The Internet commerce technologies have significantly reduced sellers' costs of collecting buyer preference information and managing multiple prices. Advanced manufacturing technologies have also i...
This exploratory research investigates the nature of explanation use and factors that influence it during users' interaction with a knowledge-based system (KBS) for decision-making. It draws upon s...
Although tacit knowledge constitutes the major part of what we know, it is difficult for organizations to fully benefit from this valuable asset. This is because tacit knowledge is inherently elusi...
This paper presents a descriptive evaluation of 54 case and field studies from 79 published papers spanning two decades of group support systems (GSS) research. It organizes the methodology and res...
An increasing number of computer applications today use multimedia content such as images, sound, and video over distributed networks of computers. Often, a dispersed set of users, with varying demands, requires ongoing access to this content. Effective placement of the multimedia content at different locations/processors thus becomes essential to ensure acceptable quality of service at a reasonable cost. Achieving this requires the consideration of a set of issues quite different from that required for traditional data distribution. These include (a) scale, both in terms of individual objects and in aggregate, (b) importance of form or appearance, making resolution levels an important, controllable variable, and (c) the temporal dimension, placing stringent demands on response time. These concerns make distribution of multimedia content more than a straightforward extension of traditional distribution approaches. We develop a model and a supporting approach to facilitate effective distribution of multimedia content, focusing on multimedia applications in corporate intranets. The model consists of multiple criteria to reflect different aspects of quality of service and cost which we formulate by leveraging variance in resolution levels to capture trade-offs among these criteria. Since the multiple-criteria allocation model is NP-complete, we propose a decision support approach that generates locally efficient solutions using designer-specified targets and evaluates them using fuzzy-set-based heuristics. The complete model and the approach have been implemented in a prototype to ensure feasibility.We demonstrate use of the prototype for a medical imaging application that illustrates applicability and usefulness of our proposals.
Advances in genetic testing and data mining technologies have increased the availability of genetic information to insurance companies and insureds (applicants and policy holders) in the individual health insurance market (IHIM). Regulators, concerned that insurance companies will use this information to discriminate against applicants who have a genetic risk factor but who are still healthy, have implemented genetic privacy legislation in at least 18 states. However, in previous work we have demonstrated that such legislation will have unintended consequences - it will reduce consumer participation in the market without making those remaining better off. This paper identifies a mechanism, a pure bundling strategy, that insurance companies may implement in this regulatory environment to restore (or maximize) consumer participation in the market and to discourage such discrimination among insureds. This problem is examined through System Dynamics, a simulation-based modeling technique. The results will have significant implications for policy designs implemented by insurance companies, and for legislation implemented by industry regulators, and therefore, for the insurability of the individuals that rely on this market for health insurance coverage.
(2000). Special Issue: Enhancing Organizations’ Intellectual Bandwidth: The Quest for Fast and Effective Value Creation. Journal of Management Information Systems: Vol. 17, No. 3, pp. 3-8.
The online database industry has annual sales of US$6.5 billion for a product that can be easily appropriated, duplicated, reused, and redistributed. This paper examines how the industry developed ...
Insider trading and asymmetric information have been the subject of a significant body of research since the 1960s. Yet little work has been directed at analyzing the impact of different market regulations. Along with difficulties in correctly identifying trades made on inside information, empirical field study methods have not been capable of analyzing the impact of different market regulations. We develop a controllable networked market trading environment that incorporates accurate identification of information possessed by each trader studied and that provides the flexibility necessary to analyze market impacts of different regulatory schemes to limit trading on inside information. We illustrate our methods through a series of controlled induced-value laboratory experiments using human subjects. Subject rewards are performance-based, with cash incentives tied to the outcomes of each market transaction. Experimental results indicate that markets with inside, privately informed traders led to greater trading volumes than markets with traders having access to private information only. In addition to reporting the results of initial sets of the experiments, we use these outcomes to frame future research issues involving the use of IT systems in surveillance and links between trading patterns and insider activity.