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題名 以自我決定論觀點討論員工使用生成式AI與其工作敬業及工作表現之關係
The Examination of the Relationship Between Employees' Use of Generative AI and Work Engagement and Job Performance from a Self-Determination Theory Perspective
作者 鮑姵文
Pao, Pei-Wen
貢獻者 胡昌亞
Hu, Chang-Ya
鮑姵文
Pao, Pei-Wen
關鍵詞 生成式人工智慧
自我決定理論
工作敬業
工作績效
Generative Artificial Intelligence
Self-Determination Theory
Work Engagement
Job Performance
日期 2025
上傳時間 4-Aug-2025 13:44:06 (UTC+8)
摘要 本研究探討生成式人工智慧於職場中之應用，是否可透過自我決定理論中 之心理需求滿足，進一步影響員工的工作敬業與績效，並使用272份具正職經驗之問卷資料進行實證研究。 研究結果指出：（1）生成式AI使用行為對工作績效具有直接正向影響，但 對工作敬業之直接效果不顯著；（2）自主滿足、能力滿足兩項心理需求皆具有顯著中介效果，能影響其敬業與績效表現；（3）生成式AI對工作敬業與績效的影響大多經由心理需求中介產生，顯示工具本身的效能需結合員工的心理感受方能發揮最大效益。 根據本研究結果，生成式AI對員工的正向影響需透過滿足員工在使用過程 中的心理需求，方能有效轉化為工作敬業與績效表現。因此，企業應在導入生成式AI時透過工作設計與支持機制提升員工的能力感，使AI成為促進動機與表現的媒介。未來研究亦可納入個體差異與組織情境作為調節因素，拓展模型解釋力。
This study investigates whether the use of generative artificial intelligence (GenAI) in the workplace influences employees’ work engagement and job performance through the satisfaction of psychological needs, based on Self Determination Theory. A total of 272 valid responses from full-time employees were analyzed. The findings reveal that: (1) GenAI use has a direct positive effect on job performance, but its direct impact on work engagement is not significant. (2) Autonomy and competence both play significant mediating roles, linking GenAI use to engagement and performance. (3) Most positive effects of GenAI occur through psychological mechanisms, highlighting the importance of employees’ internal experiences. These results suggest that organizations should enhance employees’ competence and autonomy through supportive job design to fully realize the benefits of GenAI. By enhancing employees’ sense of competence through thoughtful job design and support systems, AI can become a tool that motivates and empowers rather than one that merely automates.
參考文獻 Amazon Web Services. (2023)，生成式人工智能（GenAI）新世界：過去、現在 和未來.，https://aws.amazon.com/tw/local/hongkong/generative-ai/genai-blog- 100/，擷取日期：2025年5月19日。 Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534. Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at Work. The Quarterly Journal of Economics, 138(4), 2049-2087. Banfield, M. (2025). ‘Employees feel capable and connected’: The vital role played by good technology in job satisfaction. The Guardian. https://www.theguardian.com/the-digital-workspace- reimagined/2025/jun/27/why-good-tech-makes-employees-feel-capable-and-connected (accessed: June 29, 2025). Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014). Intrinsic motivation and extrinsic incentives jointly predict performance: A 40-year meta-analysis. Psychological Bulletin, 140(4), 980-1008. Crist, C. (2024). Early autonomy over AI can boost employee motivation, study says. HR Dive. https://www.hrdive.com/news/early-autonomy-over-ai-can- boost-employee-motivation/727349/ (accessed: June 29, 2025). Chong, X., Zhang, J., & Lee, C. (2025). The effect of job skill demands under AI embeddedness on well-being in organizations and job performance. International Journal of Environmental Research and Public Health, 22(1). Dwivedi, Y. K., Hughes, D. L., Kar, A. K., Baabdullah, A. M., Grover, P., Abbas, R., ... & Koohang, A. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 71, 102642. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268. Frese, M., & Sonnentag, S. (2002). Performance concepts and performance theory. In S. Sonnentag (Ed.), Psychological management of individual performance (1st ed., pp. 3-25). John Wiley & Sons Ltd. Gagné, M. (2003). The role of autonomy support and autonomy orientation in prosocial behavior engagement. Motivation and Emotion, 27(3), 199-223. Gagné, M., & Deci, E. L. (2005). Self‐determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331-362. Gagné, M., Parker, S. K., Griffin, M. A., & Ryan, R. M. (2022). Understanding and shaping the future of work with self-determination theory. Nature Reviews Psychology, 1(10), 648-662. Johnston, M. M., & Finney, S. J. (2010). Measuring basic needs satisfaction: Evaluating previous research and conducting new psychometric evaluations of the Basic Needs Satisfaction in General Scale. Contemporary Educational Psychology, 35(4), 280-296. Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work. Academy of Management Journal, 33(4), 692-724. Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., Schaufeli, W. B., de Vet, H. C. W., & van der Beek, A. J. (2013). Development of an individual work performance questionnaire. International Journal of Productivity and Performance Management, 62(1), 6-28. Lund, S., Manyika, J., Sanghvi, S., Dandona, G. S., Madgavkar, A., Chui, M., ... & Hasebe, P. (2023). Generative AI and the future of work in America. McKinsey Global Institute. https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america (accessed: June 20, 2025). Motowidlo, S. J., Borman, W. C., & Schmit, M. J. (1997). A theory of individual differences in task and contextual performance. Human Performance, 10(2), 71-83. McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/capabilities/mckinsey- digital/our-insights/the-economic-potential-of-generative-ai-the-next- productivity-frontier (accessed: June 3, 2025). Manyika, J., Smit, S., & Wu, D. (2025). Superagency in the workplace: Empowering people to unlock AI’s full potential at work. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our- insights/superagency-in-the-workplace (accessed: June 29, 2025). Prasad, K. D. V., & De, T. (2024). Generative AI as a catalyst for HRM practices: Mediating effects of trust. Humanities and Social Sciences Communications, 11(1), Article 1362. Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and Psychological Measurement, 66(4), 701-716. Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71-92. Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296. Van den Broeck, A., Ferris, D. L., Chang, C.-H., & Rosen, C. C. (2016). A review of self-determination theory’s basic psychological needs at work. Journal of Management, 42(5), 1195-1229. Van den Broeck, A., Vansteenkiste, M., De Witte, H., Soenens, B., & Lens, W. (2008). Capturing autonomy, competence, and relatedness at work: Construction and initial validation of the Work-related Basic Need Satisfaction scale. Journal of Occupational and Organizational Psychology, 83(4), 981-1002. White, R. W. (1959). Motivation reconsidered: The concept of com petence. Psychological Review, 66, 297-333.
描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
112363029
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112363029
資料類型 thesis
dc.contributor.advisor 胡昌亞zh_TW
dc.contributor.advisor Hu, Chang-Yaen_US
dc.contributor.author (Authors) 鮑姵文zh_TW
dc.contributor.author (Authors) Pao, Pei-Wenen_US
dc.creator (作者) 鮑姵文zh_TW
dc.creator (作者) Pao, Pei-Wenen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 13:44:06 (UTC+8)-
dc.date.available 4-Aug-2025 13:44:06 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 13:44:06 (UTC+8)-
dc.identifier (Other Identifiers) G0112363029en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158431-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 企業管理研究所(MBA學位學程)zh_TW
dc.description (描述) 112363029zh_TW
dc.description.abstract (摘要) 本研究探討生成式人工智慧於職場中之應用，是否可透過自我決定理論中 之心理需求滿足，進一步影響員工的工作敬業與績效，並使用272份具正職經驗之問卷資料進行實證研究。 研究結果指出：（1）生成式AI使用行為對工作績效具有直接正向影響，但 對工作敬業之直接效果不顯著；（2）自主滿足、能力滿足兩項心理需求皆具有顯著中介效果，能影響其敬業與績效表現；（3）生成式AI對工作敬業與績效的影響大多經由心理需求中介產生，顯示工具本身的效能需結合員工的心理感受方能發揮最大效益。 根據本研究結果，生成式AI對員工的正向影響需透過滿足員工在使用過程 中的心理需求，方能有效轉化為工作敬業與績效表現。因此，企業應在導入生成式AI時透過工作設計與支持機制提升員工的能力感，使AI成為促進動機與表現的媒介。未來研究亦可納入個體差異與組織情境作為調節因素，拓展模型解釋力。zh_TW
dc.description.abstract (摘要) This study investigates whether the use of generative artificial intelligence (GenAI) in the workplace influences employees’ work engagement and job performance through the satisfaction of psychological needs, based on Self Determination Theory. A total of 272 valid responses from full-time employees were analyzed. The findings reveal that: (1) GenAI use has a direct positive effect on job performance, but its direct impact on work engagement is not significant. (2) Autonomy and competence both play significant mediating roles, linking GenAI use to engagement and performance. (3) Most positive effects of GenAI occur through psychological mechanisms, highlighting the importance of employees’ internal experiences. These results suggest that organizations should enhance employees’ competence and autonomy through supportive job design to fully realize the benefits of GenAI. By enhancing employees’ sense of competence through thoughtful job design and support systems, AI can become a tool that motivates and empowers rather than one that merely automates.en_US
dc.description.tableofcontents 第一章 緒論 1 第一節 研究動機 1 第二節 研究目的 3 第二章 文獻探討 4 第一節 自我決定理論 4 第二節 工作敬業 7 第三節 工作績效 9 第三章 研究方法 12 第一節 研究架構與研究假設 12 第二節 研究對象 14 第三節 研究工具 15 第四章 研究結果 21 第一節 敘述統計 21 第二節 信度分析 25 第三節 中介分析 26 第五章 結論與建議 32 第一節 研究結論 32 第二節 管理意涵 34 參考文獻 37zh_TW
dc.format.extent 1231367 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112363029en_US
dc.subject (關鍵詞) 生成式人工智慧zh_TW
dc.subject (關鍵詞) 自我決定理論zh_TW
dc.subject (關鍵詞) 工作敬業zh_TW
dc.subject (關鍵詞) 工作績效zh_TW
dc.subject (關鍵詞) Generative Artificial Intelligenceen_US
dc.subject (關鍵詞) Self-Determination Theoryen_US
dc.subject (關鍵詞) Work Engagementen_US
dc.subject (關鍵詞) Job Performanceen_US
dc.title (題名) 以自我決定論觀點討論員工使用生成式AI與其工作敬業及工作表現之關係zh_TW
dc.title (題名) The Examination of the Relationship Between Employees' Use of Generative AI and Work Engagement and Job Performance from a Self-Determination Theory Perspectiveen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Amazon Web Services. (2023)，生成式人工智能（GenAI）新世界：過去、現在 和未來.，https://aws.amazon.com/tw/local/hongkong/generative-ai/genai-blog- 100/，擷取日期：2025年5月19日。 Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530-1534. Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at Work. The Quarterly Journal of Economics, 138(4), 2049-2087. Banfield, M. (2025). ‘Employees feel capable and connected’: The vital role played by good technology in job satisfaction. The Guardian. https://www.theguardian.com/the-digital-workspace- reimagined/2025/jun/27/why-good-tech-makes-employees-feel-capable-and-connected (accessed: June 29, 2025). Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014). Intrinsic motivation and extrinsic incentives jointly predict performance: A 40-year meta-analysis. Psychological Bulletin, 140(4), 980-1008. Crist, C. (2024). Early autonomy over AI can boost employee motivation, study says. HR Dive. https://www.hrdive.com/news/early-autonomy-over-ai-can- boost-employee-motivation/727349/ (accessed: June 29, 2025). Chong, X., Zhang, J., & Lee, C. (2025). The effect of job skill demands under AI embeddedness on well-being in organizations and job performance. International Journal of Environmental Research and Public Health, 22(1). Dwivedi, Y. K., Hughes, D. L., Kar, A. K., Baabdullah, A. M., Grover, P., Abbas, R., ... & Koohang, A. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 71, 102642. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268. Frese, M., & Sonnentag, S. (2002). Performance concepts and performance theory. In S. Sonnentag (Ed.), Psychological management of individual performance (1st ed., pp. 3-25). John Wiley & Sons Ltd. Gagné, M. (2003). The role of autonomy support and autonomy orientation in prosocial behavior engagement. Motivation and Emotion, 27(3), 199-223. Gagné, M., & Deci, E. L. (2005). Self‐determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331-362. Gagné, M., Parker, S. K., Griffin, M. A., & Ryan, R. M. (2022). Understanding and shaping the future of work with self-determination theory. Nature Reviews Psychology, 1(10), 648-662. Johnston, M. M., & Finney, S. J. (2010). Measuring basic needs satisfaction: Evaluating previous research and conducting new psychometric evaluations of the Basic Needs Satisfaction in General Scale. Contemporary Educational Psychology, 35(4), 280-296. Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work. Academy of Management Journal, 33(4), 692-724. Koopmans, L., Bernaards, C. M., Hildebrandt, V. H., Schaufeli, W. B., de Vet, H. C. W., & van der Beek, A. J. (2013). Development of an individual work performance questionnaire. International Journal of Productivity and Performance Management, 62(1), 6-28. Lund, S., Manyika, J., Sanghvi, S., Dandona, G. S., Madgavkar, A., Chui, M., ... & Hasebe, P. (2023). Generative AI and the future of work in America. McKinsey Global Institute. https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america (accessed: June 20, 2025). Motowidlo, S. J., Borman, W. C., & Schmit, M. J. (1997). A theory of individual differences in task and contextual performance. Human Performance, 10(2), 71-83. McKinsey & Company. (2023). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/capabilities/mckinsey- digital/our-insights/the-economic-potential-of-generative-ai-the-next- productivity-frontier (accessed: June 3, 2025). Manyika, J., Smit, S., & Wu, D. (2025). Superagency in the workplace: Empowering people to unlock AI’s full potential at work. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our- insights/superagency-in-the-workplace (accessed: June 29, 2025). Prasad, K. D. V., & De, T. (2024). Generative AI as a catalyst for HRM practices: Mediating effects of trust. Humanities and Social Sciences Communications, 11(1), Article 1362. Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and Psychological Measurement, 66(4), 701-716. Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71-92. Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296. Van den Broeck, A., Ferris, D. L., Chang, C.-H., & Rosen, C. C. (2016). A review of self-determination theory’s basic psychological needs at work. Journal of Management, 42(5), 1195-1229. Van den Broeck, A., Vansteenkiste, M., De Witte, H., Soenens, B., & Lens, W. (2008). Capturing autonomy, competence, and relatedness at work: Construction and initial validation of the Work-related Basic Need Satisfaction scale. Journal of Occupational and Organizational Psychology, 83(4), 981-1002. White, R. W. (1959). Motivation reconsidered: The concept of com petence. Psychological Review, 66, 297-333.zh_TW