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Converting consumer-generated content into an innovation resource: A user ideas processing framework in online user innovation communities

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  • Lin, Jie
  • Wang, Chao
  • Zhou, Lixin
  • Jiang, Xiaoyan

Abstract

An Online User Innovation Community (OUIC) is a space for consumers to share product usage experiences and put forward product improvement suggestions. However, as an increasing number of consumers post content in OUICs, companies face information processing challenges. Based on Organizational Information Processing Theory (OIPT), this study proposes a User Ideas Processing Framework (UIPF) to help enterprises efficiently process user ideas in OUICs and then applies it to a sample of 5,889 ideas from the Salesforce Idea Exchange. The case study results show that a UIPF can solve the information overload problem. Specifically, in Part 1 of the UIPF, we propose a new IDEA vectorization method and use it to cluster user ideas. Then, theme analysis is conducted on clusters to summarize the idea content in OUICs. This step gives us an overview of the information in OUICs. Compared with the standardized methods, our IDEA vectorization method can obtain better clustering results. Then, Part 2 of the UIPF builds a logistic regression model to identify innovative ideas from clusters. Compared with the famous “3C” method, the innovative ideas selected by the UIPF are more suitable for consumer requirements. In conclusion, the UIPF can help enterprises process information efficiently in OUICs.

Suggested Citation

  • Lin, Jie & Wang, Chao & Zhou, Lixin & Jiang, Xiaoyan, 2022. "Converting consumer-generated content into an innovation resource: A user ideas processing framework in online user innovation communities," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521007009
    DOI: 10.1016/j.techfore.2021.121266
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    References listed on IDEAS

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    Cited by:

    1. Liu, Jialing & Wei, Jiang & Liu, Yang & Jin, Duo, 2022. "How to channel knowledge coproduction behavior in an online community: Combining machine learning and narrative analysis," Technological Forecasting and Social Change, Elsevier, vol. 183(C).

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