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The link between knowledge sharing connections and employee time savings: A social network analysis

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  • Cristina Poleacovschi
  • Amy Javernick-Will
  • Tony Tong

Abstract

Social network research has shown that knowledge sharing connections facilitate access to valuable resources. However, little is known about which knowledge sharing connections save knowledge seekers the most time on their daily tasks. To address this gap, this research analyses knowledge sharing connections that provide time savings, measured as the time individuals saved on daily tasks as a result of receiving knowledge from other employees. The research evaluates time savings based on the strength of connection, the network distance between the seeker and provider and the provider’s centrality in the network. To conduct this study, the research team analysed a data-set of 10,849 knowledge sharing dyads reported through a survey administered in one department in a large construction and engineering organization. Data were analysed using social network analysis and multiple regression quadratic assignment procedure. Results showed that stronger connections were assessed as more beneficial for time savings, especially when the connections were with central actors who had higher closeness centrality scores. Knowledge seekers also had a higher assessment of time savings when they were closer (in terms of network distance) to the knowledge provider. These results show that knowledge network reach and strong knowledge connections are essential for achieving everyday work efficiency. Furthermore, the results add to theories of strong and weak connections by showing that strong ties are favourable for achieving time savings in the context of construction and engineering organizations.

Suggested Citation

  • Cristina Poleacovschi & Amy Javernick-Will & Tony Tong, 2017. "The link between knowledge sharing connections and employee time savings: A social network analysis," Construction Management and Economics, Taylor & Francis Journals, vol. 35(8-9), pages 455-467, September.
  • Handle: RePEc:taf:conmgt:v:35:y:2017:i:8-9:p:455-467
    DOI: 10.1080/01446193.2017.1287414
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    Cited by:

    1. Yan Wang & Lifan Yang & Enzo Russo & Domenico Graziano, 2021. "The Incentive Mechanism of Knowledge Sharing in Cross-Border Business Models Based on Digital Technologies," Sustainability, MDPI, vol. 13(22), pages 1-33, November.
    2. Gilson Silva jr & Jose-Maria Silveira & Henrique Richter, 2024. "Construction, extractive and mining global investment intentions: a network analysis," Economics Bulletin, AccessEcon, vol. 44(2), pages 586-600.
    3. Zhigang Li & Xu Xu, 2020. "Analysis of Network Structure and Doctor Behaviors in E-Health Communities from a Social-Capital Perspective," IJERPH, MDPI, vol. 17(4), pages 1-14, February.

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