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Identifying Useful Information from Open Innovation Community |
Li He, Zhu Linlin(), Yan Min, Liu Jincheng, Hong Chuang |
School of Management, Jilin University, Changchun 130022, China |
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Abstract [Objective] The paper aims to identify useful message from open innovation community with numerous redundant and low quality information. [Methods] First, we retrieved 23,137 users’ comments on programming bugs from the official Xiaomi MIUI Forum based on the information adoption model. Then, we applied binary logistic regression method to explore factors affecting the usefulness of these comments. [Results] The timeliness of information had positive impact on their usefulness, the integrity of information also affected their usefulness, and the semantics of information had negative effects on their usefulness. The users’ previous experience did not influence the usefulness of information. However, users’ previous contribution had positive effects on the usefulness of information. [Limitations] The research data was collected from small portion of one community, which might yield biased results. [Conclusions] This paper could help us effectively identify usefulness information from open innovation communities.
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Received: 08 April 2018
Published: 16 January 2019
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