Back

Hiring on vocational interests to simultaneously improve validity and organizational diversity.

  • Published In: International Journal of Selection & Assessment, 2023, v. 31, n. 4. P. 504 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wee, Serena; Newman, Daniel A.; Su, Rong 3 of 3

Abstract

We discuss how using vocational interests in the selection process can help address the diversity‐validity dilemma. First, we point out how incorporating vocational interests as predictors in selection could help to reduce adverse impact. We further suggest that by using optimal predictor weights, one could simultaneously improve validity while enhancing organisational diversity. Finally, the predictive validity of vocational interests arises from their ability to capture the congruence between individuals and occupations, which is a cross‐level phenomenon. Thus, when gathering validity evidence for vocational interests, multi‐occupation samples should be incorporated into validation efforts. Practitioner points: When hiring, including vocational interests as a pre‐employment device could help to improve organizational diversity.These diversity gains could be accompanied by ideal performance gains if Pareto‐optimal weighting is used to combine predictors.Vocational interests predict job performance when there is congruence between individuals and occupations; as person‐occupation interest congruence is a cross‐level phenomenon, multi‐occupation samples should be used when accumulating validity evidence for vocational interests. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Selection & Assessment. 2023/12, Vol. 31, Issue 4, p504
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:0965-075X
  • DOI:10.1111/ijsa.12443
  • Accession Number:173665119
  • Copyright Statement:Copyright of International Journal of Selection & Assessment is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.