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AbstractIn this paper we study strategies for better utilizing the network capacity of Internet Service Providers (ISPs) when they are faced with stochastic and dynamic arrivals and departures of customers attempting to log‐on or log‐off, respectively. We propose a method in which, depending on the number of modems available, and the arrival and departure rates of different classes of customers, a decision is made whether to accept or reject a log‐on request. The problem is formulated as a continuous time Markov Decision Process for which optimal policies can be readily derived using techniques such as value iteration. This decision maximizes the discounted value to ISPs while improving service levels for higher class customers. The methodology is similar to yield management techniques successfully used in airlines, hotels, etc. However, there are sufficient differences, such as no predefined time horizon or reservations, that make this model interesting to pursue and challenging. This work was completed in collaboration with one of the largest ISPs in Connecticut. The problem is topical, and approaches such as those proposed here are sought by users. © 2001 John Wiley & Sons, Inc., Naval Research Logistics 48:348–362, 2001

Evaluates the increase in inter‐ and intra‐organizational knowledge sharing capabilities brought about by the Internet‐driven "new economy" technologies and the resulting managerial implications. Presents a framework for evaluating and deploying such technologies. Firms employing knowledge networks can also use e‐knowledge to improve organizational decision making, react more quickly to changes in the economic landscape, and create dynamic custom content and consumer intimacy. Builds on the extensive literature in knowledge management and inter‐organizational systems by identifying the opportunities of each in creating "e‐knowledge networks" to support organizational collaboration. This framework is applied to four industry case studies – supply chain management networks, adserver networks, content syndication networks, and business‐to‐business exchange networks. Analysis suggests that in the new economy, characterized by ubiquitous and often automated information sharing capabilities, the ability to create knowledge‐based networks of partners will be critical to maintaining competitive advantage.

ABSTRACTThe focus of this study is on business‐to‐consumer (B2C) online auctions made possible by the advent of electronic commerce over an open‐source, ubiquitous Internet Protocol (IP) computer network. This work presents an analytical model that characterizes the revenue generation process for a popular B2C online auction, namely, Yankee auctions. Such auctions sell multiple identical units of a good to multiple buyers using an ascending and open auction mechanism. The methodologies used to validate the analytical model range from empirical analysis to simulation. A key contribution of this study is the design of a partitioning scheme of the discrete valuation space of the bidders such that equilibrium points with higher revenue structures become identifiable and feasible. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of key control factors such as the bid increment, resulting in substantial losses in a market with already tight margins. With this in mind, we put forward a portfolio of tools, varying in their level of abstraction and information intensity requirements, which help auctioneers maximize their revenues.