Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2019

Abstract

Heuristics are often viewed as inferior to “rational” strategies that exhaustively search and process information. Introducing the theoretical perspective of ecological rationality, we challenge this view and argue that under conditions of uncertainty common to managerial decision making, managers can actually make better decisions using fast-and-frugal heuristics. Within the context of personnel selection, we show that a heuristic called Δ-inference can more accurately predict which of two job applicants would perform better in the future than logistic regression, a prototypical rational strategy. Using data from 236 applicants at an airline company, we demonstrate in Study 1 that despite searching less than half of the cues, Δ-inference can lead to more accurate selection decisions than logistic regression. After this existence proof, we examine in Study 2 the ecological conditions under which the heuristic predicts more accurately than logistic regression using 1,728 simulated task environments. Finally, in Study 3, we show in an experiment that participants adapted their strategies to the characteristics of a task, and increasingly so the greater their previous experience in selection decisions. The aim of this article is to propose ecological rationality as an alternative to current views about the nature of heuristics in managerial decisions.

Keywords

ecological rationality, fast-and-frugal heuristics, comparative model testing, Δ-inference, heuristics and biases, personnel selection, selection decisions

Discipline

Human Resources Management | Industrial and Organizational Psychology

Research Areas

Organisational Behaviour and Human Resources

Publication

Academy of Management Journal

Volume

62

Issue

6

First Page

1735

Last Page

1759

ISSN

0001-4273

Identifier

10.5465/amj.2018.0172

Publisher

Academy of Management

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.5465/amj.2018.0172

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