The impact of monetary incentives and regulatory focus on worker productivity and learning in order picking

Author:

Sgarbossa FabioORCID,Glock Christoph H.ORCID,Grosse Eric H.ORCID,Calzavara Martina,de Koster René

Abstract

PurposeIn manual order picking systems, temporary workers are often employed to handle demand peaks. While this increases flexibility, it may hamper productivity, as they are usually unfamiliar with the processes and may have little experience. It is important for managers to understand how quickly inexperienced workers arrive at full productivity and which factors support workers in improving their productivity. This paper aims to investigate how learning improves the performance of order pickers, and how their regulatory focus (RF) and monetary incentives, as management actions, influence learning.Design/methodology/approachData was collected in two case studies in controlled field-lab experiments and statistically analysed. This allowed evaluating the validity of hypotheses through an ANOVA, the calculation of correlation coefficients and the application of regression models.FindingsA monetary incentive based on total order picking time and pick errors has a positive influence on order picking time, but not on pick quality. The incentive influences initial productivity, but not the learning rate. A dominant promotion-oriented RF increases the effect of the incentive on initial productivity, but it does not impact worker learning.Practical implicationsThis study contributes to behavioral and human-focused order picking management and supports managers in setting up work plans and developing incentive systems for learning and productivity enhancement, considering worker RF.Originality/valueThis work is among the few to empirically investigate the effect of monetary incentives on learning in interaction with RF. It is the first study to investigate these concepts in an order picking scenario.

Publisher

Emerald

Subject

Management of Technology and Innovation,Strategy and Management,General Decision Sciences

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