AI感知对员工心理和行为的影响及理论解释

doi:10.3724/SP.J.1042.2024.01195

摘要/Abstract

摘要： AI感知是指员工对AI运用影响其工作态度、行为、福祉和工作环境的感知。第四次工业革命已经到来, AI提升员工绩效的同时也带来了风险和不确定性, 对员工产生巨大影响。当前AI感知研究分散概念模糊, 阻碍了对AI运用如何影响员工心理和行为的理解。为探究AI运用对员工的具体影响及作用解释机制, 首先澄清了AI感知概念内涵; 其次, 揭示了AI感知影响效果; 第三, 基于资源视角、情绪视角、需求视角和环境视角阐释了AI感知的理论解释机制; 最后, 构建了AI感知的未来研究框架, 为将来相关研究提供理论借鉴, 为组织决策提供见解。

关键词: AI感知, 资源保存理论, 工作不安全感, 双刃剑效应

Abstract: AI awareness refers to an employee's perception that the use of AI affects their work attitude, behavior, well-being, and work environment. The fourth Industrial Revolution has arrived, and while AI improves employee performance, it also brings risks and uncertainties that have a huge impact on employees. Although many studies have explored the impact of AI awareness on employees' psychological and behavioral outcomes, due to scholars' academic background, current studies are more focused on the field of relative segmentation. At the same time, because the concept of AI awareness is relatively new, its name is not unified, and the ambiguity of the concept limits the public's in-depth insight into AI awareness. In addition, the action path and boundary conditions of AI awareness on employees' psychological and behavioral outcomes have not yet been clarified, and the lack of AI awareness research framework has hindered the understanding of how AI application affects employees' psychological and behavioral outcomes. In order to explore the specific impact of AI application on employees and its function explanation mechanism, firstly, the research on AI awareness was systematically reviewed, the concept connotation of AI awareness was clarified, and AI awareness was re-defined as employees' perception of the impact of AI application on their work attitude, behavior, well-being and working environment. This definition highlights the two-sided nature of AI awareness, that is, AI awareness has both positive and negative effects on employees, rather than just negative effects. Second, it reveals the effects of AI awareness, advancing the understanding of how AI awareness affects employee psychology and behavior. The positive and negative effects of AI awareness on employees' psychological state are explained from the three aspects of emotion, stress and cognition, and the positive and negative effects of AI awareness on employees' behaviors are explained from the two aspects of active and negative behaviors, so that organizations and academia can more clearly, comprehensively and systematically recognize the important effects of AI application on employees' psychological and behavioral outcomes. Promote research in related fields. Thirdly, the theoretical explanation mechanism of AI awareness is explained based on resource perspective (conservation of resources theory, JD-R model), pressure perspective (cognitive evaluation theory), psychological needs perspective (self-determination theory), and environment perspective (person-environment fit theory). Finally, the paper elaborates on five aspects: exploring the multi-level driving mechanism of AI awareness, enriching the action mechanism of AI awareness, mining the spillover effect of AI awareness and strengthening the interaction impact between AI and employees, and builds an integrated model diagram for future research on AI awareness, which will help promote local relevant research. By answering the above questions, it is expected to provide theoretical reference for the subsequent research of scholars, enhance the academic community's cognition and understanding of how the application of AI affects employees, and provide new ideas for promoting the development of AI research. At the same time, it is revealed that managers in the era of Industry 4.0 should re-examine themselves, understand, learn and trust AI technology, use AI technology to develop new skills to improve their management ability, help organizations adopt AI technology more effectively, prevent risks and promote the healthy development of organizations. Managers must clarify the use of AI technology, allow employees to participate in the process of developing and implementing AI systems, eliminate misunderstandings and mistrust, and conduct AI technology training for employees, so that employees have more understanding of AI, reduce the sense of rejection of AI, and recognize that coexistence with AI is an inevitable development of the times. At the same time, it also informs employees that the purpose of applying AI is to help rather than replace them, relieve employees' anxiety and sense of threat, reduce their fear of unemployment, enhance employees' positive cognition of the application of AI, and then reduce their negative evaluation of the application of AI, and help organizations maximize the positive side of AI and reduce the dark side brought by AI.

