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.