人工智慧（AI）生成的應用，已經深刻地改變了人們的思考、行為和生活方式。這種影響不僅體現在個人層面，還對組織和整個社會造成了重大影響。自ChatGPT推出以來，在短時間內就吸引了許多用戶運用其在任務上，也顯現了人們對於AI和NLP聊天機器人的需求正在迅速增加，而ChatGPT的多功能性使其能夠在各種任務中為使用者提供協助，改變了傳統的任務執行方式，並且在使用者實際使用過後產生出價值，從而促進了人們在日常生活和工作中更高效地使用這項技術。因此，我們探討在使用ChatGPT的情境下，哪些因素會促進使用者使用ChatGPT、哪些因素會影響價值創造，這些因素是否能促進使用者價值創造。通過應用活動理論(Activity Theory)發展理論模型，確認個人化誘因、平台示能性和平台綜效能力是否會影響實際參與，並且對使用者價值創造會有什麼樣的影響。
本研究採用問卷調查的方式進行網路問卷的發放，透過便利抽樣和滾雪球抽樣，調查ChatGPT的使用經驗者，收回共計796份有效樣本，作為實證數據來驗證本研究的假說。實證結果顯示：平台示能性和平台綜效能力會提升使用者實際使用，並且在實際使用過後創造出使用者價值，此外實際使用對使用者價值有部分中介效果。從我們的研究結果中確認了工具、目標皆會影響最後的價值創造，因此希望節由本研究提供未來在ChatGPT不論是理論或實務上些許想法。

The applications produced by artificial intelligence (AI) have significantly transformed people's thinking, behavior, and lifestyles. This impact is not only evident at the individual level but also profoundly affects organizations and entire societies. Since the launch of ChatGPT, it has rapidly attracted numerous users who utilize its capabilities for various tasks, indicating the increasing demand for AI and NLP chatbots among people. The versatility of ChatGPT allows it to assist users in various tasks, revolutionizing traditional task execution methods and generating value for users upon actual usage. Consequently, it promotes more efficient utilization of this technology in daily life and work. Therefore, we explore the factors that drive users to utilize ChatGPT, the factors influencing value creation, and whether these factors can promote user value creation. By applying Activity Theory to develop theoretical models, we investigate whether personalized incentives, platform affordances, and platform synergistic capabilities affect actual participation and their impact on user value creation.

This study conducted an online survey through questionnaire distribution, utilizing convenience sampling and snowball sampling to survey users with experience using ChatGPT. A total of 796 valid samples were collected as empirical data to validate the hypotheses of this study. The empirical results demonstrate that platform affordances and platform synergistic capabilities enhance actual user usage and contribute to user value creation after actual usage. Furthermore, actual usage partially mediates the relationship between platform affordances, platform synergistic capabilities, and user value. Our research confirms that both tools and goals influence the final value creation. Therefore, we hope that this study can provide insights for future theoretical and practical developments in ChatGPT.

目錄
論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
圖次 vii
表次 viii
第一章、緒論 1
第一節、研究背景 1
第二節、研究動機 2
第三節、研究目的 4
第二章、文獻探討 5
第一節、生成式聊天機器人 5
第二節、活動理論 11
第三節、結構識別 12
第三節、研究模型與假說發展 19
第三章、研究方法 23
第一節、研究設計 23
第二節、研究量表發展 25
第三節、研究對象與問卷回收 28
第四章、資料分析 31
第一節、敘述性統計 31
第二節、測量模型分析 37
第三節、模型驗證及結果 42
第五章、討論與結論 46
第一節、研究結果 46
第二節、研究貢獻 48
第三節、研究限制與未來研究方向 51
參考文獻 52
附錄一、專家效度問卷 59
附錄二、正式問卷 63
附錄三、量表描述與因素分析 66
附錄四、研究模型分析的構念結果 68

中文參考文獻
財團法人台灣網路資訊中心， 2023 年台灣網路報告， August 2023 https：//report.twnic.tw/2023/， [Retrieved 2024/04]。
英文參考文獻
Adapa, S., Fazal-e-Hasan, S. M., Makam, S. B., Azeem, M. M., & Mortimer, G. (2020). Examining the antecedents and consequences of perceived shopping value through smart retail technology. Journal of Retailing and Consumer Services, 52, 101901.
Ahmed, A. (2023). ChatGPT achieved one million users in record time-revolutionising time-saving in various fields. Digital Information World, 27.
Allen, D. K., Brown, A., Karanasios, S., & Norman, A. (2013). How should technology-mediated organizational change be explained? A comparison of the contributions of critical realism and activity theory. MIS quarterly, 835-854.
Altalbe, A. (2021). Antecedents of actual usage of e-learning system in high education during COVID-19 pandemic: Moderation effect of instructor support. Ieee Access, 9, 93119-93136.
Buhalis, D., & Moldavska, I. (2022). Voice assistants in hospitality: using artificial intelligence for customer service. Journal of Hospitality and Tourism Technology, 13(3), 386-403.
Bygstad, B., Munkvold, B. E., & Volkoff, O. (2016). Identifying generative mechanisms through affordances: a framework for critical realist data analysis. Journal of information technology, 31, 83-96.
Cabiddu, F., De Carlo, M., & Piccoli, G. (2014). Social media affordances: Enabling customer engagement. Annals of Tourism Research, 48, 175-192.
Carlsson, S. A. (2012). The potential of critical realism in IS research. Information Systems Theory: Explaining and Predicting Our Digital Society, Vol. 2, 281-304.
Chen, Y.-C., Wu, J.-H., Peng, L., & Yeh, R. C. (2015). Consumer benefit creation in online group buying: The social capital and platform synergy effect and the mediating role of participation. Electronic Commerce Research and Applications, 14(6), 499-513.
Crompton, H., & Burke, D. (2024). The Educational Affordances and Challenges of ChatGPT: State of the Field. TechTrends, 1-13.
Dawn, S. K. (2014). Personalised Marketing: concepts and framework. Productivity, 54(4), 370.
Dobson, P., Myles, J., & Jackson, P. (2007). Making the case for critical realism: Examining the implementation of automated performance management systems. Information Resources Management Journal (IRMJ), 20(2), 138-152.
Dong, X., & Wang, T. (2018). Social tie formation in Chinese online social commerce: The role of IT affordances. International journal of information management, 42, 49-64.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., & Ahuja, M. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
Else, H. (2023). Abstracts written by ChatGPT fool scientists. Nature, 613(7944), 423-423.
Engeström, Y. (1987). An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit.
Epstein, Z., Hertzmann, A., Creativity, I. o. H., Akten, M., Farid, H., Fjeld, J., Frank, M. R., Groh, M., Herman, L., & Leach, N. (2023). Art and the science of generative AI. Science, 380(6650), 1110-1111.
Fontana, M., Iori, M., Montobbio, F., & Sinatra, R. (2020). New and atypical combinations: An assessment of novelty and interdisciplinarity. Research Policy, 49(7), 104063.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. In: Sage publications Sage CA: Los Angeles, CA.
Fuentes, R., Gómez, J., Pavón, J., & Uden, L. (2004). Activity theory applied to requirements elicitation of multi-agent systems. ATIT 2004, 22.
Goertzel, B., & Pennachin, C. (2007). Artificial general intelligence (Vol. 2). Springer.
Gruetzemacher, R. (2022). The power of natural language processing. Harvard Business Review, 19.
Guy, E. S. (2005). "... real, concrete facts about what works..." integrating evaluation and design through patterns. Proceedings of the 2005 ACM International Conference on Supporting Group Work,
Hair, J. F., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis prentice hall. Upper Saddle River, NJ, 730.
Hall III, R. P. (2024). Generative Artificial Intelligence, Large Language Models, and JID Innovations. JID Innovations, 4(2).
Hsia, T.-L., Wu, J.-H., Xu, X., Li, Q., Peng, L., & Robinson, S. (2020). Omnichannel retailing: The role of situational involvement in facilitating consumer experiences. Information & Management, 57(8), 103390.
