Exploresearch (ISSN: 3048-815X) ( Vol. 03 | No. 2 | April - June, 2026 )

Determinants of AI-Induced Workplace Stress among IT Professionals in Gurugram: A Secondary Data Analysis

Author: Neha Saxena & Dr. Ruchi Sharma

Background: The fast incorporation of artificial intelligence in the technology industry in India has completely transformed the nature of employment, especially in Gurugram, which serves as the IT epicenter of the nation. However, while increasing international attention has been devoted to work stress generated by the use of AI in organizations, empirical research based on the Indian IT scenario remains relatively rare. Objective: This study aims to analyze the determinants of AI-induced occupational stress among the workforce employed in the IT industry in Gurugram through an evidence-based secondary data analysis for the period of 2015 to 2026. Methods: Based on the empirical literature indexed in Scopus, as well as findings from reports provided by NASSCOM, Deloitte, McKinsey, WEF, and ILO, a synthetic database of 18,743 employees working in the IT sector in Gurugram was compiled. The research incorporates insights derived from Technostress Theory (Brod, 1984; Tarafdar et al., 2015), the Job Demands-Resources model (Bakker & Demerouti, 2017), and the Technology Acceptance model (Davis, 1989; Venkatesh et al., 2016). Results: The regression model (R² = 0.67) shows that work intensification (β = 0.41, p < 0.001) and AI anxiety (β = 0.38, p < 0.001) predict stress most strongly. Technostress acts as a significant mediator in all paths (indirect effect = 0.29, 95% CI [0.21, 0.37]), whereas organizational support mitigates stress effects substantially (interaction effect β = −0.24, p < 0.01). Conclusions: The implementation of AI-based solutions without considering people's health will likely backfire because it will negate any productivity improvements. Organizational interventions can decrease stress risks among employees with technostress by around 65%.

Saxena, N. & Sharma, R. (2026). Determinants of AI-Induced Workplace Stress among IT Professionals in Gurugram: A Secondary Data Analysis. Exploresearch, 03(02), 142–151. https://doi.org/10.62823/ExRe/2026/03/02.228

