工作场所人工智能应用对员工影响的元分析

doi:10.3724/SP.J.1042.2024.01621

摘要/Abstract

摘要： 随着数字科技的发展, 人工智能为组织带来了新的机会和挑战, 其在工作场所中的应用对员工行为和心理的影响日益得到学术界的密切关注。但相关影响方向、程度和边界等研究结论尚未取得共识。本研究对包含85个结果变量, 150个效应量的64篇国内外文献进行了元分析。研究发现：工作场所AI应用有助于触发员工工作投入、组织承诺、工作幸福感等积极心理, 调动其知识共享、数字创新、工作重塑等积极行为, 但同时也会引发员工焦虑、离职倾向、工作不安全感等消极心理, 出现知识隐藏、工作退缩、服务破坏等消极行为, 且AI应用类型、行业类型以及AI应用测量方式对上述关系有不同程度的调节作用。研究结论表明工作场所AI应用是一柄双刃剑, 它既可以作为技术支持丰富员工心理资源, 激发积极行为, 亦会给员工造成威胁从而消耗心理资源, 引发消极行为。本研究在工作要求-资源模型的理论框架下, 明晰了工作场所AI应用与员工行为和心理结果变量间的关系效果以及边界条件, 对组织科学地调整AI管理方式、引导员工正确认识AI以有效发挥其价值具有指导意义。

关键词: 工作场所, 人工智能应用, 积极行为和心理, 消极行为和心理, 元分析

Abstract: Given the widespread application of artificial intelligence (AI) technologies in workplaces, there has been a rapid increase in literature exploring AI-related themes. Scholars are increasingly focused on understanding how these applications influence employee behaviors and psychology. However, consensus on the direction, boundaries, and extent of these effects remains elusive. To address this issue, this paper conducts a meticulous review and selection of literature published from January 2017 to July 2023. A meta-analysis is performed on the 64 literatures (N = 150) to advance knowledge in three main areas: (1) Explore the strength and direction of the relationship between AI application and employees’ positive behaviors and psychological effects, as well as their negative behaviors and psychological effects. This aims to clarify the inconsistent conclusions and fill gaps in quantitative integration. (2) Based on the Job Demands-Resources model, this paper delineates the theoretical rationale underlying the impact of AI on employees’ behavior and psychology within an organizational context, upon its integration as a new technology, and elucidate specific pathways of its effects. (3) Investigate whether the effects of AI application on employee behavior and psychology are potentially influenced by the type of AI application, industry context, and measurement methods. Endeavor to furnish a clearer and more comprehensive overview of the correlation between AI and employee outcomes, thereby providing a theoretical foundation for tailored AI advantages in practical settings and methodological designs for subsequent empirical research in academia.
The result finds that: (1) The application of AI in the workplace exhibits a “double-edged sword” effect, which can enrich employees' psychological resources as technical support and stimulate positive behaviors, may also threaten employees to consume psychological resources and cause negative behaviors. (2) The relationships between AI application and employee behaviors/psychological effects vary under different AI types. Assisted and augmented AI enhance employee job satisfaction by reducing task costs, thereby increasing work engagement, creativity, and productivity. Such abundance in work resources contributes to an uplift in employees' job satisfaction and happiness. Consequently, when employees experience greater job involvement, there is a notable increase in creativity and productivity. However, managerial and autonomous AI types, despite improving efficiency and autonomy to some extent, introduce stress due to their supervisory and controlling attributes, suppressing positive work experiences and fostering negative psychological states. (3) Variations in AI application effects on employee behaviors and psychological effects across different industry types are evident. Employees in labor-intensive industries, with structured work environments and lower occupational skills, perceive more negative effects from AI. Conversely, employees in knowledge-intensive industries benefit from more flexible and autonomous work environments enhanced by AI, demonstrating stronger abilities in receiving, learning, and adapting to new information and technologies. (4) The relationship between AI application and employee behavior, as well as psychological impacts, varies depending on diverse measurement of AI application. Studies using subjective evaluations tend to reveal more negative impacts of AI on employee behaviors and psychological effects compared to those using objective measurement methods.
