File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Bias in Context: Small Biases in Hiring Evaluations Have Big Consequences

TitleBias in Context: Small Biases in Hiring Evaluations Have Big Consequences
Authors
Keywordsbias
computer simulation
gender and diversity
hiring decisions
selection
Issue Date2022
Citation
Journal of Management, 2022, v. 48, n. 3, p. 657-692 How to Cite?
AbstractIt is widely acknowledged that subgroup bias can influence hiring evaluations. However, the notion that bias still threatens equitable hiring outcomes in modern employment contexts continues to be debated, even among organizational scholars. In this study, we sought to contextualize this debate by estimating the practical impact of bias on real-world hiring outcomes (a) across a wide range of hiring scenarios and (b) in the presence of diversity-oriented staffing practices. Toward this end, we conducted a targeted meta-analysis of recent hiring experiments that manipulated both candidate gender and qualifications to couch our investigation within ongoing debates surrounding the impact of small amounts of bias in otherwise meritocratic hiring contexts. Consistent with prior research, we found evidence of small gender bias effects (d = −0.30) and large qualification effects (d = 1.61) on hiring managers’ evaluations of candidate hireability. We then used these values to inform the starting parameters of a large-scale computer simulation designed to model conventional processes by which candidates are recruited, evaluated, and selected for open positions. Collectively, our simulation findings empirically substantiate assertions that even seemingly trivial amounts of subgroup bias can produce practically significant rates of hiring discrimination and productivity loss. Furthermore, we found contextual factors can alter but cannot obviate the consequences of biased evaluations, even within apparently optimal hiring scenarios (e.g., when extremely valid assessments are used). Finally, our results demonstrate residual amounts of subgroup bias can undermine the effectiveness of otherwise successful targeted recruitment efforts. Implications for future research and practice are discussed.
Persistent Identifierhttp://hdl.handle.net/10722/344510
ISSN
2023 Impact Factor: 9.3
2023 SCImago Journal Rankings: 7.539

 

DC FieldValueLanguage
dc.contributor.authorHardy, Jay H.-
dc.contributor.authorTey, Kian Siong-
dc.contributor.authorCyrus-Lai, Wilson-
dc.contributor.authorMartell, Richard F.-
dc.contributor.authorOlstad, Andy-
dc.contributor.authorUhlmann, Eric Luis-
dc.date.accessioned2024-07-31T03:04:05Z-
dc.date.available2024-07-31T03:04:05Z-
dc.date.issued2022-
dc.identifier.citationJournal of Management, 2022, v. 48, n. 3, p. 657-692-
dc.identifier.issn0149-2063-
dc.identifier.urihttp://hdl.handle.net/10722/344510-
dc.description.abstractIt is widely acknowledged that subgroup bias can influence hiring evaluations. However, the notion that bias still threatens equitable hiring outcomes in modern employment contexts continues to be debated, even among organizational scholars. In this study, we sought to contextualize this debate by estimating the practical impact of bias on real-world hiring outcomes (a) across a wide range of hiring scenarios and (b) in the presence of diversity-oriented staffing practices. Toward this end, we conducted a targeted meta-analysis of recent hiring experiments that manipulated both candidate gender and qualifications to couch our investigation within ongoing debates surrounding the impact of small amounts of bias in otherwise meritocratic hiring contexts. Consistent with prior research, we found evidence of small gender bias effects (d = −0.30) and large qualification effects (d = 1.61) on hiring managers’ evaluations of candidate hireability. We then used these values to inform the starting parameters of a large-scale computer simulation designed to model conventional processes by which candidates are recruited, evaluated, and selected for open positions. Collectively, our simulation findings empirically substantiate assertions that even seemingly trivial amounts of subgroup bias can produce practically significant rates of hiring discrimination and productivity loss. Furthermore, we found contextual factors can alter but cannot obviate the consequences of biased evaluations, even within apparently optimal hiring scenarios (e.g., when extremely valid assessments are used). Finally, our results demonstrate residual amounts of subgroup bias can undermine the effectiveness of otherwise successful targeted recruitment efforts. Implications for future research and practice are discussed.-
dc.languageeng-
dc.relation.ispartofJournal of Management-
dc.subjectbias-
dc.subjectcomputer simulation-
dc.subjectgender and diversity-
dc.subjecthiring decisions-
dc.subjectselection-
dc.titleBias in Context: Small Biases in Hiring Evaluations Have Big Consequences-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1177/0149206320982654-
dc.identifier.scopuseid_2-s2.0-85100144954-
dc.identifier.volume48-
dc.identifier.issue3-
dc.identifier.spage657-
dc.identifier.epage692-
dc.identifier.eissn1557-1211-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats