AI接受度对员工心理状态影响的双刃剑效应研究
A Double-Edged Sword Effect Study on the Influence of AI Acceptance on Employees’ Psychological State
DOI: 10.12677/ecl.2025.143853, PDF,   
作者: 辛乾民：浙江理工大学经济管理学院，浙江 杭州；郭 晗：浙江理工大学科技与艺术学院，浙江 杭州
关键词: AI接受度；挑战–阻碍压力源框架；工作繁荣感；工作倦怠感；AI Acceptance； Challenge - Blocking Stressor Framework； Job Prosperity； Job Burnout
摘要: 人工智能作为社会科学技术进步的产物，已经成为新一轮产业变革与发展的关键推动力量。人工智能的不断发展对世界经济发展，社会进步和人民幸福生活产生了重要影响。在组织层面，已有研究探讨AI技术的应用对企业绩效的影响，但是基于个人层面，对员工的影响研究较少。为研究AI接受度对员工心理状态的影响机制，本文构建了双刃剑效应框架，以224位企业员工为调查对象进行调查，结果发现：(1) AI技术接受程度对员工工作繁荣感具有正向影响，对员工工作倦怠感具有负向影响；(2) 挑战感知和阻碍感知对AI技术接受程度对员工工作繁荣感和工作倦怠影响的中介效应显著。
Abstract: As a product of social science and technology progress, artificial intelligence has become a key driving force for a new round of industrial change and development. The continuous development of artificial intelligence has had an important impact on world economic development, social progress and people’s happy life. At the organizational level, there have been studies on the impact of the application of AI technology on corporate performance, but at the individual level, there are few studies on the impact on employees. In order to study the influence mechanism of AI acceptance on employees’ psychological state, a double-edged sword effect framework was constructed, and 224 employees were surveyed. The results showed that: (1) the acceptance of AI technology had a positive impact on employees’ sense of job prosperity, and a negative impact on employees’ sense of job burnout; (2) the mediating effect of challenge perception and obstacle perception on the acceptance degree of AI technology on employees’ sense of job prosperity and job burnout is significant.
文章引用：辛乾民, 郭晗. AI接受度对员工心理状态影响的双刃剑效应研究[J]. 电子商务评论, 2025, 14(3): 1553-1562. https://doi.org/10.12677/ecl.2025.143853

参考文献

[1] 彭剑锋. 新一代人工智能对组织与人力资源管理的影响与挑战[J]. 中国人力资源开发, 2023, 40(7): 8-14.
[2] Charlwood, A. and Guenole, N. (2022) Can HR Adapt to the Paradoxes of Artificial Intelligence? Human Resource Management Journal, 32, 729-742. [Google Scholar] [CrossRef] 
[3] van den Broek, E., Sergeeva, A. and Huysman Vrije, M. (2021) When the Machine Meets the Expert: An Ethnography of Developing AI for Hiring. MIS Quarterly, 45, 1557-1580. [Google Scholar] [CrossRef] 
[4] Zirar, A., Ali, S.I. and Islam, N. (2023) Worker and Workplace Artificial Intelligence (AI) Coexistence: Emerging Themes and Research Agenda. Technovation, 124, Article ID: 102747. [Google Scholar] [CrossRef] 
[5] 崔雍浩, 商聪, 陈锶奇, 等. 人工智能综述:AI的发展[J]. 无线电通信技术, 2019, 45(3): 225-231.