This study has made several theoretical contributions: (1) Systematically integrate and evaluate the fragmented research conclusions on the effects of AI application on employee behaviors and psychology, synthesizing empirical findings and responding to calls in the literature for understanding the personal impacts of automation technologies. (2) Within the framework of Job Demands-Resources model, this paper elucidates the diverse impacts of different types of AI application on employee behavior and psychology, expands the influencing factors that could augment the positive results of AI application, and further validates the concerns regarding potential adverse consequences. (3) Enrich the boundary conditions in the relationship between workplace AI application and employee behavior and psychology. This paper explores the moderating effects of the type of AI application, industry context, and measurement methods, responding to the scholarly calls for further examination of moderating variables of AI application affecting employee experience, thereby offering new insights for inconsistent research conclusions in the academic literature. Beyond theoretical advancements, the results of this study provide guidance for organizations to scientifically adjust the management strategies of AI, accurately direct employees perceptions, and effectively maximize its value.

Key words: workplace, AI application, positive behavior and psychology, negative behavior and psychology, meta-analysis

蒋建武, 龙晗寰, 胡洁宇. (2024). 工作场所人工智能应用对员工影响的元分析. 心理科学进展 , 32(10), 1621-1639.

JIANG Jianwu, LONG Hanhuan, HU Jieyu. (2024). A meta-analysis of the impact of AI application on employees in the workplace. Advances in Psychological Science, 32(10), 1621-1639.

参考文献

(*标识纳入元分析的文献)
[1] *陈晨. (2021). 人工智能技术强度感知对员工离职意向的影响: 一个有调节的中介模型.巢湖学院学报, 23(6), 31-41.
[2] *高萍. (2021). 人工智能对金融业员工离职意愿的影响研究——基于感知组织支持和竞争心理氛围的调节作用.企业改革与管理, (12), 81-83.
[3] *韩明燕, 张毛龙, 胡恩华, 单红梅. (2023). 因参与而支持: 员工参与AI实施过程对其AI支持行为的影响.经济管理, 45(5), 151-169.
[4] *黄丽满, 宋晨鹏, 李军. (2020). 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. 资源开发与市场, 36(11), 1192-1196+1258.
[5] *刘智强, 王子婧, 程欢, 许玉平, 倪佳豪. (2024). 迎难而上: 知觉资源稀缺对员工突破性创造力的影响机制研究.管理工程学报, 38(1), 88-100.
[6] 麦劲壮, 李河, 方积乾, 刘小清, 饶栩栩. (2006). Meta分析中失安全系数的估计. 循证医学, (5), 297-300+303.
[7] 苗蕊, 吕成戍, 鲁颜. (2024). 企业社交媒体使用与员工行为及心理结果间关系的元分析.南开管理评论, 27(1), 200-212.
[8] *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.
[9] *盛晓娟, 郭辉, 何勤. (2022). 人工智能技术运用何以提高员工任务绩效?北京联合大学学报(人文社会科学版), 20(4), 85-94.
[10] 卫旭华. (主编). (2021). 组织与管理研究中的元分析方法. 北京: 科学出版社.
[11] *吴慈恩, 皮平凡, 关新华. (2023). 机器人性能特征如何影响员工工作幸福感——基于创新抵制理论与资源保存理论的双重视角.旅游导刊, 7(2), 55-78.
[12] *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.
[13] *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.
[14] *张恒, 高中华, 徐燕. (2024). AI技术替代感对工作场所人与AI合作意愿的影响机制.软科学, 38(3), 107-114.
[15] *祝楚琳, 王亚男, 何伶俐. (2022). 人工智能发展对员工工作幸福感的影响研究. 经营与管理, https://doi.org/10.16517/j.cnki.cn12-1034/f.20221227.004
[16] *朱晓妹, 任晶晶, 何勤. (2020). 人工智能技术应用会引发员工的消极情绪吗?——基于资源保存理论的视角.中国临床心理学杂志, 28(6), 1285-1288.
[17] *朱晓妹, 王森, 何勤. (2021). 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.外国经济与管理, 43(11), 15-25.
[18] *Abbas S. M., Liu Z., & Khushnood M. (2023). When human meets technology: Unlocking hybrid intelligence role in breakthrough innovation engagement via self- extension and social intelligence.Journal of Computer Information Systems, 63(5), 1183-1200.
