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Many markets have historically been dominated by a small number of best-selling products. The Pareto principle, also known as the 80/20 rule, describes this common pattern of sales concentration. However, information technology in general and Internet markets in particular have the potential to substantially increase the collective share of niche products, thereby creating a longer tail in the distribution of sales. This paper investigates the Internet's \"long tail\" phenomenon. By analyzing data collected from a multichannel retailer, it provides empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution when compared with traditional channels. Previous explanations for this result have focused on differences in product availability between channels. However, we demonstrate that the result survives even when the Internet and traditional channels share exactly the same product availability and prices. Instead, we find that consumers' usage of Internet search and discovery tools, such as recommendation engines, are associated with an increase the share of niche products. We conclude that the Internet's long tail is not solely due to the increase in product selection but may also partly reflect lower search costs on the Internet. If the relationships we uncover persist, the underlying trends in technology portend an ongoing shift in the distribution of product sales. This paper was accepted by Ramayya Krishnan, information systems.

Using a proprietary data set provided by a major manufacturer of aircraft engines, we empirically investigate how product reliability is impacted by the use of two different types of after-sales maintenance support contracts: time and material contracts (T&MC) and performance-based contracts (PBC). We offer a number of competing arguments based on the theory of incentives that establish why product reliability may increase or decrease under PBC. We build a two-stage econometric model that explicitly accounts for the endogeneity of contract choices, and find evidence of a positive and significant effect of PBC on product reliability. The estimation of our model indicates that product reliability is higher by 25%-40% under PBC compared to under T&MC, once the endogeneity of contract choice is taken into account. Our results are consistent with two mechanisms for reliability improvement under PBC: more frequent scheduled maintenance and better care performed in each maintenance event. This paper was accepted by Martin Lariviere, operations management.

As companies outsource more product design and manufacturing activities to other members of the supply chain, improving end-product quality has become an endeavor extending beyond the boundaries of the firms' in-house process capabilities. In this paper, we discuss two contractual agreements by which product recall costs can be shared between a manufacturer and a supplier to induce quality improvement effort. More specifically, we consider (i) cost sharing based on selective root cause analysis (Contract S), and (ii) partial cost sharing based on complete root cause analysis (Contract P). Using insights from supermodular game theory, for each contractual agreement, we characterize the levels of effort the manufacturer and the supplier would exert in equilibrium to improve their component failure rate when their effort choices are subject to moral hazard. We show that both Contract S and Contract P can achieve the first best effort levels; however, Contract S results in higher profits for the manufacturer and the supply chain. For the case in which the information about the quality of the supplier's product is not revealed to the manufacturer (i.e., the case of information asymmetry), we develop a menu of contracts that can be used to mitigate the impact of information asymmetry. We show that the menu of contracts not only significantly decreases the manufacturer's cost due to information asymmetry, but also improves product quality.

Organizational coordination has traditionally been viewed from anorganizational-design perspective where rules, modalities, and structuresare used to meet the information-processing demands of the environment.Fast-response organizations face unique coordination challenges as theyoperate under conditions of high uncertainty and fast decision making, wheremistakes can be catastrophic. Based on an in-depth investigation of thecoordination practices of a medical trauma center where fast-response anderror-free activities are essential requirements, we develop acoordination-practice perspective that emphasizes expertise coordination anddialogic coordination. We argue that expertise coordination practices(reliance on protocols, community of practice structuring, plug-and-playteaming, and knowledge sharing) are essential to manage distributedexpertise and ensure the timely application of necessary expertise. Wesuggest that dialogic coordination practices (epistemic contestation, jointsensemaking, cross-boundary intervention, and protocol breaking) aretime-critical responses to novel events and ensure error-free operation.However, dialogic coordination practices are highly contested because ofepistemic differences, reputation stakes, and possible blameapportionment.

We study a manufacturer that faces a supplier privileged with private information about supply disruptions. We investigate how risk-management strategies of the manufacturer change and examine whether risk-management tools are more or less valuable in the presence of such asymmetric information. We model a supply chain with one manufacturer and one supplier, in which the supplier's reliability is either high or low and is the supplier's private information. On disruption, the supplier chooses to either pay a penalty to the manufacturer for the shortfall or use backup production to fill the manufacturer's order. Using mechanism design theory, we derive the optimal contract menu offered by the manufacturer. We find that information asymmetry may cause the less reliable supplier type to stop using backup production while the more reliable supplier type continues to use it. Additionally, the manufacturer may stop ordering from the less reliable supplier type altogether. The value of supplier backup production for the manufacturer is not necessarily larger under symmetric information; for the more reliable supplier type, it could be negative. The manufacturer is willing to pay the most for information when supplier backup production is moderately expensive. The value of information may increase as supplier types become uniformly more reliable. Thus, higher reliability need not be a substitute for better information.

