TY - GEN
T1 - Predicting Employee Occupation Mobility
T2 - 29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023
AU - Li, Lun
AU - Sun, Jingyi
AU - Liu, Rong
AU - Lappas, Theodoros
N1 - Publisher Copyright:
© 2023 29th Annual Americas Conference on Information Systems, AMCIS 2023. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Predicting employees' career moves benefits both talents with career planning and firms in preparing for the gain and loss of human capital. In this paper, we follow the categorization theory to design an explainable AI artifact for the employee occupational mobility prediction problem. Under a coherent categorization theory framework, three theory-driven components explain different mobility mechanisms. The experimental results approve the effectiveness of this theory-driven approach compared to state-of-the-art baselines in terms of occupational mobility prediction.
AB - Predicting employees' career moves benefits both talents with career planning and firms in preparing for the gain and loss of human capital. In this paper, we follow the categorization theory to design an explainable AI artifact for the employee occupational mobility prediction problem. Under a coherent categorization theory framework, three theory-driven components explain different mobility mechanisms. The experimental results approve the effectiveness of this theory-driven approach compared to state-of-the-art baselines in terms of occupational mobility prediction.
KW - dynamic typicality modeling
KW - Job mobility prediction
KW - theory-driven deep learning
UR - http://www.scopus.com/inward/record.url?scp=85192944958&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85192944958&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85192944958
T3 - 29th Annual Americas Conference on Information Systems, AMCIS 2023
BT - 29th Annual Americas Conference on Information Systems, AMCIS 2023
Y2 - 10 August 2023 through 12 August 2023
ER -