圖像生成式AI的興起使其成為圖像創作者生活中不可或缺的工具，這種技術與使用者的產出績效密切相關，因此已引起本項研究探討，在使用圖像生成式AI的環境中哪些因素能促使使用者在使用圖像生成式AI後，可以獲得更好的能力與績效。
隨著這項技術不斷的優化且使用者越深入和依賴這項技術，有必要了解使用者在使用過程中，哪些因素會對績效產生影響。因此，借鑒系統理論，我們發展了一個理論模型，包括三個關鍵變數：使用者與圖像生成式AI的兼容性、圖像生成式AI的整合與投入和使用者與圖像生成式AI的綜效能力，以研究它們對使用者績效的影響。
本研究透過問卷調查法，並採便利抽樣及滾雪球抽樣經由社群平台發放問卷，最後搜集531份有效問卷來檢驗假說，結果證實：更完善的整合與投入可以提高兩者之間的兼容性來推動更大的綜效能力。最終將提高使用者的績效。此外，整合與投入可以通過提高兼容性和綜效能力來推動使用者績效。
我們的研究發現兼容性和綜效能力在調節整合與投入對使用者績效的影響有重要的作用。這些發現在實務面對圖像生成式AI的使用者以及圖像生成式AI的發展者有所貢獻。希望透過本研究有助於提供組織或是個人使用者未來在使用或導入相關技術時，有更多參考面向。

The rise of generative AI has made it an indispensable tool in the lives of image creators. This technology is closely related to user output performance, thus prompting this study to explore which factors in the environment of using generative AI can lead users to obtain better capabilities and performance.
As this technology continues to be optimized and users delve deeper and rely more on it, it becomes necessary to understand which factors will affect performance during use. Therefore, drawing on system theory, we developed a theoretical model consisting of three key variables: user compatibility with generative AI, integration and investment in generative AI, and user generative capability with generative AI, to investigate their impact on user performance.
This study conducted a questionnaire survey, utilizing convenience and snowball sampling to distribute surveys via community platforms, ultimately collecting 531 valid questionnaires to test hypotheses. The results confirm that improved integration and investment can enhance compatibility between the two, driving greater generative capability. Ultimately, this will enhance user performance. Furthermore, integration and investment can promote user performance by enhancing compatibility and generative capability. Our research found that compatibility and generative capability play important roles in moderating the impact of integration and investment on user performance. These findings contribute to practitioners using generative AI and developers of generative AI. It is hoped that this research will help provide organizations or individual users with more reference aspects for future use or implementation of related technologies.

論文審定書 i
摘要 ii
Abstract iii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第二章 文獻探討 5
第一節 人工智慧生成內容 5
第二節 系統理論 10
第三節 系統理論構念識別 12
第三章 研究模型與假說發展 19
第一節 研究模型 19
第二節 假說發展 20
第四章 研究方法 25
第一節 研究設計 25
第二節 研究量表發展 28
第三節 研究對象與問卷回收 34
第四節 資料分析 34
第五章 資料分析 36
第一節 敘述性統計 36
第二節 測量模型分析 40
第三節 模型驗證及結果 48
第六章 討論與結論 54
第一節 討論 54
第二節 研究貢獻 56
第三節 研究限制與未來研究方向 58
參考文獻 60
附錄一、專家效度問卷 67
附錄二、正式問卷 72
附錄三、交叉因素分析 79
附錄四、單因子檢定分析 80

章璟. (2023). 不只OpenAI！2023年必知13大生成式AI獨角獸. 遠見雜誌. https://www.gvm.com.tw/article/104947
Ackoff, R. L. (1971). Towards a system of systems concepts. Management science, 17(11), 661-671.
Ackoff, R. L., & Gharajedaghi, J. (2006). Designing a Replacement for the UN. In Volume 1: Rescuing the Enlightenment from Itself: Critical and Systemic Implications for Democracy (pp. 332-338). Springer.
Adobe. (2023). https://www.adobe.com/tw/sensei/generative-ai/firefly.html
Anderson, P. (1999). Perspective: Complexity theory and organization science. Organization science, 10(3), 216-232.
