support for the influence of both output quality and result demonstrability, leading to the formulation of two more propositions:

P7: Output quality has a positive direct effect on perceived usefulness of e-health.


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P8: Result Demonstrability has a positive direct effect on perceived usefulness of e-health.

In line with some previous work (Chau and Hu, 2001; Chen et al., 2002; Wu and Wang, 2005), it was also found that compatibility, apart from its direct effect on attitude, has a significant indirect impact on attitude via perceived usefulness. Hence,

P9: Compatibility has a positive direct effect on perceived usefulness of e-health.

The issue of trust and source credibility is emphasized as crucial, particularly in such an unsafe environment as the Internet (Mukherjee and McGinnis, 2007). Also, Langseng and Andreassen (2007) highlight trust in the provider as a major determinant of e-health acceptance and found support for a strong impact of trust on perceived usefulness. Conversely, van der Heijden (2003b) could not find support for a direct effect of trust on perceived usefulness or usage intentions. The eHAM integrated perceptions of the health-care provider’s credibility. By considering and testing for moderating effects, it could be established that credibility has a significant effect only on perceived usefulness for individuals who have already gained experience with e-health (users). For nonusers, on the other hand, the other three constructs (compatibility, output quality, result demonstrability) are prevalent. Consequently, the following proposition is formulated:

P10: For individuals with previous experience with e-health, the perceived credibility of the health care provider has a positive direct effect on perceivedusefulness.

As emphasized previously, risk perceptions are crucial in e-health, which is demonstrated by a direct and rather strong negative impact of perceived risk on citizens’ attitude toward the use of e-health. The results of this study demonstrate that perceptions of risk, in turn, are influenced by three eHAM constructs: compatibility, credibility, and output quality.

Although Rogers (1995) states that “compatibility reduces uncertainty” (p. 224), no records of empirical evidence for this relationship are noted in previous research. This study found that there is a clear negative link between compatibility and perceived risk, although this pertains only to nonusers. If a service is perceived as being incompatible with the individual’s way of doing things, the service will be perceived as more risky. Once the individual has gained experience with the service, risk perceptions are based on this experience, rather than on previous experiences (compatibility). Summing this up, a proposition is put forward:

P11: For nonusers compatibility has a negative direct effect on perceived risk.

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Apart from the impact of provider credibility on the perceived usefulness of the e-health service, credibility also has a strong negative effect on risk perceptions. In line with previous work (van der Heijden, 2003b; Pavlou, 2003), the results of this study demonstrate clearly that perceived risk decreases as provider credibility increases, leading to the following proposition:

P12: Perceived credibility of the health-care provider has a direct negative effect on perceived risk.

Finally, this study’s results indicate that output quality also poses an antecedent to risk perceptions. Even thought this path has not been specified before, research on perceived risk treats information quality as an element of performance risk, a risk dimension included among others, such as financial risk, psychological risk, social risk, time or convenience risk, or privacy risk (e.g., Featherman and Pavlou, 2003; Forsythe and Shi, 2003), thus providing support for the determining role of outputquality. We hence pose that:

P13: Output quality has a negative direct effect on perceived risk.

6.3.3 Ease of Use and Subjective Norm Apart from these thirteen propositions, further specific contributions to the theoretical knowledge include the rejection of three hypotheses initially put forward. No support could be found for an impact of perceived ease of use on any of the mediating or dependent constructs in the eHAM. This finding is interesting and worth noting since perceived ease of use is frequently considered the second major determinant of individuals’ intention to use a technology or system (Davis, 1989; Davis et al., 1989; Adams et al., 1992; Szajna, 1996; Lin and Lu, 2000; Venkatesh and Davis, 2000; Moon and Kim, 2001; Chen et al., 2002; Koufaris, 2002; O’Cass and Fenech, 2003; Wilson and Lankton, 2004; Wu and Wang, 2005; Klein, 2007; Lanseng and Andreassen, 2007). Compared to perceived usefulness,however, it was found in large part to be less strong. In an online context, perceivedease of use was previously found to be insignificant (e.g., Chau and Hu, 2001; Koufaris, 2002; van der Heijden et al., 2003; Curran and Meuter, 2005). An explanation for why perceived ease of use may not have an impact in the context of this study might simply be the fact that the services are not perceived to be very difficult to use. The high mean (5.30) on the summated perceived ease of use scale as well as on the individual items (5.12 – 5.55) supports this notion. Alternatively, Davis (1989), among others, has found that perceived ease of use becomes non-significant as the level of experience increases, contradicting this study’s results where perceived ease of use is of no importance for nonusers either. On the other hand, Szajna (1996) found perceived ease of use to be significant only in a post-implementation situation, which corresponds with the results of this study,


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considering that the majority of respondents actually are nonusers. The inconsistency in those findings might indicate a limitation in TAM of failing to incorporate the level of computer skills and experience with the system or technology (and in this case, the Internet).

The original TAM has often been criticized for not considering any social influence, opting instead for the inclusion of a construct called subjective norm as an antecedent to perceived usefulness. Subjective norm, however, did not prove to have a significant impact on any of the mediating or dependent constructs in the eHAM and was thus dropped. This supports findings by Davis et al. (1989), who also could not confirm any impact of subjective norm, and claim that the scale used to measure subjective norm from a psychometric standpoint is weak, which might lead to this finding.

6.4 Managerial Implications Each piece of research should not only be relevant to the research field and of interest to academics, but it should also provide some implications to practice. Based on the findings of such research, recommendations to management should be included. The practitioners to which the present study and its outcomes are relevant include both public and private health-care organizations and a variety of health-care professionals that either offer e-health currently or are considering doing so in the future. Through the identification of determinants of citizens’ attitude towards use and their intention to use e-health, health-care managers can develop and offer services that better match their citizens’ needs and wants, and initiate tailored marketing actions. The present study further examines the role of different demographical variables in the acceptance of e-health, which helps health-care managers offer services that are targeted specifically to the demographical composition of a certain health-care region. The outcome of this study may also be of interest to system developers and IT providers when developing services and technologies to be used in the provision of e-health.

First and foremost, health-care managers must make citizens aware of the e-health service alternatives available to them. The survey demonstrated that many people simply are unaware that such services exist. Yet, even those who did not know about the services expressed a decidedly positive overall attitude towards using e-health, which emphasizes the potential that can be leveraged via e-health.

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More specifically, the following recommendations can be offered to health-care managers:

- In order to market e-health services, health-care professionals must focus on communicating the benefits of e-health services to citizens, since perceptions of usefulness are a major driver of citizens’ attitude toward use.

- Access to the Internet is an issue among older citizens and those with little Internet experience. Even though the Internet coverage and usage rate in Sweden is amongst the highest, not everyone has the possibility to go online from home or work. Access can be improved by installing public kiosks at, for instance, pharmacies (some of which already exist today), hospitals, health centers and other public buildings.

- This study found that one of the strongest determinants of an individual’s intention to use e-health is the compatibility of using e-health with the individual’s lifestyle and way of doing things. Compatibility is strongly connected to individual e-readiness. This is especially applicable to the older generations that were not raised in the current e-society and may not be as e-ready as their younger counterparts. Changing citizens’ habits is no easy task, but management should focus on marketing strategies which strive to convince citizens that using e-health is not something completely new and incompatible with their way of doing things. For the time being, however, it is also extremely important to offer e-health as a complement to traditional health-care services rather than as a substitute. In this way, service quality can be improved for all citizens by offering health care through different channels depending on the citizen’s preferences.

- Perceived risks involved with using e-health influence citizens’ attitudes towards using e-health. Even in cases where no payment and thus no sensitive credit card information is disclosed (as in the case of the two e-health services investigated here), health care is a very sensitive and private issue. Therefore, it is crucial to implement risk-reducing measures and design features that recognize user privacy and security to protect citizens’ integrity. It is also essential that the measures taken are communicated to citizens and rules governing privacy and security protection should be stated clearly on the Web site.

