English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 116849/147881 (79%)
Visitors : 63916445      Online Users : 243
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/157741


    Title: 人工智慧意識對人工智慧自我效能之影響:以壓力認知評估及人智協作為中介變項
    The Impact of AI Awareness on AI Self-Efficacy: the Mediating Roles of Stress Appraisal and Human-AI Collaboration
    Authors: 陳羿熹
    Chen, Yi-Hsi
    Contributors: 陳建維
    陳羿熹
    Chen, Yi-Hsi
    Keywords: 人工智慧意識
    挑戰性評估
    阻礙性評估
    人智協作
    人工智慧自我效能
    AI Awareness
    Challenge Appraisal
    Hindrance Appraisal
    Human-AI Collaboration
    AI Self-Efficacy
    Date: 2025
    Issue Date: 2025-07-01 14:38:19 (UTC+8)
    Abstract: 隨著人工智慧(Artificial Intelligence, AI)技術迅速發展並廣泛應用於各產業 領域,個體面對人工智慧將改變工作與職涯的不確定性也日益加劇。本研究旨在探 討個體在感受到人工智慧可能影響職涯發展的情境下,所產生的心理認知與行為反 應,並進一步分析這些歷程如何影響其與人工智慧之間的互動與協作關係。研究以人工智慧意識為出發點,衡量個體對於人工智慧可能影響職涯發展的看法,並以「挑戰-阻礙壓力模型(Challenge-hindrance Stressor Framework)」為基礎,將人工智慧意識作為壓力源區分為「挑戰性評估」與「阻礙性評估」,並探討其在人智協作與人工智慧自我效能之間所扮演的中介角色。
    本研究採用量化問卷調查法,回收有效樣本共 299 份,並透過迴歸分析與中介效果檢驗進行統計驗證。研究結果顯示,人工智慧意識會正向影響挑戰性評估、負向影響阻礙性評估;挑戰性評估與人智協作對人工智慧自我效能具正向影響,而阻礙性評估具負向影響;挑戰性評估與阻礙性評估在人工智慧意識與人智協作中皆為部分中介;人智協作在挑戰性評估與人工智慧自我效能間具有完全中介效果,在阻礙性評估中則為部分中介。
    本研究結果不僅有助於理解個體面對技術變遷時的心理歷程與行為模式,也提供企業在推動人工智慧導入、員工培訓與組織文化塑造上之實務建議,協助組織與員工共同因應人工智慧時代的挑戰與轉型。
    With the rapid advancement and widespread application of Artificial Intelligence (AI) across industries, individuals face increasing uncertainty about its impact on their careers. This study explores how AI awareness influences individuals’ psychological perceptions and behavioral responses in the workplace, focusing on their collaboration with AI systems and resulting self-efficacy.
    Based on the Challenge-Hindrance Stressor Framework, the study categorizes AI awareness into challenge and hindrance appraisals and examines their mediating roles between AI awareness, human-AI collaboration, and AI self-efficacy. Using a survey of 299 participants and regression analysis, results show that AI awareness positively affects challenge appraisals and negatively affects hindrance appraisals. Challenge appraisals and human-AI collaboration enhance AI self-efficacy, while hindrance appraisals reduce it. Challenge and hindrance appraisals partially mediate the relationship between AI awareness and human-AI collaboration. Additionally, human- AI collaboration fully mediates the effect of challenge appraisals on AI self-efficacy and partially mediates the effect of hindrance appraisals.
    The findings provide insights into how individuals adapt to AI-driven change and offer practical guidance for organizations in AI implementation, employee development, and cultural transformation.
    Reference: 1. Aryee, S., & Tan, K. (1992). Antecedents and outcomes of career commitment. Journal of vocational Behavior, 40(3), 288-305.
    2. Autor, D., Mindell, D., & Reynolds, E. (2022). Why ‘the future of AI is the future of work’
    3. Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
    4. Bandura, A. (1994). Social cognitive theory and exercise of control over HIV infection. In Preventing AIDS: Theories and methods of behavioral interventions (pp. 25-59). Boston, MA: Springer US.
    5. Bandura, A., & Wessels, S. (1997). Self-efficacy (pp. 4-6). Cambridge: Cambridge University Press.
    6. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.
    7. Başer, M. Y., Büyükbeşe, T., & Ivanov, S. (2025). The effect of STARA awareness on hotel employees' turnover intention and work engagement: the mediating role of perceived organisational support. Journal of Hospitality and Tourism Insights, 8(2), 532-552.
    8. Bessen, J. (2019). Automation and jobs: When technology boosts employment. Economic Policy, 34(100), 589-626.
    9. Bloss, R. (2011). Mobile hospital robots cure numerous logistic needs. Industrial Robot: An International Journal, 38(6), 567-571.
