Journal article
An exploration and exploitation of value cocreation-based machine learning framework for automated idea screening
Decision Support Systems, Vol.196, 114504
09/2025
DOI: 10.1016/j.dss.2025.114504
Abstract
Idea screening in collaborative crowdsourcing communities poses significant challenges for firms. These challenges are primarily attributable to issues of prediction accuracy and information overload. The rapid expansion of idea pools generates a vast amount of data, making it difficult to effectively identify valuable ideas for new product development. This study introduces an interpretable framework for machine learning that integrates a novel exploration and exploitation perspective within the value cocreation model to enhance idea screening. The framework incorporates six theoretical dimensions of the exploration and exploitation of value cocreation (EEVC): the exploration and exploitation of digital resources, direct interactions, and ideas and their comments. Our evaluation reveals that the EEVC-based idea-screening system significantly outperforms the traditional 3Cs model in terms of prediction accuracy. SHAP value analysis further reveals that the exploration and exploitation of digital resources are the most influential predictors of idea implementation. The EEVC framework advances open innovation theory by clarifying how value cocreation dynamics influence idea implementation. Practically, it proposes a human–machine collaboration system that enhances expert decision-making for more effective idea selection.
Details
- Title: Subtitle
- An exploration and exploitation of value cocreation-based machine learning framework for automated idea screening
- Creators
- Qian LiuQianzhou DuChuang TangYili HongWeiguo Fan
- Resource Type
- Journal article
- Publication Details
- Decision Support Systems, Vol.196, 114504
- DOI
- 10.1016/j.dss.2025.114504
- ISSN
- 0167-9236
- eISSN
- 1873-5797
- Publisher
- ELSEVIER
- Grant note
- National Natural Science Foundation of China: 72172168, 72102106 Beijing Natural Science Foundation: 9242016 Fundamental Research Funds for the Central Universities: WK2040000093
This work was supported by the National Natural Science Foundation of China, Grant/Award Number: 72172168, 72102106; Beijing Natural Science Foundation, Grant/Award Number: 9242016; the Fundamental Research Funds for the Central Universities; the Fundamental Research Funds for the Central Universities WK2040000093.
- Language
- English
- Electronic publication date
- 07/05/2025
- Date published
- 09/2025
- Academic Unit
- Business Analytics
- Record Identifier
- 9984847246802771
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