Knowledge that Transforms

This paper examines services in which customers encounter a gatekeeper who makes an initial diagnosis of the customer's problem and then may refer the customer to a specialist. The gatekeeper may also attempt to solve the problem, but the probability of treatment success decreases as the problem's complexity increases. Given the costs of treatment by the gatekeeper and the specialist, we find the firm's optimal referral rate from a particular gatekeeper to the specialists. We then consider the principal–agent problem that arises when the gatekeeper, but not the firm, observes the gatekeeper's treatment ability as well as the complexity of each customer's problem. We examine the relative benefits of compensation systems designed to overcome the effects of this information asymmetry and show that bonuses based solely on referral rates do not always ensure first–best system performance and that an appropriate bonus based on customer volume may be necessary as well. We also consider the value of such output–based contracts when gatekeepers are heterogeneous in ability, so that two gatekeeper types face different probabilities of treatment success when given the same problem. We show that the firm may achieve first–best performance by either offering two contracts that separate the gatekeeper types or by offering a single contract that coordinates the treatment decisions of both gatekeepers.

This paper evaluates the relative performances of several well–known and widely–used incentive mechanisms under controlled experimental conditions. The scenario utilized is a delegated investment setting where effort and risk aversions contribute to moral hazard among fund managers. Analytical intractability of the problem requires a computational modeling approach to simulate comparative solutions for specific contracts under different parametric settings. Through a simulation exercise, we consider multiple agents who decide their investment strategy over several consecutive periods. Agents learn about estimation and market uncertainty through repeated realizations of investment returns. In each sequence of periods, a number of different incentive mechanisms based on the agent's communication and/or outcome are considered. Results of the computational experiments are presented. Our results overwhelmingly show the efficacy of the incentive contracts in improving the welfare of the investors. In the presence of an estimation risk, when agents learn from their past performances, the market volatility interacts with the estimation risk that makes risk–sharing arrangements such as limited liability overly important. Paying the agent to assume the risk may no longer lead to the best performance incentives.

The Internet is growing rapidly in terms of both use and infrastructure. Unfortunately, demand is outpacing the capacity of the infrastructure, as evidenced by unacceptably long response times. To support current load and further growth, we must address this problem. Several caching strategies have been proposed in the literature; many have been implemented to improve the quality of service on the Web. In this paper, we identify the main causes of delay on the Web, and provide a review of the various caching strategies employed to mitigate these delays. We also survey the application of Operations Research/Management Science (OR/MS) techniques to caching on the Web. Finally, we identify several open OR/MS research problems related to Web caching.

In goal–oriented services, consumers want to get transported from one well–defined state (start) to another (destination) state without much concern for intermediate states. A cost–based evaluation of such services should depend on the total cost associated with the service—i.e., the price and the amount of time taken for completion. In this paper, we demonstrate that the characteristics of the path to the final destination also influence evaluation and choice. Specifically, we show that segments of idle time and travel away from the final destination are seen as obstacles in the progress towards the destination, and hence lower the choice likelihood of the path. Further, we show that the earlier such obstacles occur during the service, the lower is the choice likelihood. We present an analytical model of consumer choice and test its predictions in a series of experiments. Our results show that in choosing between two services that cover the same displacement in the same time (i.e., identical average progress), consumer choice is driven by the perception of progress towards the goal (i.e., by virtual progress). In a final experiment, we show that the effects of virtual progress may outweigh the effects of actual average progress.

We consider the problem of minimizing project duration in an environment where each project activity can be executed by a number of different flexible resources. The capabilities of the flexible resources are modeled using a binary activity-resource matrixAcalled theavailabilitymatrix. Activity durations are known deterministically. We develop tight lower bounds and a variety of heuristics accompanied with extensive computational tests regarding their performance. It is shown that our algorithms consistently perform near optimally. Using these heuristics, we perform experiments on the effect of operating flexibility on project duration, where resource flexibility is measured by the number of resources available per activity, and the form of the availability matrixA. Our experiments lead to important managerial guidelines regarding the role of resource flexibility on project duration. For instance, it is found that when the flexible capabilities of the resources are evenly distributed across the activities, small improvements in resource flexibilities provide nearly the same benefits in project duration as a system of fully flexible resources.

