In much of the current literature on supply chain management, supply networks are recognized as a system. In this paper, we take this observation to the next level by arguing the need to recognize supply networks as a complex adaptive system (CAS). We propose that many supply networks emerge rather than result from purposeful design by a singular entity. Most supply chain management literature emphasizes negative feedback for purposes of control; however, the emergent patterns in a supply network can much better be managed through positive feedback, which allows for autonomous action. Imposing too much control detracts from innovation and flexibility; conversely, allowing too much emergence can undermine managerial predictability and work routines. Therefore, when managing supply networks, managers must appropriately balance how much to control and how much to let emerge.

a b s t r a c t A system of interconnected buyers and suppliers is better modeled as a network than as a linear chain. In this paper we demonstrate how to use social network analysis to investigate the structural characteristics of supply networks. Our theoretical framework relates key social network analysis metrics to supply network constructs. We apply this framework to the three automotive supply networks reported in Choi and Hong (2002). Each of the supply networks is analyzed in terms of both materials flow and contractual relationships. We compare the social network analysis results with the case-based interpretations in Choi and Hong (2002) and conclude that our framework can both supplement and complement case-based analysis of supply networks.

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a b s t r a c tSuppliers have become an increasingly important source of product and process innovation. While case studies have documented how supplier innovation can benefit a manufacturer, this relationship has not been empirically validated, nor have contingencies been explored. Using organizational learning theory we posit that the link between supplier innovativeness and manufacturer performance is moderated by the "fit" between the learning styles of the manufacturer and supplier. We empirically test our hypotheses using hierarchical linear modeling of survey responses from 148 manufacturers concerning 592 suppliers. Results indicate that supplier innovativeness has positive impacts on multiple dimensions of manufacturer performance. Results show that when the outsourced activity involves low levels of design responsibility by the supplier, it is more beneficial for the two partners to have contrasting learning styles. However, when the outsourced activity is design-intensive, it is more beneficial to have a supplier with an explorative learning style.

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.