[19] Aghaei S., Nematbakhsh M. A., & Farsani H. K. (2012). Evolution of the world wide web: From web 1.0 to web 4.0.International Journal of Web & Semantic Technology, 3(1), 1-10.
[20] Aleksander, I. (2017). Partners of humans: A realistic assessment of the role of robots in the foreseeable future.Journal of Information Technology, 32, 1-9.
[21] *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.
[22] Aylett-Bullock J., Luccioni A. S., Pham K. H., Lam C. S. N., & Luengo-Oroz M. A. (2020). Mapping the landscape of artificial intelligence applications against COVID-19.Journal of Artificial Intelligence Research, 69, 807-845.
[23] Bag S., Pretorius J. H. C., Gupta S., & Dwivedi Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities.Technological Forecasting and Social Change, 163, 120420.
[24] Bai B., Dai H., Zhang D. J., Zhang F., & Hu H. (2022). The impacts of algorithmic work assignment on fairness perceptions and productivity: Evidence from field experiments.Manufacturing & Service Operations Management, 24(6), 3060-3078.
[25] Bakker A. B., Demerouti E., & Sanz-Vergel A. I. (2014). Burnout and work engagement: The JD-R approach.Annual Review of Organizational Psychology and Organizational Behavior, 1(1), 389-411.
[26] Bakker A. B., Demerouti E., Taris T. W., Schaufeli W. B., & Schreurs P. J. (2003). A multigroup analysis of the job demands-resources model in four home care organizations.International Journal of stress management, 10(1), 16-38.
[27] Balakrishnan, J., & Dwivedi, Y. K. (2024). Conversational commerce: Entering the next stage of AI-powered digital assistants.Annals of Operations Research, 333(2), 653-687.
[28] Begg, C. B., & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias.Biometrics, 50(4), 1088-1101.
[29] Bhargava A., Bester M. S., & Bolton L. E. (2020). Employees’ perceptions of the implementation of robotics, artificial intelligence, and automation (RAIA) on job satisfaction, job security, and employability.Journal of Technology in Behavioral Science, 6(1), 106-113.
[30] Bock D. E., Wolter J. S., & Ferrell O. C. (2020). Artificial intelligence: Disrupting what we know about services.Journal of Services Marketing, 34(3), 317-334.
[31] *Brachten F., Brünker F., Frick N. R. J., Ross B., & Stieglitz S. (2020). On the ability of virtual agents to decrease cognitive load: An experimental study.Information Systems and e-Business Management, 18(2), 187-207.
[32] *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.
[33] *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.
[34] *Brougham, D., & Haar, J. (2020). Technological disruption and employment: The influence on job insecurity and turnover intentions: A multi-country study.Technological Forecasting and Social Change, 161, 120276.
[35] Budhwar P., Malik A., De Silva M. T., & Thevisuthan P. (2022). Artificial intelligence-challenges and opportunities for international HRM: A review and research agenda.The International Journal of Human Resource Management, 33(6), 1065-1097.
[36] Cao, Y. (2021). Portrait-based academic performance evaluation of college students from the perspective of big data.International Journal of Emerging Technologies in Learning, 16(4), 95-106.
[37] Challen R., Denny J. C., Pitt M. A., Gompels L., Edwards T., & Tsaneva-Atanasova K. (2019). Artificial intelligence, bias and clinical safety.BMJ Quality & Safety, 28(3), 231-237.
[38] *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, 113987.
[39] Chuang, S. (2021). An empirical study of displaceable job skills in the age of robots.European Journal of Training and Development, 45(6/7), 617-632.
[40] Cooper H., Hedges L. V., & Valentine J. C. (Eds). (2009). The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation.
[41] Corea, F. (2019). AI knowledge map: How to classify AI technologies. In F. Corea (Ed.), An Introduction to Data (Vol. 50, pp. 25-29). Springer.
[42] 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(112), 21-40.
[43] *Dabbous A., Aoun Barakat K., & Merhej Sayegh M. (2022). Enabling organizational use of artificial intelligence: An employee perspective.Journal of Asia Business Studies, 16(2), 245-266.
