The Relationship Between Algorithmic Performance Tracking Systems and Diminishing Employee Retention Rates in Contemporary Technical Sectors

Author(s): Meera Kalyani

Affiliation: Department of Human Resource Management and Organisational Studies, Symbiosis Institute of Business Management, Pune, India

Page No: 1-9

Volume issue & Publishing Year: Volume 3, Issue 7, 2026/07/01

Journal: International Journal of Modern Engineering and Management | IJMEM

ISSN NO: 3048-8230

Download PDF Cite this article

Abstract:

This quantitative study investigates the direct impact of automated performance monitoring on workforce stability in contemporary technical sectors. As digital infrastructure firms increasingly deploy algorithmic performance tracking systems (APTS) — encompassing real-time keystroke logging, productivity dashboards, automated code-commit surveillance, ticket-closure velocity metrics, and AI-driven performance scoring — questions arise about the unintended consequences of continuous metric enforcement on voluntary employee attrition. Drawing on Job Demands-Resources (JD-R) Theory, Self-Determination Theory, and Algorithmic Management Theory, this study proposes and tests a quantitative model in which APTS intensity predicts voluntary resignation intention (VRI) through the mediating pathways of perceived autonomy erosion (PAE) and metric-induced occupational stress (MIOS), with employee career stage as a moderating variable. Survey data were collected from 412 technical professionals across 22 digital infrastructure firms in Pune, Bengaluru, and Hyderabad using a two-wave time-lagged design. Hierarchical multiple regression and bootstrapped mediation analysis (5,000 replications) were employed. APTS intensity significantly predicts VRI (β = 0.44, p < 0.001, ΔR² = 0.19). Perceived autonomy erosion (β = 0.31, p < 0.001) and metric-induced occupational stress (β = 0.27, p < 0.001) are significant partial mediators, together accounting for 62% of the total APTS–VRI relationship. Early-career professionals (0–4 years’ experience) report the highest MIOS scores (M = 4.21, SD = 0.61) and the strongest APTS–VRI relationship (β = 0.58, p < 0.001), while senior professionals (≥10 years) show attenuated but still significant associations (β = 0.29, p < 0.01). The study identifies three alternative evaluation framework archetypes — Outcome-Anchored Review (OAR), Developmental Metric Portfolios (DMP), and Peer-Calibrated Contribution Scoring (PCCS) — and proposes an implementable decision matrix for firms seeking to preserve operational accountability without triggering metric-driven voluntary turnover.

Keywords:

algorithmic performance tracking, employee retention, voluntary resignation intention, autonomy erosion, metric-induced stress, job demands-resources theory, self-determination theory, algorithmic management, digital infrastructure, technical workforce

Reference:

• [1] Bakker, A. B., & Demerouti, E. (2017). Job demands-resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3), 273–285.

• [2] Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behaviour, 24(4), 385–396.

• [3] Deci, E. L., Olafsen, A. H., & Ryan, R. M. (2017). Self-determination theory in work organizations: The state of a science. Annual Review of Organizational Psychology and Organizational Behavior, 4, 19–43.

• [4] Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.

• [5] Edmondson, A. C. (1999). Psychological safety and learning behaviour in work teams. Administrative Science Quarterly, 44(2), 350–383.

• [6] Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (3rd ed.). Guilford Press.

• [7] 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.

• [8] Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. Proceedings of the ACM CHI Conference on Human Factors in Computing Systems, 1603–1612.

• [9] Mercer India. (2023). Total Remuneration Survey: IT-ITeS Sector India 2023. Mercer LLC.

• [10] 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.

• [11] Möhlmann, M., & Zalmanson, L. (2017). Hands on the wheel: Navigating algorithmic management and Uber drivers’ autonomy. Proceedings of the International Conference on Information Systems (ICIS 2017), Seoul.

• [12] NASSCOM. (2023). Digital Workforce Monitor: India IT-BPM 2023. NASSCOM Research.

• [13] Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 3758–3784.

• [14] SHRM India. (2023). Voluntary Attrition Drivers in the Indian IT Sector: Exit Interview Benchmarking Report. Society for Human Resource Management India.

• [15] Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301–328.

• [16] Veen, A., Barratt, T., & Goods, C. (2020). Platform-capital’s ‘app-etite’ for control: A labour process analysis of food-delivery work in Australia. Work, Employment and Society, 34(3), 388–406.

• [17] Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019). Good gig, bad gig: Autonomy and algorithmic control in the global gig economy. Work, Employment and Society, 33(1), 56–75.

• [18] Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.