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What is the relationship between institutional frameworks, public policy, and the governance of innovative competencies by firms? This chapter extends the varieties of capitalism theory to examine how different types of economy impede the governance of new technology firms. It argues that most new technologies attempt to create radical innovations by developing competencies along a widely diffused “Silicon Valley Model” surrounding the financing, staffing, and creation of employee incentives within firms. Liberal market economies, such as those found in the US and UK, provide strong institutional supports for the Silicon Valley Model, while coordinated market economies, such as Germany or Sweden, provide a series of constraints. A theoretical framework is developed to explore this argument. How public policy might impact competitiveness within new technology industries across the two types of economies is discussed.

This chapter discusses innovation from an organizational perspective. It identifies seven organizational factors associated with innovation: external relationships and funding, goals, organizational size, professionalization, leadership and group participation, task environment, and job satisfaction. Innovation is also affected by what has gone before, by the stages of innovations, and by the actors involved in the organization. Six key variables pertaining to organizational dynamics are discussed: previous experience of the organization with innovation, organizational discontent, type of innovation, communication and contact by organization members with the innovation, the innovation actors, and factors associated with the implementation of an innovation.

Evolutionary processes combine many features of complex systems: they are algorithmic; states co-evolve with interactions; they show power law statistics; they are selforganized critical; and they are driven non-equilibrium systems. Evolution is a dynamical process that changes the composition of large sets of interconnected elements, entities, or species over time. The essence of evolutionary processes is that, through the interaction of existing entities with each other and with their environment, they give rise to an open-ended process of creation and destruction of new entities. Evolutionary processes are critical, co-evolutionary, and combinatorial, meaning that thew entities are created from combinations of existing ones. We review the concepts of the replicator equation, fitness landscapes, cascading events, the adjacent possible. We review several classical quantitative approaches to evolutionary dynamics such as the NK model and the Bak–Snappen model. We propose a general and universal framework for evolutionary dynamics that is critical, co-evolutionary, and combinatorial.

This chapter develops the new innovation organization model in detail. This strategic, systems approach addresses the obstacles facing transformative, disruptive innovations in legacy sectors, builds a supporting talent base, and links researchers with the people and institutions involved in eventual implementation. Innovation organization encompasses the other four models of the dynamics of technology: (1) the pipeline model, in which innovation stems from basic research; (2) the induced model, in which innovation derives from business opportunities created by market niches and opportunities; (3) the extended pipeline model, in which government (usually the military) shepherds an innovation from research through to implementation; and (4) the manufacturing-led model, in which innovation is derived from improvements in production processes. A change agent is required to push innovation through the systematic barriers protecting the legacy sector, sometimes requiring the support of a higher-level meta-change agent to create the conditions in which change agents can be effective.

To understand the challenges of jobless innovation and legacy sectors, we require a new conceptual framework that better describes both the dynamics of innovation and the features of the paradigms that characterize legacy sectors and present barriers to these dynamics. Traditional theory ascribes innovation either to the outcome of inventions born of basic research (from the pipeline), or to new products induced to meet new or changed markets. To these we add three additional drivers: extended pipeline innovations that must be nurtured not just from research but all the way to market launch, manufacturing-led innovations emerging from improvements in manufacturing processes, and innovation organization innovations that require careful analysis of the many structural obstacles that hinder them from disrupting legacy sectors, especially if their advantages rest on environmental or other externalities. Innovations corresponding to all five models are needed to overcome the technology lock-in imposed by long-standing legacy paradigms.