The Impact of Nonverbal Behavior in the Job Interview

Frauendorfer, Denise; Mast, Marianne Schmid

doi:10.1057/9781137345868_11

Cited by 14 publications

(17 citation statements)

References 94 publications

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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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Section: Absence Of Visual Cues: Anxiety Effects On Ratingsmentioning

confidence: 99%

Perceived interviewee anxiety and performance in telephone interviews

Jeske

Shultz

Owen

2018

EBHRM

View full text Add to dashboard Cite

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“…Accordingly, greater amounts of eye contact are negatively associated with perceptions of deception (Bond & DePaulo, 2006). Additionally, eye contact is positively associated with conscientiousness and cognitive abilities (Frauendorfer & Mast, 2015), two of the strongest positive trait predictors of job performance (Schmidt & Hunter, 1998) and interview success (Fox & Spector, 2000;Tay et al, 2006). Thus, as interviewers may have learned through experience that individuals that make strong eye contact are more likely to be trustworthy, conscientious, and intelligent, they are likely to employ the dispositional attribution heuristic characteristic of System 1 processing in perceiving interviewees with increased eye contact as better hires.…”

Section: Taxonomy Of Nonverbal Cuesmentioning

confidence: 99%

“…Immediacy behaviors have been shown to elicit positive reactions in the interviewer via unconscious System 1 processing (Manusov, 1991), subsequently leading to higher ratings of interview performance (Frauendorfer & Mast, 2015;Imada & Hakel, 1977).…”

Section: Dual Process Theory As An Explanatory Framework For Nonverba...mentioning

confidence: 99%

Speaking without words: A meta‐analysis of over 70 years of research on the power of nonverbal cues in job interviews

et al. 2022

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What candidates say in an interview is important-but so is how they say it. We draw on dual-process theory to explain why interviewers rely on individuals' dynamic and static nonverbal cues to evaluate performance with quick, implicit inferences (System 1 processing). Yet, it remains unclear which cues most influence interviewers' judgments and whether moderators affecting interviewers' reliance on System 1 thinking (e.g., interview structure, modality, duration, and interviewee gender) impact the relationship between nonverbal cues and interview ratings. We performed the first meta-analysis to address these questions, integrating findings across 63 studies (N = 4868). The nonverbal cues demonstrating the strongest association with interview performance were professional appearance (ρ = .62), eye contact (ρ = .45), and head movement (ρ = .43). Moderator analyses highlight the persistent power of nonverbal cues, as the results were largely unaffected by interview structure, modality, or duration. Experimental design did play a role, as did interviewees' gender, with stronger effects for certain nonverbal cues (e.g., facial expressions and professional appearance) for women than men, conveying interviewers' reliance on gender-based stereotypes when judging their performance. Overall, these results suggest nonverbal cues and characteristics are an important influence on job applicants' success in employment interviews.

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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 only be done for a small subset of behaviors as humans may not be able to judge all the different possible behavioral expressions). For example, past reviews found that in the interview context, where machine learning 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: Data Bias Source 3: Behavioral Expressionmentioning

confidence: 99%

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

Tay¹,

Woo²,

Hickman³

et al. 2021

Preprint

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Given significant concerns about fairness and bias in the use of artificial intelligence (AI) and machine learning (ML) for assessing psychological constructs, 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 can empirically manifest when a trained ML model produces different predicted score levels for individuals belonging to different subgroups (e.g., race, gender) despite them having the same ground truth level 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 on how to mitigate them, recognizing that the development of new statistical and algorithmic procedures will need to follow. We also discuss how this framework brings clarity to 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 14 publications

References 94 publications

Perceived interviewee anxiety and performance in telephone interviews

Perceived interviewee anxiety and performance in telephone interviews

Speaking without words: A meta‐analysis of over 70 years of research on the power of nonverbal cues in job interviews

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

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