In today's dynamic business environment, companies are under tremendous pressure to become more innovative and maintain a steady stream of ideas that can lead to new and improved products and services. Companies have begun to explore the possibility of capturing consumers' "collective intelligence" by establishing firm-sponsored online brainstorming sites where individuals can share their ideas and offer comments on the ideas contributed by others. We term these sites "Company-Sponsored Online Co-Creation Brainstorming" (COCB). The value of this open and voluntary co-creation depends largely on members' contribution levels, the quality of the contributions, and sustained participation. In this paper, utilizing Zwass's taxonomy of co-creation value as a base, we structure a taxonomic framework of COCBs and an accompanying basic model of COCBs. We then present a series of hypotheses concerning the relationships between the model's various factors and specific COCB activities. We validate the model using empirical data collected over two and a half years, starting from the initiation of a pioneering company-sponsored online brainstorming site. Our analyses demonstrate that the level of peer feedback and the responsiveness (speed) of sponsor company feedback have significant influences on both members' contribution levels and duration of active participation. The sponsoring company's feedback, however, seems to influence only the quality of member's contribution level. On the practical side, the outcomes suggest that sponsoring companies should develop efficient processes for reviewing and responding to submitted ideas. Regarding theory, our findings provide an initial piece of contextualized research that offers implications for theory building in the COCB context, most notably the identification of key relationships between feedback (both peer and company) and participant activity levels and duration of participation.
In the work presented here, we develop and apply preference markets in evaluating early stage technology. Partnering with a Fortune 5 company, we developed and implemented two internal preference markets (field experiments). In both cases, nonmonetary (play money) incentives were utilized, but one market provided additional nonmonetary (play money) incentives. Working with the partner company, our investigation started with seven emerging technologies and expanded to a total of 17 emerging technologies. Our results suggest that even a simple form of additional nonmonetary play money incentive yielded greater price convergence, increased spread across final market prices, and greater consistency with a costly expert panel that was set up by the partner company. Based on the outcomes of our analyses, the partner company is investing in developing extended applications of preference markets as a potentially scalable approach for dealing with its ongoing and expanding strategic identification of promising emerging technologies.