- The qualitative exploratory study which consisted of several in-depth interviews indicated that apart from security and privacy issues, citizens fear the risk of receiving incorrect information. In health care, our life and body are at stake, and receiving irrelevant, outdated, or patently mistaken information could have severe consequences. Actions that can prevent this


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fear include, for instance, an indication of the source of the information and the date it was last updated.

- Also, the findings demonstrate that the credibility of the health-care providerplays a significant role in terms of risk perception and overall concerns when individuals evaluate the usefulness of the e-health service. Acknowledging the providing institution and its intent, promoting shared values, and improving communication may help contribute to a trusting relationship between citizens and the providing organization (Mukherjee and McGinnis, 2007), thus reducing risk perceptions and improving the perceived usefulness of the e-health service offered.

6.5 Limitations As with every research project, the present study is limited in certain ways. First and foremost, when using SEM techniques, the possible existence of alternative models must be acknowledged. Relative to the model, it can also be stated that the dynamic model proposed here was tested in the initial sequence of events using cross-sectional data. Still, a longitudinal study would be necessary in order to test the causality of some of the relationships proposed in the model, particularly the causal effect of intention to use on actual use, which could not be tested in the present study. Conducting a suitable longitudinal study was unfeasible due to constraints in time and resources.

A sample of 768 responses—370 responses on one service and 398 on the other—is rather large and corresponds to a sample size normally recommended for such a study. Considering the complexity of the proposed model though, a much bigger sample would have been necessary to test the entire model with full information (full scales). Primarily when examining moderating effects, a large sample is necessary as the model is tested on various smaller subsamples. Since collecting an even bigger sample was impossible in view of financial and time constraints, the scales were parcelled to overcome this problem. Parcelling generally means that information is lost, which can distort the findings (Hair et al., 2007). However, the very high reliabilities of the scales provide some confidence that the scale parceling did not have any major impact on the analyses.

Furthermore, the present study investigates the acceptance of e-health in primary health care in Sweden. Even though no particular Web site is assessed, the empirical results of this study are limited to e-health services offered by public health-care providers in Sweden. This was necessary since the respondents, mainly with respect to the credibility construct, had to be able to relate their answers to the provider of the service. Respondents were asked to relate to the county councils as being the provider, as these are the main providers of primary health care in

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Sweden. This strengthens the applicability of the results to the Swedish context; however, it limits the generalizability of the results to other countries, where the structure of the health-care sector is different and less monopolistic. A replication of this study in a private health-care sector context might thus offer additional insights, particularly on the role of service provider credibility. Also, in a country that has more competition in the health-care sector, additional factors, such as pricing issues, might become relevant and possibly influence the interplay and explanatory power of the model’s other constructs. In terms of the sample employed, it must be emphasized that 70% of the sample population is comprised of women, and that younger age groups are somewhat underrepresented, which may have introduced a bias. The sample, however, provides a rather dependable representation of those citizens who have a particular interest in health care and health-related matters, and who represent the target group of e-health services.

Unfortunately, a very common problem especially in attitudinal research is the issue of common method bias (Podsakoff et al., 2003). Common method bias can occur when a single rater is used to collect data on both independent and dependent constructs. In attitudinal research, it is difficult if not impossible to collect information from a source other than the individual him/herself. Common method bias may also have led to the very high scale reliabilities (Cronbach’s alpha of over .95) in the case of attitude, compatibility, and credibility of the service provider. It was found, however, that high scale reliabilities of above .90 and even .95 are rather common in attitudinal and technology acceptance research. Podsakoff et al.(2003) offered several tips on how to reduce common method bias, which were considered in the development of this study’s instrument.

Additional limitations must be pointed out relative to some of the measurements used in this study. Initially, the intention was to create a multidimensional measurement for perceived risk, integrating different risk domains, such as performance risk, technical risk, personal risk, etc. However, the resulting measurement did not provide for sufficient validity. Therefore, perceived risk was measured using a simple one-dimensional and overall measure. Also, limitations regarding the measurement of credibility should be noted. An originally one-dimensional scale measuring source credibility outlined by Lichtenstein and Bearden was adapted (1989), but it was demonstrated to be two-dimensional in this study. Credibility and trust are key issues in online health care, and this study’s measurements may thus be oversimplified.

6.6 Suggestions for Future Research Finally, some suggestions for further research are outlined. These are based on the findings as well as on the limitations acknowledged in the previous section. First, as already indicated in the limitations section, a longitudinal study is necessary to test


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the causality of the proposed paths to validate the eHAM and its propositions further. Especially the causal impact of intention to use on use, a commonly accepted premise that could not be tested in this study at all, should receive special attention in future research. Also, only a longitudinal study can establish whether users’ more positive attitude towards e-health is the reason for their acceptance or an outcome of use (Karahanna et al., 1999a).

Secondly, this research investigates e-health acceptance from the perspective of two common e-health services that are readily available to Swedish citizens, the online health guide and the ask-the-doctor online service. Collecting data regarding two different services helps to validate the model. Still, e-health is a very broad field involving the application of myriad information and communication technologies, including a wide range of electronic services. Continued research is therefore necessary to validate the eHAM in the context of other e-health services. Additional commercial health-care services offered in other countries with less monopolistic and government controlled health care provision would be of particular interest, as additional variables, including pricing or the availability of and satisfaction with traditional care services, might change the dynamics of the eHAM.

Some of the findings in this study question the premises commonly accepted in IS research. In particular, these are the roles played by ease of use as one of the main determinants of attitude towards use and subjective norm as an antecedent to usefulness,both of which were found to be of no impact in this study. Continued research on those determinants and the other eHAM constructs should be conducted to examine whether additional moderating effects might control their impact on usageintentions. Such moderating effects could include additional demographics, such as education, which was found to differ significantly between users and nonusers in the present study, cultural background or region of residence.

Finally, the eHAM explains up to 76% of the variance in usage intention; a rather high percentage compared to previous research in the area and more general IS research. Yet, this means that around a quarter of the variance remains unexplained. The same applies to the variance explained in attitude, of which around 26%, and in usefulness 33% to 39% are still unknown. Further research incorporating other variables that, for the sake of parsimony were omitted would thus be valuable.

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I

Appendices

Appendix A: Scale Development

Actual Use

Actual use in the original TAM constitutes the measurement for acceptance and is the final dependent variable in the model. Due to the time lag between the independent/mediating attitudinal variables and the dependent actual use variable, especially in the early stage of an innovation, actual use is difficult to measure. As this study’s sample consists of users as well as nonusers, the model will be tested in the initial sequence of events excluding the causal relationship between intention to use and actual use. Instead, intention to use will serve as the dependent variable in the model. Actual use, however, is still measured in order to assess its role as a control/moderating variable.

Following previous research (Davis et al., 1989; Moon and Kim, 2001; Chen et al.,2002), a measure of self-reported usage is used to capture actual use. Previous researchers have commonly either measured self-reported use on a seven-point Likert scale with the anchors at “very infrequent” to “very frequent” or by using an ordinal scale providing different options for frequency of use. Both measures were included in the pilot investigation and appeared to measure actual use in the same way. As interval scales generally offer a wider range of statistical analyses, it was decided to retain the Likert-scale ranging from “very infrequent” to “very frequent.”

As expected, the pretest revealed that only a small number of respondents have actually used the public e-services investigated before (20% of the pilot sample). However, some students in the pretest indicated that they had used similar services offered by other organizations (not the county councils as investigated here). Such individuals should still be considered as having gained some experience with the e-health service. Therefore, it was decided to include another single-item measure of actual use of similar services offered by other organizations or companies.

Intention to Use

The item pool for measuring intention to use consisted initially of six items taken from previous research (Venkatesh, 2000; Venkatesh and Davis, 2000; Moon and Kim, 2001; Curran and Meuter, 2005: Wilson and Lankton, 2005). All items are measured using a 7-point Likert scale with the anchors strongly disagree to strongly agree. During the scale purification process two items were dropped, leaving a four-item scale with a Cronbach alpha of .94 in the pretest.

