The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
When Does the Devil Make Work? An Empirical Study of the Impact of Workload on Worker Productivity
Abstract
We analyze a large, detailed operational data set from a restaurant chain to shed new light on how workload (defined as the number of tables or diners that a server simultaneously handles) affects servers' performance (measured as sales and meal duration). We use an exogenous shock—the implementation of labor scheduling software—and time-lagged instrumental variables to disentangle the endogeneity between demand and supply in this setting. We show that servers strive to maximize sales and speed efforts simultaneously, depending on the relative values of sales and speed. As a result, we find that, when the overall workload is small, servers expend more and more sales efforts with the increase in workload at a cost of slower service speed. However, above a certain workload threshold, servers start to reduce their sales efforts and work more promptly with the further rise in workload. In the focal restaurant chain, we find that this saturation point is currently not reached and, counterintuitively, the chain can reduce the staffing level and achieve both significantly higher sales (an estimated 3% increase) and lower labor costs (an estimated 17% decrease). This paper was accepted by Noah Gans, special issue on business analytics.
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“Workload considerations are important, as assigning a worker too many tasks can diminish the time and attention they give to their work and consequently harm their performance (Greenglass et al. 2001, Zeytinoglu et al. 2007, Tan and Netessine 2014, Fan and Smith 2017).”
From Included but undermined: How representation goals backfire (2025) · cites this paper as Tan and Netessine 2014
Citations
Cites
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A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
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Applied Regression Analysis and Other Multivariable Methods
Walker, E., Kleinbaum, D. G., Kupper, L. L., & Muller, K. E. (1989) · Technometrics · doi:10.2307/1270375 · Cited by 8,538
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. H. (1997) · Econometrica · doi:10.2307/2171753 · Cited by 7,081
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. (1994) · doi:10.3386/t0151 · Cited by 5,302 · cited 1×
“We find that all the F statistics for the joint significance of instruments excluded from the structural model are over 10, the suggested rule of thumb of weak instruments (Staiger and Stock, 1994).”
BackgroundKennedy, P. (1979) · Cited by 4,340
Toward a theory of task motivation and incentives
Locke, E. A. (1968) · Organizational Behavior and Human Performance · doi:10.1016/0030-5073(68)90004-4 · Cited by 2,409 · cited 1×
“Goal-setting theory suggests that challenges faced by workers can enhance motivation (Locke, 1968; Latham and Locke, 1979).”
Measuring and Explaining Management Practices Across Firms and Countries
Bloom, N., & Van Reenen, J. (2007) · The Quarterly Journal of Economics · doi:10.1162/qjec.2007.122.4.1351 · Cited by 2,373
With or Without U? The Appropriate Test for a U-Shaped Relationship*: Practitionersâ Corner
Lind, J. T., & Mehlum, H. (2010) · Oxford Bulletin of Economics and Statistics · doi:10.1111/j.1468-0084.2009.00569.x · Cited by 2,342
Self-determination in a work organization.
Deci, E. L., Connell, J. P., & Ryan, R. M. (1989) · Journal of Applied Psychology · doi:10.1037//0021-9010.74.4.580 · Cited by 2,064
Research works printed but not linked: 9
- Bendoly E, 2013, Production Oper. Management
- 10.1037/0021-9010.74.4.580 · DOI · Paper page
- 10.1037/0022-3514.64.5.834 · DOI
- Fields R, 2007, Restaurant Success by the Numbers
- Fitzsimmons JA, 1991, The Cornell Hotel and Restaurant Admin. Quart., 31, 94
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Cited by
Srinivasan, R., & Swink, M. (2018) · Production and Operations Management · doi:10.1111/poms.12746 · Cited by 885
Cleaning House: The Impact of Information Technology Monitoring on Employee Theft and Productivity
Pierce, L., Snow, D. C., & McAfee, A. (2015) · Management Science · doi:10.1287/mnsc.2014.2103 · Cited by 251
Doing Well by Making Well: The Impact of Corporate Wellness Programs on Employee Productivity
Gubler, T., Larkin, I., & Pierce, L. (2018) · Management Science · doi:10.1287/mnsc.2017.2883 · Cited by 222
At your service on the table: Impact of tabletop technology on restaurant performance
Tan, T. F., & Netessine, S. (2020) · Management Science · doi:10.1287/mnsc.2019.3430 · Cited by 111
Self-citationThe impact of social nudges on user-generated content for social network platforms
Zeng, Z., Dai, H., Zhang, D. J., Zhang, H., Zhang, R., Xu, Z., & Shen, Z.-J. M. (2023) · Management Science · doi:10.1287/mnsc.2022.4622 · Cited by 76
Included but undermined: How representation goals backfire
Osborne, M. R., Roberson, J. W., & Tost, L. (2025) · doi:10.31234/osf.io/cse5r_v2
Metadata
- Authors
- Tan, T. F., & Netessine, S.
- Affiliations
- Cox Business School, Southern Methodist University, Dallas, Texas 75275
- INSEAD, Singapore 138676
- Venue
- Management Science
- Published
- 2014-06
- Volume
- 60
- Issue
- 6
- Pages
- 1574-1593
- ISSN
- 0025-1909
- 1526-5501
- Cited by
- 297
- References
- 75
- Language
- en
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
