ChapterPDF Available

Management of Entrepreneurial Ecosystems

July 2017

July 2017

DOI:10.1002/9781118970812.ch19

In book: The Wiley Handbook of Entrepreneurship (pp.423-449)

Authors:

Erkko Autio

Imperial College London

Jonathan Levie

University of Galway

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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Management of Entrepreneurial Ecosystems

Erkko Autio*1 and Jonathan Levie2

* Corresponding author

1Imperial College Business School

and Tilburg University School of Economics and Management

2University of Strathclyde

Abstract

There is increasing policy interest towards entrepreneurial ecosystems. Yet, little is actually

known about how entrepreneurial ecosystems work and what the related policy challenges

are. Drawing on research on ecological economics and community governance, we develop a

theoretical framework for entrepreneurial ecosystem management. Using a Scottish

entrepreneurial ecosystem initiative as an example, we 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’ and ‘structural’ failure approaches.

Key words: entrepreneurial ecosystems; entrepreneurship policy; ecosystem management;

Scotland; stakeholder engagement

Citation

Autio, E., & Levie, J. 2017. Management of Entrepreneurial Ecosystems. In: G.

Ahmetoglu, T. Chamorro-Premuzic, B. Klinger, & T. Karcisky (Eds.). The Wiley Handbook

of Entrepreneurship: 423-449. Chichester: John Wiley & Sons

Introduction

The notion of entrepreneurial ecosystems has become increasingly prevalent in the

entrepreneurship policy literature (Acs, Autio, & Szerb, 2014; Auerswald, 2014; Drexler et

al., 2014; Stam, 2014; Spigel, 2015). However, there is little evidence that entrepreneurship

policy itself has become more systemic in nature. Like many socioeconomic policy areas,

entrepreneurship policy has traditionally employed top-down, siloed approaches designed to

address specific, well-defined market and structural failures by, for example, providing

subsidized funding for new businesses or enhancing SME access to R&D facilities

(Audretsch, 2011; Lundström, & Stevenson, 2005). However, siloed approaches may not be

effective in redressing complex, systemic challenges that span across policy domains

(Blackburn, & Schaper, 2012; Stam, 2015). In this chapter we explore alternative approaches

to managing entrepreneurial ecosystems.

Acs et al. (2014. p. 479) defined entrepreneurial ecosystems as: “dynamic,

institutionally embedded interaction between entrepreneurial attitudes, ability, and

aspirations, by individuals, which drives the allocation of resources through the creation and

operation of new ventures.” Entrepreneurial ecosystems are complex socioeconomic

structures that are brought to life by individual-level action (Spigel, 2015). This action is

embedded in complex interactions between multiple individual and organizational

stakeholders that make up the ecosystem, and it is expressed through the creation and

operation of new ventures. The ultimate outcome of this trial-and-error dynamic is the

allocation of resources toward productive uses, as entrepreneurs are more likely to abandon

the pursuit of poor-quality opportunities than they are to abandon high-quality

opportunities—that is, those which enable them to gain high returns on their resource

allocation.

In the ecological literature, the benefits generated by natural ecosystems are

commonly referred to as “ecosystem services,” and the practice of managing and enhancing

such benefits is referred to as “ecosystem management” (Anderies, Janssen, & Ostrom, 2004;

Grumbine, 1994; Seppelt, Dormann, Eppink, Lautenbach, & Schmidt, 2011). The dual

service created by entrepreneurial ecosystems is resource allocation towards productive uses

and the innovative, high-growth ventures that drive this process. Because entrepreneurial

ecosystem services are created through myriad localized interactions between ecosystem

stakeholders, it is not easy to trace gaps in ecosystem performance back to specific, well-

defined market and structural failures that could be addressed in a top-down mode. This

undermines the effectiveness of “market failure” and “system failure” approaches to policy-

making in entrepreneurial ecosystems (Bergek, Jacobsson, Carlsson, Lindmark, & Rickne,

2008; Woolthuis, Lankhuizen, & Gilsing, 2005). Yet, there has been little theoretical work

exploring alternative policy approaches in entrepreneurial ecosystems.

We address this gap by drawing on literature that discusses policy approaches in

socioeconomic and socioecological systems that are comparable to entrepreneurial

ecosystems in their complexity (e.g., socioecological ecosystems, multistakeholder

communities). We find that policy approaches to address such systems share several common

features, such as deep stakeholder engagement, reinforcement of generalized reciprocity and

prosocial behaviors, multipolar coordination, and collective action (Bowles & Gintis, 2002;

Kofinas, 2009; Vollan & Ostrom, 2010). Such approaches are yet to be discussed in the

context of entrepreneurial ecosystems. In this chapter, we contribute to entrepreneurial

ecosystem theory and practice by applying socioecological and community governance

theories to develop a model for collective management of entrepreneurial ecosystems. We

show how policymakers and other stakeholders can assume a leadership role in system

renewal by acting as stewards of the entrepreneurial ecosystem and engaging a balanced set

of relevant stakeholders to find ways to mutually coordinate their actions (Feld, 2012;

Ostrom, 1990).

We summarize our insights in three propositions for successful entrepreneurial

ecosystem management: one highlighting an approach to data and analysis, another

highlighting an approach to design of activities, and a third highlighting an approach to

implementation that set entrepreneurial ecosystem policymaking apart from traditional

approaches to entrepreneurship policy. We then reflect these propositions against a live case

of regional entrepreneurial ecosystem policymaking in practice in Scotland to check their

face validity. Finally, we draw conclusions regarding the role of policymakers in

entrepreneurial ecosystems and present normative suggestions.

We advance four contributions to the entrepreneurial ecosystem literature with this

design. First, this is the first study to identify analysis and management challenges to

policymaking, as posed by the systemic characteristics of entrepreneurial ecosystems.

Second, this is the first study to translate policy insights accumulated in other socioeconomic

and socioecological ecosystem domains to the context of entrepreneurial ecosystems. Third,

we link deep stakeholder engagement, a feature of policy approaches in socioecological

ecosystems, to stakeholder theory to generate insights on stakeholder selection and

engagement. Finally, through our empirical case, we confirm the face validity of the

propositions using a recent policymaking experience of entrepreneurial ecosystem

management and draw lessons for entrepreneurial ecosystem management in national and

regional contexts.

In the next section, we discuss characteristics of entrepreneurial ecosystems and

associated challenges for policymaking. In the subsequent section, we review policy research

that focuses on policy analysis and management in complex socioeconomic and

socioecological systems. From this review we derive three general propositions for effective

entrepreneurial ecosystem management. We follow this with a section in which we reflect

these propositions against a case study of an ongoing process of Scottish entrepreneurial

ecosystem facilitation, noting examples of good practice and instances where following the

propositions could have generated superior outcomes. We then discuss the results and finish

with a concluding section that outlines the implications of our contribution to entrepreneurial

ecosystem policy.

Entrepreneurial Ecosystems: Definitions and Policy Challenges

It is increasingly appreciated that entrepreneurial action by individuals and

entrepreneurial teams is subject to contextual influences (Autio et al., 2014). Yet, the systems

approach to entrepreneurship remains nascent and theoretical and conceptual work

conspicuously scarce (Acs, Kenney, Mustar, Siegel, & Wright, 2014; Auerswald, 2014;

Spigel, 2015). The early works in this area have been mostly practitioner-oriented and

eschewed conceptual discussion of their object (Feld, 2012; Isenberg, 2010). Such works tend

to focus on regional agglomerations and specialized resources and also cover tacit aspects

such as culture and institutions, but formal definitions are seldom offered. This problem even

extends to more formal attempts to measure and assess entrepreneurial ecosystems (Spigel,

2015; Stangler & Bell-Masterson, 2015).

The lack of coherent theoretical underpinnings is reflected in definitional diversity.

Stam (2014) suggested that an entrepreneurial ecosystem is an interdependent set of actors

that is governed in such a way that it enables entrepreneurial action. However,

entrepreneurial action is defined as the “pursuit of opportunities for innovation” (and not

opportunities for generating entrepreneurial profit), and the definition does not specify

welfare outcomes of the process

. Mason & Brown (2014, p. 5) suggested a rather more

comprehensive definition as: a set of interconnected entrepreneurial actors (both potential and

existing), organizations (e.g. firms, venture capitalists, business angels and banks),

institutions (universities, public sector agencies and financial bodies), and processes (business

birth rate, rate of [high-growth firms], number of serial entrepreneurs and blockbuster

entrepreneurs, and levels of entrepreneurial ambition and sell-out mentality in the society).

This definition lists structural, dynamic, and institutional elements attributed to

entrepreneurial ecosystems in the literature but does not specify ecosystem outcomes.

Another (unpublished) definition in the OECD LEED (Local Economic and Employment

Development) initiative was proposed by Voegel (2013, p. 6 an interactive community within

a geographic region, composed of varied and interdependent actors (e.g. entrepreneurs,

institutions and organizations) and factors (e.g. markets, regulatory framework, support

setting, entrepreneurial culture), which evolves over time and whose actors and factors

coexist and interact to promote new venture creation.

This definition is the most specific of the three in terms of its explicit focus on new

venture creation—but consequent system-level benefits are not elaborated.

The only peer-reviewed definition of entrepreneurial ecosystems, or as they called it,

systems of entrepreneurship, is the one proposed by Acs et al. (2014) and adopted in this

paper (quoted in the introduction)

. The distinctive aspect of this definition is its emphasis on

system-level resource allocation, rather than new venture creation, as the outcome of the

entrepreneurial ecosystem dynamic. In this definition, the creation of new ventures is the

mechanism that drives the resource allocation dynamic. This trial-and-error dynamic is driven

by individuals who mobilize resources to pursue opportunities they perceive. This dynamic

Stam also presents a graphical illustration which includes “framework conditions,” “systemic

conditions,” outputs (entrepreneurial businesses), and outcomes including productivity, employment, and well-

being. These are not included in the formal definition, however.

Spigel (2015) does not advance a formal definition.

should drive productivity, as if the perceived opportunity turns out not to be real, the

entrepreneurs will abandon the opportunity pursuit, and the resources will be reallocated

towards alternative uses that yield a higher return. If, however, entrepreneurs persist in

opportunity pursuit, this implies that that no alternative use of the mobilized resources can be

found that offers a higher return. This, then, means that successful opportunity pursuits will

allocate economic resources to their most productive use, implying welfare-increasing

allocation for the resources. While Acs et al.’s (2014) definition does not elaborate specific

components of the ecosystem, their discussion and proposed measurement approach

resonates with received works by highlighting elements such as attitudes, culture, institutions,

finance, technology transfer, and infrastructure.

As a context for policy action, entrepreneurial ecosystems differ from contexts usually

addressed by entrepreneurship policy: those of “markets” (industries) and “systems of

innovation” (Dodgson, Hughes, Foster, & Metcalfe, 2011). Markets and industries consist of

sets of mostly independent actors who may link to form value chains. A typical policy

challenge in such a context would arise when the market fails to perform a given activity

(say, R&D) or supply the necessary resources for entrepreneurial firms (say, funding). A

market failure mode of policy would address such gaps in a top-down mode by providing

economic incentives (usually through subsidies) to encourage specific activities; or plug

resource gaps through public intervention (Arrow, 1962; Audretsch, 2011). In contrast, a

system failure policy would address structural, institutional, and other failures in the

structure, activities, and functions of a given innovation system (Bergek et al., 2008; Carlsson

& Jacobsson, 1997; Woolthuis et al., 2005). While in this approach the structures and

mechanisms governing the production of innovations are more complex and often

multilayered, the focus of this approach remains on plugging gaps in different aspects of the

system structure by means of top-down policy analysis and design; system-level outcomes

are seen as produced by abstract “activities” and “functions” rather than by individual agents

(Markard & Truffer, 2008). As Acs et al. (2014) noted, the systems of innovation approach,

while recognizing the role of “entrepreneurial experimentation,” still fails to see the

individuals and teams behind the function they perform.

Entrepreneurial ecosystems differ from markets and systems of innovation by

positioning the entrepreneurial individual or team at the center of the system dynamic (see

also Stam, 2015). The ecosystem influences both individual-level decision-making and

aspirations (i.e., who decides to pursue opportunities through a new venture and with which

aspirations), as well as the ability of the new venture to reach its full potential (regulated

through, e.g., resource availability and governance systems). This focus on entrepreneurial

action and the realization of the welfare-generating potential set up by that action constitutes

perhaps the most important differentiator of the entrepreneurial ecosystem concept relative to

market failure and system failure approaches. In the market failure thinking, action is

assumed to follow automatically once the appropriate economic incentives and price signals

are in place. In the system failure thinking, action (in the form of functions performed by the

system) is assumed to follow automatically once the proper structures and institutions are in

place. The entrepreneurial ecosystems approach recognizes that cause–effect relationships in

an ecosystem are complex; economic incentives alone do not fully explain individual-level

motivations to act, and the welfare resulting from an action may depend on whether the

ecosystem as a whole supports the realization of the full potential of that action.

We suggest that the complexity of entrepreneurial ecosystems challenges traditional

approaches to policy-making, for four reasons. First, knowledge of the inner workings of the

ecosystem is distributed across multiple stakeholders, whose localized, often one-to-one,

interactions collectively coproduce ecosystem-level outcomes. Second, actions taken by

stakeholders can have direct and indirect cascading effects within complex causal chains,

some of which may be mutually reinforcing. Third, the stakeholders may be imperfectly

aligned, both in goals and activities. Fourth, their interlocking relationships, combined with

imperfectly distributed information, can produce a high level of inertia. We explore these

four features below.

As noted above, the services rendered by entrepreneurial ecosystems are diffuse: the

allocation of resources towards productive uses through the creation and operation of

innovative, high-growth new ventures. This trial-and-error activity is carried out in myriad

microlevel interactions, which are embedded in an idiosyncratic ecosystem structure. Because

opportunity pursuit decisions are influenced not only by opportunity size but also by local

factors such as personal opportunity costs and local social norms, much of the knowledge

relevant for understanding the ecosystem dynamic is embedded in the ecosystem structure

itself, and therefore, not easily extracted and codified (Hayek, 1945; Kirzner, 1997). This

embeddedness means that no single individual or organization is likely to have a full and

complete insight into how the ecosystem works, nor is it trivial to create comprehensive

enough statistics to support such insight. Codified data on the ecosystem inputs and outputs

alone does not yet inform us how inputs are converted into outputs.

The second challenge, that of fully understanding how exactly entrepreneurial

ecosystems work, goes beyond mapping ecosystem structure (a reasonably straightforward

exercise), because ecosystem outcomes are often produced in cascading effect chains. For

example, if a given ecosystem does not facilitate sufficient numbers of high-growth ventures,

is the problem caused by funding gaps, lack of high-growth expertise, or failure of high-

potential individuals to start new businesses? Tracking real causes of ecosystem bottlenecks

often requires a fine-grained understanding of a range of inter-related causal chains. If the

cascading effects that coproduce system-level outcomes are not fully known, policies

designed to facilitate a given favored outcome may end up producing unintended

consequences (Merton, 1936). The possibility of unintended consequences grows higher as

the cascading effect chains grow more complex.

The third feature of the challenge is stakeholder misalignment. Although

collaboration and rational alignment of stakeholders is often assumed, there is a strong

possibility of stakeholder misalignment arising from diverging and competing stakeholder

interests and imperfect information flows between stakeholders, resulting in suboptimal local

equilibria. If left unattended, this may undermine collective commitment to implementing

policy actions, sustain inefficient generalism among competing stakeholders, and inhibit

productive interactions in entrepreneurial ecosystems.

The fourth feature of the challenge is system inertia. Due to their complexity,

socioeconomic systems tend to exhibit strong inertial properties and high path dependency

(Gustafsson & Autio, 2011). Because of complex interactions between system elements, top-

down policy effort may simply dissipate into the system without leaving much visible impact

on the system dynamic.

Although these four features of entrepreneurial ecosystems—also common to natural

ecosystems—undermine the feasibility of market and system failure policies, the

entrepreneurial ecosystems literature has not discussed their implications for entrepreneurial

ecosystem management. However, these challenges have been explored in socioecological

ecosystem and collective governance literature. We next review insights and approaches from

this literature to develop theoretical propositions applicable in the context of entrepreneurial

ecosystems.

Management of Complex Socioecological Ecosystems

Socioecological and collective governance literatures have explored policy

approaches that address head on the challenges of distributed knowledge, cascading cause–

effect chains, stakeholder misalignment, and system inertia (e.g., Bowles & Gintis, 1998,

2002; Stringer et al., 2006; Vollan & Ostrom, 2010). These literatures have explored policy-

making approaches that entail multipolar coordination and generation of collective

commitment among hierarchically independent, yet mutually dependent, cospecialized agents

to manage shared resources for common good and resolve ecosystem bottlenecks to enhance

the overall functioning of the ecosystem. Examples of collective governance challenges

explored in these literatures include the management of fish stocks to prevent

overexploitation (Gutiérrez, Hilborn, &Defeo, 2011; McClanahan, Castilla, White, & Defeo,

2009) and the collective management of forests to prevent exhaustion of firewood stocks

(Vatn, 2007).

