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• W3119291878 abstract "Social network analysis (SNA) has emerged as a significant paradigm for research in data mining community for measuring and analyzing human dynamic network structure. At organizational level, SNA can enhance our understanding of work place social interactions and unveil the hidden stars embedded in informal networks by investigating nodes and edges of complex networks. For this study, we aim to formulate a network centrality based quantitative method to identify the High potential employees (HiPos) and Influencers of telecom sector and explore the relationship between degree centrality of these star employees and their turnover intention by modeling the dynamics of their workplace social ties and predictive data mining technique. We investigated the multiplex work and advice network in two leading telecom operators of Pakistan i.e. Ufone and Zong. For the statistical analysis we conducted a quantitative and visual network analysis in UCINET along with correlation and regression. Our results showed a negative correlation between HiPos out-degree centrality and turnover intention and a positive correlation between influencer’s in-degree centrality and turnover. Whereas perceived investment in employee development (PIED) was found to mediate the relationship between in-degree centrality of influencer and turnover intention. The correlation results were then verified in regression model. These findings will guide the telecom operators in designing an optimal structure for business intelligence by providing critical insights of their star employees and help them to investigate the influence of central nodes on dynamical processes of its heterogeneous networks and thus enhance employee retention before a star falls out." @default.
• W3119291878 created "2021-01-18" @default.
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• W3119291878 date "2021-01-01" @default.
• W3119291878 modified "2023-09-24" @default.
• W3119291878 title "Know Your Stars Before They Fall Apart: A Social Network Analysis of Telecom Industry to Foster Employee Retention Using Data Mining Technique" @default.
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• W3119291878 doi "https://doi.org/10.1109/access.2021.3050327" @default.
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