Mapping Review on AI, Employee Experience, and Work Performance: A Bibliometric Analysis

This bibliometric review examines the evolving relationship between artificial intelligence (AI), employee experience (EX), and work performance (WP) in organisational contexts from 2000 to 2025. As AI continues to transform human resource management practices, increasing scholarly attention has been directed toward understanding its impact on employee-centric outcomes and organisational effectiveness. The study analyses 373 peer-reviewed articles indexed in the Web of Science database, selected through the PRISMA screening process. Bibliometric and science mapping techniques were employed using Biblioshiny and VOSviewer to evaluate publication trends, leading authors and institutions, geographic contributions, keyword co-occurrence, and collaboration networks. The findings reveal a significant surge in research output after 2020, indicating heightened academic and practical interest in AI-driven HRM. Key themes identified include AI-enabled decision-making, employee engagement, performance enhancement, and organisational productivity. More recent studies increasingly focus on ethical considerations, transparency, employee well-being, and inclusivity in AI applications. This study offers a novel contribution by integrating AI, employee experience, and work performance into a single analytical framework, an area that has received limited systematic exploration. By mapping thematic evolution over 25 years, it highlights a clear shift toward human-centred and sustainability-oriented perspectives in AI research. Additionally, the study uncovers collaboration patterns and conceptual developments, providing valuable insights for future research directions in the field.

Artificial Intelligence (AI), Employee Experience (EX), Work Performance (WP), Human Resource Management (HRM), Bibliometric Review.

1. Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), 1166. https://doi.org/10.3390/su16031166 [Google Scholar] [Crossref]

2. Arora, M., Gupta, J., Mittal, A., & Prakash, A. (2024). A bibliometric review of artificial intelligence technologies in human resource management: An overview of research trends. Global Knowledge, Memory and Communication. https://doi.org/10.1108/GKMC-04-2024-0237 [Google Scholar] [Crossref]

3. Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I. (2022). Artificial intelligence and human workers interaction at team level: A conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower, 43(1), 75–88. https://doi.org/10.1108/IJM-01-2021-0052 [Google Scholar] [Crossref]

4. 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.https://doi.org/10.1016/j.techfore.2020.120420 [Google Scholar] [Crossref]

5. Bessen, J. (2019). AI and jobs: The role of demand (No. w24235). National Bureau of Economic Research. https://doi.org/10.3386/w24235 [Google Scholar] [Crossref]

6. Bhandari, A. (2024). Demystifying Organization Success: A Bibliometric Analysis and Future Research Agenda. FIIB Business Review, 15(1), 36-58. https://doi.org/10.1177/23197145231216861 [Google Scholar] [Crossref]

7. 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.https://doi.org/10.1016/j.jbusres.2020.08.018 [Google Scholar] [Crossref]

8. Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., ... & Varma, A. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606-659.10.1111/1748-8583.12524 [Google Scholar] [Crossref]

9. Cheng, K. T., Chang, K., & Tai, H. W. (2022). AI boosts performance but affects employee emotions. Information Resources Management Journal, 35(1), 1–18. https://doi.org/10.4018/irmj.314220 [Google Scholar] [Crossref]

10. 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), 100899.https://doi.org/10.1016/j.hrmr.2022.100899 [Google Scholar] [Crossref]

11. Dastane, O., & Haba, H. F. (2023). The Landscape of Digital Natives Research: A Bibliometric and Science Mapping Analysis. FIIB Business Review, 0(0). https://doi.org/10.1177/23197145221137960 [Google Scholar] [Crossref]

12. Dixon, J., Hong, B., & Wu, L. (2021). The robot revolution: Managerial and employment consequences for firms. Management Science, 67(9), 5586–5605. https://doi.org/10.1287/mnsc.2020.3812 [Google Scholar] [Crossref]

13. Erixon, F. (2018). Recipe: The economic benefits of globalization for business and consumers. https://ecipe.org/publications/the-economic-benefits-of-globalization [Google Scholar] [Crossref]

14. Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022). The dark sides of people analytics: reviewing the perils for organisations and employees. European Journal of Information Systems, 31(3), 410-435.10.1080/0960085X.2021.1927213 [Google Scholar] [Crossref]

15. Grewal, D., Benoit, S., Noble, S. M., Guha, A., Ahlbom, C. P., & Nordfält, J. (2023). Leveraging in-store technology and AI: Increasing customer and employee efficiency and enhancing their experiences. Journal of Retailing, 99(4), 487–504. https://doi.org/10.1016/j.jretai.2023.10.002 [Google Scholar] [Crossref]

