本論文聚焦於科技產業從業人員在公司已批准使用 ChatGPT 的情境下，影響其採用該工具作為生產力提升工具的因素。研究結合「任務-技術適配模型」、「技術接受模型」與「創新擴散理論」，分析 ChatGPT 的採用對工作效率的影響，並透過 SPSS 和AMOS 軟體進行數據分析，探討 ChatGPT 的適配性與工作效率提升之間的關聯性。
研究針對 510 名受試者的結果顯示，科技產業從業人員採用 ChatGPT 的意圖與其感知的有用性、易用性及與任務的適配性密切相關。將 ChatGPT 整合進日常工作流程，不僅能支援並簡化任務，還能有效提升工作效率。研究強調組織支持與技術適配性在促進 ChatGPT 採用及提升工作效率方面扮演關鍵角色。
本研究為 AI 在 IT 產業中的應用提供了重要洞見，也為企業如何運用 ChatGPT 提升員工生產力提出具體建議。同時，研究期望能促進企業對 AI 作為生產力工具的正向態度與更高接受度。

This thesis explores the factors influencing tech professionals' intention to adopt ChatGPT as a productivity tool in their workplaces where their companies have approved its integration. By employing a model that integrates Task-Technology Fit, the Technology Acceptance Model, and the Diffusion of Innovations framework, the study examines the impact of ChatGPT adoption on work efficiency. The data analysis, performed using SPSS AMOS software, explores the relationships between the perceived fit of ChatGPT adoption and the resulting improvements in work efficiency.
The results of the 510 sampling subjects indicate that tech professionals' intention to adopt ChatGPT is strongly linked to perceived usefulness, ease of use, and task alignment. Additionally, incorporating ChatGPT into daily workflows will enhance work efficiency by supporting and streamlining tasks. The study highlights the significance of organizational support and technological alignment in fostering the adoption of ChatGPT and improving work efficiency.
This research delivers significant insights into how AI is adopted within the IT industry and presents practical advice for organizations seeking to utilize ChatGPT to improve workforce productivity. Additionally, it aims to foster a positive attitude towards using AI as a productivity tool in enterprise settings.

Abdalla, A. A., Bhat, M. A., Tiwari, C. K., Khan, S. T., & Wedajo, A. D. (2024). Exploring chatGPT adoption among business and management students through the lens of diffusion of innovation theory. Computers and Education: Artificial Intelligence, 7(3), 9.
Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), 1166.
Armonk, N. Y. (2024). IBM Global AI Adoption Index 2023. Retrieved from {https://newsroom.ibm.com/2024-01-10-Data-Suggests-Growth-in-Enterprise-Adoption-of-AI-is-Due-to-Widespread-Deployment-by-Early-Adopters}. {https://newsroom.ibm.com/2024-01-10-Data-Suggests-Growth-in-Enterprise-Adoption-of-AI-is-Due-to-Widespread-Deployment-by-Early-Adopters}
Aydın, Ö., & Karaarslan, E. (2023). Is ChatGPT leading generative AI? What is beyond expectations? Academic Platform Journal of Engineering and Smart Systems, 11(3), 118-134.
Balouchi, S., & Samad, A. A. (2021). No more excuses, learn English for free: Factors affecting L2 learners intention to use online technology for informal English learning. Education and Information Technologies, 26(1), 1111-1132.
Bhargava, A., Bester, M., & Bolton, L. (2021). Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation (RAIA) on job satisfaction, job security, and employability. Journal of Technology in Behavioral Science, 6(1), 106-113.
Chandra, S., & Kumar, K. N. (2018). Exploring factors influencing organizational adoption of augmented reality in e-commerce: Empirical analysis using technology-organization-environment model. Journal of electronic commerce research, 19(3), 23.
Cheng, T. E., Lam, D. Y., & Yeung, A. C. (2006). Adoption of internet banking: An empirical study in Hong Kong. Decision support systems, 42(3), 1558-1572.
Chuma, E. L., & De Oliveira, G. G. (2023). Generative AI for business decision-making: A case of ChatGPT. Management Science and Business Decisions, 3(1), 5-11.
Combemale, B., Gray, J., & Rumpe, B. (2023). ChatGPT in software modeling. Software and Systems Modeling, 22(3), 777-779.
