OPEN INNOVATION CONTESTS IN ONLINE MARKETS: IDEA GENERATION AND IDEA EVALUATION WITH COLLECTIVE INTELLIGENCE

Abstract

To overcome constrained resources, firms can actively seek innovative opportunities from the external world. This innovation approach, called open innovation (Chesbrough 2003; Hippel 2005; Terwiesch and Ulrich 2009; Terwiesch and Xu 2008), is receiving more and more attention. Facilitated by the global Internet and emerging forms of information technology, it has become very easy for companies to generate large numbers of innovative solutions through the use of online open innovation contests or crowdsourcing contests (Archak and Sundararajan 2009; Terwiesch and Ulrich 2009; Terwiesch and Xu 2008; Yang et al. 2009).For an innovation project to succeed, it is necessary to generate not only a large number of good ideas or solutions, but also to identify those that are "exceptional" (Terwiesch and Ulrich 2009). This dissertation contains three studies that aim to improve our understanding of how best to use contests as a tool to aggregate external resources (collective intelligence) in the generation and evaluation of solutions. The first study views an innovation contest from the innovation seeker's perspective and provides insights on how to improve contest performance. The second study views an innovation contest from the innovation solver's perspective examining the characteristics and strategies of winners and solvers. Finally, in the third study, a new approach to the solution evaluation process is introduced, which is referred to as open evaluation. In this approach, a prediction market is used as an aggregation mechanism to coordinate the crowd in the evaluation of proposed solutions. These three studies make a number of contributions to the literature, addressing core issues in the area of online innovation contests. The analyses, which leverage large-scale empirical data, produce a number of profound results, which can help people to understand how best to use and design innovation contests in an online environment, for idea generation. Further, these studies present a variety of managerial implications associated with the aggregation of individual effort (collective intelligence) to evaluate the ideas that are generated by an innovation contest. We hope that our studies can help open innovation pioneers, such as Google, to systematically generate and identify exceptionally good ideas at much lower costs. By utilizing our findings, we expect that more firms will be able to adopt an open innovation strategy, both systematically and easily.