16. Hughes, C., Robert, L., Frady, K., & Arroyos, A. (2019). Artificial intelligence, employee engagement, fairness, and job outcomes. In Managing Technology and Middle- and Low-skilled Employees (The Changing Context of Managing People) (pp. 61–68). Emerald Publishing. https://doi.org/10.1108/978-1-78973-077-720191005 [Google Scholar] [Crossref]

17. Jia, Q., Guo, Y., Li, R., Li, Y., & Chen, Y. (2018). A conceptual artificial intelligence application framework in human resource management. ICEB 2018 Proceedings (pp. 91). https://aisel.aisnet.org/iceb2018/9 [Google Scholar] [Crossref]

18. Kaushal, N., Kaurav, R. P. S., & Sivathanu, B. (2023). Artificial intelligence and HRM: Identifying future research agenda using systematic literature review and bibliometric analysis. Management Review Quarterly, 73, 455–493. https://doi.org/10.1007/s11301-021-00249-2 [Google Scholar] [Crossref]

19. 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.https://doi.org/10.1108/IJCHM-07-2020-0789 [Google Scholar] [Crossref]

20. Malik, A., Budhwar, P., Mohan, H., & NR, S. (2023). Employee experience—the missing link for engaging employees: Insights from an MNE's AI-based HR ecosystem. Human Resource Management, 62(1), 97–115. https://doi.org/10.1002/hrm.22133 [Google Scholar] [Crossref]

21. Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2022). Impact of artificial intelligence on employees working in industry 4.0 led organizations. International Journal of Manpower, 43(2), 334-354.https://doi.org/10.1108/IJM-03-2021-0173 [Google Scholar] [Crossref]

22. 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), 1019-1054.10.5465/amj.2020.1516 [Google Scholar] [Crossref]

23. Mathushan, P., Gamage, A. S., & Wachissara, V. (2023). Human resource management and artificial intelligence: A bibliometric exploration. Journal of Business Research and Insights (Former Vidyodaya Journal of Management), 9(1). https://doi.org/10.31357/vjm.v9iI.6370 [Google Scholar] [Crossref]

24. Özmen, Ö. M., & Gökhan, S. (2024). Examining studies on the relationship between employee experience and digital transformation through bibliometric analysis. International Social Sciences Studies Journal, 10(8). https://doi.org/10.5281/zenodo.13383676 [Google Scholar] [Crossref]

25. Panda, G., Aggarwal, S., Kaswan, M. S., & Dhillon, K. (2025). Artificial intelligence in agile human resource practices: Systematic literature review and bibliometric analysis. International Journal of Lean Six Sigma, 16(4), 918–945. https://doi.org/10.1108/IJLSS-07-2024-0159 [Google Scholar] [Crossref]

26. Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2023). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. In Artificial intelligence and international HRM (pp. 60-82). Routledge 10.1080/09585192.2021.1879206 [Google Scholar] [Crossref]

27. 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.https://doi.org/10.1016/j.hrmr.2021.100857 [Google Scholar] [Crossref]

28. Prentice, C., Dominique Lopes, S., & Wang, X. (2020). Emotional intelligence or artificial intelligence–an employee perspective. Journal of Hospitality Marketing & Management, 29(4), 377-403.10.1080/19368623.2019.1647124 [Google Scholar] [Crossref]

29. Qamar, Y., Agrawal, R. K., Samad, T. A., & Jabbour, C. J. C. (2021). When technology meets people: The interplay of artificial intelligence and human resource management. Journal of Enterprise Information Management, 34(5), 1339–1370. https://doi.org/10.1108/JEIM-11-2020-0436 [Google Scholar] [Crossref]

30. Ramachandran, K. K., Mary, A. A. S., Hawladar, S., Asokk, D., Bhaskar, B., & Pitroda, J. R. (2022). Machine learning and role of artificial intelligence in optimizing work performance and employee behavior. Materials Today: Proceedings, 51, 2327–2331. https://doi.org/10.1016/j.matpr.2021.11.544 [Google Scholar] [Crossref]

31. Smith, A., & Anderson, J. (2020). AI, robotics, and the future of jobs. Pew Research Center. [Google Scholar] [Crossref]

32. Soulami, M., Benchekroun, S., & Galiulina, A. (2024). Exploring how AI adoption in the workplace affects employees: A bibliometric and systematic review. Frontiers in Artificial Intelligence, 7, 1473872. https://doi.org/10.3389/frai.2024.1473872 [Google Scholar] [Crossref]

33. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California management review, 61(4), 15-42.https://doi.org/10.1177/0008125619867910 [Google Scholar] [Crossref]

34. 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. https://doi.org/10.1002/smj.3322 [Google Scholar] [Crossref]

35. 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.https://doi.org/10.1080/09585192.2020.1871398 [Google Scholar] [Crossref]

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