The proliferation of interconnectivity and interactivity through Internet-based technologies enables new forms of support for new product development. This paper analyzes idea markets, which use widely distributed knowledge, the power of markets, and the Internet to support the crucial initial tasks of the new product development process, including the sourcing, filtering, and evaluation of new product ideas. Idea markets employ virtual stocks to represent new product ideas and allow participants to suggest and trade new product ideas in a virtual marketplace. This paper empirically explores the performance of idea markets in a real-world field study at a large, high-tech business-tobusiness company that includes more than 500 participants from 17 countries and features various idea sourcing tasks. The results indicate that idea markets are a feasible and promising method to support the fuzzy front end of the new product development process. Idea markets offer a platform and formal process to capture, select, and distribute ideas in an organization, which motivates employees to communicate their ideas to management. By effectively sourcing and contemporaneously filtering, idea markets help reduce the number of ideas brought to management's attention to those that seem worthy of further consideration. Because idea markets also have the ability to source many ideas, they can increase efficiency at the fuzzy front end of the new product development process.
Preference markets address the need for scalable, fast, and engaging market research in new product development. The Web 2.0 paradigm, in which users contribute numerous ideas that may lead to new products, requires new methods of screening those ideas for their marketability, and preference markets offer just such a mechanism. For faster new product development decisions, a flexible prioritization methodology is implemented for product features and concepts, one that scales up in the number of testable alternatives, limited only by the number of participants. New product preferences for concepts, attributes, and attribute levels are measured by trading stocks whose prices are based upon share of choice of new products and features. A conceptual model of scalable preference markets is developed and tested experimentally. Benefits of the methodology are found to include speed (less than one hour per trading experiment), scalability (question capacity grows linearly in the number of traders), flexibility (features and concepts can be tested simultaneously), and respondent enthusiasm for the method.
Event study methodology is a powerful procedure to quantify the impact of events and managerial decisions such as new product announcements on the value of a publicly traded company. However, for many events, appropriate financial data may not be available, either because suitable securities are not traded on financial markets or confounding effects limit the insights from financial data. In such instances, prediction markets could potentially provide the necessary data for an event study. Prediction markets are electronic markets where participants can trade stocks whose prices reflect the outcome of future events, e.g. election outcomes, sports results, new product sales or internal project deadlines. One key distinction between different prediction market applications is whether they require real money investments or play-money investment with non-monetary incentives for traders. Thus, the goal of this paper is to compare prediction markets’ ability to conduct event studies with respect to these two different incentive schemes. We empirically test the applicability of event study methodology in real-money vs. play-money prediction markets with two data sets. We show that event studies with prediction markets deliver robust and valid results with both incentive schemes.
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