Aral, S., & Weill, P. (2007). IT assets, organizational capabilities, and firm performance: How resource allocations and organizational differences explain performance variation. Organization science, 18(5), 763-780.
Ashrafi, R., & Mueller, J. (2015). Delineating IT resources and capabilities to obtain competitive advantage and improve firm performance. Information Systems Management, 32(1), 15-38.
Bell, S. (2023). The write algorithm: promoting responsible artificial intelligence usage and accountability in academic writing. BMC medicine, 21(1), 334.
Boateng, H., Adam, D. R., Okoe, A. F., & Anning-Dorson, T. (2016). Assessing the determinants of internet banking adoption intentions: A social cognitive theory perspective. Computers in Human Behavior, 65, 468-478.
Boudreau, M.-C., & Robey, D. (2005). Enacting integrated information technology: A human agency perspective. Organization science, 16(1), 3-18.
Brown, S. L., & Eisenhardt, K. M. (1995). Product development: Past research, present findings, and future directions. Academy of management review, 20(2), 343-378.
Canva. (2023). https://www.canva.com/zh_tw/help/ai-image-generation-apps/
Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., & Zhang, C. (2022). Quantifying memorization across neural language models. arXiv preprint arXiv:2202.07646.
Chen, Y., Wang, Y., Nevo, S., Benitez-Amado, J., & Kou, G. (2015). IT capabilities and product innovation performance: The roles of corporate entrepreneurship and competitive intensity. Information & Management, 52(6), 643-657.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Churchman, C. W. (1971). On the facility, felicity, and morality of measuring social change. The Accounting Review, 46(1), 30-35.
Corning, P. A. (1995). Synergy and self‐organization in the evolution of complex systems. Systems Research, 12(2), 89-121.
Cronbach, L. J. (1947). Test “reliability”: Its meaning and determination. Psychometrika, 12(1), 1-16.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management information systems, 19(4), 9-30.
DeSanctis, G., & Jackson, B. M. (1994). Coordination of information technology management: Team based structures and computer based communication systems. Journal of Management information systems, 10(4), 85-110.
Dooley, K. J. (1997). A complex adaptive systems model of organization change. Nonlinear dynamics, psychology, and life sciences, 1, 69-97.
Eggmann, F., Weiger, R., Zitzmann, N. U., & Blatz, M. B. (2023). Implications of large language models such as ChatGPT for dental medicine. Journal of Esthetic and Restorative Dentistry, 35(7), 1098-1102.
Elkins, K., & Chun, J. (2020). Can GPT-3 pass a writer’s Turing test? Journal of Cultural Analytics, 5(2).
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123.
Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840-6851.
Huber, G. P. (1984). The nature and design of post-industrial organizations. Management science, 30(8), 928-951.
Hulin, C. (2001). Can a reliability coefficient be too high? J Consum Psychol, 10(1–2), 55.
Hunt, K. M., Matthews, G. R., Pappenberger, F., & Prudhomme, C. (2022). Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States. Hydrology and Earth System Sciences, 26(21), 5449-5472.
Josh Howarth. (2024). Generative AI Market Size & Trends Statistics (2024-2026). explodingtopics. https://explodingtopics.com/blog/generative-ai-market
Kast, F. E., & Rosenzweig, J. E. (1972). General systems theory: Applications for organization and management. Academy of management journal, 15(4), 447-465.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1-10.
Kostagiolas, P., Korfiatis, N., Kourouthanasis, P., & Alexias, G. (2014). Work-related factors influencing doctors search behaviors and trust toward medical information resources. International Journal of Information Management, 34(2), 80-88.
Larsson, R., & Finkelstein, S. (1999). Integrating strategic, organizational, and human resource perspectives on mergers and acquisitions: A case survey of synergy realization. Organization science, 10(1), 1-26.
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28(4), 563-575.
Lewin, A. Y., Long, C. P., & Carroll, T. N. (1999). The coevolution of new organizational forms. Organization science, 10(5), 535-550.
Li, Z., Xia, B., Zhang, J., Wang, C., & Li, B. (2022). A comprehensive survey on data-efficient GANs in image generation. arXiv preprint arXiv:2204.08329.
Lin, T.-C., & Huang, C.-C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45(6), 410-417.
