The Effect of Turnover Intention on Tie Formation in Online Organization Networks
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Date
2020-01-07
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Turnover is costly for organizations. While existing research identifies the antecedents and effects of turnover, little research exists on how to identify individuals intending to leave an organization. We hypothesize that individuals with high turnover intention will participate in fewer communication relationships than average, and that individuals prefer communicating with others of similar levels of turnover intention. We use exponential random graph modeling (ERGM) to test our hypotheses on the email and advice networks of a technology company. ERGM allows us to simultaneously examine the effect of individual and dyadic level attributes on network formation. The results support our hypotheses in the email network, but not in the advice network. Our findings imply that organizations should examine their email networks to identify individuals with high turnover intention, and intervene with incentives if they wish to retain the employees.
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Network Analysis of Digital and Social Media, exponential random graph modeling, homophily, network formation, social network analysis, turnover intention
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10 pages
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Proceedings of the 53rd Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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