Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
The civilian unemployment rate in the United States is currently 3.8% (U.S. Bureau of Labor Statistics, 2023). This low level of unemployment has caused an increase in competition for talent across many industries and organizations. Talent also has a higher mobility, with an overall of 2.5% of workers, or around four million people, switching jobs each month on average (Kochhar et al., 2022). This means that in addition to attracting talent, it is crucial for organizations to be able to keep individuals in the applicant pool and ensure that they will accept a job offer when the time comes. Due to many organizations wanting to ensure recruitment and retention of potential employees, applicant reactions are an important factor to help achieve these goals. Automated decision making is now being used by many organizations to evaluate applicants for open positions. The extant literature on reactions to these hiring tools suggests that applicants prefer, as described by Mirowska & Mesnet (2022), ‘the devil they know’. Mirowska & Mesnet (2022) elaborate stating applicants seem to prefer human biases and intuition over that of artificial intelligence even when stating a belief that artificial intelligence evaluations are more objective. For example, Langer and colleagues (2020) found that participants included in an automatic evaluation condition felt that through the automation process of their interview, they had fewer opportunities to perform. However, there is no research on whether this is a uniform effect or if there are differences in how applicants react to automated decision making based on job type. In fact, applicant reactions literature largely ignores that the type of job individuals are applying to may make a difference in reactions by applicants. Accordingly, utilizing an experimental design with hypothetical job descriptions, and a sample of Prolific users living in the U.S., this study will examine the following hypotheses: H1: Reactions to automated decision-making will be more positive for a white-collar office job compared to a blue-collar job for a) interviews and b) resume screening. H2: Reactions to automated decision-making will be more positive for remote vs. in-person jobs for a) interviews and b) resume screening. H3: Reactions to automated resume screening will be more positive than reactions to automated interviews.
Subject
Industrial and organizational psychology
Document Type
posters
Language
English
Rights
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by/4.0/
Included in
Reactions to Automated Selection Decisions: Moderation by Job Type
The civilian unemployment rate in the United States is currently 3.8% (U.S. Bureau of Labor Statistics, 2023). This low level of unemployment has caused an increase in competition for talent across many industries and organizations. Talent also has a higher mobility, with an overall of 2.5% of workers, or around four million people, switching jobs each month on average (Kochhar et al., 2022). This means that in addition to attracting talent, it is crucial for organizations to be able to keep individuals in the applicant pool and ensure that they will accept a job offer when the time comes. Due to many organizations wanting to ensure recruitment and retention of potential employees, applicant reactions are an important factor to help achieve these goals. Automated decision making is now being used by many organizations to evaluate applicants for open positions. The extant literature on reactions to these hiring tools suggests that applicants prefer, as described by Mirowska & Mesnet (2022), ‘the devil they know’. Mirowska & Mesnet (2022) elaborate stating applicants seem to prefer human biases and intuition over that of artificial intelligence even when stating a belief that artificial intelligence evaluations are more objective. For example, Langer and colleagues (2020) found that participants included in an automatic evaluation condition felt that through the automation process of their interview, they had fewer opportunities to perform. However, there is no research on whether this is a uniform effect or if there are differences in how applicants react to automated decision making based on job type. In fact, applicant reactions literature largely ignores that the type of job individuals are applying to may make a difference in reactions by applicants. Accordingly, utilizing an experimental design with hypothetical job descriptions, and a sample of Prolific users living in the U.S., this study will examine the following hypotheses: H1: Reactions to automated decision-making will be more positive for a white-collar office job compared to a blue-collar job for a) interviews and b) resume screening. H2: Reactions to automated decision-making will be more positive for remote vs. in-person jobs for a) interviews and b) resume screening. H3: Reactions to automated resume screening will be more positive than reactions to automated interviews.
Department
University of Tennessee at Chattanooga. Dept. of Psychology