Companies are increasingly drawing on their user communities to generate promising ideas for new products, which are then marketed as “user-designed” products to the broader consumer market. We demonstrate that nonparticipating, observing consumers prefer to buy from user- rather than designer-driven firms because of an enhanced identification with the firm that has adopted this user-driven philosophy. Three experimental studies validate a newly proposed social identification account underlying this effect. Because consumers are also users, their social identities connect to the user-designers, and they feel empowerment by vicariously being involved in the design process. This formed connection leads to preference for the firm’s products. Importantly, this social identification account also effectively predicts when the effect does not materialize. First, we find that if consumers feel dissimilar to participating users, the effects are attenuated. We demonstrate that this happens when the community differs from consumers along important demographics (i.e., gender) or when consumers are nonexperts in the focal domain (i.e., they feel that they do not belong to the social group of participating users). Second, the effects are attenuated if the user-driven firm is only selectively rather than fully open to participation from all users (observing consumers do not feel socially included). These findings advance the emerging theory on user involvement and offer practical implications for firms interested in pursuing a user-driven philosophy. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1999 .

We introduce an auction design framework for large markets with hundreds of items and complex bidder preferences. Such markets typically lead to computationally hard allocation problems. Our new framework consists of compact bid languages for sealed-bid auctions and methods to compute second-price rules such as the Vickrey–Clarke–Groves or bidder-optimal, core-selecting payment rules when the optimality of the allocation problem cannot be guaranteed. To demonstrate the efficacy of the approach for a specific, complex market, we introduce a compact bidding language for TV advertising markets and investigate the resulting winner-determination problem and the computation of core payments. For realistic instances of the respective winner-determination problems, very good solutions with a small integrality gap can be found quickly, although closing the integrality gap to find marginally better solutions or prove optimality can take a prohibitively large amount of time. Our subsequent adaptation of a constraint-generation technique for the computation of bidder-optimal core payments to this environment is a practically viable paradigm by which core-selecting auction designs can be applied to large markets with potentially hundreds of items. Such auction designs allow bidders to express their preferences with a low number of parameters, while at the same time providing incentives for truthful bidding. We complement our computational experiments in the context of TV advertising markets with additional results for volume discount auctions in procurement to illustrate the applicability of the approach in different types of large markets. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.2076 .

This work examines the process of admission to a hospital’s intensive care unit (ICU). ICUs currently lack systematic admission criteria, largely because the impact of ICU admission on patient outcomes has not been well quantified. This makes evaluating the performance of candidate admission strategies difficult. Using a large patient-level data set of more than 190,000 hospitalizations across 15 hospitals, we first quantify the cost of denied ICU admission for a number of patient outcomes. We use hospital operational factors as instrumental variables to handle the endogeneity of the admission decisions and identify important specification issues that are required for this approach to be valid. Using the quantified cost estimates, we then provide a simulation framework for evaluating various admission strategies' performance. By simulating a hospital with 21 ICU beds, we find that we could save about $1.9 million per year by using an optimal policy based on observables designed to reduce readmissions and hospital length of stay. We also discuss the role of unobserved patient factors, which physicians may discretionarily account for when making admission decisions, and show that including these unobservables could result in a more than threefold increase in benefits compared to just optimizing the policy over the observable patient factors.