Supply chain management is likely to play an important role in the digital economy. In this paper, we first describe major issues in traditional supply chain management. Next, we focus our attention on the supply chain issues of visibility, supplier relationships, distribution and pricing, customization, and real-time decision technologies that have risen to importance with the prevalence of e-business. We present an overview of relevant analytical research models that have been developed in these areas, discuss their contributions, and conclude with a discussion on future modeling opportunities in this area.

This study looks at the United States biotechnology industry as a community of practice caught between two evolutionary logics by which valuable scientific knowledge and valuable innovations are selected. We analyze the publications and patents of 116 biotechnology firms during the period 1988–1995. In models that link scientific capabilities to patent citations, we show that scientific ideas are not simply inputs into inventions; important scientific ideas and influential patents follow different and conflicting selection logics. Publication, collaboration, and science intensity are associated with patented innovations; however, important scientific papers are negatively associated with high-impact innovations. These results point to conflicting logics between science and innovation, and scientists must contribute to both while inhabiting a single epistemic community. We identify individuals listed on patents and scientific papers and find they effectively integrate science with innovation, leading to more successful innovations. Our findings suggest that the role of the small, research-intensive firm is to create a repository of knowledge; to act as an organizational mechanism to combine the capabilities of versatile scientists within and outside the boundaries of the firm; and to manage the selection of scientific ideas to produce valuable technical innovations.

To investigate the conditions under which learning-by-hiring (or the acquisition of knowledge through the hiring of experts from other firms) is more likely, we study the patenting activities of engineers who moved from United States (U.S.) firms to non-U.S. firms. Statistical findings from negative binomial regressions show that mobility is more likely to result in interfirm knowledge transfer when (1) the hiring firm is less path dependent, (2) the hired engineers possess technological expertise distant from that of the hiring firm, and (3) the hired engineers work in noncore technological areas in their new firm. In addition, the results support the idea that domestic mobility and international mobility are similarly conducive to learning-by-hiring. Thus, our paper suggests that learning-by-hiring can be useful when hired engineers are used for exploring technologically distant knowledge (rather than for reinforcing existing firm expertise) and also for extending the hiring firm's geographic reach.

We model a supply chain with two retail warehouses that place replenishment orders with a common manufacturing capacity. The two retailers differ in the variability of their order-streams. The order-stream from one retail warehouse is modeled as a Poisson process and from the other as a hyperexponential renewal process. Each retail warehouse uses a base-stock policy to place replenishment orders with the manufacturer. The manufacturer is modeled as a first-come-first-serve, single exponential server queue. We analyze the supply-side impact of this mixture of order-streams received by the manufacturer on both retailers. An exact analysis of this base-model generates closed-form expressions for distributions of the lead-time, outstanding orders, and expected inventory costs for each retailer, and leads to comparative results about the two retailers- performance measures. The base-model is extended to accommodate finished goods at the manufacturer, more than two retailers, and bulk-arrivals. We use the model to suggest managerial insights about the impact of the presence of a high-variability retailer on other retailers who share capacity, the distorting impact of manufacturer finished goods inventory on retailer incentives, and the incentives for retailers to participate in variability-reduction programs in the grocery industry.

Based on measurements among 332 owner-managers, we investigate how the global shape of the utility function (i.e., S-shaped versus concave or convex over the total range of outcomes) relates to choice behavior. We find that the global shape of the utility function differs across decision makers (about one-third of the owner-managers exhibit an S-shaped utility function) and that the global shape is linked to organizational behavior (i.e., the production system employed), a result that does not change when using different methods to identify the decision maker's global shape of the utility function. The decision maker's risk attitude (risk averse or risk seeking) does not affect the choice of the production system. Whereas the degree of risk aversion, based on the local shape of the utility function, may be important in explaining owner-managers' trading behavior (Pennings and Smidts 2000), more structural organizational behavior appears to be linked to the global shape of the utility function.