Innovation Districts and Learning Regions

posted Jun 11, 2014, 9:18 PM by Greg Laudeman   [ updated Jul 31, 2014, 9:59 AM ]

Bruce Katz and Julie Wagner's work on innovation districts at Brookings is great stuff. It's powerful yet practical. But it's not unprecedented. Scholars have been studying innovation areas for quite some time, especially in Europe. The post is kind of long and academic, but is a good review of a lot the literatures. It's abstracted from my dissertation, so please excuse the dweebiness.

Agglomeration and propinquity reduces transaction costs, allowing firms to be smaller and more specialized. Thus, “much of the competitive advantage lies outside a given company or even outside its industry, residing instead in the location of its business units” (Porter, 2003, p. 254, emphasis in original). The rise and fall of regions may be understood by replacing the term “competitive advantage” in this quote with the word “knowledge.” The unique availability of a full range of specialized knowledge makes regions economically important.

Marshall emphasizes in particular the mutual knowledge and trust that reduces transaction costs in the local production system; the industrial atmosphere which facilitates the generation and transfer of skills and qualifications of workforce required by local industry; and the effect of both these aspects in promoting (incremental) innovations and innovation diffusion among small firms in industrial districts. (Asheim, 2003, pp. 415-416)

Many commentators maintain that improvements in communication should reduce the rationale for agglomeration (Cairncross, 2001). Others point out that this has not happened because collective assets and capabilities, transfer of tacit knowledge, and the very acts of creating knowledge and innovating require propinquity (Calhoun, 1998; Brown and Duguid, 2002; Boschma, 2005; Cooke and Leysdesdorff, 2005).

Research shows the role that the production of information and knowledge plays in our society (Machlup, 1962; Porat, 1977), and the roles of institutions and knowledge in economic theory (Williamson, 2000; Cortright, 2001). An emphasis on innovation by business thought leaders (Drucker, 1985, 2002/1985) makes agglomeration a practical matter. Scholars point to complementary public and private roles, and to balance between competition and cooperation, as important in agglomeration. In industrial agglomeration there is division of labor (specialization) among numerous small firms, broad and rapid dissemination of information, and a highly skilled workforce. Agglomerating involves increasing importance of continual, radical innovations, and collective cognizance of a globalizing economy. It requires learning, most of all (Asheim, 2003). Piore and Sabel (1984) see the integration of community life with productive activities as the means by which networks of firms maintain collective assets and continually innovate. As Asheim (2003) puts it:

In a learning economy, the competitive advantage of firms and regions is based on innovation, interactive learning processes. … [O]ne problematic aspect of the learning economy has been its focus on ‘catching up’ learning based on incremental innovations, and not radical innovations requiring the creation of new knowledge. (pp. 426-427)

The concept of “learning regions” was proposed to explain how agglomeration fosters continual and radical innovations, and to illuminate modern issues related to globalization, knowledge-intensive enterprises, and technological change (Florida, 1995). Amin (2008) and other scholars (Tolliday & Zeitlin, 1992; Burrows, Gilbert & Pollert, 1992) contrast these socioeconomic phenomena with large-scale, standardized production that they refer to as Fordism, in reference to industrial approaches popularized by automotive magnate Henry Ford. In a global, post-Fordist, innovation-oriented economy, regions matter more than ever because “regions themselves are becoming focal points of knowledge-creation and learning … [that] function as collectors and repositories of knowledge and ideas, providing an underlying environment or infrastructure that facilitates the flow of knowledge, ideas, and learning” (Florida, 1995, p. 528). Florida goes on to say:

Learning regions provide the crucial inputs for knowledge-intensive economic organization to flourish: a manufacturing infrastructure of interconnected vendors and suppliers; a human infrastructure that can produce knowledge workers, facilitates the development of a team orientation, and which is organized around life-long learning; a physical and communication infrastructure which facilitates and supports constant sharing of information, electronic exchange of data and information, just-in-time delivery of goods and services, and integration into the global economy; and capital allocation and industrial governance systems attend to the needs of knowledge-intensive organizations. (p. 534)

