Exploring the Impact of AI Integration on Proactive Service Behaviour Among Indian Retail Employees: A Transactional Stress and Technology Self-Efficacy Perspective
Keywords:
Artificial Intelligence, Retail Employees, AI-Enabled Process Transformation, Address of the President, Proactive Service BehaviourAbstract
As artificial intelligence (AI) continues to transform service delivery across sectors, the Indian retail industry faces growing pressure to integrate AI-driven systems into customer-facing operations. While such integration offers potential efficiencies, its impact on frontline employees remains complex and dual-faceted. Anchored in the Transactional Stress Theory, this research explores how AI-Enabled Process Transformation (AIEPT) influences Volitional Customer Service Initiative (VCSI) among retail employees in the Delhi NCR region of India.
Specifically, the study explores how employees cognitively appraise AI integration—either as a Perceived Growth Appraisal (PGA) or a Perceived Obstructive Appraisal (POA)—and how these appraisals shape downstream outcomes through Workplace Vitality and Learning (WVL) and Perceived Employment Instability (PEI), respectively.
Furthermore, this study investigates the influence of Technology Self-Efficacy (TSE) as a moderating factor in determining how employees cognitively appraise AI-enabled process transformation. Specifically, TSE is proposed to amplify the relationship between AIEPT and Perceived Growth Appraisal (PGA) while dampening the association between AIEPT and Perceived Obstructive Appraisal (POA). In addition, Perceived Institutional Support Climate (PISC) is expected to buffer the negative effects of POA and strengthen the positive influence of PGA, whereas Adaptive Psychological Capacity (APC) is theorized to mitigate the impact of POA on Perceived Employment Instability (PEI).
This study employs a time-lagged survey approach and gathers data from frontline employees working in AI-adopting retail firms within the Delhi NCR region. The proposed relationships are examined using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings are expected to offer critical insights for both theory and practice—clarifying the psychological mechanisms the process by which AI adoption bears upon employees-driven service behavior and identifying organizational levers to mitigate its negative effects while amplifying positive outcomes.
References
1. Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Press.
2. Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman and Company.
3. Bermes, C. (2021). Technostress as a risk factor for mental health in the workplace: A systematic review. Occupational Health Science, 5(1), 1–29. https://doi.org/10.1007/s41542-020-00076-0
4. Chen, Y., Wang, Y., & Sun, J. (2022). How AI reshapes work: Understanding the link between AI and employee creativity through knowledge sharing. Journal of Business Research, 146, 120–129. https://doi.org/10.1016/j.jbusres.2022.03.067
5. Cheng, G. H. L., & Chan, D. K. S. (2008). Who suffers more from job insecurity? A meta‐analytic review. Applied Psychology, 57(2), 272–303. https://doi.org/10.1111/j.1464-0597.2007.00312.x
6. Cheng, J., Gan, Y., Song, Y., & Li, H. (2021). Employee thriving at work and innovation: The role of intrinsic motivation and knowledge sharing. Human Resource Development Quarterly, 32(2), 207–223. https://doi.org/10.1002/hrdq.21411
7. Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2), 310–357. https://doi.org/10.1037/0033-2909.98.2.310
8. Deloitte. (2021). The AI journey in India: Enablers, challenges and the road ahead. Deloitte Insights. https://www2.deloitte.com
9. De Witte, H., Pienaar, J., & De Cuyper, N. (2016). Review of 30 years of longitudinal studies on the association between job insecurity and health and well-being: Is there causal evidence? Australian Psychologist, 51(1), 18–31. https://doi.org/10.1111/ap.12176
10. Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., 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. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
11. Finstad, K. (2010). Response interpolation and scale sensitivity: Evidence against 5-point scales. Journal of Usability Studies, 5(3), 104–110.
