Evaluating collaborative software in supporting organizational learning with Bayesian Networks

  • Authors:

  • Mahmoud O. Elish;David C. Rine;Joel E. Foreman

  • Affiliations:

  • George Mason University, Fairfax, VA;George Mason University, Fairfax, VA;George Mason University, Fairfax, VA

  • Venue:
  • Proceedings of the 2002 ACM symposium on Applied computing
  • Year:
  • 2002

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Abstract

Many collaborative software tools have been developed in the recent years to accelerate the growing interest of many organizations to become learning organizations. Selecting a collaborative tool that best suits an organization's needs is a challenging task, given that there are no evaluation criteria against which these tools could be evaluated with respect to various organizational learning concepts. The objective of this paper is twofold. First, it derives a generic set of criteria required to evaluate the suitability of a given collaborative tool in supporting the mental model concept of organizational learning. Second, it investigates the possibility of using Bayesian Networks as an evaluation methodology to rate the suitability of a given collaborative tool with respect to how well it meets the derived evaluation criteria.