Algorithmic Supervisor and Employee Performance
Open accessAbstract
In this article, we explore the impact of implementing an algorithmic supervisor, a data analytics tool that offers real-time feedback to employees, on their overall performance. We collaborate with a leading service company that initiated the transition from human supervisors to an algorithmic supervisor for managing call agents in their outbound call center on September 18, 2019. We collect data for 220,085 calls over a span of 6 months—that is, 3 months before and 3 months after the introduction of the algorithmic supervisor. We find that the introduction of the algorithmic supervisor improved the agents’ service quality in terms of customer satisfaction; however, this came at the expense of quantity as fewer customers were served. In particular, the adoption of the algorithmic supervisor resulted in a 15.80% reduction in the number of served customers, accompanied by a 14.91% increase in the number of satisfied customers. Moreover, our study uncovers a gender-related dimension to the impact of the algorithmic supervisor. Before its adoption, male agents outperformed their female counterparts. However, after its adoption, female agents exhibited an 11.52% increase in the number of served customers, and a remarkable 14.00% increase in the number of satisfied customers as compared to their male counterparts. Our results suggest that the adoption of the algorithmic supervisor strikes a new economic balance between service quality and service quantity and generates a social impact in differently affecting service performance of female and male employees.
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Cites
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019) · Marketing Science · doi:10.1287/mksc.2019.1192 · Cited by 1,027 · cited 2× · see citing works
“In financial services, undisclosed chatbots are as effective as proficient employees in engendering customer purchase (Luo et al., 2019).”
Ahuja, M. K., & Bennett, J. (2005) · MIS Quarterly · doi:10.2307/25148691 · Cited by 818 · cited 3× · see citing works
“Prior literature has shown that females tend to be more compliant than males (Bartel Sheehan, 1999), and females value autonomy less than males; therefore, females are more likely to accept monitoring than males (Ahuja and Thatcher, 2005; Liang et al., 2023).”
Gender and Competition at a Young Age
Gneezy, U., & Rustichini, A. (2004) · American Economic Review · doi:10.1257/0002828041301821 · Cited by 756 · see citing works
On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data
Imai, K., & Kim, I. S. (2021) · Political Analysis · doi:10.1017/pan.2020.33 · Cited by 623 · cited 1× · see citing works
“Note that recent literature has also raised concerns regarding the limitations of the two-way fixed effects for its reliance on the linear additivity assumption, for example, no dynamic changes in the effects of treatment (Imai and Kim, 2021).”
Tong, S., Jia, N., Luo, X., & Fang, Z. (2021) · Strategic Management Journal · doi:10.1002/smj.3322 · Cited by 531 · cited 1× · see citing works
“Tong et al. (2021) explore the impact of supervisory AI on employees collecting overdue payments from delinquent borrowers, and they find that employees might have negative perceptions about the deployment of AI due to algorithm aversion.”
Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
Brynjolfsson, E., Hui, X., & Liu, M. (2018) · doi:10.3386/w24917 · Cited by 408
Human-machine collaboration in managerial decision making
Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106730 · Cited by 280 · see citing works
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Artificial intelligence (AI) in sales organizations: the role of trust
Rangarajan, D., Westphal, J., Habel, J., & Rutherford, B. N. (2026) · Journal of Personal Selling and Sales Management · doi:10.1080/08853134.2026.2614068 · Cited by 9 · see citing works
“Additionally, research has begun to investigate the use of AI-driven role plays to improve sales performance (Habel et al. 2025b) as well as the use of AI for coaching sales agents (Gu, Li, and Zhang 2025; Luo et al. 2021).”
Grewal, D., Ludwig, S., Herhausen, D., Grewal, L. S., Ordenes, F. J. V., Kopalle, P. K., & Bohling, T. R. (2026) · Production and Operations Management · doi:10.1177/10591478261420001 · Cited by 3 · see citing works
“…employees are well-trained, interact effectively with customers in online service chats, and fine-tune their tone, speaking pace, and vocabulary to personalize the service interactions in order to cultivate more humanized interactions (Chamorro-Premuzic and Ahmetoglu, 2016; Gu et al., 2025).”
From tool to supervisor: Uncovering the impact of AI roles on employee performance
Chi, Y., Song, Y. (., & Bai, X. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124817 · Cited by 2 · see citing works
Mondal, S. D. (2026) · Jahangirnagar University Journal of Management Research · doi:10.59185/jujmr.v8i1.513
From Organic to Antagonistic: The Two Sides of Control Under Digital Capitalism
Bensussan, H. (2026) · The Review of Radical Political Economics · doi:10.1177/04866134261492370
Kauttonen, J., Tuomi, A., Vuori, J., Saaranen, P., & Lahtinen, A. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2750593
Confucian culture and corporate AI transformation: an ethical perspective on informal institutions
Xu, T., Tian, Y., & Jiang, X. (2026) · Asia Pacific Journal of Management · doi:10.1007/s10490-026-10196-9
AI-coaching and well-being in sales: a theory of tools perspective
Childs, D. R. N., Good, V., Fehl, A. G., Tanner, E., & Lee, N. (2026) · Journal of Personal Selling & Sales Management · doi:10.1080/08853134.2026.2738649
Fang, Z.-C., Han, L.-L., & Ma, T. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1922281
Artificial Agents as Leaders and Followers: An Integrative Role-Congruity Review
Daldrop, C., Ludwig, A., & Buengeler, C. (2026) · Group & Organization Management · doi:10.1177/10596011261476917
Will machines take over? Algorithms for human–machine collaborative decision making in healthcare
Ahsen, M. E., Mookerjee, R. V., & Ayvaci, M. (2026) · Production and Operations Management · doi:10.1177/10591478261443194
“Complementarily, “algorithmic management” studies show supervisory AI can raise quality while lowering throughput and differentially affect subgroups (Gu et al., 2025), underscoring the importance of governance, feedback, and accountability design.”
The Negative Effects of Positive Real-Time Feedback from Artificial IntelligencePreprint
Kinch, M. A. (2025) · Social Science Research Network · doi:10.2139/ssrn.5249994
- Authors
- Gu, W., Li, M., & Zhang, S.
- Affiliations
- University of Science and Technology Beijing (CN)
- University of Houston (US)
- Tianjin University (CN)
- Corresponding author
- Li, Meng
- Zhang, Shichen
- Venue
- Production and Operations Management
- Publisher
- SAGE Publications
- Published
- 2025-04-18
- Volume
- 34
- Issue
- 10
- Pages
- 3101-3118
- ISSN
- 1059-1478 (ISSN-L)
- 1937-5956
- OpenAlex
- W4408803053
- Cited by
- 10
- References
- 0
- Citations by year
- 2026: 8 · 2025: 2
- FWCI
- 12.36
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Production and Operations Management (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- real-time feedback, employee performance, call center employees, customer satisfaction, gender differences, data analysis software
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