Coursaris, C. K., Hassanein, K., Head, M. M., & Bontis, N. (2012). The impact of distractions on the usability and intention to use mobile devices for wireless data services. Computers in Human Behavior, 28(4), 1439-1449.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319.
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.
Dishaw, M., & Strong, D. (1998). Experience as a moderating variable in a task-technology fit model. AMCIS 1998 Proceedings, 242.
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & management, 36(1), 9-21.
Ebert, C., & Louridas, P. (2023). Generative AI for software practitioners. IEEE Software, 40(4), 30-38.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 19(2), 213.
Hayes, A. F. (2012). PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling. In: University of Kansas, KS.
Howard, M. C., & Rose, J. C. (2019). Refining and extending task–technology fit theory: Creation of two task–technology fit scales and empirical clarification of the construct. Information & management, 56(6), 103134.
Kaminski, J. (2011). Diffusion of innovation theory. Canadian Journal of Nursing Informatics, 6(2), 1-6.
Kumar Bhardwaj, A., Garg, A., & Gajpal, Y. (2021). Determinants of blockchain technology adoption in supply chains by small and medium enterprises (SMEs) in India. Mathematical Problems in Engineering, 2021(1), 5.
Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers and Education: Artificial Intelligence, 5(0), 10.
Letourneau-Guillon, L., Camirand, D., Guilbert, F., & Forghani, R. (2020). Artificial intelligence applications for workflow, process optimization and predictive analytics. Neuroimaging Clinics, 30(4), e1-e15.
Liu, D., & Cao, J. (2022). Determinants of collaborative robots innovation adoption in small and medium-sized enterprises: An empirical study in China. Applied Sciences, 12(19), 10.
Lu, H.-P., & Yang, Y.-W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34(0), 323-332.
Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017). A future that works: AI, automation, employment, and productivity. McKinsey Global Institute Research, Tech. Rep, 60(0), 1-135.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal access in the information society, 14(0), 81-95.
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.
Mukhopadhyay, S. (2023). Chat GPT adoption in cultural domains in India: A diffusion of innovation analysis. Asian Journal of Management and Commerce, 4(2), 112-117.
Raman, R., Mandal, S., Das, P., Kaur, T., Sanjanasri, J., & Nedungadi, P. (2024). University students as early adopters of ChatGPT: Innovation Diffusion Study. Human Behavior and Emerging Technologies, 2024(1), 6.
Retkowsky, J., Hafermalz, E., & Huysman, M. (2024). Managing a ChatGPT-empowered workforce: Understanding its affordances and side effects. Business Horizons, 62(5), 511.
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations (Vol. 0): Routledge.
Roussou, I., & Stiakakis, E. (2016). Adoption of digital currencies by companies in the European Union: a research model combining DOI and TAM. Paper presented at the 4th International Conference on Contemporary Marketing Issues (ICCMI).
Solomovich, L., & Abraham, V. (2024). Exploring the influence of ChatGPT on tourism behavior using the technology acceptance model. Tourism Review, 1(0), 12.
Soon, K. W. K., Lee, C. A., & Boursier, P. (2016). A study of the determinants affecting adoption of big data using integrated Technology Acceptance Model (TAM) and diffusion of innovation (DOI) in Malaysia. International journal of applied business and economic research, 14(1), 17-47.
Sturm, T., & Peters, F. (2020). The impact of artificial intelligence on individual performance: Exploring the fit between task, data, and technology. Paper presented at the In Proceedings of the 28th European Conference on Information Systems (ECIS), An Online AIS Conference.
Taherdoost, H. (2016). Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in a research. International Journal of Academic Research in Management (IJARM), 5(3), 28.
Thormundsson, B. (2024). Generative artificial intelligence (AI) - statistics & facts. Retrieved from https://www.statista.com/topics/10408/generative-artificial-intelligence/#topicOverview. https://www.statista.com/topics/10408/generative-artificial-intelligence/#topicOverview
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
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, 36(1), 157.
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.-L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136.
Zigurs, I., & Buckland, B. K. (1998). A theory of task/technology fit and group support systems effectiveness. MIS quarterly, 22(3), 313.