Increasing worker motivation using a reward scheme with probabilistic elements

Adrian R. Camilleri, Katarina Dankova, Jose M. Ortiz and Ananta Neelim

Organizational Behavior and Human Decision Processes, 2023, vol. 177, issue C

Abstract: The purpose of this research was to investigate the effectiveness of a probabilistic reward scheme to motivate workers and increase their performance. Across seven experiments (three of which are in the online appendices) testing three different real effort tasks, we compared two novel probabilistic reward schemes with two traditional non-probabilistic reward schemes. In our flagship “single lottery” probabilistic scheme, worker performance was associated with the accumulation of lottery tickets in the worker’s own personal lottery with a moderate jackpot on offer. It was possible for the worker to accumulate all tickets and thus guarantee the jackpot. We found that the single lottery scheme increased motivation and performance relative to other probabilistic and non-probabilistic schemes with the same expected values. There was also evidence that the single lottery scheme was particularly effective for lower-ability workers relative to the non-probabilistic schemes. We argue that the single lottery scheme uniquely benefited from optimism bias and the goal gradient effect. Considering perceptions of (un)fairness associated with probabilistic reward schemes – at least at first – we discuss what labor contexts are appropriate for the introduction of a probabilistic reward scheme.

Keywords: Experiment; Motivation; Performance; Reward; Incentives; Fairness; Justice; Ability (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0749597823000328
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jobhdp:v:177:y:2023:i:c:s0749597823000328

DOI: 10.1016/j.obhdp.2023.104256

Access Statistics for this article

Organizational Behavior and Human Decision Processes is currently edited by John M. Schaubroeck

More articles in Organizational Behavior and Human Decision Processes from Elsevier
Bibliographic data for series maintained by Catherine Liu ().