ENTREPRENEURIAL DYNAMICS AND THE
ORIGIN AND GROWTH OF HIGH-TECH
CLUSTERS
Colin Mason
Hunter Centre for Entrepreneurship
University of Strathclyde
Glasgow G1 1XH, Scotland.
colin.mason@strath.ac.uk
Version 2
August 2006
Chapter 6.2 in C Karlsson (ed) Handbook of Research on Clusters: Theories, Policies
and Case Studies (Edward Elgar), to be published in 2007.
1. INTRODUCTION
This chapter develops the proposition that entrepreneurial activity has been the central
mechanism in the emergence of high tech clusters. It might be expected that new
technologies would be exploited by incumbent firms which dominated the existing
technology. However, this is not the case. Existing firms are too preoccupied with
their existing businesses, and so under-emphasise their significance, or are unwilling
or unable to exploit them because it would involve cannibalising or writing-off much
of their existing activities (Christensen, 1997; Kenney and von Burg, 1999). The
essence of high tech regions such as Silicon Valley and Route 128 “lies in the[ir]
continuous ability to create firms” (Kenney and Von Burg, 1999: 72). By exploiting
emerging technologies that established firms either resist or fail to react to, this
process of ‘entrepreneurial spawning’ results in an upgrading of the regional economy
(Castilla et al, 2000).
The genesis of most technology clusters can be traced to a few individuals in a region
who left their existing organisations in order to start their own companies to
commercialise technological advances that they had been exposed to in their
employment. Once seeded, the cluster becomes part of a self-reinforcing cycle. The
examples of the pioneering entrepreneurs prompt imitation, generating further spinoffs from the original ‘anchor’ organisation(s) and from the first generation new
companies, thereby fuelling the initial growth of the cluster. Since spin-offs generate
distinctive innovations from those of their parents they provide a source of innovative
diversity (Klepper, 2001). Meanwhile the entrepreneurial environment is enhanced as
successful entrepreneurs become mentors of new entrepreneurs, investors in new
businesses and engage in institution building (Wolfe, 2002), specialist support
1
infrastructure is established, suppliers and service providers emerge (Saxenian, 1994;
Kenney and von Burg, 1999) and local universities develop new teaching and research
programmes to meet the needs of companies for skilled labour. The effect of these
developments is to lower the barriers to entry compared with other locations (Porter,
2000). The process accelerates over time, so that within a couple of decades there is a
sizeable cluster of high tech companies.
The outcome of this process of entrepreneurial activity is illustrated by the
‘genealogical trees’ that have been constructed for several high-tech clusters to show
organisational origins of the founders of new businesses (Figure 1). Examples include
New England (Hekman and Strong, 1981), Austin/San Antonio (Smilor et al, 1988),
Cambridge (SQW, 1985; Garnsey and Heffernan, 2005; Myint et al, 2005), San Diego
(Innovation Associates Inc, 2000), Boulder (Neck et al, 2004), and the wireless
clusters in Calgary (Langford et al, 2002) and North Jutland (Dahl et al, 2005). These
genealogical trees show that in the vast majority of clusters a small number of key
organisations are the source of a disproportionate number of multiple entrepreneurs.
For example, Neck et al (2004) notes that the cluster of high tech firms in Boulder, Co
that has emerged since the mid-1960s can be traced back to just seven organisations –
four companies (including IBM and one of its early spin-offs), two government
scientific research institutes and a university (Figure 1). In San Diego, the main
sources of high tech firms have been University of California San Diego (UCSD)
(especially its Centre for Wireless Communication and School of Engineering),
medical and bioscience labs and Department of Defense contractors. In Cambridge
there have been three main sources of spin-offs: Cambridge University Engineering
Department, Acorn Computers and Cambridge Consultants (Garnsey and Heffernan,
2
2005). In Austin/San Antonio TRACOR, itself a spin-off from the University of
Texas, has been a key source of spin-off companies (Smilor et al, 1988), while much
of medical devices industry in Southern California can be traced back to spin-offs
from Edwards Laboratories (de Vet and Scott, 1992).
Figure 1. Boulder’s genealogical tree
Source: Neck et al (2004)
Meanwhile, the growth in the numbers of locally-formed companies creates
agglomeration economies which attracts companies from other regions and countries
in order to tap into local sources of knowledge and expertise. This process takes two
forms: the establishment of new operations and the acquisition of local technology
businesses. For example, Cambridge has attracted new inward investment by
multinational companies, including investment in ‘embedded laboratories’.1 It has
also experienced high rates of acquisition of its indigenous technology companies
(Garnsey and Cannon-Brookes, 1993; Garnsey and Heffernan, 2005).2 Similarly,
Ottawa began to attract R&D investments by large foreign technology companies
from the early 1990s (Ghent-Mallett, 2002) and many of its local technology
companies have been acquired, especially by US firms (Doyletech Corporation,
2004).
1
These are private sector R&D units located on university or hospital sites, sometimes even sharing
buildings with university laboratories. Microsoft provides one example of a company that has
established this type of facility in Cambridge (Longhi and Keeble, 2000).
2
Lindholm Dahlstrand (2000) also notes the prevalence of acquisitions of successful small technology
companies in Sweden. See Lindholm Dahlstrand (2000: 174-6) and Mason and Harrison (2006) for
discussions of the advantages and disadvantages of the acquisition of small technology companies for
regional economic development.
