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Digital Affordances, Spatial Affordances, and The Genesis of Entrepreneurial Ecosystems


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Entrepreneurial ecosystems command increasing attention from policy-makers, academics, and practitioners, yet the phenomenon itself remains under-theorized. Specifically, the conceptual similarities and differences of entrepreneurial ecosystems relative to, e.g., clusters, ‘knowledge clusters’, regional systems of innovation, and ‘innovative milieus’ remain unclear. Drawing on research on industrial districts and agglomerations, clusters, and systems of innovation, we suggest that entrepreneurial ecosystems differ from traditional clusters by their emphasis on the exploitation of digital affordances; by their organization around entrepreneurial opportunity discovery and pursuit; by their emphasis on business model innovation; by voluntary horizontal knowledge spillovers; and by cluster-external locus of entrepreneurial opportunities. We highlight how these distinctive characteristics set entrepreneurial ecosystems apart from other cluster types, propose a structural model of entrepreneurial ecosystems, summarize the papers in this special issue, and suggest promising avenues for future research.
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Erkko Autio*
Imperial College Business School, London, United Kingdom
and Tilburg University School of Economics and Management
Satish Nambisan
Weatherhead School of Management, Case Western Reserve University
Llewellyn D W Thomas
Imperial College Business School, London, United Kingdom
Mike Wright
Center for Management Buyout Research,
Imperial College Business School, London, United Kingdom and ETH, Zurich
Forthcoming in the Strategic Entrepreneurship Journal.
14 July 2017
*corresponding author
Entrepreneurial ecosystems command increasing attention from policy-makers, academics,
and practitioners, yet the phenomenon itself remains under-theorized. Specifically, the
conceptual similarities and differences of entrepreneurial ecosystems relative to, e.g.,
clusters, ‘knowledge clusters’, regional systems of innovation, and ‘innovative milieus’
remain unclear. Drawing on research on industrial districts and agglomerations, clusters, and
systems of innovation, we suggest that entrepreneurial ecosystems differ from traditional
clusters by their emphasis on the exploitation of digital affordances; by their organization
around entrepreneurial opportunity discovery and pursuit; by their emphasis on business
model innovation; by voluntary horizontal knowledge spillovers; and by cluster-external
locus of entrepreneurial opportunities. We highlight how these distinctive characteristics set
entrepreneurial ecosystems apart from other cluster types, propose a structural model of
entrepreneurial ecosystems, summarize the papers in this special issue, and suggest promising
avenues for future research.
Entrepreneurial ecosystems command increasing attention from policy-makers, academics,
and practitioners. We suggest that entrepreneurial ecosystems differ from traditional clusters
by their emphasis on the exploitation of digital affordances; by their organization around
entrepreneurial opportunity discovery and pursuit; by their emphasis on business model
innovation; by voluntary horizontal knowledge spillovers; and by cluster-external locus of
entrepreneurial opportunities. We highlight how these distinctive characteristics set
entrepreneurial ecosystems apart from regional cluster phenomena discussed in received
economic geography and innovation literatures. We suggest policymakers need to adopt
novel approaches to stimulate entrepreneurial ecosystems that differ from those in place to
develop industrial clusters or support already established small- and medium-sized
Keywords: entrepreneurial ecosystem; digital affordance; spatial affordance; business model
innovation; architectural knowledge; startup; scale-up; cluster
The idea that regional entrepreneurial landscapes can be usefully viewed as complex,
evolving ecosystems has rapidly gained traction in the entrepreneurship practitioner and
policy literatures (Acs, Autio and Szerb, 2014; Auerswald, 2014; Drexler et al., 2014; Feld,
2012; Isenberg, 2010, 2016; Spigel, 2017). This phenomenon coincides with the rapid global
diffusion of new entrepreneurial practices and related organizational innovations such as the
‘Lean Entrepreneurship’ movement (Blank, 2013; Reis, 2011), new venture accelerators
(Miller and Bound, 2011; Pauwels, Clarysse, Wright and Van Hove, 2016), and the
proliferation of venturing festivals such as the annual ‘Slush’ event in Finland and the
StartmeupHK festival in Hong Kong (Hixon, 2015; StartmeupHK, 2017). However, while the
‘entrepreneurial ecosystems’ phenomenon commands considerable policy and practitioner
attention, the literature in this area remains largely practitioner-centric and theoretical
treatments of the phenomenon few. We explore the theoretical and conceptual underpinnings
of the entrepreneurial ecosystem phenomenon and propose directions for further research.
As an object of study, the phenomenon (and concept) of entrepreneurial ecosystems
resembles concepts previously explored by economic geographers and innovation researchers
– such as ‘clusters’, ‘knowledge clusters’, ‘industrial districts’, ‘innovative milieus’, and
regional and national systems of innovation (Arıkan and Schilling, 2011; Crevoisier, 2004;
Delgado, Porter and Stern, 2010; Doloreux, 2002; Marshall, 1890; Piore and Sabel, 1984;
Pyke, Becattini and Sengenberger, 1990; Tallman, Jenkins, Henry and Pinch, 2004). We
therefore ask: Does the concept and phenomenon of entrepreneurial ecosystems differ
meaningfully from what came before, and if so, how? Exploring the theoretical contours of
the entrepreneurial ecosystem concept is important to establish both the concept’s theoretical
distinctiveness and to provide guidance for researchers and policy-makers alike regarding
relevant research questions and policy approaches. Therefore, we systematically compare the
entrepreneurial ecosystem concept against theoretical constructs evoked in the economic
geography, innovation, and management literatures.
Broadly characterizing, the economic geography tradition has sought to understand
economic (and sometimes also social and institutional) rationales that might explain regional
agglomeration patterns of businesses and industries (Camagni, 1995; Crevoisier, 2004;
Maskell, 2001; Maskell and Kebir, 2006; Pinch, Henry, Jenkins and Tallman, 2003; Hervas-
Oliver et al, 2005). The ‘systems of innovation’ literature seeks to explain the capacity of
national and regional economies to generate ‘innovation’ (Cooke, Uranga and Etxebarria,
1997; Lundvall, Johnson, Andersen and Dalum, 2002; Cooke, 2001). The management
tradition has sought to explain mechanisms that underpin firm- and cluster-level competitive
advantage (Arikan, 2009; Porter, 1998; Tallman et al., 2004). Entrepreneurs and small- and
medium-sized businesses feature in each of these traditions, albeit in diverse ways. However,
although some work in these traditions assigns entrepreneurs a significant role (Delgado et
al., 2010; Feldman, Francis and Bercovitz, 2005; Feldman and Francis, 2004; Glaeser, Kerr
and Ponzetto, 2010; Zahra and Nambisan, 2011), none of the previous frameworks have
treated entrepreneurial opportunity pursuit as the defining aspect of the cluster dynamic. Even
in the nascent literature on entrepreneurial ecosystems, there have been few attempts to
explore what drives the phenomenon itself.
We suggest that it is useful to view entrepreneurial ecosystems as a digital economy
phenomenon that harnesses technological affordances to facilitate entrepreneurial opportunity
pursuit by new ventures through radical business model innovation. Although entrepreneurial
ecosystems inevitably incorporate structures that evolved before the current digital era, a
digitalization prism can support important insight into how they work. For example, we
believe that it is no coincidence that the world’s first modern new venture accelerator, the Y-
Combinator, started its operations in Silicon Valley in 2005, only one year after the moniker:
‘Web 2.0’ was coined – also in Silicon Valley in a Web developer conference. This, and
subsequent monikers of ‘Web 3.0’ and ‘Web 4.0’, signals the phase change of the Internet
from a one-way content distribution medium into a global interaction platform that can
support complex transactions amongst multiple stakeholders reliably, asynchronously, and
regardless of location (Constantinides and Fountain, 2008; John, 2012). The rapid global
diffusion of the new venture accelerator concept even in sub-Saharan Africa is consistent
with our argument that the adoption is driven by a globally operating mechanism – i.e.,
rapidly evolving digital infrastructures (Sofia, 2016). To understand modern entrepreneurial
ecosystems, therefore, one should first understand how digitalization shapes value creation,
delivery, and capture in the economy and society (Nambisan, Lyytinen, Majchrzak and Song,
2017; Yoo, Henfridsson and Lyytinen, 2010).
We build a conceptual model of entrepreneurial ecosystems as a distinct type of
cluster that specializes in harnessing technological affordances (Gibson, 1977) created by
digital technologies and infrastructures (which we call digital affordances), and combines
them with spatial (i.e., proximity-related) affordances to support a distinctive cluster dynamic
that is expressed through the creation and scale-up of new ventures. Digital affordances
derive from the technical architecture of digital infrastructures, and they support an economy-
wide redesign of value creation, delivery, and capture processes. Spatial affordances in
entrepreneurial ecosystems support the cultivation and dissemination of cluster-level
architectural knowledge on a generic business process: effective business model innovation
and entrepreneurial startup and scale-up (Tallman et al., 2004). This means that
entrepreneurial ecosystems constitute the only documented cluster type that is not specific to
a given (set) of industry sector(s) or technology domain(s)1. Relative to other cluster types,
1 There are examples of industry- or technology-specific entrepreneurial ecosystems, but such specificity is not
required for the effective operation of an entrepreneurial ecosystem uniquely among documented cluster types.
additional distinctive features of entrepreneurial ecosystems concern the organization of
cluster externalities around entrepreneurial opportunity discovery and pursuit; the
predominance of business model innovation (as opposed to product, process, and linear,
‘technology-push’ innovation); the prevalence of voluntary horizontal knowledge spillovers
(as opposed to vertical spillovers in user-producer dyads); and the locus of entrepreneurial
opportunities outside the cluster (as opposed to being intrinsic to the cluster). The
characteristic structural elements of entrepreneurial ecosystems (notably, new venture
accelerators, co-working spaces and makerspaces) reflect this specialization on facilitating
business model experimentation and associated horizontal knowledge spillovers.
