Which ideas are more likely to be implemented in online user innovation communities? An empirical analysis

• We study how the likelihood of idea implementation is affected in user innovation communities.
• Our model is validated using logistic regression analysis on secondary data of 19,964 user ideas.
• The results show significant impacts of the characteristics of its contributor as well as the characteristics of a submitted idea and its presentation.
• We also identify important differences in their effects for hybrid versus professional user innovation communities.

Online user innovation communities are increasingly being deployed by firms to garner innovation ideas from customers or users. However, very few ideas from such communities are successful in getting selected for implementation by the host firm. Given the limited understanding of the phenomenon, this study examines the determinants of firms' implementation of customers' ideas from user innovation communities. Drawing on theories of message persuasion and cognitive overload, we develop a conceptual model to explain how the likelihood of idea implementation is affected by the characteristics of its contributor as well as the characteristics of a submitted idea and its presentation. Specifically, we study the effects of the contributor's prior participation and prior implementation rate, as well as the idea's popularity, length, and supporting evidence on the idea's implementation likelihood. Our model is validated through logistic regression on a secondary dataset of 19,964 user ideas collected from two large user innovation websites, Salesforce.com IdeaExchange and Dell IdeaStorm. The results show significant impacts of these characteristics on idea implementation likelihood and also reveal important differences in their effects for hybrid (i.e., Dell IdeaStorm) versus professional (i.e., Salesforce.com IdeaExchange) user innovation communities.