Innovation and Entrepreneurship
Innovation and Entrepreneurship
Edward J. Malecki, The Ohio State University
Ben Spigel, University of Edinburgh Business School
Abstract: We follow Schumpeter in attributing to entrepreneurs the spark to bring new combinations to market by combining knowledge, perceived opportunity, and other resources to form new firms. A link between innovation and entrepreneurship was first seen in new firms exploiting new technologies in high-technology regions. This context set the tone for research, which we explore in this paper. We identify five topics: entrepreneurship in high-tech contexts, spinoffs from university research, science parks (or research parks), the local/regional ecosystem or innovation system, and flows of knowledge within social and professional networks. Underlying these five attributes of high-tech innovation are cultural outlooks and orientations. Without an understanding of how culture influences entrepreneurial and innovative activities, it is difficult to study their relationship with the cultural contexts in which they take place. Building on a nexus-based view of innovation and entrepreneurship, we argue that culture is best understood as a process through which actors interpret the world around them and which can either encourage or discourage entrepreneurial and innovative activity.
Keywords: Economic development, Innovation, Policy and Practice
Introduction
The connection between innovation and entrepreneurship originated with Schumpeter’s (1934) emphasis on new combinations – new goods, new methods or processes, new markets, or the new organization of an industry – introduced by entrepreneurs. “New combinations are, as a rule, embodied, as it were, in new firms” (Schumpeter 1934: 66). As Landström et al. (2012: 1155) observe, “Schumpeter’s idea was … that economic growth resulted not from capital accumulation, but from innovations or ‘new combinations’ that create a disequilibrium on the market.” Despite the common origins of research on both innovation and entrepreneurship in the work of Schumpeter, they have “evolved over time as two largely separate research fields” and remain “surprisingly disconnected from the neighbouring field of innovation studies” (Landström et al. (2012: 1171-1172).
The definition of entrepreneurship has evolved over time (Braunerhjelm 2011; Hébert and Link 2006). Drucker (1985) combines innovation and entrepreneurship and stresses that entrepreneurs must be innovative, which requires systematic search and analysis of innovative opportunities. A key aspect of new firms is that entrepreneurs have more to learn from their local environment and actors within it than do large firms (Zahra et al. 2006).
In entrepreneurial capitalism, small, innovative firmsplay a significant role(Baumol et al. 2007). Such firms are the “fruit flies” of innovation, contributing to its heterogeneity (de Jong and Marsili 2006). There are several types of innovative entrepreneurship, including new New technologyTechnology-based Based firms Firms (NTBFs), spinoff firms, and high-growth startups(Stam and Nooteboom 2011). NTBFs are acquirers, synthesizers and introducersof new technology, and they contribute to the local or regional economy (Fontes and Coombs 2001). Academic spinoffs are typically in research and development (R&D)-intensive high technology, associated with new fields and emerging sectors (Lawton Smith and Bagchi-Sen 2012).
Generally speaking, the stylized facts of innovation leave little space for entrepreneurs. Exceptions are found in the literature on nursery cities (Duranton and Puga 2001) and cluster formation (Bresnahan and Gambardella 2004). While The the conventional wisdom and standard models focus on cities as the locus of innovation,; innovation occurs in small and isolated places as well(Shearmur 2012). Recent research highlights how knowledge sparks innovation and how it lubricates entrepreneurship (Bae and Koo 2009; Braunerhjelm et al. 2010; Iammarino and McCann 2006; Qian et al. 2013).
We review the literatureon entrepreneurship and innovation in regions, highlighting dynamic processes and supporting institutions (Stam and Nooteboom 2011). We stress that this paper is not about the topic of spillovers and agglomeration (Feldman and Avnimelech 2011; Feldman and Kogler 2010; van Oort and Bosma 2013). As Shearmur (2012: 2352) suggests, “innovation dynamics do not necessarilyoccur in agglomerations (although ofcourse they can occur there).”
We identify five core topics in this paper. First, much early research on entrepreneurship focused on high-tech contexts (for example, biotech and ICT). Second, the phenomenon of firms spinning out of university research put a spotlight on academic and basic research as the starting point of innovative entrepreneurship. Third, science parks (research parks in the USA) were identified as particularly fertile locations for high-tech entrepreneurs. Fourth, the local or regional ecosystem or innovation system has been identified as a nexus of social and economic links and support for entrepreneurship. Fifth, flows of knowledge within social and professional networks allow diffusion of knowledge to new places. Underlying these topics are the regional and organizational cultures that influence the willingness of actors to engage with the risks of the entrepreneurship process.
The high-technology context
Empirical observation of the link between innovation and entrepreneurship largely grew out of the experience in California’s Silicon Valley and Route 128 outside Boston beginning in the 1960s, and set the tone for understanding the links between high-tech innovation and entrepreneurship. By the mid-1980s, it was well-established that places where high-technology, R&D-intensive firms were active and abundant generated spinoffs and grew into (what would later be called) clusters (Cooper 1986; Cooper and Folta 2000; Malecki 2011).
Silicon Valley was not the product only of entrepreneurs; large multilocational firms also were attracted to the region and its universities and clusters of firms, and they continue to be a lure (Adams 2011; Poon et al. 2006). Globalization of R&D has spread the potential for high technology to new places (Malecki 2010). Large firms, together with entrepreneurs, co-create regional high-tech clusters (Smilor et al. 2007). A vibrant global competition for high-tech success continues (Anttiroiko 2004; Castells and Hall 1994).
Technology clusters differ significantly from other industrial clusters in that they are tied more to the early stages of industry life cycles, and resources at the regional level support growth and innovation (St. John and Pouder 2006). Therefore, what is considered “high tech” changes over time, encompassing innovative sectors that present new opportunities. The principal activity of technology-based sectors is research, and their main input as well as output is knowledge. For firms, locating near sources of knowledge (such as universities and research centers) and clustering in specialized labor markets maximizes opportunities for collective learning and exploitation of entrepreneurial opportunities (Audretsch et al. 2006). We may be able to identify tendencies such as those above but, as Iammarino and McCann (2006) point out, diversity and heterogeneity continue to operate, so that we are unable to predict with accuracy where innovative clusters will emerge.
Therefore, high technology is typically defined as innovative, and measured by through inputs of expenditure on R&D and employment of scientists, engineers, and technicians (Hecker 2005). Schoales (2006) expands the concept of newness to include all industries in which product life cycles are very short, thereby embracing several service sectors including advertising, design, fashion, finance, and others. Stoneman (2010) likewise includes products of the ‘creative industries’, encompassing culture, media, and the arts, as examples of soft innovation, which places emphasis on aesthetic rather than technological characteristics. Finally, knowledgeKnowledge-intensive Intensive business Business services Services (KIBS) are a “hidden engine” of high technology (Probert et al. 2013).
For several decades, we have come to consider high-tech industries and regions as innovative and entrepreneurial. By studying innovative entrepreneurship not only in prominent high-tech regions, but also in other locales, we have learned that innovation and entrepreneurship are found in many, though by no means all, places.
University spinoffs
Universities are frequently among the “incubator organizations” that are thethat act as the foundation of innovative entrepreneurial regions (Mayer 2007). Spinoffs in the Boston area were largely from MIT, rather than Harvard or the other higher education institutions in the Boston area (Roberts 1991). Similarly, Silicon Valley evolved both from corporate spinoffs and from entrepreneurial activity related to Stanford University.These two models are difficult for otherregions to imitate, since the numbers of spinoff firms from top researchinstitutions are not possible to match in other settings (Degroof and Roberts,2004). Shane (2004) summarizes the findings for the USA, updated by Grimaldi et al. (2011).
Both the US and European experiences have created an expectation regarding the regional role of a university:to contribute to the regional economy through spinoffs of new firmsbased on innovative technologies that flowflowing from university research (Lerner 2005;Wright et al. (2007). Etzkowitz (1983) has documented the role of entrepreneurial scientists and entrepreneurial universities in American academic science. His picture of entrepreneurial researchers and science parks has had broad influence, which hasgrown further with the development of the “triple helix” model (Etzkowitz and Leydesdorff, 2000). Universities are not monolithic; within each, four sets of actors – individuals, research groups, departments, and the university as an organization – compete for resources, prestige, and recognition within universities (Deiaco et al. 2012; Etzkowitz 2003). For academic entrepreneurs, a decision to start a firm is rooted within the values of an academic career (Franzoni and Lissoni 2009).
