Applying conventional agency theory to prediction of employee self-reporting performance behaviour
International Journal of Productivity and Performance Management
ISSN: 1741-0401
Article publication date: 30 July 2020
Issue publication date: 18 October 2021
Abstract
Purpose
The purpose of this study is to test various hypotheses regarding if managers' voluntarily prefer honesty in self-reported managerial performance (HPR).
Design/methodology/approach
This study uses an experimental approach with a data set of 300 Ghanaian employees.
Findings
The results confirm that the current trend where employee contracts are underpinned by the classical agency theory (CAT) is problematic, ineffective and costly because it does not appropriately explain the observed behaviour of honesty and partial honesty in self-reported performance or the dishonesty in reporting performance when there is no financial reward to be gained by employees. Therefore MNCs may benefit from a consideration of wider and alternative perspectives. Additionally, stakeholders must consider a strategy of delaying performance-related bonuses (pay-offs) to improve HPR and avoid capping performance-related pay off with an arbitrary threshold. This is because the setting of arbitrary thresholds reduces the established relationship between effort and reward and introduces gaming into the managerial performance reporting process.
Originality/value
Unlike other prior studies that rely on students as surrogates for employees, this study uses actual employees to test the experimental constructs. Aside from the comparatively large data set, this study is the first exploration of the differential effects of national characteristics on HPR in Ghana.
Keywords
Citation
Nsor-Ambala, R. (2021), "Applying conventional agency theory to prediction of employee self-reporting performance behaviour", International Journal of Productivity and Performance Management, Vol. 70 No. 7, pp. 1728-1750. https://doi.org/10.1108/IJPPM-03-2019-0144
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited