Parametric empirical Bayes estimation of individual time-pressure reactivity

Ji-Eun Kim and David A. Nembhard

International Journal of Production Research, 2018, vol. 56, issue 7, 2452-2463

Abstract: As the global workplace becomes increasingly industrialised and competitive, organisations are finding that it is crucial to manage their time more efficiently. An important topic in time management is the influence of time pressure under deadlines on individual performance. People often do less work when deadlines are far off; they then increase their work rate as deadlines approach. Although researchers have studied from various perspectives how individuals work towards deadlines, the measurement of individual differences in pacing styles has been based mostly on self-report questionnaire instruments or on frequentist estimation, which often relies on sparse data in practice. The purpose of this study is to estimate distributions of individuals’ time-pressure reactivity using a parametric empirical Bayesian estimation (PEB) approach, and to determine an adequate sample size to estimate posterior distributions. The use of PEB approach was motivated by the varied nature of sample sizes across individuals and task types. In this study, two data-sets were used, one from an online course and another from an Anti-Air Warfare Coordinator task. From these two different data-sets, we generated informative individualised posterior distributions for time-pressure activity, and the PEB approach was validated by showing the intervals of posterior distribution were smaller than the intervals of point estimates for time-pressure reactivity. In addition, we found that 18–40% of the actual number of samples was sufficient to estimate posterior distributions to within 10% error, and this finding is advantageous in cases in which collecting large sets of data may be time-consuming or expensive. This study demonstrates the effectiveness of Bayesian estimation in determining individual differences in time-pressure reactivity to deadlines using individualised posterior distributions rather than point estimates, which is beneficial for understanding differences in behaviour across a diverse population.

Date: 2018
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DOI: 10.1080/00207543.2017.1380321

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