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Attitude toward Use

The attitude toward something is generally conceptualized as an affective or evaluative judgment of an object or event by the individual (Fishbein and Ajzen, 1975). This has most often been measured using semantic, differential scales with bi-polar end-points (e.g., good/bad). For this study, however, it was decided to have the same anchors for all items in a scale, as a standardized format requires less cognitive processing and makes it easier for the respondent to answer the questions. As emphasized by Ajzen (1991), from a measurement perspective, it makes no difference whether bi-polar or unipolar scales are used. Items were thus built by forming statements from the adverbs used as bi-polar scale anchors in previous research, which were also then measured on a seven-point Likert scale capturing the extent to which one strongly disagrees/strongly agrees. The original item pool of six items was reduced to a three-item scale based on the pretest results achieving a Cronbach alpha of .94.

Perceived usefulness

Perceived usefulness as has been proposed by Davis (1989) has been touted as being difficult to measure. In its origin, it is defined as “the degree of which a person believes that using a particular system would enhance his or her job performance” (Davis et al., 1989, p. 985). This definition however does not apply to the consumer service context. Moore and Benbasat (1991) explain that as innovations typically are developed with certain purposes in mind, they should be perceived as fulfilling those purposes better than traditional products in order to be considered useful. The six items proposed by Davis et al. (1989) that have been used most often since then measure exactly that; the benefits the system offers an employee when doing his/her job (more effective, easier, more quickly, increase performance, increase productivity, useful).

In order to measure perceived usefulness in the context of this study, the benefits offered by e-health over traditional health services thus had to be identified and captured in the items. Based on the exploratory interviews presented in the previous chapter as well as an extensive literature search, the advantages of the two e-health services were gathered. The three main advantages identified are convenience, time savings, and availability. The item pool created for measuring perceived usefulness thus consisted of items that were created to capture those advantages, as well as items from previous research that were considered applicable to e-health.

The initial item pool consisted of 15 items measuring different aspects of perceived usefulness, but with many very similar items. Based on the results of the pilot study, the scale could be reduced to eight items capturing the various domains of the

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III

construct (convenient, time savings, availability, overall usefulness). This overall perceived usefulness scale achieved a Cronbach alpha of .90.


The initial item pool created for measuring perceived ease of use consisted of eight items from previous research that were considered suitable for the e-health context. The intention was to retain the original items as proposed by Davis et al. (1989). However, two items were not included as they did not transfer well to the consumer service context. Based on the scale analysis during the pilot study, two more items were dropped, thus yielding a five-item scale with an overall Cronbach alpha of .88.

Subjective Norm

The social influence suggested in previous research and reflected in the construct subjective norm, has previously been measured with two items (SN1 and SN2). Yet, as explained by Hair et al. (2007), in order to avoid under-identification, one should attempt to have at least three items in a multiple-item scale. For this reason, five more items that measure social influence in the e-health context were created and added to the item pool, of which three were dropped again during scale purification based on the pilot study. A Cronbach alpha of .97 was calculated for the remaining four-item scale of subjective norm.

Output Quality

Venkatesh and Davis (2000) who proposed output quality as an independent construct in TAM 2, measured the construct with a two-item scale. Further items were created based on the definition of the construct in order to be able, after scale purification, to obtain a minimum of three items in the scale. The output of the e-health services investigated here is information, and information quality in health care depends on several aspects: understandability, relevance, usefulness, and reliability (Song et al., 2006). Thus, five additional items were created targeting those information quality-related aspects. After the pilot study, one of the original items was dropped, resulting in a six-item scale with a Cronbach alpha of .93.


Result demonstrability in previous studies (Venkatesh and Davis, 2000; Moore and Benbasat, 1991; An, 2005) has been measured on a four-item scale. Those four items were taken and adapted to the context of this study. Based on the results of the pilot study, however, one item was dropped as it significantly lowered the overall scale alpha (Cronbach alpha with all four items .77). This item was reverse coded which might have led to this abnormality. Dropping the item resulted in a Cronbach alpha of .84.

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IV

Perceived Credibility of the Health-Care Provider

Source credibility as discussed by Newell and Goldsmith (1997) constitutes the perceived expertise, trustworthiness, and/or attractiveness of the information source: i.e., in this case, the service provider. Lichtenstein and Bearden (1989) established a scale for source credibility that was considered suitable to the e-health context and was thus adapted without major reformulation. As the scale performed well in the pilot study with a Cronbach’s alpha of .94, and since five items is considered an appropriate number for items in a scale, all items were retained.

Perceived Accessibility

The initial item pool of the perceived accessibility scale consisted of three items adapted from O’Reilly (1982), Rice and Shook (1988), Culnan (1984) and Davis etal. (1989). Perceived accessibility of the e-health services can be limited in two respects. The first access issue is the more straightforward one: having access to the Internet. Internet access can be related to terminal or physical accessibility as proposed in previous research (e.g., Culnan, 1988). Yet another item was added to the initial pool to capture the lack of access stemming from the county councils not offering a service as such. That item, however, was dropped again during scale purification, since most individuals in the pretest simply did not know whether their county councils provide such an e-health service. Also, the three items that are retained after scale purification are rather general items measuring overall perceived accessibility, and which capture all aspects connected to perceived accessibility.

Perceived Risk

Risk perceptions tend to be high in situations where there is uncertainty (Pavlou, 2003), which is the case in online environments and particularly in an online health-care context. Perceived risk as investigated in the online context consists of several facets including performance risk, financial risk, time risk, psychological risk, socialrisk, and privacy risk (Featherman and Pavlou, 2003). In addition, perceived risk can also be measured on an overall level, evaluating all criteria together (e.g., Featherman and Pavlou, 2003). In general, overall risk perceptions are measured on a semantic differential scale with bi-polar endpoints, such as significantrisk/insignificant risk, very negative/very positive situation¸ high/low potential for negative/positive outcomes. Yet again, being consistent in measuring one construct by using only one anchor for all items makes it easier for the individual to respond.

The exploratory study demonstrated that individuals are afraid or concerned about a number of different things regarding e-health apart from security and privacy as the main concerns with online services. For this reason, the exploratory study, an extensive literature survey, as well as expert interviews were used to identify all types of risk that are associated with using the e-health services studied here. The risks that were brought up consistently, namely the quality of the information, technical

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V

issues, and negative outcomes, are considered domains of the risk construct in this study. Those risk domains build the basis for creating new items that were added to the item pool. This procedure has also been used previously for creating a measurement of perceived risk (e.g. Garbarino and Strahilevitz, 2004). It resulted in the formation of eleven items.

Even though the scale analysis based on the pretest suggested items for elimination, all items were retained. Comments from the students in the pretest show that there was difficulty and misunderstandings regarding one of the items in the scale (R3), the item that demonstrated the highest alpha if deleted. It was decided to retain the item, but to adapt it based on the students’ feedback to make it clearer and more understandable. No items were dropped from the risk scale, as the pretest sample consisted of Internet-experienced students and thus risk perceptions might be rather different from those of the public. The overall Cronbach alpha of the scale including all 11 items can be considered sufficient at .84.

Compatibility

The compatibility scale was taken and adapted from Moore and Benbasat (1991), who suggested three items for a “short” compatibility scale. As it was assumed that compatibility in this context would be characterized by an individual who likes to use online services and the Internet for different purposes, initially two more items were suggested. Yet, because they lowered the Cronbach alpha of the scale, both items were dropped after the pretest and the original three were remained.

Demographics

Several demographical variables are included in the questionnaire to examine whether there are any demographical differences between users and nonusers and to test for moderating effects in the model. These variables include age, gender, the level of education and the level of Internet experience (measurements outlined in table A1) below. Age, gender and the level of Internet experience emerged as key influencing variables in previous work and are hypothesized to moderate several relationships in the eHAM. For measuring the level of Internet experience, one item was used, which measured how experienced the individual perceives him/herself to be with the Internet on a seven point Likert-type scale.

