Title Page
Contents
ABSTRACT 9
Chapter 1. Introduction 12
1.1. Research Background 13
1.2. Organization of Dissertation 15
Chapter2. Development of the Telemedicine Service Acceptance Model to explain the behavioral intention for Physicians in South Korea 16
2.1. Introduction 17
2.2. Theoretical Background 20
2.2.1. Technology Acceptance Model 20
2.2.2. Provider's perception in telemedicine service acceptance 22
2.3. Research Model and Hypotheses 24
2.3.1. Perceived usefulness and perceived ease of use 26
2.3.2. Clinical factor 27
2.3.3. Individual factor 29
2.3.4. Regulatory factor 30
2.4. Methods 31
2.4.1. Sample organizations and respondents 31
2.5. Results 31
2.5.1. Sample demographics 31
2.5.2. Confumatory factor analysis 32
2.5.3. Hypotheses testing 36
2.6. Discussion 40
2.6.1. Summary of finding 40
2.6.2. Theoretical and practical implications 41
2.6.3. Limitation and future research 42
Appendix 43
Chapter3. Development of the Telemedicine Service Acceptance Model for Chronically III Patients 44
3.1. Introduction 45
3.2. Theoretical Background 48
3.2.1. Unified Theory of Acceptance and Use of Technology: UTAUT 48
3.2.2. Innovation Diffusion Theory: IDT 50
3.2.3. Perceived Risk 51
3.2.4. Patients' perception in Telemedicine Service Acceptance 52
3.2.5. Patients' technology acceptance model 54
3.3. Research Model and Hypotheses 55
3.3.1. Research model 55
3.3.2. Research hypotheses 56
3.4. Research Methodology 67
3.4.1. Instrument development 67
3.4.2. Sample and data collection 68
3.4.3. Sample characteristics 68
3.4.4. Non-response bias test 70
3.5. Data Analysis and Results 71
3.5.1. Measurement model 71
3.5.2. Dimensionality, convergent and discriminant validity of performance expectancy 77
3.5.3. First-order versus second-order factor model 79
3.5.4. Hypotheses testing 81
3.6. Conclusions 85
3.6.1. Summary of findings 85
3.6.2. Theoretical and practical implications 87
3.6.3. Limitation and future research 88
Appendix 89
Chapter4. Suggesting Telemedicine Service Strategy Based on Health Lifestyle for People over the Age of 40: Data Mining Approaches 91
4.1. Introduction 92
4.2. Literature Review 95
4.2.1. Health lifestyle 95
4.2.2. Telemedicine service and consumer 97
4.2.3. Association rule mining 100
4.2.4. Decision tree 103
4.3. Research Design 105
4.3.1. Data collection and subjects 105
4.3.2. Measurement items 106
4.4. Results 108
4.4.1. Decision tree: rule induction method (C5.0) 108
4.4.2. Association rule mining 113
4.5. Conclusions 124
4.5.1. Summary of finding 124
4.5.2. Implication for practice 126
4.5.3. Limitations and future research 127
Appendix 128
References 129
Table1.1. Organization of dissertation 15
Table2.1. Provider's perception in telemedicine service 23
Table2.2. Definition of the constructs 25
Table2.3. Descriptive statistics of respondent characteristics and result of one-way ANOVA analysis 33
Table2.4. Overall fit of the CFA model 34
Table2.5. Measurements; confinnatory factor analysis 35
Table2.6. Discriminant validity 36
Table2.7. Overall fit of the research model 37
Table2.8. Structural model results 39
Table2.9. List of questionnaires 43
Table3.1. Patients' perception in telemedicine service acceptance 53
Table3.2. Definition of the constructs 66
Table3.3. The modified patients' perception 67
Table3.4. Descriptive statistics of respondent characteristics 69
Table3.5. T-test results for non-response bias test 70
Table3.6. KMO and Bartlett's test 71
Table3.7. Loadings, cross-loadings, and reliability 74
Table3.8. Measurements; confinnatory factor analysis 75
Table3.9. Descriptive statistics, correlations and discriminant validity 76
Table3.10. Model comparison for second-order factor 78
Table3.11. Model comparison for significant of the structural links that link measurement models 80
Table3.12. Proposed fit of the research model 82
Table3.13. Summary of research results 84
Table3.14. The full list of questionnaires 89
Table4.1. Prevalence rate of chronic illness (over age 30) 98
Table4.2. Descriptive statistics of respondent characteristics 105
Table4.3. Summary of variables 106
Table4.4. Loadings, cross-loadings, and reliability 107
Table4.5. Results of health lifestyle of middle-aged subjects over 40 108
Table4.6. Variable ranking affecting health lifestyle of middle-aged subjects over 40 111
Table4.7. Definition of health lifestyle rule 111
Table4.8. Descending results by confidence 114
Table4.9. Descending results by support 114
Table4.10. Descending results by lift 115
Table4.11. Descending results by confidence 116
Table4.12. Descending results by support 116
Table4.13. Descending results by lift 117
Table4.14. Descending results by confidence 118
Table4.15. Descending results by support 118
Table4.16. Descending results by confidence 119
Table4.17. Descending results by support 120
Table4.18. Descending results by lift 120
Table4.19. Descending results by confidence 121
Table4.20. Descending results by support 121
Table4.21. Descending results by lift 122
Table4.22. Descending results by confidence 123
Table4.23. Descending results by support 123
Table4.24. Telemedicine service pattern related to health lifestyle for people over the age of 40 124
Table4.25. List of questionnaires 128
Fig.2.1. Research model 24
Fig.2.2. Hypotheses testing results: structural equation model 37
Fig.3.1. Research model 55
Fig.3.2. Research model 56
Fig.3.3. Measurement models corresponding to Model 3-2 and Model 4 80
Fig.3.4. Measurement models corresponding to Model 5-1 and Model 5-2 81
Fig.3.5. Results of Structural Model Analysis 83
Fig.3.6. Results of Structural Model Analysis 83
Fig.4.1. Results of rule induction method (C5.0) 109
Fig.4.2. Association rule graph for taste-seeking style 113
Fig.4.3. Association rule graph for exercise seeking style 115
Fig.4.4. Association rule graph for meal managing style 117
Fig.4.5. Association rule graph for heavy drinker style 119
Fig.4.6. Association rule graph for exercise managing style 121
Fig.4.7. Association rule graph for wellbeing seeking style 122