We begin our argument by exploring digitalization, “the sociotechnical process of
applying digitizing techniques to broader social and institutional contexts that render digital
technologies infrastructural2 and associated affordances (Majchrzak and Markus, 2013;
Tilson, Lyytinen and Sørensen, 2010: 749). We then scan literature on agglomerations and
clusters, overviewing categories of spatial affordances and highlighting sources of
entrepreneurial opportunities discussed in received agglomeration and cluster literature. We
build on this review to highlight distinctive characteristics of entrepreneurial ecosystems,
focusing specifically on the role of entrepreneurs, the locus and drivers of opportunities for
entrepreneurship, and sources of cluster advantage. We then present a structural model of
entrepreneurial ecosystems and introduce the articles of this Special Issue against the model,
highlighting the contributions they make towards understanding the entrepreneurial
ecosystem phenomenon. We conclude with implications for research, policy, and practice.
2 In contrast, digitization is the technical conversion of analogue information into digital form.
In their milestone contribution, Freeman and Perez (1988) highlighted the cyclical pattern of
the interplay between technological development and economic organization in society. Since
the late 1700’s, every half century or so, a set of interconnected technological breakthroughs
would emerge that fundamentally transformed patterns of industrial activity and economic
organization. These they dubbed ‘techno-economic paradigms’. Not only would such
breakthroughs give rise to new industries, they would often transform how existing industries
worked and prompt new organizational forms that were more suited to take advantage of the
new affordances3 created by the new technologies (Gibson, 1977; Hutchby, 2001). For
example, the classic ‘M Form’ structure of multi-unit, multi-divisional enterprise emerged
from the 1850s onwards, when the emergence of railroad networks and telegraph systems
(combined with the emergence of new production technologies) afforded the pursuit of
unprecedented economies of scale by managing and coordinating the flow of supplies and
products across geographical distance such that investment in large-scale manufacturing
facilities became economically feasible (Chandler, 1977, 1986).
The emergence of the ‘M Form’ structure highlights how technological disruptions
can sometimes precipitate new forms of economic organization, particularly when the new
technology affords more efficient economic interactions (e.g., logistics, transportation,
production, distribution) or better management and coordination. For the ‘M Form’, advances
in production technologies supported hitherto unseen economies of scale. To support the
capital investment in manufacturing, much greater and more reliable flows of materials and
products were necessary than could be supported by animal-driven transportation and
3 The noun ‘affordance’ is derived from the verb ‘afford’ to signal the potentiality to perform a new function or
perform existing functions more efficiently. What those new functions are, however, may not be immediately
obvious: they remain to be discovered, often by entrepreneurs. Thus, an affordance is not a characteristic of the
new technology (e.g., computing power): it is a potentiality that needs to be discovered and articulated (e.g.,
computer simulations to model thermal flows).
fragmented suppliers and distributors. Railway and telegraph could support new corporate
structures that exploited both upstream and downstream integration in a new business model,
that of a large-scale, multidivisional corporation.
More specifically to entrepreneurship, we suggest that the rapid evolution of digital
technologies and infrastructures is again creating new affordances that affect the organization
of economic activity (Majchrzak and Markus, 2013; Nambisan, 2017; Zammuto, Griffith,
Majchrzak, Dougherty and Faraj, 2007).4 Digitalization supports three key affordances that
shape both the locus of entrepreneurial opportunities in the economy, as well as the effective
practices to pursue such opportunities. First, digitalization promotes de-coupling between
form and function, and consequently, shifts the determinants of and likely reduces the
importance of asset specificity in regulating power and dependency relationships within
manufacturing value chains (Tilson et al., 2010; Yoo et al., 2010). Second, digitalization
promotes disintermediation, reducing the power of middlemen in value chains, and
conferring greater freedom for product and service providers to configure the activity systems
for the delivery of their products and services (Bakos, 1998; Gellman, 1996; Katz, 1988).
Finally, digitalization drives generativity, enabling the coordination of geographically
dispersed audiences and opening new ways to build and harness platform momentum thus
created (Nambisan, 2017; Thomas, Autio and Gann, 2014; Yoo, Boland Jr, Lyytinen and
Majchrzak, 2012; Zittrain, 2006). These affordances enable new ventures to re-invent how
they create, deliver, and capture value, thereby enabling new ventures to disrupt incumbents
with radical new business models (Prahalad and Ramaswamy, 2003).
4 We use the term: ‘digital technologies and infrastructures’ to refer to both applications of information and
communication (ICT) technologies as well as associated infrastructures. Digital technologies comprise devices,
such as smartphones and sensors, applications such as computer software and information systems, and
infrastructures such as fixed-line and wireless infrastructures, transmission systems, and data and computing
applications that can be accessed through such infrastructures (e.g., ‘cloud-based’ services).
First, for de-coupling, we observe that the re-programmability of digital technologies
weakens the coupling between form and function, thus undermining traditional drivers of
asset specificity in vertical transactions or the degree to which a given asset can be
redeployed to alternative uses and by alternative users without sacrifice of productive value
(De Vita, Tekaya and Wang, 2011; Williamson, 1988). All digital technologies and
infrastructures are Turing machines: both their instruction sets and the ‘raw material’ of their
operations (i.e., data) are expressed in bits (Hopcroft, Motwani and Ullman, 2006). This
makes digital technologies inherently flexible, as bits represent the most elementary form of
information, and all other forms of information are ultimately reducible to them. As both the
inputs, instruction sets, and outputs of digital devices are expressed in the same generic form,
this greatly increases the flexibility of digital devices not only in terms of the range of
functions they can be programmed to perform, but also, in terms of the underlying digital
infrastructures that can be called upon to perform a given function or service (Tilson et al.,
2010; Yoo et al., 2010). In ‘physical’ technologies, form and function are closely coupled
because a specific physical form is typically required to perform a given function. As
physical forms are expressed in poorly reversible arrangements of atoms in matter,
investments in physical assets tend to be more asset specific than investments in re-
programmable digital devices. Such digitalization-induced reduction in asset specificity
shapes the locus and emergence of entrepreneurial opportunities in value chains and clusters,
as elaborated in the next section.
Second, digitalization-induced disintermediation both reduces dependency on
location-specific value chain assets and resources and opens new opportunities for value-
creating interactions with end users. As such, disintermediation – or the ability of the Internet
to support direct interactions between service providers and users, thereby enabling them to
bypass intermediaries – is a long-recognized affordance of the Internet (Bakos, 1998;
Gellman, 1996; Jallat and Capek, 2001). Disintermediation is the product of, first, the ability
to directly and seamlessly communicate with end users using web-based applications; and
second, the ability to dissociate the flow of goods and services from the flow of associated
information (Evans and Wurster, 1998). This affords producers and suppliers greater control
over material flows and activities within the value chain and reduces their dependency on
location-specific intermediaries as sources of information necessary to coordinate their
operations. The resulting flexibility in outsourcing practices enables even new and
entrepreneurial ventures to more flexibly configure and coordinate the activity systems
necessary for the delivery of their products and services. Increased flexibility in outsourcing
reduces dependency on locally available and cluster-specific resources. These trends
undermine traditional cluster advantages, as recognized in received cluster theory.
Finally, generativity, or the ability of the Internet to facilitate unprompted innovative
inputs from large, uncoordinated audiences (Zittrain, 2006), is the outcome of several
architectural features that jointly reduce transaction costs in interactions conducted through
the Internet and infuse an extent of unpredictability and fluidity into entrepreneurial and
innovation outcomes. These include leverage (the ability to produce an output disproportional
to the size of the input); adaptability (the ease with which a given system can be modified to
produce different functionalities); ease of mastery; and accessibility (Zittrain, 2007). These
properties make it easy and attractive for varied audiences to engage with offerings and
resources made available over the Internet. For example, digital platforms allow for a
recombination of elements and for assembly, extension and redistribution of functionality
(Yoo et al., 2010), thereby contributing to the dynamic emergence and evolution of
entrepreneurial opportunities and outcomes (Nambisan, 2017). Such an affordance is further
enhanced by inbuilt trust mechanisms and technologies of the Internet, such as online
certification and reputation mechanisms, location-unspecific Internet intermediaries (e.g.,
two-sided market facilitators, payment platforms), and distributed ledgers, which support
different parts of complex transactions, such as payment verification, payment processing,
dispute settlement, and even self-executing contracts (Catalini and Gans, 2016). The
Internet’s architectural trust mechanisms can potentially offer a near full substitute for social
and relational trust that is non-localized and does not depend on geographical proximity.
Summarizing, digitalization creates potent digital affordances that likely have a
transformative effect upon the organization of economic activity by supporting radical
business model innovation (Nambisan et al., 2017). Moreover, as the digital affordances are
not location-specific, they likely challenge or even alter some spatial affordances at play
within clusters and agglomerations because of their effect on local resource dependency,
dependence on local networks, the build-up of and leveraging of trust and legitimacy signals
by new ventures, and the locus of entrepreneurial opportunities. These trends give rise to a
distinctively different cluster dynamic in entrepreneurial ecosystems relative to other types of
clusters discussed in received literature.
The entrepreneurial ecosystems literature intermingles with a rich tradition of economic
geography, management, and microeconomic research on industrial districts, clusters, and
innovative milieus, as well as with the innovation research tradition exploring ‘systems of
innovation’ (Camagni, 1995; Cooke et al., 1997; Marshall, 1890; Piore and Sabel, 1984;
Porter, 1998). All these literatures emphasize the importance of spatial mechanisms in
facilitating and regulating economic activity and their effects on productivity and innovation.
These mechanisms we call them spatial affordances for coherence are distinct from digital
affordances, which do not operate spatially. We next briefly review the agglomeration and
cluster literatures, focusing specifically on the general view of the cluster, on cluster-level
economic benefits, on the role(s) entrepreneurs play in the cluster, on drivers of
entrepreneurial opportunity, on the locus of opportunity drivers, on dominant types of
knowledge spill-over, on characteristic structural elements of the cluster, and on the function
of cluster-specific institutions.
Spatial affordances, productivity, and innovation
The cluster and agglomeration literature identifies two major benefits facilitated by
spatial affordances: enhanced productivity and increased innovation. The original insight of
Alfred Marshall captured both outcomes in his recognition of external economies of scale
that accrue within regional agglomerations due to specialization effects, labor (and resource)
pooling, and knowledge spillovers (Marshall, 1920). Specialization effects derive from
regional concentration of supply and demand within value chains, which makes it easier for
agglomeration participants to discover niches to specialize in (Piore and Sabel, 1984). The
pooling of skilled labor drives down the cost of recruiting productive employees. The
knowledge spillover effects – or Marshall’s (1920) ‘mysteries of trade in the air’ (Audretsch
and Feldman, 1996) – were primarily due to businesses in an agglomeration being able to
better collaborate with one another in innovative projects, and also, to competitively observe
one another and identify and adopt effective practices (Bathelt, Malmberg and Maskell, 2004;
Maskell, 2001). These spatial affordances support external economies of scale and scope, or
productivity-enhancing pecuniary (i.e., price-related) and non-pecuniary externalities that
enhance the productivity and innovative output of agglomeration participants.