[44] Dai N. T., Kuang X., & Tang G. (2018). Differential weighting of objective versus subjective measures in performance evaluation: Experimental evidence.European Accounting Review, 27(1), 129-148.
[45] DeChurch, L. A., & Mesmer-Magnus, J. R. (2010). The cognitive underpinnings of effective teamwork: A meta-analysis.Journal of Applied Psychology, 95(1), 32-53.
[46] *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.
[47] *Ding, L. (2022). Employees’ STARA 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.
[48] Dunlap, R. D., & Lacity, M. C. (2017). Resolving tussles in service automation deployments: Service automation at Blue Cross Blue Shield North Carolina (BCBSNC).Journal of Information Technology Teaching Cases, 7(1), 29-34.
[49] *Dutta, D., & Mishra, S. K. (2021). Chatting with the CEO’s virtual assistant: Impact on climate for trust, fairness, employee satisfaction, and engagement.AIS Transactions on Human-Computer Interaction, 13(4), 431-452.
[50] *Dutta D., Mishra S. K., & Tyagi D. (2022). 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.
[51] Dwivedi Y. K., Hughes L., Ismagilova E., Aarts G., Coombs C. R., 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.
[52] Egger, M., & Smith, G. D. (1997). Meta-analysis: Potentials and promise.BMJ, 315(7119), 1371-1374.
[53] Fossen, F. M., & Sorgner, A. (2022). New digital technologies and heterogeneous wage and employment dynamics in the United States: Evidence from individual- level data. Technological Forecasting and Social Change, 175, 121381.
[54] Gentilini U., Almenfi M. B. A., Orton I., & Dale P. (2020). Social protection and jobs responses to COVID-19: A real-time review of country measures. Retrieved July 10, 2020, from https://hdl.handle.net/10986/33635
[55] Grønsund, T., & Aanestad, M. (2020). Augmenting the algorithm: Emerging human-in-the-loop work configurations.The Journal of Strategic Information Systems, 29(2), 101614.
[56] Guo, H., & Polák, P. (2021). Artificial intelligence and financial technology FinTech: How AI is being used under the pandemic in 2020. In A. Hamdan, A. Ella Hassanien, A. Razzaque, & B. Alareeni (Eds.), The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success (Vol. 935, pp. 169-186). Springer Cham.
[57] Haenlein, M., & Kaplan, A. M. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence.California Management Review, 61(4), 14-15.
[58] Hakanen J. J., Schaufeli W. B., & Ahola K. (2008). The job demands-resources model: A three-year cross-lagged study of burnout, depression, commitment, and work engagement.Work & stress, 22(3), 224-241.
[59] *Henkel A. P., Bromuri S., Iren D., & Urovi V. (2020). Half human, half machine-augmenting service employees with AI for interpersonal emotion regulation.Journal of Service Management, 31(2), 247-265.
[60] Hobfoll, S. E. (1990). Loss as the active ingredient in stress: Response to Arnold Lazarus.American Psychologist, 45(11), 1275-1276.
[61] Hobfoll S. E.(2011). Conservation of resources theory: Its implication for stress, health, and resilience. In S. Folkman (Ed.), The Oxford handbook of stress, health, and coping (pp. 127-147). Oxford University Press.
[62] Hobfoll S. E., Halbesleben J. R. B., 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(1), 103-128.
[63] Howard, A., & Borenstein, J. (2020). AI, robots, and ethics in the age of COVID-19. Retrieved May 18, 2020, from https://sloanreview.mit.edu/article/ai-robots-and-ethics-in-the-age-of-covid-19/
[64] Huang J., Saleh S., & Liu Y. (2021). A review on artificial intelligence in education.Academic Journal of Interdisciplinary Studies, 10(3), 206-206.
[65] Huang J., Wang Y., & You X. (2016). The job demands- resources model and job burnout: The mediating role of personal resources.Current Psychology, 35(4), 562-569.
[66] Hunter J. E., Schmidt F. L., & Judiesch M. K. (1990). Individual differences in output variability as a function of job complexity.Journal of Applied Psychology, 75(1), 28-42.
[67] Ivanov S., Webster C., & Berezina K. (2017). Adoption of robots and service automation by tourism and hospitality companies.Revista Turismo & Desenvolvimento, 27(28), 1501-1517.
