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Understanding applicants' reactions to asynchronous video interviews though self-reports and nonverbal cues

Muralidhar, Skanda and Kleinlogel, Emmanuelle Patricia and Mayor, Eric and Bangerter, Adrian and Schmid Mast, Marianne and Gatica-Perez, Daniel. (2020) Understanding applicants' reactions to asynchronous video interviews though self-reports and nonverbal cues. ICMI '20: International Conference On Multimodal Interaction. pp. 566-574.

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Official URL: https://edoc.unibas.ch/79348/

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Abstract

Asynchronous video interviews (AVIs) are increasingly used by organizations in their hiring process. In this mode of interviewing, the applicants are asked to record their responses to predefined interview questions using a webcam via an online platform. AVIs have increased usage due to employers' perceived benefits in terms of costs and scale. However, little research has been conducted regarding applicants' reactions to these new interview methods. In this work, we investigate applicants' reactions to an AVI platform using self-reported measures previously validated in psychology literature. We also investigate the connections of these measures with nonverbal behavior displayed during the interviews. We find that participants who found the platform creepy and had concerns about privacy reported lower interview performance compared to participants who did not have such concerns. We also observe weak correlations between nonverbal cues displayed and these self-reported measures. Finally, inference experiments achieve overall low-performance w.r.t. to explaining applicants' reactions. Overall, our results reveal that participants who are not at ease with AVIs (i.e., high creepy ambiguity score) might be unfairly penalized. This has implications for improved hiring practices using AVIs.
Faculties and Departments:07 Faculty of Psychology > Departement Psychologie > Forschungsbereich Klinische Psychologie und Neurowissenschaften > Klinische Psychologie und Epidemiologie (Lieb)
UniBasel Contributors:Mayor, Eric Marcel
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Association for Computing Machinery
ISBN:978-1-4503-7581-8
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:03 Nov 2021 16:01
Deposited On:03 Nov 2021 16:01

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