Key words: AI awareness, conservation of resources theory, job insecurity, double-edged sword effect

中图分类号:

B849

王涛, 占小军, 余薇. (2024). AI感知对员工心理和行为的影响及理论解释. 心理科学进展 , 32(7), 1195-1208.

WANG Tao, ZHAN Xiaojun, YU Wei. (2024). The influence of AI awareness on employee's psychological and behavioral outcomes and its theoretical explanation. Advances in Psychological Science, 32(7), 1195-1208.

参考文献

[1] 陈文晶, 康彩璐, 杨玥, 万岩. (2022). 人工智能潜在替代风险与员工职业能力发展: 基于员工不安全感视角.中国人力资源开发, 39(1), 84-97.
[2] 崔淼, 肖咪咪, 王淑娟. (2019). 组织创新氛围研究的元分析.南开管理评论, 22(1), 98-110.
[3] 郝夏靓, 陈毅文. (2023). 不合规任务与知识隐藏的关系研究——基于双过程模型和量子型领导视角.技术经济与管理研究, 10, 75-80.
[4] 李燕萍, 陶娜娜. (2022). 员工人工智能技术采纳多层动态影响模型:一个文献综述.中国人力资源开发, 39(1), 35-56.
[5] 裴嘉良, 刘善仕, 钟楚燕, 谌一璠. (2021). AI算法决策能提高员工的程序公平感知吗?外国经济与管理, 43(11), 41-55.
[6] 彭剑锋. (2023). 新一代人工智能对组织与人力资源管理的影响与挑战.中国人力资源开发, 40(7), 8-14.
[7] 涂艳, 蒿坡, 龙立荣. (2023). 工作替代还是工作转型?技术型工作不安全感的内涵、影响后果及来源.心理科学进展, 31(8), 1359-1373.
[8] 王海波, 严鸣, 吴海波, 黎金荣, 王晓晖. (2019). 恶意报复还是认同驱动?新员工的角色社会化程度对其职场排斥行为的作用机制.心理学报, 51(1), 128-140.
[9] 徐广路, 王皓天. (2022). 技术冲击意识对员工变革支持意愿的影响研究——以人工智能发展为背景.华东经济管理, 36(6), 119-128.
[10] 徐广路, 王皓天. (2023). 人工智能冲击意识对员工职业满意度的影响:工作压力和目标导向的作用.中国人力资源开发, 40(7), 15-33.
[11] 张志学, 华中生, 谢小云. (2024). 数智时代人机协同的研究现状与未来方向.管理工程学报, 38(1), 1-13.
[12] Alshahrani A., Dennehy D., & Mäntymäki M. (2022). An attention-based view of AI assimilation in public sector organizations: The case of Saudi Arabia.Government Information Quarterly, 39(4), 101617.
[13] Arias-Pérez, J., & Vélez-Jaramillo, J. (2022). Understanding knowledge hiding under technological turbulence caused by artificial intelligence and robotics.Journal of Knowledge Management, 26(6), 1476-1491.
[14] Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation.Journal of Economic Perspectives, 29(3), 3-30.
[15] Bankins S., Ocampo A. C., Marrone M., Restubog S. L. D., & Woo S. E. (2024). A multilevel review of artificial intelligence in organizations: Implications for organizational behavior research and practice.Journal of Organizational Behavior, 45(2), 159-182.
[16] Basu S., Majumdar B., Mukherjee K., Munjal S., & Palaksha C. (2023). Artificial Intelligence-HRM interactions and outcomes: A systematic review and causal configurational explanation.Human Resource Management Review, 33(1), 100893.
[17] Belanche D., Casaló L. V., Schepers J., & Flavián C. (2021). Examining the effects of robots' physical appearance, warmth, and competence in frontline services: The humanness-value-loyalty model.Psychology & Marketing, 38(12), 2357-2376.
[18] Braganza A., Chen W., Canhoto A., & Sap S. (2021). Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust.Journal of Business Research, 131, 485-494.
[19] Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (AI): Employees' perceptions of our future workplace.Journal of Management & Organization, 24(2), 239-257.
[20] Cao L., Chen C., Dong X., Wang M., & Qin X. (2023). The dark side of AI identity: Investigating when and why AI identity entitles unethical behavior. Computers in Human Behavior, 143, Article 107669.
[21] Chen Y., Hu Y., Zhou S., & Yang S. (2023). Investigating the determinants of performance of artificial intelligence adoption in hospitality industry during COVID-19.International Journal of Contemporary Hospitality Management, 35(8), 2868-2889.
[22] Cheng B., Lin H., & Kong Y. (2023). Challenge or hindrance? How and when organizational artificial intelligence adoption influences employee job crafting. Journal of Business Research, 164, Article 113987.
