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- W3201740317 abstract "Collaborative crowdsourcing communities help firms obtain ideas generated by the public at a lower cost compared to those generated in-house. However, the growth of these communities has led to a large influx of ideas of mixed quality, which has made it difficult for firm experts to select and implement ideas. In this paper we propose a novel theoretical framework to (1) extract important features from a collaborative crowdsourcing community and (2) apply them to the practice of recommending ideas that are most likely to be implemented in the future. More specifically, we adopt the design science research paradigm, introduce the knowledge persuasion model as the kernel theory, operate users’ persuasive attempts and firm experts’ persuasive coping, and identify a rich set of features as predictors of the likelihood of idea implementation. We test our prediction framework on a large-scale collaborative crowdsourcing community. The results of our data analysis show that the proposed framework is effective and efficient in predicting the likelihood of idea implementation. To increase the interpretability of the prediction model, we also implement the SHapley Additive exPlanations (SHAP) analysis and discuss the relationships between important features and idea implementation. We conclude by discussing the theoretical and practical implications of these findings." @default.
- W3201740317 created "2021-10-11" @default.
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- W3201740317 date "2021-01-01" @default.
- W3201740317 modified "2023-09-23" @default.
- W3201740317 title "Idea Recommendation in Open Innovation Platforms: A Design Science Approach" @default.
- W3201740317 doi "https://doi.org/10.2139/ssrn.3898894" @default.
- W3201740317 hasPublicationYear "2021" @default.
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