Richard A. Bettis

Bio: Richard A. Bettis is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Strategic management & Competitive advantage. The author has an hindex of 37, co-authored 70 publications receiving 11734 citations. Previous affiliations of Richard A. Bettis include Southern Methodist University & North Carolina State University.

The dominant logic: A new linkage between diversity and performance

Abstract: Current research offers alternative explanations to the “linkage” between the pattern of diversification and performance At least four streams of research can be identified None of these can be considered to be a reliable, predictive theory of successful diversification They are, at best, partial explanations The purpose of this paper is to propose an additional “linkage,” conceptual at this stage, that might help our understanding of the crucial connection between diversity and performance The conceptual argument is intented as a “supplement” to the current lines of research, rather than as an alternative explanation

The dominant logic: retrospective and extension

Abstract: This paper briefly reviews some history of the concept of dominant logic, and then elaborates some of the ways in which the authors have further developed this concept in recent years. Discussion focuses on the dominant logic as a filter, on the dominant logic as a level of strategic analysis, on the unlearning (forgetting) curve, on the dominant logic as an emergent property of organizations as complex adaptive systems, and on the relationship between organizational stability and the dominant logic. Throughout emphasis is given to the inherent nonlinear nature of organizations and the mental models that they create.

The new competitive landscape

Abstract: Technology is rapidly altering the nature of competition and strategy in the late twentieth century, moving us toward a ‘new competitive landscape’ in the twenty-first century. The new competitive landscape presents new issues, new concepts, new problems and new challenges. This essay examines the broad nature of the technological changes that are occurring and identifies some of the important implications of these changes for strategic management. The purpose of the paper is to stimulate further research into these issues in strategic management and to provide an overall context for the other papers appearing in this special issue.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Absorptive Capacity: A Review, Reconceptualization, and Extension

Abstract: Researchers have used the absorptive capacity construct to explain various organizational phenomena. In this article we review the literature to identify key dimensions of absorptive capacity and offer a reconceptualization of this construct. Building upon the dynamic capabilities view of the firm, we distinguish between a firm's potential and realized capacity. We then advance a model outlining the conditions when the firm's potential and realized capacities can differentially influence the creation and sustenance of its competitive advantage.

Organizational Learning: The Contributing Processes and the Literatures

Abstract: This paper differs from previous examinations of organizational learning in that it is broader in scope and more evaluative of the literatures. Four constructs related to organizational learning knowledge acquisition, information distribution, information interpretation, and organizational memory are articulated, and the literatures related to each are described and critiqued. The literature on knowledge acquisition is voluminous and multi-faceted, and so the knowledge acquisition construct is portrayed here as consisting of five subconstructs or subprocesses: 1 drawing on knowledge available at the organization's birth, 2 learning from experience, 3 learning by observing other organizations, 4 grafting on to itself components that possess knowledge needed but not possessed by the organization, and 5 noticing or searching for information about the organization's environment and performance. Examination of the related literatures indicates that much has been learned about learning from experience, but also that there is a lack of cumulative work and a lack of integration of work from different research groups. Similarly, much has been learned about organizational search, but there is a lack of conceptual work, and there is a lack of both cumulative work and syntheses with which to create a more mature literature. Congenital learning, vicarious learning, and grafting are information acquisition subprocesses about which relatively little has been learned. The literature concerning information distribution is rich and mature, but an aspect of information distribution that is central to an organization's benefitting from its learning, namely how units that possess information and units that need this information can find each other quickly and with a high likelihood, is unexplored. Information interpretation, as an organizational process, rather than an individual process, requires empirical work for further advancement. Organizational memory is much in need of systematic investigation, particularly by those whose special concerns are improving organizational learning and decision making.