[6] Yoon, S.N. and Lee, D. (2018) Artificial Intelligence and Robots in Healthcare: What Are the Success Factors for Technology-Based Service Encounters? International Journal of Healthcare Management, 12, 218-225. [Google Scholar] [CrossRef] 
[7] Jarrahi, M.H. (2018) Artificial Intelligence and the Future of Work: Human-Ai Symbiosis in Organizational Decision Making. Business Horizons, 61, 577-586. [Google Scholar] [CrossRef] 
[8] Spencer, D.A. (2018) Fear and Hope in an Age of Mass Automation: Debating the Future of Work. New Technology, Work and Employment, 33, 1-12. [Google Scholar] [CrossRef] 
[9] Piszczek, M.M. (2016) Boundary Control and Controlled Boundaries: Organizational Expectations for Technology Use at the Work-Family Interface. Journal of Organizational Behavior, 38, 592-611. [Google Scholar] [CrossRef] 
[10] Kellogg, K.C., Valentine, M.A. and Christin, A. (2020) Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14, 366-410. [Google Scholar] [CrossRef] 
[11] Frey, C.B. and Osborne, M.A. (2017) The Future of Employment: How Susceptible Are Jobs to Computerisation? Technological Forecasting and Social Change, 114, 254-280. [Google Scholar] [CrossRef] 
[12] 王才, 周文斌, 赵素芳. 机器人规模应用与工作不安全感——基于员工职业能力调节的研究[J]. 经济管理, 2019, 41(4): 111-126.
[13] Johnson, A., Dey, S., Nguyen, H., Groth, M., Joyce, S., Tan, L., et al. (2020) A Review and Agenda for Examining How Technology-Driven Changes at Work Will Impact Workplace Mental Health and Employee Well-Being. Australian Journal of Management, 45, 402-424. [Google Scholar] [CrossRef] 
[14] Brougham, D. and Haar, J. (2017) Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ Perceptions of Our Future Workplace. Journal of Management & Organization, 24, 239-257. [Google Scholar] [CrossRef] 
[15] Shaikh, F., Afshan, G., Anwar, R.S., Abbas, Z. and Chana, K.A. (2023) Analyzing the Impact of Artificial Intelligence on Employee Productivity: The Mediating Effect of Knowledge Sharing and Well‐Being. Asia Pacific Journal of Human Resources, 61, 794-820. [Google Scholar] [CrossRef] 
[16] Davis, F.D. (1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319-340. [Google Scholar] [CrossRef] 
[17] Spreitzer, G., Sutcliffe, K., Dutton, J., Sonenshein, S. and Grant, A.M. (2005) A Socially Embedded Model of Thriving at Work. Organization Science, 16, 537-549. [Google Scholar] [CrossRef] 
[18] Spreitzer, G.M. and Porath, C. (2012) Creating Sustainable Performance. Harvard Business Review, 90, 92-99.
[19] 张柏楠, 徐世勇. 高参与人力资源实践对员工创新行为的影响: 一个中介与调节模型[J]. 科技进步与对策, 2021, 38(7): 141-150.
[20] 朱晓妹, 王森, 何勤. 人工智能嵌入视域下岗位技能要求对员工工作旺盛感的影响研究[J]. 外国经济与管理, 2021, 43(11): 15-25.
[21] Qiu, H., Li, M., Bai, B., Wang, N. and Li, Y. (2022) The Impact of AI-Enabled Service Attributes on Service Hospitableness: The Role of Employee Physical and Psychological Workload. International Journal of Contemporary Hospitality Management, 34, 1374-1398. [Google Scholar] [CrossRef] 
[22] Chatterjee, S., Rana, N.P., Dwivedi, Y.K. and Baabdullah, A.M. (2021) Understanding AI Adoption in Manufacturing and Production Firms Using an Integrated TAM-TOE Model. Technological Forecasting and Social Change, 170, Article ID: 120880. [Google Scholar] [CrossRef] 
[23] Maslach, C. and Jackson, S.E. (1981) The Measurement of Experienced Burnout. Journal of Organizational Behavior, 2, 99-113. [Google Scholar] [CrossRef] 
[24] Maslach, C., Schaufeli, W.B. and Leiter, M.P. (2001) Job Burnout. Annual Review of Psychology, 52, 397-422. [Google Scholar] [CrossRef] [PubMed]
[25] 黄丽满, 宋晨鹏, 李军. 旅游企业员工人工智能焦虑对知识共享的作用机制——基于技术接受模型[J]. 资源开发与市场, 2020, 36(11): 1192-1196, 1258.