3
However, the role of entrepreneurship in seeding the growth of high tech clusters
remains both under-appreciated and poorly understood. Indeed, Wolfe and Gertler
(2004: 1076) suggest that the entrepreneurial process “is often one of the least well
documented, but most critical, elements of successful clusters.” Many of the
conditions that are commonly identified as vital in cluster development, such as
agglomeration economies and venture capital, actually lag rather than lead cluster
emergence, arising as a consequence of entrepreneurial activity rather than being
causal factors (Feldman, 2001; Wolfe, 2002). In the case of Ottawa, for example, the
spawning of new technology firms began in the 1960s, but until the late 1990s it had
just one institutional venture capital fund (Mason et al, 2002). So, what drives the
spin-off process? And why does it only occur in certain locations? Research on the
‘entrepreneurial event’ indicates that it is the outcome of individual-situational
outcomes (e.g. Shapero, 1984; Shapero and Sokol, 1982; Krueger, 1993; 2000). The
decision of an individual to start a business is made in a specific social and
environmental context which creates motivation and shapes perceptions of its
feasibility. Accordingly, this chapter focuses some key environmental factors which
the literature suggests are critical influences on the spin-off process.
2. INSTITUTIONAL ENVIRONMENT
Clusters do not emerge just anywhere. Rather, entrepreneurially-led high-tech clusters
emerge in areas that already have an established and highly regarded science base
which employ significant numbers of scientists and engineers. This can take the form
of universities and research institutes, government research establishments or
research-based companies. These organisations are the source of skilled personal who
are able to start new firms and without which there is no opportunity to initiate the
4
virtuous cycle of new firm formation, the rapid growth of some businesses, IPOs,
further spin-offs and yet more successes (Kenney and Patton, 2005).
Accounts of the emergence of high-tech clusters as varied as Route 128, Silicon
Valley, San Diego, Austin-San Antonio, Calgary, Cambridge (UK) and Jena
(Germany) all emphasise the critical significance of their respective universities,
research institutes and ‘anchor’ companies (Roberts and Wainer, 1968; Saxenian,
1994; Innovation Associates Inc, 2000; Smilor et al, 1988; Langford et al, 2002;
Garnsey and Lawton Smith; 1998; Hassink and Wood, 1998). For example, Smilor et
al (1988: 150) note that the Austin-San Antonio Corridor could not have begun to be
developed as a high-tech cluster if its major research universities - University of
Texas at Austin, University of Texas Health Science Centre and the University of San
Antonio – “were not in place and had not attained an acceptable level of overall
excellence” (p 150). The research strengths of Ottawa originate with public scientific
labs which were mostly established in the early post-war and corporate R&D labs,
notably Northern Electric (now Nortel) established from the 1960s (Ghent Mallett,
2002; 2005). The development of Calgary’s wireless cluster was underpinned by
NovAtel, a merger of two Alberta-government owned companies to grow a cell phone
industry, the University of Calgary and TR labs, a university-industry-research
consortium for telecoms research (Langford et al, 2002).
These research institutions perform several vital roles in seeding clusters. First, they
undertake cutting edge research which generate technological advances and scientific
discoveries that form the basis for the creation of entrepreneurial businesses. These
5
may take the form of the spin-out of independent companies by employees or, less
often, the creation of internal ventures by the research institutions themselves. 3
Second, their reputation for research provides the region with visibility and makes it
appealing to researchers in similar and complementary fields. This attracts talented
individuals in the form of eminent scholars, gifted students and ambitious scientists
and engineers, the effects of which are to further boost the technological capacity of
the region, expand the pool of individuals who might become future entrepreneurs and
key employees in new entrepreneurial ventures, and increase its attraction as a
location for ‘high tech’ firms based in other locations. Norton (2001) notes that the
lead entrepreneurs in the Silicon Valley businesses which pioneered the PC and
Internet revolutions were typically ‘provincials’ who were born and brought up in
other regions of the USA.4 Harrison et al (2004) note that the majority of technology
entrepreneurs in Ottawa are not local but were attracted by jobs in government
research laboratories and Bell Northern Research/Nortel or, less often, to study at
university (also see Ghent Mallett, 2002). A similar situation is evident in Cambridge
(SQW, 1985; Keeble, 1989), although in this case the university has been the main
‘talent magnet’. Saxenian (2000; 2001) has highlighted the increasing numbers of
Taiwanese and Indian scientists and engineers who starting companies in Silicon
Valley in the 1990s.
3
Licensing to established companies is another means of commercialising knowledge, especially by
universities. However, the licensees are less likely to be locally based than in the case of spin-offs and
corporate ventures. The reasons for the localised nature of the new firm formation process are
discussed later in this chapter.
4
Norton (2001: 140-141) notes that they ‘outsiders’ from a geographical perspective but ‘insiders’ in
the sense that they typically came from privileged ethnic and social backgrounds.
6
Third, the research base attracts research funding from government and the private
sector. Indeed, a common theme amongst technology cluster studies is that clusters
have typically been underpinned by substantial government expenditure. The basic
research that has supported business formation and growth in both the information
and communications technologies (ICT) and biotechnology industries has been
developed in universities, government research labs or private firms with government
funding. In addition, Government is often a major customer for technology firms. For
example, Silicon Valley’s technological base has been created by defense spending
throughout the post-war period which has helped to create the semi-conductor,
computer, software and internet industries (Brown and Duguid, 2000). The Military
has also been a major customer of its leading firms (including those better known for
consumer products) and Stanford University has been a major recipient of defence
contracts (Leslie, 2000). The origins of Tel Aviv’s ICT cluster also lie in military
spending on research and purchasing. Entrepreneurial activity has been based on the
human capital that was released in the late 1980s following the (temporary) easing of
the geo-political situation in the Middle East (Roper and Grimes, 2005). And in some
extreme cases, such as Sophia-Antipolis in France, government is almost entirely
responsible for the emergence of the cluster, locating state R&D facilities, using tax
incentives to attract multinational R&D units and developing the physical
infrastructure (Longhi, 1999).
