To date, the phenomenon of entrepreneurship has lacked a conceptual framework. In this note we draw upon previous research conducted in the different social science disciplines and applied fields of business to create a conceptual framework for the field. With this framework we explain a set of empirical phenomena and predict a set of outcomes not explained or predicted by conceptual frameworks already in existence in other fields.

The Promise of Entrepreneurship as a Field of Research

This paper draws on the social and behavioral sciences in an endeavor to specify the nature and microfoundations of the capabilities necessary to sustain superior enterprise performance in an open economy with rapid innovation and globally dispersed sources of invention, innovation, and manufacturing capability. Dynamic capabilities enable business enterprises to create, deploy, and protect the intangible assets that support superior long- run business performance. The microfoundations of dynamic capabilities—the distinct skills, processes, procedures, organizational structures, decision rules, and disciplines—which undergird enterprise-level sensing, seizing, and reconfiguring capacities are difficult to develop and deploy. Enterprises with strong dynamic capabilities are intensely entrepreneurial. They not only adapt to business ecosystems, but also shape them through innovation and through collaboration with other enterprises, entities, and institutions. The framework advanced can help scholars understand the foundations of long-run enterprise success while helping managers delineate relevant strategic considerations and the priorities they must adopt to enhance enterprise performance and escape the zero profit tendency associated with operating in markets open to global competition. Copyright  2007 John Wiley & Sons, Ltd.

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Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance

What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences. The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables. The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection. The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.

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Causation, prediction, and search

The paper is concerned with spatial clustering of economic activity and its relation to the spatiality of knowledge creation in interactive learning processes. It questions the view that tacit knowledge transfer is confined to local milieus whereas codified knowledge may roam the globe almost frictionlessly. The paper highlights the conditions under which both tacit and codified knowledge can be exchanged locally and globally. A distinction is made between, on the one hand, the learning processes taking place among actors embedded in a community by just being there dubbed buzz and, on the other, the knowledge attained by investing in building channels of communication called pipelines to selected providers located outside the local milieu. It is argued that the co-existence of high levels of buzz and many pipelines may provide firms located in outward-looking and lively clusters with a string of particular advantages not available to outsiders. Finally, some policy implications, stemming from this argumen...

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Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation

This article introduces the concept of the capability lifecycle (CLC), which articulates general patterns and paths in the evolution of organizational capabilities over time. The capability lifecycle provides a structure for a more comprehensive approach to dynamic resource-based theory. The analysis incorporates the founding, development, and maturity of capabilities in a manner that helps to explain the sources of heterogeneity in organizational capabilities. In addition, the analysis includes the "branching" of an original capability into several possible altered forms.

The Dynamic Resource-Based View: Capability Lifecycles

Regularities concerning how entry, exit, market structure, and innovation vary from the birth of technologically progressive industries through maturity are summarized. A model emphasizing differences in firm innovative capabilities and the importance of firm size in appropriating the returns from innovation is developed to explain the regularities. The model also explains regularities regarding the relationship within industries between firm size and firm innovative effort, innovative productivity, cost, and profitability. It predicts that over time firms devote more effort to process innovation but the number of firms and the rate and diversity of product innovation eventually wither.

Entry, Exit, Growth, and Innovation over the Product Life Cycle

This study attempts to measure and analyse the diffusion of product innovations. Diffusion is defined as the spread in the number of producers engaged in manufacturing a new product. Thus, the term refers to the net entry rate in the market for a new product. We trace the history of diffusion for 46 new products and examine the inter-relations among diffusion, other aspects of technological change, price, output, and certain attributes of the relevant markets. To explain the 46 product histories, we construct a theory of the development of industries for new products. Our theory combines elements of traditional, neoclassical models with what Nelson and Winter (I974) have termed an evolutionary theory. A novel feature is that the historical sequence, or time path, of events is viewed as a critical determinant of the ultimate structure of new product markets. Thus the time path of events determines not only the course traversed in reaching the end result but the ultimate market structure itself. The paper is organised in four sections. In Section I we present our theory. In Section II we construct a series of alternative theories of the development of industries for new products based on approaches to be found in received literature. The evidence from the 46 new product histories is examined in Section III. Finally, a brief summary of principal findings follows in Section IV.

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Time Paths in the Diffusion of Product Innovations

Numerous studies have shown that, within industries, the propensity to perform R&D and the amount of R&D conducted by performers are closely related to the size of the firm, while R&D productivity declines with firm size. These findings have been widely interpreted to indicate that there is no advantage to large firm size in conducting R&D. The authors show how a simple model based on the idea of R&D cost spreading can explain the prior findings about the R&D-firm size relationship, as well as additional features of the R&D-firm size relationship, implying an advantage to large size in R&D.

A Reprise of Size and R & D

The effect of firm size on the allocation of R&D effort between process and product innovation is examined. It is hypothesized that, relative to product innovations, process innovations are less saleable in disembodied form and spawn less growth. This implies that the returns to process R&D will depend more on the firm's output at the time it conducts its R&D than the returns to product R&D. Incorporating this distinction in a simple model, the authors derive and test predictions about how the fraction of R&D devoted to process innovation varies with firm size within industries.

Firm Size and the Nature of Innovation within Industries: The Case of Process and Product R&D

Evidence on entry, exit, firm survival, innovation and firm structure in new industries is reviewed to assess whether industries proceed through regular cycles as they age. A leading depiction of the evolution of new industries, the product life cycle, is used to organize the evidence it is shown that the product life cycle captures the way many industries evolve through their formative eras, but regular patterns occur when industries are mature that are not predicted by the product life cycle. Regularities in entry, exit, firm survival and firm structure are also developed for industries whose evolution departs significantly from the product life cycle. Opportunities for further research on the nature of industry life cycles and the factors that condition which life cycle pattern an industry follows are discussed. Copyright 1997 by Oxford University Press.

Steven Klepper

Papers

Entry, Exit, Growth, and Innovation over the Product Life Cycle

Time Paths in the Diffusion of Product Innovations

A Reprise of Size and R & D

Firm Size and the Nature of Innovation within Industries: The Case of Process and Product R&D

Industry Life Cycles