Maerten, A.-S., & Soydaner, D. (2023). From paintbrush to pixel: A review of deep neural networks in AI-generated art. arXiv preprint arXiv:2302.10913.
Markus, M. L. (2004). Technochange management: using IT to drive organizational change. Journal of Information technology, 19, 4-20.
Markus, M. L., & Robey, D. (1988). Information technology and organizational change: Causal structure in theory and research. Management science, 34(5), 583-598.
Mettler, S. (2016). The policyscape and the challenges of contemporary politics to policy maintenance. Perspectives on Politics, 14(2), 369-390.
Microsoft. (2024). https://www.bing.com/images/create
Miles, I. (2008). Patterns of innovation in service industries. IBM Systems journal, 47(1), 115-128.
Mitroff, I. I., Mason, R. O., & Pearson, C. M. (1994). Radical surgery: what will tomorrow's organizations look like? Academy of Management Perspectives, 8(2), 11-21.
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.
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.
Nielsen, B. B. (2005). The role of knowledge embeddedness in the creation of synergies in strategic alliances. Journal of business research, 58(9), 1194-1204.
Nunnally, J. C. (1978). An overview of psychological measurement. Clinical diagnosis of mental disorders: A handbook, 97-146.
Oke, A., Burke, G., & Myers, A. (2007). Innovation types and performance in growing UK SMEs. International Journal of Operations & Production Management, 27(7), 735-753.
OpenAI. (2023). https://openai.com/dall-e-3
Oppenlaender, J. (2022). The creativity of text-to-image generation. Proceedings of the 25th International Academic Mindtrek Conference,
Orlikowski, W. J., & Hofman, J. D. (1997). An improvisational model for change management: The case of groupware technologies. MIT Sloan Management Review.
Orr, G. (2003). Diffusion of innovations, by Everett Rogers (1995). Retrieved January, 21, 2005.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of management, 12(4), 531-544.
Reynolds, L., & McDonell, K. (2021). Prompt programming for large language models: Beyond the few-shot paradigm. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems,
Robey, D., & Boudreau, M.-C. (1999). Accounting for the contradictory organizational consequences of information technology: Theoretical directions and methodological implications. Information systems research, 10(2), 167-185.
Sarkar, M. B., Echambadi, R., Cavusgil, S. T., & Aulakh, P. S. (2001). The influence of complementarity, compatibility, and relationship capital on alliance performance. Journal of the academy of marketing science, 29(4), 358-373.
Schneider, M., & Somers, M. (2006). Organizations as complex adaptive systems: Implications of complexity theory for leadership research. The Leadership Quarterly, 17(4), 351-365.
Shenkar, O., & Li, J. (1999). Knowledge search in international cooperative ventures. Organization science, 10(2), 134-143.
Shutterstock. (2023). https://www.shutterstock.com/zh-Hant
Sirisha. (2022). Illustrators are Out of Job, Thanks to Google’s Imagen and OpenAI’s Dall-E 2. Analyticsinsight. Retrieved 06/12 from https://www.analyticsinsight.net/illustrators-are-out-of-job-thanks-to-googles-imagen-and-openais-dall-e-2/
Slotegraaf, R. J., & Dickson, P. R. (2004). The paradox of a marketing planning capability. Journal of the academy of marketing science, 32(4), 371-385.
Straub, D. W., & Watson, R. T. (2001). Research commentary: Transformational issues in researching IS and net-enabled organizations. Information systems research, 12(4), 337-345.
Tang, H. (1998). An integrative model of innovation in organizations. Technovation, 18(5), 297-309.
Wu, J.-H., Kao, H.-Y., & Sambamurthy, V. (2016). The integration effort and E-health compatibility effect and the mediating role of E-health synergy on hospital performance. International Journal of Information Management, 36(6), 1288-1300.
Yen-Chun Lin. (2020). Exploring Trust Transfer and Status Quo Bias on the Usage Intention of Internet-Only Bank. https://hdl.handle.net/11296/c295qp
Zhang, N., Li, L., Chen, X., Deng, S., Bi, Z., Tan, C., Huang, F., & Chen, H. (2021). Differentiable prompt makes pre-trained language models better few-shot learners. arXiv preprint arXiv:2108.13161.