Idea Recommendation in Open Innovation Platforms: A Design Science Approach

59 Pages Posted: 10 Aug 2021

See all articles by Qian Liu

Qian Liu

Central University of Finance and Economics

Qianzhou Du

Nanjing University

Yili Hong

University of Miami Herbert Business School

Weiguo Fan

University of Iowa

Date Written: August 3, 2021

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.

Keywords: idea recommendation, persuasion attempt, persuasion coping, prediction model, interpretable machine learning

Suggested Citation

Liu, Qian and Du, Qianzhou and Hong, Yili and Fan, Weiguo, Idea Recommendation in Open Innovation Platforms: A Design Science Approach (August 3, 2021). China Center for Internet Economy Research (CCIE) Research Paper, Available at SSRN: https://ssrn.com/abstract=3898894 or http://dx.doi.org/10.2139/ssrn.3898894

Qian Liu

Central University of Finance and Economics ( email )

770 Middle Road
Dresden, ME 04342
United States

Qianzhou Du

Nanjing University ( email )

Nanjing, Jiangsu 210093
China

Yili Hong (Contact Author)

University of Miami Herbert Business School ( email )

P.O. Box 248126
Florida
Coral Gables, FL 33124
United States

Weiguo Fan

University of Iowa ( email )

S262 JPP Business Building
Iowa City, IA 52242-1097
United States

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