Before we review different aspects of the clusters and agglomerations literature and
highlight distinctive elements of entrepreneurial ecosystems as a type of cluster, we point out
one important characteristic of the clusters and agglomerations literature. That is, virtually all
definitions of an industrial district, cluster, innovative milieu, or regional system of
innovation define them as concentrations of related businesses and supporting institutions
and structures that are specific to a given industry, a set of related industries, or a given
technology (see Cruz and Teixeira, 2011, for an overview of cluster definitions). Most
entrepreneurial ecosystems are agnostic relative to industry or technology domain, although
there are exceptions. This is not to suggest that entrepreneurial ecosystems lack a shared
knowledge base. The nature of this knowledge base is distinctively different, however.
Broadly characterizing, the different streams in the cluster and agglomeration
literature (including the literature on ‘systems of innovation’) take different views of the
cluster and emphasize different cluster benefits. The literature on industrial and Marshallian
districts emphasizes productivity outcomes and views the cluster as a flexibly specialized
production system. This literature argues that a region that carries many small, specialized
units of production (often alongside with larger firms) in one or related industries will be
inherently more flexible than will one comprising large manufacturing plants only (Dumais,
Ellison and Glaeser, 2002; Piore and Sabel, 1984). Many specialized, independent units of
production can more flexibly recombine their outputs in response to changing customer
demands, and numbers also provide insurance against shocks that might adversely affect
individual businesses (Pyke et al., 1990). Spatial proximity also supports pecuniary (i.e.,
price-related) advantages by reducing search and transaction costs and attracting valuable
resources such as specialized human capital and specialized services (Markusen, 1996).
These, and the coordination and logistical benefits facilitated by proximity create external
economies that show up as increased productivity and enhanced profitability both at the firm
and value chain levels.
In contrast with the economic and price-based (i.e., pecuniary) mechanisms
emphasized in the industrial and Marshallian districts literature, both the ‘knowledge cluster’,
‘innovative milieus’, and ‘regional systems of innovation’ literatures emphasize the role of
non-pecuniary mechanisms in facilitating learning and innovation in regional agglomerations
(Camagni, 1995; Crevoisier, 2004; Cooke, 1997; Bathelt, Malmberg and Maskell, 2004;
Maskell, 2001; Maskell and Kebir, 2006; Cooke, 2001) Such mechanisms include, e.g., an
appropriate mix of knowledge-producing research organizations, bridging organizations, and
companies; relational trust; shared social norms; and a regional culture that encourage
knowledge-intensive collaborations and ‘collective learning’ in regional agglomerations
(Boschma, 2005; Cooke, 2001; Maskell, 2001). Although the different streams emphasize
different mechanisms, all three share the view of the regional agglomeration as a localized
system of learning and innovation (Asheim, Smith and Oughton, 2011). This system creates
three major types of learning and innovation benefits: product innovation, process innovation,
and linear, ‘technology-push’ innovation (i.e., the translation of scientific knowledge and
research advances into commercial application). Important for the creation of these learning
and innovation benefits is knowledge relatedness: in order to learn from one another, the
cluster participants need to be able to understand one another (Arikan, 2009; Baptista and
Swann, 1998; Nahapiet and Ghoshal, 1998). Knowledge exchange is facilitated by a shared
focus on the same industry or a set of related industries – or on the same technology or a set
of related technologies, as would be the case of regional systems of innovation (Asheim et al.,
2011; Boschma, 2005; Cooke, 2001; Ter Wal and Boschma, 2011).
As a type of cluster, entrepreneurial ecosystems are distinctively different: they are
not flexible systems of production, nor are they systems of learning and innovation in the
sense described in the received literature. Instead, entrepreneurial ecosystems are systems of
entrepreneurial opportunity discovery and pursuit (Acs et al., 2014). Entrepreneurial
ecosystems are the only cluster type where cluster externalities and cluster-specific structural
elements are explicitly organized around the entrepreneurial process of opportunity
discovery, pursuit, and scale-up of new ventures (e.g., new venture accelerators, co-learning
spaces, makerspaces, business angel networks, networking events, and so on). In both the
‘production system’ and ‘learning and innovation system’ streams entrepreneurs are seen
more as by-products of spatial affordances, which apply similarly to all cluster occupants. In
contrast, entrepreneurial ecosystems revolve around entrepreneurial opportunity discovery
and pursuit, of which entrepreneurs and their ventures are the central agents.
In addition to the organization of their cluster-specific externalities, entrepreneurial
ecosystems are also distinguished by their exploitation of digitalization affordances. In our
review of the digitalization literature, we highlighted how digitalization affordances allow
firms to re-think how they create, deliver, and capture value. Entrepreneurial ecosystems
represent the only cluster type where the dominant cluster-level benefit is business model
innovation, and not process, product, or ‘technology’ push innovation, as is the case for
flexible production systems and learning and innovation systems. This is an important
distinction: entrepreneurial ecosystems are the only cluster type to facilitate a shared
knowledge base relating to a generic business process rather than to a specific industry or
technology, as is the case for all other cluster types (Asheim et al., 2011; Ter Wal and
Boschma, 2011). In entrepreneurial ecosystems, the shared knowledge base relates to
business model innovation and entrepreneurial opportunity pursuit and scale-up. Although
many new ventures apply digital technologies in opportunity pursuit, this is not obligatory.
The emphasis on generic business model innovation also has wider policy implications, as
this characteristic makes entrepreneurial ecosystems a potent policy tool to advance
economy-wide digitalization through the diffusion of new business models that translate
digitalization affordances into more efficient ways to create, deliver, and capture not only
economic, but also, societal value. We elaborate policy implications in the final chapter.
Characteristic knowledge spillovers
The focus of entrepreneurial ecosystems on a generic business process rather than
industry- or technology-specific knowledge facilitation means that they also feature
distinctive patterns and types of knowledge spillover and dissemination. In different cluster
types covered in the clusters and agglomerations literature, the characteristic patterns of
knowledge spillover vary, both in terms of their directionality, their voluntary or involuntary
character, as well as the mechanisms that facilitate such spillovers. When the cluster is
composed of a regional agglomeration of productive firms organized along an industry value
chain, the dominant form of voluntary knowledge spillovers is predominantly vertical and
operates in user-producer dyads (Maskell, 2001, 2004). This applies to both the flexible
production system as well as the learning and innovation streams of the clusters and
agglomerations literature. In the flexible production system streams, vertical and voluntary
knowledge spillovers are facilitated by a shared interest to increase the efficiency of user-
producer interactions, as well as the build-up of relational trust facilitated by geographical
proximity (Bathelt, Malmberg and Maskell, 2004; Maskell, 2001; Maskell and Kebir, 2006;
Boschma, 2005). In vertical value chains, many such spillovers support process innovation,
or at least a more efficient organization of interactions and logistics in user-producer
relationships. The voluntary nature of vertical knowledge spillovers is ensured by the
complementary nature of the user-producer dyad, of which the occupants target different
markets the ‘producer’ selling to the ‘user’, and the ‘user’ targeting downstream markets
(Arikan, 2009).
In contrast to voluntary spillovers in user-producer dyads, horizontal knowledge
spillovers tend to be involuntary in both flexible production systems, and also, in learning and
innovation system –type clusters. This is because these cluster types are composed of
vertically networked firms who compete horizontally. Therefore, the dominant form of
horizontal knowledge spillover in traditional clusters tends to take the form of isomorphic
copying of competitive practices and promising product concepts. As such, also involuntary
knowledge spillovers drive learning and innovation and can manifest themselves both as
product and process innovation (Audretsch and Feldman, 1996). There are also special cases
where collaborations among small- and medium-sized companies support horizontal
knowledge spillover – for example, in sponsored networks of small-and medium-sized firms
(SMEs) that collaborate to gain market power or improve their access to foreign markets
(Wincent, Anokhin, Örtqvist and Autio, 2009). Furthermore, especially the ‘innovative
milieus’ literature highlights how frequent interactions and embeddedness in social networks
may encourage the formation of an ‘innovative culture’, which punishes free-riding among
regional SMEs and encourages horizontal collaboration (Camagni, 1995; Crevoisier, 2004).
Vertical spillovers also characterize regional systems of innovation, where the
structuring of the institutional framework in the region (e.g., universities, research institutes,
industry, users and producers) shapes knowledge-intensive interactions among participants of
the innovation system (Cooke, 2001). In this literature, the spillovers do not occur exclusively
along the vertical value chain, but rather, along the knowledge maturation chain from basic
research to applied research to commercial application (Cooke et al., 1997; Lundvall et al.,
2002; Nelson and Nelson, 2002). A well-structured innovation system supports broad and
effective interactions between research institutions and industry, and thus, will have a greater
capacity to create product and process innovation and support the translation of scientific
advances into industrial application. In such a system, entrepreneurial firms can act as a
knowledge filter that translates the cluster’s endogenous advances in research knowledge into
commercial application (Acs et al., 2009).
In entrepreneurial ecosystems, the patterns and character of knowledge spillovers are
distinctively different from those reviewed above: entrepreneurial ecosystems are
characterized by horizontal, voluntary knowledge spillovers. This is, first, because of how
value-creating activity is organized in entrepreneurial ecosystems, and second, because of
their specialization in facilitating generic business knowledge. In contrast to other types of
clusters, which are composed of vertically networked firms that compete horizontally,
entrepreneurial ecosystems are composed of horizontally networked firms that compete
vertically – against incumbents that operate outside the cluster. The disintermediation
affordance of digitalization tends to break up vertical value chains and reorganize value-
creating activity around digital platforms (Tilson et al., 2010). Such platforms offer good
opportunities to experiment with alternative value propositions and service concepts, and they
also make it possible to directly engage end users for value co-creation (Lusch and
Nambisan, 2015)5. As digital platforms can support a wide variety of value propositions in a
wide range of markets, new ventures in entrepreneurial ecosystems typically do not directly
compete against one another. In contrast, new ventures exploiting digital platforms for
business model experimentation will have an incentive to share their experiences, as
reciprocal sharing of such knowledge will help all occupants of the entrepreneurial ecosystem
to become more effective in business model innovation. As we will elaborate later, the
distinctive structural elements of entrepreneurial ecosystems, such as new venture
accelerators, co-working spaces, and makerspaces, also serve as a forum for cultivating
knowledge on effective business model experimentation and the horizontal sharing of it.
