Explaining Complex Organizational Dynamics
提出一个模型，区分组织事件时间序列中的周期、混沌、白噪声和粉红噪声四种动态模式，帮助研究者推断背后的因果机制，并指出混沌动态可能反映控制与合作而非混乱。
Studies of organizational processes can yield observations in the form of event time series that can be analyzed to determine whether they reflect periodic, chaotic, white noise, or pink noise dynamic patterns. These different patterns each imply different underlying generative mechanisms and hence, different process theories. In this paper we present a model that describes how these four dynamical patterns are different from one another. Specifically, a causal system can be characterized by its dimensionality, and by the nature of interaction between causal factors. Low dimensional causal systems yield periodic and chaotic dynamics, while high dimensional causal systems yield white and pink noise dynamics. Periodic and white noise dynamics stem from systems where causal factors act independently, or in a linear fashion, while chaotic and pink noise systems stem from systems where causal factors act interdependently, in a nonlinear fashion. Thus, given a diagnosis of an observed event time series, we can hypothesize a particular story, or causal process theory, that might explain in organization-specific terms why such dynamics came about. In doing so, we also propose that the observation of chaotic organizational dynamics may often signify the presence of control and/or cooperation, rather than a lack of it, as implied by the vernacular use of the term. We conclude by challenging organizational researchers to define new models that capture such observed behavior.