TY - GEN
T1 - Unraveling the Impact of Visual Cues in Online Portraits on Workers' Employability in Digital Labor Markets
AU - Jiang, Yuting
AU - Rossi, Matti
AU - Tuunainen, Virpi Kristiina
AU - Cai, Zhao
AU - Tan, Chee Wee
N1 - Publisher Copyright:
© 2024 IEEE Computer Society. All rights reserved.
PY - 2024
Y1 - 2024
N2 - Online portraits constitute a pervasive and critical signal in digital labor markets in that workers can boost their employability by manipulating select visual cues embedded in these portraits. Consequently, we attempt to unravel how visual cues embedded in workers' portraits within digital labor markets can collectively influence constituent dimensions of employability. Notably, we advance a non-verbal cues classification model that differentiates among demographic, physical appearance, image quality, and non-verbal behavioral cues as focal determinants affecting one's employment status, the number of job offers received, and rehiring probability. Employing computer vision and deep learning algorithmic techniques to analyze the online portraits and personal information of 53,950 workers on Upwork.com, we demonstrate that visual cues embedded in profile portraits exert a significant effect on workers' employability in digital labor markets.
AB - Online portraits constitute a pervasive and critical signal in digital labor markets in that workers can boost their employability by manipulating select visual cues embedded in these portraits. Consequently, we attempt to unravel how visual cues embedded in workers' portraits within digital labor markets can collectively influence constituent dimensions of employability. Notably, we advance a non-verbal cues classification model that differentiates among demographic, physical appearance, image quality, and non-verbal behavioral cues as focal determinants affecting one's employment status, the number of job offers received, and rehiring probability. Employing computer vision and deep learning algorithmic techniques to analyze the online portraits and personal information of 53,950 workers on Upwork.com, we demonstrate that visual cues embedded in profile portraits exert a significant effect on workers' employability in digital labor markets.
KW - deep learning
KW - digital labor market
KW - non-verbal classification model
KW - Visual cues
UR - http://www.scopus.com/inward/record.url?scp=85199775982&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85199775982
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 693
EP - 702
BT - Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
A2 - Bui, Tung X.
PB - IEEE Computer Society
T2 - 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
Y2 - 3 January 2024 through 6 January 2024
ER -