Development of a multi-dimensional measure for assessing entrepreneurial ecosystems

Abstract

Researchers and theorist have put great effort into defining and examining entrepreneurial ecosystems and how business clusters develop in certain regions. Favourable entrepreneurship ecosystems are thought to drive business and innovation. However, a commonly accepted measure of entrepreneurial ecosystem favourableness has yet to be developed. The purpose of the present research is to contribute to ecosystems research by taking a two-study approach to developing and validating a perceptual measure of entrepreneurial ecosystems. The perceptual measure is based upon prior conceptual frameworks that outline ecosystems. In developing this measure, we are able to further unpack and illuminate the factor structure of ecosystems, the results of which have direct scholarly and practitioner uses.

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Entrepreneurship & Regional Development
An International Journal
ISSN: 0898-5626 (Print) 1464-5114 (Online) Journal homepage: http://www.tandfonline.com/loi/tepn20
Development of a multi-dimensional measure for
assessing entrepreneurial ecosystems
Eric Liguori, Josh Bendickson, Shelby Solomon & William C. McDowell
To cite this article: Eric Liguori, Josh Bendickson, Shelby Solomon & William C. McDowell
(2019) Development of a multi-dimensional measure for assessing entrepreneurial ecosystems,
Entrepreneurship & Regional Development, 31:1-2, 7-21, DOI: 10.1080/08985626.2018.1537144
To link to this article: https://doi.org/10.1080/08985626.2018.1537144
Published online: 05 Nov 2018.
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Development of a multi-dimensional measure for assessing
entrepreneurial ecosystems
Eric Liguori
a
, Josh Bendickson
b
, Shelby Solomon
c
and William C. McDowell
d
a
Department of Management and Entrepreneurship, Rowan University, Glassboro, USA;
b
Department of
Management, University of Louisiana at Lafayette, Lafayette, USA;
c
Department of Management, Roger Williams
University, Bristol, USA;
d
Department of Management, Technology and Law, Bradley University, Peoria, USA
ABSTRACT
Researchers and theorist have put great effort into defining and examin-
ing entrepreneurial ecosystems and how business clusters develop in
certain regions. Favourable entrepreneurship ecosystems are thought to
drive business and innovation. However, a commonly accepted measure
of entrepreneurial ecosystem favourableness has yet to be developed.
The purpose of the present research is to contribute to ecosystems
research by taking a two-study approach to developing and validating
a perceptual measure of entrepreneurial ecosystems. The perceptual
measure is based upon prior conceptual frameworks that outline ecosys-
tems. In developing this measure, we are able to further unpack and
illuminate the factor structure of ecosystems, the results of which have
direct scholarly and practitioner uses.
KEYWORDS
Entrepreneurship
ecosystems; clusters;
innovation; measurement;
scale development
Introduction
Clusters of innovative firms emerge under certain conditions (Isaksen 2016; Oerlemans, Meeus,
Boekema 2001; Uyarra and Ramlogan 2016) and many antecedents dictate success and failure of
firms, including firm innovation, within such clusters. For example, geographical aspects of local
ecosystems affect firm innovation (Herstad 2017). We refer to these as entrepreneurial ecosystems
(EE), which we define as conditions that make ecosystems more or less favourable for entrepre-
neurship activity. According to the EE perspective, firms cluster in advantageous regions and thrive
on various environmental resources such as information, networks and labour markets (Coenen,
Moodysson, and Martin 2015).
Research in this space includes topics such as ecosystem entrepreneurs (Nambisan and Baron
2013; Velt, Torkkeli, and Saarenketo 2018), social and sustainable entrepreneurship ecosystems
(McMullen 2018; Muñoz and Dimov 2015), entrepreneurial university ecosystems (Hayter 2016;
Shaeffer and Matt, 2016) and location-based innovative clusters for high growth and other geo-
graphic implications (Breschi and Malerba 2001; Li et al. 2016), all of which help us better under-
stand where firms may cluster to innovate and thrive. However, although conceptual attempts to
clarify how ecosystems are measured (e.g. Isenberg 2010a,2011a,2011b), empirical evidence still
lags behind. The purpose of this study is to better define and measure entrepreneurship ecosys-
tems through the development of a Multidimensional Entrepreneurial Ecosystem Scale (MEES)
capable of assessing six specific domains within an entrepreneurial ecosystem. In doing so, we help
to unpack the elements of geographical regions that encourage firm clustering and innovation.
CONTACT William C. McDowell wmcdowell@bradley.edu Bradley University, 1501 W. Bradley Ave., Peoria, IL 61525,
USA
ENTREPRENEURSHIP & REGIONAL DEVELOPMENT
2019, VOL. 31, NOS. 1–2, 7–21
https://doi.org/10.1080/08985626.2018.1537144
© 2018 Informa UK Limited, trading as Taylor & Francis Group

EE researchers no longer surround questioning if territory or environment are important elements,
yet now acknowledge the importance of territory and environment as undeniable; in doing so, the
research has shifted towards understanding and validating the effectiveness and efficiency of environ-
mental configurations (Boutillier, Carre, and Lavrato 2016). Conceptually, an EE is a collection of six non-
causal critical domains of entrepreneurship. These include policy, finance, culture, supports, human
capital and markets (Isenberg 2011b). Within each of these six domains reside many sub-domains. For
example, within policyreside leadership and government, each has sub-domain items. Culture includes
success stories and societal norms, which also have sub-domain items. Not of surprise, the market
domain includes early customers, but it also includes networks. Isenberg (2011a) provides a more
comprehensive overview. Given new products, new jobs and economic growth in the United States are
largely a result of the entrepreneurial sector (Hisrich and Grachev 1993; Baumol and Strom 2007), and
considering that many economies have changed drastically in the last fifteen years due to technology
and globalization, the study of EE is prudent to better understand what drives entrepreneurship and
what can make a region more or less suitable for sustaining an entrepreneurial cluster.
A number of indices and barometers exist, each purportedly measuring unique aspects of the EE
at one level or another. For example, efforts such as the Global Economic Monitor (GEM) and Doing
Business (DB) help capture EE aspects at the national level, enabling cross-national comparison.
Yet, these efforts are difficult to scale down to a more local level. The National Federation of
Independent Business’s National Small Business Poll (NSBP), designed to capture a snapshot of the
United States as a whole, is more easily adapted to the state or regional level, but lacks some of the
robustness of GEM and DB. In the United States, popular press outlets such as Site Selection
Magazine and Development Consultants International offer metrics to rank state level ecosystems
(e.g. Guttman 2015; Stamer 2005). Yet, a critical review of these metrics call their validity into
question given they typically use small, non-representative convenience samples that lack any
theoretical foundation (e.g. Arend 2004; Newman 2015).
Therefore, we seek to address these issues and contribute to the literature by developing a new,
individual level, multidimensional measure of an EE: the MEES. Using Isenberg’s conceptual work
(2010a,2011a), we provide needed systemization of the six domains of the entrepreneurial ecosystem
(policy, finance, culture, supports, human capital and markets) as a framework. A fundamental
contribution of the MEES is that it can be used to assess ecosystems at any level; all the researcher
must do is define the scope of the ecosystem when employing the measure. Thus, MEES can look at
ecosystems at a city, county, state, region, or national level. Second, we tap into ecosystems as a
cognitively assessed, rather than objectively assessed, construct. We believe this is a strength of our
study as it is perceptions that influence behaviours and ‘perceptions can be more discriminating than
objective measures’(Powell 1996, 336). We accomplish our goal by assessing policy, finance, culture,
supports, human capital and markets through the perception of individuals within the given ecosys-
tem. Fourth, we argue that understanding which characteristics lead to entrepreneurship is of upmost
importance and that too few efforts have been made to understand the broader context. Lastly, given
entrepreneurship and clustering is of critical importance to economic growth and innovation (Herstad
2017; Wennekers and Thurik 1999), having an accurate measurement tool provides a great number of
practical contributions (i.e. what conditions are conducive to entrepreneurship).
The remainder of the manuscript is as follows: we first address current measures of entrepre-
neurship ecosystems. Next, we provide a more in-depth overview of our guiding principles and the
six domains of the MEES. Third, we describe our item generation procedure. This is followed by
Study 1, Study 2 and the results of our analyses. Lastly, we discuss the results, implications,
limitations and opportunities for future research.
Current measures of entrepreneurial ecosystems
The existing literature is dominated by three means to assess entrepreneurial ecosystems (viz.,
GEM, PSED, and Kauffman), each of which we will discuss further. We will not explore the popular
8E. LIGUORI ET AL.

press approaches noted earlier given their lack of scientific rigour. First, the Global
Entrepreneurship Monitor (GEM) is arguably the largest and most long-standing option, allowing
for ‘. . .a deep understanding of the environment for entrepreneurship’(GEM Consortium, 2017
para. 5). To date, GEM studies have been conducted in over 100 countries and involve over 300
academic and research institutions. GEM, however, is primarily focused at the national level, with
past GEM research resulting in the development of ‘country profiles’or comparing differences in
nations, not individual communities. Second, the Panel Study of Entrepreneurial Dynamics (PSED),
was ‘designed to enhance the scientific understanding of how people start businesses’and data
are collected on the process of business formation from nationally representative samples.
Included in the PSED are some environmental impact items hitting at some of the core aspects
of the entrepreneurial ecosystem. Like GEM, this measure has been used primarily at the national
level (Parker and Belghitar 2006; Reynolds and Curtain 2009), but unlike GEM the focus is more on
the person and process and less on the environment.
Third, the Kauffman Foundation has published a variety of research reports and guides to
assessing and exploring entrepreneurial ecosystems. We draw specific attention to their March
2015 white paper titled ‘Measuring an Entrepreneurial Ecosystem’by Dane Stangler and Jordan
Bell-Masterson. Stangler and Bell-Masterson (2015) posit that while some attempts to measure
entrepreneurship include only one or two indicators, others try to include the kitchen-sink
approach and suggest there should be some common ground to better understand this puzzle.
Together, GEM, PSED and the Kauffman Foundation research reports have been the largest and
most influential sources of insight to those who study entrepreneurial ecosystems. Yet, none of the
three enables policy makers and other interested parties to fully nor adequately assess the
favourableness of the ecosystem at the community level and from the perspective of the consti-
tuent. For this reason, we chose to create the MEES.
Creating a new instrument: MEES
Given many past measures have either been specific to the national level, rooted solely in
economic indicators, or having been ad hoc to study only a small subset of phenomenon, we
sought to develop a more comprehensive measure of the entrepreneurial ecosystem within a given
community. Isenberg (2011b) posited a six-domain taxonomy for considering entrepreneurial
ecosystems at the local level: policy, finance, culture, supports, human capital, and markets.
Using these six domains, and two guiding principles, the MEES was developed.
Guiding principle #1 –applicability
First, we began with the contention that this measure should be broad enough to work in any
geography, yet specific enough to capture the uniqueness of a given locality. Consider Fresno,
California for example. Fresno has a growing entrepreneurial ecosystem, especially in regard to
technology and agriculture (e.g. Fallows 2015; Santos 2018). Yet, despite this activity, few would
call Fresno a major tech or start-up hub. Now consider Silicon Valley, the ‘gold standard’of
technology. Both Fresno and Silicon Valley are California cities, subject to California labour laws,
California tax and legal structures, and California geography; all factors that constrain either cities
ability to differ from one another across some major domains. Despite standardized state and
federal labour laws, state income tax rates, and west coast location, Fresno suffers from a severe
brain drain problem (i.e. an inability to effectively retain its most promising human capital) while
Silicon Valley arguably leads the nation in human capital.
Given that firms seek out knowledge-based human capital (Coder, Peake, and Spiller 2017;
Roper, Love, and Bonner 2017), this presents an interesting regional difference. While these two
locations vary in a very small amount from a policy perspective, they vary greatly in culture, access
to markets, and human capital, all of which have the ability to impact growth (Li et al. 2016).
ENTREPRENEURSHIP & REGIONAL DEVELOPMENT 9

Thinking across the United States one can quickly identify numerous cities that vary across multiple
domains despite them both residing within the same nation, region, and state (Austin vs. Houston;
Nashville vs. Memphis; Miami vs. Pensacola; etc.). Isenberg (2010a) suggested that to build a
successful entrepreneurial ecosystem one must embrace and exploit the local conditions of the
specific community rather than trying to emulate what works in other communities (viz., stop
trying to replicate Silicon Valley), so a tool enabling a locality to better understand its specific
environmental context is needed.
Guiding principle #2 –attitudes affect behaviour
The second guiding principle included is that behaviour is best predicted by intent (Armitage and
Conner 2001). Given intent is heavily derived from attitudes (Ajzen 2001), understanding how a
particular community views a given domain is critical to our understanding of each domain,
especially at the local level. While individual views may be informed by objective reality, bounded
rationality and information asymmetry are at play. Considering the finance domain, for example, a
steadily increasing number of SBA loan applications approved or the cumulative dollar total of SBA
lending within a community rising over a 5-year period may not equate to the community
recognizing improved access to capital. If there is a general sentiment within a community that
banks will not lend, there will be fewer loan applications received. In sum, we presume that
understanding the intentions of the community at large is critical to fully understanding the
strengths and weaknesses of the community’s ecosystem.
Domain #1 –policy
Policy is the extent to which government and leadership not just support and advocate for
entrepreneurial activity, but also have rules and regulations in place to govern it. At the national
level this could be the difference between the intellectual property protection systems of the
United States vs. Asia; at the state level this could be the difference between tax and entity
structures in Texas vs. California; and at local level this could be the permitting, zoning, and
occupational licensing processes in place that vary from city to city and county to county.
Regulation that impacts venturing is prevalent given most countries have experienced impactful
regulation increases over the past century (e.g. Kleiner 2000; Kleiner and Krueger 2010).
Domain #2 –finance
Finance centres primarily around access to capital. This includes not just the presence of venture
capitalists, angel investors, and traditional bank lenders, but also state and community run micro-
loan programmes (e.g. Benjamin, Rubin, and Zielenbach 2004). Given 67.2 per cent of new ventures
are financed from founder savings (Kauffman 2015), finance also includes the wealth of the
individuals in the community (this impact can be profound given friends and family account for
another 28.4% per cent of new venture financing nationally; Kedrosky and Stangler 2011).
Domain #3 –culture
Organizational researchers have long recognized the importance of culture in stimulating creativity
and innovation (Martins and Terblanche 2003) and affecting organizational performance (Den
Hartog and Verburg 2004). Moreover, research conducted by the Organization for Economic
Cooperation and Development (OCED) found that entrepreneurial culture is a key determinant of
EE success (Boutillier, Carré, and Levratto 2016). EE Culture is largely individually driven by people
advocating values that promote innovation and venturing as viable career opportunities
(Audretsch 2007; Hechavarria and Ingram 2018). Mack and Mayer (2016) showed the impact
10 E. LIGUORI ET AL.

success story visibility has in building culture. Others work has shown how a tolerance for risk and
acceptance of failure are important cultural characteristic. For example, up until the 1980s, Ireland
was heavily stigmatized by failure (viz., loan default or bankruptcy) to the point where children to
coached take corporate or government jobs and not start new ventures, but some successes in the
1990s caused a change in how failure is viewed in Ireland and enterprising has become much more
socially acceptable (Isenberg 2010a).
Domain #4 –supports
Supports for entrepreneurship go a long way to enabling and empowering a community to truly
facilitate entrepreneurial behaviour. Supports include infrastructure (transportation; high-speed
Internet access; energy), support professions (legal, accounting), and entrepreneurship friendly
institutions and programmes (small business resource centres, chambers of commerce, business
plan competitions). Tampa, FL is a good example of how supports can both help and hinder the
entrepreneurial ecosystem. Tampa has 62 entrepreneurial support organizations (White et al. 2016),
access to accounting and legal expertise, the promise of Google Fibre internet infrastructure
(Trigaux 2015), and a plethora of entrepreneurial programming open to the community (Startup
Weekends, Startup Week, co-working spaces, Small Business Development Centers). All of these
factors contribute to Tampa’s continual rise in numerous rankings related to entrepreneurship. But,
Tampa also has a fatal infrastructure flaw that hinders its ecosystem: transportation. Tampa has
poor public transportation and the ability to connect the main research and technology hubs of
the city with the core business community is often cited as a major impediment to the Tampa EE
(Kritzer 2016). Similarly, research into Estonia’s EE has found infrastructure is a hindrance to their
growth and prosperity (Kshetri 2014; Velt, Torkkeli, and Saarenketo 2018; fortunately, this same
work notes that Estonia’s culture is especially strong and responsible for the boom of entrepre-
neurial activity the country has experienced in the last 15 years).
Domain #5 –human capital
Access to human capital has long been a factor impacting entrepreneurial venturing (Bendickson
et al. 2017; Huggins, Prokop, and Thompson 2017). Whereas in some industries access to human
capital has become less of an issue given technological advances (e.g. videoconferencing, cloud-
based collaboration tools, etc.), other industries still very much rely on the availability of adequate
human capital in their geographic vicinity making this a critical EE success factor. Randall Kemper,
Executive Director of the Aspen Network of Development Entrepreneurs, notes that access to
human capital is an especially impactful hindrance for many for smaller companies in emerging
markets (NewsGhana 2016). Moreover, even in developed markets access to adequate human
capital regularly affects both venture growth and success (Siepel, Cowling, and Coad 2017).
Domain #6 –markets
Markets include access to early adopters, distribution channels, diaspora networks, etc. South Korea
serves as a good example of how access to markets can help or hinder the EE. South Korean
entrepreneurs rely heavily on diaspora-based networks in the United States and Europe to not just
access resources but also develop out their markets (Kitching, Smallbone, and Athayde 2009).
Similarly, major U.S. companies test new products in cities that serve as microcosms to the nation
as a whole (e.g. Nashville, Orlando, Cincinnati, etc.; Pilny 2014).
ENTREPRENEURSHIP & REGIONAL DEVELOPMENT 11

Item generation
Following systematic item generation procedures (cf. Netemeyer, Bearden, and Sharma 2003;
Hinkin 1995,1998), several sources were scoured for potential items that could reflect the six
posited domains. These sources included the Global Entrepreneurship Monitor, Doing Business, the
National Federation of Independent Business’National Small Business Poll, the Panel Study for
Entrepreneurial Dynamics, Feld’s(2012) Startup Communities, and the entrepreneurship literature
(cf., Fetters, Greene, and Rice 2010; Hitt et al. 2011; Isenberg 2010a,2010b,2011a,2011b; Isserman
1993; Schwarzkopf 2016; Venkataraman 1997).
Then, following a deductive approach (Schwab 1980; Spector 1992), additional items were
developed based upon semi-structured interview discussions with four subject matter experts
spanning various business domains (e.g. accounting, finance, marketing, management). Each
expert was provided with a standardized definition of an entrepreneurial ecosystem from
Isenberg (2010b). The lead author then asked the interviewees about what characteristics the
expert considered to be the most impactful indicators of a strong entrepreneurial ecosystem. Once
their responses were recorded the experts were shown the six-dimension model and asked what
indicators they would expect to see in their community under strong and weak domain conditions.
To ensure the breadth of coverage was not limited to academics or the academic literature, two
full-time Small Business Development Center Counsellors who interact regularly with nascent
entrepreneurs were also consulted and given an opportunity to help generate items from the
entrepreneur’s perspective. Lastly, to ensure no age or culture biases exist, two diverse focus
groups (N= 80; 74% Caucasian, μ= 21.54 years of age; 55% female) of undergraduate students
at a large research university were conducted to help the research team generate items. The use of
students was limited only to item generation and much prior research substantiates the relevance
of considering adults at 21 years of age or higher as potential entrepreneurs (Turker and Selcuk
2009) given they have an increased propensity for new venturing (Linan and Santos 2007).
In addition to the item generation procedures followed above, three expert judges, each familiar
with both psychometric theory and the entrepreneurial ecosystem literature, were solicited and
agreed to assist in purifying the item pool following Haynes, Richard, and Kubany’s(1995) content
validation guidelines. Working independently, judges were asked to review each item for clarity,
meaningfulness, and appropriateness. Judges were provided the latitude to rephrase or eliminate
items they deemed redundant, ambiguous, misleading or poorly worded. Likewise, they were also
empowered to write or suggest new items. This process resulted in 43 useable items.
Sample 1
Two independent samples were used to develop and validate the MEES. Following recommenda-
tions from Prescott and Soeken (1989) to pre-test the measure as a means of sharpening the design
of a proposed study prior to formal data collection, we collected data (viz., Sample 1) from a
snowball sample of nascent entrepreneurs across the United States (N= 198). Sample 1 spanned
the Northeast, Southeast and West Coast. Sample 1 was used to explore the basic factor structure
of the MEES and its correlates.
Sample 2
Next, to confirm and validate the MEES, we surveyed entrepreneurs in a mid-sized metropolitan
city on the West Coast (viz., Sample 2). Respondents for Sample 2 were recruited using a targeted
sampling approach allowing for access to multiple organizations (Schneider 1987; Schneider,
White, and Paul 1998; Organ and McFall 2004; Organ, Podsakoff, and MacKenzie 2006) across
multiple industries (N= 267). Respondents represented industries including restaurants, hospitality,
financial services, construction, technology, retail trade and healthcare.
12 E. LIGUORI ET AL.

Validity testing
Following recommendations from Netemeyer, Bearden, and Sharma (2003) we explored both
translation- and criterion-related validity. The first, translation validity, was established via the
item generation and development process employed. The second, criterion-related validity is
evidenced via convergent validity. More specifically, across both samples the internal consistency
reliability estimates for each domain were at or above the generally accepted 0.70 level, and the
items, by and large, loaded cleanly on the theoretically hypothesized domain.
Results
Exploratory factor analysis
To reduce the original set of items down to a number of items that best reflected each dimension
of the MEES, we used an exploratory factor analysis (EFA) technique on our sample of nascent
entrepreneurs (i.e. sample 1). EFA is a dependence technique that categorizes items together based
on how strongly they load onto a common factor (Pedhazur and Schmelkin 1991). Specifically, we
performed six common factor analyses (viz., one factor per theorized dimension) thus forcing the
analysis to settle upon a single factor solution for each of the analyses in order to identify items
that did not fit its respective domain well. Items were then eliminated on the basis of poor factor
loadings (i.e. we eliminated items that displayed loadings of less than .7 until all items within each
factor had loadings of greater than .7, or the set of items representing each factor was limited
down to a set of three
1
). This process yielded a set of 22 items that can be expected to consistently
tap the dimensions of people’s perceptions of a given entrepreneurial ecosystem. See Table 1 for a
list of the items and their respective factor loadings that were obtained through this process. Note
that when analyzed individually each factor analysis yielded loadings that greatly exceed the .5 cut-
offvalue required for practical significance (Hair et al. 2006).
Additionally, all of the loadings were significant at the .05 level. The factor solution also
maintained its structure when all of the items were entered into the analysis together and analyzed
as a group, see Table 2.
2
That is, the items representing each respective factor consistently loaded
together and there were no meaningful or problematic dual loadings. Although, some of the
loadings within this final EFA were rather low, the loadings do hover around the .5 cut-offlevel (i.e.,
.49–.8) suggested for practical purposes such as data reduction (Hair et al. 2006). Furthermore, all of
the factor loadings were significant at the .05 level.
Confirmatory factor analysis
After reducing the data down to a smaller set of items that were most reflective of the focal
constructs, the data from the sample of entrepreneurs (sample 2) were then analyzed through the
use of confirmatory factor analysis (CFA). CFA was selected as the ideal method to test the structure
of our data, because it allows the researcher to test the extent to which their hypothesized factor
model fits the data. Specifically, CFA is used to test how well the hypothesized factor model is able
to reproduce the observed covariances between a set of items. Hence, in the present study CFA
allowed us to test how well the proposed factor structure of the MEES fits the actual data. We
estimated a model that contained all six dimensions of the MEES with each set of respective items
loaded onto their corresponding dimension to test our hypothesized model.
Upon the first run of the CFA with the set of 22 items identified during the EFA we yielded an
inadequate fitting model that did not meet the standard cut-offvalue for model fit in terms of
incremental fit. In addition, the root mean square error of approximation (RMSEA) value was also
too large at first run. We recognized that a number of poor fitting (viz., items with loadings of less
than .7 within factors that had more than three items) within the factors of Supports and Finance
were the root cause of this problem. Specifically, we found that items 3 and 4 within the Supports
ENTREPRENEURSHIP & REGIONAL DEVELOPMENT 13

factor displayed a low set of standardized loadings and items 4 and 5 within the Finance also
displayed a similarly low set of standardized loadings. Therefore, we rejected the four poor fitting
items and ultimately yielded a set of 18 items to represent the six factors of the MEES (three items
per factor). See Figure 1 for an overview of the final factor structure.
We assessed model feasibility and validity through the use of MPLUS as well as via a number of
model fit indices in order to demonstrate goodness-of-fit, incremental (or comparative) fit, and a
lack of badness-of-fit (Hair et al. 2006). See Table 3 for a summary of the model fit statistics. First,
we used the chi-square goodness-of-fit statistic to asses overall model fit. However, the classic chi-
square goodness-of-fit statistic is heavily influenced by sample size, that is, tests of model fit
involving samples of even moderate sizes (e.g. N= 100–400) are often rejected as poor fitting (i.e.
P< .05) (Marsh, Balla, and McDonald 1988). Hence, the fact that our data sample and CFA model
failed this test was a rather predictable and non-problematic result. Therefore, to provide an
additional test of model fit that is able to compensate for larger sample sizes, we also used the
normed-chi-square goodness-of-fit statistic. The normed-chi-square goodness-of-fit statistic is use-
ful because it is a statistic based on dividing the goodness-of-fit statistic through by the number of
degrees of freedom, to discount for model parsimony (Kline 2005). Additionally, the CFI was used
to test for incremental model fit and the RMSEA was used to test for badness-of-fit.
Table 1. Summary of individual EFAs.
Factor Loading
Finance
Finance_2: There are local individual investors in my community who are willing to financially support
entrepreneurial venturing.
0.82
Finance_5: Bankers in my community work hard to help entrepreneurs obtain financing. 0.76
Finance_3: Financing for entrepreneurship is available in my local community. 0.76
Finance_4: Information on what funding programmes are available for entrepreneurs is easily accessible. 0.74
Finance_1: My community has a sufficient number of banks who are willing to lend to entrepreneurs. 0.72
Supports
Supports_2: My community has the infrastructure necessary to start and run most businesses (e.g.
telecommunication, transportation, energy).
0.77
Supports_3: My community has many entrepreneur-friendly organizations such as Rotary Clubs or Chambers of
Commerce.
0.76
Supports_6: Professional services (e.g. lawyers and accountants) for entrepreneurs are readily available in my
community.
0.75
Supports_5: I believe the resources in my community are well designed to support business growth. 0.74
Supports_4: Local organizations, such as incubators and Small Business Development Centers, are active in
supporting local entrepreneurs.
0.71
Culture
Culture_17: The social values and culture of the community emphasize creativity and innovativeness. 0.85
Culture_16: The social values and culture of the community encourage entrepreneurial risk-taking. 0.85
Culture_15: The social values and culture of the community emphasize self-sufficiency, autonomy, and personal
initiative.
0.77
Human Capital
Human Capital_3: Local educational institutions offer specialized courses in entrepreneurship. 0.87
Human Capital_4: There are entrepreneurial training programs, such as entrepreneurship bootcamps, available in
my local community.
0.80
Human Capital_2: There are ample local institutions of higher education (universities, community colleges, trade
schools) within my community.
0.80
Markets
Markets_7: The diversity in my community provides a great test market for many other locations. 0.84
Markets_6: My community networks could help me distribute new products across a variety of new markets. 0.83
Markets_5: My community’s multinational diversity helps keep me connected the global economy. 0.82
Policy
Policy_4: The local government actively seeks to create and promote entrepreneurship-friendly legislation. 0.89
Policy_3: The local government has programmes in place to help new entrepreneurs, such as seed funding
programmes or entrepreneurship training programmes.
0.79
Policy_5: Local community leaders regularly advocate for entrepreneurs. 0.67
*Sample size N= 198. All factor loadings are significant at p< .05
14 E. LIGUORI ET AL.

Although, our CFA model did not pass the traditional chi-square goodness-of-fit test, the more
commonly applied test of fit is the normed-chi-square goodness-of-fit measure. The critical cut off
value for the normed-chi-square goodness-of-fit test is three and lower (Kline 2005). As shown in
Table 2. Rotated component matrix
a
.
Component
123456
Finance_2 0.79
Finance_4 0.73
Finance_5 0.71
Finance_1 0.71
Finance_3 0.65
Supports_3 0.74
Supports_4 0.71
Supports_2 0.69
Supports_5 0.64
Supports_6 0.59
Culture_17 0.79
Culture_16 0.79
Culture_15 0.68
Human Capital_3 0.8
Human Capital_2 0.76
Human Capital_4 0.71
Markets_6 0.8
Markets_5 0.77
Markets_7 0.69
Policy_3 0.79
Policy_4 0.77
Policy_5 0.49
Extraction Method: Common Factor
Rotation Method: Orthogonal
a. Rotation converged in seven iterations; Loadings below .4 are not displayed
**All factor loadings are significant at P< .05; N= 198
Finance
Human
Capital
Market
Policy
Culture
Supports
Finance_1
Finance_3
Finance_2
Human Capital_3
Human Capital_2
Human Capital_4
Market_7
Market_5
Market_6
Policy_5
Policy_3
Policy_4
Culture_15
Culture_16
Culture_17
Supports_5
Supports_6
Supports_2
Figure 1. The final CFA structure of the MEES.
ENTREPRENEURSHIP & REGIONAL DEVELOPMENT 15

Table 3 our data sample and model passes the normed chi-square goodness-of-fit and model
parsimony test. Furthermore, the accepted threshold for the CFI is .9 (Bentler 1990). Hence, our CFA
model passed the CFI test of incremental fit, as we achieved a CFI value of .9. Finally, the RMSEA
value for the CFA model fell below the traditional .08 cut-offvalue (MacCallum, Browne, Sugawara,
1996). Thus, the CFA model was able to pass all of the model fit tests except for the chi-square
goodness-of-fit test, which is most likely a function of our large sample size. In sum, the data fit the
model based on the results of the commonly accepted fit statistics.
In addition to displaying acceptable fit statistics, the model also displayed a desirable set of
convergent validity statistics. That is, all of the loadings were greater than the traditional minimum
cut-offof .5 and most of the loadings were greater than .7, also all of the loadings were statistically
significant (Hair et al. 2006). Moreover, each of the factors within the overall MEES displays
excellent internal convergent validity properties in terms of composite reliability. Each of the
factors achieves composite reliability coefficients of greater than the standard .7 cut-offvalue
(Bagozzi and Yi 1988; Nunnally 1978), and range from .79 to .73 with an average reliability of .77.
Furthermore, the robustness of this models convergent validity is demonstrated by the average
variance extracted (AVE) coefficients that fall largely above the cut-offvalue of .5 (Bagozzi and Yi
1988), as the AVEs for each factor ranged between .49 and .57 with an average value of .53.
Accordingly, the items of the MEES on average explained the majority of the variance within each
factor. See Table 4 for a summary of the loadings and convergent validity statistics.
Table 3. Summary of fit statistics.
Sample 2 (Entrepreneur Sample)
Sample Size 267
Number of Parameters 69
DF 120
Chi-Sq Goodness-of-Fit 292.99
Chi-Sq P_Value 0.00
Normed Chi-Sq 2.44
CFI 0.90
RMSEA 0.07
Table 4. Summary of loadings and convergent validity statistics.
Loadings PReliability AVE
Finance Items 0.79 0.49
Finance_1 0.64 ***
Finance_3 0.74 ***
Finance_2 0.86 ***
Human Capital Items 0.75 0.56
Human Capital_4 0.62 ***
Human Capital_2 0.81 ***
Human Capital_3 0.68 ***
Market Items 0.77 0.55
Markets_7 0.63 ***
Markets_5 0.78 ***
Markets_6 0.76 ***
Policy Items 0.78 0.53
Policy_5 0.59 ***
Policy_3 0.75 ***
Policy_4 0.86 ***
Culture Items 0.79 0.50
Culture_15 0.61 ***
Culture_16 0.84 ***
Culture_17 0.78 ***
Support Items 0.73 0.57
Supports_5 0.65 ***
Supports_6 0.78 ***
Supports_2 0.64 ***
16 E. LIGUORI ET AL.

To provide further support for the construct validity of the MEES, we also tested the discriminate
validity properties of the MEES. To provide a test of the discriminant validity of the measure, we
applied the conservative test of comparing each factor’s AVE statistic to the squared correlations
between each of the factors (see Table 5 for a summary of correlations and squared correlations
between factors) (Farrell 2010; Fornell and Larcker 1981). In all cases, the AVEs for each factor were
greater than the squared correlations between the factors, as the squared correlations ranged
between .09 and .40. Our results suggest that although the factors within the MEES are related to
one another, the items that comprise each factor tap a distinct component of the entrepreneurial
ecosystem.
In sum, the CFA yielded a set of results that were supportive of the MEES across a number of
dimensions. First, the MEES displayed a set of adequate fit statistics; the CFA model represented the
data very well. Second, the model demonstrated convergent validity, as each of the items
representing the factors significantly loaded on to their corresponding factor. Moreover, the
standardized loadings were also large enough to represent the factors on a meaningful level (i.e.
loadings ranged from .59–.86) (Hair et al. 2006). The MEES was able to also achieve acceptable
convergent validity statistics in terms of both reliabilities and AVEs (Bagozzi and Yi 1988). Third,
each of the factors demonstrated a high degree of discriminant validity, as the AVEs for each factor
were all greater than the squared correlations between factors (Fornell and Larcker 1981).
Therefore, the triangulation of the evidence of model fit, convergent validity, and discriminant
validity serve to establish the overall construct validity of the MEES.
Discussion
We began by inquiring about drivers of entrepreneurship and what can make a region more or less
suitable for sustaining an entrepreneurial cluster. While current studies and literature have added
value to this topic, the applicability is disconnected such that it is difficult to compare varying
entrepreneurial ecosystems through the use of the current measures. We set out to create a
measure that can be utilized across levels as a means to help solve this problem given its large-
scale importance to overall economic conditions. To do so, we grounded the MEES in Isenberg’s
(2010a,2011a,2011b) conceptual work and push it forward by systematizing the six domains of an
entrepreneurship ecosystem by determining individuals’perceptions of their entrepreneurship
communities. As such, we confirm Isenberg’s conceptual model (2010a,2011a,2011b) by illustrat-
ing a stable factor structure in the MEES. That is, each factor of the MEES captures a unique and
important piece of any given entrepreneurial system.
We further contribute to the entrepreneurship ecosystem literature by demonstrating ecosys-
tems can be measured through perceptual techniques. As a result of our research effort, we built a
psychometrically robust measure to capture entrepreneurs’perceptions of the surrounding eco-
system. As stated previously, we designed the MEES to be a measure capable of capturing
perceptions of the ecosystem of on many levels (i.e. city, state, region, etc.) provided the research-
ers are able to draw from a representative sample. Going forward this is a tool that not only has
academic implications, but also practical value. More specifically, by knowing which domains
Table 5. Summary of correlations and squared correlations between factors.
Culture Policy Finance Supports Human Capital Markets
Culture - 0.27 0.11 0.24 0.08 0.13
Policy 0.52 - 0.09 0.27 0.13 0.13
Finance 0.33 0.30 - 0.37 0.26 0.21
Supports 0.49 0.52 0.61 - 0.40 0.38
Human Capital 0.27 0.36 0.51 0.63 - 0.38
Markets 0.36 0.36 0.46 0.62 0.62 -
*Standard bivariate correlations are listed below the diagonal, squared correlations are listed above the diagonal
**All correlations between factors are significant P< .05
ENTREPRENEURSHIP & REGIONAL DEVELOPMENT 17

within a given ecosystem are strengths and weaknesses, policy makers can gain richer and deeper
insight to the fundamental dynamics driving the success of their community ecosystem. This
knowledge can lead to more targeted and informed interventions, thus yielding more promising
outcomes. Given the decline in the number of start-ups in the United States over the last few
decades coupled with the known impact of entrepreneurship on the U.S. economy (Clifton and
Badal 2014), any tool that empowers policy makers to improve their ecosystems is much
welcomed.
Limitations and future research directions
Our study is not without limitations. First, our data was collected from various areas of the
United States, including the West, South, Midwest, and Southeast, but not the Northeast. While
this could minimally limit the generalizability of the MEES, post hoc analyses of differences
between the regions from which data was collected did not reveal differences that would
meaningfully suggest the measurement is regionally biased within the United States based
sample. Future research, however, is needed to test the generalizability of the MEES outside of
the United States.
A second potential limitation is a lack of depth within each domain. It is possible to continue to
break down the six domains even further. One example could be breaking the culture domain into
two smaller domains (viz. cultural norms and narratives), both of which are known to be relevant
cultural factors (Mack and Mayer 2016; Isenberg 2010a). That said, the MEES is a tool designed to
help researchers and policy makers better and more quickly assess the areas of strengths and
weaknesses within an ecosystem from the perception of the community. The tool is designed to
enable these stakeholders to look at the domains and determine which are strong and which are
weak, so tactically a weak domain can then be explored in more depth to determine how it can be
strengthened. It is not designed to explore a given domain in great detail, so while it is possible to
break it down even further, our work stops short of diving into each domain. This limitation is
reasonable given ‘academic research can rarely deliver fully developed solutions to any practical
problem’(Davidsson 2009, 213). Future work should explore measurement of each of these
domains in greater detail.
In closing the MEES is a step towards better understanding what makes any geographical
area more or less conducive towards entrepreneurship. We set out to develop a measure that
could be used to better and more quickly assess the strengths and weaknesses of a given
ecosystem using the domains posited by the Babson Entrepreneurial Ecosystem Project (e.g.
Isenberg 2010a,2011b). What resulted is a measure, the MEES, which does just that, and several
economic development organizations have begun to look to use the MEES in their regions
(Chicago, Philadelphia, Tampa). Despite the acknowledged limitations, our hope is that scholars
will continue to explore more and better ways to measure entrepreneurial ecosystem dynamics,
and that policy makers begin to make more data-driven decisions as it related to fostering
entrepreneurship in their regions.
Notes
1. At least three items were always retained because the ultimate goal was to use the items in a confirmatory
factor analysis, and three items are required in order achieve model identification for each factor within a
confirmatory factor analysis.
2. The model was specified to settle upon six-factor solution.
Disclosure statement
No potential conflict of interest was reported by the authors.
18 E. LIGUORI ET AL.

References
Ajzen, I. 2001.“Nature and Operation of Attitudes.”Annual Review of Psychology 52: 27–58. doi:10.1146/annurev.
psych.52.1.27.
Arend, M. 2004.“Texas Triumphs: The Lone Star State Ends a Tarheel Toehold on the Top Business-Climate Slot.”Site
Selection Magazine.
Armitage, C. J., and C. Conner. 2001.“Efficacy of the Theory of Planned Behaviour: A Meta-Analytic Review.”British
Journal of Social Psychology 40: 471–499.
Audretsch, D. B. 2007.The Entrepreneurial Society. New York: Oxford University Press.
Bagozzi, R. P., and Y. Yi. 1988.“On the Evaluation of Structural Equation Models.”Journal of the Academy of Marketing
Science 16 (1): 74–94. doi:10.1007/BF02723327.
Baumol, W. J., and R. J. Strom. 2007.“Entrepreneurship and Economic Growth.”Strategic Entrepreneurship Journal 1(1–
2): 233–237. doi:10.1002/sej.26.
Bendickson, J., J. Muldoon, E. Liguori, and C. Midgett. 2017.“High Performance Work Systems: A Necessity for
Startups.”Journal of Small Business Strategy 27 (2): 1–12.
Benjamin, L., J. S. Rubin, and S. Zielenbach. 2004.“Community Development Financial Institutions: Current Issues and
Future Prospects.”Journal of Urban Affairs 26 (2): 177–195. doi:10.1111/j.0735-2166.2004.00196.x.
Bentler, P. M. 1990.“Comparative Fit Indexes in Structural Models.”Psychological Bulletin 107: 238–246.
Boutillier, S., D. Carré, and N. Levratto. 2016.Entrepreneurial Ecosystems. Hoboken, NJ: John Wiley & Sons.
Breschi, S., and F. Malerba. 2001.“The Geography of Innovation and Economic Clustering: Some Introductory Notes.”
Industrial and Corporate Change 10 (4): 817–833. doi:10.1093/icc/10.4.817.
Clifton, J., and S. B. Badal. 2014.Entrepreneurial Strengths Finder. New York: Gallup Press.
Coder, L., W. Peake, and M. Spiller. 2017.“Do High Performance Work Systems Pay for Small Firms? an Intellectual
Capital Building Perspective.”Journal of Small Business Strategy 27 (2): 13–35.
Coenen, L., J. Moodysson, and H. Martin. 2015.“Path Renewal in Old Industrial Regions: Possibilities and Limitations for
Regional Innovation Policy.”Regional Studies 49 (5): 850–865. doi:10.1080/00343404.2014.979321.
Davidsson, P. 2009.The Entrepreneurship Research Challenge. Northhampton, MA: Edward Elgar Publishing.
Den Hartog, D. N., and R. M. Verburg. 2004.“High Performance Work Systems, Organisational Culture and Firm
Effectiveness.”Human Resource Management Journal 14 (1): 55–78. doi:10.1111/hrmj.2004.14.issue-1.
Fallows, J. 2015.“California’s Centers of Technology: Bay Area, L.A., San Diego, and . . . Fresno?”Accessed 2 April 2016.
http://www.theatlantic.com/national/archive/2015/03/californias-centers-of-technology-bay-area-la-san-diego-and-
fresno/387808/
Farrell, A. M. 2010.“Insufficient Discriminant Validity: A Comment on Bove, Pervan, Beatty, and Shiu (2009).”Journal of
Business Research 63: 324–327. doi:10.1016/j.jbusres.2009.05.003.
Feld, B. 2012.Startup Communities: Building an Entrepreneurial Ecosystem in Your City. New York: John Wiley & Sons.
Fetters, M., P. G. Greene, and M. P. Rice. 2010.The Development of University-Based Entrepreneurship Ecosystems: Global
Practices. Cheltenham, UK: Edward Elgar Publishing.
Fornell, C., and D. F. Larcker. 1981.“Evaluating Structural Equation Models with Unobservable Variables and
Measurement Error.”Journal of Marketing Research 18: 39–50. doi:10.2307/3151312.
Global Entrepreneurship Monitor. 2017. Accessed 30 December 2017. http://www.gemconsortium.org/
Guttman, A. 2015 April 21. “A‘Round the World Ticket to the Best Entrepreneurial Ecosystems on the Planet.”
Accessed 12 September 2018 https://www.forbes.com/sites/amyguttman/2015/04/10/a-round-the-world-ticket-to-
the-best-entrepreneurial-ecosystems-on-the-planet/#3e9363c16b44
Hair, J. F., W. C. Black, B. K. Babin, R. E. Anderson, and R. L. Tatham. 2006.Multivariate Data Analysis. Vol. 6. Upper
Saddle River, NJ: Pearson Prentice Hall.
Haynes, S. N., D. Richard, and E. S. Kubany. 1995.“Content Validity in Psychological Assessment: A Functional
Approach to Concepts and Methods.”Psychological Assessment 7 (3): 238–247. doi:10.1037/1040-3590.7.3.238.
Hayter, C. S. 2016.“A Trajectory of Early-Stage SpinoffSuccess: The Role of Knowledge Intermediaries within an
Entrepreneurial University Ecosystem.”Small Business Economics 47 (3): 633–656. doi:10.1007/s11187-016-9756-3.
Hechavarría, D. M., and A. E. Ingram. 2018.“Entrepreneurial Ecosystem Conditions and Gendered National-Level
Entrepreneurial Activity: A 14-Year Panel Study of GEM.”Small Business Economics 1–28. doi:10.1007/s11187-018-
9994-7.
Herstad, S. J. 2017.“Innovation Strategy Choices in the Urban Economy.”Urban Studies. doi:10.1177/
0042098017692941.
Hinkin, T. R. 1995.“A Review of Scale Development Practices in the Study of Organizations.”Journal of Management 21
(5): 967–988. doi:10.1177/014920639502100509.
Hinkin, T. R. 1998.“A Brief Tutorial on the Development of Measures for Use in Survey Questionnaires.”Organizational
Research Methods 1 (1): 104–121. doi:10.1177/109442819800100106.
Hisrich, R. D., and M. V. Grachev. 1993.“The Russian Entrepreneur.”Journal of Business Venturing 8 (6): 487–497.
doi:10.1016/0883-9026(93)90034-3.
ENTREPRENEURSHIP & REGIONAL DEVELOPMENT 19

Hitt, M. A., R. D. Ireland, D. G. Sirmon, and C. A. Trahms. 2011.“Strategic Entrepreneurship: Creating Value for
Individuals, Organizations, and Society.”Academy of Management Perspectives 25 (2): 57–75.
Huggins, R., D. Prokop, and P. Thompson. 2017.“Entrepreneurship and the Determinants of Firm Survival within
Regions: Human Capital, Growth Motivation and Locational Conditions.”Entrepreneurship & Regional Development
29 (3–4): 357–389. doi:10.1080/08985626.2016.1271830.
Isaksen, A. 2016.“Cluster Emergence: Combining Pre-Existing Conditions and Triggering Factors.”Entrepreneurship &
Regional Development 28 (9–10): 704–723. doi:10.1080/08985626.2016.1239762.
Isenberg, D. 2011b, May 25. “Introducing the Entrepreneurship Ecosystem: Four Defining Characteristics.”Accessed 2
April 2016. http://www.forbes.com/sites/danisenberg/2011/05/25/introducing-the-entrepreneurship-ecosystem-
four-defining-characteristics/#642c216f38c4
Isenberg, D. J. 2010a.“How to Start an Entrepreneurial Revolution.”Harvard Business Review, June 40-50.
Isenberg, D. J. 2010b November 16. “Can Policy Makers Create an Ecosystem for Entrepreneurship?.”The Economist,
April 5 2016.
Isenberg, D. J. 2011a.“The Entrepreneurship Ecosystem Strategy as a New Paradigm for Economic Policy: Principles for
Cultivating Entrepreneurship.”In Paper Presented at the Institute of International European Affairs. Dublin, Ireland.
Isserman, A. M. 1993.“State Economic Development Policy and Practice in The United States: a Survey Article.”
International Regional Science Review 16 (1-2): 49-100.
Kauffman Foundation. 2015.“Entrepreneurship Policy Digest.”Accessed 19 June 2016. http://www.kauffman.org/~/
media/kauffman_org/resources/2015/entrepreneurship%20policy%20digest/june%202015/how_entrepreneurs_
access_capital_and_get_funded.pdf.
Kedrosky, P., and D. Stangler. 2011.“Financialization and Its Entrepreneurial Consequences.”Ewing Marion Kauffman
Foundation Research Paper.
Kitching, J., D. Smallbone, and R. Athayde. 2009.“Ethnic Diasporas and Business Competitiveness: Minority-Owned
Enterprises in London.”Journal of Ethnic and Migration Studies 35 (4): 689–705. doi:10.1080/13691830902765368.
Kleiner, M. M. 2000.“Occupational Licensing.”The Journal of Economic Perspectives 14 (4): 189–202. doi:10.1257/
jep.14.4.189.
Kleiner, M. M., and A. B. Krueger. 2010.“The Prevalence and Effects of Occupational Licensing.”British Journal of
Industrial Relations 48 (4): 676–687. doi:10.1111/bjir.2010.48.issue-4.
Kline, R. B. 2005.Principles and Practice of Structural Equation Modeling. 2nd ed. New York: Guilford Press.
Kritzer, A. G. 2016 March 1. “Vinik: Transportation Is the ‘Achilles Heel’to Tampa Bay Economic Development.”Tampa
Bay Business Journal. Accessed 26 April 2017. http://www.bizjournals.com/tampabay/news/2016/03/01/vinik-trans
portation-is-the-achilles-heel-to-tampa.html.
Kshetri, N. 2014.“Developing Successful Entrepreneurial Ecosystems: Lessons from a Comparison of an Asian Tiger
and a Baltic Tiger.”Baltic Journal of Management 9 (3): 330–356. doi:10.1108/BJM-09-2013-0146.
Li, M., S. J. Goetz, M. Partridge, and D. A. Fleming. 2016.“Location Determinants of High-Growth Firms.”
Entrepreneurship & Regional Development 28 (1–2): 97–125. doi:10.1080/08985626.2015.1109003.
Liñán, F., and F. J. Santos. 2007.“Does Social Capital Affect Entrepreneurial Intentions?.”International Advances in
Economic Research 13 (4): 443-453.
MacCallum, R. C., M. W. Browne, and H. M. Sugawara. 1996.“Power Analysis and Determination of Sample Size for
Covariance Structure Modeling.”Psychological Methods 1 (2): 130–149. doi:10.1037/1082-989X.1.2.130.
Mack, E., and H. Mayer. 2016.“The Evolutionary Dynamics of Entrepreneurial Ecosystems.”Urban Studies 53 (10): 2118–
2133. doi:10.1177/0042098015586547.
Marsh, H. W., J. R. Balla, and R. P. McDonald. 1988.“Goodness-of-Fit Indexes in Confirmatory Factor Analysis: The Effect
of Sample Size.”Psychological Bulletin 103 (3): 391–410. doi:10.1037/0033-2909.103.3.391.
Martins, E. C., and F. Terblanche. 2003.“Building Organisational Culture that Stimulates Creativity and Innovation.”
European Journal of Innovation Management 6 (1): 64–74. doi:10.1108/14601060310456337.
McMullen, J. S. 2018.“Organizational Hybrids as Biological Hybrids: Insights for Research on the Relationship between
Social Enterprise and the Entrepreneurial Ecosystem.”Journal of Business Venturing 33 (5): 575–590. doi:10.1016/j.
jbusvent.2018.06.001.
Muñoz, P., and D. Dimov. 2015.“The Call of the Whole in Understanding the Development of Sustainable Ventures.”
Journal of Business Venturing 30 (4): 632–654. doi:10.1016/j.jbusvent.2014.07.012.
Nambisan, S., and R. A. Baron. 2013.“Entrepreneurship in Innovation Ecosystems: Entrepreneurs’Self-Regulatory
Processes and Their Implications for New Venture Success.”Entrepreneurship, Theory and Practice 37 (5): 1071–1097.
doi:10.1111/etap.2013.37.issue-5.
Netemeyer, R. G., W. O. Bearden, and S. Sharma. 2003.Scaling Procedures: Issues and Applications. London, UK: Sage
Publications.
Newman, K. M. 2015 August 03. The Top 20 Startup Ecosystems Ranked. Accessed 12 September 2018. https://tech.co/
top-20-startup-ecosystems-ranked-2015-07
NewsGhana. 2016 March 22. Accessed 12 March 2017. https://www.newsghana.com.gh/lack-of-human-capital-hinder-
growth-of-entrepreneurs/
Nunnally, J. C. 1978.Psychometric Theory. 2nd ed. New York: McGraw-Hill.
20 E. LIGUORI ET AL.

