Title Page
Abstract
Contents
Chapter 1. Introduction 11
1.1. Research Background 11
1.2. Objective of the Study 13
1.3. Outline of the Study 14
Chapter 2. Literature Review 17
2.1. Overview of the Diffusion of Innovation 17
2.2. Diffusion Models 22
2.2.1. Aggregate Models 23
2.2.2. Agent-Based Models 30
2.2.3. Diffusion models based on individual behavior 36
2.3. Interaction-Based Model 41
2.4. Research Motivation 46
Chapter 3. Interaction-Based Diffusion Model 51
3.1. Utility Model 51
3.2. Structure of Social Network 62
3.3. Diffusion Process 71
Chapter 4. Interpretation of the Interaction-Based Diffusion Model 75
4.1. Specification of Simulation 75
4.2. Simulation Results 84
4.2.1. Effect of Price Coefficients 88
4.2.2. Effect of Social Interactions 97
4.3. Summary 102
Chapter 5. Empirical Availability of the Interaction-Based Diffusion Model 109
5.1. Adjustment of the Model for Fitting 109
5.2. Analysis of Real Market Data 121
5.2.1. Fitting Procedure 121
5.2.2. Analysis Results 124
5.3. Summary 129
Chapter 6. Conclusion 131
6.1. Concluding Remarks 131
6.2. Contributions and Limitations 132
6.3. Future Research Topics 135
Bibliography 137
Appendix 153
Abstract (Korean) 155
Table 1. Extensions of the Bass model and their characteristics 29
Table 2. Average path length of the S-W network due to the rewiring probability 84
Table 3. List of scenarios used in the simulation 88
Table 4. Simulation results for two different price strategies by time 95
Table 5. Adopter categories of the IBDM for three cases 106
Table 6. Estimation results for the conditional probability of social distance 115
Table 7. Specification of initial parameter grid for the brute-force algorithm 123
Table 8. Estimation results of normalized annual price for mobile subscriptions 125
Table 9. Mean absolute percentage error of each model 126
Table 10. Estimated parameters of the IBDM drawn from the brute-force algorithm 129
Table 11. Raw data of mobile communication subscriptions in three countries 153
Figure 1. Adopter categorization on the basis of innovativeness 21
Figure 2. Adopters due to external and internal influences in the Bass model 25
Figure 3. Relation of this study to previous research studies 50
Figure 4. Diagram of interaction weights in the IBDM 60
Figure 5. Four different network structure topologies 64
Figure 6. Random rewiring procedure in the S-W network 67
Figure 7. Average path length and clustering coefficients due to rewiring probability 69
Figure 8. Conceptual diagram of interaction weight in an IBDM 70
Figure 9. Small-world networks resulting from the rewiring probability 76
Figure 10. 10 cases drawn when N=1,000 80
Figure 11. 10 cases drawn when N=10,000 81
Figure 12. Diffusion patterns resulting from the rewiring probability 82
Figure 13. Cumulative sales in the conformity model 86
Figure 14. Simulation results of Case 1 91
Figure 15. Bass diffusion curves due to the innovation coefficient 92
Figure 16. Simulation Results of Case 2 93
Figure 17. Comparison between two price strategies 96
Figure 18. Simulation results of Case 3 98
Figure 19. Simulation Results of Case 4 99
Figure 20. Bass diffusion curves due to the imitation coefficient 100
Figure 21. Adopters due to both influences in the IBDM 101
Figure 22. Adopter categories based on the Bass model 104
Figure 23. Adopter categories in the IBDM: The base case 105
Figure 24. Two small-world networks with the same specifications 110
Figure 25. Probability distribution of social distance 112
Figure 26. Joint probability distribution of the social distance and rewiring probability 113
Figure 27. Conditional probability distribution of a rewiring probability 114
Figure 28. Number of agents due to a minimum distance to adopters 117
Figure 29. Conceptual diagram of diffusion in the IBDM 119
Figure 30. Fitting curves to real market data: USA 127
Figure 31. Fitting curves to real market data: Germany 127
Figure 32. Fitting curves to real market data: Korea 128