Finally, research organisations are potential customers for new firms. This not only
provides such businesses with revenue but also with important endorsement which
helps to overcome the ‘liability of newness’.
7
Research-oriented universities can also make a number of distinctive contributions to
the emergence of technology clusters in addition to those discussed above. First, they
often attract major technology firms wishing to enhance their R&D efforts through
closer engagement with university researchers. This also contributes to the attraction
of highly skilled labour. Second, through their teaching and other educational
programmes universities provide a source of skilled scientists, engineers and other
graduates for organisations in the cluster and enables existing employees to upgrade
their skills and knowledge. Third, universities may play a key role in the
establishment of what Keeble (2000) terms ‘regional collective initiatives’. Typically
these take the form of business support organisations designed to overcome
constraints on business growth, facilitate collaborative activity or promote and market
the region. One of best documented examples is CONNECT Program in San Diego
which was established by UCSD in 1985 to foster university-industry co-operation
and promote the growth of technology businesses (Innovation Associates Inc, 2000).
However, universities tend to be most active in such initiatives once the cluster has
achieved some level of maturity and the spin-off process has developed momentum. It
should also be noted that a university is not a necessary (or sufficient) condition for
the emergence of a successful technology cluster (Feldman, 1994; 2001).
3. TECHNOLOGY AND INDUSTRY CONDITIONS
By no means all R&D activities lead to the emergence of entrepreneurially led
clusters. Three points are important here. First, technological advances which create
‘technological discontinuities’ produce the most new opportunities (Kenney and von
Burg, 1999). Disruptive technologies overturn the established order. Whereas
established firms can react to sustaining innovations through their own R&D or
8
acquiring technology from external sources (e.g. by licensing or acquisition), their
accumulated investment in the established technology deters them from committing to
the new and superior technology. However, new firms do not have this handicap
(Christensen, 1997). For example, the origins of Silicon Valley are linked to the
replacement of the thermionic valve by the transistor. The first firms to seize the
opportunity were new businesses, rather than the incumbents who had dominated the
old technology (Owen, 2001). Indeed, only two of the 10 thermionics firms in 1953
made the switch to transistors and survived as producers of transistors and later
integrated circuits. Four of these firms failed to pursue the technology at all, and three
bet on the wrong technology (Norton, 2001). One of the key factors why Ottawa has
generated a cluster of firms in telecoms and related industries can be attributed to the
discontinuities associated with the switch from electro-mechanical to digital and
latterly to optical telecommunication systems (Chamberlin and de la Mothe, 2003).
Silicon Valley has gone on to ride subsequent disruptive technologies caused by the
personal computer and the Internet, as well as developing a biotechnology cluster
(Henton, 2000).
Second, the technological trajectory is important because it conditions the possibilities
for how the technology might be exploited. Kenney and von Burg (1999) contrast
semi-conductors – which were an enabling technology for nearly every important
electronic innovation – and mini computers, which were a product segment in the
computer industry. The semi-conductor found a much greater variety of applications
than the mini computer, making many other products possible, whereas the market
for mini computers eventually stagnated and declined in the face of competition from
workstations. The general point is that entrepreneurial opportunities are much greater
9
for components which open up new economic spaces because of their wide range of
applications.
Third, the technology has to create market opportunities if entrepreneurs are to start
businesses. Thus, the timing of cluster emergence depends on the emergence of
markets for new technologies. This may be a function of the development of the
technology, or of regulatory change or government decisions and strategies. For
example, in the case of Cambridge, the commercial take-off of the CAD and
microcomputer areas in the late 1970s provided the market opportunities for the initial
wave of spin-off companies (SQW, 1985). Similarly, new entrepreneurial
opportunities are being created by the drive to enable the Internet to become pervasive
and easy to use. This requires whole new classes of telecoms equipment to be
developed for routing and switching data signals over a network designed for voice
traffic (Banatao and Fong, 2000).
Industry conditions also influence the scope of spin-offs. Entrepreneurial activity will
flourish where there is open technology standards and full technical compatibility
between every component market because this will allow innovation to occur
independently across the system (Norton, 2001). A specific example of this process
was the displacement of the mainframe computer by the PC which, in turn, resulted in
the shift in the computer industry from proprietary to open standards (notably
Microsoft’s DOS operating system and Intel microprocessor: the so-called Wintel
standard). This directly led to the disintegration of the old vertically-integrated
industry organised on a firm basis and the creation of a new horizontal one organised
by industry segment (Figure 2). This has enabled software firms to develop products
10
for fundamentally similar computers based on powerful general purpose programs in
a variety of business contexts. This new horizontally organised computer industry in
which innovation was driven by users, resulted in an explosion of start-ups in Silicon
Valley and elsewhere from the 1980s onwards (Rowen, 2000).
Figure 2. The Transformation of the Computer Industry
Source: Grove 1996.
In general, spin-offs are more conducive in the emergent phase of industries where no
single product design is dominant. Because new products are continually being
created there is no scope for incumbent firms to benefit from scale or learning
economies. Spin-offs are also more likely where there are market niches permitting
narrow product lines and which may not be widely known or well understood outside
of the industry. They are also more likely when the customer makes the purchasing
decision on product attributes rather than price (Garvin, 1983).
