stockouts are a common phenomenon and prod- uct availability remains a key issue in marketing and operations. In environments in which consumers.
MANAGEMENT SCIENCE
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Vol. 55, No. 5, May 2009, pp. iv–vi issn 0025-1909 eissn 1526-5501 09 5505 00iv
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Management Insights Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity (p. 697) Daniel Fleder, Kartik Hosanagar
An Optimal Contact Model for Maximizing Online Panel Response Rates (p. 727) Scott A. Neslin, Thomas P. Novak, Kenneth R. Baker, Donna L. Hoffman
The last ten years have seen an extraordinary increase in the number of products available. This trend is part of the “long tail” phenomenon, and many believe that it could amount to a cultural shift from hit to niche goods. A difficulty that arises, however, is how consumers will find their ideal, niche products among myriad choices. Recommender systems are one solution. These systems use data on purchases and user profiles to identify which products are best suited to each user. Although recommenders have been assumed to diversify choice, we show why some systems may do the opposite. Recommenders can create self-reinforcing cycles in which popular items are recommended more, recommended items are purchased more, purchased items are recommended even more, and so on. These cycles reduce diversity. Consequently, consumers and niche producers may be underserved if there exist better product matches outside of the hits, and retailers may find that they offer the right assortment but their recommender system may be promoting a narrow range of products. We recommend that managers consider design modifications to ensure that their recommender system limits these popularity effects and promotes exploration.
This paper develops and field tests a model for maximizing response rates for online survey research panels. The model includes several important features: (i) it accounts for the “state” of each panelist (e.g., previous response rate); (ii) it can plan for several studies at a time; (iii) it recognizes that current decisions may influence future response rates; (iv) it allows the user to stipulate the desired sample size and demographic makeup for the sample; and (v) it anticipates growth in the panel. In a field test conducted for four studies, the model yields an average response rate of 43% per study, compared to 25% for the heuristic currently used by an online panel’s manager and 14% for random selection. These results suggest that managers could use the model to improve upon current practice either by obtaining the same sample size while soliciting fewer panelists (thus avoiding panelist “burnout”) or by increasing the sample size while soliciting the same number of panelists (thus providing smaller standard errors and hence more accurate results). Contagion of Wishful Thinking in Markets (p. 738) Nicholas Seybert, Robert Bloomfield
On the Value of Commitment and Availability Guarantees When Selling to Strategic Consumers (p. 713) Xuanming Su, Fuqiang Zhang
How does one person’s behavior affect the decisions of others when all parties are making risky decisions to increase their wealth? We show that investors in a stock market have a tendency to engage in “wishful betting,” where they invest or bet as if desirable outcomes are unreasonably likely. When one investor engages in wishful betting and purchases additional shares of stock (as if he believes shares are undervalued), other investors may fail to adjust for this bias, thereby leading them to hold unreasonably optimistic beliefs, which we term “wishful thinking.” Wishful thinking could occur in a variety of contexts, including managerial decisions influenced by competitor behavior, and individual career path decisions influenced by peer behavior. The results of our studies suggest that people should be cautious when interpreting others’ behavior; otherwise, they may unwittingly sacrifice their own wealth when making investments.
Product availability plays an important role in attracting consumer demand. Despite technological and managerial advances, industry evidence shows that stockouts are a common phenomenon and product availability remains a key issue in marketing and operations. In environments in which consumers make choices based on product availability, we propose two strategies that firms can use to improve profits. First, firms can make upfront commitments to consumers that at least a certain quantity will be stocked. Second, firms can provide availability guarantees to compensate consumers in the event of stockouts. Interestingly, firms may have an incentive to overcompensate consumers during stockouts. To attain maximum possible profits, we show that firms need to use both strategies in conjunction. iv
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Management Science 55(5), pp. iv–vi, © 2009 INFORMS
Quasi-Robust Multiagent Contracts (p. 752) Anil Arya, Joel Demski, Jonathan Glover, Pierre Liang Incentive contracting has been a much-studied topic in information economics. However, it has been noted that the theoretically derived optimal contracts are often at odds with observed practice in that they are highly fine-tuned to the details of the environment. Many of these theoretically optimal contracts would perform quite poorly if the environment were even slightly different from that assumed. This paper attempts to contribute to the emerging theory of robust contracts in the hope that such contracts will help us better understand observed practice. A distinguishing feature of our approach is that we assume a manager must choose the design of a contract mechanism before some key information is known about the environment. We use our method to provide insights into an auction that has to be designed for a variety of bidders and bidder correlations. Multiple Sourcing and Procurement Process Selection with Bidding Events (p. 763) Tunay I. Tunca, Qiong Wu We study the process selection problem of an industrial buyer who employs online reverse auctions for procurement. We compare two types of procurement processes: (1) simple reverse auctions (“single-stage” processes) and (2) processes where the buyer makes additional price-quantity adjustments with the winning suppliers after the auction (“two-stage” processes). If there is a large number of bidding suppliers and production is not scalable (i.e., capacity is rigid), then we find that single-stage procurement is preferred. However, the two-stage process tends to be relatively more attractive as the number of bidders decreases or as capacity becomes more scalable (i.e., an increase in quantity does not generate a considerable increase in the per-unit cost). Information Sharing and Order Variability Control Under a Generalized Demand Model (p. 781) Li Chen, Hau L. Lee Information sharing between partners is one means to improve supply chain performance, e.g., through mitigation of the bullwhip effect. However, when a retailer shares point-of-sales data, the supplier can fully exploit this information only if the supplier also has knowledge of the characteristics of the demand process and the retailer’s order policy. How can a supplier realize the value of information sharing when such knowledge is lacking? Our paper shows that this can be achieved by having the retailer share its projections of future orders and their revisions.
A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms (p. 798) Victor DeMiguel, Lorenzo Garlappi, Francisco J. Nogales, Raman Uppal We provide a general framework for finding portfolios that perform well out-of-sample in the presence of estimation error. This framework relies on solving the traditional minimum-variance problem but subject to the additional constraint that the norm of the portfolio-weight vector be smaller than a given threshold. We show that several established approaches in the literature are actually special cases of our framework. We use five data sets to compare the out-of-sample performance of our method with 10 known strategies and find that our method often yields a higher Sharpe ratio. Optimal Policies and Approximations for a Bayesian Linear Regression Inventory Model (p. 813) Katy S. Azoury, Julia Miyaoka We consider the inventory management problem when demand is estimated using a regression model. We assume the regression parameters are unknown, and a Bayesian approach is used to update the distribution on the regression parameters as new information becomes available. Within our framework we identify the optimal inventory policy. However, it is computationally complex to implement, so we propose heuristic policies and demonstrate that they yield near-optimal performance. Information Market-Based Decision Fusion (p. 827) Johan Perols, Kaushal Chari, Manish Agrawal In many decision-making scenarios, such as fraud detection and bankruptcy prediction, the decisions of multiple human experts and/or software are fused to determine the overall decision. This paper provides an innovative approach for decision fusion based on information markets. Our computational results indicated that our approach is superior to other existing approaches. This information market-based method can help organizations lower costs and facilitate new decision-making systems that combine the expertise of humans and software. Private Network EDI vs. Internet Electronic Markets: A Direct Comparison of Fulfillment Performance (p. 843) Yuliang Yao, Martin Dresner, Jonathan Palmer How can purchasing organizations decrease cycle times and improve order fulfillment? The key to these performance improvements may lie in the supply chain technology used for transaction exchanges. Using a data set comprised of 2.8 million transactions
vi placed through the U.S. government’s Federal Supply Services, we provide a direct comparison between private network electronic data interchange (EDI) systems and Internet-based electronic market systems. We find that when purchasers use the Internet-based electronic market, cycle times are reduced by two days and complete orders fulfilled are increased by two percentage points, compared to the competing EDI system. Loss Functions in Option Valuation: A Framework for Selection (p. 853) Dennis Bams, Thorsten Lehnert, Christian C. P. Wolff We investigate the importance of different loss functions when estimating and evaluating option pricing models. Our analysis shows that it is important to take into account parameter uncertainty because this leads to uncertainty in the predicted option price. We find strong evidence to support the idea that the absolute pricing error criterion may serve as a general-purpose loss function in option valuation applications. At the same time, we provide a first yardstick to evaluate the
Management Insights Management Science 55(5), pp. iv–vi, © 2009 INFORMS
adequacy of the loss function. This is accomplished through a data-driven method to deliver not just a point estimate of the pricing error, but a distribution. Additive Utility in Prospect Theory (p. 863) Han Bleichrodt, Ulrich Schmidt, Horst Zank Decision making in managerial environments where alternatives consist of risky multiple-attribute outcomes is becoming a very difficult task because of the complex way in which individuals evaluate risks. Utility measurement tools have been developed for single-attribute outcomes but those tools may be inappropriate for multiple-attribute outcomes if loss aversion is attribute specific. For example, when individuals evaluate job offers, the effect of loss aversion relating to a drop in salary income may be of different magnitude than the effect of loss aversion relating to how onerous the job is. We show how utility measurement can be improved if loss aversion is understood as an attribute-specific feature, and we provide new decision models that improve the empirical toolbox of decision makers and managers.
