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Data architectures for an organizational memory information system

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  • Kevin E. Dow
  • Gary Hackbarth
  • Jeffrey Wong

Abstract

A framework is developed that supports the theoretical design of an organizational memory information system (OMIS). The framework provides guidance for managing the processing capabilities of an organization by matching knowledge location, flexibility, and processing requirements with data architecture. This framework is tested using three different sets of data attributes and data architectures from 147 business professionals that have experience in IS development. We find that trade‐offs exist between the amount of knowledge embedded in the data architecture and the flexibility of data architectures. This trade‐off is contingent on the characteristics of the set of tasks that the data architecture is being designed to support. Further, the match is important to consider in the design of OMIS database architecture.

Suggested Citation

  • Kevin E. Dow & Gary Hackbarth & Jeffrey Wong, 2013. "Data architectures for an organizational memory information system," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(7), pages 1345-1356, July.
    • Handle: RePEc:bla:jamist:v:64:y:2013:i:7:p:1345-1356
    DOI: 10.1002/asi.22848
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    Cited by:

    1. Yan (Mandy) Dang & Yulei (Gavin) Zhang & Susan A. Brown & Hsinchun Chen, 2020. "Examining the impacts of mental workload and task-technology fit on user acceptance of the social media search system," Information Systems Frontiers, Springer, vol. 22(3), pages 697-718, June.
    2. Atif Ahmad & Kevin C. Desouza & Sean B. Maynard & Humza Naseer & Richard L. Baskerville, 2020. "How integration of cyber security management and incident response enables organizational learning," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(8), pages 939-953, August.

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