Kevin J. Dooley

Bio: Kevin J. Dooley is an academic researcher from Arizona State University. The author has contributed to research in topics: Quality (business) & Supply chain. The author has an hindex of 42, co-authored 135 publications receiving 8529 citations. Previous affiliations of Kevin J. Dooley include Arizona's Public Universities & University of Minnesota.

A Complex Adaptive Systems Model of Organization Change

Abstract: The study of complex adaptive systems has yielded great insight into how complex, organic-like structures can evolve order and purpose over time. Business organizations, typified by semi-autonomous organizational members interacting at many levels of cognition and action, can be portrayed by the generic constructs and driving mechanisms of complex adaptive systems theory. The purpose of this paper is to forge a unified description of complex adaptive systems from several sources, and then investigate the issue of change in a business organization via the framework of complex adaptive systems. The theory of complex adaptive systems uses components from three paradigms of management thought: systems theory, population ecology, and information processing. Specific propositions regarding the nature of dynamical change will be developed, driven by the complex adaptive systems model. Supporting evidence for these propositions is then sought within the existing management theory literature. In doing so, the complex adaptive systems approach to understanding organization change will be better grounded in domain-specific theory, and new insights and research areas will come to light.

Organizational Change and Innovation Processes: Theory and Methods for Research

Abstract: In a world of organizations that are in constant change, scholars have long sought to understand and explain how they change. This book introduces research methods that are specifically designed to support the development and evaluation of organizational process theories. The authors are a group of highly regarded experts who have been doing collaborative research on change and development for many years.

Explaining Complex Organizational Dynamics

Abstract: 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.

The Structure and Function of Complex Networks

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Out of the Crisis

Abstract: According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Impacts of climate change on the future of biodiversity.

Abstract: Many studies in recent years have investigated the effects of climate change on the future of biodiversity. In this review, we first examine the different possible effects of climate change that can operate at individual, population, species, community, ecosystem and biome scales, notably showing that species can respond to climate change challenges by shifting their climatic niche along three non-exclusive axes: time (e.g. phenology), space (e.g. range) and self (e.g. physiology). Then, we present the principal specificities and caveats of the most common approaches used to estimate future biodiversity at global and sub-continental scales and we synthesise their results. Finally, we highlight several challenges for future research both in theoretical and applied realms. Overall, our review shows that current estimates are very variable, depending on the method, taxonomic group, biodiversity loss metrics, spatial scales and time periods considered. Yet, the majority of models indicate alarming consequences for biodiversity, with the worst-case scenarios leading to extinction rates that would qualify as the sixth mass extinction in the history of the earth.