簡易檢索 / 詳目顯示

研究生: 官佳棋
Chia-Chi Kuan
論文名稱: ChatGPT科技接受度探討:擬人化重要嗎?
A Study on ChatGPT Technology Acceptance: Is Anthropomorphism Important?
指導教授: 盧希鵬
Hsi-Peng Lu
口試委員: 羅天一
Tain-Yi Luor
黃世禎
Sun-Jen Huang
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理系
Department of Information Management
論文出版年: 2023
畢業學年度: 111
語文別: 英文
論文頁數: 46
中文關鍵詞: ChatGPT感知擬人化科技接受模型感知有用性人工智慧接受度
外文關鍵詞: ChatGPT, perceived anthropomorphism, technology acceptance model, perceived usefulness, acceptance of AI tools
相關次數: 點閱:281下載:8
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
  • 隨著資通訊科技的進步,人工智慧技術在不斷地在進行創新,在人工智慧技術的應用也逐漸擴展,2022年一項跨時代的應用工具—ChatGPT橫空出世,顛覆了過往對於人工智慧技術的應用,ChatGPT結合自然語言處理技術與生成式對話,打造與過往不同的使用者體驗。在過去的人工智慧工具使用之中,發現人工智慧工具在溝通上無法採用自然語言處理的方式進行,限制了其與使用者之間的溝通效果與降低使用意願。如今,ChatGPT的發展即跨越此藩籬,以與過往不同之對話技術進行互動。,本研究提出感知擬人化變數搭配科技接受模型,希望藉此探討在ChatGPT此人工智慧工具之中,感知擬人化技術的重要性。
    本研究將以量化的方式搭配問卷進行研究,探討使用者對於ChatGPT之使用經驗。藉由問卷分析發現:(1) 感知擬人化(交流)變數並不會直接對使用者態度與使用意圖有所影響,而是經由感知有用性間接影響使用者態度與使用意圖;(2) 於拓展之科技接受度模型之中,可以發現感知有用性之重要性大於感知易用性。綜上所述,本研究結合了感知擬人化技術和TAM模型,了解到使用者使用ChatGPT的真正原因取決於工具之有用性,而感知擬人化的存在可以間接地增強使用者對ChatGPT之態度與使用意願。


    With the advancements in information and communication technology, artificial intelligence (AI) technologies continue to innovate and expand their applications. In 2022, a groundbreaking AI tool called ChatGPT emerged, revolutionizing the previous understanding of AI applications. ChatGPT combines natural language processing and generative dialogue to create a unique user experience. However, traditional AI tools often face limitations in effective communication due to the inability to process natural language, which hinders user engagement and reduces user acceptance. ChatGPT overcomes this barrier and enables interaction using a novel conversation technique. In light of this, our study proposes the incorporation of perceived anthropomorphism variables with the Technology Acceptance Model (TAM) to investigate the importance of perceived anthropomorphism in ChatGPT, an AI tool.
    This study adopts a quantitative approach, employing a questionnaire to investigate users' experiences with ChatGPT. Through questionnaire analysis, the following findings emerge: (1) Perceived anthropomorphism does not directly influence user attitude and intention to use ChatGPT but indirectly affects them through perceived usefulness; (2) In the extended TAM, perceived usefulness outweighs perceived ease of use in terms of its impact on technology acceptance. In summary, this study integrates perceived anthropomorphism with the TAM model and reveals that users' adoption of ChatGPT is primarily driven by its usefulness, while the presence of perceived anthropomorphism indirectly enhances user attitudes and intentions towards ChatGPT.

    摘 要 I Abstract II 誌謝 III Table of Contents IV List of Figures VI List of Tables VII 1 Introduction 8 1.1 Research Background 8 1.2 Research Questions 12 1.3 Value of Research 12 1.4 Overview 13 2 Literature Review 14 2.1 ChatGPT 14 2.2 Technology Acceptance Model 16 2.3 Acceptance of AI Tools 18 2.4 Perceived Anthropomorphism 20 3 Proposed Model and Research Hypotheses 22 4 Methodology 25 4.1 Research Design 25 4.2 Sample 27 4.3 Data Analysis 28 5 Result 31 6 Conclusion and Limitation 33 6.1 Theoretical contributions 33 6.2 Management implications 33 6.3 Limitations 34 6.4 Future research 34 Reference 36 Appendix 42

