Job performance during interviews is typically assessed by hiring managers. However, in recent years, companies have increasingly experimented with automated interview evaluations, with significant technological advancements. For example, the American company HireVue (HireVue, 2018) assesses applicants’ interview recordings based on facial expressions and body posture. This has not only promoted the use of automated interview evaluations but also generated research indicating that automated assessments can predict job performance. For instance, Naim (2015) demonstrated a relationship between applicant behavior in automated assessments and interview performance.
The majority of applicants use impression management (IM) techniques during job interviews (Roulin et al., 2014), which can be either assertive or defensive (Levashina & Campion, 2006). For example, assertive IM involves highlighting one’s skills and abilities, while defensive IM aims to conceal negative traits (Roulin et al., 2015). Additionally, IM behaviors can be honest or dishonest (Roulin et al., 2015): applicants using honest IM might exaggerate their positive contributions to past projects, while those who misrepresent past work experiences engage in deceptive IM. Particularly, deceptive IM has been shown to have negative effects. Previous research has explored methods to reduce IM, and aside from using highly structured interviews (Barrick et al., 2009), most attempts to detect or reduce IM have been unsuccessful.
The use of technology has many positive effects on organizations but also has drawbacks, particularly concerning applicant reactions. Current research focuses on applicant reactions most likely influenced by algorithm-based interview evaluations: opportunities for performance, job relevance, privacy concerns, and general fairness perceptions.
The purpose of this study is to investigate how perceptions of automated interview evaluations impact IM behaviors and applicant reactions. Regarding IM behaviors, the findings suggest that using automated interview evaluations may reduce the use of deceptive IM by applicants, indicating that organizations concerned about the negative impact of deceptive IM on interview effectiveness might consider incorporating automated evaluations into their interview processes. Regarding the impact of automated assessments on applicants, the study also suggests that this method might deter some applicants who react negatively to this novel interview evaluation process. Organizations should be aware of the trade-offs between controlling deceptive IM behaviors and ensuring positive applicant reactions.
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