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This paper aims to provide an overview of the concept ‘organizational unlearning’ and its development since it was first introduced to the management literature and presents a useful perspective that can help to advance the conceptual development of this topic.

Through a conversation with celebrated scholar William H. Starbuck, this paper discusses several topics that are still up for debate in the organizational unlearning literature and argues for a number of viewpoints relevant to the application of this concept.

Unlearning is an important requirement for organizational learning and adaptation. Change cannot occur in organizations until old knowledge and practices are replaced by new ideas and methods. Researchers and managers should pay attention to the distinction between individual behavior and organizational behavior regarding unlearning.

The discussion of the contested topics of unlearning and their implications for organizational learning and adaptation will be of value to academic researchers as well as managers working in a context of environment change.

This paper aims to summarize four decades of research into organizational adaptation, learning and unlearning.

The most important contributions to this stream of research have been case studies of organizations struggling to survive serious crises.

Very diverse kinds of organizations run into serious crises, and many organizations fail because they respond poorly or slowly to these crises. Slow and inadequate initial responses result in organizations going into a stage of “unlearning” that is demoralizing, harmful and very difficult to manage.

The research involved a small number of longitudinal case studies.

Organizations may survive longer if they deal with serious crises more effectively. Top management teams especially may benefit from discussing how to spot and mitigate crises. The paper proposes a few actions that organizations might usefully take before and after trouble becomes visible.

Almost all organizations have short lives, and many organizations fail when they encounter unexpected, serious crises. The consequences include unemployment, dislocation, emotional turmoil, wasted resources and cynicism.

This paper reviews published research. Its value depends on the plausibility of its interpretations and the practical usefulness of the advice offered.

Purpose – This chapter reports on a rapidly growing trend in the analysis of data about emerging market (EM) economies – the use of baseline models as comparisons for explanatory models. Baseline models estimate expected values for the dependent variable in the absence of a hypothesized causal effect but set higher standards than do traditional null hypotheses tests that expect no effect.

Design/methodology/approach – Although the use of baseline models research originated in the 1960s, it has not been widely discussed, or even acknowledged, in the EM literature. We surveyed published EM studies to determine trends in the use of baseline models.

Findings – We categorize and describe the different types of baseline models that scholars have used in EM studies, and draw inferences about the differences between more effective and less effective uses of baseline models.

Value – We believe that comparisons with baseline models offer distinct methodological advantages for the iterative development of better explanatory models and a deeper understanding of empirical phenomena.

Null-hypothesis significance tests (NHST) are a very troublesome methodology that dominates the quantitative empirical research in strategy and management. Inherent limitations and inappropriate applications of NHST impede the accumulation of knowledge and fill academic journals with meaningless “findings,” and they corrode researchers' motivation and ethics. Inherent limitations of NHST include the use of point null hypotheses, meaningless null hypotheses, and dichotomous truth criteria. Misunderstanding of NHST has often led to applications to inappropriate data and misinterpretation of results.

Researchers should move beyond the ritualistic and often inappropriate use of NHST. The chapter does not advocate a best way to do research, but suggests that researchers need to adapt their methods to reflect specific contexts and to use evaluation criteria that are meaningful for those contexts. Researchers need to explain the rationales that guided the selection of evaluation measures and they should avoid excessively complex models with many variables. The chapter also offers four more focused recommendations: (1) Compare proposed hypotheses with naïve hypotheses or the outcomes of alternative treatments. (2) Acknowledge the uncertainty that attends research findings by stating confidence limits for parameter estimates. (3) Show the substantive relevance of findings by reporting effect sizes – preferably with confidence limits. (4) Use statistical methods that are robust against deviations from assumptions about population distributions and the representativeness of samples.

This chapter reports on a rapidly growing trend in data analysis – analytic comparisons between baseline models and explanatory models. Baseline models estimate values for the dependent variable in the absence of hypothesized causal effects. Thus, the baseline models discussed in this chapter differ from the baseline models commonly used in sequential regression analyses.Baseline modelling entails iteration: (1) Researchers develop baseline models to capture key patterns in the empirical data that are independent of the hypothesized effects. (2) They compare these patterns with the patterns implied by their explanatory models. (3) They use the derived insights to improve their explanatory models. (4) They iterate by comparing their improved explanatory models with modified baseline models.The chapter draws on methodological literature in economics, applied psychology, and the philosophy of science to point out fundamental features of baseline modelling. Examples come from research in international business and management, emerging market economies and developing countries.Baseline modelling offers substantial advantages for theory development. Although analytic comparisons with baseline models originated in some research fields as early as the 1960s, they have not been widely discussed or applied in international management. Baseline modelling takes a more inductive and iterative approach to modelling and theory development. Because baseline modelling holds substantial potential, international-management scholars should explore its opportunities for advancing scientific progress.

Cognitive perspectives have emerged from many years of struggle for recognition, and grown into a dominant theme in psychology. The purpose of this paper is to discuss what Karl Weick expressed as important themes in this struggle, made major contributions to the content of cognitive psychology, and helped to make cognition relevant for organizational behavior.

This paper reviews key developments in the history of psychology, points out central issues, and summarizes Weick’s contributions.

Weick brought sensemaking into sharp focus as a major activity of people and organizations. His writings established information processing as the core of organizational activities. He also showed how sensemaking affects organizational reliability.

Weick is one of the authors whom management scholars cite very often because he has been a thought leader. The paper places Weick’s work in historical context and points to his major contributions.

This chapter explores the origins of the theme of competitive advantage in 19th and early 20th century economics. This theme, which forms the core of modern Strategic Management, was a battleground for debates about the value of abstract theory versus observations about real-life events. Intellectual genealogies, citations, and other sources show the central roles played by the University of Vienna and Harvard University. These two institutions strongly influenced the theory of monopolistic competition as well as all three modern views of competitive advantage – the industrial as expressed by Porter, the resource-based as expressed by Penrose, and the evolutionary as expressed by Schumpeter.

This paper aims to provide some comments on the four papers, other than the author’s own, that were included in a recent special issue on organizational unlearning.

The author carefully reads these papers and identify problems that may hinder unlearning research.

While each paper has its own merits, a common problem shared by the papers is that the concept of organizational unlearning is unnecessarily eulogized, complicated or straitjacketed.

The author’s discussion focuses only on a problem related directly to the concept of unlearning, and does not cover other, though less serious, issues.

The author’s comments help readers better understand the concept of unlearning and so facilitate the further development of unlearning research.