Integrating Multi-source Heterogeneous Online Reviews to Predict the Adoption of Ideas in Open Innovation Communities
Integrating internal and external innovation resources to gain market advantages has always been a concern of enterprises,because their innovation ability directly impacts their success or failure.In previous research,online reviews were used as a data source for predicting the adoption of ideas in open innovation communities,and such research mainly relied on a single text feature.Based on heuristic systematic persuasion theory,we propose a new predictive model for idea adoption in open innovation communities.Based on value co-creation theory and trust transfer theory,our model summa-rizes the definition of multi-source heterogeneous online reviews and describes creativity based on features at three levels:heuristic reviewer features,heuristic review features,and systematic review features.In addition,we introduce a graph at-tention network to realize the integration of the three levels of idea features.We then collected data sets from a real open in-novation community,with which we verified the proposed model and its various characteristics.The comprehensive perfor-mance of the model in accurately predicting the adoption of ideas is about 97%.The results also show that the classifica-tion prediction results of the graph model with feature fusion are better than the traditional machine learning classification algorithm.These findings not only demonstrate the effectiveness of the graph model in feature fusion,but the integration of the features of reviews and reviewers also makes a methodological and theoretical contribution to the study of idea adop-tion.
万方数据
维普