Through spinoffs – and profiting from spinoffs – universities have broadened the scope of their activities beyond teaching and research. The “entrepreneurial university” model includes patenting, commercialization, and technology transfer as a “third stream” of revenue and/or as a way to engage with the local communitycontribute to the local economy (Clark 1998). European universities are “learning to compete[BS1]” and becoming multidimensional in their entrepreneurialism and other aspects of the “knowledge business” (McKelvey and Holmén 2009). The allure of profit has been especially strong in biomedical fields (Åstebro and Bazzasian (2011). Rothaermel et al. (2007) and Siegel (2011) review the burgeoning literature on academic entrepreneurship and university technology transfer.
European countries have attempted to imitate the favorable conditions found for spin-off formation in US high-tech regions (Mustar et al. 2008; Wright et al. 2007). The variability among national academic cultures is overwhelmed by the “imitation effect” that has“led to a convergence of policies toward the same goal: to foster a larger number of academic spin-offs.”[BS2] Imitation also leads nearly all imitators to target biomedical and nanotechnology fields. Such policies, however, have not imitated faculty mobility, university autonomy, and generous support of basic research found in the US (Franzoni and Lissoni 2009; Howells et al. 2012).
Spinoffs started by professors, researchers and other university employees are are relatively easily easy to tracktracked when professors, researchers, and other university employees are the entrepreneurs. However, former students (including alumni) also start firms, utilizing knowledge and contacts that originated at a university. Many spinoffs are by graduates (Åstebro et al. 2012), but such firms are not readily identified in available data sets (see also Bathelt et al. 2011). Thus, recent research, reviewed by Grimaldi et al. (2011), Mustar et al. (2006), and Pirney et al. (2003), scans moves beyond university faculty and research staff to identify a broader cadre of university-related entrepreneurs. Broad surveys of alumni have been conducted by MIT (Hsu et al. 2007; Roberts and Easley 2011) and Stanford (Eesley and Miller 2012). However, A a focus on graduates or alumni will missmisses entrepreneurs who drop out (e.g., Michael Dell and Bill Gates) as well as those who benefit indirectly from the university environment.
The principal challenge of understanding university startups is that far more knowledge transfer occurs than is imagined in the linear technology transfer model. Research “spills over” and informal transfer of knowledge takes place continually as research is conducted and as student entrepreneurship is encouraged in other ways that are underestimated and understudied (Grimaldi et al. 2011; Nelson 2012). Many spinoffs are “spontaneous” rather than planned, growing out of informal activities in the “grey zone” where tacit knowledge is shared and transfused into society (Bathelt et al. 2010; Nilsson et al. 2010; Rappert et al 1999). Many university-related start-ups arise from “decentralized idea development” that may have originated in a classroom or lab or from social ties that are informal and related to the university only indirectly. Such tacit knowledge is basically unknown to and uncontrollable by a university, yet it may well represent the majority of knowledge transferred (Karnani 2013). Knowledge-related collaboration by academic researchers with non-academic organizations, whether patentable or not, is innovative (Perkmann et al. 2013).
Universities produce many outputs, including new knowledge and human capital. They transfer existing know-how and produce technologicalinnovation. They also provide regional leadership, co-producing the regional knowledgeinfrastructure. Together, the mechanisms by which university R&D activity stimulates economic development are both broader and more diverse than spinoffs, patenting, and licensing activity (Benneworth and Charles 2005; Goldstein 2009; Lendel 2010).
As the discussion above suggests, research on academic spinoffs has been empirically rich but “mainly atheoretical” (Autio 2000: 332). Spinoffs are part of the culture of entrepreneurial universities, but they can hardly be planned and they vary greatly in nature. Academic culture is not necessarily at odds with entrepreneurship. Scientific productivity enhances entrepreneurial activity rather than substituting for it (Franzoni and Lissoni 2009; Van Looy et al. 2010). In developing a spinoff strategy, a university should focus on developing a critical mass of world class research in a few areas in which they can attract industrial partners (Siegel et al. 2007). The exceptional university culture also builds an institutional structure of incentives, such as profit shared among the university, departments, and inventors. However, a research-intensive institutional culture can discourage technology transfer or commercialization activities if they are seen as distracting from basic research and publication.