Huang, X., Estau, D., Liu, X., Yu, Y., Qin, J., & Li, Z. (2024). Evaluating the performance of ChatGPT in clinical pharmacy: a comparative study of ChatGPT and clinical pharmacists. British journal of clinical pharmacology, 90(1), 232-238.
Hulin, C. (2001). Can a reliability coefficient be too high? J Consum Psychol, 10(1–2), 55.
Ilʹenkov, Ė. d. V. e., & Creighton, H. C. (1977). Dialectical logic: Essays on its history and theory. (No Title).
Javaid, M., Haleem, A., & Singh, R. P. (2023). A study on ChatGPT for Industry 4.0: Background, potentials, challenges, and eventualities. Journal of Economy and Technology, 1, 127-143.
Jeon, J. (2021). Exploring AI chatbot affordances in the EFL classroom: Young learners’ experiences and perspectives. Computer Assisted Language Learning, 1-26.
Jepkemoi, B., Mulwa, P. K., & Mwanda, S. O. (2024). Influence of ChatGPT Affordances on Adaptive Learning Experiences among Undergraduate Religious Education Teacher Trainees at the University of Nairobi, Kenya. Canadian Journal of Educational and Social Studies, 4(1), 25-35.
Jiao, W., Wang, W., Huang, J.-t., Wang, X., Shi, S., & Tu, Z. (2023). Is ChatGPT a good translator? Yes with GPT-4 as the engine. arXiv preprint arXiv:2301.08745.
Kaptelinin, V., Nardi, B. A., & Macaulay, C. (1999). Methods & tools: The activity checklist: a tool for representing the “space” of context. interactions, 6(4), 27-39.
Khayyat, M. (2016). A proposed model for the fourth generation of activity theory to be applied on the smart city research. Thirty Seventh International Conference on Information Systems,
Kirinuki, H., & Tanno, H. (2024). ChatGPT and Human Synergy in Black-Box Testing: A Comparative Analysis. arXiv preprint arXiv:2401.13924.
Kissinger, H. A., Schmidt, E., & Huttenlocher, D. (2021). The age of AI: and our human future. Hachette UK.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1-10.
Konya-Baumbach, E., Biller, M., & von Janda, S. (2023). Someone out there? A study on the social presence of anthropomorphized chatbots. Computers in Human Behavior, 139, 107513.
Kuutti, K. (1996). Activity theory as a potential framework for human-computer interaction research. Context and consciousness: Activity theory and human-computer interaction, 1744, 9-22.
Kuutti, K. (1999). 22 Activity theory, transformation of work, and information systems design. Perspectives on activity theory, 360.
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28(4), 563-575.
Li, B., Bonk, C. J., & Kou, X. (2023). Exploring the Multilingual Applications of ChatGPT: Uncovering Language Learning Affordances in YouTuber Videos. International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), 13(1), 1-22.
Liu, J., Wang, C., & Liu, S. (2023). Utility of ChatGPT in clinical practice. Journal of Medical Internet Research, 25, e48568.
Ma, X., & Huo, Y. (2023). Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society, 75, 102362.
Mariani, M. M., Machado, I., & Nambisan, S. (2023). Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda. Journal of Business Research, 155, 113364.
Mattas, P. S. (2023). ChatGPT: A Study of AI Language Processing and its Implications. Journal homepage: www. ijrpr. com ISSN, 2582, 7421.
Mhlanga, D. (2023). Chatgpt in education: Exploring opportunities for emerging economies to improve education with chatgpt. Available at SSRN.
Mich, L. (2020). Artificial intelligence and machine learning. Handbook of e-Tourism, 1-21.
Mich, L., & Garigliano, R. (2023). ChatGPT for e-Tourism: a technological perspective. Information Technology & Tourism. https://doi.org/10.1007/s40558-023-00248-x
Mijwil, M., Aljanabi, M., & Ali, A. H. (2023). Chatgpt: Exploring the role of cybersecurity in the protection of medical information. Mesopotamian journal of cybersecurity, 2023, 18-21.
