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Cited by 365

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Monitoring, Motivation, and Management: The Determinants of Opportunistic Behavior in a Field Experiment

Nagin, Daniel S, Rebitzer, James B, Sanders, Seth, Taylor, Lowell J·2002·American Economic Review
DOI 10.1257/00028280260344498
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Abstract

Economic models of incentives in employment relationships are based on a specific theory of motivation: employees are “rational cheaters,” who anticipate the consequences of their actions and shirk when the marginal benefits exceed costs. We investigate the “rational cheater model” by observing how experimentally induced variation in monitoring of telephone call center employees influences opportunism. A significant fraction of employees behave as the “rational cheater model” predicts. A substantial proportion of employees, however, do not respond to manipulations in the monitoring rate. This heterogeneity is related to variation in employee assessments of their general treatment by the employer.

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Metadata

Authors
Nagin, D. S., Rebitzer, J. B., Sanders, S., & Taylor, L. J.
Venue
American Economic Review
Publisher
American Economic Association
Published
2002-08-01
Volume
92
Issue
4
Pages
850-873
ISSN
0002-8282 (ISSN-L)
DOI
10.1257/00028280260344498
OpenAlex
W3125047309
Cited by
365
References
33
FWCI
29.83
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Monitoring, Motivation, and Management: The Determinants of Opportunistic Behavior in a Field Experiment. https://app.cochat.ai/papers/10.1257/00028280260344498
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Nagin, D. S., Rebitzer, J. B., Sanders, S., & Taylor, L. J. (2002). Monitoring, Motivation, and Management: The Determinants of Opportunistic Behavior in a Field Experiment. American Economic Review. https://doi.org/10.1257/00028280260344498

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.