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研究生:包栯綺
研究生(外文):Yu-Chi Bau
論文名稱:類神經網路技術應用於預測塑膠技術研發服務廠商之顧客回應
論文名稱(外文):A Case Study of Neural Network Technique Applied to Buyer''s Response Prediction for a Plastic Technique R&D Service Provider in Taiwan
指導教授:周世玉周世玉引用關係
學位類別:碩士
校院名稱:國立中興大學
系所名稱:行銷學系所
學門:商業及管理學門
學類:行銷與流通學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:54
中文關鍵詞:類神經網路模型市場導向預測模型顧客回應
外文關鍵詞:Neural Networkmarket orientationpredicting modelbuyer’s response
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In recent years companies in various industries tended to outsource part of their R&D services to a professional third party to save their costs and speed up new product development. In light of customer retention, a customer’s response to the R&D service has great impact on the possibility of reusing the service from the same service provider. Realizing how customers respond has become a critical issue for a R&D service provider, such as PIDC (Plastic Industry Development Center). Besides, if PIDC know their buyer’s loyalty tendency and propensity to switch to other providers, it can implement adequate relationships management policy with limited marketing resources.
The independent variables, including effective communication, trust, satisfaction, commitment, and customer’s cultural market orientation, are used to predict buyer’s response. The predictive performance of models, neural network, decision tree, linear regression, logistic regression and discriminant analysis, are compared. In the prediction of buyer’s response, neural network is a better predictive model than other models. Finally, we suggest that PIDC use the neural network model as their predictive model to manage its relationship marketing policies effectively and efficiently.
ACKNOWLEDGEMENTS i
ABSTRACT ii
TABLE OF CONTENTS iii
LISTS OF TABLES vi
LIST OF FIGURE vii
CHAPTERⅠ INTRODUCTION 1
1.1 Research Background and Motivation 1
1.2 Research Questions 4
1.3 Research Objectives 4
1.4 Thesis Organization 5
CHAPTERⅡ LITERATURE REVIEW 6
2.1 The Target Variables 6
2.1.1 Buyer’s Loyalty 6
2.1.2. Buyer’s Propensity to Switch to Other Providers 6
2.2 The Prediction of Buyer’s Response in the Literature 7
2.2.1 Buyer’s Cultural Market Orientation 8
2.2.2 Effective Communication between Buyers and the Service Provider 9
2.2.3 Trust 9
2.2.4 Satisfaction with the Service Provider 10
2.2.5 Commitment to the Relationship 10
2.3 The Antecedents of Buyer’s Response 11
2.3.1 Buyer’s Cultural Market Orientation Effects on Buyer’s Response 11
2.3.2 Effective Communication Effects on Buyer’s Response 12
2.3.3 Trust Effects on Buyer’s Response 13
2.3.4 Satisfaction with the Service Provider Effects on Buyer’s Response 14
2.3.5 Commitment to the Relationship Effects on Buyer’s Response 15
2.4 The Predicting Model of Buyer’s Response 16
CHAPTERⅢ RESEARCH METHOD 18
3.1 Research Variables Definition 18
3.2 Sampling Plan 20
3.3 Surveyed Sample Profiling 21
3.4 Measurement Validation 22
3.4.1 Reliability Analysis 22
3.4.2 Validation Analysis 24
3.5 Introduction to Models Used 25
3.5.1 Neural Network Model (NN) 25
3.5.2 Decision Tree Model (DT) 26
3.5.3 Linear Regression Model (RE) 27
3.5.4 Logistic Regression Model (LR) 27
3.5.5 Discriminant Analysis (DC) 28
CHAPTERⅣ MODEL DEVELOPMENT AND 29
DATA ANALYSIS 29
4.1. The Processes of the SAS Program 30
4.1.1. Neural Network Model (NN) 30
4.1.2. Decision Tree Model (DT) 30
4.1.3. Linear Regression Model (RE) 31
4.1.4. Logistic Regression Model (LR) 31
4.1.5. Discriminant Analysis Model (DC) 31
4.2. Buyer’s Response Using Continuous Variables 32
4.3. Buyer’s Response Using Categorical Variables 38
4.4. Why Both Target Variables as Buyer’s Response 44
CHAPTERⅤ DISCUSSION 45
5.1. Managerial Implications 47
5.2. Research Limitation and Future Research Directions 47
REFERENCES 49
APPENDICES 56
Appendix1. The Questionnaire 56
Appendix2. The Predictive Performance of Buyer’s Loyalty Using Continuous Variable among Three Models 59
Appendix2.1 The Data Partition of 75%-25% of Both Data Sets 59
Appendix2.2 The Data Partition of 80%-20% of Both Data Sets 60
Appendix2.3 The Data Partition of 85%-15% of Both Data Sets 61
Appendix2.4 The Data Partition of 90%-10% of Both Data Sets 62
Appendix3. The Predictive Performance of Buyer’s Propensity to Switch to Other Providers Using Continuous Variable among Three Models 63
Appendix3.1 The Data Partition of 75%-25% of Both Data Sets 63
Appendix3.2 The Data Partition of 80%-20% of Both Data Sets 64
Appendix3.3 The Data Partition of 85%-15% of Both Data Sets 65
Appendix3.4 The Data Partition of 90%-10% of Both Data Sets 66
Appendix4. The Predictive Performance of Buyer’s Loyalty Using Categorical Variable among Four Models 67
Appendix4.1 The Data Partition of 75%-25% of Both Data Sets 67
Appendix4.2 The Data Partition of 80%-20% of Both Data Sets 68
Appendix4.3 The Data Partition of 85%-15% of Both Data Sets 69
Appendix4.4 The Data Partition of 90%-10% of Both Data Sets 70
Appendix5. The Predictive Performance of Buyer’s Propensity to Switch to Other Providers Using Categorical Variable among Four Models 71
Appendix5.1 The Data Partition of 75%-25% of Both Data Sets 71
Appendix5.2 The Data Partition of 80%-20% of Both Data Sets 72
Appendix5.3 The Data Partition of 85%-15% of Both Data Sets 73
Appendix5.4 The Data Partition of 90%-10% of Both Data Sets 74
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