Finally, technology and market conditions influence the potential growth of the
cluster. In the case of the ICT sector, the commanding technological lead of firms in
Silicon Valley and the existence of de facto standard setting, as in much of
computing, has created strong barriers to entry, a process which Kenney and Patton
(2005: 224) describe as “winner take all regional dynamics”. For example,
Cambridge’s growth has been limited because its product space is similar to that of
Silicon Valley (Bresnahan et al, 2001). Because of the dominance of US companies in
the vertical markets, ICT clusters in other countries are only able to grow by
specialising in sector niches and value chain segments that are complementary to
11
existing ICT technologies (which are mainly sold by US-based multinationals) and
based on their ties into the US market (e.g. language, cultural connections, diaspora).
However, the emergence of fast-growing wireless hardware clusters in the Nordic
regions, centred around Nokia and Ericsson, has been based on an unoccupied vertical
market which was created by the establishment of the GSM European standard in
mobile telephony (Bresnahan et al, 2001). It may be easier for biotechnology clusters
to emerge because of the absence of a single, dominant global-class biotechnology
cluster (Kenney and Patton, 2005). This may reflect the greater variety of sub-sectors
that comprise the biotechnology sector. Alternatively, it may be because the source of
entrepreneurs is not as concentrated in existing firms and exhibits greater dependence
on universities (Kenney and Patton, 2005).
4. INCUBATOR ORGANISATIONS
The origins of a new firm are when an individual realises the potential for a
technology. This “requires a sophisticated understanding of consumer needs, existing
markets for product innovation and factors inputs, and prevailing production
technology (Feldman et al, 2005: 131). Such individuals typically derive this
knowledge from previous employment. The literature emphasises the importance of
the incubator organisation – the organisation that an entrepreneur worked for
immediately prior to starting his/her own business, although as Harrison et al (2004)
emphasise, most entrepreneurs have had several jobs, either in different organisations
or within the same organisation, before starting their own business. Indeed, it is often
through this process of job mobility that scientist-managers and engineer-managers,
who are particularly important in the spin-off process, gain their management
experience (Harrison et al, 2004). The incubator organisation is where entrepreneurs
12
acquire technical skills and product and market knowledge and gain access to
information about appropriate organisational structures, strategies and systems. It is
also where in the course of their work experience they identify market opportunities
and notice ways of exploiting them. Because the knowledge needed to start a new
firm is tacit and therefore difficult to transfer, it will only be possessed by those with
direct technical experience. Similarly, the technological possibilities (and
applications) and market opportunities will be most visible to those whose work is
intimately related to the technology (Kenney and von Burg, 1999; Stuart and
Sorenson, 2003). However, according to Klepper (2001) founders appear not to
exploit their knowledge about the specific technology of their incubator organisation.
Rather, they draw more narrowly on their work experience in the incubator as
embedded in its organisational ‘routines’. It is this knowledge which gives the spinoff company a key source of competitive advantage. The incubator organisation is
also where entrepreneurs establish reputations and professional contacts with future
partners, suppliers, customers and other key stakeholders.
The incubators also typically provide the stimulus to start. Entrepreneurs make the
decision to start a business for either positive or negative reasons. Negative reasons
tend to dominate. The most common reasons for starting a business are because of
frustrations when their ideas and not endorsed by senior management, conflicts with
their boss and redundancy.5 Saxenian’s (1994: 113) comment that “Silicon Valley
5
The most famous spin-off of all, the eight individuals who left Shockley Semiconductor to found
Fairchild Semiconductor in 1957 did so in response to Shockley’s eccentric and authoritarian
management style. Shockley was co-inventor of the transistor in 1947 in Bell Labs, for which he
subsequently won the Nobel Prize. Fairchild itself became the incubator for multiple Silicon Valley
spin-offs, including Intel in 1968, in reaction its hierarchical structure (dress codes, reserved parking
spaces, closed offices, executive dining rooms and, most importantly, restriction of stock options to
management). Gordon Moore and Andrew Grove, founders of Intel, wanted stock options to be part of
the compensation for all employees – from janitor to bosses (see Castilla et al, 2000; Lécuyer, 2000;
Norton, 2001).
13
entrepreneurs … were typically engineers who were frustrated by unsuccessful
attempts to pursue new ideas within the region’s established companies” has much
wider generality. Established companies may prevent their employees from pursuing
their own discoveries because they are in financial difficulties, have organisational
difficulties, have too many opportunities to be able to pursue them all, or because they
threaten their existing competence. Successful entrepreneurs also play a crucial role
by providing role models which encourage imitation. This is illustrated by Dorfman’s
(1983: 308) quote from an entrepreneur in the Route 128 region in the Boston area.
Speaking of former colleagues who left to start their own firm he observed that “these
guys were just like you and me. There was nothing unique or special about them. So I
figured if they can do it, why can’t I?”
However, organisations vary quite significantly in terms of their effectiveness as
incubators. Highly innovative firms and firms with a rich and broad knowledge base
spawn the most spin-offs (Klepper, 2001). These firms - on the cutting edge of
technology will create too many commercial possibilities for one company to take
advantage, prompting teams of engineers with rejected projects to resign and start
their own firms. Rapidly growing firms operating at the frontiers of knowledge, active
in the early phases of the industry and encountering rapid shifts in market acceptance
of competing designs and technologies are also likely to be effective incubators.