12. Folkman, S., & Lazarus, R. S. (1988). Manual for the Ways of Coping Questionnaire. Consulting Psychologists Press.
13. 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. https://doi.org/10.1016/j.techfore.2016.08.019
14. Grant, A. M., & Ashford, S. J. (2008). The dynamics of proactivity at work. Research in Organizational Behavior, 28, 3–34. https://doi.org/10.1016/j.riob.2008.04.002
15. Jensen, J. M., Patel, P. C., & Messersmith, J. G. (2017). High-performance work systems and job control: Consequences for anxiety, role overload, and turnover intentions. Journal of Management, 39(6), 1699–1724. https://doi.org/10.1177/0149206314566066
16. Keng, S. L., Smoski, M. J., & Robins, C. J. (2011). Effects of mindfulness on psychological health: A review of empirical studies. Clinical Psychology Review, 31(6), 1041–1056. https://doi.org/10.1016/j.cpr.2011.04.006
17. KPMG. (2020). Retail trends in India 2020. https://assets.kpmg/content/dam/kpmg/in/pdf/2020/01/Retail-Trends-Report-2020.pdf
18. Marinova, D., Ye, J., & Singh, J. (2017). Frontline employee customer-oriented behaviors: Retail climate, perceived supervisor support, and performance outcomes. Journal of the Academy of Marketing Science, 46(3), 337–356. https://doi.org/10.1007/s11747-017-0545-0
19. NASSCOM. (2022). AI adoption in Indian enterprises: Survey insights. https://nasscom.in/knowledge-center/publications
20. Pan, Y., & Zhang, L. (2018). The dark side of artificial intelligence in customer service: Emotional labor and burnout. Service Industries Journal, 38(3–4), 301–322. https://doi.org/10.1080/02642069.2017.1387533
21. Parker, S. K., & Collins, C. G. (2010). Taking stock: Integrating and differentiating multiple proactive behaviors. Journal of Management, 36(3), 633–662. https://doi.org/10.1177/0149206308321554
22. PwC. (2021). How AI is reshaping jobs in India. PricewaterhouseCoopers. https://www.pwc.in
23. Qiu, Y., Xia, Y., & Tang, T. L. P. (2022). How AI shapes service employees’ behaviors: A social cognitive theory perspective. Journal of Service Research, 25(3), 325–341. https://doi.org/10.1177/10946705211052721
24. RAI (Retailers Association of India). (2021). State of AI in Indian retail. https://www.rai.net.in
25. Raisch, S., & Krakowski, S. (2020). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
26. Rhoades, L., & Eisenberger, R. (2002). Perceived organizational support: A review of the literature. Journal of Applied Psychology, 87(4), 698–714. https://doi.org/10.1037/0021-9010.87.4.698
27. Ryan, R. M., & Frederick, C. (1997). On energy, personality, and health: Subjective vitality as a dynamic reflection of well-being. Journal of Personality, 65(3), 529–565. https://doi.org/10.1111/j.1467-6494.1997.tb00326.x
28. Sharma, A., & Singh, R. (2023). AI in Indian retail: Innovation, integration, and implications. Journal of Retail Technology & Strategy, 15(1), 22–35.
29. Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2021). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 63(3), 56–83. https://doi.org/10.1177/0008125621992226
30. Singh, R., & Gupta, M. (2020). Digital literacy and frontline employee outcomes in India’s retail sector. Indian Journal of Industrial Relations, 56(2), 235–250.
31. Song, L. J., Almeida, D. M., & Wu, C. (2018). Job insecurity and employee performance: A meta-analytic review. Academy of Management Proceedings, 2018(1), 12142. https://doi.org/10.5465/AMBPP.2018.12142abstract
32. Staufenbiel, T., & König, C. J. (2010). A model for the effects of job insecurity on performance, turnover intention, and absenteeism. Journal of Occupational and Organizational Psychology, 83(1), 101–117. https://doi.org/10.1348/096317908X401912
33. Sun, R., Wang, C., & Song, Z. (2023). AI-powered innovation in service: A review and future agenda. Service Science, 15(1), 1–20. https://doi.org/10.1287/serv.2022.0329
34. Tugade, M. M., & Fredrickson, B. L. (2004). Resilient individuals use positive emotions to bounce back from negative emotional experiences. Journal of Personality and Social Psychology, 86(2), 320–333. https://doi.org/10.1037/0022-3514.86.2.320
35. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
36. Webster, J. R., Beehr, T. A., & Love, K. (2011). Extending the challenge-hindrance model of occupational stress: The role of appraisal. Journal of Vocational Behavior, 79(2), 505–516. https://doi.org/10.1016/j.jvb.2011.02.001
37. Yan, L., Qian, X., & Yu, H. (2023). Employee resilience in technology-driven workplaces: The mediating role of adaptability. International Journal of Human Resource Management, 34(5), 870–890. https://doi.org/10.1080/09585192.2022.2066597
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dr. Anju
This work is licensed under a Creative Commons Attribution 4.0 International License.
