The Impact of Nonverbal Behavior in the Job Interview

Frauendorfer, Denise; Mast, Marianne Schmid

doi:10.1057/9781137345868.0017

Cited by 6 publications

(7 citation statements)

References 50 publications

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“…Interviewee Impression Management Blacksmith et al (2016) suggested that one reason for the lower ratings in technology-mediated interviews is that interviewees are restricted in their use of IM tactics (see Chapman & Rowe, 2002, for a similar suggestion). Usually, interviewees can use verbal IM tactics such as emphasizing potential strengths or playing down potential weaknesses or failures or non-verbal IM tactics such as making eye contact, smiling, nodding, and maintaining a specific body posture (Frauendorfer & Schmid Mast, 2015). Previous research repeatedly found that the use of such IM behaviors correlates with better interview performance ratings (Barrick, Shaffer, & DeGrassi, 2009;Burnett & Motowidlo, 1998;Levashina et al, 2014).…”

Section: Ratings Of Interviewees' Performance In Ftf and Technology-mmentioning

confidence: 99%

It Takes More Than a Good Camera: Which Factors Contribute to Differences Between Face-to-Face Interviews and Videoconference Interviews Regarding Performance Ratings and Interviewee Perceptions?

et al. 2020

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Due to technological progress, videoconference interviews have become more and more common in personnel selection. Nevertheless, even in recent studies, interviewees received lower performance ratings in videoconference interviews than in face-to-face (FTF) interviews and interviewees held more negative perceptions of these interviews. However, the reasons for these differences are unclear. Therefore, we conducted an experiment with 114 participants to compare FTF and videoconference interviews regarding interview performance and fairness perceptions and we investigated the role of social presence, eye contact, and impression management for these differences. As in other studies, ratings of interviewees’ performance were lower in the videoconference interview. Differences in perceived social presence, perceived eye contact, and impression management contributed to these effects. Furthermore, live ratings of interviewees’ performance were higher than ratings based on recordings. Additionally, videoconference interviews induced more privacy concerns but were perceived as more flexible. Organizations should take the present results into account and should not use both types of interviews in the same selection stage.

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Section: Ratings Of Interviewees' Performance In Ftf and Technology-mmentioning

confidence: 99%

It Takes More Than a Good Camera: Which Factors Contribute to Differences Between Face-to-Face Interviews and Videoconference Interviews Regarding Performance Ratings and Interviewee Perceptions?

et al. 2020

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“…Interviewers in telephone interview settings will use verbal indicators to form impressions about a candidate's characteristics. These impressions may be based, in the absence of visual information, on interviewees' vocal fluency, voice intensity and pitch, energy, affect, emotional expressiveness and voice modulation (DeGroot and Gooty, 2009;Frauendorfer and Schmid Mast, 2014;Riggio and Riggio, 2002). Yet many of these verbal indicators are negatively affected by anxiety (Gilboa-Schechtman and Shachar-Lavie, 2013).…”

Section: Absence Of Visual Cues: Anxiety Effects On Ratingsmentioning

confidence: 99%

Perceived interviewee anxiety and performance in telephone interviews

Jeske

Shultz

Owen

2018

EBHRM

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Purpose -The purpose of this paper is to focus on the role of interviewee anxiety as a predictor of perceived hireability (Study 1, n ¼ 82) and job suitability (Study 2, n ¼ 74). Design/methodology/approach -Using an experimental design, participants were randomly allocated to one of two conditions (an audio recording of either a confident or anxious job candidate with identical scripts) and asked to take the role of an interviewer. Findings -The anxious interviewee (played by an actor) was consistently rated as less hireable (in a combined sample based on Studies and 2), less suitable to the job and received less favorable hiring recommendations (as assessed in Study 2) than the confident interviewee (played by the same actor).Research limitations/implications -The study was conducted with students who may have less interview experience than experienced interviewers. Practical implications -The results suggest that anxiety has a negative biasing effect on perceived hireability and job suitability ratings. In other words, the behavioral manipulation of anxiety affects hireability ratings, independent of any subjective assessment of anxiety. Originality/value -The findings provide evidence of an anxiety bias in telephone interview settings. The results highlight the importance of considering anxiety cues when training employment interviewers.

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“…Because it is important not to confound computer-extracted features with behavioral expressions, the focus should be on using human raters to examine base rates of behaviors (realistically, this can be done only for a small subset of behaviors because humans may not be able to judge large numbers of behaviors reliably). For example, past reviews found that in the interview context, in which ML is increasingly applied (Hickman et al, 2019), females smile and nod more than males (Frauendorfer & Mast, 2015). Significant differences when ground-truth distributions are matched would suggest behavioral expression bias.…”

Section: Identifying Mlmb Sources and Mitigating Mlmb In Psychologica...mentioning

confidence: 99%

A Conceptual Framework for Investigating and Mitigating Machine-Learning Measurement Bias (MLMB) in Psychological Assessment

Tay

Woo

Hickman

et al. 2022

Advances in Methods and Practices in Psychological Science

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Given significant concerns about fairness and bias in the use of artificial intelligence (AI) and machine learning (ML) for psychological assessment, we provide a conceptual framework for investigating and mitigating machine-learning measurement bias (MLMB) from a psychometric perspective. MLMB is defined as differential functioning of the trained ML model between subgroups. MLMB manifests empirically when a trained ML model produces different predicted score levels for different subgroups (e.g., race, gender) despite them having the same ground-truth levels for the underlying construct of interest (e.g., personality) and/or when the model yields differential predictive accuracies across the subgroups. Because the development of ML models involves both data and algorithms, both biased data and algorithm-training bias are potential sources of MLMB. Data bias can occur in the form of nonequivalence between subgroups in the ground truth, platform-based construct, behavioral expression, and/or feature computing. Algorithm-training bias can occur when algorithms are developed with nonequivalence in the relation between extracted features and ground truth (i.e., algorithm features are differentially used, weighted, or transformed between subgroups). We explain how these potential sources of bias may manifest during ML model development and share initial ideas for mitigating them, including recognizing that new statistical and algorithmic procedures need to be developed. We also discuss how this framework clarifies MLMB but does not reduce the complexity of the issue.

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The Impact of Nonverbal Behavior in the Job Interview

Cited by 6 publications

References 50 publications

It Takes More Than a Good Camera: Which Factors Contribute to Differences Between Face-to-Face Interviews and Videoconference Interviews Regarding Performance Ratings and Interviewee Perceptions?

It Takes More Than a Good Camera: Which Factors Contribute to Differences Between Face-to-Face Interviews and Videoconference Interviews Regarding Performance Ratings and Interviewee Perceptions?

Perceived interviewee anxiety and performance in telephone interviews

A Conceptual Framework for Investigating and Mitigating Machine-Learning Measurement Bias (MLMB) in Psychological Assessment

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