The role of an individual’s education is far less well researched, but has been found to have an impact on IT adoption as well (e.g. Agarwal and Prasad, 1999). It was thus decided to include this variable in the questionnaire as an additional demographic, which will make it possible to conduct further comparisons between users and nonusers of e-health. The level of education is measured on a five-category ordinal scale from “comprehensive school,” which is the minimum in Sweden, to “Licentiate or Ph.D. degree.”

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VI

Table A1: Measurement of Demographics and Control Variables

Variable MeasureGender I am: Male/FemaleAge I am: ___ years of ageLevel of Education Comprehensive school (or equivalent)

2 years of upper secondary school (or equivalent) at least 3 years of upper secondary school (or equivalent) University degreeLicentiate or Ph.D. degree

Internet Experience and Use

EXP1 – am an experienced Internet user

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VII

Appendix B1: Questionnaire (Swedish Version – HG) Please note that the format of the questionnaire has been adapted to to fit the margins of the thesis print. Page breaks and number of pages thus differ from the original.

Vårdguider på Internet – Vad tycker DU? Jag är intresserad av din åsikt beträffande en elektronisk vårdservice hädanefter kallad ”Vårdguide på Internet”, som erbjuds av bl.a. Sjukvårdsrådgivningen, en organisation som ägs av Sveriges landsting. En vårdguide på Internet är en webbplats som tillhandahåller allmän hälso- och sjukvårdsinformation i form av ett lexikon. Här kan du söka information om sjukdomar, symtom och behandlingsformer. Symtom och sjukdomar förklaras, och du får tips och råd om hur Du kan behandla enklare åkommor.

Vårdguiden på Internet är inte avsedd att ersätta läkarbesök. Den ska fungera som ett komplement, så att du bättre kan sköta din och din familjs hälsa.

Du behöver inte veta någonting om tjänsten för att fylla i detta frågeformulär. Jag är intresserad av dina förväntningar på en sådan tjänst och din allmänna inställning till att använda den. När du fyller i frågeformuläret kan det underlätta om du föreställer dig att du söker information om en specifik vårdfråga som intresserar dig. Även om det finns andra organisationer och företag som erbjuder liknande tjänster, omfattar denna undersökning endast ”Vårdguide på Internet” som tillhandahålls av Sveriges landsting.

I de flesta frågor ställs du inför ett antal påståenden. Ringa in den siffra mellan 1 och 7 som bäst motsvarar i vilken utsträckning du håller med eller inte håller med om påståendet (1 = Jag instämmer inte alls; 7 = Jag instämmer helt). Det tar 10 till 15 minuter att besvara alla frågorna.

Tack så mycket för hjälpen!!!

1. Medvetenhet – Kände du redan tidigare till att vårdguide på Internet finns?

Mycketomedveten

Mycket medveten

a) Hur medveten är du om att Sveriges landsting erbjuder vård-guide på Internet? 1 2 3 4 5 6 7

b) Hur medveten är du om att andra organisationer och företag erbjuder vårdguide på Internet? 1 2 3 4 5 6 7

__ Appendices _______________________________________

VIII

2. Användbarhet – Hur användbar tror du att en Vårdguide på Internet skulle vara?

Instämmer inte alls Instämmer helt

a) En vårdguide på Internet skulle göra det enklare att inhämta vårdinformation. 1 2 3 4 5 6 7

b) En vårdguide på Internet skulle göra vårdinformation mer lättillgänglig. 1 2 3 4 5 6 7

c) Med en vårdguide på Internet skulle jag snabbare få svar på mina vårdfrågor. 1 2 3 4 5 6 7

d) Med en vårdguide på Internet skulle jag hantera vården mer effektivt. 1 2 3 4 5 6 7

e) En vårdguide på Internet skulle vara användbar när det gäller egenvård. 1 2 3 4 5 6 7

f) En vårdguide på Internet skulle underlätta för mig att skaffa den vårdinformation jag vill ha. 1 2 3 4 5 6 7

g) Med en vårdguide på Internet skulle jag kunna få mer vård-information. 1 2 3 4 5 6 7

h) Fördelarna med att använda en vårdguide på Internet överstiger vida nackdelarna. 1 2 3 4 5 6 7

3. Resultat – Vad anser du om det troliga resultatet av att använda en vårdguide på Internet?

Instämmer inte alls

Instämmer helt

a)Jag skulle inte ha svårt att berätta för andra om fördelarna med att använda en vårdguide på Internet.

1 2 3 4 5 6 7

b)Jag skulle kunna berätta för andra om resultatet av att använda en vårdguide på Internet.

1 2 3 4 5 6 7

c) Fördelarna med att använda en vårdguide på Internet är uppenbara för mig. 1 2 3 4 5 6 7

4. Innehållsmässig kvalitet – Vilken kvalitet tror du det är på den information du skulle få från en vårdguide på Internet?


Instämmer helt

a) Kvaliteten på den information som jag inhämtar från en vårdguide på Internet skulle vara hög. 1 2 3 4 5 6 7

b) En vårdguide på Internet skulle fungera väl. 1 2 3 4 5 6 7

c) En vårdguide på Internet skulle ge bra information. 1 2 3 4 5 6 7

__________________________________________________ Appendices __

IX

Question 4 continued

d) En vårdguide på Internet skulle ge korrekt information. 1 2 3 4 5 6 7

e) En vårdguide på Internet skulle ge aktuell information. 1 2 3 4 5 6 7

f) En vårdguide på Internet skulle ge relevant information. 1 2 3 4 5 6 7

5. Omgivningens inflytande – Vad skulle personer i din omgivning tycka om att du använde en vårdguide på Internet?


Instämmer helt

a) Personer som har inflytande över mig skulle tycka att jag borde använda en vårdguide på Internet. 1 2 3 4 5 6 7

b) Personer som är betydelsefulla för mig skulle tycka att jag borde använda en vårdguide på Internet. 1 2 3 4 5 6 7

c)Personer som är betydelsefulla för mig skulle uppmuntra mig att använda en vårdguide på Internet.

1 2 3 4 5 6 7

d)Personer som har inflytande över mig skulle tycka att det var en god idé att använda en vårdguide på Internet.

1 2 3 4 5 6 7

6. Överensstämmelse – Hur väl överensstämmer användandet av en vårdguide på Internet med ditt sätt att göra saker?


Instämmer helt

a)Att använda en vårdguide på Internet skulle passa bra med det sätt som jag tycker om att göra saker på.

1 2 3 4 5 6 7

b) Att använda en vårdguide på Internet skulle passa min livsstil. 1 2 3 4 5 6 7

c)Att använda en vårdguide på Internet skulle stämma väl överens med det sätt som jag tycker om att göra saker på.

1 2 3 4 5 6 7

7. Förtroende – Känner du förtroende för landstinget som tillhandahållare av en vårdguide på Internet?


Instämmer helt

a) Som leverantör av en vårdguide på Internet är landstinget pålitligt. 1 2 3 4 5 6 7

b) Som leverantör av en vårdguide på Internet är landstinget trovärdigt. 1 2 3 4 5 6 7

__ Appendices _______________________________________

X


c) Som leverantör av en vårdguide på Internet är landstinget övertygande. 1 2 3 4 5 6 7

d) Som leverantör av en vårdguide på Internet är landstinget uppriktigt. 1 2 3 4 5 6 7

e) Som leverantör av en vårdguide på Internet är landstinget ärligt. 1 2 3 4 5 6 7

8. Användarvänlighet – Hur lätt eller svår att använda tror du att en vårdguide på Internet skulle vara?


Instämmer helt

a) Att lära mig använda en vårdguide på Internet skulle vara lätt. 1 2 3 4 5 6 7

b) Mitt samspel med en vårdguide på Internet skulle vara enkelt. 1 2 3 4 5 6 7

c) Det skulle vara lätt för mig att bli skicklig på att använda vårdguide på Internet. 1 2 3 4 5 6 7

d) Jag skulle tycka att en vårdguide på Internet var lätt att använda. 1 2 3 4 5 6 7

e) Det skulle vara lätt att komma ihåg hur man använder en vårdguide på Internet. 1 2 3 4 5 6 7

f) En vårdguide på Internet skulle vara svår att använda. 1 2 3 4 5 6 7

9. Risk – Anser du att det skulle vara riskabelt att använda en vårdguide på Internet?


Instämmer helt

a) Att använda en vårdguide på Internet skulle vara mycket riskabelt. 1 2 3 4 5 6 7

b)Jag skulle känna mig orolig för att bli hypokondriker om jag använder en vårdguide på Internet.

1 2 3 4 5 6 7

c) Jag skulle känna mig orolig för mitt privatliv om jag använde en vårdguide på Internet. 1 2 3 4 5 6 7

d) Jag skulle känna mig säker om jag använde en vårdguide på Internet. 1 2 3 4 5 6 7

e) Jag skulle känna mig trygg om jag använde en vårdguide på Internet. 1 2 3 4 5 6 7

f) Risken att någonting kan gå snett när man använder en vårdguide på Internet är liten. 1 2 3 4 5 6 7

__________________________________________________ Appendices __

XI


g)Jag skulle känna mig orolig för att feltolka informationen som inhämtas från en vårdguide på Internet.

1 2 3 4 5 6 7

h)Jag skulle känna mig orolig för kvaliteten på informationen som inhämtas från en vårdguide på Internet.

1 2 3 4 5 6 7

i) Jag skulle känna mig orolig för att felaktig information skulle ges på en vårdguide på Internet. 1 2 3 4 5 6 7

j)Jag skulle känna mig orolig för att ha svårt att bedöma kvaliteten på informationen som ges på en vårdguide på Internet.

1 2 3 4 5 6 7

k)Felaktig behandling av en åkomma kan bli resultatet om jag använder en vårdguide på Internet.

1 2 3 4 5 6 7

10. Tillgänglighet – Hur lätt eller svårt skulle det vara för dig att få tillgång till en vårdguide på Internet?


Instämmer helt

a) Jag tror att det skulle vara lätt att få tillgång till en vårdguide på Internet. 1 2 3 4 5 6 7

b) Jag ser inga problem med att få tillgång till en vårdguide på Internet. 1 2 3 4 5 6 7

c) En vårdguide på Internet skulle vara mycket tillgänglig för mig. 1 2 3 4 5 6 7

11. Inställning – Vilken är din inställning till att använda en vårdguide på Internet?


Instämmer helt

a) Jag skulle tycka om att använda en vårdguide på Internet. 1 2 3 4 5 6 7

b) Det skulle kännas bra att använda en vårdguide på Internet. 1 2 3 4 5 6 7

c) Att använda en vårdguide på Internet är klokt. 1 2 3 4 5 6 7

__ Appendices _______________________________________

XII

12. Avsikt att använda en vårdguide på Internet – Kommer du att använda en vårdguide på Internet i framtiden?


Instämmer helt

a) Jag kommer att använda en vårdguide på Internet regelbundet i framtiden. 1 2 3 4 5 6 7

b) Jag förutspår att jag kommer att använda en vårdguide på Internet. 1 2 3 4 5 6 7

c) Jag har för avsikt att använda en vårdguide på Internet i framtiden. 1 2 3 4 5 6 7

d) Jag kommer varmt rekommendera andra att använda en vårdguide på Internet. 1 2 3 4 5 6 7

13. Användning – Har du någonsin använt en vårdguide på Internet?

a) Har du använt dig av Sveriges landstings vårdguide på Internet?

Nej (fortsätt till fråga 14c)Ja

Väldigt sällan

Väldigt ofta

b) Om ja, hur ofta använder du den? 1 2 3 4 5 6 7

c)Har du använt dig av en vårdguide på Internet-tjänst av någon annan organisation eller företag?

Nej (fortsätt till fråga 15)Ja

Väldigt sällan

Väldigt ofta

d) Om ja, hur ofta använder du den tjänsten? 1 2 3 4 5 6 7

14. Om mig - ALL INFORMATION BEHANDLAS KONFIDENTIELLT!!!

a) Jag är: Man Kvinna

b) Jag är: ____ år

c) Min högsta utbildning är: Grundskola, folkskola eller liknande 2-årig gymnasium, realskola. folkhögskola

eller liknande Minst 3-årigt gymnasium eller gymnasieskola Universitets- eller högskoleutbildningForskarutbildning

d)

__________________________________________________ Appendices __

XIII



Instämmer helt

e) Jag är en van Internetanvändare. 1 2 3 4 5 6 7

Om du har några kommentarer beträffande Internetanvändning i vårdsyfte eller beträffande undersökningen och detta frågeformulär, använd gärna följande rader:__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

Var vänlig och kontrollera att du har besvarat alla frågor.

Tack så mycket för din medverkan!!!

__ Appendices _______________________________________

XIV

Appendix B2: Questionnaire (English Version – HG)

Online Health Guides – What do YOU think?

I am interested in your opinion about an e-health service hereafter referred to as “Online Health Guide”, which is offered by for instance Sjukvårdsrådgivning, an organization owned by the county councils. An Online Health Guide is a website, on which general healthcare information is provided in form of a dictionary. Here you can look for information on diseases, symptoms, and forms of treatments. Symptoms and diseases are explained and tips and advice on how to treat smaller complaints is given.

The Online Health Guide is not meant to replace a visit to a doctor, but to serve as an additional tool for you to take better care of your and your family’s health.

You do not need to know anything about this service to fill in this questionnaire. I am interested in your expectations of such a service and your general attitude toward using it. When completing the questionnaire, it is helpful to imagine yourself looking for information on a specific health issue that is of interest to you. Even though there are other organizations that offer health guides online, this questionnaire covers Online Health Guides as offered by the county councils only.

For each question you will be given a number of statements. Please circle the number between 1 and 7 that best reflects the extent to which you agree or disagree with each statement (1 = strongly disagree; 7 = strongly agree). It will take you 10 to 15 minutes to answer all questions.

Thank you so much for your help!!!

1. Awareness – Did you know about the existence of an Online Health Guide before?

Veryunaware

Very aware

c) How aware are you of the existence of the county councils’ Online Health Guides? 1 2 3 4 5 6 7

d)How aware are you of the existence of Online Health Guides offered by other organizations or companies?

1 2 3 4 5 6 7

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XV

2. Usefulness - How useful do you expect an Online Health Guide to be?Strongly disagree

Strongly agree

i) An Online Health Guide would make obtaining health information more convenient. 1 2 3 4 5 6 7

j) An Online Health Guide would make health information more accessible. 1 2 3 4 5 6 7

k) An Online Health Guide would enable me to find answers to my health questions more quickly. 1 2 3 4 5 6 7

l) An Online Health Guide would enhance my effectiveness in managing health care. 1 2 3 4 5 6 7

m) An Online Health Guide would be useful for managing my health care. 1 2 3 4 5 6 7

n) An Online Health Guide would make it easier for me to gain the health information I want. 1 2 3 4 5 6 7

o) An Online Health Guide would offer additional health information. 1 2 3 4 5 6 7

p) The advantages of using an Online Health Guide far outweigh the disadvantages. 1 2 3 4 5 6 7

3. Results – What do you think about the likely outcome of using an Online Health Guide?

Strongly disagree

Strongly agree

d) I would have no difficulty telling others about the advantages of using an Online Health Guide.

1 2 3 4 5 6 7

e) I would be able to communicate to others the results of using an Online Health Guide.

1 2 3 4 5 6 7

f) The advantages of using an Online Health Guide are apparent to me.

1 2 3 4 5 6 7

4. Output Quality – What do you think the quality of information you get from an Online Health Guide would be?

Strongly disagree

Strongly agree

g) The quality of the information I obtain from an Online Health Guide would be high.

