由於現今網路與AI人工智慧服務的普及與發展迅速，並延伸發展出許多有趣且實用的應用軟體服務，根據研究指出，現代人的生活中有許多層面皆可透過AI人工智慧完成，例如透過智能聊天機器人能獲得即時解答，可以向它提問各種問題，從常見問題到特定領域的知識都可以涵蓋；或是透過生成式AI人工智慧模型可以探索創造性想法，如果您需要在寫作、設計或其他創造性領域中獲得靈感，可以為人們提供一些有趣的思路；可以作為學習工具，幫助人們理解各種領域的概念，學習新的事實和見解；如果需要幫助完成某個任務或項目，AI人工智慧模型能夠可以提供指導和建議，可以向它諮詢有關計劃、寫作、編程等方面的問題，並獲得有用的提示和建議。以上所述，生成式AI人工智慧模型ChatGPT皆能夠完成現代人們的需求，故而在2023年2月，在社群媒體上形成一股潮流，並隨之發佈的同時，在科技產業投下革命性震撼彈!而在此浪潮之下，其他相關AI人工智慧模型應用，也隨之蓬勃發展。本研究以整合科技接受模型 UTAUT與延伸性整合科技接受模型 UTAUT2，探討使用者對於對話式AI人工智慧服務之接受程度，以ChatGPT為例說明之。

ChatGPT，全稱聊天生成型預訓練變換模型(Chat Generative Pre-trained Transformer），是OpenAI基於GPT（Generative Pre-trained Transformer）模型開發而成的聊天生成型預訓練變換模型開發的人工智慧聊天機器人程式。以下將其以ChatGPT代稱。

ChatGPT是一種「文本生成」的技術，透過你輸入的文字讓AI做判斷，而產出相對應的回覆。不過在技術的初期可能會因為AI資料庫有限，以及計算能力與方式的限制等因素有答非所問或不完全準確的回答的情形，然而，隨著更多的訓練數據和改進的模型架構和技術的日趨成熟，在分析過大量文本後就能給出越來越精準的答覆。像是大家常用的iPhone Siri就是一種透過AI來給予回答的工具。ChatGPT支援多種語言，這使得它能夠應對不同語言背景的使用者需求。它在發布後引起了廣泛的關注和討論，並引發了許多重要的議題，例如機器倫理、隱私保護和誤導性資訊的問題。為了提高ChatGPT的使用安全性和效果，OpenAI正在持續改進模型並采取相應的措施來解決這些問題。

本研究根據Viswanath Venkatesh et al. (2003) 所建立之整合科技接受模型UTAUT以及Viswanath Venkatesh et al. (2012) 所建立之延伸性整合科技接受模型UTAUT2，為主要的理論基礎，同時參考過去多個與科技接受相關的理論文獻，建構出以ChatGPT為研究對象的模型，而其中自變數包含：績效預期、易用預期、社會影響、享樂動機、促進條件、產品涉入、產品知識，並且以使用者態度為中介，進而分析使用者對ChatGPT的使用意願或是行為意圖，同時根據本研究之目的刪除原理論架構之所有調節變數對整體進行討論。

本研究希望透過量化的實證分析，探討使用者使用ChatGPT的心理因素，以
了解其持續使用之行為意圖，本研究透過網路進行問卷的發放，總共蒐集了509份問卷，而有效問卷共507份，題項的內容藉由結構方程進行信度與效度分析後，再由路徑分析和迴歸分析得出研究結果。而從研究中發現，模型UTAUT和UTAUT2 中的自變數與新加入之自變數產品知識和產品涉入對於消費者使用ChatGPT的行為意圖有很高的解釋能力。

With the widespread adoption and rapid development of the internet and artificial intelligence, AI, services today, numerous interesting and practical software applications have emerged. Research indicates that artificial intelligence, AI, can accomplish various aspects of modern life. For example, AI chatbots provide instant answers and can be asked a wide range of questions, covering both common inquiries and specialized domain knowledge. Generative AI models allow the exploration of creative ideas, offering intriguing insights and inspiration in areas such as writing, design, and other creative domains. They can also serve as learning tools, aiding individuals in understanding concepts across different fields and acquiring new facts and perspectives. Moreover, when assistance is needed to complete a task or project, artificial intelligence, AI, models can provide guidance and suggestions. They can be consulted for questions related to planning, writing, programming, and more, yielding useful tips and advice. As described above, generative AI model ChatGPT can fulfill the needs of modern individuals.

