1,598
Views
10
CrossRef citations to date
0
Altmetric
Research Articles

Effects of Hourly, Low-Incentive, and High-Incentive Pay on Simulated Work Productivity: Initial Findings With a New Laboratory Method

&
Pages 21-42 | Published online: 23 Feb 2011
 

Abstract

The failures of previous studies to demonstrate productivity differences across different percentages of incentive pay may be partially due to insufficient simulation fidelity. The present study compared the effects of different percentages of incentive pay using a more advanced simulation method. Three payment methods were tested: hourly, low-incentive, and high-incentive (0%, 10%, and 100%) pay. Four participants performed a simulated work task for 30 6-hr sessions. Productivity under the 100% incentive condition was consistently higher than under the 10% condition for all participants. Productivity under the 10% condition was higher than under the 0% condition for two participants. Results suggest that different percentages of incentive pay may in fact produce productivity differences under more realistic simulated work conditions.

Acknowledgments

This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion Fund) (KRF-2008-327-H00037). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. M10740030003-07N4003-00310).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 485.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.