1 2 3 4 5 6 7

h) An Online Health Guide would perform well. 1 2 3 4 5 6 7

i) An Online Health Guide would offer good information.

1 2 3 4 5 6 7

j) An Online Health Guide would offer accurate information.

1 2 3 4 5 6 7

__ Appendices _______________________________________

XVI


k) An Online Health Guide would offer up-to-date information.

1 2 3 4 5 6 7

l) An Online Health Guide would offer relevant information.

1 2 3 4 5 6 7

5. Subjective Norm – What would people around you think of you using and Online Health Guide?

Strongly disagree

Strongly agree

e) People who influence me would think that I should use an Online Health Guide. 1 2 3 4 5 6 7

f) People who are important to me would think that I should use an Online Health Guide. 1 2 3 4 5 6 7

g) People who are important to me would encourage me to use an Online Health Guide. 1 2 3 4 5 6 7

h) People who influence me would think that using an Online Health Guide is a good idea. 1 2 3 4 5 6 7

6. Compatibility – How compatible is using an Online Health Guide with your way of doing things?

Strongly disagree

Strongly agree

d) Using an Online Health Guide would fit well with the way I like to do things. 1 2 3 4 5 6 7

e) Using an Online Health Guide would fit into my life style. 1 2 3 4 5 6 7

f) Using an Online Health Guide would be compatible with the way I like to do things. 1 2 3 4 5 6 7

7. Trust – Do you trust the county council as provider of an Online Health Guide?

Strongly disagree

Strongly agree

f) As provider of an Online Health Guide, the county council is dependable.

1 2 3 4 5 6 7

g) As provider of an Online Health Guide, the county council is trustworthy.

1 2 3 4 5 6 7

h) As provider of an Online Health Guide, the county council is credible.

1 2 3 4 5 6 7

i) As provider of an Online Health Guide, the county council is sincere.

1 2 3 4 5 6 7

j) As provider of an Online Health Guide, the county council is honest.

1 2 3 4 5 6 7

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XVII

8. Ease of Use – How easy or difficult to use do you expect an Online Health Guide to be?

Strongly disagree

Strongly agree

g) Learning to operate an Online Health Guide would be easy for me. 1 2 3 4 5 6 7

h) My interaction with an Online Health Guide would be simple. 1 2 3 4 5 6 7

i) It would be easy for me to become skilful at using an Online Health Guide 1 2 3 4 5 6 7

j) I would find an Online Health Guide easy to use. 1 2 3 4 5 6 7

k) It would be easy to remember how to use an Online Health Guide. 1 2 3 4 5 6 7

l) An Online Health Guide would be difficult to use. 1 2 3 4 5 6 7

9. Risk – Do you think it is risky to use an Online Health Guide? Strongly disagree

Strongly agree

a) Using an Online Health Guide would be highly risky. 1 2 3 4 5 6 7

b) I would be concerned that I would become hypochondriac when using an Online Health Guide. 1 2 3 4 5 6 7

c) I would be concerned about my privacy when using an Online Health Guide. 1 2 3 4 5 6 7

d) I would feel secure using an Online Health Guide. 1 2 3 4 5 6 7

e) I would feel safe using an Online Health Guide. 1 2 3 4 5 6 7

f) There is little danger that anything would go wrong when using an Online Health Guide. 1 2 3 4 5 6 7

g) I would be concerned about misinterpreting the information obtained from an Online Health Guide. 1 2 3 4 5 6 7

h) I would be concerned about the quality of the information obtained from an Online Health Guide. 1 2 3 4 5 6 7

i) I would be concerned that wrong information would be provided via an Online Health Guide. 1 2 3 4 5 6 7

j)I would be concerned about having difficulty assessing the quality of the information that is provided via an Online Health Guide.

1 2 3 4 5 6 7

k) Wrong treatment of a health matter can be an outcome of using an Online Health Guide. 1 2 3 4 5 6 7

__ Appendices _______________________________________

XVIII

10. Accessibility – How easy or difficult would it be for you to get access to an Online Health Guide?

Strongly disagree

Strongly agree

d) I expect it to be easy for me to access an Online Health Guide. 1 2 3 4 5 6 7

e) I do not foresee any problems getting access to an Online Health Guide. 1 2 3 4 5 6 7

f) An Online Health Guide would be very accessible for me. 1 2 3 4 5 6 7

11. Attitude – What is your attitude to using an Online Health Guide?Strongly disagree

Strongly agree

d) I would like using an Online Health Guide. 1 2 3 4 5 6 7

e) I would feel good about using an Online Health Guide.

1 2 3 4 5 6 7

f) Using an Online Health Guide is wise. 1 2 3 4 5 6 7

12. Intention to Use – Will you use an Online Health Guide in the future?Strongly disagree

Strongly agree

e) I will use an Online Health Guide on a regular basis in the future. 1 2 3 4 5 6 7

f) I predict I will use an Online Health Guide. 1 2 3 4 5 6 7

g) I intend to use an Online Health Guide in the future. 1 2 3 4 5 6 7

h) I will strongly recommend others to use an Online Health Guide. 1 2 3 4 5 6 7

13. Use – Have you ever used an Online Health Guide before?

e) Have you used an Online Health Guide offered by the county councils before?

No (please continue with Question 14c)Yes

Veryinfrequent

Very frequent

f) If yes, how often do you use it? 1 2 3 4 5 6 7

g)Have you used an Online Health Guide offered by another organization or company before?

No (please continue with Question 15)Yes

__________________________________________________ Appendices __

XIX


Veryinfrequent

Very frequent

h) If yes, how often do you use this service? 1 2 3 4 5 6 7

14. About Me - ALL INFORMATION WILL BE TREATED CONFIDENTIALLY!!!!a) I am: Male

Female

b) I am: _____ Years of age

c) My highest education is: Comprehensive school (or equivalent) 2 years of upper secondary school (or equivalent) at least 3 years of upper secondary school

(or equivalent) University degreeLicentiate or Ph.D. degree

Strongly disagree

Strongly agree

d) I am an experienced Internet user. 1 2 3 4 5 6 7

If you have any comments regarding the use of the Internet for health-care purposes or on the study and this questionnaire, please use the space below: __________________________________________________________________

__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

__________________________________________________________________

Please make sure you have answered all questions.

Thank you very much for participating!!!

__ Appendices _______________________________________

XX

Appendix C1: Cover Letter (Swedish Version)

Hej!

Mitt namn är Marie-Louise Jung och jag är doktorand vid Luleå tekniska universitet. I min doktorsavhandling genomför jag en undersökning av medborgares förväntningar på sjukvårdstjänster via Internet. Internet erbjuder många möjligheter för att stödja och förbättra dagens sjukvårdstjänster. Resultatet från undersökningen kommer att hjälpa landsting och privata sjukvårdsleverantörer att utveckla sina Internettjänster så att de på bästa sätt matchar behoven hos invånare och patienter.

Du är en av de personer som blivit slumpmässigt utvalda att delta i undersökningen. Din adress har hämtats från konsumentregistret POSTIAD, där Du har angett att Du är intresserad av ”friskvård och hälsokost”. Jag är intresserad av Din allmänna inställning till och Dina förväntningar på den Internettjänst som beskrivs i bifogat frågeformulär. Du behöver inte känna till något om tjänsten och Dubehöver inte ha använt den tidigare för att kunna besvara frågorna.

Jag uppskattar om Du kan fylla i frågeformuläret och returnera det till mig i det portofria svarskuvertet så snart som möjligt. Frågeformuläret är HELT ANONYMT. Det finns alltså ingen möjlighet att identifiera vad just Du har svarat.

För att resultatet ska bli så representativt som möjligt och för att säkerställa att undersökningen håller hög kvalitet är det väldigt viktigt att så många personer som möjligt svarar. Jag uppskattar därför om Du kan ägna 10 till 15 minuter av Din tid åt att fylla i frågeformuläret. Genom att besvara enkäten hjälper Du inte bara mig, utan bidrar även till den framtida utvecklingen av sjukvårdstjänster via Internet.