    Ajzen, I. (2015). Consumer attitudes and behavior: the theory of planned behavior applied to food consumption decisions. Italian Review of Agricultural Economics, 70(2), 121–138. https://doi.org/10.13128/rea-18003
    Biever, C. (2023). ChatGPT broke the Turing test — the race is on for new ways to assess AI. Nature. https://www.nature.com/articles/d41586-023-02361-7#ref-CR3
    Brachten, F., Kissmer, T., & Stieglitz, S. (2021). The acceptance of chatbots in an enterprise context – A survey study. International Journal of Information Management, 60, 102375. https://doi.org/10.1016/j.ijinfomgt.2021.102375
    Caldarini, G., Jaf, S., & McGarry, K. (2022). A Literature Survey of Recent Advances in Chatbots. Information, 13(1), 41. https://doi.org/10.3390/info13010041
    Cassell, J., Bickmore, T. Negotiated Collusion: Modeling Social Language and its Relationship Effects in Intelligent Agents. User Model User-Adap Inter 13, 89–132 (2003). https://doi.org/10.1023/A:1024026532471
    Chen, H., Widarso, G. V., & Sutrisno, H. (2020). A ChatBot for Learning Chinese: Learning Achievement and Technology Acceptance. Journal of Educational Computing Research, 58(6), 1161–1189. https://doi.org/10.1177/0735633120929622
    Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P. A. (2019). In the shades of the uncanny valley: An experimental study of human–chatbot interaction. Future Generation Computer Systems, 92, 539–548. https://doi.org/10.1016/j.future.2018.01.055
    Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/bf02310555
    Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Management Information Systems Quarterly, 13(3), 319. https://doi.org/10.2307/249008
    Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-machine Studies, 38(3), 475–487. https://doi.org/10.1006/imms.1993.102
    Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864–886. https://doi.org/10.1037/0033-295x.114.4.864
    Fayad, R., & Paper, D. (2015). The Technology Acceptance Model E-Commerce Extension: A Conceptual Framework. Procedia. Economics and Finance, 26, 1000–1006. https://doi.org/10.1016/s2212-5671(15)00922-3
    Ghazizadeh, M., Lee, J. D., & Boyle, L. N. (2011). Extending the Technology Acceptance Model to assess automation. Cognition, Technology & Work, 14(1), 39–49. https://doi.org/10.1007/s10111-011-0194-3
    Golossenko, A., Pillai, K. G., & Aroean, L. (2020). Seeing brands as humans: Development and validation of a brand anthropomorphism scale. International Journal of Research in Marketing, 37(4), 737–755. https://doi.org/10.1016/j.ijresmar.2020.02.007
    Gonçalves, B. (2022). The Turing Test is a Thought Experiment. Minds and Machines, 33(1), 1–31. https://doi.org/10.1007/s11023-022-09616-8
    Haenlein, M., & Kaplan, A. M. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
    Hair, J. F., Anderson, R. E., & Tatham, R. L. (1988). Multivariate Data Analysis with Readings. Journal of the Royal Statistical Society, 151(3), 558. https://doi.org/10.2307/2983017
    Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 49, 245–250. https://doi.org/10.1016/j.chb.2015.02.026
    IBM. (n.d.). What is natural language processing (NLP)? Retrieved June 26, 2023, from https://www.ibm.com/topics/natural-language-processing
    Kim, A. J., Yang, J., Jang, Y., & Baek, J. S. (2021). Acceptance of an Informational Antituberculosis Chatbot Among Korean Adults: Mixed Methods Research. Jmir Mhealth and Uhealth, 9(11), e26424. https://doi.org/10.2196/26424
    Kim, S., & Garrison, G. (2008). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323–333. https://doi.org/10.1007/s10796-008-9073-8
    Koufaris, M. (2002). Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior. Information Systems Research, 13(2), 205–223. https://doi.org/10.1287/isre.13.2.205.83
    Leiphone (2017). Why is Pepper in trouble? INSIDE. Retrived from https://www.inside.com.tw/article/9979-why-is-pepper-going-down
    Lu, J., Yu, C. X., Liu, C., & Yao, J. C. (2003). Technology acceptance model for wireless Internet. Internet Research, 13(3), 206–222. https://doi.org/10.1108/10662240310478222
