More human-likeness, less self-disclosure? Avatars' form realism and job applicants' self-disclosure in AI interviews

Author

Yamin XU, City University of Hong Kong
Keng SIAU, Singapore Management UniversityFollow
Fiona Fui-hoon NAH, Singapore Management UniversityFollow

Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2024

Abstract

The rise of AI in recruitment promises to revolutionize how organizations evaluate job candidates. The quality of AI evaluations is determined by the input data, which depends on job applicants' self-disclosure. However, little is known about how the design elements of AI interview systems, particularly avatar interviewers, influence job applicants' self-disclosure during these interactions. This study aims to address this gap by specifically focusing on how the form realism of avatar interviewers affects job applicants' self-disclosure through their perceptions. In addition, the study will examine the effects of job type as a moderator. Drawing on the Stimulus-Organism-Response (S-O-R) model, this study proposes hypotheses on the influence of avatar form realism on selfdisclosure. We will design and conduct experiments to test the hypotheses and draw conclusions. This research will improve understanding of avatar design's influence on self-disclosure in AI interviews and provide valuable knowledge for organizations using AI in recruitment.

Keywords

AI interview, Avatar, Form realism, Trustworthiness, Fairness, Privacy, Self-disclosure

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Information Systems and Management

Publication

17th China Summer Workshop on Information Management: CSWIM 2024: Xiamen, China, June 29-30: Proceedings

First Page

497

Last Page

502

Publisher

CSWIM

City or Country

China

Citation

XU, Yamin; SIAU, Keng; and NAH, Fiona Fui-hoon. More human-likeness, less self-disclosure? Avatars' form realism and job applicants' self-disclosure in AI interviews. (2024). 17th China Summer Workshop on Information Management: CSWIM 2024: Xiamen, China, June 29-30: Proceedings. 497-502.
Available at: https://ink.library.smu.edu.sg/sis_research/9993

Copyright Owner and License

Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Additional URL

https://2024.cswimworkshop.org/wp-content/uploads/2024/06/CSWIM2024-Proceddings.pdf