Knowledge-sharing ties and equivalence in corporate online communities: A novel source to understand voluntary turnover

Yuan Chen, Hsing Kenneth Cheng, Yang Liu, Jingchuan Pu, Liangfei Qiu, Ning Wang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

An increasing number of companies are using corporate online communities, a new information technology tool, to facilitate internal knowledge sharing. The corporate online community also offers companies a novel source to understand employee's behaviors such as voluntary turnover, an important part of workforce management. Little is known, however, about whether and to what extent an employee's voluntary turnover is related to their knowledge-sharing activities in the corporate online community. In this study, we address this critical issue by jointly considering in-degree ties (i.e., knowledge acquisition) and out-degree ties (i.e., knowledge contribution), which we combine into two novel knowledge-sharing indicators: (i) the existence of bidirectional (vs. unidirectional) knowledge-sharing ties and (ii) the equivalence of knowledge-sharing ties (i.e., the balance between in-degree centrality and out-degree centrality). We theorize the relationships between these two indicators and the likelihood of voluntary employee turnover, and we test our hypotheses by collaborating with a large company to collect official voluntary turnover records and a unique dataset of detailed knowledge-sharing behaviors in its corporate online community. A survival model and a series of robustness checks consistently indicate that voluntary turnover is less likely among employees who establish bidirectional knowledge-sharing ties (rather than unidirectional ties) and that voluntary turnover is less likely among those with a higher equivalence of knowledge-sharing ties. In light of the critical role of workforce management and the extensive use of online communities, our study offers important managerial implications and can help companies better understand or predict employees’ voluntary turnover from their online knowledge-sharing activities.

Original languageEnglish (US)
Pages (from-to)3896-3913
Number of pages18
JournalProduction and Operations Management
Volume31
Issue number10
DOIs
StatePublished - Oct 2022

All Science Journal Classification (ASJC) codes

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Management of Technology and Innovation

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