These firms often have an entrepreneurial culture, led by strong entrepreneurial
characters and with decentralised decision-making and creative management. As an
example, accounts of the Cambridge technology cluster highlights the importance of
Acorn Computer as a source of spin-outs which Garnsey and Heffernan (2005: 1135)
describe as “a learning organisation for the whole area, providing expertise to many
14
local entrepreneurs and managers. The range and depth of competence developed at
Acorn made it possible for former members to start large numbers of local spin-offs”
(see Figure 3). Mature firms undergoing change as a result of the need to adopt new
technologies to stay competitive can also be effective incubators. Although the R&D
efforts may be successful in producing technological breakthroughs such companies
may be slow or unable to adopt them, prompting frustrated scientists and engineers to
leave in order to exploit them on their own account.
Figure 3. New firms started by founders and employees of Acorn Computers
(Cambridge, UK)
Source: Garnsey and Heffernen (2005)
Effective incubators also need to provide their employees with exposure to best
practice technology and intimate knowledge of markets in order to uncover business
opportunities based on novel applications. However, access to such information is
likely to be limited to people working at corporate level (Miller and Côté, 1987).
Thus, organisations with a truncated range of management jobs – typically a
characteristic of branch plants – are unlikely to be effective incubators. Branch plants
are poor incubators on account of being production-dominated and with limited
exposure to the market place, having little or no R&D capacity, operating on an
assembly line basis, and with limited local purchases capability. These factors explain
why branch plants of electronics companies that were attracted to the declining
industrial regions of advanced economies throughout much of the post-war period
under regional policy in an attempt to offset the decline of traditional sectors have
failed to stimulate indigenous development in these regions, and may actually have
15
depressed new firm formation rates. This is well illustrated in the case of Scotland’s
‘Silicon Glen’ (Clarke and Beaney, 1993; McCalman, 1992; Turok, 1993a; 1993b).
However, Glasmeier (1988) notes that technical branch plants (stand alone profit
centres with product-related R&D and employing both technical and non-technical
workers) may also be poor incubators, depending upon the nature of the products and
production process and extent of local supply linkages.
Government research laboratories are also very ineffective incubators. Government
research laboratories lack exposure to markets and their research often does not have
any obvious immediate market (Miller and Côté, 1987; Lawton Smith, 1996).
Although there are some prominent exceptions, most universities are also poor
incubators for the same reason, namely that the research is not dictated by market
needs6 (Malecki, 1997). However, universities and research institutes are more
important as incubators in the biotechnology sector (Mitton, 1986; Haug, 1995;
Leibovitz, 2004), at least as a source first generation spin-offs, some of which then act
as incubators for further spin-offs (Haug, 1995; Niosi and Banik, 2005). Miller and
Côté (1987) emphasise that technical and scientific knowledge is insufficient:
research needs to be performed in market-driven settings so that scientific activities
are related to market needs and would-be entrepreneurs are exposed to hands on
experience of state of the art technology. They suggest that the most effective
research-oriented incubators are those which are engaged in market driven research,
such as the advanced laboratories of technology-based firms and contract research
laboratories. This is supported by a range of evidence: the importance of technology
6
The importance of universities is much greater if the subsequent spin-offs from university spin-offs,
businesses started by former students and employees who have had other jobs since leaving the
university and firms set up by university employees but not based on university technology are
included.
16
consultants as major sources of spin-offs in Cambridge (SQW, 1985; Garnsey and
Heffernan, 2005); the dominance of development-oriented rather than researchoriented work experience amongst Route 128 technology entrepreneurs (Roberts and
Wainer, 1968); and evidence from the genealogy studies that research institutes (i.e.
‘boundary spanning’ organisations to promote university-industry interaction and
technology transfer) generate more spin-offs than university academic departments.
Clustering occurs because of the overwhelming tendency for spin-off companies to be
located in close proximity to the incubator, reflecting the general tendency for
entrepreneurship to be a local event (Cooper and Folte, 2000). There are four principal
reasons for this. First, entrepreneurs need to utilise their social networks of business
associates and fellow employees to access the industry-specific tacit knowledge,
human capital and specific resources (e.g. finance) required to start and grow their
business. People almost always have more, more diverse and stronger ties to contacts
in the region in which they reside. This suggests that the social networks used for
resource mobilisation are geographically localised (Stuart and Sorenson, 2003). The
consequence is that “these networks … bind entrepreneurs to the locations in which
they reside because only there do they have the access to the resources and social
support requires to sustain their entrepreneurial ventures” Sorenson (2003: 524).
Second, new technology firms typically begin on a part-time basis while the founder
is still employed (i.e. as a ‘garage’ start-up), delaying full-time commitment until the
venture seems sufficiently promising. The links that are built up at this stage with
customers, suppliers, advisors, employees, and so on combine to embed the business,
thereby limiting its locational flexibility when the transfer to full-time operations
occurs. Third, family ties encourage locational inertia. The spouse can remain in
17
employment so income continues to flows to the family and the aspects of the
entrepreneur’s life remain the same so that their full energies can be devoted to the
start-up (Cooper and Folte, 2000). Finally, locational preferences (which may have
discouraged relocation in response to corporate downsizing or other forms of
turbulence) often play a role. Indeed, it is probably no coincidence that many high
tech clusters (e.g. Ottawa, Calgary, Cambridge) have emerged in locations with high
residential amenity. For example, in accounting for the growth of the Austin-San
Antonio Corridor, Smilor et al (1988) identify the role of Tracor as a major source of
spin-out companies. They go on to comment that “one of the main reasons that Tracor
located and grew in Austin, and one of the reasons that Tracor spin-outs were able to
and wanted to locate in Austin was the affordable quality of life.” Accounts of the
Cambridge cluster in England similarly identify its residential attractiveness as a
reason why entrepreneurs started their businesses locally (Keeble, 1989). However, it
is important to recall that whereas the spin-off process is local, the origin of most
entrepreneurs is not; rather most have moved into the area at some time in their
career, either to attend university or to work in one of the region’s existing
organisations.
