AI Chatbots and Employee Experience: Implications for Engagement, Satisfaction, and Retention
DOI:
https://doi.org/10.64882/ijrt.v14.iS2.1170Keywords:
AI Chatbots, employee experience, service sector, employee engagement, retention intentionAbstract
The high rate at which AIs have been deployed to create Chatbots in human resource management has significantly transformed service delivery, especially in industries that are always facing human-organizational contact. Although previous research has given more attention to efficiency and cost benefits, the findings of the impact of such Chatbots on the employee experience and the outcomes are limited as far as empirical studies are concerned. The study aims to fill this gap by evaluating the quality of the HR Chabot usage on the experience of employees and the impact of this experience on their engagement, job satisfaction, and intention to remain in service-sector organisations.
A cross-sectional survey was used as the method of collecting data, and the sample of the research included employees working in IT/ITES, banking, healthcare, education, and hospitality organisations, where HR Chatbots are utilized regularly to receive HR-related services. A multiple regression analysis was done with adjustment of major demographic variables. The findings of the analysis indicate that employee experience is greatly predicted by the quality of HR Chabot use. Moreover, employee experience has a significant and positive effect on employee engagement and job satisfaction which are both significant predictors of retention intention.
Moreover, the results show that the proportion of variance of retention intention explained by employee experience is significantly higher than by the quality of HR Chabot use alone, which highlights the central role of experiential perceptions in technology-facilitated HR systems.
The research contributes significantly to the digital HRM literature through the evidence presented in regression form that is based on the service industry. It highlights how experience-based Chabot design is strategic in terms of boosting employee engagement, satisfying, and retention.
References
Bondarouk, T., & Brewster, C. (2016). Conceptualising the future of HRM and technology research. The International Journal of Human Resource Management, 27(21), 2652–2671.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30.
Grandey, A. A., & Gabriel, A. S. (2015). Emotional labor at a crossroads: Where do we go from here? Annual Review of Organizational Psychology and Organizational Behavior, 2, 323–349.
https://doi.org/10.1007/s00146-022-01490-7
https://doi.org/10.1037/0021-9010.63.4.408
https://doi.org/10.1080/07421222.2003.11045748
https://doi.org/10.1080/09585192.2016.1232296
https://doi.org/10.1108/02683940610690169
https://doi.org/10.1108/ER-01-2021-0036
https://doi.org/10.1108/ER-08-2023-0402
https://doi.org/10.1108/JOSM-09-2024-0381
https://doi.org/10.1111/j.2044-8325.1985.tb00196.x
https://doi.org/10.1145/3351095.3372828
https://doi.org/10.1146/annurev-orgpsych-032414-111400
https://doi.org/10.1177/2397002220921131
https://doi.org/10.2307/249008
https://doi.org/10.2307/256287
https://us.sagepub.com/en-us/nam/commitment-in-the-workplace/book204036
https://vpr.psych.umn.edu/instruments/msq-minnesota-satisfaction-questionnaire
https://www.wiley.com/en-us/The+Employee+Experience+Advantage-p-9781118877241
Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work. Academy of Management Journal, 33(4), 692–724.
Meyer, J. P., & Allen, N. J. (1997). Commitment in the workplace: Theory, research, and application. Sage Publications.
Mobley, W. H., Horner, S. O., & Hollingsworth, A. T. (1978). An evaluation of precursors of hospital employee turnover. Journal of Applied Psychology, 63(4), 408–414.
Morgan, J. (2017). The employee experience advantage: How to win the war for talent by giving employees the workplaces they want, the tools they need, and a culture they can celebrate. Wiley.
Murugesan, S. (2023). Artificial intelligence in human resource management: Applications and challenges. AI & Society, 38(4), 1281–1293.
Nawaz, N. (2024). Adoption of artificial intelligence in HRM: Evidence from service-sector organizations. Employee Relations. Advance online publication.
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 469–481.
Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21(7), 600–619.
Spector, P. E. (1985). Measurement of human service staff satisfaction: Development of the Job Satisfaction Survey. Journal of Occupational Psychology, 58(2), 103–115.
Strohmeier, S. (2020). Digital human resource management: A conceptual clarification. German Journal of Human Resource Management, 34(3), 345–365.
Valtonen, A., Turunen, T., & Saarijärvi, H. (2025). Human–AI collaboration and employee experience in digital service systems. Journal of Service Management. Advance online publication.
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. Employee Relations, 44(3), 513–543.
Weiss, D. J., Dawis, R. V., England, G. W., & Lofquist, L. H. (1967). Manual for the Minnesota Satisfaction Questionnaire. University of Minnesota.
Yang, Y., Guo, Y., & Chen, J. (2024). Employee responses to AI-enabled workplace systems: The mediating role of perceived usefulness. Information & Management, 61(2), 103730. https://doi.org/10.1016/j.im.2023.103730
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