Drivers of entrepreneurial opportunity
For the most part, the cluster and agglomeration literature has not focused explicitly
on entrepreneurship. Although small and medium-sized businesses and also entrepreneurs
feature in the cluster and agglomeration literatures, sometimes prominently, there have been
relatively few studies exploring specifically how spatial affordances create opportunities for
entrepreneurship within clusters (Rocha and Sternberg, 2005; Wennberg and Lindqvist,
2010). At least six such mechanisms have been suggested. First, the concentration of value
5 The practice of ‘Lean Entrepreneurship’ is itself largely inspired by digitalization, as digital platforms are able
to support cost-efficient and rapid experimentation with alternative value propositions (Reis, 2011; Blank,
chains in a geographically confined space makes it easier for prospective entrepreneurs to
spot gaps for niche players to occupy (Porter, 1998). Second, clusters attract resources such
as specialized labor, knowledge, and services that may lower the cost of entrepreneurial entry
(Delgado et al., 2010; Dumais et al., 2002; Stuart and Sorenson, 2003). Third, geographical
proximity facilitates the build-up of relational trust and increases transparency in the pricing
of supplies, thus driving down transaction costs and making it easier and cheaper for
entrepreneurial ventures to enter the market (Storper, 1995). Fourth, the regional
concentration of demand provides an environment where it is easier for specialized niche
players both to enter the market, and also, survive post-entry (Wennberg and Lindqvist,
2010). Fifth, advances in knowledge due to investment in R&D and innovation create
‘technology-push’ opportunities for entrepreneurs to exploit and act as agents of knowledge
spill-overs in clusters (Acs, Braunerhjelm, Audretsch and Carlsson, 2009). Finally, partly
through the actions of entrepreneurs, clusters may develop institutional structures and
specialized services that support and facilitate entrepreneurial entry (Feldman et al., 2005;
Feldman and Francis, 2004, 2006).
Entrepreneurial ecosystems exhibit distinctively different opportunity drivers because
of the way value-creating activities are organized within them and because of the way they
exploit digital affordances. As reviewed previously, digital affordances alter the balance
between digital and spatial affordances in terms of how these drive and constrain
entrepreneurial action within clusters. For example, the in-built trust mechanisms of the
Internet may substitute for relational trust, thus reducing the dependence of new ventures on
spatial proximity. This alleviates some of the legitimacy constraints that encumber new
ventures and enables them to more easily pursue opportunities outside the cluster.
Disintermediation reduces the dependency of new ventures on local intermediaries, also
reducing cluster lock-in. By enhancing the ability of the new venture to mobilize momentum
outside the cluster, also generativity reduces its dependency on local markets. Thus,
digitalization has the general effect of reducing the dependency of new ventures on cluster-
specific spatial affordances for entrepreneurial opportunities, while also alleviating some of
the spatial constraints of opportunity pursuit and enabling new ventures to experiment with
and discover business models that exploit opportunities external to the cluster.
Locus of opportunity drivers
The entrepreneurial opportunity drivers in the ‘production system’ and ‘learning and
innovation system’ perspectives are all intrinsic to the cluster: value chain concentration
supporting specialization opportunities; resource concentration enhancing resource access
and mobilization opportunities while reducing the cost of resource access; relational trust
driving down transaction costs while also facilitating knowledge exchanges; cluster-specific
knowledge advances opening commercialization opportunities. All these drivers are afforded
by spatial proximity, which restricts the locus of the resulting entrepreneurial opportunities to
the cluster itself. In some cases, such as niche specialization within the value chain, this also
limits the scope of the entrepreneurial opportunities thus created. In other cases, this may
increase the dependency of the new venture on cluster-specific resources and stakeholders. In
contrast, the key affordances exploited by entrepreneurial ecosystems are not intrinsic to the
cluster itself, but rather, opened by advances in a location-nonspecific element – i.e., the
digital infrastructure. This means that the locus of entrepreneurial opportunities exploited by
new ventures in entrepreneurial ecosystems are largely external to the cluster.
Role of entrepreneurs in the cluster
The review of entrepreneurial opportunity drivers also implies specific roles assigned
to entrepreneurs in the cluster and agglomeration literatures. In the flexible production system
perspective, entrepreneurs are mostly seen as occupants of specialist niches in the production
system. Entrepreneurs may also play an important role in initiating and shaping cluster
institutions (Feldman and Francis, 2004, 2006). In addition, the ‘learning and innovation
system’ stream assigns entrepreneurs the role of exploiting industry- and technology-specific
knowledge externalities facilitated by spatial affordances. The innovative outputs from such
activities would take the form of product innovation, as would be the case in many Italian
fashion clusters, for example (Porter, 1998), or technology-based products and services
produced by technology-based new firms that exploit the spillover of knowledge from
research to industry (Autio, 1997). The roles of specialist niche occupants, makers of
industry-specific products and services, or developers of technology-specific applications are
quite different from those typically seen in entrepreneurial ecosystems, where new ventures
harness digitalization affordances to innovate and scale up new business models that usually
address opportunities outside the cluster. This activity entails several subsidiary activities, the
main one being active experimentation with value propositions and associated systems for
value creation and delivery.
Characteristic structural elements of the cluster and their function
The characteristic structural elements of different cluster types reflect their internal
dynamic. In flexibly specialized production systems, one typically sees structures whose role
is to advance the interests of the industry, e.g., by lobbying or by providing generic training
and other services for the cluster occupants. Such functions are performed by, e.g., regional
chambers of commerce and industry associations. Similar structures are also typically
observed in knowledge clusters and innovative milieus. Regional systems of innovation add
the element of bridging organizations, the function of which is to facilitate knowledge
creation, transfer, and combination activities in the technology-push flow of knowledge from
basic research to commercial application. Science parks represent one such structure.
In contrast, entrepreneurial ecosystems characteristically exhibit structural elements
whose main function is to facilitate horizontal sharing and dissemination of experiences from
business model experimentation and support the adoption of ‘lean entrepreneurship’
practices. Such structural elements include new venture accelerators, co-working spaces, and
makerspaces. Entrepreneurial ecosystems also exhibit structures facilitating the self-selection
of individuals into entrepreneurship (e.g., innovation challenges), as well as those facilitating
the scale-up of new ventures with robust and scalable business models (e.g., venture
capitalists). We will elaborate on the functions of the characteristic structural elements in the
next section, where we present our structural model of entrepreneurial ecosystems.
[Insert Table 1 here]
The distinctive characteristics of entrepreneurial ecosystems relative to other types of
clusters documented in received literature are summarized in Table 1. Although derived from
conceptual review, our insights in Table 1 are supported by our observations and direct
engagement with entrepreneurial ecosystems in four continents (Acs, Szerb, Autio, and
Ainsley, 2017; Autio, 2014, 2016; Autio and Rannikko, 2016, 2017). We nevertheless
emphasize that our insights are not categorical. All entrepreneurial ecosystems evolve within
idiosyncratic local conditions with vague and porous boundaries, as is the case of virtually
any agglomeration covered in received literature. We also see practices and lessons pioneered
in entrepreneurial ecosystems adopted in other types of clusters. Despite these limitations, it
is important to highlight the interplay between globally operating technological affordances
and localized spatial affordances, as it is this dynamic that sets entrepreneurial ecosystems
apart from other cluster types. Understanding this aspect is important from a policy
perspective, as we will elaborate in the policy section.
While highlighting distinctive features, our insights do not comment on specific
operations of entrepreneurial ecosystems nor the question of what makes some ecosystems
tick. To understand this, we next focus on this aspect and build a structural model.
Figure 1 below depicts a structural framework of entrepreneurial ecosystems that
encapsulates our discussion so far. As noted previously, the combination of digital and spatial
affordances facilitates business model innovation for entrepreneurial opportunity discovery
and pursuit that in turn characterizes entrepreneurial ecosystems. In what follows, we expand
on this framework by focusing on the structural elements and knowledge-based processes that
assume significance in entrepreneurial ecosystems and which facilitate entrepreneurial
ecosystem outcomes, contingent on contextual factors relating to local policies, regulations,
and culture (Autio, Kenney, Mustar, Siegel and Wright, 2014; Zahra and Wright, 2011). We
organize our discussion according to the entrepreneurial process and distinguish between
‘stand-up’, ‘startup’, and ‘scaleup’ activities in entrepreneurial ecosystems.
[Insert Figure 1 around here]
To examine the knowledge-based processes, we draw on the model by Tallman et al.
(2004), who explored how clusters promote cluster- and firm-level competitive advantage.
Extending Henderson and Clark’s (1990) and Matusik and Hill’s (1998) terminology to the
levels of the firm and cluster, they defined firm-level component knowledge as specific firm-
level knowledge that relates to identifiable parts of an organizational system, and also to
external conditions and laws (Tallman et al., 2004). Architectural knowledge they defined as
understandings relating to entire systems, developed at the regional cluster level through the
routinization of the interactions, interdependencies, and interests of cluster members
(Tallman et al., 2004). Whereas component knowledge lends itself more readily for
codification and transfer between firms, architectural knowledge tends to be more complex
and less readily transferred across clusters.
Drawing on the terminology developed by Tallman et al. (2004), entrepreneurial
ecosystems can be usefully viewed as structures that specialize in the facilitation and
cultivation of a specific type of architectural knowledge – notably, knowledge about ‘what
works’ in terms of organizing for business model innovation and entrepreneurial opportunity
pursuit and scaleup. This process is largely agnostic with regard to technologies, products,
and industries to which this architectural knowledge is applied. The cluster-level architectural
knowledge is then combined with entrepreneur- and venture-specific knowledge concerning
how specific technologies, products, and industries work. We suggest that it is the
combination of cluster-level architectural knowledge regarding effective heuristics for
business model innovation and entrepreneurial opportunity pursuit and scaleup with venture-
specific component knowledge regarding how specific technologies, products, and industry
sectors work that constitutes the essential, distinctive modus operandi of entrepreneurial
ecosystems relative to other cluster types.
[Insert Table 2 around here]
We use the architectural-component knowledge lens to explore the structural and
processual aspects of entrepreneurial ecosystems, specifically those related to the ‘stand-up’,
startup’, and ‘scaleup’ activities (see Table 2 above). The ‘stand-up’ stage covers all
activities and mechanisms associated with the self-selection of individuals and teams into the
entrepreneurial process: a well-functioning stand-up operation will attract high-potential
individuals and teams into entrepreneurship. The ‘startup’ stage covers all activities and
mechanisms associated with the actual startup of new ventures, including concept search and
refinement and business model experimentation. In our model, startup continues beyond the
actual incorporation of the new venture and covers the business model experimentation to
discover a robust and scalable business model. Finally, the ‘scaleup’ stage covers scaleup
activities once a robust and scalable business model has been discovered.
In our observation, although there may be variations in local policies, regulation and
culture, entrepreneurial ecosystems around the world have developed a broad range of
characteristic structures and processes to support entrepreneurial stand-up. Attracting
entrepreneurial talent is a key activity in many entrepreneurial ecosystems, which will only
prosper if there is a continuous flow of interesting business ideas and talented individuals to
take them on. Consistent with the notion of entrepreneurial ecosystems specializing in
harnessing digital affordances, we see many entrepreneurial ecosystems organizing
challenge-driven activities, such as hackathons and innovation challenges. Such events can
play an important role in attracting and motivating entrepreneurial talent. Many accelerators
are active in talent scouting, often internationally, and can go to great lengths to persuade
talented individuals to re-locate. Speed dating and networking events connect individuals
with prospective venture ideas, often in universities where students may be looking for
summer jobs and extra income. Also, entrepreneurship programs, offered both through
educational institutions and accelerators, foundations, and private-sector educational
institutions, serve to both build, motivate, and attract entrepreneurial talent. These structural
elements and processes help achieve the key outcomes related to the development of rich
entrepreneur networks and a steady stream of new venture concepts, that in turn feed the next
two stages.
During the startup stage, the focus is on team building and business model
experimentation. It is almost standard practice among new venture accelerators to offer
training and coaching in lean entrepreneurship practices such as business model
experimentation (Reis, 2011). Such experimentation does not need to be confined to
accelerators, though, and not all new ventures channel through them: also shared co-working
spaces, makerspaces, startup academies, and similar arrangements cultivate and disseminate
cluster-specific architectural knowledge and facilitate horizontal experience sharing among
new ventures. In addition to furthering the goal associated with architectural knowledge
cultivation and dissemination, entrepreneurial ecosystems also support the concentration of
specialized resources, notably, active funding and specialized human capital, and associated
structures to support team formation and initial funding of new ventures.
The distinctive characteristics and processes of entrepreneurial ecosystems are
typically at their most intense at the startup stage. However, we have seen entrepreneurial
ecosystems also develop specialized resources (including infrastructure) and activities to
support the scaleup of successful business models. The classic such function is the provision
of angel and venture capital funding – both of which tend to be regionally concentrated. In
addition, entrepreneurial ecosystems can also attract specialized human labor for recruitment
purposes and offer mechanisms and structures to support mentoring for budding scaleups.
Some entrepreneurial ecosystems also cultivate cross-cluster linkages that are most likely to
come into play in the scaleup stage – the Slush event being one example. Further, ventures
that go on to successful IPOs and trade sales help generate financial returns to venture owners
and investors, a portion of which is channeled back to new ventures as investments, thereby
completing an ecosystem-wide feedback loop in value creation.
Our structural framework and model imply important avenues for future research, as
well as policy prescriptions. In the remainder of this paper, we consider the papers that are
part of this special issue and then discuss the agenda for future research and policy.
The papers in this special issue were selected following a general open call. Papers that were
not desk rejected were subject to the usual SEJ review procedures, with the three papers
presented here successfully navigating this process. The papers are summarized in Table 3.
[Insert Table 3 around here]
The papers adopt several theoretical perspectives, notably field theory (Thompson et
al.), socially-situated entrepreneurial cognition (Goswami et al), and clusters and regional
innovation systems (Spigel and Harrison). These perspectives reflect the diverse levels of
analysis in the papers. The papers also adopt several methodological approaches including
both qualitative theory building from empirical data and the development of conceptual
frameworks from existing literature. Data sources are varied but are marked by rich
longitudinal data including both archival sources and detailed interviews.
All three papers explore process issues in entrepreneurial ecosystems. The papers
emphasize the importance of the expertise of actors and interactions between them in the
emergence and evolution of entrepreneurial ecosystems. Digitalization is shown to facilitate
horizontal knowledge sharing and interactions that reinforce the ecosystem community via
websites, blogs, and other media (Thompson et al; Goswami et al.). While websites and blogs
provide for some degree of disintermediation, Goswami et al show that the expertise of
intermediaries such as accelerators can still be important in helping to build commitment to
the ecosystem community as well as being important for venture outcomes in terms of the
validation of venture viability.
The paper by Thompson et al provides a fascinating account of the formation of a
Seattle-based entrepreneurial ecosystem specializing on social entrepreneurship. Illustrating
the point that entrepreneurial ecosystems build on what came before, the case describes the
formation of an entrepreneurial ecosystem that started out almost as a small-scale social
movement that aimed to promote the legitimacy of a new organizational field, that of Social
Purpose Corporations (SPC). The case describes how, starting around 2012, this movement
started to reshape itself into an entrepreneurial ecosystem by adopting distinctive structural
elements of these, such as a co-working space, networking events, and even a fledgling
community of informal investors specializing in socially minded ventures. The case also
describes the creation of mechanisms to cultivate and horizontally disseminate ecosystem-
level architectural knowledge on social purpose enterprise in the form of a specialized MBA
program, field-specific terminology and language, a co-working space, and networking
events. This case illustrates that entrepreneurial ecosystems need not be limited to exclusively
profit-oriented business, and they may sometimes organize around a shared set of social goals
in this case inherited from its social movement origin. The social enterprise focus of this
ecosystem probably explains the lack of a dedicated accelerator a structural element
typically seen in purely profit-oriented entrepreneurial ecosystems. Although key structural
elements and practices in entrepreneurial ecosystems may have emerged to exploit
digitalization affordances, this case shows how those elements and practices are now
sufficiently mature to be able to be co-opted even for social missions that do not exclusively
operate in the digital space.
The paper by Goswami et al provides a detailed analysis of the interplay between
accelerators (a key structural element of entrepreneurial ecosystems), ventures, and the rest of
the Bangalore entrepreneurial ecosystem. This ecosystem had its origins in the large firm -
dominated IT cluster that developed around Bangalore over the past decades, and it
developed a more explicit entrepreneurial focus by adopting characteristic structural elements
of entrepreneurial ecosystems. The rapid adoption of the accelerator concept in Bangalore
since 2008 (by end 2016, 14 were in operation) supports the global salience of digitalization
affordances in driving the emergence of entrepreneurial ecosystems even in emerging
economies. The case study of Bangalore also adds important nuance to the structural
ecosystem model presented in this paper, by providing a close-up view of how exactly
accelerators facilitate both the cultivation of cluster-level architectural knowledge, the
dissemination of it across distinct parts of the ecosystem, as well as facilitating the absorption
of it at the level of entrepreneurial ventures. The case highlights accelerators as critical
elements in both cultivating such knowledge through commitment, validation, and
additionality activities; and in facilitating the horizontal spillover of it through selection,
coordination, development, and connection activities.
The paper by Spigel and Harrison provides a conceptual review and typology of
entrepreneurial ecosystems. Their review echoes several elements of the structural model
presented in this article, and their focus on processes provides a good complement to the
structural model. Echoing this article’s notion of architectural knowledge, they emphasize the
importance of knowledge about the entrepreneurial process, which emphasizes opportunity
identification, business planning, and pitching for investment. Their model also emphasizes
the role of entrepreneurs in leading entrepreneurial ecosystem – in this respect echoing some
aspects of Feldman and Francis’ work (e.g., 2006). Consistent with our model, they
emphasize the industry agnostic character of entrepreneurial ecosystems. The main emphasis
of their model, however, is on network processes and resource flows, and they present a
typology of entrepreneurial ecosystems on this basis.
While providing additional insight and nuance, the three papers do not cover all
aspects of our framework and hence give rise to opportunities for further research. For
example, the papers each focus on fairly well-defined urban conurbations (Seattle and
Bangalore) rather than a wider geographical spread that digitalization might be anticipated to
afford. While each focusing on ecosystem processes, the papers also do not explore the roles
digitalization play in facilitating these. With respect to entrepreneurial opportunity
identification and pursuit, we have few insights regarding the role of non-localized, less-
predefined entrepreneurial agency. These provide pointers for future research.
Future research
We now outline several salient issues and themes for future research based on the elements in
our structural model (see Figure 1 and Table 2).
Digital affordances: The centrality of digitalization in the conceptualization of
entrepreneurial ecosystems implies the need to further investigate the role of digital
technologies and related affordances. First, we need to know more about how digitalization
influences the structures and processes that comprise an entrepreneurial ecosystem. For
example, what are the new types of institutions and institutional arrangements afforded by
digitalization, and what are their roles in entrepreneurial ecosystems? How do the distinctive
structural elements of entrepreneurial ecosystems overlap and interact with more traditional
cluster structures? How do digital infrastructures and their associated sociotechnical
processes impact the development and sharing of cluster-level architectural knowledge in
entrepreneurial ecosystems? Second, it is also necessary to consider how digitalization shapes
the outcomes of entrepreneurial ecosystems. For instance, how do the qualities of the digital
infrastructure in a given region regulate the quality of an entrepreneurial ecosystem’s startup
and scaleup activities in that region? How and to what extent does digitalization enhance
learning speed in entrepreneurial ecosystems? Third, we identified three broad affordances
associated with digitalization. Future research may identify other digital affordances and
explore their impact on the nature and structure of entrepreneurial ecosystems. A more
granular consideration of specific digital infrastructures and technologies may offer more
nuance on how these create digital affordances and shape entrepreneurial ecosystem
structures and outcomes.