Entrepreneurial ecosystems resemble socioecological ecosystems in that stakeholders

need to undertake essentially voluntary action to generate common-good benefits that

materialize in the future (Autio & Levie, 2016). The collective governance literature suggests

that voluntary action to enhance ecosystem functioning cannot be motivated by economic

self-interest alone, as short-term financial incentives for participation in the common project

may distort and even crowd out common-good motivations (Vollan, 2008). Therefore,

collaborative governance literature emphasizes deep forms of stakeholder engagement, which

harness intrinsic motivations, foster a stewardship attitude towards the ecosystem, and

encourage voluntary contribution (Bowles & Gintis, 1998; Das & Teng, 2002; Ekeh, 1974;

Kofinas, 2009). Although the emphasis is on the generation of common-good benefits, this is

not purely altruistic behavior, as common-good outcomes through collective action benefit all

ecosystem stakeholders. Applied in the context of entrepreneurial ecosystems, ecosystem

management needs to rely on voluntary participation motivated by “enlightened

entrepreneurial self-interest” that recognizes that the pursuit of private and common good

benefits can be more effective when aligned (Van de Ven, Sapienza, & Villanueva, 2007).

Stakeholder engagement is central for multipolar policy-making and implementation.

The depth of engagement with ecosystem stakeholders can range from shallow top-down

communication to bottom-up consultation, and to the deepest form, participation, where

information flows both ways in an iterative fashion (Stringer et al., 2006). Deep stakeholder

engagement can tap knowledge within the ecosystem and uncover hidden interactions and

cause–effect chains. It can also facilitate multipolar coordination and collective governance

(Seppelt et al., 2011). By facilitating mutual coalignment and specialization among

ecosystem stakeholders, deep stakeholder engagement can preempt potential conflicts due to

lack of trust and mutual awareness (Lichtenstein, 2014). Even when hard facts are available,

they may not be accepted by key stakeholders if these have not been constructively engaged

in the ecosystem analysis and management processes (McClanahan et al., 2009; van den Belt,

2004, p. 7). In summary, deep stakeholder engagement can overcome system inertia by

allowing stakeholders to become active participants in ecosystem analysis and management,

thus facilitating joint action to resolve ecosystem constraints (Pahl-Wostl, 2002; Smit, &

Wandel, 2006).

Several approaches have been developed to promote deep stakeholder engagement

and discourage free-riding in socioecological and socioeconomic systems. These include

adaptive governance (Ostrom, 1990), polycentric governance (Pahl-Wostl, 2002), adaptive

management (Stringer et al., 2006), community governance (Bowles & Gintis, 2002),

generative leadership (Lichtenstein, 2014), and transition management (Nill & Kemp, 2009).

These approaches harness the knowledge, attitudes, and actions of individual stakeholders to

produce a broader and deeper understanding of the ecosystem dynamic, promote collective

commitment to resolving problems, and incorporate learning into policy analysis and

management through feedback loops that engage these actors (Bowles & Gintis, 2002; Vollan

& Ostrom, 2010; Wilson & Howarth, 2002). We next review stakeholder consultation and

participation approaches and elaborate implications for entrepreneurial ecosystem

management.

Stakeholder Consultation

Stakeholder consultation facilitates bottom-up flow of information from ecosystem

stakeholders to policy-makers (Stringer et al., 2006). With consultation, ecosystem

stakeholders are identified and consulted in order to help policymakers understand the

ecosystem, sometimes with the help of approaches such as Integrated Assessment (IA)

methodologies (Rotmans, 1998) or systems dynamics modeling (van den Belt, 2004). The

breadth and depth of information gleaned in basic stakeholder consultation may vary with the

method of engagement. These range from semistructured individual or group interviews

(Kaplowitz & Hoehn, 2001) to participant observation and focus groups (Smit &Wandel,

2006; Kahan, 2001; Markova, Linell, Grossen, & Salazar Orvig, 2007) to surveys (BenDor,

Shoemaker, Thill, Dorning, & Meentemeyer, 2014).

Whereas stakeholder interviews and surveys are typically broad-based, focus groups

are designed to produce insight into more narrowly defined aspects of the ecosystem (Kahan,

2001; Kaplowitz & Hoehn, 2001). Interviews and surveys solicit broad understandings of the

ecosystem as a whole, drawing on inputs from a range of different stakeholders. In contrast,

focus groups facilitate deep insight on specific issues. This has implications for how focus

groups are organized. As the purpose is digging deep into a specific issue, coherence in

participant backgrounds takes precedence over diversity. Focus group participants need to be

well aware of the domain area, and their participants need to be able to communicate

effectively with one another. Also, focus group discussions tend to be more narrowly focused

on sets of closely related issues, in lieu of exploring and uncovering previously unexplored

issues. Finally, consensus-building and commitment-building play less of a role in focus

groups, whose purpose is more on the production of well-defined outputs such as specific

calls to action. This tends to imply less free-flowing and more intense sessions that tend to be

shorter in duration than stakeholder discussion groups (Kahan, 2001).

Stakeholder Participation

Stakeholder participation assigns a more active role than stakeholder consultation to

ecosystem stakeholders, engaging stakeholders not just in ecosystem analysis but also in

ecosystem management (Stringer et al., 2006). Several methods have been reported in the

socioecological and collective governance literatures. For example, adaptive management

uses both “hard” facts (i.e., codified data) and insight derived through deep stakeholder

engagement to encourage social learning and develop simulation models to better manage

socioecological systems (Holling, 1978; Stringer et al., 2006). Feedback mechanisms are

built into this approach to enhance adaptation. Collective action draws on the theory of

adaptive governance (Olstom, 1990) as an alternative to top-down governance (Meinzen,

Dick, & Di Gregorio, 2004). This approach is increasingly deployed in large-scale renewal of

neighbourhoods and even cities, under the term “collective impact” (Hanley-Brown, Kania,

& Kramer, 2012). It also has parallels in the transition approach to sustainable innovation

policy in the Netherlands (Nill & Kemp, 2009).

Collective action requires careful and sensitive management. The ecosystem needs to

be at a scale that is big enough to be self-sustaining but small enough so that the major

stakeholders know each other. Some key principles to avoid unintended consequences,

misalignment, and inertia include reciprocity, recognition, validation, and differentiation

(Meilasari-Sugiana, 2012). These help bind people collectively to the mission, whether it is to

facilitate a sustainable yield of biomass or of high-quality entrepreneurs.

The approach known as collective impact follows similar principles: common agenda,

shared measurement systems, mutually reinforcing activities, continuous communication, and

the presence of a backbone organization (Hanley-Brown et al., 2012). Transition management

exhibits the following features: long-term thinking (at least 25 years) as a framework for

short-term action; thinking in terms of multiple domains; and a focus on learning, including

learning-by-doing, doing-by-learning, and learning about a variety of options (Nill & Kemp,

2009).

In stakeholder participation, stakeholders engage with each other, repeatedly, with a

view to their coordinated, collective action to pursue a shared vision. Policymakers can

facilitate this by creating relational space for stakeholders, that is, opportunities for

“reflective learning across organizational boundaries, which is enabled by, and in turn gives

rise to, collaborative projects” (Bradbury-Huang, Lichtenstein, Carroll, & Senge, 2010, p.

109).

Finally, Lichtenstein (2014) provides a generalized five-stage model of ecosystem

emergence and renewal. They include disrupting existing patterns, encouraging experiments,

surfacing conflict, supporting rich interaction, catalysing collective action, correlating the

system, recombining resources, leaders accepting tags as role models, and leveraging local

resources. Many of these techniques can also be seen in descriptions of the collective

management of other complex socioeconomic systems (e.g., Meilasari-Sugiana, 2012;

Meinzen-Dick & Di Gregorio, 2004; Nill & Kemp, 2009). By linking them to phases of

emergence, Lichtenstein offers a manual to complex system managers on managing system

change.

Summarizing, the ecosystem management approaches reviewed above share

numerous commonalities, deep stakeholder engagement being the defining one. This is

employed to uncover hard-to-access information on ecosystem interactions, to enhance

multipolar coordination, and to motivate and commit stakeholders to ecosystem

improvement. These characteristics respond to ecosystem management challenges created by

distributed knowledge, cascading cause–effect chains, stakeholder misalignment, and

resulting system inertia. The arguments suggest three theoretical conjectures that we explore

next in our case study of the Scottish innovation-based entrepreneurial ecosystem:

1. In entrepreneurial ecosystems, deep stakeholder engagement will produce richer

and more actionable insight into ecosystem workings than will information and

data derived from external observation alone.

2. An understanding of cascading effects will help generate more productive insight

(from a policy perspective) than will the study of market and structural failures in

entrepreneurial ecosystems.

3. A careful consideration of entrepreneurial ecosystem stakeholders, their

motivations, and power relations will enable more efficient policy action than one

that considers all stakeholders as homogenous.

