We investigate the feasibility of unstructured direct-elicitation (UDE) of decision rules consumers use to form consideration sets. With incentives to think hard and answer truthfully, tested formats ask respondents to state non-compensatory, compensatory, or mixed rules for agents who will select a product for the respondents. In a mobile-phone study two validation tasks (one delayed 3 weeks) ask respondents to indicate which of 32 mobile phones they would consider from a fractional 4 5 x2 2 design of features and levels. UDE predicts consideration sets better, across profiles and across respondents, than a structured direct-elicitation method (SDE).It predicts comparably to established incentive-aligned compensatory, non-compensatory, and mixed decompositional methods. In a more-complex (20x7x5 2 x4x3 4 x22 ) automobile study, noncompensatory decomposition is not feasible and additive-utility decomposition is strained, but UDE scales well. Incentives are aligned for all methods using prize indemnity insurance to award a chance at $40,000 for an automobile plus cash. UDE predicts consideration sets better than either additive decomposition or an established SDE method (Casemap). We discuss the strengths and weaknesses of UDE relative to established methods.

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Accurate measurement of consumer preferences reduces development costs and leads to successful products. Some product‐development teams use quantitative methods such as conjoint analysis or structured methods such as Casemap. Other product‐development teams rely on unstructured methods such as direct conversations with consumers, focus groups, or qualitative interviews. All methods assume that measured consumer preferences endure and are relevant for consumers' marketplace decisions. This article suggests that if consumers are not first given tasks to encourage preference self‐reflection, unstructured methods may not measure accurate and enduring preferences. This paper provides evidence that consumers learn their preferences as they make realistic decisions. Sufficiently challenging decision tasks encourage preference self‐reflection which, in turn, leads to more accurate and enduring measures. Evidence suggests further that if consumers are asked to articulate preferences before self‐reflection, then that articulation interferes with consumers' abilities to articulate preferences even after they have a chance to self‐reflect. The evidence that self‐reflection enhances accuracy is based on experiments in the automotive and mobile phone markets. Consumers completed three rotated incentive‐aligned preference measurement methods (revealed‐preference measures [as in conjoint analysis], a structured method [Casemap], and an unstructured preference‐articulation method). The stimuli were designed to be managerially relevant and realistic (53 aspects in automobiles, 22 aspects for mobile phones) so that consumers' decisions approximated in vivo decisions. One to three weeks later, consumers were asked which automobiles (or mobile phones) they would consider. Qualitative comments and response times are consistent with the implications of the measures of predictive ability.

The pharmaceutical industry leads all industries in terms of R&D spend. Portfolio management in new drug development is extremely challenging due to long drug development cycles and high probabilities of failure. In 2010, a pharmaceutical company like GlaxoSmithKline (GSK) spent over USD 6 billion in R&D expenditure and managed a total of 147 R&D projects across 13 therapeutic areas in different stages of development. There are a lot of challenges in deciding on how to allocate resources to these projects in order to achieve the maximum returns. For example, how to evaluate the value and risk of each project, how to choose new projects for both short-term cash flow and long-term development, how to decide which projects to prioritize and which projects to remove from the portfolio, how to design drug development unit and incentive schemes to maximize the likelihood of success, and so forth.This chapter reviews both practice and the state-of-the-art research and summarizes the latest insights from both industry and academia. For a manager, it provides a guide to the tools they need in portfolio management in the new drug development context. For an academic, it provides a quick overview of the extant research and points out some promising research directions.