Algorithmic Management and the Erosion of Managerial Trust: A Conceptual Framework for Human-AI Supervisory Relations in Organizations
Keywords:
Algorithmic management, organizational justice, algorithmic control, employee voice, human resource management, psychological contract, managerial trustAbstract
Supervising, monitoring, scheduling, evaluating performance, and sometimes even disciplining, are becoming increasingly delegated to algorithmic systems. The primary reason for implementing algorithmic management is to enhance efficiency, consistency, and scalability in managing employees. The paper claims that the same systems can, at the same time, take away the trust of managers, a different and not well-studied cost of an organization, which disproportionately affects the least powerful actors in the organization: workers. The paper is based on Algorithmic Management Theory, Psychological Contract Theory and Organizational Justice Theory and integrates the characteristics of algorithmic management, the opacity of algorithms, the intensity of surveillance, automated evaluation and constrained worker voice with a mediating process of managerial trust erosion, which consists of procedural injustice, interactional injustice and psychological contract breach. The framework identifies the factor of organizational outcomes as turnover intention, discretionary effort, employee voice, and organizational legitimacy as responsible for this erosion. Moderators include algorithmic transparency, human-override discretion, worker voice mechanisms, algorithmic literacy, justice climate and occupational context. Eight testable propositions are presented and the framework is expanded to hybrid, white-collar knowledge work, where algorithmic supervision is becoming ubiquitous with the guise of workplace analytics and productivity software. The paper ends by outlining a research agenda for developing algorithmic supervision without undermining the trust that sustains the authority of management.
References
Abraham, M., Niessen, C., Schnabel, C., Lorek, K., Grimm, V., Möslein, K., & Wrede, M. (2019). Electronic monitoring at work: The role of attitudes, functions, and perceived control for the acceptance of tracking technologies. Human Resource Management Journal, 29(4), 657–675. https://doi.org/10.1111/1748-8583.12250
Alge, B. J. (2001). Effects of computer surveillance on perceptions of privacy and procedural justice. Journal of Applied Psychology, 86(4), 797–804. https://doi.org/10.1037/0021-9010.86.4.797
Ball, K. (2010). Workplace surveillance: An overview. Labor History, 51(1), 87–106. https://doi.org/10.1080/00236561003654776
Bujold, A., Parent-Rocheleau, X., & Gaudet, M.-C. (2022). Opacity behind the wheel: The relationship between transparency of algorithmic management, justice perception, and intention to quit among truck drivers. Computers in Human Behavior Reports, 8, Article 100245. https://doi.org/10.1016/j.chbr.2022.100245
Colquitt, J. A. (2001). On the dimensionality of organizational justice: A construct validation of a measure. Journal of Applied Psychology, 86(3), 386–400. https://doi.org/10.1037/0021-9010.86.3.386
Colquitt, J. A., Scott, B. A., Rodell, J. B., Long, D. M., Zapata, C. P., Conlon, D. E., & Wesson, M. J. (2013). Justice at the millennium, a decade later: A meta-analytic test of social exchange and affect-based perspectives. Journal of Applied Psychology, 98(2), 199–236. https://doi.org/10.1037/a0031757
Curchod, C., Patriotta, G., Cohen, L., & Neysen, N. (2020). Working for an algorithm: Power asymmetries and agency in online work settings. Administrative Science Quarterly, 65(3), 644–676. https://doi.org/10.1177/0001839219867024
open? Academy of Management Journal, 50(4), 869–884. https://doi.org/10.5465/amj.2007.26279183
Dirks, K. T., & Ferrin, D. L. (2002). Trust in leadership: Meta-analytic findings and implications for research and practice. Journal of Applied Psychology, 87(4), 611–628. https://doi.org/10.1037/0021-9010.87.4.611
Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–132. https://doi.org/10.1111/1748-8583.12258
Fulmer, C. A., & Gelfand, M. J. (2012). At what level (and in whom) we trust: Trust across multiple organizational levels. Journal of Management, 38(4), 1167–1230. https://doi.org/10.1177/0149206312439327
Jeske, D., & Santuzzi, A. (2015). Monitoring what and how: Psychological implications of electronic performance monitoring. New Technology, Work and Employment, 30(1), 62–78. https://doi.org/10.1111/ntwe.12039
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. https://doi.org/10.5465/annals.2018.0174
Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: A systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research, 13(3), 795–848. https://doi.org/10.1007/s40685-020-00134-w
Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1603–1612). Association for Computing Machinery. https://doi.org/10.1145/2702123.2702548
Morrison, E. W. (2023). Employee voice and silence: Taking stock a decade later. Annual Review of Organizational Psychology and Organizational Behavior, 10, 79–107. https://doi.org/10.1146/annurev-orgpsych-120920-054654
Möhlmann, M., Zalmanson, L., Henfridsson, O., & Gregory, R. W. (2021). Algorithmic management of work on online labor platforms: When matching meets control. MIS Quarterly, 45(4), 1999–2022. https://doi.org/10.25300/MISQ/2021/15333
Newman, D. T., Fast, N. J., & Harmon, D. J. (2020). When eliminating bias isn't fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes, 160, 149–167. https://doi.org/10.1016/j.obhdp.2020.03.008
Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), Article 100838. https://doi.org/10.1016/j.hrmr.2021.100838
Rahman, H. A. (2021). The invisible cage: Workers' reactivity to opaque algorithmic evaluations. Administrative Science Quarterly, 66(4), 945–988. https://doi.org/10.1177/00018392211010118
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Ravid, D. M., Tomczak, D. L., White, J. C., & Behrend, T. S. (2020). EPM 20/20: A review, framework, and research agenda for electronic performance monitoring. Journal of Management, 46(1), 100–126. https://doi.org/10.1177/0149206319869435
Robinson, S. L. (1996). Trust and breach of the psychological contract. Administrative Science Quarterly, 41(4), 574–599. https://doi.org/10.2307/2393868
of Uber's drivers. International Journal of Communication, 10, 3758–3784. https://doi.org/10.2139/ssrn.2686227
Rousseau, D. M. (1995). Psychological contracts in organizations: Understanding written and unwritten agreements. Sage Publications. https://doi.org/10.4135/9781452231594
Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. Academy of Management Review, 20(3), 571–610. https://doi.org/10.5465/amr.1995.9508080331
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
Tyler, T. R. (2006). Psychological perspectives on legitimacy and legitimation. Annual Review of Psychology, 57, 375–400. https://doi.org/10.1146/annurev.psych.57.102904.190038
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. https://doi.org/10.1177/0950017018785616