By drawing on the entrepreneurial ecosystem facilitation case in Scotland, we can see

how deep and participative stakeholder engagement approaches can be harnessed to enhance

the functioning of entrepreneurial ecosystems.

Scottish Innovation-Based Entrepreneurial Ecosystem

Method

Because of the novelty of the ecosystems approach to entrepreneurship policy, there

have been relatively few policy initiatives that qualify as entrepreneurial ecosystem analysis

and management initiatives, and there is no database storing data on entrepreneurial

ecosystem policy effectiveness. A quantitative method of testing hypotheses drawn from our

propositions is therefore not feasible. We therefore adopt systematic process analysis as our

research method (Hall, 2006). Systematic process analysis uses rich longitudinal data and

reflects on findings against what the researchers would expect to see. It enables the

investigation of the face validity of propositions. Specifically, we chose a qualitative

approach called “pattern matching” (Geels & Penna, 2015; Yin, 1994), in which the pattern

of events predicted by theory is compared with the pattern of events exhibited in the case. To

avoid confirmation bias, we actively sought chains of events that did not fit our theory, and

compared theorized patterns against competing propositions.

Because the still ongoing initiative has progressed in stages, we are able to compare

insights produced by codified secondary data alone to insights produced through stakeholder

engagement, allowing us to compare the effectiveness of alternative approaches to ecosystem

facilitation. Because one of the authors has participated in the initiative as a participant-

observer, we had rich data available to gauge levels of stakeholder alignment, commitment,

and inertia during the initiative, enabling us to compare the effectiveness of participative

approaches against more traditional approaches to entrepreneurship policy. We next describe

our empirical context.

REAP Scotland

The Scottish entrepreneurial ecosystem policy initiative was triggered by participation

of a Scottish team in a series of workshops organized by Massachusetts Institute of

Technology (MIT) from 2012 to 2014. This programme, known as REAP (Regional

Entrepreneurship Acceleration Program) sought to facilitate policy targeting high-potential

innovation-based entrepreneurship (IBE). The Scottish team comprised two senior enterprise

agency managers, a university representative (the second author), and three entrepreneurs.

The team met regularly (usually once a month) from December 2011 to March 2015, and

there were also four intensive three-day sessions with participating teams from other

countries between 2012 and 2014.

The Scottish exercise used secondary data and interviews to assess regional

innovation capacity and entrepreneurship capacity under six themes: people, funding,

infrastructure, policy, rewards and norms, and demand. Potential growth clusters were then

identified. Early work in the Scottish exercise found that while good quality secondary data

was often available at the regional level for innovation capacity and clusters, measures of

entrepreneurial capacity were unsatisfactory. There was also a need for rigorous

benchmarking of elements of the regional ecosystem against each other and against

equivalent elements in other regions or equivalent sized nations.

The Scottish team decided to ground their analysis of the Scottish entrepreneurial

ecosystem on data provided by the Global Entrepreneurship and Development Index (GEDI)

method (Acs et al., 2014). The GEDI methodology builds on the systems of entrepreneurship

theory and portrays the quality (instead of quantity) of the entrepreneurially driven resource

allocation dynamic in entrepreneurial ecosystems. It does so by combining individual-level

data on entrepreneurial attitudes, ability, and aspirations with data describing the context

within which these processes are expressed or repressed. The outcome of this methodology is

an index composed of a set of interactions between national-level measures of rates of

individual attitudes, ability, and aspirations and institutional-level variables that moderate the

impact of individual-level variables on productivity growth. Unlike measures quantifying

rates of self-employment, which tend to decline with increasing rates of economic

development at a decreasing rate (Carree & Thurik, 2008), the GEDI is positively associated

with gross domestic product (GDP) per capita for most of its range (Acs et al., 2014).

The distinctive characteristics of the GEDI methodology reflect well the complexity

of entrepreneurial ecosystems, and therefore, the policy challenges described earlier. First,

the GEDI methodology contextualizes individual-level data by weighting it with data

describing a country’s framework conditions for entrepreneurship, thereby seeking to capture

embedded complexity in entrepreneurial ecosystems. Complexity is further reflected in

GEDI’s use of 14 context-weighted measures to portray entrepreneurial attitudes, ability and

aspirations in the ecosystem. The GEDI methodology also allows different index pillars to

interact and, thus, coproduce ecosystem performance. This last feature also captures the

notion that national entrepreneurial performance may be held back by “bottleneck factors”—

that is, poorly performing pillars that may constrain system performance (see Acs et al., 2014

for a description of the Penalty for Bottleneck methodology).

The distinctive aspects of the GEDI approach fitted well with the aim of the Scottish

team to describe, diagnose, and enhance the Scottish entrepreneurial ecosystem. For example,

the GEDI features a holistic approach in which a deficiency in one factor can have knock-on

effects on other parts of the ecosystem, simulating the notion of cascading effects. The index

also appeared to provide comprehensive coverage of all elements of an entrepreneurial

ecosystem outlined above with the exception of policy measures, though it did cover current

institutions which could reflect past and current. It also contained a combination of multiple

input and output measures, which is necessary in any assessment of an ecosystem.

What

GEDI did not do was engage in any form of sectoral cluster analysis. Fortunately, a great deal

of work on clusters had already been done and designated growth sectors formed part of the

Scottish Government’s Economic Strategy (Scottish Government, 2011). At no time did the

Scottish team consider that additional analysis was required in this area.

The Scottish team adapted the GEDI methodology to a regional level of analysis and

used this to identify possible gaps between the areas of current policy focus and bottlenecks

in the entrepreneurial ecosystem suggested by the GEDI analysis. It was recognized, first,

that the GEDI analysis was only as good as the quality and choice of data, and second, that it

could stimulate wider debate on the health of an innovative entrepreneurial ecosystem, but

should not be used as a computerized “policy-creating machine.”

Policymakers want to know how they can achieve most leverage in enhancing an

entrepreneurial ecosystem. Understanding the strength of the links between pillars that appear

A table of these measures is available from the authors on request.

to be linked might help reveal critical leverage points. Unfortunately, the GEDI analysis did

not reveal the strength of links between pillars. Because all ecosystems are unique, the GEDI

methodology assumes that all links have the same strength and that all pillars cost the same to

change. Furthermore, it could not reveal whether the bottlenecks were causal or merely

symptoms of underlying, deep-seated weaknesses in an innovation-based entrepreneurial

ecosystem. Therefore, the Scottish team decided it had to be supplemented by expert

stakeholder judgment.

The method of entrepreneurial ecosystem analysis and management developed by the

Scottish team is outlined in Figure 19.1. The process begins with data collection using the

GEDI framework. Once bottlenecks have been identified using sensitivity analysis, the next

step is to test the convergent validity of the bottlenecks themselves by employing alternative

measures of institutional variables. If the bottleneck measures are robust to alternative

specifications, the next step is to group them under themes and then test them on groups of

expert stakeholders. Assuming the GEDI assessment passes this test, one could explore

possible links between bottleneck pillars, underlying causes, and priorities for action with the

experts. If there are a limited number of underlying causes, this should be apparent in the

degree of agreement across the different stakeholder groups, providing both convergent

validity and face validity to the assessment.

Assuming this consultative phase delivers a consensus on a limited number of linked

causes and priorities for action, the next stage is to appoint short-term task groups of lead

stakeholders to actively participate in the process by developing solutions and taking action.

This, in turn, could lead to a monitoring and learning phase in which momentum is

maintained and the effect of different actions is compared.

[!Figure 19.1 about here!]

Field Trial in Scotland

The Scottish core team drew up a project specification which, first, requested a GEDI-

type assessment of Scotland’s entrepreneurial performance against benchmark nations within

and outside the UK on the set of six priority themes and, second, requested a sensitivity

analysis of the results to identify likely bottlenecks to the acceleration of innovative

entrepreneurship in Scotland.

The first stage was to regionalize the individual and institutional variables that the

GEDI research team had found to best represent the quality and size of the entrepreneurial

ecosystem at the national level. The actual individual and framework variables employed are

described in Levie et al. (2013), and the pillars are described in Autio et al. (2012). This list

was debated by the Scottish team both before and after the list was populated with data,

resulting in several changes to the data specification, including a change from two-year to

four-year moving averages because of the low frequency of entrepreneurs in the population,

and a change in the institutional measure used for risk capital, which was subsequently

adopted more generally by GEDI. This corresponds to the first step of the method.

Six of the 14 measures were taken from perceptual measures generated for the Global

Competitiveness Index by senior corporate managers in different countries, and it was not

possible to find equivalent regional measures. These measures were of business risk,

technology absorption capability, staff training, market dominance, technology transfer, and

business strategy. Another three measures—business freedom, globalization, and venture

capital—all drawn from different global indices with national-level indicators, were assumed

to differ little at the home nation level of the UK. Truly regional estimates were found from

published sources for seven of the 14 institutional measures.

