Predicting Employee Occupation Mobility: A Theory-Drive Deep Learning Approach

Lun Li, Jingyi Sun, Rong Liu, Theodoros Lappas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication29th Annual Americas Conference on Information Systems, AMCIS 2023
ISBN (Electronic)9781713893592
StatePublished - 2023
Event29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023 - Panama City, Panama
Duration: 10 Aug 202312 Aug 2023

Publication series

Name29th Annual Americas Conference on Information Systems, AMCIS 2023

Conference

Conference29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023
Country/TerritoryPanama
CityPanama City
Period10/08/2312/08/23

Keywords

  • dynamic typicality modeling
  • Job mobility prediction
  • theory-driven deep learning

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