[68] Jia N., Luo X., Fang Z., & Liao C. (2024). When and how artificial intelligence augments employee creativity.Academy of Management Journal, 67(1), 5-32.
[69] Jiang, L., & Lavaysse, L. M. (2018). Cognitive and affective job insecurity: A meta-analysis and a primary study.Journal of Management, 44(6), 2307-2342.
[70] Kellogg K. C., Valentine M. A., & Christin A. (2020). Algorithms at work: The new contested terrain of control.Academy of Management Annals, 14(1), 366-410.
[71] *Kensbock, J. M., & Stöckmann, C. (2021). “Big brother is watching you”: Surveillance via technology undermines employees’ learning and voice behavior during digital transformation.Journal of Business Economics, 91(4), 565-594.
[72] *Khaliq A., Waqas A., Nisar Q. A., Haider S., & Asghar Z. (2022). Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective.Technology in Society, 68, 101807.
[73] *Kim, Y. (2023). Examining the impact of frontline service robots service competence on hotel frontline employees from a collaboration perspective.Sustainability, 15(9), 7563.
[74] *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.
[75] Lane M.,& Williams, M. (2023). "Defining and classifying AI in the workplace". OECD Social, Employment and Migration Working Papers. OECD Publishing, Paris. https://doi.org/10.1787/59e89d7f-en
[76] Langer, M., & Landers, R. N. (2021). The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers.Computers in Human Behavior, 123, 106878.
[77] Lesener T., Gusy B., & Wolter C. (2019). The job demands-resources model: A meta-analytic review of longitudinal studies.Work & stress, 33(1), 76-103.
[78] Lewig K. A., Xanthopoulou D., Bakker A. B., Dollard M. F., & Metzer J. C. (2007). Burnout and connectedness among Australian volunteers: A test of the job demands- resources model.Journal of vocational behavior, 71(3), 429-445.
[79] Li, D., & Du, Y. (Eds). (2017). Artificial intelligence with uncertainty. CRC press.
[80] *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.
[81] Li P., Sun J.-M., Taris T. W., Xing L., & Peeters, M. C. W. (2021). Country differences in the relationship between leadership and employee engagement: A meta-analysis.Leadership Quarterly, 32(1), 101458.
[82] *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, 104564.
[83] *Lingmont, D. N., & 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, 120302.
[84] Lipsey, M. W., & Wilson, D. B. (Eds). (2001). Practical meta-analysis. SAGE publications, Inc.
[85] *Loureiro S. M. C., Bilro R. G., & Neto D. (2022). Working with AI: Can stress bring happiness?Service Business, 17(1), 233-255.
[86] *Ma, C., & Ye, J. (2022). Linking artificial intelligence to service sabotage.The Service Industries Journal, 42(13-14), 1054-1074.
[87] Malik A., Budhwar P., & Kazmi B. A. (2023). Artificial intelligence (AI)-assisted HRM: Towards an extended strategic framework.Human Resource Management Review, 33(1), 100940.
[88] Malik A., Thevisuthan P., & De Sliva, T. (2022). Artificial intelligence, employee engagement, experience, and HRM. In A. Malik (Ed.), Strategic human resource management and employment relations: An international perspective (pp. 171-184). Springer International Publishing.
[89] Malik N., Tripathi S. N., Kar A. K., & Gupta S. (2021). Impact of artificial intelligence on employees working in industry 4.0 led organizations.International Journal of Manpower, 43(2), 334-354.
[90] *Man Tang P., 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.
[91] *Marikyan D., Papagiannidis S., Rana O. F., Ranjan R., & Morgan G. (2022). “Alexa, let’s talk about my productivity”: The impact of digital assistants on work productivity.Journal of Business Research, 142, 572-584.
[92] *Matsunaga, M. (2021). Uncertainty management, transformational leadership, and job performance in an AI-powered organizational context.Communication Monographs, 89(1), 118-139.
[93] Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance.Information & Management, 58(3), 103434.
[94] *Mirbabaie M., Brünker F., Möllmann Frick, N. R. J., & Stieglitz S. (2021). The rise of artificial intelligence- understanding the AI identity threat at the workplace.Electronic Markets, 32(1), 73-99.
