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This article evaluates large group interventions as organizational change methods that address more adequately than traditional models the complexity, unpredictability, and turbulence associated with today's organizations. Large group interventions are presented as a means to facilitate organizational change from a complexity science perspective. The author argues that such interventions increase an organization's potential for amplifying ideas and generating radical change through self-organization: By equipping organizations to rely on their ability to reference and rearrange existing resources into more complex states, they create a balance between structure and information flow. The article concludes with a discussion of the implications of large group interventions for organizational change.
For many organizations, successful change remains an elusive goal. Indeed, most change initiatives end in failure (Smith, 2002; Sterbel, 1996; Kotter, 1995). One reason for this failure may be the widespread use of mechanistic models of change that emphasize centralized control, routine behavior, and the prediction of specific outcomes (Morgan, 1998). These models are dependant upon a few underlying assumptions: only a few critical variables need to be evaluated, the sum of the parts is equivalent to the whole, causality is a linear relationship and decisions are efficiency centric (Olson & Eoyang, 2001). While such models are appropriate for understanding and managing change in stable environments, they are ill-suited to turbulent environments (Holman & Devane, 1999; Burns & Stalker, 1961)-precisely the kinds of environments that have become the norm in organizations (Axelrod & Cohen, 1999, Wheatley, 1992). Recognizing this new reality, some practitioners have turned to complexity science for models of change that address distributed knowledge, creative behavior, and the prediction of unfolding patterns of events.
Over the past 40 years, complexity theory has become an increasingly important tool for explaining a range of phenomena in such sciences as physics (Grebogi, Ott, & Yorke, 1987), chemistry (Prigogine & Stengers, 1984), biology (Kauffman, 1993), and meteorology (Lorenz, 1963). More recently, it has diffused into the social sciences, where it may have even greater relevance (Boisot & Cohen, 2000). In contrast to the dominant theories of organizational change, which rely on traditional assumptions of reductionism, linear causality, and objective observation, complexity theory adopts the emerging assumptions of holism, mutual causaUty, and perspectival observation (Olson & Eoyang, 2001). Such assumptions allow...