[26] Cudré-Mauroux, A. (2011) Staff and Challenging Behaviours of People with Developmental Disabilities: Influence of Individual and Contextual Factors on the Transactional Stress Process. The British Journal of Development Disabilities, 57, 21-40. [Google Scholar] [CrossRef] 
[27] Lazarus, R.S. (1984) Stress, Appraisal, and Coping. Springer.
[28] LePine, M.A., Zhang, Y., Crawford, E.R. and Rich, B.L. (2016) Turning Their Pain to Gain: Charismatic Leader Influence on Follower Stress Appraisal and Job Performance. Academy of Management Journal, 59, 1036-1059. [Google Scholar] [CrossRef] 
[29] Cavanaugh, M.A., Boswell, W.R., Roehling, M.V. and Boudreau, J.W. (2000) An Empirical Examination of Self-Reported Work Stress among U.S. Managers. Journal of Applied Psychology, 85, 65-74. [Google Scholar] [CrossRef] [PubMed]
[30] Chang, P., Zhang, W., Cai, Q. and Guo, H. (2024) Does Ai-Driven Technostress Promote or Hinder Employees’ Artificial Intelligence Adoption Intention? A Moderated Mediation Model of Affective Reactions and Technical Self-Efficacy. Psychology Research and Behavior Management, 17, 413-427. [Google Scholar] [CrossRef] [PubMed]
[31] Ding, L. (2021) Employees’ Challenge-Hindrance Appraisals toward STARA Awareness and Competitive Productivity: A Micro-Level Case. International Journal of Contemporary Hospitality Management, 33, 2950-2969. [Google Scholar] [CrossRef] 
[32] LePine, M.A. (2022) The Challenge-Hindrance Stressor Framework: An Integrative Conceptual Review and Path Forward. Group & Organization Management, 47, 223-254. [Google Scholar] [CrossRef] 
[33] Schepman, A. and Rodway, P. (2020) Initial Validation of the General Attitudes Towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1, Article ID: 100014. [Google Scholar] [CrossRef] [PubMed]
[34] Aryee, T.E. (2015) Corporate Governance, Fundraising and Operational Efficiency of Non-Governmental Organizations in Ghana. Doctoral Dissertation, University of Ghana.
[35] Venkatesh, V. and Bala, H. (2008) Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39, 273-315. [Google Scholar] [CrossRef] 
[36] Porath, C., Spreitzer, G., Gibson, C. and Garnett, F.G. (2011) Thriving at Work: Toward Its Measurement, Construct Validation, and Theoretical Refinement. Journal of Organizational Behavior, 33, 250-275. [Google Scholar] [CrossRef] 
[37] Alsheibani, S., Cheung, Y. and Messom, C. (2018) Artificial Intelligence Adoption: AI-Readiness at Firm-Level. 22nd Pacific Asia Conference on Information Systems, PACIS 2018, Yokohama, 26-30 June 2018, 37.
[38] Lepine, J.A., Podsakoff, N.P. and Lepine, M.A. (2005) A Meta-Analytic Test of the Challenge Stressor-Hindrance Stressor Framework: An Explanation for Inconsistent Relationships among Stressors and Performance. Academy of Management Journal, 48, 764-775. [Google Scholar] [CrossRef] 
[39] 万金, 周雯珺, 张银普, 等. 挑战-阻碍性压力对企业科技人员工作繁荣的影响: 有调节的中介模型[J]. 科技管理研究, 2021, 41(9): 115-121.
[40] 赵简, 张西超. 工作压力与工作倦怠的关系——心理资本的调节作用[J]. 河南师范大学学报: 自然科学版, 2010, 38(3): 139-143.
[41] 张韫黎, 陆昌勤. 挑战性-阻断性压力(源)与员工心理和行为的关系: 自我效能感的调节作用[J]. 心理学报, 2009, 41(6): 501-509.
[42] Podsakoff, N.P., LePine, J.A. and 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, 438-454. [Google Scholar] [CrossRef] [PubMed]
[43] Wang, Q., Liu, X. and Huang, K. (2024) Investigating Employees’ Occupational Risks and Benefits Resulting from Artificial Intelligence: An Empirical Analysis. Information & Management, 61, Article ID: 104036. [Google Scholar] [CrossRef] 

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