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Utility analysis for the probationary selection decision to obtain a fixed quota of successful selectees

Bibliographic Data

ID12168029
AuthorsWilfried De Corte (0000-0001-7400-3181, Ghent University, corresponding author)
Year1997
Volume18
Issue1
Pages67-82
Publication date1997-01-01
Open AccessYes
TypeARTICLE
VenueJournal of Organizational Behavior (JOURNAL)
Journal identifiersISSN: 0894-3796 • E-ISSN: 1099-1379
PublisherWiley (PUBLISHER • GB)
DOI10.1002/(sici)1099-1379(199701)18:1<67::aid-job773>3.0.co;2-b
OpenAlexW2024545463
LanguageEN

This article presents the extension of the one-cohort probationary selection (OCPS) framework (De Corte, 1994) to a multiple cohort model and shows how the extension can be applied to estimate the utility of probationary selection with replacement (PSWR) when a fixed quota of successfully performing selectees is required. Next, formulas to compute the net present value of the OCPS and the PSWR approach to the selection of a fixed quota of successful employees are derived. The equations indicate that the latter approach will usually lead to a higher utility, but not necessarily to a higher net present value of the predictor selected workforce. This finding, which is corroborated by the results of an example application, is of immediate practical relevance because it justifies the probationary selection with replacement practice that is adopted in many West European countries. Finally, the example results suggest also that the traditional Brogden–Cronbach–Gleser framework generally overestimates the incremental utility of using a predictor when the objective is to select a fixed quota of successful selectees. © 1997 John Wiley & Sons, Ltd

Actuarial science · Economics · Extension (predicate logic · Operations management · Operations research · Political science · Relevance (law · Selection (genetic algorithm · Statistics · Value (mathematics · Workforce · Advanced Causal Inference Techniques · Computer Science · Labor market dynamics and wage inequality · Mathematics · Spatial and Panel Data Analysis · Artificial Intelligence

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