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How to Find the Key Participants in Crowdsourcing Design? Identifying Lead Users in the Online Context Using User-Contributed Content and Online Behavior Analysis

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  • Jing Zhang

    (Tianjin Key Laboratory of Equipment Design and Manufacturing Technology, Tianjin University, Tianjin 300072, China
    Department of Mechanical Engineering, Tianjin Ren’ai College, Tianjin 301636, China)

  • Wei Guo

    (Tianjin Key Laboratory of Equipment Design and Manufacturing Technology, Tianjin University, Tianjin 300072, China
    Department of Mechanical Engineering, Tianjin Ren’ai College, Tianjin 301636, China)

  • Ruoyu Liang

    (School of Design, Jiangnan University, Wuxi 214122, China)

  • Lei Wang

    (Tianjin Key Laboratory of Equipment Design and Manufacturing Technology, Tianjin University, Tianjin 300072, China)

  • Zhonglin Fu

    (Tianjin Key Laboratory of Equipment Design and Manufacturing Technology, Tianjin University, Tianjin 300072, China)

  • Jiang Sun

    (Department of Mechanical Engineering, Tianjin Ren’ai College, Tianjin 301636, China)

Abstract

Lead users are the most valuable innovation sources in crowdsourcing design; how to identify these users is a research hotspot in the field of design and management. Existing approaches to discover lead users in the context of the online community, such as the manual method and ordering algorithm, have some limitations, for instance, low coverage and accuracy. To address these deficiencies, this article proposes a method that applies text-mining techniques, analysis of user behavior, and contributed content to identify lead users. We suggest a three-step analytical approach: First, a criterion system to evaluate the user’s leading-edge status is constructed. Second, we utilize a fuzzy analytical hierarchy process to assess the weighted value of each indicator and develop the reference sequence of the indicators. Third, grey relational analysis is employed to analyze the correlations between users’ indicators and reference sequences, and lead users are recognized based on each user’s correlation ranking. An empirical analysis is used to examine the effectiveness of the proposed method. The results reveal that the method has good precision and recall rate, can automatically process large-scale data, and has no strict requirements for respondents. Finally, the article discusses the limitations and provides possible directions for future research.

Suggested Citation

  • Jing Zhang & Wei Guo & Ruoyu Liang & Lei Wang & Zhonglin Fu & Jiang Sun, 2022. "How to Find the Key Participants in Crowdsourcing Design? Identifying Lead Users in the Online Context Using User-Contributed Content and Online Behavior Analysis," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2094-:d:747741
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    References listed on IDEAS

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