As a result, in February 2023, a trend emerged on social media platforms, accompanied by the release of ChatGPT, which made a revolutionary impact on the technology industry. This wave of development has also led to the flourishing growth of other related artificial intelligence model applications.

This study using the Extend Unified Theory of Acceptance and Use of Technology
(UTAUT2) as the main theoretical basis. As the same time, this study referring to
numbers of theoretical literatures which related to technology acceptance in the past and
constructed a model with ChatGPT as the research target. This study aims to explore the psychological factors influencing users' continued usage intention of ChatGPT through quantitative empirical analysis. A web-based questionnaire was distributed for data collection, resulting in a total of 509 responses, out of which 507 were deemed valid. The content of the questionnaire items underwent reliability and validity analysis using structural equation modeling, followed by path analysis and regression analysis to obtain research findings. The study revealed that the independent variables from the UTAUT and UTAUT2 models, along with the newly added independent variables of product knowledge and product involvement, have a high explanatory power on consumers' usage intention of ChatGPT.

中文參考文獻：
1. 王郁雅. (2018). 以 UTAUT 探討消費者對安全監視系統接受度之研究. (碩士), 國立高雄應用科技大學, 高雄市.
2. 朱莉芳. (2010). 探討影響電子書使用意圖之因素，計劃性行為理論與科技接受模式觀 點 . ( 碩 士 ), 國 立 成 功 大 學 , 台 南 市
3. 江志卿, 黃興進, & 嚴紀中. (2005). 中小企業採用網路科技之比較: 創新擴散通用模式的整合觀點. 資訊管理學報, 12(2), 103-139.
4. 何胤呈. (2019) 線上影音串流平台 4C 策略行銷分析: 以愛奇藝, NETFLIX, LINE TV 為例
5. 李佩君. (2016). 以 UTAUT 與 TTF 整合模式探討醫師與藥師 使用健保雲端藥歷系統意圖之研究. (碩士), 國立高 雄應用科技大 學, 高雄市
6. 李靜芳. (2019). 以 UTAUT 模式進行 Beacon 微定位技術採用探討. [Using UTAUT Model to Explore Beacon Micro-Positioning Technology Adoption]. 管理資訊計算, 8(2), 11-19. doi:10.6285/mic.201909_8(2).0002
7. 周英奇. (2016). 以 UTAUT2 模式理論探討銀髮族對社群軟體接受度. (碩士), 國立高雄應用科技大學, 高雄市
8. 林昱豪. (2018). 內容供應商與 OTT 串流影音平台的交易模式研究
9. 林勝蒝. (2020). 消費者對美食外送平台使用意圖之探討—以台灣市場為例. 淡江大學, Available from Airiti AiritiLibrary database. (2020 年)
10. 胡庠卉. (2011). 影響高等教育學生使用數位學習系統意願因素之研究—應用 UTAUT 模型分析
11. 翁晨語, & 黃惠萍. (2017). 以延伸整合型科技接受模式和數位生活型態探討 LINE TV的使用行為. [A Study on the Use Behavior of LINE TV Based on the UTAUT2 and Digital Lifestyle Scale]. 資 訊 社 會 研 究 (33), 17-63.
12. 張巧真, & 陳筠惠. (2014). 應用延伸型整合科技接受模式探討線上購買意願-以雙媒介之觀點. Electronic Commerce Studies, 12(2), 143-168.
13. 張志銘, 甯晏然, 許子淵, & 張世沛. (2019). 以健康信念模式結合計畫行為理論探討國小教師運動參與行為意圖之影響. [The study of the influence of health belief model combined with theory of planned behavior on the sports behavior intention participated by Changhua elementary school teachers]. 管理實務與理論研究, 13(2), 40-56. doi:10.29916/jmpp.201912_13(2).0003
14. 張維熊. (2016). 行動銀行知覺易用性、使用者體驗與忠誠度之關聯性研究. 國立交通大學, Available from Airiti AiritiLibrary database. (2016 年)
15. 曹金成. (2008). 物業管理系統使用行為之研究－UTAUT 模型之應用. 國立臺北大學, Available from Airiti AiritiLibrary database. (2008 年)
16. 黃璿吉. (2010). 影響消費者採用創新服務之因素研究—以提供雲端閱讀服務網站為例 . ( 碩 士 ), 國 立 交 通 大 學 , 新 竹 市
17. 楊雅婷. (2018). OTT 影音平台的使用者研究：以整合科技接受與資訊系統成功模式探討行為意圖與付費意願. 國立政治大學, Available from Airiti AiritiLibrary database. (2018 年)
18. 葉宗樺. (2017). 消費者對智慧型手環穿戴式科技產品使用意願之研究－UTAUT2 模型之 應 用 . ( 碩 士 ), 國 立 臺 北 大 學 , 新 北 市
19. 賴俊杉. (2017). 擴充 UTAUT 理論探討高齡者使用行動掛號 App 的意圖. (碩士), 國立高雄應用科技大學, 高雄市
20. 賴禹心, 鄭宇庭, & 蘇志雄. (2020). 使用 OTT 影音平台的關鍵因素與行為模式-以YouTube 為例. Journal of Data Analysis, 15(5), 21-43

