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

Jie Lin, Chao Wang, Lixin Zhou and Xiaoyan Jiang

Technological Forecasting and Social Change, 2022, vol. 174, issue C

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.

Keywords: Online user innovation community; User ideas; Text clustering; Organizational information processing theory (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521007009

DOI: 10.1016/j.techfore.2021.121266

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