Interaction between digital and spatial affordances: Future research should explore
more in detail how digital affordances shape spatial affordances and alleviate spatial
constraints. Although, for example, digital crowdfunding platforms have proliferated, to what
extent do they substitute for local funding? Similarly, research could also consider how
specific digital affordances (e.g., generativity) interact with specific spatial affordances. Do
particular spatial and digital affordances support particular structural elements of
entrepreneurial ecosystems (e.g., accelerators, innovation challenges, business angels)?
Facilitating processes and mechanisms: Future research should consider the main
facilitating mechanisms of entrepreneurial ecosystems. We identified several facilitating
processes, ranging from motivation, concept development, and opportunity development for
stand-up processes, through horizontal knowledge spillovers, business model
experimentation, and team-building for startup processes, and resourcing and cross-cluster
linkages for scaleup processes. What are the interrelationships between these mechanisms?
For instance, are there systematic relationships between the key stand-up, startup and scaleup
processes? More specifically, is there a systematic relationship between horizontal
knowledge spillovers and the key stand-up processes (such as talent development, concept
development and opportunity identification)? Are there specific institutional arrangements in
entrepreneurial ecosystems that are more effective in facilitating horizontal knowledge
sharing? Future research should also consider how the facilitating processes and mechanisms
interact with the structural elements: Do differences in the quality of new venture accelerators
and other structures specializing in horizontal knowledge spillover facilitation shape the
intensity of horizontal spillovers across entrepreneurial ventures? How do team-building
structures such as accelerators and co-working spaces, and the regional concentration of
associated human capital influence the quality of human capital in entrepreneurial teams?
Further research should also consider the influence of these facilitating mechanisms on
ecosystem outcomes. Given that cluster-level architectural knowledge tends to be sticky, how
does the quality and innovativeness vary among new venture business models that emerge
from different entrepreneurial ecosystems? How do these mechanisms facilitate the evolution
and scalability of business models? What are the ‘dark side’ aspects (such as unequal access,
bias or social inequity) of entrepreneurial ecosystems resulting from digitalization? When can
such aspects be alleviated through formal mechanisms (e.g., regulations), and when is self-
regulation more effective (e.g., shared social norms)?
Entrepreneurial ecosystem outcomes and shared goals: Another set of questions
relates to entrepreneurial ecosystem outcomes. What is the range of outcomes created by
entrepreneurial ecosystems, and how can these be measured? The paper by Thompson et al
provides a reminder that these outcomes do not need to be restricted to economic outcomes
only, as some ecosystems seem to emphasize social and public good benefits. Also, we need
to know more how the outcomes created by the constituent elements of entrepreneurial
ecosystems contribute to and relate with ecosystem-level outcomes. And there is a need to
know more about how entrepreneurial ecosystems affect the performance and survival
outcomes at the level of individual entrepreneurial ventures. What is the impact on the
success or failure of individual ventures if the entrepreneurial ecosystem they are part of fails
or declines? To what extent can they be mobile in order to survive?
The Seattle case also raises the interesting question concerning ecosystem-level
shared goals. It is likely that many entrepreneurial ecosystems do not develop a shared sense
of mission and goals, which does seem to have emerged in the Seattle case.6 And, the
absence of ecosystem-level shared goals (combined with absence of membership
arrangements and mechanisms for collective decision-making) may constitute an important
differentiator between entrepreneurial and innovation ecosystems (Gulati et al., 2012). On the
other hand, as research on open source and virtual communities has shown, there might be
alternative mechanisms for shared goals to emerge in entrepreneurial ecosystems. Further,
digital affordances may also play a role in facilitating the emergence of such shared goals in
virtual forums (e.g., crowdsourcing, crowdfunding, 3D printing platforms) associated with
entrepreneurial ecosystems (Majchrzak and Malhotra, 2013). What are some of the structural
elements, mechanisms, and contextual factors that influence the emergence and evolution of
6 Note that ecosystem-level shared goals are not the same thing as shared social norms and culture. Most
entrepreneurial ecosystems we have seen seem to develop strong social norms that celebrate success, emphasize
openness and collaboration, and punish freeriding.
shared goals in entrepreneurial ecosystems? How long do such ecosystem-level shared goals
persist over time, and how much do they enable or constrain the actions of individual
ventures? Would such shared goals have greater salience for non-for profit missions such as
social and environmental sustainability? What is the relative role of ecosystem-level shared
goals (when they do exist) and ecosystem-specific shared social norms and culture in
encouraging horizontal knowledge spillover and other ecosystem benefits? These questions
also highlight the need to study ecosystem governance structures more closely. For example,
Spigel and Harrison emphasized the importance of entrepreneurs ‘leading’ entrepreneurial
ecosystems. When is this necessary? Are entrepreneurs always more effective? And when do
entrepreneurial ecosystems evolve governance structures?
Contextual factors: Another stream of research should examine contextual aspects and
their effects on entrepreneurial ecosystems and their outcomes. One set of questions may
relate to boundary conditions: How does the role of digitalization vary depending on whether
the outcomes of ecosystems are commercial or social? What are the factors that determine the
vertical and horizontal sector scope and boundaries of an entrepreneurial ecosystem? How do
these differ across different geographic regions and sectors? What configurations of actors
within an entrepreneurial region are most conducive to discovering and exploiting
opportunities? Another set of questions should consider the relationship between
entrepreneurial ecosystems. For instance, to what extent does architectural knowledge spill
over across entrepreneurial ecosystems?
Evolution of entrepreneurial ecosystems: Entrepreneurial ecosystems are continually
evolving, often from pre-existing regional clusters. What do we know about the life-cycle of
entrepreneurial ecosystems? How do they emerge, grow, reinvigorate, decline? How do the
boundaries of an entrepreneurial ecosystems evolve over time? What ‘frictions’ are there in
changing these boundaries? What sequencings of structural elements are important to the
emergence of entrepreneurial ecosystems? How adaptable are entrepreneurial ecosystems to
changes in digital technologies and infrastructures?
Entrepreneurial ecosystems and innovation ecosystems: Another promising avenue
for research relates to the comparison and overlap of entrepreneurial ecosystems and
platform-based innovation ecosystems. As described previously, entrepreneurial ecosystems
are communities of stakeholders and external resources organized around the process of
entrepreneurial opportunity discovery, pursuit, and scaleup, whereas platform-centric
innovation ecosystems are communities of co-specialised and complementary producers,
organised around a shared set of resources and standards they exploit (Autio and Thomas,
2014). While in the case of entrepreneurial ecosystems, the primary medium of value creation
is new ventures and associated market capitalization, in the case of innovation ecosystems it
tends to be innovation that exploits platform complementarity. At the same time, some
platform leaders (e.g., Salesforce) have recognized the merits of advancing their platforms by
promoting entrepreneurial ecosystem -type structures and mechanisms that are solely focused
on the digital platform they control — e.g., by creating platform-specific venture accelerators
and seed funding mechanisms. Although some early empirical work has considered the
relationship between entrepreneurial and innovation ecosystems (see, for instance Thomas,
Sharapov and Autio, 2017), there is a need for future research that examines the nature and
effectiveness of such platform-specific entrepreneurial ecosystems and the boundary
conditions associated with this new phenomenon.
Implications for policy and practice
Our structural framework suggests important implications for policy and practice. In
particular, policymakers need to adopt novel approaches to stimulate entrepreneurial
ecosystems that differ from those in place to develop industrial clusters or support already
established small- and medium-sized companies (Autio, 2016; Autio and Rannikko, 2017).
Entrepreneurial ecosystems are distributed structures, the constituent elements of which ‘co-
create’ ecosystem outputs. Therefore, traditional, siloed approaches optimized to fixing static
market failures or specific structural elements in isolation are not likely to be effective (Autio
and Levie, 2017). Ecosystem failures are likely to be dynamic and trace back to interactions
among ecosystem structures and participants. Therefore, in addition to facilitating specific
structural elements of entrepreneurial ecosystems, policymakers need to support interactions
among these such that they effectively facilitate business model experimentation and
associated horizontal knowledge spillovers. Traditional, siloed and ‘top-down’ policy
interventions are not likely to work, and policy-makers should consider more holistic,
participative, and facilitative approaches that engage all ecosystem stakeholders into a sense-
making process that seeks to understand specific ecosystem dynamics and fix bottlenecks
through bottom-up initiative (Autio and Levie, 2017; Feld, 2012). In addition, policy-makers
should also recognize the wider role of entrepreneurial ecosystems as hotbeds of business
model innovation and the diffusion of radical, digitally-enabled business models in the
economy (Autio and Rannikko, 2017). As we noted previously, digitalization is an economy-
wide process that is likely to transform how economic and societal value is created, delivered,
and captured. The specialization of entrepreneurial ecosystems in supporting business model
innovation makes them an important structure to advance the digital transformation of the
economy and society, one that policy-makers should not ignore.
Finally, our conceptual review offers implications for prospective entrepreneurs and
investors. Entrepreneurial ecosystems cultivate specialized structures and cluster-specific
knowledge to support business model experimentation and venture scaleup. Therefore, there
should be differences across entrepreneurial ecosystems in terms of how well they support
these processes. Such differences should inform new venture location decisions. Practitioners
should also recognize the role of different ecosystem structures in facilitating horizontal
knowledge spillovers and invest effort into cultivating these for the benefit of their own
In this paper we have sought to strengthen the theoretical grounding of the burgeoning
literature on entrepreneurial ecosystems by illustrating how they represent a novel cluster
type. We hope this grounding will help inform both practitioners and policy-makers, not only
by highlighting distinctive characteristics of entrepreneurial ecosystems, but also, by
identifying key drivers of the entrepreneurial ecosystem phenomenon and the roles
entrepreneurial ecosystems may play in the wider economic and societal context. The three
papers included in this special issue reflect the rich opportunities entrepreneurial ecosystems
research offers, and we hope that the research questions proposed in this paper will help
shape the emergent research agenda on this important, global phenomenon.