5. EXOGENEOUS FACTORS
Many accounts of clusters highlight the importance of chance events which either
kick start or fuel the spin-off process (e.g. Pounder and St John, 1996; Wolfe, 2002).
Bresnahan et al (2001) highlight the role of luck in the emergence of clusters.
Entrepreneurs located in nascent clusters have to gamble on new technology
trajectories before their potential becomes apparent, and, pursue specific business
ideas before it is clear that they represent genuine opportunities. Some degree of risk
18
is therefore inevitable since only some opportunities will materialize. Thus, “many
attempts at creating new clusters and successful new firms in certain industrial and
technological trajectories will fail, and they will fail in spite of the fact that the key
actors have done all the right things that are to be done in these contexts. … It appears
that luck and skill are complements: those initiatives that embody a superior business
model or technology are more likely to find the ‘luck’ they need” (Bresnahan et al,
2001: 845).
Neck et al (2004) emphasise the importance of ‘critical moments’ which “can have
dramatic effects on the incidences of spawning” (p 192). These critical moments can
refer to the evolution of technologies or companies. We have already noted that
commercialisation opportunities for new technologies arise at particular points in
time. There are also critical moments in the history of companies which result in
companies leaving to start new ventures. Spin-offs are often associated with
companies that are undergoing some form of crisis or turbulence: examples include
the shift of the organisation in a new direction, the appointment of an outsider as the
CEO, and significant changes in corporate governance, for example, following an IPO
or acquisition (Romanelli and Schoonhavem, 2001).
The contraction or closure of particular technology companies may also provide a
stimulus for spin-off activity, especially in locations offering an attractive quality of
life which discourages the affected individuals from moving to seek employment
elsewhere. Spin-off activity in Ottawa was given a boost in the 1970s with the failure
of Microsystem International, a two year old subsidiary of Northern Telecom which
had attracted many highly skilled engineers and scientists to Ottawa. Its bankruptcy
19
released a number of engineers and scientists who went on to launch a number of
start-up businesses (Ghent Mallett, 2005; Corona et al, 2006). The wireless cluster in
Calgary was boosted when NovAtel hit troubled times in the early 1990s, with
employees preferring to start their own businesses or find employment with local
firms (and often launch their own businesses later) rather than seek employment
elsewhere because of the highly desirable lifestyle offered by Calgary, notably
outdoor recreation possibilities in the nearby Rocky Mountains and the community
spirit crated by the successful Winter Olympics in 1988 (Corona et al, 2006). De Vet
and Scott (1992) identify the contraction of the aerospace industry in southern
California following Department of Defense cuts as an important factor in the
emergence of a medical devices cluster by prompting key personnel in aerospace
firms to apply their expertise – typically in advanced electronics – in other sectors.
Feldman (2001) places considerable emphasis on exogenous factors to account for the
emergence of a technology cluster in the Washington DC region during the 1970s and
1980s . First, the downsizing of the federal government led to the deterioration of
employment conditions and future prospects for its employees. Hence, individuals in
the prime of their careers – particularly those with strong personal ties to the region –
“found entrepreneurship a viable career option” (Feldman, 2001: 873-4). The effect of
the deterioration in conditions of employment in the government sector was to lower
the threshold for risk-taking. Second, increased outsourcing by government provided
opportunities to provide goods and services back into Federal Government. In
particular, the growth of the ‘Star Wars’ initiative created a demand for technical and
software attributes of armaments systems, such as electronics, design and systems
management. Third, a change in the IP regime involving the removal of commercial
20
restrictions on the use of the internet (which had been developed by the Department of
Defense) also opened up opportunities for commercial applications of the technology.
Finally, Federal Government-led changes relating to the transfer of federally-funded
technology and financial support for new technology businesses were also conducive
to technology entrepreneurship. According to Feldman (2001), entrepreneurship in the
region was a response to these exogenous factors.
6. CLUSTER DYNAMICS
Once the spin-off process gathers momentum it sets in motion a virtuous, selfreinforcing process which leads to the creation of an ecosystem that nurtures and
supports further entrepreneurial activity. One of the consequences is that founding a
firm at an early stage in a cluster’s development is very different to founding a firm
when the cluster is established (Bresnahan et al, 2001).
First, successful businesses provide role models and create legitimacy for further
entrepreneurial activity. As Jurvetson (2000: 125-6) observes in the context of Silicon
Valley, “for those who live in the region, there are many within a couple of degrees of
separation who say ‘Hey, I can do that too! I could be a Marc Andreessen [founder of
Netscape] or a Jerry Yang [co-founder of Yahoo!]. The process of entrepreneurship
seems less mysterious and daunting to them than to those outside the region, to whom
it can seem very magical and mystifying. Distance can be distancing.” He makes the
further observation of the effect of successful entrepreneurs: “[they] catalyse fencesitters. If you have been thinking about starting a company, or thinking about joining
one, there’s nothing like a local success story to give you that extra push” (Jurvetson,
2000: 126).