[23] Choi S., Mattila A. S., & Bolton L. E. (2021). To err is human (-oid): How do consumers react to robot service failure and recovery? Journal of Service Research, 24(3), 354-371.
[24] Chowdhury S., Dey P., Joel-Edgar S., Bhattacharya S., Rodriguez-Espindola O., Abadie A., & Truong L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), Article 100899.
[25] Del Giudice M., Scuotto V., Orlando B., & Mustilli M. (2023). Toward the human-centered approach. A revised model of individual acceptance of AI. Human Resource Management Review, 33(1), Article 100856.
[26] Ding, L. (2021). Employees' challenge-hindrance appraisals toward AI awareness and competitive productivity: A micro-level case.International Journal of Contemporary Hospitality Management, 33(9), 2950-2969.
[27] Ding, L. (2022). Employees' AI awareness and innovative work behavioural intentions: Evidence from US casual dining restaurants. In S. Tabari & W. Chen (Eds.), Global strategic management in the service industry: A perspective of the new era (pp. 17-56): Emerald Publishing Limited.
[28] Dutta D., Mishra S. K., & Tyagi D. (2023). Augmented employee voice and employee engagement using artificial intelligence-enabled chatbots: A field study.The International Journal of Human Resource Management, 34(12), 2451-2480.
[29] Einola, K., & Khoreva, V. (2023). Best friend or broken tool? Exploring the co-existence of humans and artificial intelligence in the workplace ecosystem.Human Resource Management, 62(1), 117-135.
[30] Fiske S. T., Cuddy A. J., & Glick P. (2007). Universal dimensions of social cognition: Warmth and competence.Trends in Cognitive Sciences, 11(2), 77-83.
[31] Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?Technological Forecasting and Social Change, 114, 254-280.
[32] Fried, E. I. (2017). The 52 symptoms of major depression: Lack of content overlap among seven common depression scales.Journal of Affective Disorders, 208, 191-197.
[33] Gregory R. W., Henfridsson O., Kaganer E., & Kyriakou H. (2021). The role of artificial intelligence and data network effects for creating user value.Academy of Management Review, 46(3), 534-551.
[34] Halbesleben, J. R. B., & Buckley, M. R. (2004). Burnout in Organizational Life.Journal of Management, 30(6), 859-879.
[35] Hanisch, K. A., & Hulin, C. L. (1990). Job attitudes and organizational withdrawal: An examination of retirement and other voluntary withdrawal behaviors.Journal of Vocational Behavior, 37(1), 60-78.
[36] Harris-Watson A. M., Larson L. E., Lauharatanahirun N., DeChurch L. A., & Contractor N. S. (2023). Social perception in Human-AI teams: Warmth and competence predict receptivity to AI teammates. Computers in Human Behavior, 145, Article 107765.
[37] He C., Teng R., & Song J. (2024). Linking employees' challenge-hindrance appraisals toward AI to service performance: the influences of job crafting, job insecurity and AI knowledge.International Journal of Contemporary Hospitality Managemen, 36(3), 975-994.
[38] Hobfoll S. E., Halbesleben J., Neveu J. P., & Westman M. (2018). Conservation of resources in the organizational context: The reality of resources and their consequences. Annual Review of Organizational Psychology and Organizational Behavior, 5, 103-128.
[39] Hu J.,& Wang, R.(2023). Familiarity Breeds Trust? The relationship between dating app use and trust in dating algorithms via algorithm awareness and critical algorithm perceptions. International Journal of Human-Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2023.2217014