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Figure 1 Structural Framework of Entrepreneurial Ecosystems
Table 1 Stylized differences between ‘traditional’ clusters and entrepreneurial
Industrial and
Marshallian districts
Knowledge clusters,
innovative milieus,
systems of innovation
View of the
Flexibly specialized
production system
Localized system of
learning and innovation
System of
opportunity discovery,
pursuit, and scaleup
productivity, pecuniary
advantages, process and
product innovation
Product and process
innovation, ‘technology-
push’ innovation, non-
pecuniary advantages
Business model
innovation and the
diffusion of radical new
business models in the
Vertical and voluntary
Vertical and voluntary
relationships, linear
technology translation)
and horizontal and
involuntary (competitive
Horizontal and voluntary
(sharing of experiences
from business model
Role of
Participants in regional
production system,
shapers of cluster
Participants in regional
knowledge and learning
system, shapers of
cluster institutions,
champions of
Business model
experimenters, scalers-
up of successful business
Drivers of
Market concentration,
value chain
specialized resource
transaction cost
economization through
relational trust
Voluntary vertical
knowledge spillovers,
voluntary and
involuntary horizontal
knowledge spillovers,
spillovers of generic
research knowledge
Digitalization and
technology affordances
to business model
Locus of
Internal to the cluster
Internal to the cluster
Largely external to the
Chambers of commerce,
industry associations
Chambers of commerce,
industry associations,
science parks
New venture
accelerators, co-working
spaces, makerspaces,
networking events,
innovation challenges
(e.g., hackathons)
Function of
Facilitate flexible
coordination of
distributed production
Facilitate knowledge
creation, transfer, and
combination, as well as
Facilitate business model
experimentation and
associated experience
sharing, rapid scaleup of
successful business
models and new
Table 2 Structural model of entrepreneurial ecosystems
Hackathons, innovation
challenges, talent
scouting, speed dating
events, networking
events, entrepreneurship
Accelerators, co-working
spaces, makerspaces,
networking events, startup
academies, crowdfunding,
angel investors
Business angels,
crowdfunding, venture
capital, mentoring
Key processes
Motivation, talent
development, concept
Architectural knowledge
cultivation and
dissemination, business
model experimentation,
team building
Resourcing, cross-
cluster linkages,
provisioning, supply of
specialized human
Entrepreneur network,
new venture concepts
New ventures,
architectural knowledge
on business model
innovation and
opportunity pursuit and
Scaled-up ventures,
IPOs, trade sales
Table 3 Papers in this Special Issue
Research Question
Data and Method
Findings and Conclusions
Purdy, Ventresca
How does an
ecosystem for social
impact business begins
to take form in the midst
of an ongoing business
and commercial context?
Field theory
Data relating to the
landscape for social impact
and related entrepreneurial
activity in the greater Seattle
area from early 2000 to
2014; Public records,
websites, news outlets and
blogs, to capture webpages,
documents and data on new
ventures created through
SPCs and B Corporations;
attendance at six events for
and about social businesses;
multiple interviews with 40
individuals involved in
starting and supporting social
impact ventures; Analyzed
using NVivo 10.
Entrepreneurial ecosystems form through
endogenous, bottom-up, and time-patterned
processes. What actors do and how the interactions
change over time supports a two-period model of
ecosystem formation where a phase transition
occurs from distributed, disparate activity to
coordinated and integrated activity. In the initial
period, efforts to generate shared meanings through
language and to create community give initial form
to a nascent ecosystem via cultural-cognitive and
material foundations, respectively. The second
period is marked by shifts in the kind and quality of
interactions, becoming more frequent, formalized
and dense or interconnected across sets
of actors. Interactions and shared meanings
relatively separate in the initial period undergo a
phase transition to become more mutually
reinforcing in the second, facilitating the sustained
effort over time that establishes durable patterns of
activity in an entrepreneurial ecosystem and garners
legitimacy needed to coexist with or change
existing conventions of market-based capitalism.
How do accelerators
affect venture success or
failure? (2) How do
accelerators affect
Interpretive qualitative
approach to elaborate
existing theory; Accelerators
in the ecosystem of
Bangalore; Face-to-face
interviews with 51
accelerator managers,
accelerator mentors,
entrepreneurship educators,
Four different kinds of meso-level,
accelerator expertiseconnection expertise,
development expertise, coordination expertise, and
selection expertisecan influence commitment to
the regional entrepreneurial ecosystem, venture
validation (success or failure) and ecosystem
additionality through ecosystem intermediation
Research Question
Data and Method
Findings and Conclusions
founder participants in
accelerators, graduates of
accelerators and policy
makers; online archival
material from 49 websites,
13 online video interviews of
actors within the ecosystem,
26 online news articles and
blog posts and 301 pages of
policy documents and
industry reports; Analyzed
using NVivo.
Accelerators act as intermediaries in regional
entrepreneurial ecosystems through commitment
engagement processes, venture development
processes, and ecosystem development processes.
Spigel, Harrison
How can a process-
based view of
ecosystems provide a
better framework to
understand their role in
supporting new venture
Critical examination of the
literature concerning
relationships within
ecosystems and comparisons
with clusters and regional
innovation systems;
development of process-
based view of ecosystems;
implications for a typology
of ecosystem types.
Demonstrate that EE are a unique domain of study
distinct to related work in clusters and regional
innovation systems as they focus on entrepreneurs
and new ventures that require different types of
knowledge and support than older and more
established firms and acquire the resources they
need through different means; EE better understood
as ongoing processes through which entrepreneurs
acquire resources, knowledge, and support,
increasing their competitive advantage and ability
to scale up; as new ventures grow, they strengthen
the overall EE;; develop a typology to assess the
health of entrepreneurial ecosystems; Identifies four
types of Entrepreneurial ecosystems dependent on
resource munificence and network strength: strong,
arid, irrigated and weak

Supplementary resource (1)

... Against the backdrop of continuous breakthroughs in the updating and iteration of digital technologies, digital transformation has been the general trend for enterprises [1]. It increasingly becomes a "mandatory topic" related to the survival and long-term development of enterprises, showing strong resilience and potential. ...
... where Digitalit is the degree of digital transformation of firm i in year t, Connectionit is the level of executive connections of firm i in year t, Mediatorit indicates the degree of asset specificity of firm i in year t (ASit), Moderatorit indicates the dynamic of the environment of firm i in year t (EDit) and the dynamic capability of firm i in year t (DCit), respectively, Controlsit is the set of control variables, are year-fixed effects, are region-fixed effects, and are random disturbance terms. In this paper, we test Hypothesis H1 using model (1), test Hypothesis H2 using models (2) and (3), and test Hypotheses H3a and H3b using model (4). ...
... First, in column (2), we find a significantly negative coefficient (−0.017) on Connection with asset specificity as the dependent variable, suggesting that the executive connection can effectively reduce the investment of enterprise-specific assets. Further, after including the mediating variable (AS) in the regression model (1), we document a positive and significant coefficient (0.780) on Connection in column (3), which is lower than the estimated coefficient (1.006) in column (1), and the coefficient on AS is also significant and negative, revealing that the executive connection could boost digital transformation by lowering asset specificity of firms. In addition, the confidence interval obtained based on the bootstrap test is (0.197, 0.324), being a positive interval excluding the value of 0, and the z-statistic value of AS in the Sobel test is positive and significant (7.555). ...
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In the context of the digital economy, the external connection of executives provides enterprises with a good idea to amplify their potential for digital transformation with the help of external forces. Therefore, we conduct a theoretical exploration and an empirical analysis of the relationship between executive connections and enterprise digital transformation. As the research sample, we use the A-share manufacturing companies listed in China from 2012 to 2021. According to sufficient verifications, we discover that executive connections can effectively support digital transformation. From the perspective of each subdivision dimension, executive business connections, executive technical connections, and executive financial connections can significantly promote digital transformation, among which executive technical connections have the greatest favorable impact. However, the impact of executive political connections on digital transformation is not obvious. Additionally, executive connections primarily foster enterprise digital transformation by reducing enterprise asset specificity. The results of the boundary mechanism test demonstrate that the external environmental dynamics and the internal dynamic capabilities reinforce the positive effect of executive connections on digital transformation. These findings contribute to a deeper understanding of the role of executive connections in digital transformation and provide practical guidance for firms to accelerate digital transformation.
... Researchers and others loosely used digitization and digitalization interchangeably because of the limited leveraging of digital resources. As a result, the "transformative" scope was small and the outcomes were relatively marginal (Autio et al., 2018;Verhoef et al., 2021). With information technology (IT) readiness and evolution, firms began to broadly embed new digital technologies, such as artificial intelligence, blockchain and machine learning, into multiple aspects of operations to improve business processes and exploit new business models (Li et al., 2021). ...
Purpose This study conceptualizes the digital transformation (DT) strategy in a supply chain context, identifies its drivers from intra- and inter-organizational perspectives and examines the effect of the DT strategy on the strategic agility and financial performance of Chinese manufacturing firms. Design/methodology/approach The authors constructed a theoretical model by synthesizing the diffusion of innovation and organizational information processing theory (OIPT) and provided a set of hypotheses. The authors empirically tested the arguments using partial least squares structural equation modeling using data from a sample of 200 manufacturing firms in China. Findings The findings indicate that while supply chain connectivity positively affects DT adoption and DT routinization, data analytics capability and organizational learning positively influence DT adoption but not DT routinization. The mediation analysis also shows that DT strategy has significant direct effects on financial performance and a stronger indirect influence on financial performance via improved strategic agility. Research limitations/implications This study responds to repeated calls for a new understanding of supply chain DT strategy. In addition, the study offers important contributions to the literature by identifying the potential discord between the existing DT strategy and the supply chain context and proposes a new framework that provides essential theoretical underpinnings. Originality/value This study enriches the literature by conceptualizing and validating the dimensions, driving factors and performance implications of DT strategy in strategic supply chain management.
... These skills cloud enhance both of flexibility and efficiency of all organizations' activities, particularly for those that arise from the critical digitalisation to digital entrepreneurship. This also could help the digital business to assess clearly and exploit the possible emerging opportunities like employees and customers and to be able work together (Autio et al., 2018). Collaboration among the projects also without slow or unnecessary offline interaction can interoperate with the different cloud systems. ...
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digital entrepreneurial activities have become most crucial for the modern business entrepreneurs due to the growing changes among the business environments. This study aims to address the effects of the different digital entrepreneurial activities (entrepreneurial skills, entrepreneurial infrastructure, digital transformation) on the entrepreneurship from the perspective of the entrepreneurs in Jordan. To perform this research and fulfill the research objective, it used a quantitative research method to collect the data from entrepreneurs are running businesses in Jordan. To analysis the data, the study used PLS-SEM approach to conduct the analysis part with a total of 212 participants represent the entrepreneurs in Jordan. The results showed a significant effect of all hypothesized research statements and with a positive perspective of the entrepreneurs in Jordan towards this issue. The research findings also offered some research implications to support new evidence among the relevant literature, and would contribute to cover the existed knowledge research gaps for further insights in this area for better understanding in this topic.
