Title Page
Contents
Abstract 14
Overview 18
References 20
Part I. Factors Affecting Employees' Compliance Support System Appropriation and Their Impacts on Compliance Intention: The Longitudinal Study 22
1. Introduction 24
2. Theoretical background and literature review 27
2.1. Adaptive Structuration Theory 27
2.2. Theory of Planned Behavior 30
2.3. Case study of compliance support system in Korea 33
3. Research model and hypotheses 39
3.1. CSS appropriation and compliance intention 41
3.2. CSS quality and CSS appropriation 42
3.3. Compliance knowledge and CSS appropriation 43
3.4. The effect of compliance behavioral belief on compliance intention, CSS quality, and compliance knowledge 44
3.5. The effect of perceived social pressure on compliance intention and compliance knowledge 45
3.6. Changes in the influences over time 46
3.7. Summary of hypotheses 47
4. Methodology 49
4.1. Instrument development 49
4.2. Data collection and sampling 51
4.3. Demographic characteristics of respondents 53
5. Results 58
5.1. Assessment of measurement model 58
5.2. Common method bias test 65
5.3. Hypotheses testing 67
5.4. Comparison between usage stages 71
6. Discussion 75
6.1. Summary of findings 75
6.2. Implications 79
6.3. Limitations and future research 83
6.4. Conclusion 84
References 86
APPENDIX 97
APPENDIX I. Questionnaire items 97
APPENDIX II. S-OIL CSS Screenshot 100
Part II. The Appropriation of Compliance Support System in Organizations: The Firm Level Analysis 106
1. Introduction 108
1.1. Background and purpose of the study 108
1.2. Scope of the study and research procedure 112
1.3. Organization of the study 114
2. Theoretical background and literature review 116
2.1. Literature Review on Corporate Compliance 116
2.2. Adaptive Structuration Theory 120
2.3. Resource Based View 126
2.4. Knowledge relatedness 135
2.5. IT relatedness 140
3. Research model and hypotheses 143
3.1. Research model 143
3.2. Theoretical development 145
4. Methodology 157
4.1. Operationalization of the variables 157
4.2. Instrument development 167
4.3. Data collection and sampling 172
5. Results 175
5.1. Demographic characteristics of respondents 175
5.2. Measurement model test 178
5.3. Hypotheses testing 191
6. Discussion 195
6.1. Summary of findings 195
6.2. Implications 199
6.3. Limitations and future research 201
6.4. Conclusion 204
References 206
APPENDIX 229
APPENDIX I. Questionnaire items 229
APPENDIX II. Questionnaire items (Korean version) 235
Part I 37
Table 2.1. S-Oil's CSS based on COSO framework 37
Table 3.1. Summary of hypotheses 48
Table 4.1. Operationalization of the variables 50
Table 4.2. Demographic characteristics of respondents (t₁ & t₂) 54
Table 4.3. Demographic characteristics of respondents (n=423, same respondents) 56
Table 5.1. Result of principal component factor analysis (t₁) 60
Table 5.2. CR, AVE, and correlations of the latent variables (t₁) 61
Table 5.3. Descriptive statistics and K-S test results (t₁) 61
Table 5.4. Result of principal component factor analysis (t₂) 63
Table 5.5. CR, AVE, and correlations of the latent variables (t₂) 64
Table 5.6. Descriptive statistics and K-S test results (t₂) 64
Table 5.7. Harman's single-factor test result 66
Table 5.8. Summary of Structural Model Test (t₁) 69
Table 5.9. Summary of Structural Model Test (t₂) 71
Table 5.10. Differences in the perception of constructs between usage stages 72
Table 5.11. Results of PLS-MGA between usage stages 73
Table 6.1. Summary of findings 76
Part II 117
Table 2.1. Examples of the compliance requirements in a company 117
Table 2.2. The existing studies on corporate compliance 118
Table 2.3. Research projects grounded in AST in the field of IS 125
Table 2.4. Classification of knowledge according to researchers 130
Table 2.5. Definition of knowledge management by researchers 132
Table 2.6. Study examples of knowledge relatedness 136
Table 3.1. Summary of hypotheses 156
Table 4.1. Summary of operationalization of the variables 166
Table 4.2. Guidelines for choosing a measurement model mode 168
Table 4.3. Composition of questionnaire 171
Table 4.4. Sample size required for PLS-SEM assuming statistical power of 80% 174
Table 5.1. Demographic characteristics of the respondents (firm) 176
Table 5.2. Demographic characteristics of the respondents (individual) 177
Table 5.3. Items that have been removed as a result of reliability and validation 180
Table 5.4. Descriptive statistics and K-S test results 180
Table 5.5. Result of principal component factor analysis 181
Table 5.6. Reliability and discriminant validity 182
Table 5.7. VIF of the indicators 186
Table 5.8. Results of the outer weighting signification test 187
Table 5.9. Result of Harman's single factor analysis 190
Table 5.10. Results of path analysis 193
Table 6.1. Summary of findings 196
Part I 31
Figure 2.1. Theory of planned behavior 31
Figure 3.1. Research model and hypothesized relationship 40
Figure 4.1. Steps for data collection 51
Figure 5.1. Data analysis results (t₁) 68
Figure 5.2. Data analysis results (t₂) 70
Part II 122
Figure 2.1. Adaptive Structuration Theory 122
Figure 2.2. Resource-based model of competitive advantage 127
Figure 2.3. IT relatedness, knowledge management capability, and... 138
Figure 2.4. Performance effects of Knowledge synergies: The... 138
Figure 3.1. Research model and hypothesized relationship 144
Figure 5.1. Steps to test formative measurement model 183
Figure 5.2. Assessment of convergent validity (APP) 185
Figure 5.3. Assessment of convergent validity (CLR) 185
Figure 5.4. Structural model test result 192
Figure 5.5. Interaction plot with IT relatedness as moderator 194