Knowledge that Transforms

Information specialists in enterprises regularly use distributed information retrieval (DIR) systems that query a large number of information retrieval (IR) systems, merge the retrieved results, and display them to users. There can be considerable heterogeneity in the quality of results returned by different IR servers. Further, because different servers handle collections of different sizes and have different processing and bandwidth capacities, there can be considerable heterogeneity in their response times. The broker in the DIR system has to decide which servers to query, how long to wait for responses, and which retrieved results to display based on the benefits and costs imposed on users. The benefit of querying more servers and waiting longer is the ability to retrieve more documents. The costs may be in the form of access fees charged by IR servers or user's cost associated with waiting for the servers to respond. We formulate the broker's decision problem as a stochastic mixed-integer program and present analytical solutions for the problem. Using data gathered from FedStats—a system that queries IR engines of several U.S. federal agencies—we demonstrate that the technique can significantly increase the utility from DIR systems. Finally, simulations suggest that the technique can be applied to solve the broker's decision problem under more complex decision environments.

Online participation engenders both the benefits of knowledge sharing and the risks of harm. Vigilant interaction in knowledge collaboration refers to an interactive emergent dialogue in which know...

Although companies have spent a great deal of money to adopt CRM (customer relationship management) technologies, many have not seen satisfactory returns on their CRM implementations. We study optimal CRM implementation strategies and the impact of CRM investments on profitability. For our analysis, we classify CRM technologies into two broad categories: targeting-related and support-related technologies. While targeting CRM improves the success rate of distinguishing between nonloyal and loyal customers, support CRM increases the probability of retaining the loyalty of existing customers. We also consider the costs of implementing each CRM type separately as well as both types simultaneously. We show that the optimal CRM implementation strategy depends on the initial mass of loyal customers and diseconomies of scale in simultaneous implementation. We also find that the two types of CRM technologies are substitutive rather than complementary in generating revenue. We discuss why it is difficult to avoid overinvestments in CRM when the nature of the investments is misunderstood. We study the optimal CRM implementation scope and the impact of different types of CRM on customers. We develop a model that not only considers both the revenue and costs sides but is also helpful in determining the deployment of right CRM technology in the right scope.

We study the problem where a decision maker needs to discover a classification rule to classify intelligent, self-interested agents. Agents may engage in strategic behavior to alter their characteristics for a favorable classification. We show how the decision maker can induce a classification rule that anticipates such behavior while still satisfying an important risk minimization principle.