[40] Jin Y., Cai W., Chen L., Htun N. N., & Verbert K. (2019). MusicBot: Evaluating critiquing-based music recommenders with conversational interaction. Proceedings of the 28th ACM International Conference(pp. 951-960). Sydney. Australia: Information and Knowledge Management.
[41] Kang D. Y., Hur W. M., & Shin Y. (2023). Smart technology and service employees' job crafting: Relationship between AI awareness, performance pressure, receiving and giving help, and job crafting. Journal of Retailing and Consumer Services, 73, Article 103282.
[42] Kang E. Y. N., Chen D. R., & Chen Y. Y. (2023). Associations between literacy and attitudes toward artificial intelligence-assisted medical consultations: The mediating role of perceived distrust and efficiency of artificial intelligence. Computers in Human Behavior, 139, Article 107529.
[43] Kieslich K., Lünich M., & Marcinkowski F. (2021). The threats of artificial intelligence scale (TAI) development, measurement and test over three application domains. International Journal of Social Robotics, 13, 1563-1577.
[44] Kong H., Jiang X., Zhou X., Baum T., Li J., & Yu J. (2024). Influence of artificial intelligence (AI) perception on career resilience and informal learning.Tourism Review, 79(1), 219-233.
[45] 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.
[46] Kurniawan D. T., Prameka A. S., & Praktikto H. (2022). AI is in Sight: The effect of AI on job outcome through job insecurity perception about future workplace on private companies in East Java, Indonesia. Paper presented at the 2022 3rd International Conference on Big Data Analytics and Practices (IBDAP).
[47] Lan J., Yuan B., & Gong Y. (2022). Predicting the change trajectory of employee robot-phobia in the workplace: The role of perceived robot advantageousness and anthropomorphism. Computers in Human Behavior, 135, Article 107366.
[48] Lazarus, R. S., & Folkman, S. (1987). Transactional theory and research on emotions and coping.European Journal of Personality, 1(3), 141-169.
[49] LePine J. A., Podsakoff N. P., & 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(5), 764-775.
[50] Lestari N. S., Rosman D., & Millenia E. (2023). The association between smart technology, artificial intelligence, robotics, and algorithms (AI) awareness, job stress, job insecurity, and job satisfaction among hotel employees during COVID-19 pandemic. In E3S Web of Conferences(Vol. 388, p. 3021). EDP Sciences.
[51] Li B., Chen Y., Liu L., & Zheng B. (2023). Users' intention to adopt artificial intelligence-based chatbot: A meta-analysis.The Service Industries Journal, 43(15-16), 1117-1139.
[52] Li J., Bonn M. A., & Ye B. H. (2019). Hotel employee's artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate.Tourism Management, 73, 172-181.
[53] Liang X., Guo G., Shu L., Gong Q., & Luo P. (2022). Investigating the double-edged sword effect of AI awareness on employee's service innovative behavior. Tourism Management, 92, Article 104564.
[54] Lingmont, D. N. J., & Alexiou, A. (2020). The contingent effect of job automating technology awareness on perceived job insecurity: Exploring the moderating role of organizational culture. Technological Forecasting and Social Change, 161, Article 120302.
[55] Ma, C., & Ye, J. (2022). Linking artificial intelligence to service sabotage.The Service Industries Journal, 42(13-14), 1054-1074.
[56] Malik A., Budhwar P., & Kazmi B. A. (2022). Artificial intelligence (AI)-assisted HRM: Towards an extended strategic framework. Human Resource Management Review, 33(1), Article 100940.
[57] Maslach C., Schaufeli W. B., & Leiter M. P. (2001). Job burnout.Annual Review of Psychology, 52(1), 397-422.