Tack på förhand!

Med vänliga hälsningar,

Marie-Louise Jung Industriell marknadsföring och e-handelLuleå tekniska universitet E-Post: [email protected]

mailto:[email protected]

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XXI

Appendix C2: Cover Letter (English Version)

Hello!

My name is Marie-Louise Jung and I am a doctoral student at Luleå University of Technology. Within the scope of my doctoral thesis I am conducting a survey on citizens’ expectations of health-care services on the Internet. The Internet offers many opportunities to support and improve today’s health-care services. This survey will help the county councils and private health-care providers to develop online services that best match their citizens’ and patients’ needs.

I randomly selected a group of individuals to participate in this investigation and you are one of them. I obtained your address from a consumer register called POSTIAD where you once indicated that you are interested in “friskvård och hälsokost”. I am interested in your general opinion and your expectations of an online service described in the attached questionnaire. You do not need to know anything about the service and you do not need to have used it inorder to be able to answer the questions.

I would kindly ask you to complete this questionnaire and return it to me using the enclosed prepaid envelope. This questionnaire is COMPLETELY ANONYMOUS and your answers will only be reflected in statistical summaries once all of the data is collected.

In order to get representative results and to ensure a high quality of the investigation, it is very important that as many people as possible respond. I would therefore greatly appreciate you could take 10 to 15 minutes of your time to fill in this questionnaire. By doing so, you are not only helping me, but are also contributing to the future development and success of health-care services on the Internet.

Thank you very much in advance!

Best regards,

Marie-Louise Jung Industrial Marketing and e-Commerce Luleå University of Technology E-mail: [email protected]


__ Appendices _______________________________________

XXII

Appendix C3: Reminder Postcard (Swedish Version)

Hej!

Nyligen fick Du ett brev med ett frågeformulär avseende vad Du tycker om sjukvårdstjänster på Internet och vilka dina förväntningar på sådana är. Om Du redan har svarat vill jag tacka Dig så mycket för Din medverkan och Din hjälp!

Om Du inte har haft möjligheten att svara ännu vill jag be Dig att göra det nu. För att resultatet ska bli så representativt som möjligt och för att säkerställa att undersökningen håller hög kvalitet är det viktigt att så många personer som möjligt svarar. Därför är Dina svar särskilt värdefulla för mig.

Om Du inte har kvar frågeformuläret kan Du skicka ett e-postmeddelande till mig med Din adress så skickar jag ett nytt.

Tusen tack för Din hjälp!

Med vänliga hälsningar,

Marie-Louise Jung Industriell marknadsföring och e-handel Luleå tekniska universitet 971 87 Luleå E-post. [email protected]


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XXIII

Appendix C4: Reminder Postcard (English Version)

Hello!

You recently received a letter with a questionnaire on your opinion and initial expectations of online services in health care. If you have already responded I would like to thank you very much for your interest and help!

If you have not been able to respond yet, I would kindly ask you to do so now. In order to get representative results and to ensure the high quality of the investigation, it is very important that as many people as possible respond. Your opinion is thus of particular value to me.

If you don’t have the questionnaire any longer, please email me with your address and I send you a new one.

Thank you so much for your help!

Best regards,

Marie-Louise Jung Industrial Marketing and e-Commerce Research Group Luleå University of Technology [email protected]


__ Appendices _______________________________________

XXIV

Appendix D: Testing for Non-Response BiasTable D.1: Independent Sample t-Test to test for Response Bias

Key Variable t dfSig.

(2-tailed) MeanDiff.

Std. Error Diff.

U5_useful for managing health care ,131 382 ,896 ,021 ,159

OQ1_high quality -,128 380 ,898 -,019 ,151

RD3_advantages are apparent -,192 382 ,848 -,036 ,190

SN1_people who influence think I should use

-,923 380 ,356 -,173 ,188

CB1_fit well with the way I like to do things -,391 381 ,696 -,077 ,195

T2_trustworthy ,533 380 ,594 ,089 ,167

EU4_easy to use ,523 381 ,602 ,090 ,171

ACC1_expect it to be easy to access -,257 382 ,798 -,042 ,162

ATT3_using it is wise ,087 382 ,931 ,016 ,180

ITU3_I intend to use it ,935 382 ,350 ,188 ,200

Exp1_experienced Internet user -,143 377 ,886 -,028 ,196

* Equal variances assumed * Group 1: first quartile of respondents; group 2: last quartile of respondents

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Appendix E: Individual Construct Measure Validation and Purification

Perceived Ease of Use During the CFAs, EU6 was particularly disturbing, as were EU1 and EU2. As dropping EU6 resulted in an even worse fit on both samples, EU6 was retained, and first EU1 then EU2 were dropped. After dropping those two items, the fit indices passed the thresholds on all indices investigated. Even though the fit indices demonstrate acceptable fit on both samples (see table E1 below), EU6 still loads substantially lower and only achieves a multiple square root of .28 which is far below the threshold of .5 proposed by Hair et al. (2003). Calculating the Cronbach’s alpha on the four-item scale revealed that EU6 does load significantly lower and decreases Cronbach’s alpha with almost .06 in the health guide sample. Since in the ask-the-doctor sample EU6 reduces Cronbach’s alpha from .94 to .91 and as modification indices were proposed connected to EU6, this item was eventually dropped leaving a three-item ease of use scale.

Table E.1: GOF-Indices of the four item Pereived Ease uf Use Scale (EU3, EU4, EU5, EU6)

CMIN/DF

GFI CFI AGFI RMSEA Cronbach’s Alpha

Health Guide ,121 1,000 1,000 ,998 ,000 ,89Ask-the-Doctor 3,831 ,990 ,996 ,951 ,084 ,91

Perceived Usefulness In order to achieve a good fit on both the health guide and the ask-the-doctor sample, three of the eight usefulness items were dropped one by one (U1, U4, U6), based on lowest regression weights as well as suggested modification indices. Fit indices of the CFA on the remaining five items are provided below.

Table E.2: GOF-Indices for the five-item Perceived Usefulness Scale (U2, U3, U5, U7, U8)

CMIN/ DF

GFI CFI AGFI RMSEA Cronbach’s Alpha

Health Guide ,465 ,997 1,000 ,992 ,000 ,92Ask-the-Doctor ,803 ,996 1,000 ,988 ,000 ,91

Even though the refined five-indicator usefulness scale performed well on the individual CFA, item U8 caused problems with other items and constructs in the measurement model. Also, examining the scale’s Cronbach’s alpha revealed that

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XXVI

U8 loaded substantially lower than the other indicators and lowered overall scale reliability. Thus, U8 was also dropped.

Output Quality and Result Demonstrability The experts consulted during scale development had difficulty distinguishing between the two antecedents to usefulness, result demonstrability and output quality. Also, since the two constructs were expected to be highly correlated, they were assessed together in a CFA. In order to achieve a good fit, the first item eliminated from the output quality scale was OQ2. Likewise, RD3, the reverse coded item in the result demonstrability scale was eliminated. In order to achieve good fit on both the HG and the ATD samples, OQ1 and OQ3 were ultimately eliminated. From a theoretical standpoint, this is justifiable, as the remaining three items still represent all aspects of health information quality, the output quality in the context of this study.

The CFA, with the two-indicator result demonstrability construct and the three-indicator output quality construct, achieves a good fit on both samples (see table E3 below).

Table E.3: CFA on Result Demonstrability (RD1, RD2) and Output Quality (OQ4, OQ5, OQ6)

Cronbach’s Alpha

CMIN/ DF

GFI CFI AGFI RMSEA

RD OQHealth Guide 1,962 ,991 ,997 ,968 ,051 .90 .92 Ask-the-Doctor 2,239 ,991 ,997 ,967 ,056 .88 .94

Subjective Norm The CFA shows unacceptable model fit indices for the four indicators on both samples. A scale analysis using Cronbach’s alpha does not provide conclusive results either, as all items positively contribute to the scale’s alpha, which is very high at .97. As mentioned previously, Hair et al. (2007) point out that closer attention should be paid to whether the items might ask the same question when Cronbach’s alpha is higher than .95. Even though SN1 and SN2 were items taken from previous research where the scale performed acceptably, translating the items into the Swedish language might have caused difficulty differentiating between them. Thus, SN2 (the items with the lowest alpha when deleted) was dropped in order to achieve a more satisfactory scale alpha.

