跳到主要內容

簡易檢索 / 詳目顯示

回結果列表
研究生： 林俊良
Rykelin
論文名稱： AI驅動的個人化對網路購買意願的影響：歐洲與東南亞消費者之比較研究
The Impact of AI-Driven Personalization on E-Commerce Purchase Intention: A Comparative Study of European and Southeast Asian Consumers
指導教授： 蔡政憲
Jason Tsai
口試委員: 黃孝慈
湯美玲
學位類別： 碩士
Master
系所名稱： 商學院 - 國際經營管理英語碩士學位學程(IMBA)
International MBA Program College of Commerce(IMBA)
論文出版年： 2026
畢業學年度： 115
語文別： 英文
論文頁數： 89
中文關鍵詞： AI驅動的個人化 、電子商務 、網路購買意願 、信任 、隱私顧慮
外文關鍵詞： AI-driven personalization, e-commerce, purchase intention, trust, privacy concern
附註 Yes I confirmed that the expected graduation date is January 2027
相關次數： 點閱：44  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 本研究旨在探討AI驅動的個人化（AI-driven Personalization）對歐洲與東南亞消費者網路購買意願之影響，並進一步分析信任（Trust）的中介效果，以及隱私顧慮（Privacy Concern）與地區差異（Regional Differences）之調節效果。
    本研究採用量化研究方法，透過問卷調查蒐集德國、法國、義大利、印尼、泰國及越南六個國家之消費者資料，並使用 IBM SPSS Statistics 與 PROCESS Macro 進行資料分析。
    研究結果顯示，AI驅動的個人化對消費者信任及網路購買意願皆具有顯著正向影響。消費者信任亦對網路購買意願具有顯著正向影響，並在AI驅動的個人化與網路購買意願之間發揮部分中介效果。此外，隱私顧慮對AI驅動的個人化與網路購買意願之間的關係具有顯著調節效果。然而，歐洲與東南亞消費者之間的地區差異並未對上述關係產生顯著調節效果。
    本研究結論指出，在建立消費者信任及採取負責任的資料管理措施之情況下，AI驅動的個人化能有效提升消費者的網路購買意願。研究結果亦建議電子商務企業應妥善管理消費者的隱私顧慮，並依據不同地區市場特性調整其個人化策略，以提升消費者接受度與購買意願。

    This study examines the impact of AI-driven personalization on e-commerce purchase intention among consumers in Europe and Southeast Asia. It also analyzes the mediating role of trust and the moderating effects of privacy concern and regional differences.
    A quantitative survey was conducted in six countries: Germany, France, Italy, Indonesia, Thailand, and Vietnam. Data were analyzed using IBM SPSS Statistics and PROCESS Macro.
    The results show that AI-driven personalization positively influences both consumer trust and purchase intention in e-commerce platforms. Consumer trust also positively affects purchase intention and partially mediates the relationship between AI-driven personalization and purchase intention. In addition, privacy concern significantly moderates the relationship between AI-driven personalization and purchase intention. However, no significant moderating effect was found between consumers in Europe and Southeast Asia.
    The study concludes that AI-driven personalization can enhance consumers’ purchase intention when supported by trust-building mechanisms and responsible data practices. The findings also suggest that e-commerce firms should carefully manage consumer privacy concerns while adapting personalization strategies across different regional markets.