[58] Mirbabaie M., Brünker F., Möllmann N. R., & Stieglitz S. (2022). The rise of artificial intelligence-understanding the AI identity threat at the workplace.Electronic Markets, 32(1), 73-99.
[59] Nam, T. (2019). Technology usage, expected job sustainability, and perceived job insecurity.Technological Forecasting and Social Change, 138, 155-165.
[60] Paluch S., Tuzovic S., Holz H. F., Kies A., & Jörling M. (2022). “My colleague is a robot” -exploring frontline employees' willingness to work with collaborative service robots.Journal of Service Management, 33(2), 363-388.
[61] Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world.Applied Psychology, 71(4), 1171-1204.
[62] Parvez M. O., Arasli H., Ozturen A., Lodhi R. N., & Ongsakul V. (2022). Antecedents of human-robot collaboration: Theoretical extension of the technology acceptance model.Journal of Hospitality and Tourism Technology, 13(2), 240-263.
[63] Paul, A., & Ahmed, S. (2024). Computed compatibility: Examining user perceptions of AI and matchmaking algorithms.Behaviour & Information Technology, 43(5), 1001-1015.
[64] 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), Article 100857.
[65] Presbitero, A., & Teng-Calleja, M. (2023). Job attitudes and career behaviors relating to employees' perceived incorporation of artificial intelligence in the workplace: A career self-management perspective.Personnel Review, 52(4), 1169-1187.
[66] Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox.Academy of Management Review, 46(1), 192-210.
[67] Rathi S., Majumdar A., & Chatterjee C. (2024). Did the COVID-19 pandemic propel usage of AI in pharmaceutical innovation? New evidence from patenting data. Technological Forecasting and Social Change, 198, Article 122940.
[68] Salancik, G. R., & Pfeffer, J. (1978). A social information processing approach to job attitudes and task design.Administrative Science Quarterly, 23(2), 224-253.
[69] Shin H., Perdue R. R., & Kang J. (2019). Front desk technology innovation in hotels: A managerial perspective.Tourism Management, 74, 310-318.
[70] Shoss, M. K. (2017). Job insecurity: An integrative review and agenda for future research.Journal of Management, 43(6), 1911-1939.
[71] Stumpf S. A., Colarelli S. M., & Hartman K. (1983). Development of the career exploration survey (CES).Journal of Vocational Behavior, 22(2), 191-226.
[72] Tan, K. L., Gim, G. C.W., Hii, I. S. H., & Zhu, W. Q.(2023). AI fight or flight: A two-wave time-lagged study of challenge and hindrance appraisal of AI awareness on basic psychological needs and individual competitiveness productivity among hospitality employees. Current Issues in Tourism. Advance online publication. https://doi.org/10.1080/13683500.2023.2224550
[73] Tang P. M., Koopman J., Mai K. M., De Cremer D., Zhang J. H., Reynders P., … Chen I. H. (2023). No person is an island: Unpacking the work and after-work consequences of interacting with artificial intelligence.Journal of Applied Psychology, 108(11), 1766-1789.
[74] Tang P. M., Koopman J., McClean S. T., Zhang J. H., Li C. H., De Cremer D., … Ng, C. T. S. (2022). When conscientious employees meet intelligent machines: An integrative approach inspired by complementarity theory and role theory.Academy of Management Journal, 65(3), 1019-1054.
[75] Tang P. M., Koopman J., Yam K. C., De Cremer D., Zhang J. H., & Reynders P. (2023). The self-regulatory consequences of dependence on intelligent machines at work: Evidence from field and experimental studies.Human Resource Management, 62(5), 721-744.
[76] Teng R., Zhou S., Zheng W., & Ma C. (2023). Artificial intelligence (AI) awareness and work withdrawal: Evaluating chained mediation through negative work-related rumination and emotional exhaustion. International Journal of Contemporary Hospitality Management. Advance online publication. https://doi.org/10.1108/IJCHM-02-2023-0240
[77] Varma A., Dawkins C., & Chaudhuri K. (2023). Artificial intelligence and people management: A critical assessment through the ethical lens. Human Resource Management Review, 33(1), Article 100923.
[78] Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation.MIS Quarterly, 23(2), 239-260.
[79] Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing.Academy of Management Discoveries, 4(4), 404-409.
[80] Wang H., Zhang H., Chen Z., Zhu J.,& Zhang, Y.(2022). Influence of artificial intelligence and robotics awareness on employee creativity in the hotel industry. Frontiers in Psychology, 13, Article 834160. https://doi.org/10.3389/fpsyg.2022.834160
[81] Webb, M. (2019. The impact of artificial intelligence on the labor market. Retrieved Jan. 20, 2024, from 2019). The impact of artificial intelligence on the labor market. Retrieved Jan. 20, 2024, from http://dx.doi.org/10.2139/ssrn.3482150
[82] Wirtz J., Patterson P. G., Kunz W. H., Gruber T., Lu V. N., Paluch S., & Martins A. (2018). Brave new world: Service robots in the frontline.Journal of Service Management, 29(5), 907-931.
[83] Xu G., Xue M., & Zhao J. (2023). The association between artificial intelligence awareness and employee depression: The mediating role of emotional exhaustion and the moderating role of perceived organizational support. International Journal of Environmental Research and Public Health, 20(6), Article 5147.
[84] Yam K. C., Goh E. Y., Fehr R., Lee R., Soh H., & Gray K. (2022). When your boss is a robot: Workers are more spiteful to robot supervisors that seem more human. Journal of Experimental Social Psychology, 102, Article 104360.
[85] Yam K. C., Tang P. M., Jackson J. C., Su R., & Gray K. (2023). The rise of robots increases job insecurity and maladaptive workplace behaviors: Multimethod evidence.Journal of Applied Psychology, 108(5), 850-870.
[86] Yanit M., Yanit M., & Wan F. (2023). Right agent, wrong level of hedonism: How high (vs low) hedonic values in AI-performed tasks lead to decreased perceptions of humanlikeness, warmth, and less consumer support. Computers in Human Behavior, 147, Article 107870.
[87] Yin M., Jiang S., & Niu X. (2024). Can AI really help? The double-edged sword effect of AI assistant on employees' innovation behavior. Computers in Human Behavior, 150, Article 107987.
[88] Yu H., Shum C., Alcorn M., Sun J., & He Z. (2022). Robots can't take my job: Antecedents and outcomes of Gen Z employees' service robot risk awareness.International Journal of Contemporary Hospitality Management, 34(8), 2971-2988.
[89] Zhang M., Geng R., Hong Z., Song W., & Wang W. (2020). The double-edged sword effect of service recovery awareness of frontline employees: From a job demands- resources perspective. International Journal of Hospitality Management, 88, Article 102536.
[90] Zhang, X., & Jin, H. (2023). How does smart technology, artificial intelligence, automation, robotics, and algorithms (STAARA) awareness affect hotel employees' career perceptions? A disruptive innovation theory perspective.Journal of Hospitality Marketing & Management, 32(2), 264-283.
[91] Zhao J., Hu E., Han M., Jiang K., & Shan H. (2023). That honey, my arsenic: The influence of advanced technologies on service employees' organizational deviance. Journal of Retailing and Consumer Services, 75, Article 103490.