The Scottish team requested four benchmarking assessments: Scotland versus all 78

economies for which data was available; Scotland versus 27 innovation-driven economies

according to the 2011 World Economic Forum’s Global Competitiveness Report 2011;

Scotland versus “Arc of Prosperity” countries, a Scottish Government term for small modern

nations located around Scotland (Ireland, Iceland, Norway, Denmark, and Finland); and

Scotland versus other home nations within the UK (England, Wales, and Northern Ireland).

A second work package consisted of a sensitivity analysis of the results, which used

the Penalty for Bottleneck methodology to simulate how weaknesses in one component might

affect the entrepreneurial ecosystem as a whole. In this analysis, weak pillars were artificially

boosted to gauge the effect of additional policy effort to improve them. Due to simplifying

assumptions, the purpose of this sensitivity analysis was not to be prescriptive, but rather, to

serve as a basis for discussion by stakeholders in the next phase of assessment.

Figure 19.2 is an example of benchmarking using a spider diagram, in this case

against Arc of Prosperity countries. It plots Scotland’s scores and shows visually where

Scotland fits relative to the other countries. Denmark appears strong in pillars where Scotland

is relatively weak, such as process innovation and networking, and where it is absolutely

weak, such as Opportunity Perception. Ireland follows a similar pattern to Scotland, and is

worse in some pillars, such as Opportunity Perception and Opportunity Start-up. Iceland fares

worse than Scotland in Competition but better in some pillars where Scotland is weak, such

as Process Innovation and Networking. Finland does better than Scotland in some Attitudes

measures and most aspiration measures.

[!Figure 19.2 about here!]

Against the UK’s other home nations—England, Wales, and Northern Ireland—

Scotland appeared as performing less well than England in Opportunity Perception and Start-

up Skills, and better than Wales and Northern Ireland in some ability measures and

Opportunity Perception. (A full set of benchmark figures and tables is included in Levie et

al., 2013).

In relation to 27 innovation-driven countries, three weak institutional variables were

apparent: the current level of participation in postsecondary education among young adults

(aged 18–22), the level of internet usage, and Gross Expenditure in R&D (GERD). These had

knock-on effects on their respective pillars: Start-up skills, Networking, and Process

Innovation. Most individual aspiration variables were also relatively weak. Scotland was in

the fourth quartile of innovation-driven nations in Process Innovation, Product Innovation

and Risk Capital. The benchmarking also revealed areas where Scotland compared well with

other countries. Scotland ranked second out of 78 countries in the Tech Sector, third in the

Competition pillar, and fourth in the Opportunity Start-up pillar.

A sensitivity analysis estimated “optimum” additional allocation of policy effort for a

20% improvement in Scotland’s GEDI score, based on simplifying assumptions. This

improvement would bring Scotland from 16th place to around 4th place in the rank of 78

countries in the GEDI database, behind the United States, Denmark, and Sweden and just

ahead of Australia. The analysis suggested that almost 50% of additional allocation should be

focused on aspiration pillars, with another 35% on three Attitudes pillars. This sensitivity

analysis assumed that the cost of improving each pillar is the same, which of course is

unrealistic. It nevertheless provided a basis for stakeholders to debate alternative scenarios, a

useful guide to possible areas for further investigation, and facilitated debate as to where the

goals should be set. The team reached a consensus that an absolute score of less than 0.5 or a

ranking of 19 or higher justified further investigation of a given pillar. Three attitude pillars

(Opportunity Perception, Start-up Skills, Networking) and all five aspiration pillars met this

criterion.

Having identified eight pillars to focus on, the next stage was to validate the

bottleneck pillars with four stakeholder consultation meetings in February 2013, to which a

mix of prominent and representative members of Scotland’s entrepreneurial ecosystem were

invited to debate between one and three of the eight pillars. (In October 2012, these

individuals had all been invited to a dinner in Edinburgh. The Scottish core team felt that this

would help to give a sense of purpose and legitimacy to subsequent stakeholder engagement.)

Four different sets of about a dozen stakeholders attended one of four consultation meetings,

which were chaired by members of the core team and recorded verbatim by a professional

court recorder. These consultation meetings suggested a set of perceived weaknesses in

Scotland’s entrepreneurial ecosystem that cross-linked the bottlenecks. A 23-page summary

report was written based on a content analysis of the stakeholder consultation meetings.

Identified concerns included: networking and networks (67 mentions); business, management

and commercial skills (28 mentions) and in particular sales and selling skills (21 mentions);

global outlook (10 mentions) and the need to connect with other cultures (11 mentions); the

contribution of Scottish universities (12 mentions); mentors (12 mentions); role models (eight

mentions); access to markets (four mentions) and finance (12 mentions) including those

outside Scotland; and exits (four mentions). Several participants noted how individuals who

had had the opportunity to experience entrepreneurial environments such as Boston returned

to Scotland fired with enthusiasm.

In a 24-hour retreat in May 2013, the core team whittled down the identified issues

(and underlying causes) into five priority themes: financing for growth (including exits for

investors in angel-backed companies, increasing access to institutional and international

funds, etc.); effective connections (this included networks but was more fundamental than

mere networking); skills for growth for leadership teams within IBE ventures; role of the

universities in the IBE ecosystem; and role models and positive messages. Chairs and

members of the stakeholder community were identified for high-level task groups who would

be charged with developing and implementing solutions to each of the five themes. (This

corresponds to “stakeholder participation”: the deepest level of stakeholder engagement

identified in the literature review above.) At least one member of the Scottish core team was

appointed to each task group to facilitate information flows between task groups. In

developing the briefs for the task groups, the team agreed to adopt the collective impact

approach of Hanley Brown et al. (2012) that seeks to get wide stakeholder buy-in and

consensus on the direction of travel rather than a top-down directive approach. As a reminder,

the five key aspects of this approach are a common agenda, shared measurement systems,

mutually reinforcing activities, continuous communication, and the presence of a backbone

organization.

Between May 2013 and August 2015, the task groups evolved in different ways. The

Financing for Growth task group was already formed as a separate committee of the Royal

Society of Edinburgh, charged with coming up with proposals for the Scottish Government

on the financing of early-stage growth companies. It published an advice paper in June 2014.

An online questionnaire to assess the demand for growth finance was piloted. The effective

connections group developed a guide to networking within Scotland for entrepreneurs, which

was updated annually, and the group organized an event to bring networking organizations

together in November 2014. The universities group developed a set of actions and planned a

series of five workshops for relevant university staff on each action point in 2015–2016 and a

manual on enhancing university entrepreneurial ecosystems, sponsored by an enterprise

agency. The Scottish team also consulted with experienced entrepreneurs to draw up a list of

critical skills for CEOs of growth companies under the “skills for growth” bottleneck. The

enterprise agencies took this forward to create SCALE, a skills for growth program for early-

stage entrepreneurs, launched in August 2015 with 70 entrepreneurs and input from MIT and

Harvard Business School; and the University of Strathclyde launched the Growth Advantage

program for 20 more established CEOs in May 2015. Under the role models theme, an

enterprise agency ran a series of events and initiatives to encourage women to become

entrepreneurs and to encourage women entrepreneurs to serve as role models to other women.

It also greatly expanded its online suite of videos of Scottish entrepreneurs of growing

businesses.

In June 2014, a 48-page report was released by the Scottish team to all stakeholders

who had been consulted or had participated in the Scottish exercise (Chisholm et al., 2014).

The team deliberately chose a soft launch with minimal publicity, as it wished to ensure that

the process did not end with a report that might, like so many before it, be shelved and

forgotten. The report listed the calls for action under each of the five themes, and also called

for a backbone organization independent of government agencies to take on the management

of the process. At the time of the report, the team thought that a new organization might need

to be formed to do this. However, coinciding with the release of the report, the merger of two

prominent independent stakeholder organizations in the Scottish entrepreneurial ecosystem,

the Entrepreneurial Exchange and the Saltire Foundation, was announced. Subsequently, the

incoming chief executive of this new organization, Entrepreneurial Scotland, was invited to

join the core team and, at the time of writing, a proposal that Entrepreneurial Scotland serve

as the backbone organization was under active consideration.

Case Reflection

We now consider the applicability of alternative policy approaches against the chain

of events presented in the previous section. A traditional approach to correcting system

failures would rely on secondary data describing the structures and processes of the system to

be improved. Based on our literature review above, we suggest that codified (i.e., externally

observable) data alone is not likely to be sufficient to fully understand the complexities of

emergent systemic processes and uncover cascading effect chains. If the traditional approach

holds (i.e., that of relying on externally observable data alone and not engaging the multiple

stakeholders), then a policymaker that engages stakeholders for entrepreneurial ecosystem

analysis should find discrepancies between the understanding supported by codified data and

the understanding emerging from stakeholder consultations. We illustrate this logical

implication with examples of stakeholders questioning and finding weaknesses in the

secondary data, finding gaps in the secondary data, and generating actions in priority areas

that were not highlighted by the secondary data. Together, these examples suggest that a

participative stakeholder engagement approach is likely to produce better results than

external observation alone.

First, the core team driving the process of entrepreneurial ecosystem management in

Scotland actively questioned the results in several of the items included in the secondary data

analysis conducted for it by a third party. For example, the UK scored relatively poorly on the

institutional measure of risk capital used by GEDI (a measure of corporate managers’

perceptions of the availability of venture capital to early-stage ventures, published by the

Global Competitiveness Index), but measured relatively well on other international measures

of venture capital availability. The Scottish REAP team included several experts in young

venture finance, and their knowledge of the UK risk capital market conflicted with this

measure. On reflection, the GEDI team realized that the perceptions of middle managers in

corporations might not accurately reflect financial flows on the ground and this measure was

replaced with a measure of the Depth of the Capital Market for Venture Capital published by

the IESE - Ernst & Young PE-VC Country Attractiveness Index. This did not rely on

perceptions of managers with little exposure to venture capital, but instead was composed of

measures of capital flows relevant to venture exits. This measure correlated highly with

actual venture capital flows at the national level, and also reflected the expectations of the

experts in the Scottish team. It was subsequently adopted by GEDI as the default measure of

institutional risk capital.

In another example, secondary data indicated that Scotland has a relatively low

proportion of individuals who invest in other individuals’ new businesses, leading the

secondary analysis to identify risk capital as a bottleneck. But stakeholders pointed out in

consultation meetings that Scotland has a relatively well-developed business angel

infrastructure, with some 22 angel syndicates and a transparent angel market, and the UK has

exceptionally attractive incentives for wealthy individuals to invest in new ventures. Also this

discrepancy triggered debate leading to the identification of funding flows (rather than

business angels) as the real constraint of the Scottish ecosystem.

Further reflection on the pillars in which Scotland did well raised some doubts within

the core team as to what was being measured within these pillars. For example, in the

Competition pillar, the individual-level measure was the percentage of early-stage

entrepreneurs who operate in markets where not many businesses offer the same product. The

GEDI authors intended it to be a relative measure of product-market uniqueness, but it could

also be interpreted as lack of competition due to low overall levels of entrepreneurship. Thus,

Scotland appeared to do well on this measure, but it could actually be a sign of weakness

rather than strength, because of the way the original survey item was worded. The

institutional measure for this pillar was for the UK rather than Scotland, and might therefore

not reflect the dominance of firms in a regional market like Scotland. Again, this could have

flattered Scotland’s ranking.

Because Scotland had relatively few necessity-driven entrepreneurs, it scored highly

on a secondary measure of nascent and new early-stage entrepreneurs who initiated their

business because of opportunity start-up motive. This measure intended to penalize countries

with high proportions of necessity-driven, and by implication low-quality, start-ups. But, in

Scotland, which had a relatively advanced social welfare system and a strong class-based

society, it may simply have reflected the lack of perceived economic need or self-efficacy on

the part of those without employment.

A second implication of our first theoretical conjecture was that by combining

codified (secondary) data and stakeholder insights, a policymaker will produce a more

complete understanding of the ecosystem. There is evidence to support this from the Scottish

case. The four stakeholder consultation meetings conducted by the Scottish team identified

links between the bottlenecks in the entrepreneurial ecosystem suggested by the secondary

data. This is something the hard data itself did not do. Following a content analysis of the

stakeholder consultation meetings and a full day of discussion by the core team, combining

the GEDI and stakeholder analyses, the Scottish team was able to identify five linked issues

that, if tackled comprehensively, could lift a range of pillars. These five themes only partially

overlapped with the bottlenecks thrown up by the hard data analysis. For example, one issue

raised repeatedly by entrepreneurs in the stakeholder group sessions was the relatively low

perceived contribution by universities to the Scottish entrepreneurial ecosystem. This was not

identified as an issue by the GEDI analysis. If indeed the university sector was a bottleneck,

proceeding with policy based on the hard data alone would have omitted a bottleneck in the

ecosystem. If it was not, a false attribution of a bottleneck in the system would have been

identified. In the end, while the empirical evidence did not fully support the view of the

stakeholders (and this evidence was published in the June 2014 report), the high-level task

group on universities identified that the contribution of universities was uneven across

Scotland and that there was much that Scottish universities could learn from each other.

The university theme is interesting because it was raised repeatedly as a bottleneck by

entrepreneurs. Yet many of their claims were based on misunderstandings and not borne out

by hard evidence subsequently collected by the Scottish team and incorporated into its June

2014 report. Examples included the belief that entrepreneurs taught all entrepreneurship

classes in US universities, that universities were detached from industry, that it was

extremely difficult to get technology out of a university, and that academics and researchers

were unwilling to network. In fact, Scotland had one of the highest rates of R&D expenditure

in Higher Education (HERD) and one of the lowest rates of R&D expenditure in the business

sector (BERD) in the OECD, and Scottish universities had relatively high rates of knowledge

exchange compared with the rest of the UK (Chisholm et al., 2014, p. 19). One of the tasks of

the university task group, composed of “entrepreneurial professors” rather than professors of

entrepreneurship, became not just how to enhance the connectivity of Scottish universities

but also to educate entrepreneurs on how Scottish universities do engage in the ecosystem.

On the other hand, it demonstrated that the degree of engagement varied greatly across the

university sector and that universities had much to learn from each other: pockets of best

practice were scattered through the university sector and not visible. This led the university

task group to the idea of the sharing of best practice within the sector.

Our second theoretical conjecture stated that deep stakeholder engagement produces

causative insights that identify areas where concerted action can bring about productive

change within an entrepreneurial ecosystem, lower the risk of unintended consequences in

entrepreneurial ecosystem management, improve stakeholder alignment, and reduce the risk

of ecosystem inertia dissipating the effect of policy action. If this proposition holds, then we

should see instances where deep stakeholder engagement produced causative insights that

identified areas where concerted action could bring about productive change within the

Scottish entrepreneurial ecosystem. These could include recommendations for action that

were identified as a result of the stakeholder participation but for which secondary data did

not suggest a gap, thus demonstrating what the Scottish team might have missed by relying

on the secondary data alone. Crucially, in order for this proposition to be supported, these

actions would not be ones that provided incentives to address perceived market failure.

In the Scottish case, five priority themes were identified following stakeholder

engagement: ‘financing for growth’; ‘effective connections’, ‘skills for growth’, ‘role of the

universities in the IBE ecosystem’, and ‘role models and positive messages’. One of these,

the role of universities, was not suggested by secondary data, while the priority action within

the financing for growth theme, enabling an exit mechanism for investors, was not

immediately obvious from the secondary data. Similarly, it was not obvious from the low

networking score in the secondary data that, although Scotland had many networking

organizations for entrepreneurs, many entrepreneurs did not know this and the networking

organizations were not themselves well networked to each other. This prevented the Scottish

team from needlessly developing new networking organizations but instead led it to create a

guide to networking and an event that brought networking organizations together to inform

each other on their activities. These actions improved alignment, lowered the risk of

unintended consequences, and prevented inertia, which would have been the likely result of

action that created a new networking organization. Significantly, the Scottish entrepreneurial

ecosystem support landscape began to shift significantly in 2014 with the merger of two

major support organizations.

In another example, in addition to agreeing on the sharing of best practice, the

entrepreneurial professors group agreed a set of stretch goals for the sector, including

“enterprise for all.” This combination of sharing of best practice to produce quick wins and

long-term ambitious goals helped promote alignment and avoid inertia. Where one might

have expected competitive stances between universities, instead a positive spirit of

cooperation developed. This initiative developed independently of the existing range of

financial incentives for universities to engage in knowledge exchange activity.

Under the skills for growth theme, there is evidence of actions that could be

interpreted as market failure interventions and actions that were independent of market failure

approaches. Both were initiated by stakeholders active in Scottish. The enterprise agencies

created SCALE, a skills for growth programme for early-stage entrepreneurs completely

subsidized by the Scottish Funding Council and the enterprise agencies, launched in August

2015 with 70 entrepreneurs in the first cohort and led by staff from MIT and Harvard

Business School. The University of Strathclyde launched the Growth Advantage programme

for 20 established CEOs in May 2015, which was not free but paid for in part by the

participants and in part sponsored by Santander Bank.

Our third theoretical conjecture stated that in selecting and engaging stakeholders in

an entrepreneurial ecosystem, policymakers need to understand and balance the power,

legitimacy and urgency of different stakeholders. The Scottish core team looked for a balance

between entrepreneurs, entrepreneurial financiers, government, corporates, and universities

among participating stakeholders. A prime example of the wisdom of this approach is the

issue of finance, where the two finance experts on the panel queried the measure of risk

finance used by GEDI. Another example is where the stakeholder consultations raised the

issues of exit for investors and the universities as bottlenecks, areas not covered by the GEDI

framework. Because of the wide differences in background within the core team and the

stakeholders they engaged with through consultation and participation, many differences of

perspective on the problem were raised and as learning developed, core team members and

task team members changed the way they perceived they and their organizations could

address the goal.

The makeup of the Scottish team was a difficult issue for the enterprise agencies who

funded the Scottish exercise and invited stakeholders to join it. While civil servants

responsible for entrepreneurship policy in Scotland were kept informed of the team’s

progress, they were not invited to become core team members. On the one hand, the Scottish

team was concerned that in the heated political atmosphere leading up to the Scottish

independence referendum in September 2014, part-formulated policy solutions might be

prematurely adopted and announced rather than analyzed fully and managed bottom-up. In

other words, a civil servant as a core team member might have too much power and urgency,

upsetting the stakeholder balance. But on the other hand, absence of the central

policymaker’s perspective may have led to some blind spots on the part of the core team, and

a chance was missed for social learning by civil servants.

Another constituency that was missing from the core team was corporate Scotland.

While a representative group of senior corporate executives did participate at a separate

stakeholder workshop, the presence of a corporate representative on the core team might have

changed the dynamics of the team. The three sectors represented—enterprise agencies,

universities and entrepreneurs—all had their disagreements at times and sometimes felt they

had to defend their sectors. A corporate representative might have been seen as an honest

broker, more objective, and more results-focused—and was, in fact, recommended by MIT.

Balance within the high-level task groups was also important. The core team opted to

invite senior entrepreneurial professors in the main Scottish research universities to form the

high-level task group for universities rather than professors of entrepreneurship or officials in

technology transfer or enterprise centers. Entrepreneurial professors possessed relatively high

levels of power, legitimacy and urgency within their institutions. They bought into the vision

relatively quickly and recognized the value that could be gained from sharing best practice

across the sector. They also attracted similar individuals from other universities, expanding

the group to cover almost all higher education institutes in Scotland.

Experience with the high-level task groups to date suggests that regular monitoring

and “holding of feet to the fire” is necessary to maintain momentum and prevent inertia. The

core team regularly reviewed activity under each action plan and it became clear that “what

gets measured gets done.” At the same time, however, the high-level task groups represent a

form of relational space, that might otherwise be competitive, which stakeholders can

collaborate and devise new actions or spread existing successful ones in ways that

policymakers might not have imagined (Bradbury-Huang et al., 2010, p. 109). The actions

produced by the effective connections and university groups were examples of this.

In summary, our empirical analysis provides consistent support for the propositions

we derived from our theoretical analysis of entrepreneurial ecosystems, demonstrating the

importance of deep stakeholder engagement in entrepreneurial ecosystem policy

interventions.

Discussion

This chapter was inspired by the observation that while the concepts of

entrepreneurial ecosystems and start-up ecosystems have rapidly gained currency in policy

practitioner circles, both the concepts themselves and their implications for policy analysis

and management of policy implementation have remained undertheorized. To address this

gap, we have drawn on policy research in ecological economics to infer distinctive challenges

faced by entrepreneurial ecosystem policy analysis and management, and, also, elaborated on

the implications they pose for policy practice. Our core observation was that in complex,

multipolar entrepreneurial ecosystems, where system performance is coproduced in localized,

embedded interactions among ecosystem stakeholders, performance gaps are not easily

reducible to well-defined market failures that can be addressed by top-down approaches in

which stakeholder engagement is reduced to communication, such as those described by

Arshed, Carter, & Mason, (2014). Instead, stakeholder consultation and participation are

required to enhance the understanding of how the system works, identify coherent policy

actions that are more likely to yield a desired impact, realign stakeholders, and build

stakeholder commitment to overcome systems inertia. Moreover, the choice of stakeholder

should be determined by their current and potential influence on the ecosystem. We used the

context provided by a Scottish entrepreneurial ecosystem facilitation initiative to explore the

face validity of these propositions.

As such, participative approaches to policy design and implementation are not new—

even to the increasing number of ecosystem management exercises currently under way in

the area of entrepreneurship. Nevertheless, conceptual and theoretical underpinnings have

lagged behind burgeoning policy practice. In this chapter, we have provided a theoretical

treatment of the concept of entrepreneurial ecosystems, highlighting how they differ from

other concepts that have guided policy theory and thinking— notably, those of markets and

innovation systems. In our theoretical review, we highlighted four distinct challenges that

characterize entrepreneurial ecosystems—and potentially undermine the applicability of

market and structural failure approaches: (1) the creation of the ecosystem service through

localized interactions among hierarchically independent, yet mutually codependent,

cospecialized system stakeholders; (2) the potential for emergent cascading effects created by

cross-dyad influences; (3) the resulting potential for ecosystem inertia; and (4) the need for

commitment, action, and multipolar coordination by ecosystem stakeholders. Our major

conceptual contribution has been to highlight and explicate such challenges and elaborate

why received approaches to entrepreneurial ecosystem policy are not well equipped to deal

with them.

Our main contribution to policy practice was to review policy approaches discussed in

socioecological ecosystem and collective governance literatures and apply these insights in

the context of entrepreneurial ecosystem management. Specifically, we highlighted the

importance of soliciting deep stakeholder engagement in entrepreneurial ecosystem

management and reviewed policy approaches for achieving this goal. We have illustrated

why it is important to go beyond secondary, quantified data in entrepreneurial ecosystem

analysis and why exactly deep stakeholder engagement matters in practice. While these

approaches may seem like common sense to many a policy practitioner, examples of success

in entrepreneurial ecosystem management nevertheless remain frustratingly few. As

highlighted by Arshed et al. (2014), top-down policy declarations arguably remain the norm

in entrepreneurship and policy initiatives vulnerable to capture by politicians. Similarly, most

policy initiatives remain siloed, consistent with the market and structural failure approaches,

and comprehensive, ecosystem-wide actions are few. This perhaps helps explain why the

majority of attempts to replicate successful entrepreneurial hot-spots such as Silicon Valley

have failed. By explicating and theoretically justifying principles of good management of

entrepreneurial ecosystems, and by illustrating the application of these principles with rich

case evidence from Scotland, we hope to contribute toward a wider and more meaningful

adoption of an ecosystem management approach to entrepreneurship policy. We contend that,

from a policy perspective, entrepreneurship should be viewed as a complex, dynamic

ecosystem, and the effective management of such ecosystems is only possible when their

distinctive management challenges are clearly understood.

Although we have illustrated advantages of an ecosystem approach to

entrepreneurship policy, we are not implying that traditional policy approaches have no role

to play in entrepreneurial ecosystem management. In the Scottish case, while our explorations

supported the validity of Propositions 1, 2 and 3, there was also evidence that participative

approaches alone—in the absence of codified data—may, in themselves, provide an

insufficient picture of entrepreneurial ecosystem dynamics for policy analysis and

management. In the Scottish case, evidence of the importance of codified data included

stakeholders being informed and even surprised by the hard data. For example, Scottish team

members were surprised that in relation to other innovation-driven countries, the current

gross enrolment ratio in tertiary education in Scotland (proportion of 18- to 22-year-olds

undergoing third level education) was relatively low. A second surprise was that Scotland

fared best in the ability pillars. The quantity of entrepreneurship in Scotland has long been

perceived as relatively low. This demonstrated the advantage of the GEDI choice of ability

variables that reflect innovative entrepreneurship, not all entrepreneurship. Examples like this

illustrate how successful ecosystem practices should be employed to complement and

enhance insights achieved through market and structural failure practices, not blindly replace

them.

Interestingly, throughout the whole Scottish exercise, no suggestions were made to

add dimensions to the GEDI framework, only to improve certain measures of constructs. The

hard data provided a foil, or basis for discussion rather than being accepted as the last word

on the state of the ecosystem. It also enabled statements made by stakeholders to be

compared with the hard data. In some cases, these statements were found to be untrue. In

other cases, knowledge held by stakeholders revealed that the measures used for the hard data

were unsuitable.

In an example where the systematic analysis of the hard data countered subjective

bias, the sensitivity analysis highlighted the need to focus on areas of absolute weakness,

rather than relative weakness. Scotland ranks in the fourth quartile of innovation-driven

countries for three aspiration pillars but no attitude pillars. Yet the worse score of any pillar

was Opportunity Perception, and this was identified as the bottleneck deserving the greatest

allocation of additional effort. Because the GEDI methodology is based on the premise that

the weakest pillars, not the relatively weak pillars, hold the entire entrepreneurial ecosystem

in check, the sensitivity analysis spotlighted Opportunity Perception more than the relatively

weak aspiration pillars. This example again suggests that “soft” insights complement, but do

not replace, “hard” facts.

While it is too early to conclusively assess the full impact of the ecosystem

management approach in Scotland, it is striking that not one of the actions proposed in the

Scottish report summarizing its findings required government to introduce new policies or

change old ones. The actions focused on other stakeholders in the system, challenging them

to commit to specific actions; through the high-level task groups, critical stakeholders had

already been co-opted, helped to shape, and were committed to these actions. This stands in

stark contrast to contemporary practices in the UK of superficial conference-style

communication with stakeholders and ministerial “surprise” announcements of policy

decisions (Bridge, 2010; Arshed et al., 2014). This ecosystem analysis and management

approach seems more humble, more cautious, more engaged, and more long term-oriented;

the analysis took around two years, and the action phase has only just begun and is intended

to last for the foreseeable future.

A limitation of this study is that we have focused on a single, albeit rich, case

example, that of Scotland. In addition to allowing deep longitudinal immersion into rich data,

this choice nevertheless carries obvious limitations, particularly where it comes to possible

bias induced by idiosyncratic institutional conditions. For example, one reason for the

absence of a clear role for central government in the actions may be that the general

regulatory background for entrepreneurship in Scotland is relatively favorable. In 2014, the

UK ranked eighth in the World Bank Ease of Doing Business ranking (World Bank, 2014).

This may be why regulatory issues were not identified as bottlenecks. This raises the

possibility that our propositions may be contingent on the relative maturity of the regulatory

regime for entrepreneurship in a country. In countries where regulation is burdensome or rule

of law is weak, there may indeed be a need for government as the central policy actor, at least

in relation to the regulatory regime (Autio & Fu, 2015; Levie & Autio, 2011). Given that

entrepreneurial ecosystem management engages stakeholder communities, there is little doubt

that local and regional cultural and social norms will influence the effectiveness of alternative

approaches to such engagement, for example. Future research should compare the

effectiveness of attempts at analysis and management in different entrepreneurial ecosystems,

using systematic process analysis to compare across cases (Hall, 2006).

Another limitation is that we have focused more on country-level rather than regional-

level analysis. Although part of the UK, Scotland has its own parliament and considerable

independence in economic and fiscal policy. The focus of the Scottish exercise was on the

Scottish national ecosystem framework, within which a number of regional clusters can be

found (e.g., Scotland’s “Silicon Glen”). It is quite well recognized (although not sufficiently

theorized) that country-level and regional entrepreneurial ecosystems are different and may

exhibit different dynamics (Sobel & Hall, 2008; Szerb, Acs, Autio, Ortega-Argiles, &

Komlosi, 2013). It is left for future research to tease out nuances of entrepreneurial ecosystem

management in national and regional contexts.

Conclusion

In conclusion, this chapter has explored the various challenges an ecosystems

approach to entrepreneurship policy presents for policy analysis and management. After 30 or

so years of a market failure approach to entrepreneurship policy, an ecosystems management

approach offers the potential of new insights and, if correctly implemented, higher

effectiveness. This chapter has provided conceptual grounding to understand entrepreneurial

ecosystems, elaborated resulting challenges for entrepreneurship policy, derived principles of

successful management of entrepreneurial ecosystems, and highlighted these using rich case

data from Scotland. We hope that our study will inspire further explorations of this important,

yet underresearched domain.

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Figure 19.1 Process model of the Scottish entrepreneurial ecosystem exercise.

Validity test of bottleneck pillars

Group bottlenecks under themes

Smaller short-term task groups of stakeholders focused

on solutions to bottlenecks/underlying causes

Set of actions agreed and monitoring of actors in place

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How do you measure your entrepreneurial ecosystem? How should you interpret the data about your startup community? What economic indicators should matter for vibrancy and growth? These questions come up repeatedly in conversations with entrepreneurs, program heads, event organizers, investors, policymakers, and others. The frequency of these queries reflects the phenomenon: With the rapid spread of efforts to build entrepreneurial ecosystems, it’s only natural to wonder what outcomes should be tracked. And, what you track depends on what you’re trying to achieve.In some places, the desired outcome is simply more: more entrepreneurs, more companies, and more jobs. Other communities design their ecosystem efforts around a particular type of company or type of job. Some regions, moreover, see the “entrepreneurial ecosystem” as a marketing effort, and focus on a particular type of individual they hope to attract to their area. For other cities, the only thing that matters is the “exit” - initial public offerings and acquisitions. These are all worthy objectives, and communities must define their own goals. Yet where most places fail is in reliance on a handful of limited input metrics rather than outcomes. To judge the vibrancy of their entrepreneurial ecosystems, many states and regions focus on things like research and development funding at universities, available investment capital, and engineering degrees. These may be associated with more entrepreneurial activity, but they are inputs, not necessarily the outcomes to be tracked. Other regions focus on patents or technology licenses out of universities - these are a piece of the puzzle, but they’re not necessarily the leading indicators of entrepreneurial vibrancy. At the other end of the spectrum is the kitchen-sink approach - because every part of an entrepreneurial ecosystem is critically important, you must track everything. This approach has the admirable quality of avoiding Campbell’s Law but provides no sense of prioritization or focus for those community leaders involved in the ecosystem. There must be some middle ground between trying to capture every dimension of an entrepreneurial ecosystem and overly focusing on only one or two indicators. There are also different levels of measurement for entrepreneurial ecosystems. In this paper, we focus on the overall performance of the ecosystem in terms of outcomes and vibrancy. In future work, we will explore measurement indicators that can be instituted at the level of programs and organizations.

Clio and the economics of QWERTY

Article

Full-text available

May 1985

Paul A. David

"Nowhere does history indulge in repetitions so often or so uniformly as in Wall Street," observed legendary speculator Jesse Livermore. History tells us that periods of major technological innovation are typically accompanied by speculative bubbles as economic agents overreact to genuine advancements in productivity. Excessive run-ups in asset prices can have important consequences for the economy as firms and investors respond to the price signals, resulting in capital misallocation. On the one hand, speculation can magnify the volatility of economic and financial variables, thus harming the welfare of those who are averse to uncertainty and fluctuations. But on the other hand, speculation can increase investment in risky ventures, thus yielding benefits to a society that suffers from an underinvestment problem.

Mediated Modeling; a system dynamics approach to environmental consensus building

Book

Full-text available

Jan 2004

Marjan van den Belt

Mediated modeling is an innovative new approach that enhances the use of computer models as invaluable tools to guide policy and management decisions. Rather than having outside experts dispensing answers to local stakeholders, mediated modeling brings together diverse interests to raise the shared level of understanding and foster a broad and deep consensus. It provides a structured process based on system dynamics thinking in which community members, government officials, industry representatives, and other stakeholders can work together to produce a coherent, simple but elegant simulation model. Mediated Modeling by Marjan Van Den Belt is a practical guide to participatory modeling for both practitioners and students, one that is firmly theoretically grounded in the field of systems dynamics and environmental modeling. Five in-depth case studies describe the successful use of the technique in a variety of settings, and a final chapter synthesizes the lessons highlighted by the case studies. Mediated Modeling's step-by-step description of the techniques and practical advice regarding implementation offer a real-world solution for all those seeking to make sound decisions about the environment. - See more at: http://www.islandpress.org/book/mediated-modeling#sthash.G3iNjmCc.dpuf

Entrepreneurship Policy: Theory and Practice

Book

Jan 2005

Government, SMEs and Entrepreneurship Development

Book

Apr 2016

Robert A. Blackburn

Economic Welfare and the Allocation of Resources for Invention

Chapter

Dec 1962

Kenneth J. Arrow

Entrepreneurial discovery and the competitive market process: An Austrian approach

Article

Jan 1977
J ECON LIT

I. Kirzner

Economic welfare and the allocation of resources for invention

Article

Jan 1962

K. Arrow

Rethinking Enterprise Policy: Can Failure Trigger New Understanding?

Book

Aug 2010

Simon Bridge

Policies to increase the level of enterprise and entrepreneurship, in many countries and regions, have often failed. This book explores this and gives alternative views to derive a different model, based on social influence, which is consistent with the evidence and which might therefore lead to better policy.

Social capital and community governance

Article

Jan 1999
Focus

Samuel Bowles

Management of Entrepreneurial Ecosystems

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