    TABLE OF CONTENTS
    Chapter 1: Introduction 1
    1.1. Background of the Study 1
    1.2. Problem Statement 4
    1.3. Research Objectives 5
    1.4. Research Questions 6
    1.5. Significance of the Study 6
    1.5.1. Academic Significance 6
    1.5.2. Managerial Significance 7
    1.5.3. Societal Significance 7
    1.6. Scope of the Study 7
    1.7. Organization of the Thesis 8
    Chapter 2: Literature Review 9
    2.1. Introduction 9
    2.2. Artificial Intelligence in E-Commerce 9
    2.2.1. Concept of Artificial Intelligence 9
    2.2.2. AI Applications in E-Commerce 10
    2.2.3. AI and Consumer Experience 10
    2.3. AI-Driven Personalization 11
    2.3.1. Definition of Personalization 11
    2.3.2. Benefits of Personalization 11
    2.3.3. Risks of Personalization 12
    2.4. Purchase Intention 12
    2.4.1. Definition of Purchase Intention 12
    2.4.2. Purchase Intention in E-Commerce 13
    2.4.3. Relevance to This Study 13
    2.5. Trust in E-Commerce 13
    2.5.1. Definition of Trust 13
    2.5.2. Trust as a Predictor of Purchase Intention 13
    2.5.3. Trust and Personalization 14
    2.6. Privacy Concern 14
    2.6.1. Definition of Privacy Concern 14
    2.6.2. Privacy Concern and Personalization 14
    2.6.3. Moderating Role of Privacy Concern 15
    2.7. Cross-Regional Consumer Behaviors: Europe and Southeast Asia 15
    2.7.1. Consumer Behaviors in Europe 15
    2.7.2. Consumer Behaviors in Southeast Asia 17
    2.7.3. Why Regional Comparison Matters 19
    2.8. Theoretical Foundations 19
    2.8.1. Stimulus-Organism-Response (S-O-R) Model 19
    2.8.2. Technology Acceptance Model (TAM) 22
    2.8.3. Theory of Planned Behavior (TPB) 23
    2.8.4. Privacy Calculus Theory 23
    2.9. Research Gap 24
    2.10. Conceptual Framework 24
    2.11. Hypotheses Development 25
    2.12. Chapter Summary 27
    Chapter 3: Research Methodology 28
    3.1. Introduction 28
    3.2. Research Design 28
    3.2.1. Quantitative Research Approach 28
    3.2.2. Cross-Sectional Survey Design 29
    3.3. Target Population and Unit of Analysis 29
    3.4. Geographic Scope and Country Selection 29
    3.5. Sampling Technique 30
    3.5.1. Non-Probability Quota Sampling 30
    3.6. Sample Size Justification 31
    3.7. Data Collection Method 32
    3.8. Research Instrument 32
    3.8.1. Questionnaire Structure 32
    3.8.2. Measurement Scale 32
    3.9. Variable Operationalization 36
    3.10. Pilot Testing 37
    3.11. Reliability and Validity Assessment 37
    3.11.1. Reliability 37
    3.11.2. Construct Validity 37
    3.11.3. Content Validity 38
    3.12. Data Screening and Cleaning 38
    3.13. Data Analysis Techniques 39
    3.13.1. Descriptive Analysis 39
    3.13.2. Correlation Analysis 39
    3.13.3. Crosstab and Chi-Square Analysis 39
    3.13.4. Multiple Regression Analysis 39
    3.13.5. Independent Samples t-Test 40
    3.13.6. Mediation Analysis 40
    3.13.7. Moderation Analysis 40
    3.13.8. Moderated Moderation Analysis 41
    3.13.9. Hypothesis and Decision Rules 41
    3.14. Ethical Considerations 41
    3.15. Chapter Summary 42
    Chapter 4: Data Analysis and Results 43
    4.1. Introduction 43
    4.2. Response Rate and Final Sample 43
    4.3. Country Distribution of Respondents 43
    4.4. Demographic Profile of Respondents 44
    4.5. Platform Usage Analysis 46
    4.6. Online Shopping Behaviour 46
    4.7. Crosstab Analysis Between European and Southeast Asian Consumers 47
    4.8. Descriptive Statistics of Main Variables 50
    4.9. Reliability Analysis 50
    4.10. Validity Assessment 51
    4.10.1. KMO and Bartlett’s Test 51
    4.10.2. Factor Loadings 51
    4.11. Correlation Analysis 52
    4.12. Multiple Regression 53
    4.13. Hypothesis Testing 55
    4.13.1. H1 - AI-driven Personalization Positively Affects Consumer Trust In E-Commerce Platform 55
    4.13.2. H2 - Consumer Trust Positively Affects Purchase Intention. 56
    4.13.3. H3 - AI-driven Personalization Positively Affects Purchase Intention 57
    4.13.4. H4 - Trust Mediates The Relationship Between AI-driven Personalization And Purchase Intention 58
    4.13.5. H5 - Privacy concern negatively moderates the relationship between AI-driven personalization and purchase intention 59
    4.13.6. H6-H9 - There Are Significant Differences in AI-Driven Personalization, Trust, Privacy Concern, and Purchase Intention Between European and Southeast Asian Consumers 62
    4.13.7. H10 - The Relationship Between AI-driven Personalization And Purchase Intention Differs Between Consumers In Europe and Southeast Asia 64
    4.13.8. H11 - The Relationship Between AI-driven Personalization and Trust Differs Between Consumers In Europe and Southeast Asia 65
    4.13.9. H12 - The Relationship Between Trust and Purchase Intention Differs Between Consumers In Europe and Southeast Asia 66
    4.13.10. H13 - The Moderating Effect Of Privacy Concern On The Relationship Between AI-driven Personalization And Purchase Intention Differs Between Consumers In Europe and Southeast Asia 68
    4.14. Summary of Hypothesis Results 69
    4.15. Chapter Summary 71
    Chapter 5: Discussion, Conclusion, and Recommendations 72
    5.1. Summary of the Study 72
    5.2. Discussion of Findings 73
    5.2.1. Effect of AI-Driven Personalization on Purchase Intention (H3) 73
    5.2.2. Effect of AI-Driven Personalization on Trust (H1) 74
    5.2.3. Effect of Trust on Purchase Intention (H2) 75
    5.2.4. Mediating Role of Trust (H4) 76
    5.2.5. Moderating Role of Privacy Concern (H5) 77
    5.2.6. Regional Differences Between Europe and Southeast Asia (H6–H13) 78
    5.3. Theoretical Implications 80
    5.4. Managerial Implications 81
    5.4.1. Personalization Should Prioritize Relevance 81
    5.4.2. Trust Must Accompany Technology 82
    5.4.3. Privacy Communication Is Essential 82
    5.4.4. Regional Adaptation May Be Necessary 82
    5.5. Limitations of the Study 83
    5.6. Recommendations for Future Research 84
    5.7. Conclusion 85
    References 87

