利用人格、工作岗位和工作行为之间的联系:来自联合贝叶斯学习的证据

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Management Information Systems Pub Date : 2023-07-12 DOI:10.1145/3607875
Dazhong Shen, Hengshu Zhu, Keli Xiao, Xi Zhang, Hui Xiong
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引用次数: 0

摘要

性格一直被认为是工作投入的驱动因素,它显著影响人们在工作中的角色表现。虽然已有研究对人格特质与员工工作行为之间的关系提供了一些直观的认识,但仍然缺乏有效的量化工具来同时对人格特质、工作岗位特征和员工工作行为进行建模。为此,在本文中,我们引入了一种数据驱动的联合贝叶斯学习方法joint - pjb,从大量的个性和职位相关行为数据中发现可解释的联合模式。具体来说,Joint-PJB是在四象限行为模型DISC (Dominance, Influence, Steadiness, Conscientiousness)的知识指导下设计的。joint - pjb基于从一家高科技公司收集的真实数据,旨在从人格特征、工作职责和工作行为三个方面突出人格-工作-行为的联合模式。该模型可以根据员工的性格特征和工作岗位特征来衡量员工与其工作行为的匹配程度。我们发现这种匹配度与员工离职倾向之间存在显著的负相关。此外,我们还展示了如何利用已识别的模式来支持现实世界的人才管理决策。案例研究和定量实验都验证了联合pjb在理解不同工作情境下人格特质及其对工作行为的影响方面的有效性。
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Exploiting Connections among Personality, Job Position, and Work Behavior: Evidence from Joint Bayesian Learning
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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来源期刊
ACM Transactions on Management Information Systems
ACM Transactions on Management Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
6.30
自引率
20.00%
发文量
60
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