Abstract
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.
Recommended Citation
Blohm, Ivo; Riedl, Christoph; Leimeister, Jan Marco; and Krcmar, Helmut, "Idea Evaluation Mechanisms for Collective Intelligence in Open Innovation Communities: Do Traders outperform Raters?" (2011). ICIS 2011 Proceedings. 19.
https://aisel.aisnet.org/icis2011/proceedings/onlinecommunity/19
Idea Evaluation Mechanisms for Collective Intelligence in Open Innovation Communities: Do Traders outperform Raters?
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.