New Product Development 2.0: Preference Markets—How Scalable Securities Markets Identify Winning Product Concepts and Attributes^*

Abstract

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.

Rate or Trade? Identifying Winning Ideas in Open Idea Sourcing

“…Next, we measured idea quality based on a preference marketbased procedure in which four experts from similar domains as used in the consensual assessment technique traded ideas on a preference market for one hour. Previous research has shown that prediction and preference markets can produce accurate results in such short trading periods in cases where all users use the market synchronously (Dahan et al 2010(Dahan et al , 2011. This is contrary to our experimental task in which users traded ideas asynchronously, making a longer trading period necessary (LaComb et al 2007, Soukhoroukova et al 2012.…”

“…These findings complement research on aggregating individual opinions in the domain of crowdsourcing by showing that mechanism accuracy is not only a function of the aggregation mechanism, as frequently assumed in research on prediction (e.g., Arrow et al 2008, Hanson 2003, Spann and Skiera 2003 and preference markets (e.g., Dahan et al 2010, LaComb et al 2007), but is also reflected in the decisions formed on the user level, which may be systematically hampered by evaluation mechanisms that are too difficult to use.…”

“…Such markets, e.g., for idea evaluation, measure individual expectations about others' value perceptions or preferences for a given set of alternatives for which they create a price-based ranking (Chen et al 2009, Dahan et al 2010. As these markets cannot be tied to actual events, user payouts are commonly based on market-based measures such as price convergence (Chen et al 2009) or artificial external events, e.g., expert evaluations (Soukhoroukova et al 2012).…”

“…LaComb et al (2007) and Soukhoroukova et al (2012) show how such markets can be used with employees, while Dahan et al (2011) focus on use by customers. Trading behavior (Spears et al 2009) or design choices such as contract design (Dahan et al 2010), payout structures (Slamka et al 2012) or incentives (Chen et al 2009) have also been investigated. However, to our knowledge, it is as yet unanswered how the perception of the trading tasks that represent users' choice-based decisions affects users' ability to effectively operate the IT-based task representation, their ability to acquire information relevant to the choices they make, and the chance of making errors in their decision.…”

Prediction Markets for Economic Forecasting

“…These experiments vary many of the fine details of market structure, and hence are not easily summarized. Details can be found in Lavoie (2009) Dahan, Soukhoroukova and Spann (2010). An unusually detailed description of the design and evaluation of a prediction market to help with technology assessment can be found in (Gaspoz, 2011).…”