    10. Boswell, W. R., Olson-Buchanan, J. B., & LePine, M. A. (2004). Relations between stress and work outcomes: The role of felt challenge, job control, and psychological strain. Journal of Vocational Behavior, 64(1), 165-181.
    11. Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257.
    12. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & company.
    13. Cao, S., Gomez, C., & Huang, C. M. (2023). How time pressure in different phases of Decision-Making influences Human-AI collaboration. Proceedings of the ACM on Human-computer Interaction, 7(CSCW2), 1-26.
    14. Cavanaugh, M. A., Boswell, W. R., Roehling, M. V., & Boudreau, J. W. (2000). An empirical examination of self-reported work stress among US managers. Journal of applied psychology, 85(1), 65.
    15. Crawford, E. R., LePine, J. A., & Rich, B. L. (2010). Linking job demands and resources to employee engagement and burnout: a theoretical extension and meta- analytic test. Journal of applied psychology, 95(5), 834.
    16. Darvishmotevali, M., Arasli, H., & Kilic, H. (2017). Effect of job insecurity on frontline employee’s performance: Looking through the lens of psychological strains and leverages. International Journal of Contemporary Hospitality Management, 29(6), 1724-1744.
    17. Davenport, T. H., & Kirby, J. (2016). Just how smart are smart machines?. MIT Sloan Management Review, 57(3), 21.
    18. Ding, L. (2021). Employees’ challenge-hindrance appraisals toward STARA awareness and competitive productivity: a micro-level case. International Journal of Contemporary Hospitality Management, 33(9), 2950-2969.
    19. Ding, L. (2022). Employees’ STARA awareness and innovative work behavioural intentions: Evidence from US casual dining restaurants. In Global strategic management in the service industry: A perspective of the new era (pp. 17-56). Emerald Publishing Limited.
    20. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994.
    21. Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62-70.
    22. Frey, C. B., & Osborne, M. (2013). The future of employment.
    23. Frymier, A. B., Shulman, G. M., & Houser, M. (1996). The development of a learner empowerment measure. Communication education, 45(3), 181-199.
    24. Gao, R. X., Krüger, J., Merklein, M., Möhring, H. C., & Váncza, J. (2024). Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions. CIRP Annals. Russell 與 Norvig(2021)
    25. Gilboa, S., Shirom, A., Fried, Y., & Cooper, C. (2008). A meta-analysis of work demand stressors and job performance: examining main and moderating effects. Personnel psychology, 61(2), 227-271.
    26. Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of management annals, 14(2), 627-660.
    27. Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda✰. Technological Forecasting and Social Change, 162, 120392.
    28. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), 5-14.
    29. Hasan, B. (2007). Examining the effects of computer self-efficacy and system complexity on technology acceptance. Information Resources Management Journal (IRMJ), 20(3), 76-88.
    30. Hodges, C. B., & Murphy, P. F. (2009). Sources of self-efficacy beliefs of students in a technology-intensive asynchronous college algebra course. The Internet and Higher Education, 12(2), 93-97.
    31. Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of research on technology in education, 43(4), 343-367.
    32. Hong, J. W. (2022). I was born to love AI: The influence of social status on AI self-efficacy and intentions to use AI. International Journal of Communication, 16, 20.
    33. Hur, W. M., & Shin, Y. (2024). Service employees’ STARA awareness and proactive service performance. Journal of Services Marketing, 38(4), 426-442.
    34. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
    35. Jia, N., Luo, X., Fang, Z., & Liao, C. (2023). When and how artificial intelligence augments employee creativity. Academy of Management Journal, 67(1), 5-32.
    36. Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., & Siemens, G. (2023). Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education: Artificial Intelligence, 4, 100138.
    37. Kang, D. Y., Hur, W. M., & Shin, Y. (2023). Smart technology and service employees’ job crafting: Relationship between STARA awareness, performance pressure, receiving and giving help, and job crafting. Journal of Retailing and Consumer Services, 73, 103282.
    38. Karatepe, O.M., Rezapouraghdam, H. and Hassannia, R. (2020), “Job insecurity, work engagement and their effects on hotel employees’ non-green and nonattendance behaviors”, International Journal of Hospitality Management, Vol. 87, p. 102472.
    39. Kong, H., Yin, Z., Baruch, Y., & Yuan, Y. (2023). The impact of trust in AI on career sustainability: The role of employee–AI collaboration and protean career orientation. Journal of Vocational Behavior, 146, 103928.
    40. Kong, H., Yin, Z., Chon, K., Yuan, Y., & Yu, J. (2024). How does artificial intelligence (AI) enhance hospitality employee innovation? The roles of exploration, AI trust, and proactive personality. Journal of Hospitality Marketing & Management, 33(3), 261-287.