    McNeese, N. J., Schelble, B. G., Canonico, L. B., & Demir, M. (2021). Who/What Is My Teammate? Team Composition Considerations in Human–AI Teaming. IEEE Transactions on Human-Machine Systems, 51(4), 288–299. https://doi.org/10.1109/thms.2021.3086018
    McKinsey& Company. (2023). What is generative AI? Retrieved June 26, 2023, from https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai
    Moon, J., & Kim, Y. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217–230. https://doi.org/10.1016/s0378-7206(00)00061-6
    Norfolk, L., & O’Regan, M. (2020). Biometric technologies at music festivals: An extended technology acceptance model. Journal of Convention & Event Tourism, 22(1), 36–60. https://doi.org/10.1080/15470148.2020.1811184
    NVIDIA (n.d.). What is Generative AI? Retrieved June 26, 2023, from https://www.nvidia.com/en-us/glossary/data-science/generative-ai/
    Pelau, C., Dabija, D., & Ene, I. (2021). What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior, 122, 106855. https://doi.org/10.1016/j.chb.2021.106855
    Perobot (n.d.). Hello, I am Pepper. Retrieved June 18, 2023, from http://www.perobot.com.tw/pepper/index
    Phaosathianphan, N., & Leelasantitham, A. (2021). An intelligent travel technology assessment model for destination impacts of tourist adoption. Tourism Management Perspectives, 40, 100882. https://doi.org/10.1016/j.tmp.2021.100882
    Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/ijchm-04-2020-0259
    Rahimi, B., Nadri, H., Afshar, H. L., & Timpka, T. (2018). A Systematic Review of the Technology Acceptance Model in Health Informatics. Applied Clinical Informatics, 09(03), 604–634. https://doi.org/10.1055/s-0038-1668091
    Raida, R. E., & Néji, B. (2013). The Adoption of the E-Banking: Validation of the Technology Acceptance Model. Technology and Investment, 04(03), 197–203. https://doi.org/10.4236/ti.2013.43023
    Ruijten, P. a. M., Haans, A., Ham, J., & Midden, C. (2019). Perceived Human-Likeness of Social Robots: Testing the Rasch model as a method for measuring anthropomorphism. International Journal of Social Robotics, 11(3), 477–494. https://doi.org/10.1007/s12369-019-00516-z
    Samadzadeh, G. R., Rigi, T., & Ganjali, A. (2013). Comparison of Four Search Engines and their efficacy With Emphasis on Literature Research in Addiction (Prevention and Treatment). International Journal of High Risk Behaviors and Addiction, 1(4). https://doi.org/10.5812/ijhrba.6551
    Schlicht, M. (2016). The Complete Beginner’s Guide To Chatbots. Chatbots Magazine. Retrieved June 13, 2023, from https://chatbotsmagazine.com/the-complete-beginner-s-guide-to-chatbots-8280b7b906ca
    Similarweb. (n.d.). Retrieved June 19, 2023, from https://www.similarweb.com/website/chat.openai.com/#overview
    Snapchat support. (n.d.). Retrieved June 18, 2023, from https://help.snapchat.com/hc/en-gb/articles/13266788358932-What-is-My-AI-on-Snapchat-and-how-do-I-use-it-
    Turing, A. (1950). I.—COMPUTING MACHINERY AND INTELLIGENCE. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/lix.236.433
    Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
    Ujhelyi, A., Almosdi, F., & Fodor, A. (2022). Would you pass the Turing test? Influencing factors of the turing decision. Psihologijske Teme, 31(1), 185–202. https://doi.org/10.31820/pt.31.1.9
    Wang, S., Yu, H., Hu, X., & Li, J. (2020). Participant or spectator? Comprehending the willingness of faculty to use intelligent tutoring systems in the artificial intelligence era. British Journal of Educational Technology, 51(5), 1657–1673. https://doi.org/10.1111/bjet.12998
    Yalalov, D. (2022). ChatGPT passes the Turing test News Report Technology. Metaverse Post. Retrieved August 2, 2023, from https://mpost.io/chatgpt-passes-the-turing-test/
    Yin, Z. Q., Yan, J., Fang, S., Wang, D., & Han, D. (2022). User acceptance of wearable intelligent medical devices through a modified unified theory of acceptance and use of technology. Annals of Translational Medicine, 10(11), 629. https://doi.org/10.21037/atm-21-5510
    Yu, C.-H. (2023). E-Sun Bank started to use ChatGPT to assist KYC account opening investigation, and Chatbot customer service will also use it in the future. iThome. Retrived from https://www.ithome.com.tw/news/15611

    無法下載圖示
    全文公開日期 2028/08/16 (校外網路)
    全文公開日期 2028/08/16 (國家圖書館:臺灣博碩士論文系統)
    QR CODE