Stairway to Fairness: The Impact of Explanations on Applicant Reactions to Automated Video Interviews
type: CreativeWork
description: Advancements in technology have transformed the landscape of organizational selection systems. In order to remain competitive and identify top talent, organizations have increasingly adopted new tools like automated interviews and evaluations to help in the decision-making process (Jaser et al., 2022). One such tool is asynchronous video interviews (AVIs), which offer the benefits of streamlining the hiring process and efficiently reviewing a large pool of candidates (Saurabh, 2022). However, applicant reactions to these systems have generally been negative, with concerns that automation may negatively impact perceptions of fairness (e.g., Langer et al., 2021). Explanations have been found to be a useful approach for improving applicant reactions, however, not all explanations are equally effective (Gilliland and Steiner, 2012; Langer et al., 2021). Therefore, this study aims to test Gilliland's (1993) model of applicants' reactions to employment selection systems in the context of AVIs, specifically focusing on the aftermath of receiving a negative selection decision. Data were collected from 380 individuals in the USA and the UK through online crowdsourcing platforms. These participants took part in a brief AVI session consisting of three interview questions. Subsequently, all participants received the same negative selection decision, accompanied by two different “should” explanations tailored to address the should counterfactual for each condition (Folger & Cropanzano, 2001). The strength-based explanation focused on aspects of the automated interviewing and evaluation that are considered strengths of the system, such as its consistency in evaluating applicants and efforts to mitigate bias (e.g., Noble et al., 2021). Conversely, the weakness-based explanation focused on addressing the system's shortcomings and emphasized features that could assist applicants in showcasing their KSAOs. The explanations were manipulated using a 2 (strength-based vs. no strength-ba…
abstract: Advancements in technology have transformed the landscape of organizational selection systems. In order to remain competitive and identify top talent, organizations have increasingly adopted new tools like automated interviews and evaluations to help in the decision-making process (Jaser et al., 2022). One such tool is asynchronous video interviews (AVIs), which offer the benefits of streamlining the hiring process and efficiently reviewing a large pool of candidates (Saurabh, 2022). However, applicant reactions to these systems have generally been negative, with concerns that automation may negatively impact perceptions of fairness (e.g., Langer et al., 2021). Explanations have been found to be a useful approach for improving applicant reactions, however, not all explanations are equally effective (Gilliland and Steiner, 2012; Langer et al., 2021). Therefore, this study aims to test Gilliland's (1993) model of applicants' reactions to employment selection systems in the context of AVIs, specifically focusing on the aftermath of receiving a negative selection decision. Data were collected from 380 individuals in the USA and the UK through online crowdsourcing platforms. These participants took part in a brief AVI session consisting of three interview questions. Subsequently, all participants received the same negative selection decision, accompanied by two different “should” explanations tailored to address the should counterfactual for each condition (Folger & Cropanzano, 2001). The strength-based explanation focused on aspects of the automated interviewing and evaluation that are considered strengths of the system, such as its consistency in evaluating applicants and efforts to mitigate bias (e.g., Noble et al., 2021). Conversely, the weakness-based explanation focused on addressing the system's shortcomings and emphasized features that could assist applicants in showcasing their KSAOs. The explanations were manipulated using a 2 (strength-based vs. no strength-ba…
author: Falls, Benjamin E.
keywords: applicant reactions, asynchronous video interviews, data privacy concerns, Organizational justice, Organizational Behavior and Theory
date published: 2023-01-01T08:00:00Z
date modified: 2026-07-24T01:40:22Z
provider: Claremont Graduate University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Claremont Graduate University