    41. Kong, H., Yuan, Y., Baruch, Y., Bu, N., Jiang, X., & Wang, K. (2021). Influences of artificial intelligence (AI) awareness on career competency and job burnout. International Journal of Contemporary Hospitality Management, 33(2), 717-734.
    42. Koo, B., Curtis, C., & Ryan, B. (2021). Examining the impact of artificial intelligence on hotel employees through job insecurity perspectives. International Journal of Hospitality Management, 95, 102763.
    43. Lazarus, R. S. (1984). Stress, appraisal, and coping (Vol. 464). Springer.
    44. Lee, H. J., Probst, T. M., Bazzoli, A., & Lee, S. (2022). Technology advancements and employees’ qualitative job insecurity in the Republic of Korea: Does training help? Employer-provided vs. self-paid training. International Journal of Environmental Research and Public Health, 19(21), 14368.
    45. Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human factors, 46(1), 50-80.
    46. Liang, H., Tsui, B. Y., Ni, H., Valentim, C. C., Baxter, S. L., Liu, G., ... & Xia, H. (2019). Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nature medicine, 25(3), 433-438.
    47. Lorentziadis, M. L. (2014). A short history of the invasion of robots in surgery. Hellenic Journal of Surgery, 86(3), 117–121.
    48. Marinova, D., de Ruyter, K., Huang, M. H., Meuter, M. L., & Challagalla, G. (2017). Getting smart: Learning from technology-empowered frontline interactions. Journal of Service Research, 20(1), 29-42.
    49. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
    50. Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33(1), 100857.
    51. Podsakoff, N. P., LePine, J. A., & LePine, M. A. (2007). Differential challenge stressor-hindrance stressor relationships with job attitudes, turnover intentions, turnover, and withdrawal behavior: a meta-analysis. Journal of applied psychology, 92(2), 438.
    52. Repenning, N.P. (2000), “Drive out fear (unless you can drive it in): the role of agency and job security in process improvement”, Management Science, Vol. 46 No. 11, pp. 1385-1396.
    53. Rigotti, T., Mohr, G., & Isaksson, K. (2015). Job insecurity among temporary workers: Looking through the gender lens. Economic and Industrial Democracy, 36(3), 523-547.
    54. Russell, S., Norvig, P., Popineau, F., Miclet, L., & Cadet, C. (2021). Intelligence artificielle: une approche moderne (4e édition). Pearson France.
    55. Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66-83.
    56. Spreitzer, G. M. (1995). An empirical test of a comprehensive model of intrapersonal empowerment in the workplace. American journal of community psychology, 23(5), 601-629.
    57. Staufenbiel, T., & König, C. J. (2010). A model for the effects of job insecurity on performance, turnover intention, and absenteeism. Journal of occupational and organizational psychology, 83(1), 101-117.
    58. Tan, K. L., Gim, G. C., Hii, I. S., & Zhu, W. (2024). STARA fight or flight: a two- wave time-lagged study of challenge and hindrance appraisal of STARA awareness on basic psychological needs and individual competitiveness productivity among hospitality employees. Current Issues in Tourism, 27(13), 2151-2169.
    59. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
    60. Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404- 409.
    61. Vössing, M., Kühl, N., Lind, M., & Satzger, G. (2022). Designing transparency for effective human-AI collaboration. Information Systems Frontiers, 24(3), 877-895.
    62. Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2023). Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. Artificial intelligence and international HRM, 172-201.
    63. Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance education, 34(3), 302-323.
    64. Wang, H. J., Lu, C. Q., & Siu, O. L. (2015). Job insecurity and job performance: The moderating role of organizational justice and the mediating role of work engagement. Journal of applied psychology, 100(4), 1249.
    65. Webster, J. R., Beehr, T. A., & Love, K. (2011). Extending the challenge-hindrance model of occupational stress: The role of appraisal. Journal of Vocational Behavior, 79(2), 505-516.
    66. Weng, Y., Wu, J., Kelly, T., & Johnson, W. (2024). Comprehensive overview of artificial intelligence applications in modern industries. arXiv preprint arXiv:2409.13059.
    67. Zhao, X., Deng, Y., Yang, M., Wang, L., Zhang, R., Cheng, H., ... & Xu, R. (2023). A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers. arXiv preprint arXiv:2306.02051.
    68. Zimmerman, B. J., Bandura, A., & Martinez-Pons, M. (1992). Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American educational research journal, 29(3), 663-676.
    Description: 碩士
    國立政治大學
    國際經營與貿易學系
    112351025
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112351025
    Data Type: thesis
    Appears in Collections:[國際經營與貿易學系 ] 學位論文

    Files in This Item:

    File Description SizeFormat
    102501.pdf1315KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback