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How to Analyze and Enhance Participation in Electronic Networks of Practice

Author

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  • Charkhsaz Fatemeh

    (Graduate School of Management and Economics, Sharif University of Technology, Tehran, IRAN)

  • Isaai Mohammad Taghi

    (Graduate School of Management and Economics, Sharif University of Technology, Tehran, IRAN)

  • Dayani Malihe

    (Kermanshah University of Medical Sciences, Kermanshah, IRAN)

Abstract

This study proposes an exploratory methodology for analyzing participation in Electronic Networks of Practice, focusing on a network of 500 specialists in breast cancer treatment as a case study. The research addresses a critical gap in understanding how conversational dynamics influence participation in professional forums. Conversation measures and participation measures reported in the literature are identified and categorized, and new ones are suggested. This study by correlation analysis reveals three key participation triggers: initiator features, timing of initiation, and feedback, while also identifying non-associated variables like conversation topic and expression. Clustering categorizes conversations into four types, each exhibiting participation patterns and attributes. Social network analysis further reveals the network’s structure and participants’ interrelations. These findings provide a comprehensive understanding of participation in Electronic Networks of Practice and show the methodology’s broader applicability in analyzing professional networks. The study’s insights are vital for practitioners and theorists by offering a perspective on fostering knowledge sharing.

Suggested Citation

  • Charkhsaz Fatemeh & Isaai Mohammad Taghi & Dayani Malihe, 2024. "How to Analyze and Enhance Participation in Electronic Networks of Practice," Foundations of Management, Sciendo, vol. 16(1), pages 103-126.
    • Handle: RePEc:vrs:founma:v:16:y:2024:i:1:p:103-126:n:1007
    DOI: 10.2478/fman-2024-0007
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    References listed on IDEAS

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    1. Zhang, Yixiang & Fang, Yulin & Wei, Kwok-Kee & Chen, Huaping, 2010. "Exploring the role of psychological safety in promoting the intention to continue sharing knowledge in virtual communities," International Journal of Information Management, Elsevier, vol. 30(5), pages 425-436.
    2. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    3. Shichao Pang & Peng Bao & Wenyuan Hao & Jaewoong Kim & Wei Gu, 2020. "Knowledge Sharing Platforms: An Empirical Study of the Factors Affecting Continued Use Intention," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    4. Kwayu, Shirumisha & Abubakre, Mumin & Lal, Banita, 2021. "The influence of informal social media practices on knowledge sharing and work processes within organizations," International Journal of Information Management, Elsevier, vol. 58(C).
    5. Scott, John, 1988. "Social Network Analysis and Intercorporate Relations," Hitotsubashi Journal of commerce and management, Hitotsubashi University, vol. 23(1), pages 53-68, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Electronic Networks of Practice; Knowledge Sharing; Participation; Conversation; Exploratory Data Analysis;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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