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Getting from valid to useful : end user modifiability and human capital analytics implementation in selection
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Downes, Patrick E., Harris, T. Brad and Allen, David G. (2023) Getting from valid to useful : end user modifiability and human capital analytics implementation in selection. Human Resource Management, 62 (6). pp. 917-932. doi:10.1002/hrm.22179 ISSN 0090-4848.
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Official URL: http://dx.doi.org/10.1002/hrm.22179
Abstract
A major problem in employee selection coalesces around convincing decision-makers (e.g., hiring managers) to use analytically derived models. Existing recommendations in the literature largely focus on convincing executives to adopt analytical models and then exert their top-down influence on lower-level hiring decisions. In contrast to these solutions, we explore end user modifiability (i.e., allowing decision-makers to modify a statistical model before use) as a bottom-up approach for increasing hiring managers' implementation of analytical recommendations. From a utility standpoint, we consider how incorporating end user modifiability into hiring decisions will result in a less statistically valid, but potentially more valuable, organizational selection process. We explore these ideas in two studies. In Study 1, we experimentally test whether model modification increases decision-maker reliance on a statistical model, as well as how much decision-makers need to modify a model in order to use it. In Study 2, we examine the extent that modifiability introduces implicit biases that might adversely affect marginalized groups. Results suggest that modifiability can increase decision-makers' perceived usefulness of a model and, importantly, that only a small amount of modifiability is needed to elicit this effect. Further, end user modifications were statistically insignificant predictors of hiring rates across race-based subgroups, though supplementary analyses suggest important cautionary nuance. Given that analytical models are rarely perfectly or wholly implemented, end user modifiability may offer a viable solution for organizations seeking to increase the implementation of algorithmic guidance in selection decisions, even if it deviates modestly from a statistical optimality.
Item Type: | Journal Article | ||||||||
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Subjects: | H Social Sciences > HD Industries. Land use. Labor | ||||||||
Divisions: | Faculty of Social Sciences > Warwick Business School > Entrepreneurship, Innovation & Management Faculty of Social Sciences > Warwick Business School |
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Library of Congress Subject Headings (LCSH): | Human capital, Human capital -- Decision making, Personnel management | ||||||||
Journal or Publication Title: | Human Resource Management | ||||||||
Publisher: | John Wiley & Sons, Inc. | ||||||||
ISSN: | 0090-4848 | ||||||||
Official Date: | November 2023 | ||||||||
Dates: |
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Volume: | 62 | ||||||||
Number: | 6 | ||||||||
Page Range: | pp. 917-932 | ||||||||
DOI: | 10.1002/hrm.22179 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Re-use Statement: | This is the peer reviewed version of the following article: Downes, P. E., Harris, T. B., & Allen, D. G. (2023). Getting from valid to useful: End user modifiability and human capital analytics implementation in selection. Human Resource Management, which has been published in final form at https://doi.org/10.1002/hrm.22179. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 1 June 2023 |
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