英文參考文獻：
1. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control (pp. 11-39): Springer.
2. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
3. Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of experimental social psychology, 22(5), 453-474.
4. Ajzen, I. (1996) The directive influence of attitudes on behavior. In: The psychology of action. Linking cognition and motivation to behavior. Eds: Gollwitzer, PM and Bargh, JA NewYork: Guilford Press. 385-454.
5. Ajzen, I. (1988) Attitudes, personality and behavior.
6. Ajzen, I. (2001) Perceived behavioral control, self-efficacy, locus of control, and the Theory of Planned Behavior. Journal of Applied Social Psychology 32, 11-20.
7. Aviv Shachak, Craig Kuziemsky, Carolyn Petersen (2019). Beyond TAM and UTAUT: Future directions for HIT implementation research.
8. Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122.
9. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986(23-28).
10. Bhawan, M. D., & Marg, J. L. N. (2015). Regulatory Framework for Over-the-top (OTT) services. Telecom Regulatory Authority of India, 1-118.
11. Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 399-426.
12. Carl Benedikt Frey and Michael A. Osborne. (2017). The future of employment: How susceptible are jobs to computerization.
13. Celik, H. (2016). Customer online shopping anxiety within the Unified Theory of Acceptance and Use Technology (UTAUT) framework. Asia Pacific journal of Marketing and logistics.
14. Chan, K. Y., Gong, M., Xu, Y., & Thong, J. (2008). Examining user acceptance of SMS: An empirical study in China and Hong Kong. PACIS 2008 Proceedings, 294.
15. Charng, H.-W., Piliavin, J. A., & Callero, P. L. (1988). Role identity and reasoned action in the prediction of repeated behavior. Social psychology quarterly, 303-317.
16. Chen, Y.-N. K. (2019). Competitions between OTT TV platforms and traditional television in Taiwan: A Niche analysis. Telecommunications Policy, 43(9), 101793.
17. Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. In (pp. vii-xvi): JSTOR. 56
18. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
19. Cronbach, L. J. (1947). Test “reliability”: Its meaning and determination. Psychometrika, 12(1), 1-16.
20. Daniel Le Métayer et al. (2015). PRIPARE: Integrating Privacy Best Practices into a Privacy Engineering Methodology.
21. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Mis Quarterly, 319-340.
22. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
23. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology, 22(14), 1111-1132.
24. Decharms, R., & Carpenter, V. (1968). Measuring motivation in culturally disadvantaged school children. The Journal of Experimental Education, 37(1), 31-41.
25. Dodds, W. B., & Monroe, K. B. (1985). The effect of brand and price information on subjective product evaluations. ACR North American Advances.
26. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of marketing research, 28(3), 307-319.
27. Dorsey Press. Ajzen, I. (1991) The theory of planned behavior. Organizational Behavior and Human Decision Process 50, 179-211.
28. Fishbein, M. Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
29. Fishbein, M. Ajzen, I. (1985) From intentions to actions: A theory of planned behavior. In: Action control from cognition to behavior. Eds: Khul, J. and Beckmann, J. Berlin: Verlag. 10-39.
30. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
31. Gefen, D., ＆ Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the association for Information Systems, 1(1), 8.
32. Gefen, D., Karahanna, E., ＆ Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 27(1), 51-90.
33. Hair, J. F. (2009). Multivariate data analysis.
34. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
35. John Howard MD. (2019). Artificial intelligence: Implications for the future of work.
36. Kaiser, H. F. (1970). A second generation little jiffy.
37. Keaveney, S. M., & Parthasarathy, M. (2001). Customer switching behavior in online services: An exploratory study of the role of selected attitudinal, behavioral, and demographic factors. Journal of the academy of marketing science, 29(4), 374-390.
38. Kim, J., Kim, S., & Nam, C. (2016). Competitive dynamics in the Korean video platform market: Traditional pay TV platforms vs. OTT platforms. Telematics and Informatics, 33(2), 711-721.
39. Kim, J. O., Forsythe, S., Gu, Q., & Moon, S. J. (2002). Cross‐cultural consumer values, needs and purchase behavior. Journal of Consumer marketing.
40. Kim, S., Baek, H., & Kim, D. H. (2021). OTT and live streaming services: Past, present, and future. In (Vol. 45, pp. 102244): Elsevier.
41. Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management science, 51(5), 741-755.
42. Kraemer, H. C., & Blasey, C. M. (2004). Centring in regression analyses: a strategy to prevent errors in statistical inference. International journal of methods in psychiatric research, 13(3), 141-151.
43. Kwateng, K. O., Atiemo, K. A. O., & Appiah, C. (2018). Acceptance and use of mobile banking: an application of UTAUT2. Journal of enterprise information management.
44. Lepper, M. R. (1985). Microcomputers in education: Motivational and social issues. American psychologist, 40(1), 1.
45. Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 705-737.
46. Matthew B Hoy. (2018). Alexa, Siri, Cortana, and More: An Introduction to Voice Assistants. DOI: 10.1080/02763869.2018.1404391.
47. Michaelidou, N., & Dibb, S. (2009). Brand switching in clothing: the role of variety‐seeking drive and product category‐level characteristics. International Journal of Consumer Studies, 33(3), 322-326.
48. Miles Brundage et al. (2018). The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation.
49. Michał Choraś & Michał Woźniak. (2021). The double-edged sword of AI: Ethical Adversarial Attacks to counter artificial intelligence for crime.
50. Mishra, D., Akman, I., & Mishra, A. (2014). Theory of Reasoned Action application for Green Information Technology acceptance. Computers in Human Behavior, 36, 29-40. Retrieved from <Go to ISI>://WOS:000338387300004.
51. Mitchell, T. R., & Biglan, A. (1971). Instrumentality theories: Current uses in psychology. Psychological Bulletin, 76(6), 432.
52. Mittal, B. (1988). Achieving higher seat belt usage: The role of habit in bridging the attitude‐behavior gap 1. Journal of applied social psychology, 18(12), 993-1016.
53. Montano, D. E., ＆ Kasprzyk, D. (2015). Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. Health behavior: Theory, research and practice.
54. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
55. Moore, G. C., & Benbasat, I. (1996). Integrating diffusion of innovations and theory of reasoned action models to predict utilization of information technology by endusers. In Diffusion and adoption of information technology (pp. 132-146): Springer.
56. Murray, K. B., Liang, J., & Häubl, G. (2010). ACT 2.0: the next generation of assistive consumer technology research. Internet Research.
57. Nick Bostrom & Eliezer Yudkowsky. (2020). The Ethics of Artificial Intelligence.
58. Niininen, O., Szivas, E., & Riley, M. (2004). Destination loyalty and repeat behavior: An application of optimum stimulation measurement. International Journal of Tourism Research, 6(6), 439-447.
59. Norouzi, S., Moghaddam, F., & Sharafi, F. (2018). Effectiveness of Trainings Based on the Theory of Reasoned Action in the Choice of Natural Delivery by Pregnant Women. International Journal of Womens Health and Reproduction Sciences, 6(2), 181-186.
60. Raju, P. S. (1980). Optimum stimulation level: Its relationship to personality, demographics, and exploratory behavior. Journal of consumer research, 7(3), 272-282.
61. Ramdas, K. (2003). Managing product variety: An integrative review and research directions. Production and operations management, 12(1), 79-101.
62. Ramdas, K., & Sawhney, M. S. (2001). A cross-functional approach to evaluating multiple line extensions for assembled products. Management science, 47(1), 22-36.