This paper introduces a method to measure regret theory, a popular theory of decision under uncertainty. Regret theory allows for violations of transitivity, and it may seem paradoxical to quantitatively measure an intransitive theory. We adopt the trade-off method and show that it is robust to violations of transitivity. Our method makes no assumptions about the shape of the functions reflecting utility and regret. It can be performed at the individual level, taking account of preference heterogeneity. Our data support the main assumption of regret theory, that people are disproportionately averse to large regrets, even when event-splitting effects are controlled for. The findings are robust: similar results were obtained in two measurements using different stimuli. The data support the reliability of the trade-off method: its measurements could be replicated using different stimuli and were not susceptible to strategic responding.

Most online feedback mechanisms rely on voluntary reporting of privately observed outcomes. This introduces the potential for reporting bias, a situation where traders exhibit different propensities to report different outcome types to the system. Unless properly accounted for, reporting bias may severely distort the distribution of public feedback relative to the underlying distribution of private transaction outcomes and, thus, hamper the reliability of feedback mechanisms. This study offers a method that allows users of feedback mechanisms where both partners of a bilateral exchange are allowed to report their satisfaction to \"see through\" the distortions introduced by reporting bias and derive unbiased estimates of the underlying distribution of privately observed outcomes. A key aspect of our method lies in extracting information from the number of transactions where one or both trading partners choose to remain silent. We apply our method to a large data set of eBay feedback. Our results support the widespread belief that eBay traders are more likely to post feedback when satisfied than when dissatisfied and are consistent with the presence of positive and negative reciprocation among eBay traders. Most importantly, our analysis derives unbiased estimates of the risks that are associated with trading on eBay that, we believe, are more realistic than those suggested by a naïve interpretation of the unusually high (>99%) levels of positive feedback currently found on that system.

Failure of many engineering systems usually results from a gradual and irreversible accumulation of damage, a degradation process. Most degradation processes can be monitored using sensor technology. The resulting degradation signals are usually correlated with the degradation process. A system is considered to have failed once its degradation signal reaches a prespecified failure threshold. This paper considers a replacement problem for components whose degradation process can be monitored using dedicated sensors. First, we present a stochastic degradation modeling framework that characterizes, in real time, the path of a component's degradation signal. These signals are used to predict the evolution of the component's degradation state. Next, we formulate a single-unit replacement problem as a Markov decision process and utilize the real-time signal observations to determine a replacement policy. We focus on exponentially increasing degradation signals and show that the optimal replacement policy for this class of problems is a monotonically nondecreasing control limit policy. Finally, the model is used to determine an optimal replacement policy by utilizing vibration-based degradation signals from a rotating machinery application.

We study robust formulations of assortment optimization problems under the multinomial logit choice model. The novel aspect of our formulations is that the true parameters of the logit model are assumed to be unknown, and we represent the set of likely parameter values by a compact uncertainty set. The objective is to find an assortment that maximizes the worst-case expected revenue over all parameter values in the uncertainty set. We consider both static and dynamic settings. The static setting ignores inventory consideration, whereas in the dynamic setting, there is a limited initial inventory that must be allocated over time. We give a complete characterization of the optimal policy in both settings, show that it can be computed efficiently, and derive operational insights. We also propose a family of uncertainty sets that enables the decision maker to control the trade-off between increasing the average revenue and protecting against the worst-case scenario. Numerical experiments show that our robust approach, combined with our proposed family of uncertainty sets, is especially beneficial when there is significant uncertainty in the parameter values. When compared to other methods, our robust approach yields over 10% improvement in the worst-case performance, but it can also maintain comparable average revenue if average revenue is the performance measure of interest.

Performance-based contracting is reshaping service support supply chains in capital-intensive industries such as aerospace and defense. Known as \"power by the hour\" in the private sector and as \"performance-based logistics\" (PBL) in defense contracting, it aims to replace traditionally used fixed-price and cost-plus contracts to improve product availability and reduce the cost of ownership by tying a supplier's compensation to the output value of the product generated by the customer (buyer). To analyze implications of performance-based relationships, we introduce a multitask principal-agent model to support resource allocation and use it to analyze commonly observed contracts. In our model the customer (principal) faces a product availability requirement for the \"uptime\" of the end product. The customer then offers contracts contingent on availability to n suppliers (agents) of the key subsystems used in the product, who in turn exert cost reduction efforts and set spare-parts inventory investment levels. We show that the first-best solution can be achieved if channel members are risk neutral. When channel members are risk averse, we find that the second-best contract combines a fixed payment, a cost-sharing incentive, and a performance incentive. Furthermore, we study how these contracts evolve over the product deployment life cycle as uncertainty in support cost changes. Finally, we illustrate the application of our model to a problem based on aircraft maintenance data and show how the allocation of performance requirements and contractual terms change under various environmental assumptions.