Entrepreneurs penetrate the “knowledge filter” between research and economic use (Carlsson et al. 2009). They are able to do so when an entrepreneur has the ability “to understand new knowledge, recognize its value, andsubsequently commercialize it by creating a firm”(Qian and Acs 2013: 191). This ability, which Qian and Acs call entrepreneurial absorptive capacity, involves two dimensions: scientific knowledge as well as market or business knowledge. A pool of academic researchers and their new firms adds to the regional knowledge base for further innovative entrepreneurship.
University spinoffs are innovative, applying cutting-edge research to new purposes. They also are an unusual type of entrepreneurship, because academic entrepreneurship is counter to long-standing scholarly models (Martin 2012). New skills must be acquired, and new networks formed, to assemble a new spinoff firm and for it to succeed(Clarysse et al. 2005; Vohora et al. 2004). Without question, university spinoffs combine innovation and entrepreneurship and are the archetypes of new combinations.
Science parks and research parks[BS3]
The Silicon Valley experience also highlighted the Stanford Research Park (Stanford Industrial Park until the 1970s) as a site for the emergence and growth of firms. As the first university-owned industrial park, it has inspired imitators since the 1970s (Anttiroiko 2004; Miller and Coté 1987). Because of the diverse nature of science parks and the firms that locate in them, they serve several functions and appeal to different types of firmsat once (Johannisson et al. 1994). Consequently, much recent research has focused on creating typologies to understand this diversity (Mustar et al. 2007; Nicolau and Birley 2003; Pirnay et al. 2003). In general, science parks of research-oriented universities, and parks that are older are nearer to the main university campus, are more likely to contain university spinoff companies (Link and Scott 2005).
Many science parks also have an incubator function, but investigations into the survival and performance of tenant firms have been investigated unevenly and incompleteely (Bergek and Norrman 2008; Phan et al. 2005). Because of their diversity, typologies also have been constructed to understand the roles and relative success of incubators (Bergek and Norrman 2008). As science park occupants – many of them university technology-based spinoffs – go through various growth stages, different incubator services are needed in each stage(Chan and Lau 2005; van Geenhuizen and Soetanto 2009).
Science parks not only incubate new firms but also attract established companies. For large firms, science parks allow collaborative links to be established with a recognized academic ‘center of excellence’ and to take advantage of an agglomeration of skilled researchers and new graduates. Proximity to a center of excellence may attract large firms, but does not necessarily indicate any linkage or interaction with the local universities – or with one another (Johannisson et al. 1994).
According to Macdonald and Deng (2004: 3), “what little evidence there is does not conclude that science parks offer the optimum location for high technology firms. Indeed, it would seem that the science park offers little advantage at all”. The most imitated regions were the product of serendipity rather than of planning – but grounded in the benefits of agglomeration economies, externalities, networking, and clustering, with most information flow taking place informally. Despite their rather poor proof of success, science parks have remained extremely popular as a policy tools (van Geenhuizen and Soetanto 2008).
Overall, science parks continue to be an attractive policy for universities and other regional players. Because they are property-based, the temptation is always present to keep them filled – with large tenants as well as small, with technology lifestyle businesses as well as high-growth potential start-ups (Harrison and Leicht 2010). Not surprisingly, they have difficulty to fulfill the needs of this diverse mix of firms to have positive impact, and to enhance the reputation of the university sponsor (Chan and Lau 2005). Much depends on what is being evaluated. Studies of science parks have focused on firms (numbers of spinoff and other firms, innovativeness, survival, growth), the science park itself (employment, networking), and the regional economy (numbers and types of firms, employment) (Van Geenhuizen and Soetanto 2008). The variety of purposes and functions of science parks remains problematic. Perhaps, as van Geenhuizen and Soetanto (2008: 106) suggest, we “need to investigate more thoroughly why Science Parks exist in the first place.”