Credibility of the Health-Care Provider The unidimensional trust scale as proposed by Lichtenstein and Bearden (1989) achieved a bad fit. Since even dropping different items did not improve fit and the

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Cronbach’s alpha did not suggest the elimination of any items, it was suspected that the scale consisted of more than one dimension. Figure E1 shows the two-dimensional model that resulted after further analyses, and finally the exclusion of indicator T3.

Figure E.1: 2-dimensional Credibility Construct (HG sample)

This two-dimensional trust scale received high fit indices in both samples as presented in Table E4 below. Dimension one can be themed “trust” as the two items relate to the trustworthiness of the health-care provider. Dimension two is labeled “honest” and relates to the honesty and sincerity of the health-care provider.

Table E.4: GOF-Indices of the Resulting Credibility Construct (dimensions trust and honesty)

Cronbach Alpha CMIN/DF GFI CFI AGFI RMSEA Trust Honesty

Health Guide 1.616 .998 1.000 .978 .041 .97 .97Ask-the-Doctor 2.526 .997 .999 .968 .062 .97 .97

Perceived Risk The initial risk scale consisted of 11 indicators covering different risks that are associated with the use of e-health services. Two of the indicators have heavily skewed data (R2 and R3) and load substantially lower than other indicators in the scale. They were thus excluded.

,96

dim trust

,94

T1E1,97

,95

T2E2,97

,89

dim honesty

Credibility

,98

,94,93

T4E4,95

T5E5

,97

,98

E6

E7

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XXVIII

As suspected, the scale’s bad fit with the remaining 9 indicators in the CFA signifies that the scale could be multidimensional. This makes sense, as an individual might perceive the use of the service as being very risky in terms of receiving a bad outcome, yet from a security perspective, he might feel safe. Some previous studies that have focused on certain risks rather than an overall risk perception, suggest risk dimensions such as technical risk, financial risk, personal risk and performance risk (e.g., Featherman and Pavlou, 2003). In the context of this study two major risks are involved: loss of privacy and security, which is referred to as technical risk, and the danger that the service might not perform well and that users might receive wrong information, which is referred to as performance risk. A two-dimensional risk scale was specified with the two dimensions performance and technical risk, as illustrated in Figure E2 below.

Figure E.2: 2-dimensional Risk Construct (HG sample)

The CFA on the two risk dimensions achieved good fit on both samples and produced the following results:

Table E.5: GOF-indices of the two-dimensional Perceived Risk Construct

Cronbach’s Alpha CMIN/DF GFI CFI AGFI RMSEA TRisk PRisk

Health Guide .266 .999 1.000 .996 .000 .91 .93Ask-the-Doctor .377 .998 1.000 .994 .000 .94 .91

Risk

,84

R5E10

,85

R4E7

,76

R10

,90R9E5

,81

R8E4 ,43

,46

Technical

,90

,95

,87

,92

,92

,66

,68

E13

E12

Perform-ance

E6

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Even though the two-dimensional scale performed well in the CFA, when running the whole measurement model including all constructs simultaneously errors occurred that were related to the second-order scale. When examining the indicators that account for the two dimensions and the correlations in the measurement model, it appeared as if R4 and R5, which were used to measure technical risk, served more as an overall measure of risk, which may have led to this problem. Featherman and Pavlou (2003), who also proposed a multidimensional second-order perceived risk construct, realized the difficulty with multicollinearity across the dimensions and item cross-loadings. In order not to disturb further analysis and the interplay among the risk construct and the other constructs in the model, it was decided to use the overall risk dimension only (with the indicators R4 and R5), as a reflection of all types of risk involved. However, the results of this study, in line with those found previously by Featherman and Pavlou (2003), point to a multidimensionality of the construct, thus making further research on this issue both interesting and imperative.

Compatibility The compatibility scale was assessed using Cronbach’s alpha when tested individually. The three-indicator scale achieved a very high scale alpha of .97 on both samples and worked well together with the other constructs in the measurement model.

Perceived Accessibility Also, the three-indicator perceived accessibility scale was assessed using Cronbach’s alpha, which again was very high with .95 on both samples and good loadings and fit in the measurement model.

Attitude toward Use Attitude toward use initially consisted of three indicators. Even though a high Cronbach’s alpha was achieved on both samples when analyzing the scale separately, the CFA on the measurement model revealed some disturbance of ATT3 in combination with other constructs and their measurements. ATT3, the only reverse coded indicator in the scale, was thus dropped.

Intention to Use The CFA on the four-item scale showed a decidedly unacceptable fit as well as a high chi-square significant at the .01 level. The regression coefficients and the proposed modification indices on both samples suggest that ITU4 might be the problematic item. A scale analysis using Cronbach’s alpha reveals that, although minor, it is indeed ITU4 that lowers the alpha. The item was thus dropped,

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resulting in a three-item scale with a Cronbach’s alpha of .94 in both samples and good fit in combination with other constructs in the measurement model.

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Appendix F: Metric Invariance – Results of the Multigroup Analysis

Table F.1: Multigroup Analysis to test for Metric Invariance

Measurement Model

Construct Indicator p Level of Metric Invariance

EU3 n.s. EU4 n.s.


EU5 n.s. - Full metric invariance

SN1 n.s. SN3 n.s.

Subjective Norm

SN4 n.s. - Full metric invariance

RD1 n.s. ResultDemonstrability RD2 n.s.

- Full metric invariance

OQ4 n.s. OQ5 n.s.

Output Quality

OQ6 n.s. *

- Full metric invariance with p<.001 - Partial metric invariance with p<.01

CB1 n.s. CB2 n.s. *

Compatibility

CB3 n.s.

- Full metric invariance with p < .001 - Partial metric invariance with p<.01

ACC1 n.s. ACC2 n.s.


ACC3 n.s. - Full metric invariance

T1 n.s. honestT2 n.s. T4 n.s.

Model 1 (exogenous constructs)

Credibility of the Health Provider

trustT5 n.s.


U2 n.s. U3 n.s. U5 n.s.

PerceivedUsefulness

U7 n.s.


ATT1 n.s. Attitude toward Use ATT2 n.s.


ITU1 n.s. ITU2 n.s.

Intention to Use

ITU3 n.s. - Full metric invariance

R4 n.s.

Model 2 (endogenous constructs)

Perceived Risk R5 n.s.


* p<.01

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Appendix G: Overview of eHAM Fit Indices

Table G.1: GOF-Indices of the Empricially Derived eHAM

Index CMIN/DF GFI AGFI CFI NFI RMSEA Rule of Thumb < 5 > .9 > .8 > .9 > ,9 < .1 eHAM total sample 4,467 ,978 ,939 ,989 ,986 ,067 eHAM HG 3,890 ,961 ,894 ,981 ,975 ,089 eHAM ATD 2,976 ,971 ,920 ,988 ,982 ,071 eHAM nonusers n = 602

3,152 ,980 ,945 ,991 ,987 .060

eHAM usersn = 166

3,671 ,920 ,780 ,954 ,940 ,127

eHAM malen = 201

2,530 ,955 ,876 ,982 ,970 ,087

eHAM female n = 565

3,246 ,978 ,939 ,990 ,986 ,063

eHAM low exp. n = 188

2,057 ,960 ,889 ,989 ,975 ,075

eHAM high exp. n = 360

2,060 ,978 ,940 ,992 ,985 ,054

eHAM younger n = 194

2,894 ,946 ,852 ,971 ,957 ,099

eHAM older n = 194

1,733 ,967 ,910 ,992 ,981 ,062

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