Oerlemans, L. A., M. T. Meeus, and F. W. Boekema. 2001.“Firm Clustering and Innovation: Determinants and Effects.”
Papers in Regional Science 80 (3): 337–356. doi:10.1007/PL00013637.
Organ, D. W., and J. B. McFall. 2004. Personality and citizenship behavior in organizations. In B. Schneider & D. B. Smith
(Eds.), Personality and organizations: 291-314. Mahwah, NJ: Erlbaum.
Organ, D. W., P. M. Podsakoff, and S. B. MacKenzie. 2006.Organizational Citizenship Behavior: Its Nature, Antecedents
and Consequences. Thousand Oaks, CA: Sage.
Parker, S. C., and Y. Belghitar. 2006.“What Happens to Nascent Entrepreneurs? An Econometric Analysis of the PSED.”
Small Business Economics 27 (1): 81–101. doi:10.1007/s11187-006-9003-4.
Pedhazur, E. J., and L. P. Schmelkin. 1991.Measurement, Design, and Analysis: An Integrated Approach. Hillsdale, NJ:
Lawrence Erlbaum Associates.
Pilny, C. 2014, August 28. “Top U.S. Microcosm Cities to Test Market a National Product.”Accessed 10 April 2017.
http://smallbusiness.com/product-development/best-u-s-cities-to-test-market-a-national-product/
Powell, T. 1996.“How Much Does Industry Matter? An Alternative Empirical Test.”Strategic Management Journal 17 (4):
323–334. doi:10.1002/(ISSN)1097-0266.
Prescott, P. A., and K. L. Soeken. 1989.“The Potential Uses of Pilot Work.”Nursing Research 38 (1): 60–62.
Reynolds, P. D., and R. T. Curtin. 2009.New Firm Creation in the United States: Initial Explorations with the PSED II Data
Set. Vol. 23. New York: Springer Science & Business Media.
Roper, S., J. H. Love, and K. Bonner. 2017.“Firms’Knowledge Search and Local Knowledge Externalities in Innovation
Performance.”Research Policy 46 (1): 43–56. doi:10.1016/j.respol.2016.10.004.
Santos, E. 2018 May 19. “Why You Should Invest in Fresno and the Central Valley.”Accessed 15 September 2018.
https://venturebeat.com/2018/05/19/why-you-should-invest-in-fresno-and-the-central-valley/
Schaeffer, V., and M. Matt. 2016.“Development of Academic Entrepreneurship in a Non-Mature Context: The Role of
the University as a Hub-Organisation.”Entrepreneurship & Regional Development 28 (9–10): 724–745. doi:10.1080/
08985626.2016.1247915.
Schneider, B. 1987.“The People Make the Place.”Personnel Psychology 40 (3): 437–453. doi:10.1111/peps.1987.40.
issue-3.
Schneider, B., S. S. White, and M. C. Paul. 1998.“Linking Service Climate and Customer Perceptions of Service Quality:
Tests of a Causal Model.”Journal of Applied Psychology 83 (2): 150. doi:10.1037/0021-9010.83.2.150.
Schwab, D. P. 1980.“Construct Validity in Organization Behavior.”In Research in Organizational Behavior, edited by B.
M. Staw and L. L. Cummings, 3–43. Vol. 2. Greenwich, CT: JAI Press.
Schwarzkopf, C. 2016.Fostering Innovation and Entrepreneurship: Entrepreneurial Ecosystem and Entrepreneurial
Fundamentals in the USA and Germany. Karlsruhe, Germany: Springer Gabler.
Siepel, J., M. Cowling, and A. Coad. 2017.“Non-Founder Human Capital and the Long-Run Growth and Survival of
High-Tech Ventures.”Technovation 59: 34–43. doi:10.1016/j.technovation.2016.09.001.
Spector, P. E. 1992.Summated Rating Scale Construction: An Introduction. Thousand Oaks, CA: Sage.
Stamer, R. (2005). “Top Micropolitans.”Site Selection Magazine.
Stangler, D., and J. Bell-Masterson. 2015.Measuring an Entrepreneurial Ecosystem. Kansas City, MO: Kauffman
Foundation.
Trigaux, R. 2015, October 29. “Why Did Google Fiber Pick Tampa? Credit the Area’sEmerging Tech and Startup
Scenes.”.Tampa Bay Times. Accessed 2 January 2018. http://www.tampabay.com/news/business/why-did-google-
fiber-pick-tampa-credit-the-areas-emerging-tech-and-startup/2251736.
Turker, D., and S. Sonmez Selçuk. 2009.“Which Factors Affect Entrepreneurial Intention Of University Students?.”
Journal Of European Industrial Training 33 (2): 142-159.
Uyarra, E., and R. Ramlogan. 2016.“The Impact of Cluster Policy on Innovation.”In Handbook of Innovation Policy
Impact, edited by J. Edler, P. Cunningham, A. Gök, and P. Shapira, 196–225. Cheltenham: Edward Elgar.
Velt, H., L. Torkkeli, and S. Saarenketo. 2018.“The Entrepreneurial Ecosystem and Born Globals: The Estonian Context.”
Journal of Enterprising Communities: People and Places in the Global Economy 12 (2): 117–138. doi:10.1108/JEC-08-
2017-0056.
Venkataraman, S. 1997.“The Distinctive Domain of Entrepreneurship Research: An Editor’s Perspective.”In Advances in
Entrepreneurship, Firm Emergence, and Growth, edited by J. Katz and R. Brockhaus, 119–138. Vol. 3. Greenwich, CT:
JAI Press.
Wennekers, S., and R. Thurik. 1999.“Linking Entrepreneurship and Economic Growth.”Small Business Economics 13 (1):
27–56. doi:10.1023/A:1008063200484.
White, R. J., S. Terjesen, D. Hechavarria, D. Aldrich, and Z. Acs. 2016 November 15. “State of the Tampa Bay
Entrepreneurial Ecosystem 2016..”Presentation Given to Community Leaders in Tampa, FL.
ENTREPRENEURSHIP & REGIONAL DEVELOPMENT 21