[95] *Mirbabaie M., Stieglitz S., Brünker F., Hofeditz L., Ross B., & Frick N. R. (2021). Understanding collaboration with virtual assistants-the role of social identity and the extended self.Business & Information Systems Engineering, 63, 21-37.
[96] *Nguyen, T. M., & Malik, A. (2022a). A two-wave cross-lagged study on AI service quality: The moderating effects of the job level and job role.British Journal of Management, 33(3), 1221-1237.
[97] *Nguyen, T. M., & Malik, A. (2022b). Impact of knowledge sharing on employees' service quality: The moderating role of artificial intelligence.International Marketing Review, 39(3), 482-508.
[98] Nauman S., Zheng C., & Naseer S. (2020). Job insecurity and work-family conflict: A moderated mediation model of perceived organizational justice, emotional exhaustion and work withdrawal.International Journal of Conflict Management, 31(5), 729-751.
[99] *Odugbesan J. A., Aghazadeh S., Al Qaralleh R. E., & Sogeke O. S. (2023). Green talent management and employees’ innovative work behavior: The roles of artificial intelligence and transformational leadership.Journal of knowledge management, 27(3), 696-716.
[100] O'Neill T. A., Allen N. J., & Hastings S. E. (2013). Examining the "pros" and "cons" of team conflict: A team-level meta-analysis of task, relationship, and process conflict.Human Performance, 26(3), 236-260.
[101] Oosthuizen, R. M. (2019). Smart technology, artificial intelligence, robotics and algorithms (STARA): Employees’ perceptions and wellbeing in future workplaces. In Potgieter, I., Ferreira, N., & Coetzee, M.(Eds)., Theory, research and dynamics of career wellbeing
[102] Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis.Journal of Applied Psychology, 90(1), 175-181.
[103] Prasad Agrawal,K.(2023). Towards adoption of generative AI in organizational settings. Journal of Computer Information Systems, https://doi.org/10.1080/08874417. 2023.2240744
[104] *Prentice C., Wong I. A., & Lin Z. (2023). Artificial intelligence as a boundary-crossing object for employee engagement and performance.Journal of Retailing and Consumer Services, 73, 103376.
[105] *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.
[106] *Qiu H., Li M., Bai B., Wang N., & 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(4), 1374-1398.
[107] Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox.Academy of Management Review, 46(1), 192-210.
[108] Raj, M., & Seamans, R. (2019). Primer on artificial intelligence and robotics.Journal of Organization Design, 8(1), 1-14.
[109] Rampersad, G. (2020). Robot will take your job: Innovation for an era of artificial intelligence.Journal of Business Research, 116, 68-74.
[110] Ren, J. (2021). Research on financial investment decision based on artificial intelligence algorithm.IEEE Sensors Journal, 21(22), 25190-25197.
[111] Rosenthal, R. (1979). The file drawer problem and tolerance for null results.Psychological Bulletin, 86(3), 638-641.
[112] Rothstein H. R., Sutton A. J., & Borenstein M. (2005). Publication bias in meta-analysis. In Rothstein, H. R., Sutton, A. J., & Borenstein, M.(Eds.), Publication bias in meta‐analysis: Prevention, assessment and adjustments
[113] *Rožman M., Oreški D., & Tominc P. (2023). Artificial- intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA environment.Sustainability, 15(6), 5019.
[114] *Shaikh F., Afshan G., Anwar R. S., Abbas Z., & 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(4), 794-820.
[115] *Singh, R., & Tarkar, P. (2022). Future of work: How artificial intelligence will change the dynamics of work culture and influence employees work satisfaction post-covid-19. In V. Goyal, M. Gupta, S. Mirjalili, & A. Trivedi (Eds.), Proceedings of International Conference on Communication and Artificial Intelligence: ICCAI 2021(pp. 239-260). Springer Nature Singapore, Singapore.
[116] Smith, A., & Anderson, J. (2014). AI, robotics, and the future of jobs.Pew Research Center, 6, 51.
[117] *Song Y., Zhang M., Hu J., & Cao X. (2022). Dancing with service robots: The impacts of employee-robot collaboration on hotel employees’ job crafting.International Journal of Hospitality Management, 103, 103220.