Purpose This study aims to explore the internationalization trajectory of emerging country digital economy ventures by specifically concentrating on how ambidexterity facilitates international market expansion. Further, this paper examines how these ventures develop dynamic capabilities by using their ambidextrous skills in the entrepreneurial ecosystem (EE). Design/methodology/approach This study adopts a multiple-case research design where data were gathered from five digital economy ventures in Turkey, serving an international array of customers. Findings The analyses reveal that, to a large extent, internationalization is enabled by the extensive use of ambidextrous skills in the ecosystem domain. We found evidence for practicing exploration and exploitation while interacting with several ecosystem pillars grouped as founder-related, firm-related and business context-related factors. These interactions portray how ventures sense, seize and transform resources to support their international expansion. Originality/value This study extends the current literature on internationalization by discussing the role of ambidexterity as a dynamic capability. The findings also demonstrate the EE as a construct to explain international entrepreneurial activity. Further, the study extends the existing literature by considering the calls for research on dynamic capabilities of international new ventures (INVs). Finally, the findings point to several implications both for practitioners and policymakers.
Small and medium-sized firms are increasingly adopting digital technologies to transform themselves. Yet, the ability of top-management teams to embark on strategic transformations depends on entrepreneurial ideas and initiatives that arise across the firm. We conducted a qualitative pre-study of manufacturing companies to understand their challenges in engaging in and implementing digital transformation. The study drew attention to the prevalence of internal secrecy that inhibits knowledge sharing across units and, therefore, complicates the identification and elaboration of customer-centric innovations based on digital data. Building on our initial findings and research, we then developed and tested hypotheses that relate organisational secrecy, competitive pressures, and the range of digital technologies in use to top management’s shift in focus towards digital innovation. We find, in particular, that organisational secrecy impeds a shift in top management attention towards those forms of digital innovation that require complex cross-unit coordination. We also found that perceived external pressures from competition were associated with an increase in top management’s focus on digital innovation. Our study contributes to the literature on digital transformation and strategy processes more generally by elaborating on how organisational secrecy can hamper strategic renewal. The findings suggest that a culture of openness and transparency can facilitate strategic renewal in established companies.
Conference Paper
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This research aims to identify and examine the underlying factors of entrepreneurial intentions among seniors. Besides, it examines whether the HDI level of the EU nations, combined with individual characteristics, affect the choices of the seniors. The empirical findings aim to shed some light on the connection between the ecosystem and senior entrepreneurship, providing relevant insights to build the economic programmes endorsed by development agencies, to promote these initiatives.
Purpose This study aims to outline the influence of various combinations of antecedent conditions for startups being accepted into business incubators in Italy and Romania. The degree to which these conditions affect acceptance is referred to here as the Business Ideas Acceptance Degree (BIAD). The antecedent conditions considered are business idea potential, business plan quality, entrepreneurial team features, business project progress stage, available financial resources, debts of potential incubated companies, commitment to apply for national/EU funds, business area related to incubator mission, proposed technological content level, technological transfer from university/research centres and spin-off of a partner-entity of the incubator. Design/methodology/approach The methodological toolkit used was mixed: correlation-based analysis (CBA), machine learning (ML) techniques and fsQCA. Principal component analysis enabled the selection of the most representative antecedent conditions from both business incubator samples in Italy and Romania, further used in fsQCA analyses. XGBoost algorithm has been also used. K-Means clustering, an unsupervised learning algorithm that groups unlabeled dataset into different clusters, led to the configuration of two clusters associated to each of the countries involved in this study (Romania and Italy). Findings The findings reveal the differences between the different antecedent conditions that can contribute to startups being accepted into business incubators in Italy and Romania. The validation of the fsQCA equifinality principle in both samples shows that the selected antecedent conditions, mixed in combinations of “causal recipes”, lead to a high BIAD by business incubators from both countries. Originality/value This study reveals the differences between different antecedent conditions, capable to contribute to the start-up acceptance within business incubators from Italy and Romania. Furthermore, the validation of fsQCA equifinality principle in both samples highlight that the selected antecedent conditions, mixed in combinations of causal recipes, lead to a high degree of business ideas' acceptance in business incubators.
Heritage forms of entrepreneurship are less well-understood in the literature, which is unusual given that heritage should be a consideration of any entrepreneurial decision. As a result, this chapter focuses on heritage entrepreneurship but also links it to research regarding business model innovation. This enables research on heritage entrepreneurship to continue to gain a reputation as being a topical and relevant field of inquiry. The reason for this is due to the need to consider historical and contextual factors when analysing entrepreneurial behaviour thereby focusing on two streams of research of practical significance. This chapter provides some ideas and questions about where the field is heading and what needs to be done in order to ensure this is successful.
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The challenges that managers face in performing their managerial functions during the covid19 pandemic are numerous. The aim of this paper is to investigate the problems that managers face in performing their managerial functions. The three biggest problems are noticeable, the psychological aspect (care for employees who are under stress due to the covid-19 pandemic), rising prices of raw materials needed for business and the inability to plan due to instability in business caused by the covid-19 pandemic. During the covid 19 pandemic, managers are more exposed to stress in performing managerial functions and spend more time caring for employee satisfaction and maintaining the health of the collective. Also, the increase in raw material prices makes the company's current operations more difficult. In the part of the research, an interview is conducted with 35 operational managers in Croatian companies, in January 2022, with pre-prepared questions related to the daily performance of managerial functions in companies during the covid-19 pandemic. The data obtained by the interview are statistically presented in the chapter research results. The scientific contribution of this paper is reflected in the research of the problems that managers face in performing their managerial functions during the covid-19 pandemic. Managers point out that they are more stressed, as are employees. It is more difficult for them to perform all managerial functions, and they have the most problems with the planning function, because there is an unplanned absence of workers in the workplace every day, which directly affects another managerial function, organization. The problems caused by the covid-19 pandemic are visible in the performance of all managerial functions, and least of all in the performance of the control function
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Research Summary This research uses insights from field theory to explore the early moments of how entrepreneurial ecosystems form through everyday interactions. We examine the cultural-cognitive and material micro-dynamics of activities occurring in support of social impact entrepreneurs and businesses from 2000 to 2014 in the Seattle WA region using archival and interview data sources. The pattern of results about what actors do and how interactions change over time supports a two-period model of ecosystem formation where initial distributed and disparate activity undergoes a phase transition to coalesce into a more coordinated and integrated social order. The findings point to endogenous sources of structuring, including language and interaction, rather than exogenous sources such as government action or instrumental policy goals. Managerial Summary How do the ecosystems that support entrepreneurs form? Rather than being created through top-down actions of governments and other powerful actors, we argue that entrepreneurial ecosystems form through the everyday interactions of individuals striving to create shared meaning, resources and infrastructure needed to support their new ventures. This is especially true in ecosystems focused on creating social impact, which don’t always offer the high returns expected in a market-based capitalistic system. Our study shows how the initial activities of distributed, disparate individuals and groups rather suddenly coalesce into more coordinated, integrated and durable patterns of social interaction, creating the methods, resources and legitimacy needed for an entrepreneurial ecosystem for social impact businesses to coexist with or change existing conventions of market-based capitalism.
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To understand the intermediary role of accelerators in the developing regional entrepreneurial ecosystem of Bangalore, we analyze data from 54 interviews with accelerator graduates, accelerator managers, and other ecosystem stakeholders, and from 49 websites, 13 online video interviews, 26 online news sources and 301 pages of policy documents. Specifically, we adopt a socially-situated entrepreneurial cognition approach to theorize how accelerator expertise, existing at a meso-level, intermediates between (micro-level) founders and the (macro-level) ecosystem. In our model, four types of accelerator expertise—connection, development, coordination, and selection—together increase stakeholders’ commitment to the entrepreneurial ecosystem, leading to venture validation (success or failure) and ecosystem additionality. These findings indicate that accelerators contribute to ecosystems in a way that is distinct from, but supportive of, building individual ventures.
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There is an increasing policy interest toward entrepreneurial ecosystems. Yet, little is actually known about how an entrepreneurial ecosystem works and what the related policy challenges are. Drawing on research on ecological economics and community governance, this chapter develops a theoretical framework for entrepreneurial ecosystem management. Using a Scottish entrepreneurial ecosystem initiative as an example, the authors conclude that policy approaches that emphasize deep stakeholder engagement are likely to give rise to better informed, targeted, and more effectively implemented policy initiatives in entrepreneurial ecosystems than will market failure and structural failure approaches.
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Entrepreneurial ecosystems have emerged as a popular concept within entrepreneurship policy and practitioner communities. Specifically, they are seen as a regional economic development strategy based around creating supportive environments that foster innovative startups. However, existing research on entrepreneurial ecosystems has been largely typological and atheoretical and has not yet explored how they influence the entrepreneurship process. This paper critically examines the relationships between ecosystems and other existing bodies of work such as clusters and regional innovation systems. Drawing on this background, the paper suggests that a process-based view of ecosystems provides a better framework to understand their role in supporting new venture creation. This framework is used to explain the evolution and transformation of entrepreneurial ecosystems and to create a typology of different ecosystem structures.
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This year's Global Entrepreneurship Index features an exploration of digital entrepreneurship ecosystems. The Global Entrepreneurship Index both captures the context features of entrepreneurship and fills a gap in the measurement of development. Building on recent advances in entrepreneurship and economic development, the authors have created an index that offers a measure of the quality of the business formation process in 137 countries. The authors expertly capture the contextual feature of entrepreneurship by focusing on entrepreneurial attitudes, entrepreneurial abilities and entrepreneurial aspirations. The data and their contribution to the business formation process are supported by three decades of research into entrepreneurship across a host of countries. The unique index construction of individual and institutional measures integrates 31 variables from various data sources into 14 pillars, three sub-indexes and a 'super index'. The relationship between entrepreneurship and economic development appears to be more or less mildly S-shaped. The findings suggest moving away from simple measures of entrepreneurship across countries illustrating a U-shaped or L-shaped relationship to more complex measures, which are positively related to development. The model has important implications for development policy. This unique book will be invaluable for researchers, policymakers and entrepreneurs keen to expand their understanding of entrepreneurship and development.