The challenges for firms, Florida (1995) maintains, are to “adopt new organizational and management systems that harness knowledge and intelligence,” to maintain “a balance between cutting edge innovation and high-quality and efficient production,” “to spur individual genius and creativity … and the collective mobilization of knowledge,” and “to build integrated and dense global webs of innovation and production” (p. 534). All of which implies a shift away from “the increasingly dysfunctional Fordist model” to sustainable advantage based on “continuous improvement of technology, continuous development of human resources, the use of clean production technology, elimination of waste, and a commitment to continuous environmental improvement” (p. 535). The implication is that firms cannot do these things on their own but require an intellectual, physical, and social environment that enables them to do them collectively: a learning region.

The learning-region paradigm emphasizes the networks or associational characteristics of a region in which firms are embedded, subsuming individual entrepreneurs and workers to consider how they function together rather than operate independently (Granovetter, 1985; Saxenian, 1994; Morgan, 1997). Various forms of proximity—cognitive, organizational, social, institutional, and geographical—provide stability and enable interactive learning (Boschma, 2005), but excessive stability and static interactions resulting from “institutional thickness” can impede innovation (Amin and Thrift, 1995; Hauser, Tappeiner & Walde, 2007). Dynamic social networks with abundant weak ties within regions allow for knowledge spillovers and new opportunities for interactive learning. Broader extra-local networks bring new capabilities, ideas, and technologies into regions (MacKinnon, Cumbers, and Chapman, 2002). Both types of ties reduce transaction costs for knowledge as well as other resources, contributing to innovation by making it easy to connect disparate chunks of information into usable and useful knowledge.

Embeddedness must be balanced by autonomy. If it is not, regions can get locked into a particular technology, following it from boom to bust. “It is the type of network relationships between organizations (firms, institutions) rather than their spatial clustering alone that determines the ability of regions to adapt” (Boschma and Lambooy, 1999, p. 393). Dynamism in social networks—lots of weak ties and shifting relationships—allows clusters (or the communities or regions in which they operate) to diversify, reinvent and revitalize themselves, and avoid technologically-determined path-dependency. Stronger, more stable ties provide governance, particularly to and through institutions, and reduce uncertainty, making it more practical for actors to take risks (Morgan, 1997).

Some regional-learning literature, particularly prior to Florida’s explication of the concept, extends resource-based theories of organizations to explain why firms cluster together by industry (see, for example, Porter, 1990). Breznitz and Taylor (2010) contrast such factor-oriented theoretical perspectives with others that focus on social structure of regions, such as the dynamics of the “triple helix” of academia, government, and industry (Etzkotwitz and Leydesdorff, 1997; Etzkowitz, 2008) and an interactive model of innovation (Morgan, 1997). Cooke (2002) suggests that regional learning is essentially collaborative economic action by a localized socioeconomic system in response to natural socioeconomic disequilibrium. This requires social connections that are dynamic yet resilient: “knowledge is in the network,” Cooke (2002) maintains, “because each move in the interactive innovation process requires learning from other than those involved in the preceding move” (pp. 2 – 3, emphasis in the original). Breznitz and Taylor (2010) conclude that social structure is as important for innovation as economic factors, and more important to growing and retaining producers of technological innovations.

The triple-helix theory (Etzkowitz & Leydesdorff, 1997; Etzkotwitz, 2008) posits that a general type of social-network structure must exist for regional learning, and evolves in a particular way to sustain innovation. Generally, Etzkotwitz (2008) maintains, triple-helix regional learning emerges as the distinction between business and science is blurred, and as government facilitates this blurring with resources and regulatory relief. Civil society and voluntary associations provide the space for the helices to connect and overlap. “A triple helix regime typically begins as university, industry, and government enter into a reciprocal relationship with each other in which each attempts to enhance the performance of the other” (Etzkotwitz, 2008, p. 8). As industries become more knowledge-intensive, government and university play more important roles as enablers.  The triple helix evolves as each strand—academia, government, or industry—takes on new roles similar to the roles of the others in support of the others’ core competencies, as each strives to make the others successful. Performance of each helix improves as individuals and information circulate through it, and individuals and information circulating between the helices fuels innovation.

