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Exploiting Connections among Personality, Job Position, and Work Behavior: Evidence from Joint Bayesian Learning

Authors:

Hui XiongAuthors Info & Claims

ACM Transactions on Management Information Systems, Volume 14, Issue 3

Article No.: 26, Pages 1 - 20

https://doi.org/10.1145/3607875

Published: 12 September 2023 Publication History

Abstract

Personality has been considered as a driving factor for work engagement, which significantly affects people’s role performance at work. Although existing research has provided some intuitive understanding of the connection between personality traits and employees’ work behaviors, it still lacks effective quantitative tools for modeling personality traits, job position characteristics, and employee work behaviors simultaneously.

To this end, in this article, we introduce a data-driven joint Bayesian learning approach, Joint-PJB, to discover explainable joint patterns from massive personality and job-position-related behavioral data. Specifically, Joint-PJB is designed with the knowledgeable guidance of the four-quadrant behavioral model, namely, DISC (Dominance, Influence, Steadiness, Conscientiousness). Based on the real-world data collected from a high-tech company, Joint-PJB aims to highlight personality-job-behavior joint patterns from personality traits, job responsibilities, and work behaviors. The model can measure the matching degree between employees and their work behaviors given their personality and job position characteristics. We find a significant negative correlation between this matching degree and employee turnover intention. Moreover, we also showcase how the identified patterns can be utilized to support real-world talent management decisions. Both case studies and quantitative experiments verify the effectiveness of Joint-PJB for understanding people’s personality traits in different job contexts and their impact on work behaviors.

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Cited By

Hao MCai MFang MYou L(2024)SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation SystemsACM Transactions on Intelligent Systems and Technology10.1145/364386115:2(1-20)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3643861
Guo PXiao KZhu HMeng Q(2023)Preference-Constrained Career Path Optimization: An Exploration Space-Aware Stochastic Model2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00021(120-129)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00021
Sun YJi YZhu HZhuang FHe QXiong H(undefined)Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement LearningACM Transactions on Information Systems10.1145/3704998
https://dl.acm.org/doi/10.1145/3704998

Index Terms

Exploiting Connections among Personality, Job Position, and Work Behavior: Evidence from Joint Bayesian Learning
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 14, Issue 3

September 2023

184 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3605933

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

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Publication History

Published: 12 September 2023

Online AM: 12 July 2023

Accepted: 21 June 2023

Revised: 07 March 2023

Received: 25 August 2022

Published in TMIS Volume 14, Issue 3

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Cited By

Hao MCai MFang MYou L(2024)SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation SystemsACM Transactions on Intelligent Systems and Technology10.1145/364386115:2(1-20)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3643861
Guo PXiao KZhu HMeng Q(2023)Preference-Constrained Career Path Optimization: An Exploration Space-Aware Stochastic Model2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00021(120-129)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00021
Sun YJi YZhu HZhuang FHe QXiong H(undefined)Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement LearningACM Transactions on Information Systems10.1145/3704998
https://dl.acm.org/doi/10.1145/3704998

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