Computational experimentation on new organizational forms: Exploring behavior and performance of Edge organizations

  • Authors:
  • Mark E. Nissen

  • Affiliations:
  • Naval Postgraduate School, Monterey, U.S.A 93943-5000

  • Venue:
  • Computational & Mathematical Organization Theory
  • Year:
  • 2007

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Abstract

New organizational forms are being conceived and proposed continually, but because many such organizations remain conceptual--and hence have no basis for empirical assessment--their putative advantages over extant organizational forms are difficult to evaluate. Moreover, many such organizational forms are proposed without solid grounding in our cannon of organization theory; hence understanding their various theoretical properties in terms of our familiar, archetypal forms remains difficult. This poses problems for the practitioner and researcher alike. The Edge represents one such, recent, conceptual organizational form, which lacks readily observable examples in practice, and the conceptualization of which is not rooted well in our established organization theory. Nonetheless, proponents of this new form argue its putative advantages over existing counterparts, with an emphasis upon complex, dynamic, equivocal environmental contexts; hence the appeal of this form in today's organizational environment. The research described in this article employs methods and tools of computational experimentation to explore empirically the behavior and performance of Edge organizations, using the predominant and classic Hierarchy as a basis of comparison. We root our models of these competing forms firmly in Organization Theory, and we make our representations of organizational assumptions explicit via semi-formal models, which can be shared with other researchers. The results reveal insightful dynamic patterns and differential performance capabilities of Hierarchy and Edge organizations, and they elucidate theoretical ramifications for continued research along these lines, along with results amenable to practical application. This work also highlights the powerful role that computational experimentation can play as a complementary, bridging research method.