Modeling Distributed Knowledge Processes in Next Generation Multidisciplinary Alliances

  • Authors:
  • Alaina G. Kanfer;Bertram C. Bruce;Caroline Haythornthwaite;Nicholas Burbules;James Wade;Geoffrey C. Bowker;Joseph Porac

  • Affiliations:
  • -;-;-;-;-;-;-

  • Venue:
  • AIWORC '00 Proceedings of the Academia/Industry Working Conference on Research Challenges
  • Year:
  • 2000

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Abstract

Current research on distributed knowledge processes suggests a critical conflict between knowledge processes in groups and the technologies built to support them. The conflict centers on observations that authentic and efficient knowledge creation and sharing is deeply embedded in an interpersonal face to face context, but that technologies to support distributed knowledge processes rely on the assumption that knowledge can be made mobile outside these specific contexts. This conflict is of growing national importance as work patterns change from same site to separate site collaboration, and millions of government and industrial dollars are invested in establishing academic-industry alliances and building infrastructures to support distributed collaboration and knowledge.In this paper, we describe our multi-method approach for studying the tension between embedded and mobile knowledge in a project funded by the U.S. National Science Foundation's program on Knowledge & Distributed Intelligence. This project examines knowledge processes and technology in distributed, multidisciplinary scientific teams in the National Computational Science Alliance (Alliance), a prototypical Next Generation enterprise. First, we review evidence for the tension between embedded and mobile knowledge in several research literatures. Then we present our three-factor conceptualization that considers how the interrelationships among characteristics of the knowledge shared, group context, and communications technology contribute to the tension between embedded and mobile knowledge. Based on the conceptualization we suggest that this dichotomy does not fully explain distributed multidisciplinary knowledge processes. Therefore, we propose some alternate models of how knowledge is shared. Finally, we describe the data collection methodologies and present the status of the project.