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Application-driven, technology-intensive research is critically needed to meet the challenges of globalization, interactivity, high productivity, and rapid adaptation faced by business organizations. Information systems researchers are uniquely positioned to conduct such research, combining computer science, mathematical modeling, systems thinking, management science, cognitive science, and knowledge of organizations and their functions. We present an agenda for addressing these challenges as they affect organizations in heterogeneous and distributed environments. We focus on three major capabilities enabled by such environments: Mobile Computing, Intelligent Agents, and Net-Centric Computing. We identify and define important unresolved problems in each of these areas and propose research strategies to address them.

Younir-dul Kim and Sahiatore T, March Comparing Data Modeling Formalisms ccuiate specification and validation of information rec|iiiieinents is critical to the development of organizational information systems. Semantic data models were developed to provide a precise and unambiguous representation of organizational information requirements [9, 17], They serve as a communication vehicle between analysts and users. Alter analyzing 11 semantic data models, Biller and Neuhold [3] conclude that there are essentially only two types of data modeling forinalisms: entity-attribute-relationship (EAR) models and object-relationship (OR) models. Proponents of each claim their model yields "better" representations [7] than the other.There is, however, little empirical evidence to substantiate these claims. This article presents an empirical study that compares two popular semantic data models: the extended entityrelationship (EER) model (an FAR nu)del) [23], and the Nijsseii information analysis melhodotoiry (NIAM) nu)dcl (an

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