Improving Online Idea Generation Platforms and Customizing the Task Structure on the Basis of Consumers' Domain-Specific Knowledge

The authors explore how firms can enhance consumer performance in online idea generation platforms. Most, if not all, online idea generation platforms offer all consumers identical tasks in which (1) participants are granted access to ideas from other participants and (2) ideas are classified into categories, but consumers can navigate freely across idea categories. The former is linked to stimulus ideas, and the latter may be viewed as a first step toward problem decomposition. The authors propose that the effects of both stimulus ideas and problem decomposition are moderated by consumers' domain-specific knowledge. In particular, concrete cues such as stimulus ideas are more beneficial to low-knowledge consumers, and high-knowledge consumers are better served with abstract cues such as the ones offered by problem decomposition. The authors' hypotheses are supported by an extensive empirical investigation involving more than 6,000 participants. The findings suggest that online idea generation platforms should use problem decomposition more explicitly and that firms should not immediately show other participants' ideas to high-knowledge consumers when they access the platform. In other words, online idea generation platforms should customize the task structure on the basis of each participant's domain-specific knowledge.

• 저널Journal of Marketing · 79(5) · 100–114
• 토픽Open Source Software Innovations · Computer Science Applications
• DOI10.1509/jm.13.0212
• 저자Lan Luo, Olivier Toubia