  1. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Sage.
  2. Baber, H., Bhardwaj, P., & Chauhan, G. (2023). AI anxiety and psychological distress among Indian IT professionals. Information Technology & People, 36(4), 1548–1572. https://doi.org/10.1108/ITP-03-2022-0189
  3. Bakker, A. B., & Demerouti, E. (2007). The Job Demands-Resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115
  4. Bakker, A. B., & Demerouti, E. (2017). Job demands-resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/ocp0000056
  5. Ball, K. (2021). Electronic monitoring and surveillance in the workplace. Publications Office of the European Union. https://doi.org/10.2767/6382
  6. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182.
  7. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Wiley.
  8. Brod, C. (1984). Technostress: The human cost of the computer revolution. Addison-Wesley.
  9. Brynjolfsson, E., & McAfee, A. (2017). The second machine age. W. W. Norton & Company.
  10. Chandra, S., & Sharma, A. (2022). Technostress and psychological well-being among IT employees in NCR. Vikalpa, 47(2), 88–104. https://doi.org/10.1177/02560909221104726
  11. Cheng, B., Zhou, X., Guo, G., & Yang, K. (2021). Perceived AI stress and employee well-being. Journal of Applied Psychology, 106(7), 1026–1043. https://doi.org/10.1037/apl0000887
  12. Chua, R. Y. J., & Konar, E. (2021). When AI judges. Organizational Behavior and Human Decision Processes, 165, 72–88. https://doi.org/10.1016/j.obhdp.2021.04.001
  13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  14. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
  15. (2022). Global technology leadership study 2022. Deloitte Insights.
  16. (2023). Global human capital trends 2023. Deloitte Insights.
  17. Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512.
  18. Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71(3), 500–507.
  19. Feldman, D. C. (1996). The nature, antecedents and consequences of underemployment. Journal of Management, 22(3), 385–407.
  20. Green, F. (2004). Work intensification, discretion, and the decline in well-being at work. Eastern Economic Journal, 30(4), 615–625.
  21. Haryana State Employment Exchange. (2022). Annual report on employment in Gurugram district. Government of Haryana.
  22. Hong, Q. N., et al. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018. Education for Information, 34(4), 285–291.
  23. Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172.
  24. International Labour Organization (ILO). (2022). Working towards mental health in the workplace. ILO.
  25. International Labour Organization (ILO). (2023). World employment and social outlook 2023. ILO.
  26. Johnston, M. P. (2017). Secondary data analysis: A method of which the time has come. Qualitative and Quantitative Methods in Libraries, 3(3), 619–626.
  27. Langer, M., König, C. J., & Hemsing, V. (2021). Is anybody home? Computers in Human Behavior, 119, 106711.
  28. Maier, C., Laumer, S., Wirth, J., & Weitzel, T. (2019). Technostress and the hierarchical levels of personality. European Journal of Information Systems, 28(5), 496–522.
  29. Mazzola, J. J., & Disselkamp, R. (2019). Unpacking technology demands and job strain. Journal of Occupational Health Psychology, 24(1), 102–115.
  30. McKinsey Global Institute. (2022). The state of AI in 2022. McKinsey & Company.
  31. McKinsey Global Institute. (2023). Generative AI and the future of work in America. McKinsey & Company.
  32. Molino, M., et al. (2020). Wellbeing costs of technology use during Covid-19 remote working. Sustainability, 12(15), 5911.
  33. Moore, P. V. (2018). The quantified self in precarity. Routledge.
  34. (2022). Future of work: India IT workforce skills and AI readiness survey 2022. NASSCOM.
  35. (2023). India AI landscape 2023. NASSCOM.
  36. NASSCOM & Deloitte. (2022). Workforce wellness in the Indian IT sector. NASSCOM.
  37. (2023). OECD guidelines for responsible business conduct and AI in the workplace. OECD Publishing.
  38. Page, M. J., et al. (2021). The PRISMA 2020 statement. BMJ, 372, n71.
  39. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing indirect effects. Behavior Research Methods, 40(3), 879–891.
  40. PricewaterhouseCoopers (PwC). (2023). Workforce intelligence report 2023. PwC.
  41. Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users. Information Systems Research, 19(4), 417–433.
  42. Rhoades, L., & Eisenberger, R. (2002). Perceived organizational support: A review. Journal of Applied Psychology, 87(4), 698–714.
  43. Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.
  44. Schaufeli, W. B., & Taris, T. W. (2014). A critical review of the Job Demands-Resources Model. In G. F. Bauer & O. Hämmig (Eds.), Bridging occupational, organizational and public health (pp. 43–68). Springer.
  45. Schwab, K. (2016). The fourth industrial revolution. World Economic Forum.
  46. Spurk, D., & Straub, C. (2020). Flexible employment relationships and careers in times of COVID-19. Journal of Vocational Behavior, 119, 103435.
  47. Sverke, M., Hellgren, J., & Näswall, K. (2002). No security: A meta-analysis of job insecurity. Journal of Occupational Health Psychology, 7(3), 242–264.
  48. Tarafdar, M., Tu, Q., Ragu-Nathan, T. S., & Ragu-Nathan, B. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301–328.
  49. Tarafdar, M., Bolman Pullins, E., & Ragu-Nathan, T. S. (2015). Technostress: Negative effect on performance and possible mitigations. Information Systems Journal, 25(2), 103–132.
  50. Tarafdar, M., Cooper, C. L., & Stich, J.-F. (2019). The technostress trifecta. Information Systems Journal, 29(1), 6–42.
  51. 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.
  52. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology. Journal of the Association for Information Systems, 17(5), 328–376.
  53. Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48.
  54. World Economic Forum (WEF). (2020). The future of jobs report 2020. WEF.
  55. World Economic Forum (WEF). (2023). The future of jobs report 2023. WEF.

DOI:

Article DOI: 10.62823/ExRe/2026/03/02.228

Download Full Paper: