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DIGITAL AFFORDANCES, SPATIAL AFFORDANCES, AND THE GENESIS OF ENTREPRENEURIAL ECOSYSTEMS Erkko Autio* Imperial College Business School, London, United Kingdom and Tilburg University School of Economics and Management [email protected] Satish Nambisan Weatherhead School of Management, Case Western Reserve University [email protected] Llewellyn D W Thomas Imperial College Business School, London, United Kingdom [email protected] Mike Wright Center for Management Buyout Research, Imperial College Business School, London, United Kingdom and ETH, Zurich [email protected] Forthcoming in the Strategic Entrepreneurship Journal . 14 July 2017 1

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Page 1: Digital affordances, spatial affordances, and the genesis of ... · Web viewand related entrepreneurial activity in the greater Seattle area from early 2000 to 2014; Public records,

DIGITAL AFFORDANCES, SPATIAL AFFORDANCES, AND THE GENESIS OF ENTREPRENEURIAL ECOSYSTEMS

Erkko Autio*Imperial College Business School, London, United Kingdom

and Tilburg University School of Economics and [email protected]

Satish NambisanWeatherhead School of Management, Case Western Reserve University

[email protected]

Llewellyn D W ThomasImperial College Business School, London, United Kingdom

[email protected]

Mike WrightCenter for Management Buyout Research,

Imperial College Business School, London, United Kingdom and ETH, [email protected]

Forthcoming in the Strategic Entrepreneurship Journal.

14 July 2017

*corresponding author

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ABSTRACT

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.

MANAGERIAL SUMMARY

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 companies.

Keywords: entrepreneurial ecosystem; digital affordance; spatial affordance; business model innovation; architectural knowledge; startup; scale-up; cluster

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INTRODUCTION

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

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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:

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‘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,

additional distinctive features of entrepreneurial ecosystems concern the organization of

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.

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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 infrastructural”2 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.

DIGITAL AFFORDANCESAND THE ORGANIZATION OF ECONOMIC ACTIVITY

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

2 In contrast, digitization is the technical conversion of analogue information into digital form.

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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

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

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).

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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).

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

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).

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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

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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

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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.

SPATIAL AFFORDANCES, CLUSTERS, AND ENTREPRENEURIAL ECOSYSTEMS

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

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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

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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

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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

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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

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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

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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

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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

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 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, 2013).

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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.

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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

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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

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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.

ENTREPRENEURIAL ECOSYSTEMS: 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

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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

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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

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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

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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.

PAPERS IN THIS SPECIAL ISSUE

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

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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

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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

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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 AND POLICY IMPLICATIONS

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

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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

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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

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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

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

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.

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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,

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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

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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

ventures.

Conclusion

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

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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

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Table 1 Stylized differences between ‘traditional’ clusters and entrepreneurial ecosystems

Industrial and Marshallian districts

Knowledge clusters, innovative milieus,

systems of innovation

Entrepreneurial ecosystems

View of the cluster

Flexibly specialized production system

Localized system of learning and innovation

System of entrepreneurial opportunity discovery, pursuit, and scaleup

Cluster-level economic benefit

Manufacturing 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 economy

Dominant knowledge spillovers

Vertical and voluntary (user-producer relationships)

Vertical and voluntary (user-producer relationships, linear technology translation) and horizontal and involuntary (competitive emulation)

Horizontal and voluntary (sharing of experiences from business model experiments)

Role of entrepreneurs

Participants in regional production system, shapers of cluster institutions

Participants in regional knowledge and learning system, shapers of cluster institutions, champions of technology-based innovation

Business model experimenters, scalers-up of successful business models

Drivers of entrepreneurial opportunity

Market concentration, value chain specialization, specialized resource agglomeration, 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 innovation

Locus of opportunity drivers

Internal to the cluster Internal to the cluster Largely external to the cluster

Characteristic structural elements

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 cluster-specific structural elements

Facilitate flexible coordination of distributed production processes

Facilitate knowledge creation, transfer, and combination, as well as ‘technology-push’ innovation

Facilitate business model experimentation and associated experience sharing, rapid scaleup of successful business models and new ventures

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Table 2 Structural model of entrepreneurial ecosystems

  Stand-up Startup ScaleupStructuralelements

Hackathons, innovation challenges, talent scouting, speed dating events, networking events, entrepreneurship programs

Accelerators, co-working spaces, makerspaces, networking events, startup academies, crowdfunding, angel investors

Business angels, crowdfunding, venture capital, mentoring initiatives

Key processes and mechanisms

Motivation, talent development, concept development, opportunity identification

Architectural knowledge cultivation and dissemination, business model experimentation, team building

Resourcing, cross-cluster linkages, infrastructure provisioning, supply of specialized human capital

Outcomes Entrepreneur network, new venture concepts

New ventures, architectural knowledge on business model innovation and entrepreneurial opportunity pursuit and scaleup

Scaled-up ventures, IPOs, trade sales

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Table 3 Papers in this Special Issue

Authors Research Question Theory Data and Method Findings and Conclusions

Thompson, Purdy, Ventresca

How does anentrepreneurial 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 businessand 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 throughendogenous, 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 setsof 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.

Goswami, Mitchell, Bhagavatula

How do accelerators affect venture success or failure? (2) How do accelerators affect regionalentrepreneurial ecosystems?

Socially-situated entrepreneurial cognition

Interpretive qualitative approach to elaborate existing theory; Accelerators in the ecosystem of Bangalore; Face-to-face interviews with 51 accelerator managers, accelerator mentors,

Four different kinds of meso-level,accelerator expertise—connection expertise, development expertise, coordination expertise, and selection expertise—can influence commitment to the regional entrepreneurial ecosystem, venture validation (success or failure) and ecosystem additionality through ecosystem intermediation processes.

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Authors Research Question Theory Data and Method Findings and Conclusions

entrepreneurship educators, 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 creation?

Clusters, regional innovation systems

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

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