63. Ram, S. (1987). A model of innovation resistance. NA-Advances in Consumer Research Volume 14.
64. Ram, S. (1987). A model of innovation resistance. ACR North American Advances.
65. Ram, S., ＆ Sheth, J. N. (1989). Consumer resistance to innovations: the marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5-14.
66. Reuben Binns. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR 81:149-159.
67. Roberts, C., & Muscarella, V. (2015). Defining Over-The-Top (OTT) Digital Distribution. The Entertainment Merchants Association, 3-4.
68. Rogers, E.M. (1962). Diffusion of Innovations. New York: The Free Press, 1th Edition.
69. Rogers, E.M. (1983). Diffusion of Innovations. New York: The Free Press, 3th Edition.
70. Rogers, E.M. (1995). Diffusion of Innovations. New York: The Free Press, 4th Edition.
71. Rogers, E. M. (1995). Diffusion of Innovations: modifications of a model for telecommunications. In Die diffusion von innovation in der telecommunication (pp. 25-38): Springer.
72. Rogers, E.M. (2005). Diffusion of Innovations. New York: The Free Press, 5th Edition.
73. Roose, Kevin. The Brilliance and Weirdness of ChatGPT. The New York Times. 2022-12-05 [2022-12-26].
74. Schifter, D. E., & Ajzen, I. (1985). Intention, perceived control, and weight loss: an application of the theory of planned behavior. Journal of personality and social psychology, 49(3), 843.
75. Sheth, J. N., ＆ Stellner, W. H. (1979). Psychology of innovation resistance: The less developed concept (LDC) in diffusion research: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign Urbana-Champaign, IL.
76. Sheng, T.-W. (2020). Product Differentiation, Customer Equity, and Purchase Intention: An Empirical Study of Original Content on Video Streaming Service.
77. Steenkamp, J.-B. E., & Baumgartner, H. (1992). The role of optimum stimulation level in exploratory consumer behavior. Journal of consumer research, 19(3), 434-448.
78. Sountongnoma Martial Anicet Kiemde & Ahmed Dooguy Kora. (2022). Towards an ethics of AI in Africa: rule of education.
79. Sumak, B., Hericko, M., Polancic, G., & Pusnik, M. (2010). Investigation of E-Learning System Acceptance using UTAUT. International Journal of Engineering Education, 26(6), 1327-1342.
80. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS quarterly, 125-143.
81. Thong, J. Y., Hong, S.-J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of human-computer studies, 64(9), 799-810.
82. Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang. (2019). Mitigating Gender Bias in Natural Language Processing: Literature Review.
83. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
84. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
85. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
86. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). CONSUMER ACCEPTANCE AND USE OF INFORMATION TECHNOLOGY: EXTENDING THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY. Mis Quarterly, 36(1), 157-178.
87. Verplanken, B. (2006). Beyond frequency: Habit as mental construct. British Journal of Social Psychology, 45(3), 639-656.
88. Virginia Dignum. (2018). Ethics in artificial intelligence: introduction to the special issue.
89. Vroom, V. H. (1964). Work and motivation.
90. Wu, J.-H., ＆ Wang, S.-C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information ＆ management, 42(5), 719-729.
91. Wu, J.-H., Wang, S.-C., ＆ Lin, L.-M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International journal of medical informatics, 76(1), 66-77.
92. Wu, J.-H., Shen, W.-S., Lin, L.-M., Greenes, R. A., ＆ Bates, D. W. (2008). Testing the technology acceptance model for evaluating healthcare professionals' intention to use an adverse event reporting system. International Journal for Quality in Health Care, 20(2), 123-129.
93. Yang, K. (2010). Determinants of US consumer mobile shopping services adoption: implications for designing mobile shopping services. Journal of consumer marketing, 27(3), 262-270.