21
Second, spin-offs create the critical mass which stimulates the emergence of an
entrepreneurial support network (Kenney and Patton, 2005) that sustains and
nourishes the creation and growth of entrepreneurial businesses. This comprises three
types of service:

Specialist business services: notably, law firms with deep expertise in handling
IP, marketing firms, executive search firms, accountancy practices that are
familiar with the unique needs of technology start-ups, technology marketing
and PR firms, management consultants, and technology assessment
consultants

Technical services: precision machining, prototyping, precision moulding,
testing, etc

Finance providers: venture capital firms, investment banks specialising in
IPOs
These support services facilitate the process of business start-up and growth by
enabling new firms to focus on their area of expertise while buying-in specialist
service and support (Saxenian, 1994).
Third, high level expertise and competencies are diffused within the region when
individuals carrying technical and management know how and ‘embodied expertise’
move to new organisations as founders or key employees, taking ideas that they have
acquired in other local organisations, creating a process of regional collective learning
(Keeble, 2000).
22
Fourth, Bahrami and Evans (1995) highlight a process of entrepreneurial recycling
that has occurred in Silicon Valley, but is also observable elsewhere (Mason and
Harrison, 2006), in which entrepreneurs and other members of successful start-ups
recycle and re-invest their capabilities and capital gains by becoming serial
entrepreneurs, investors in new companies, mentors to new entrepreneurs and
institution builders (Feldman, 2001). Key individuals – such as Terry Matthews in
Ottawa (Ghent Mallett, 2005) and Hermann Hauser in Cambridge (Garnsey and
Hefferman, 2005; Myint et al, 2005) - can be identified in many clusters who are
involved in multiple businesses as serial entrepreneur, investor or board member.
Managers and engineers who held equity in their businesses and are now
independently wealthy, are able to join start-ups. Universities may also benefit from
the philanthropy of successful entrepreneurs, providing a boost to the quality of their
research and ability to attract top class academics. For example, 20 of Stanford
University’s 41 engineering chairs have been endowed by Silicon Valley-based high
tech companies, entrepreneurs and venture capitalists (Huffman and Quigley, 2002).
As the spin-off process gathers momentum so institutions emerge – often through the
collective action of the technology community - to nurture and encourage the
formation of new firms and to solve problems which individual firms cannot solve
individually, skilled labour is attracted to the region, and local institutions to develop
specialist training courses (Wolfe and Gertler, 2004). These are of two main types
(Corona et al, 2006): (i) technology incubation mechanisms, such as incubators,
innovation centres and science parks, to provide physical space and intangible support
to new technology based firms; these can be public or private sector and often
designated as not-for-profit; and (ii) partnership organisations, usually comprising
23
government, universities and the private sector, to promote networking and
collaboration between members and which can ‘champion’ the region both internally,.
The important point to note is that the supportive conditions for entrepreneurship
spontaneously follow the process in which entrepreneurship takes hold in a cluster.
This is particularly the case with the availability venture capital – which is widely
seen as a necessary attribute for technology clusters (Malecki, 1997; Norton, 2001).
But as several authors have noted, venture capital lags rather than leads the emergence
of entrepreneurial activity: it is not part of the initial environmental conditions
(Saxenian, 1994; Feldman, 2001; Mason et al, 2002; Garnsey and Heffernan, 2005).
The Ottawa example suggests that the investors in the initial waves of new technology
businesses are often private individuals and families who had made their money from
earlier technologies, or from the service economy, and from ‘old economy’ companies
(Mason et al, 2002; Doyletech Corporation, 2005). However, venture capital is needed
for the sustained growth and development of a cluster (Llobrera et al, 2000): without
venture capital the cluster is likely to stagnate or decline (Feldman et al, 2005).
Clusters will evolve and change over time. Two mechanisms can be identified. First,
and most common, the specialist local competences will mutate as technology sectors
mature and others emerge. This is well illustrated by the wireless communications
cluster in North Jutland, Denmark. The original firms in the cluster specialised in
maritime radio communications. However, as the mobile phone industry evolved, the
spin-offs used their competences and experience from maritime telecommunications
to diversify into the new industry. The establishment a common European standard
24
for mobile telephony (GSM) was the stimulus for a further wave of spin-offs based on
this second generation mobile phone technology (Dahl et al, 2005). Cambridge has
seen the relative decline of computer-aided design and emergence of Geographical
Information Systems (Garnsey and Heffernan, 2005). Second, and probably less
common, clusters may evolve as a consequence of the efforts of the entrepreneurial
support network to promote the development of new activities. For example, in the
case of Minneapolis, Llobrera et al (2000) highlight how the entrepreneurial support
mechanisms that grew out of its mainframe computer industry – notably venture
capital firms and various public-private partnerships – enabled it to develop a medical
instruments cluster when the computer industry went into terminal decline in the
1980s. “With venture capitalists looking away from the declining computer
mainframe industry to other high technology sectors, the medical technology industry
was in a position to become the focal point of the region’s high technology sector.
Medical technology firms, therefore, had ready access to the seed and start-up capital
pool that had been supporting the computer mainframe industry” (Llobrera et al,
2000: 87).
7. CONCLUSION
This chapter has argued that entrepreneurial activity drives the emergence and growth
of high tech clusters. It therefore follows that an understanding of entrepreneurial
activity must be central to any attempt to understand cluster development. Although
the emergence and growth of technology clusters is path dependent and idiosyncratic
the material that has been reviewed suggests that there is sufficient commonality to
propose a four distinct phase model (following Feldman et al, 2005 and Crone, 2005).
