Don E. Kash

Self-organizing innovation networks: implications for globalization

Abstract The most valuable and complex technologies are increasingly innovated by networks that self-organize. Networks are those linked organizations (e.g., firms, universities, government agencies) that create, acquire, and integrate the diverse knowledge and skills required to create and bring to the market complex technologies (e.g., aircraft, telecommunications equipment). In other words, innovation networks are organized around constant learning. Self-organization refers to the capacity these networks have for combining and recombining these learning capabilities without centralized, detailed managerial guidance. The proliferation of self-organizing innovation networks may be linked to many factors, but a key one seems to be increasing globalization. Indeed, globalization and self-organizing innovation networks may be coevolving. Changes in the organization of the innovation process appear to have facilitated the broadening geographical linkages of products, processes, and markets. At the same time, globalization seems to induce cooperation among innovative organizations. Research on innovation networks is used to illustrate these ideas. The evolution of the automotive industry provides a test case.

Patterns of innovating complex technologies: a framework for adaptive network strategies

Abstract Self-organizing networks have become the dominant innovators of complex technologies. This paper presents a framework that offers insight into the three distinctive patterns of innovation that are evident in the evolution of six technologies. It is at those points that networks must change from one pattern to another and that major adaptations in company strategies and public policy are required. Four indicators of pattern changes are discussed. Effective strategies and policy would benefit from the development of a different set of concepts and the science of complexity offers some of those concepts.

Emerging patterns of complex technological innovation

Technological innovation is increasingly concerned with complex products and processes. The trend toward greater complexity is suggested by the fact that in 1970 complex technologies comprised 43% of the 30 most valuable world goods exports, but by 1996 complex technologies represented 84% of those goods. These technologies are innovated by self-organizing networks. Networks are those linked organizations that create, acquire, and integrate the diverse knowledge and skills required to innovate complex technologies. Accessing tacit knowledge (i.e., experienced-based, unwritten know-how) and integrating it with codified knowledge is a particular strength of many networks. Self-organization refers to the capacity networks have for reordering themselves into more complex structures (e.g., replacing individual managers with management teams), and for using more complex processes (e.g., evolving strategies) without centralized, detailed managerial guidance. Case studies of the innovation pathways traced by six complex technologies indicate that innovations can be grouped into three quite distinct patterns. Transformation: the launching of a new trajectory by a new coevolving network and technology. Normal: the coevolution of an established network and technology along an established trajectory. Transition: the coevolutionary movement to a new trajectory by an established network and technology. Policy makers and managers face the greatest challenge during those periods of movement from one innovation trajectory to another. These are periods of turbulence; they are the embodiment of Schumpeters “gales of creative destruction.” This paper investigates how, in six case studies, core capabilities, complementary assets, organizational learning, path dependencies, and the selection environment varied among the innovation patterns. The paper builds on work reported in a recent book by the authors entitled: The Complexity Challenge: Technological Innovation for the 21st Century, Pinter, London, 1999.

Path Dependence in the Innovation of Complex Technologies

This article investigates three factors that affect the path dependence of complex technologies and the organizational networks that carry out their innovation: culture and institutions, organizational learning, and technology design. Evidence from the rapidly growing body of literature on path dependence and from six case studies of complex technologies (i.e. turbine blades, cardio-imaging technology, audio compact discs (CDs), radiation therapy technology, micro-floppy disks, and microprocessors) is used to investigate the impact of culture, organizational learning, and technology design on path dependence. Three innovation patterns associated with co-evolution of technologies and organizational networks provide the framework used to structure the investigation.

Patents in a world of complex technologies

The changes brought about by the Patent Act of 1952 were needed to adjust the administration of patents to the reality that invention and innovation now primarily result from investment rather than from individual creativity. An unintended result of these changes has been to make it difficult for firms in most non-chemical technologies and especially smaller firms involved in the innovation of complex technologies to obtain the protection they need. Also, denying firms in complex technologies the power to operate patent pools has forced them to multiply their patents as bargaining chips: these large numbers of interlocking patents effectively bar entry by newcomers to complex technologies, thus reducing competition. Reforms are possible and they could enable the patent system to fulfill its proper function in relation to complex technologies and transform the economic climate for small-firm innovation, replacing the patent systems present emphasis on serving large firms in simple technologies. Copyright , Beech Tree Publishing.

Complex technology and community: implications for policy and social science

A new technological imperative which punishes individualism and conflict and rewards community and cooperation is impacting the world. Lack of a conceptual framework which allows understanding of that imperative is a barrier to technology based economic policy in the US. A key component of the needed conceptual framework will be the role of community in the continuous innovation of complex technologies. Three characteristics of complex innovation are integral to the conceptual framework needed to inform technologically based economic policy: complexity, networks, and collaboration. These characteristics both require and contribute to community

To manage complex innovation, ask the right questions

This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

Technology policy: Fitting concept with reality

Abstract We have crossed an historical Rubicon into an era in which the capacity to innovate increasingly complex technologies has created a set of opportunities and problems that make our reductionist and simplistic linear models of policy and governance obsolete. America must base its technology policy on a synthesis of both international experience and new conceptualizations. The most important recent experience with successful technological innovation has taken place in Asia, whereas the most rapid conceptual advances appear to be occurring in Europe and the U.S. Complexity is the key factor in both.

Technology policy in a complex world

Abstract President Clinton has given commercial technology policy a priority position amid signs that there is growing consensus in America that such policy is needed. However, while there is growing consensus on the need, there is widespread disagreement about exactly what initiatives we should undertake. Insight into the choice of policy options is provided by the new science of complexity. Four concepts are developed to guide technology policy: self- organization, learning, positive feedback, and emergence.

Steering Complex Innovation

OVERVIEW: Participation in self-organizing networks of firms that exist to carry out the repeated innovation of complex technologies is becoming increasingly important. To achieve and maintain leadership, these networks must achieve a mutually reinforcing fit among organizational core capabilities, complementary assets, learning, and linkages to the external environment. Three distinct patterns have been identified within which these networks operate: transformational, normal and transitional. Each presents its own management opportunities and requirements, but all call for a premium to be placed on exploratory, experimental approaches to decision making.