    References
    Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34-49. Retrieved from https://doi.org/10.1016/j.jretai.2014.09.005
    Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. Retrieved from https://doi.org/10.1016/0749-5978(91)90020-T
    Bleier, A., & Eisenbeiss, M. (2015). The importance of trust for personalized online advertising. Journal of Retailing, 91(3), 390-409. Retrieved from https://doi.org/10.1016/j.jretai.2015.04.001
    Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
    Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust. Organization Science, 10(1), 104-115. Retrieved from https://doi.org/10.1287/orsc.10.1.104
    Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42. Retrieved from https://doi.org/10.1007/s11747-019-00696-0
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. Retrieved from https://doi.org/10.2307/249008
    Eroglu, S., Machleit, K., & Davis, L. (2001). Atmospheric qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54(2), 177-184. doi:10.1016/S0148-2963(99)00087-9
    European Commission. (2023). Data protection and GDPR implementation reports. Retrieved from https://commission.europa.eu/law/law-topic/data-protection_en
    Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. Retrieved from https://doi.org/10.2307/30036519
    Google; Temasek; Bain & Company. (2023). e-Conomy SEA 2023. Retrieved from https://economysea.withgoogle.com/
    Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8 ed.). Boston: Cengage Learning.
    Hayes, A. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3 ed.). New York, NY: The Guilford Press.
    Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50. Retrieved from https://doi.org/10.1007/s11747-020-00749-9
    Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25. Retrieved from https://doi.org/10.1016/j.bushor.2018.08.004
    Lemon, K., & Verhoef, P. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. doi:10.1509/jm.15.0420
    Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press.
    OECD. (2023). Digital economy outlook 2023. Retrieved from https://www.oecd.org/digital/
    Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134. Retrieved from https://doi.org/10.1080/10864415.2003.11044275
    Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6), e04284. Retrieved from https://doi.org/10.1016/j.heliyon.2020.e04284
    Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach. (4, Ed.) Pearson.
    Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 989-1015. Retrieved from https://doi.org/10.2307/41409970
    Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7 ed.). New York: Pearson.
    Tam, K. Y., & Ho, S. Y. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS Quarterly, 30(4), 865-890. Retrieved from https://www.jstor.org/stable/25148757
    UNCTAD. (2023). Digital economy report 2023. Retrieved from https://unctad.org/
    World Bank. (2023). World development indicators / digital development datasets. Retrieved from https://databank.worldbank.org/

    無法下載圖示 全文公開日期 2027/07/02
    QR CODE
    :::