EPM 20/20: A Review, Framework, and Research Agenda for Electronic Performance Monitoring
Abstract
Electronic performance monitoring (EPM) refers to the use of technological means to observe, record, and analyze information that directly or indirectly relates to job performance. The last comprehensive review of the EPM literature was published in 2000. Since 2000, dramatic advances in information technologies have created an environment in which organizations are able to monitor employees to a greater extent and with greater intensity than was previously possible. Moreover, since that time, considerable research has been devoted to understanding the effects of EPM on individual performance and attitudes. Contradictory findings in the EPM literature exist, suggesting that EPM is a multidimensional phenomenon and one for which contextual and psychological variables are pertinent. Thus, we propose a theory-based typology of EPM characteristics and use this typology as a framework to review the EPM literature and identify an agenda for future research and practice.
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Negative effects of monitoring can be reduced if monitoring is used constructively for employee development through feedback, and when employees participate in design and control of monitoring systems.
“The negative effects of monitoring can also be reduced if monitoring is used constructively to help employees develop through feedback129–133, and when employees participate in the design and control of the monitoring systems134,135.”
From Understanding and shaping the future of work with self-determination theory (2022) · cites this paper as [133]
Cites
Motivational processes affecting learning.
Dweck, C. S. (1986) · American Psychologist · doi:10.1037/0003-066x.41.10.1040 · Cited by 6,971 · see citing works
Task-Technology Fit and Individual Performance
Goodhue, D. L., & Thompson, R. L. (1995) · MIS Quarterly · doi:10.2307/249689 · Cited by 6,006 · see citing works
The Parable of Google Flu: Traps in Big Data Analysis
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014) · Science · doi:10.1126/science.1248506 · Cited by 2,523 · cited 2× · see citing works
“Theory-free algorithms can produce overfitted models that describe relationships among data points but cannot explain causal mechanisms or endure inevitable changes in the individuals or contexts they describe (Lazer et al., 2014).”
BackgroundWang, Y., & Kosinski, M. (2018) · Journal of Personality and Social Psychology · doi:10.1037/pspa0000098 · Cited by 766 · cited 1× · see citing works
“Wang and Kosinski (2018) recently showed that facial detection software could be used to identify an individual’s sexual orientation with relative accuracy, Yaden and colleagues (2018) used linguistic patterns to predict religious affiliation, and online behaviors have been shown to reliably…”
MethodChiviacowsky, S., & Wulf, G. (2002) · Research Quarterly for Exercise and Sport · doi:10.1080/02701367.2002.10609040 · Cited by 369 · see citing works
Designing experiments that generalize
Highhouse, S. (2009) · Organizational Research Methods · doi:10.1177/1094428107300396 · Cited by 353 · cited 1× · see citing works
“For example, many professionals are reluctant to use decision aids due to mistrust of statistical forecasting as compared to professional judgment (Highhouse, 2009).”
BackgroundPotential Employees' Ethical Perceptions of Active Monitoring: The Dark Side of Data Analytics
Holt, M., Lang, B., & Sutton, S. G. (2017) · Journal of Information Systems · doi:10.2308/isys-51580 · Cited by 44 · see citing works
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Achieving Effective Remote Working During the COVID‐19 Pandemic: A Work Design Perspective
Wang, B., Liu, Y., Qian, J., & Parker, S. K. (2021) · Applied Psychology · doi:10.1111/apps.12290 · Cited by 1,722 · see citing works
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022) · The International Journal of Human Resource Management · doi:10.1080/09585192.2020.1871398 · Cited by 1,134 · see citing works
“…forms are already widely used (e.g. call and internet usage monitoring, electronic medication administration records) and it is increasingly argued that technologies such as microchip wrist implants and body heat sensor desk hardware may be the future of work monitoring (e.g. Ravid et al., 2020).”
BackgroundMethodAlgorithms as work designers: How algorithmic management influences the design of jobs
Parent-Rocheleau, X., & Parker, S. K. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100838 · Cited by 512 · see citing works
Understanding and shaping the future of work with self-determination theory
Gagné, M., Parker, S. K., Griffin, M. A., Dunlop, P. D., Knight, C., Klonek, F. E., & Parent-Rocheleau, X. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00056-w · Cited by 434 · see citing works
Siegel, R., König, C. J., & Lazar, V. (2022) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2022.100227 · Cited by 121 · see citing works
Charbonneau, É., & Doberstein, C. (2020) · Public Administration Review · doi:10.1111/puar.13278 · Cited by 80 · see citing works
Lee, A., Inceoglu, I., Hauser, O., & Greene, M. (2022) · The Leadership Quarterly · doi:10.1016/j.leaqua.2020.101426 · Cited by 48 · see citing works
Nian, P., Liu, M., Pei, J., & Liu, S. (2026) · Journal of Business Research · doi:10.1016/j.jbusres.2026.116176
- Authors
- Ravid, D. M., Tomczak, D. L., White, J. C., & Behrend, T. S.
- Affiliations
- George Washington University (US)
- Corresponding author
- Behrend, Tara S.
- Venue
- Journal of Management
- Publisher
- SAGE Publications
- Published
- 2019-08-20
- Volume
- 46
- Issue
- 1
- Pages
- 100-126
- ISSN
- 0149-2063 (ISSN-L)
- 1557-1211
- OpenAlex
- W2969968060
- Cited by
- 277
- References
- 7
- Citations by year
- 2026: 66 · 2025: 51 · 2024: 65 · 2023: 38 · 2022: 29 · 2021: 20 · 2020: 8
- FWCI
- 29.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Management (publishedVersion)
- Topics
- Social Sciences › Demography › Cyberloafing and Workplace Behavior
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Job Satisfaction and Organizational Behavior
- Social Sciences › Human Factors and Ergonomics › Employee Performance and Management
- Keywords
- electronic performance monitoring, employee monitoring, employee performance, employee attitudes, information technology in organizations, research agenda, employee privacy
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