[118] *Tahir K. H. K., Iqbal A., & Khudai M. S. (2021). Articulating manager’s skills and employee performance management through artificial intelligence. Multicultural Education, 7(10), Article e5646563. http://doi.org/10.5281/zenodo.5646563
[119] *Tang P. M., Koopman J., Elfenbein H. A., Zhang J. H., De Cremer D., Li C. H., & Chan E. T. (2022). Using robots at work during the COVID-19 crisis evokes passion decay: Evidence from field and experimental studies.Applied Psychology, 71(3), 881-911.
[120] Terminio R.,& Rimbau Gilabert, E. (2018). The digitalization of the working environment: The advent of robotics, automation, and artificial intelligence (RAAI) from the employees perspective-a scoping review In MCoeckelbergh, J Loh, M Funk, J Seibt, & M Nørskov (Eds), Envisioning robots in society-power, politics and public space (pp 166-177) IOS Press, Amsterdam The advent of robotics, automation, and artificial intelligence (RAAI) from the employees perspective-a scoping review.
[121] *Tong S., Jia N., Luo X., & Fang Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance.Strategic Management Journal, 42(9), 1600-1631.
[122] Torre D. L., Colapinto C., Durosini I., & Triberti S. (2021). Team formation for human-artificial intelligence collaboration in the workplace: A goal programming model to foster organizational change.IEEE Transactions on Engineering Management, 70(5), 1966-1976.
[123] Tversky, A., & Kahneman, D. (1996). On the reality of cognitive illusions.Psychological Review, 103(3), 582-591.
[124] Vaish A., Grossmann T., & Woodward A. (2008). Not all emotions are created equal: The negativity bias in social-emotional development.Psychological bulletin, 134(3), 383-403.
[125] *Verma, S., & Singh, V. (2022). Impact of artificial intelligence-enabled job characteristics and perceived substitution crisis on innovative work behavior of employees from high-tech firms. Computers in Human Behavior, 131, 107215.
[126] Walsh G., Yang Z., Dose D., & Hille P. (2015). The effect of job-related demands and resources on service employees’ willingness to report complaints: Germany versus China.Journal of Service Research, 18(2), 193-209.
[127] *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, 834160.
[128] *Wang W., Chen L., Xiong M., & Wang Y. (2023). Accelerating AI adoption with responsible AI signals and employee engagement mechanisms in health care.Information Systems Frontiers, 25(6), 2239-2256.
[129] Wang X., Lin X., & Shao B. (2022). How does artificial intelligence create business agility? Evidence from chatbots.International Journal of Information Management, 66, 102535.
[130] *Wijayati D. T., Rahman Z., Rahman M. F. W., Arifah I. D. C., & Kautsar A. (2022). A study of artificial intelligence on employee performance and work engagement: The moderating role of change leadership.International Journal of Manpower, 43(2), 486-512.
[131] *Xu G., Xue M., & Zhao J. (2023a). 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), 5147.
[132] *Xu G., Xue M., & Zhao J. (2023b). The relationship of artificial intelligence opportunity perception and employee workplace well-being: A moderated mediation model.International Journal of Environmental Research and Public Health, 20(3), 1974.
[133] *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, 104360.
[134] *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.
[135] Zel, S., & Kongar, E. (2020, September). Transforming digital employee experience with artificial intelligence. In 2020 IEEE/ITU International Conference on Artificial Intelligence for Good (AI4G) (pp. 176-179). IEEE.
[136] *Zeng X., Li S., & Yousaf Z. (2022). Artificial intelligence adoption and digital innovation: How does digital resilience act as a mediator and training protocols as a moderator?Sustainability, 14(14), 8286.
[137] Zhang H., Cui N., Chen D., Zou P., Shao J., Wang X., … Zheng D. (2021). Social support, anxiety symptoms, and depression symptoms among residents in standardized residency training programs: The mediating effects of emotional exhaustion.BMC Psychiatry, 21, 1-8.
[138] 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, 102536.
[139] *Zhu, Y. Q., & Kanjanamekanant, K. (2022). Human-bot co-working: Job outcomes and employee responses.Industrial Management & Data Systems, 123(2), 515-533.
[140] Zirar A., Ali S. I., & Islam N. (2023). Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda.Technovation, 124, 102747.