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Population ecologists suggest that inertial pressures make it difficult for organizations to adapt their strategies and structures in response to environmental changes. One way in which organizations may attempt to overcome these inertial tendencies is by selecting executive successors with different career specializations than their predecessors, which may enable them to better cope with changing environmental contingencies. The present study examined the relationship between the previous CEO's career specialization, the corporate strategy in place at the time of the succession event, and the career specialization of the new CEO from 1981-1990. The results suggest that organizations often chose successors with different career specializations than their predecessors, and that previous corporate strategy was a relatively poor predictor of successors' specializations. These results differ from earlier research findings concerning executive succession between 1957-1981. Possible reasons for these differences are discussed, and the implications for future research are considered.

This paper presents a career specialization typology that is specifically designed to assess a CEO's career specialization, but more generally can be used to assess other top executives' career specialization as well. The typology is based on the results of a study involving 544 chief executive officers of Fortune 1000 companies. The typology integrates the functional and institutional specializations of the executives into a single typology of career specialization and empirically groups the executives into one of three career "types" based on their functional and institutional specializations.

Over the past two decades, several new perspectives have emerged in the physical and natural sciences and are collectively referred to as the complexity sciences. Insights from these emerging perspectives have implications that merit consideration for developments and extensions of existing work at the metatheoretical, theoretical, and methodological levels in organization theory. The purpose of this manuscript is to: (a) provide an overview of the complexity sciences, (b) provide a justification and rationale for their inclusion into the social sciences, and (c) review the current organizational literature which utilizes and applies concepts from the complexity sciences to organizational phenomena.

ABSTRACTAs noted in the literature, comparatively little research has examined either descriptive or prescriptive modeling of the planning process. A major reason for this lack of research appears to be the difficulty of comparing and testing planning methods within and across actual organizations. The purpose of this paper is to present a prescriptive planning model based on information‐processing research and strategies. This model explicitly considers the linkages among the environment, the organization's adaptation to that environment, the type of planning, and information‐processing strategies.

It is a generally accepted proposition in the literature on organizations that there is a monotonic relationship among increasing environmental complexity, organizational interdependence, and increasing environmental turbulence. Moreover, this proposition also assumes that increasing environmental complexity for the organization and increasing organizational interdependencies lead to increased uncertainty for organizational growth and survival. This article challenges this latter assumption by arguing that organizational theory generally has ignored the industrial organization economics literature. This literature contradicts the increasing uncertainty assumption considered by many to be a major contribution to the literature by Emery and Trist.

In the past three decades we have seen the emergence of the generalized linear model (GLM) techniques for analyzing discrete multivariate data when the independent and dependent variables are categorical, ordinal, or mixed. The primary statistical techniques are loglinear modeling, probit, and logistic regression. The purpose of this article is to (a) briefly describe the emergence of these discrete multivariate techniques in the medical and social sciences, (b) disclose their relationship to one another, and (c) demonstrate the utility of hierarchical loglinear modeling in managerial research

There has been much debate in the management literature between neo-Darwinists (who believe in the natural selection of populations of organizations) and adaptationists (who contend that changes in organization structure and behavior occur in response to the environment). The general thesis of neo-Darwinism is that species are blindly selected for survival by the environment. The latest empirical support for the dominant neo-Darwinism perspective adopted by most biologists is based primarily on the experiments conducted by Salvador Luria who claims to have conclusively demonstrated that genes mutate randomly. Recently, however, biologists have re-examined Luria's research methods and, after replications of his experiments, now question some aspects of the validity of his results. Moreover, there is now new research which provides support for the earlier adaptationist position, namely, the existence of evolutionary drivers and directors existing within self-organizing systems. Of particular importance to the present study is the experimental indication that self-organizing systems play a conscious role in their own evolution. We propose that similar mechanisms or processes operate in organizational adaptation, thus pointing toward a theoretical modification of neo-Darwinism that embraces both adaptation and natural selection in a general, unified theory.