Innovation is an interactive learning process, says Morgan (1997), with powerful feedback loops incorporating common and tacit knowledge, “that is shaped by a variety of institutional routines and social conventions” (Morgan, 1997, p. 493). Agglomeration, or clusters of complementary specialized entities in proximity and cooperating with each other, is a hallmark of learning regions. But the clusters are byproducts of the innovation process, of social propagation of knowledge from individuals to community, and of mobility within the region between firms (Cooke, 2002). These are made possible in turn by norms of reciprocity and trust—social capital—that facilitate network development, support interactive learning and innovation, and thereby provide competitive advantage. Cook maintains that “[C]lustering [exists] for learning, knowledge transfer, collaboration, and the exploitation of spillovers” (p. 3). The “innovation as interactive learning” theory further explicates and supports the “evolving triple helix as source of sustained radical innovation” theory: The triple helix provides the institutional infrastructure for interactive learning, and interactive learning provides the means by which the triple helix evolves.

Brown and Duguid (2002) maintain that the only means of constructing regional advantage is to capitalize on local knowledge that is simultaneously “leaky” and “sticky.” Such knowledge inevitably leaks out of particular organizations but sticks in a particular region because it inheres to an embedded boundary, spanning local social networks.  Also consider what may be called “optimal proximity,” presented by Boschma (2005): a loosely coupled system, balancing local “buzz” with extra-local linkages, combining community and market relations, and providing institutional checks and balances, to create a common knowledge base with diverse but complementary capabilities. This involves cognitive, organizational, social, and institutional capabilities across and within geographical limits. Brown and Duguid (2002) and Boschma (2005) make essentially the same point: Sustained innovation capacity comes from leveraging unique local human assets for acquiring relevant global human assets and constantly recombining them. These perspectives are essentially elaborations on Marshall (Asheim, 2003), Piore and Sabel (1984), and others.

In the same ways that interactive learning enables firms to generate marketable innovations, case studies have shown that interactive learning by policy makers (Hassink, 2005) and boundary spanning by civic organizations (Safford, 2009) can be important for regions to recover from path dependency and revitalize. Cooke and Leysdesdorff (2005) maintain that regions provide “constructed advantage”—as opposed to comparative and competitive advantages—by intentionally aligning and integrating the regional economy, governance, knowledge infrastructure, and community and culture. Constructed advantage involves combining symbolic/creative, synthetic/technical, and analytic/scientific forms, linking the subsystems for knowledge creation, exploration, and exploitation, enabled by the triple helix. Gertler and Wolfe (2004) look at regional foresight exercises as interactive learning by individuals, organizations, and regions that allow them to adapt and innovate. Such broad-based collective learning can overcome the barriers to learning intrinsic to capitalism. Cross-sector interactive learning allows for the creation of the entirely new organizations necessary for the creative forgetting and unlearning by organizations and social systems. It leads to deep regional economic restructuring (Johnson, 1992; Hudson, 1999; Boschma and Lambooy, 1999).

A new kind of organisation is spearheading the phenomenon: knowledge-based communities, i.e. networks of individuals striving, first and foremost, to produce and circulate new knowledge and working for different, even rival, organisations. One sign that a knowledge-based economy is developing can be seen when such individuals penetrate conventional organisations to which their continuing attachment to an “external” knowledge-based community represents a valuable asset. As these communities develop their activities, they become agents of change for the economy as a whole. (David & Foray, 2002, p. 9)

A knowledge-intensive community is a community where a large proportion of members is involved in the production and reproduction of knowledge and, hence, the creation of a public (or semi-public) space where knowledge is circulated and where codification and dissemination costs have been radically reduced through the use of new information and communication technologies. (David & Foray, 2002, p. 14)