First, the seeds of the future cluster are put in place. This will typically involve
25
investment in universities and other types of research institutions. It may also involve
the establishment of anchor firms, typically the research-intensive functions of large
companies. These organisations give the region visibility and attract talented
individuals. At this stage the region is prominent in terms of its research activity but is
largely unindustrialised. Several decades may pass before there is significant
entrepreneurial activity. A proto-cluster emerges when exogenous events prompt a
few pioneering individuals to leave established organisations in the region to start
their own businesses. The emergent phase is characterised by increased
entrepreneurial activity as new entrepreneurs spin-out of the anchor organisations and
from the pioneering spin-outs in a narrow range of technologies. The beginnings of a
supportive habitat emerge as capital sources emerge, entrepreneurial support services
are established to help these companies, networks are established and strengthened
and public and private sector initiatives lead to the creation of support infrastructure.
Entrepreneurs and associated actors forge a collective sense of community. Some of
the early entrepreneurs have been able to cash-out by selling their business or floating
it on the stock market (termed an IPO or Initial Public Offering) and are now involved
in various recycling processes – such as serial entrepreneurs, investors, mentors and
institution builders. By the end of this stage the cluster has become self-sustaining.
The cluster can be said to be a fully functioning entrepreneurial environment when
spin-offs are occurring across a range of related technologies, local sources of venture
capital are established, the support habitat offers a wide range of customers, suppliers
and specialised service organisations, region-wide support networks are established,
and universities and colleges recognise the need to offer programmes that satisfy the
demand for trained personnel. A few of the earlier waves of spin-offs will now be
large, publicly-listed companies and multinational companies will have a significant
26
presence in the cluster through new investment and acquisition. Established clusters
will therefore be characterised by considerable diversity, in terms of technology,
function firm size and ownership. Government becomes actively involved in
supporting the cluster, notably financing initiatives, grants and infrastructure
development.
Three observations can be made in closing. First, the literature upon which this
account is based is limited in terms of its geographical coverage. It draws heavily
upon various accounts of Silicon Valley while the range of clusters that are used for
support and illustrative purposes are largely those which have emerged in previously
un-industrialised regions, such as Silicon Valley (rural), Cambridge (market and
university town), Ottawa and Washington DC (capital cities). It is not clear the extent
to which the same themes and processes are relevant in the case of ‘new economy’
clusters, such as new media (Diebold Institute, 2000; Indergaard, 2004) and music
(Power and Jansson, 2004; Braunerhjelm, 2005). In contrast to ICT and biotechnology
clusters which started as greenfield developments, new economy clusters have
emerged in densely development central city districts “layered by the rise and fall of
past industries” (Indergaard, 2004: 16). A further difference is that the key talent in
new economy clusters are ‘creatives’ rather than technologists and the outputs are
cultural rather than material products which are based on the application of new ICT
technologies to existing sectors to create and distribute new products and services.
Nevertheless, a recent account of Stockholm’s music cluster echoes several themes
identified here. These include the following: the accumulation of knowledge which
can be traced back several decades; the importance of educational institutions (music
schools); an igniting spark; the importance of in-migrants; knowledge spillovers and
27
network connectivity; vertical disintegration and the exploitation of niches; and the
emergence of a critical mass of activity which generated agglomeration economies
which attracted key international businesses (e.g. record companies) (Braunerhjelm,
2005).
Second, this typology is not deterministic. A cluster can ossify or goes into decline at
any stage if the spin-off process stops. This, could occur in response to changes in
market conditions, technological shifts and rigidities in prevailing business practices.
Indergaard (2004) describes the brief but spectacular growth of a new media cluster
(termed ‘Silicon Alley’) in downtown New York (which was estimated by the
Diebold Institute, 2002, to have seen the creation of 4,000 firms between 1994 and
1999, many of whom raised billions of dollars in venture capital) and its subsequent
decline from 2000 following the dot-com fallout. Some attention has also been given
to old industrial regions – for example those which specialised in iron, steel and metal
work, automobiles or heavy engineering and have been in decline for several decades.
Tödling and Trippl (2004) conclude that the revitalization of older industrial clusters
may be possible if a well developed regional innovation system is present, but that it
ultimately depends on market conditions, the degree of ‘lock in’ to old technology
paths and ‘institutional sclerosis, and its size and diversity. However, according to
Kenney and von Burg (1999) the ability of a cluster to bounce back from adverse
economic conditions, to change its technological focus or to develop multiple
technological foci is critically dependent on entrepreneurial support networks (which
they term ‘Economy 2’) – those institutions (in particular, venture capital firms)
which do not simply nurture new firm formation but actually help to ‘produce’ new
firms through their alertness to new opportunities created by technological advances.
28
Third, government plays a critical indirect role through its financing of the research
base in clusters. However, its ability to play a significant role in facilitating the
processes involved in cluster emergence and growth is limited, at least until the
cluster is approaching maturity. For example, technology incubation mechanisms
(science parks, incubators, etc) have had mixed success in Canada (Corona et al,
2006). Indeed, the implication from our analysis is that the scope for government
intervention is limited to three areas: (i) creating the enabling conditions at the macro
scale (e.g. markets, innovation programmes), (ii) putting the initial institutional
conditions in a region in place through the location of public sector research facilities,
providing research grants to the local university, and (iii) “relentlessly encourag[ing]
co-operation between the regional actors” (Corona et al, 2006: 214) – industry,
government, universities, and financial services.
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