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Manage. Inf. Syst."],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>Personality has been considered as a driving factor for work engagement, which significantly affects people\u2019s role performance at work. Although existing research has provided some intuitive understanding of the connection between personality traits and employees\u2019 work behaviors, it still lacks effective quantitative tools for modeling personality traits, job position characteristics, and employee work behaviors simultaneously.<\/jats:p>\n          <jats:p>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\u2019s personality traits in different job contexts and their impact on work behaviors.<\/jats:p>","DOI":"10.1145\/3607875","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T11:45:41Z","timestamp":1689162341000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Exploiting Connections among Personality, Job Position, and Work Behavior: Evidence from Joint Bayesian Learning"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3947-4153","authenticated-orcid":false,"given":"Dazhong","family":"Shen","sequence":"first","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-643X","authenticated-orcid":false,"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Career Science Lab, BOSS Zhipin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6494-1174","authenticated-orcid":false,"given":"Keli","family":"Xiao","sequence":"additional","affiliation":[{"name":"Stony Brook University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1105-9417","authenticated-orcid":false,"given":"Xi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6016-6465","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[{"name":"The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), China and The Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2023,9,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/ICIMTech.2016.7930305","volume-title":"2016 International Conference on Information Management and Technology (ICIMTech)","author":"Agung Anak Agung Gde","year":"2016","unstructured":"Anak Agung Gde Agung and Irna Yuniar. 2016. 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