Idea Evaluation Mechanisms for Collective Intelligence in Open Innovation Communities: Do Traders Outperform Raters?

摘要

The increasing popularity of open innovation approaches has led to the rise of various open innovation communities on the Internet which might contain several thousand user-generated ideas. However, a company's absorptive capacity is limited regarding such an amount of ideas so that there is a strong need for mechanisms supporting the evaluation of these ideas. In this paper, we focus on the evaluation of such mechanisms for collective idea evaluation. Applying a multi-method approach, we compare six different configurations of a prediction market with a multi-criteria rating scale that performed best in previous research. We combine a web-based experiment with 448 participants, data from a participant survey, and an independent expert jury. Based on cognitive load theory, we explain why a multi-criteria rating scale outperforms prediction markets in terms of evaluation accuracy and evaluation satisfaction. This study contributes to theory building in the emerging field of collective intelligence.