The Influence of Sentiment Orientation in Open Innovation Communities: Empirical Evidence from a Business Analytics Community

Abstract

This study aims to investigate the influence of presentation and sentiment orientation of user-generated ideas and reviews on idea adoption in open innovation communities (OICs). Drawing on the social influence theory, this study develops a research model that divides idea components into informational and normative determinants. The sentiment orientation of the idea title, description, and associated reviews is determined using a lexicon-based sentiment analysis approach. The research model is empirically tested using logistic regression analysis based on a dataset from the Microsoft community for business analytics products. The results reveal that the sentiment orientation of idea title has a negative influence on idea adoption, whilst the sentiment orientation of description has no influence on idea adoption. The sentiment orientation of the associated reviews has a positive influence on idea adoption, and this influence is moderated by the number of reviews. In addition, both idea title length and description length have a positive influence on idea adoption. These results offer several theoretical and practical implications and should therefore contribute to a better understanding of how user-generated ideas can be leveraged to drive innovation development and sustainability in OICs.