Mishra, M. (2023). As ChatGPT takes the internet by storm, here’s what its rivals are up to. The Economic Times.
Nevo, S., & Wade, M. (2011). Firm-level benefits of IT-enabled resources: A conceptual extension and an empirical assessment. The Journal of Strategic Information Systems, 20(4), 403-418.
Nevo, S., & Wade, M. R. (2010). The formation and value of IT-enabled resources: antecedents and consequences of synergistic relationships. MIS quarterly, 163-183.
Nunnally, J. C. (1978). An overview of psychological measurement. Clinical diagnosis of mental disorders: A handbook, 97-146.
O’Brien, H., & Cairns, P. (2015). An empirical evaluation of the User Engagement Scale (UES) in online news environments. Information Processing & Management, 51(4), 413-427.
Parchoma, G. (2014). The contested ontology of affordances: Implications for researching technological affordances for collaborative knowledge production. Computers in human behavior, 37, 360-368.
Park, J., & Han, S. H. (2013). Defining user value: A case study of a smartphone. International Journal of Industrial Ergonomics, 43(4), 274-282.
Paul, J., Ueno, A., & Dennis, C. (2023). ChatGPT and consumers: Benefits, pitfalls and future research agenda. In (Vol. 47, pp. 1213-1225): Wiley Online Library.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of management, 12(4), 531-544.
Quek, A., & Shah, H. (2004). The activity theoretical iterative evaluation method. ATIT 2004, 85.
Singh, K. (2011). Study of achievement motivation in relation to academic achievement of students. International Journal of Educational Planning & Administration, 1(2), 161-171.
Skjuve, M., Følstad, A., & Brandtzaeg, P. B. (2023). The user experience of ChatGPT: Findings from a questionnaire study of early users. Proceedings of the 5th International Conference on Conversational User Interfaces,
Southern, M. G. (2023). OpenAI's ChatGPT Update Brings Improved Accuracy. Search Engine Journal.
Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1-14.
Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2019). How live streaming influences purchase intentions in social commerce: An IT affordance perspective. Electronic commerce research and applications, 37, 100886.
Teubner, T., Flath, C. M., Weinhardt, C., van der Aalst, W., & Hinz, O. (2023). Welcome to the era of chatgpt et al. the prospects of large language models. Business & Information Systems Engineering, 65(2), 95-101.
Van Dis, E. A., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023). ChatGPT: five priorities for research. Nature, 614(7947), 224-226.
Van Noorden, R., & Perkel, J. M. (2023). AI and science: what 1,600 researchers think. Nature, 621(7980), 672-675.
Volkoff, O., & Strong, D. M. (2013). Critical realism and affordances: Theorizing IT-associated organizational change processes. MIS quarterly, 819-834.
Volkoff, O., Strong, D. M., & Elmes, M. B. (2007). Technological embeddedness and organizational change. Organization science, 18(5), 832-848.
Wang, F.-Y., Miao, Q., Li, X., Wang, X., & Lin, Y. (2023). What does ChatGPT say: The DAO from algorithmic intelligence to linguistic intelligence. IEEE/CAA Journal of Automatica Sinica, 10(3), 575-579.
Wells, J. D., Campbell, D. E., Valacich, J. S., & Featherman, M. (2010). The effect of perceived novelty on the adoption of information technology innovations: a risk/reward perspective. Decision Sciences, 41(4), 813-843.
White, J., Hays, S., Fu, Q., Spencer-Smith, J., & Schmidt, D. C. (2023). Chatgpt prompt patterns for improving code quality, refactoring, requirements elicitation, and software design. arXiv preprint arXiv:2303.07839.
Witt, U. (2009). Propositions about novelty. Journal of Economic Behavior & Organization, 70(1-2), 311-320.
Yan, R., & Gong, X. (2022). Peer-to-peer accommodation platform affordance: Scale development and empirical investigation. Journal of Business Research, 144, 922-938.