Why Is It So Hard to Apply Professional Selection Methods in Business Practice?

Chorągwicka, Beata; Janta, Barbara

doi:10.1111/j.1754-9434.2008.00062.x

Cited by 5 publications

(5 citation statements)

References 11 publications

(11 reference statements)

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“…This means approaching algorithm aversion as a project of behavior change in which hardwired organizational routines and social norms pose as major obstacles. A number of suggestions have been made along these lines: Choragwicka and Janta () suggest framing the benefits of algorithm utilization in relatable terminology, Alexander et al () propose manipulating the perceived social consensus, and Fisher (), Klimoski and Jones (), and Kuncel () advocate for localized reward schemes that apply to specific decision‐making roles in organizations. Each of these holds promise, and the most effective incentivization program is likely to vary by environment.…”

Section: Resultsmentioning

confidence: 99%

A systematic review of algorithm aversion in augmented decision making

Burton

Stein

Jensen

2019

Behavioral Decision Making

375

222

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Despite abundant literature theorizing societal implications of algorithmic decision making, relatively little is known about the conditions that lead to the acceptance or rejection of algorithmically generated insights by individual users of decision aids. More specifically, recent findings of algorithm aversion—the reluctance of human forecasters to use superior but imperfect algorithms—raise questions about whether joint human‐algorithm decision making is feasible in practice. In this paper, we systematically review the topic of algorithm aversion as it appears in 61 peer‐reviewed articles between 1950 and 2018 and follow its conceptual trail across disciplines. We categorize and report on the proposed causes and solutions of algorithm aversion in five themes: expectations and expertise, decision autonomy, incentivization, cognitive compatibility, and divergent rationalities. Although each of the presented themes addresses distinct features of an algorithmic decision aid, human users of the decision aid, and/or the decision making environment, apparent interdependencies are highlighted. We conclude that resolving algorithm aversion requires an updated research program with an emphasis on theory integration. We provide a number of empirical questions that can be immediately carried forth by the behavioral decision making community.

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Section: Resultsmentioning

confidence: 99%

A systematic review of algorithm aversion in augmented decision making

Burton

Stein

Jensen

2019

Behavioral Decision Making

375

222

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“…Single‐attribute utility analysis is the most established form of utility analysis (e.g., Brogden, 1949; Choragwicka & Janta, 2008; Cronbach & Gleser, 1965), even though the reactions to it often are ambivalent (see e.g., Carson, Becker, & Henderson, 1997). It calculates the benefit of an HRM intervention based on a multiplicative combination of factors related to the quality, quantity, and costs of an HRM intervention (Macan & Foster, 2004).…”

mentioning

confidence: 99%

Single‐attribute Utility Analysis May Be Futile, but This Can’t Be the End of the Story: Causal Chain Analysis as an Alternative

Winkler

König

Kleinmann

2010

Personnel Psychology

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Research on providing single-attribute utility analysis has shown moderate or even negative effects on the acceptance of selection and training tests by human resource decision makers. In this study, we contrasted the perceived utility of single-attribute utility analysis with causal chain analysis as an alternative way of conducting utility analysis. Causal chain analysis focuses on measuring the linkages between HRM interventions and organizational outcomes mediated by employee attitudes and customer perceptions. We compared 144 managers' reactions to both methods of utility analysis concerning the variables understandability, information quality, perceived usefulness, user information satisfaction, and intention to use. Causal chain analysis yielded higher results than single-attribute analysis for these variables, and a compound measure of these constructs supported this finding. This indicates that causal chain analysis is a valuable alternative method of communicating the utility of HRM interventions.

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“…The purpose of a job analysis is to gather information about a role, such as the content, purpose and output required, but also the preferred knowledge, skills and attributes of the job holder (Brannick and Levine, 2002). Unlike large organizations, SMEs have fewer resources to conduct job analyzes (Barber et al, 1999) and are less likely to have a human resources department to facilitate this (Bartram et al, 1995;Choragwicka and Janta, 2008). This is supported by findings in the recent Workplace Employee Relations Survey (Forth et al, 2006) that SMEs were more likely to use a structured recruitment method when they had an employment relations specialist.…”

Section: Best Practice Methodology In Selectionmentioning

confidence: 99%

Advancing selection in an SME: Is best practice methodology applicable?

Wyatt

Pathak²,

Zibarras

2010

International Small Business Journal

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Effective selection tools are important for identifying high caliber employees in SMEs, yet few SMEs use tools created using 'best practice' methodology. Selection literature tends to focus on large organizations and is conceptual rather than empirical; which may make it difficult for SMEs to use a best practice approach. This article addresses this by providing an empirical account of the design and validation of two selection tools in a medium-sized recruitment consultancy using a best practice selection methodology. Two work sample tests were developed using critical incident technique interviews, and validated using a concurrent design with existing recruitment consultants who were 'high' or 'low' performers according to sales output. Results indicated that the tools significantly differentiated between high and low performers, and there was a significant correlation between test performance and individual sales output. Findings are discussed in relation to implications for research and practice in SMEs and selection.

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Why Is It So Hard to Apply Professional Selection Methods in Business Practice?

Abstract: The first author of the article is a Spanish Ministry of Education and Science grant holder for PhD students (FPU, AP2005-3807).

Cited by 5 publications

References 11 publications

A systematic review of algorithm aversion in augmented decision making

A systematic review of algorithm aversion in augmented decision making

Single‐attribute Utility Analysis May Be Futile, but This Can’t Be the End of the Story: Causal Chain Analysis as an Alternative

Advancing selection in an SME: Is best practice methodology applicable?

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