Innovative entrepreneurial behavior in high-income European countries

Abstract

Purpose
This study investigates the personal factors influencing innovative entrepreneurship combined with additional contextual insights from high-income European countries. Specifically, this study has three main objectives: (i) to measure differences in the level of entrepreneurial innovativeness activity among high-income European regions; (ii) to uncover key factors leading to appropriate levels of entrepreneurial innovativeness and (iii) to suggest policies that may enhance the regional level of entrepreneurial innovation.

Design/methodology/approach
A sample of 4,430 nascent and new entrepreneurs from 16 different high-income European countries drawn from the Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) was used in conjunction with macroeconomic indicators. Data were analyzed using a logistic regression analysis.

Findings
There are significant differences in the conditions that influence entrepreneurial innovativeness in European regions. These variations in entrepreneurial activity can be explained using contextual factors and individual characteristics. Although technological novelty increases the probability of innovative entrepreneurship, the technology effect is significantly greater in Western Europe than other regions across Europe.

Originality/value
This study illustrates how a contextualized view of entrepreneurship enriches the knowledge of the human and dynamic socioeconomic drivers that motivate innovative entrepreneurial action in high-income European countries.

Full text

Innovative entrepreneurial
behavior in high-income
European countries
Jos
e Fernando L
opez-Mu~
noz
Department of Management, ESIC Business & Marketing School, Valencia, Spain
Josefina Novejarque-Civera
Department of Finance, ESIC Business & Marketing School, Valencia, Spain, and
Mabel Pis
a-B
o
Department of Economics, ESIC Business & Marketing School, Valencia, Spain
Abstract
Purpose –This study investigates the personal factors influencing innovative entrepreneurship combined
with additional contextual insights from high-income European countries. Specifically, this study has three
main objectives: (i) to measure differences in the level of entrepreneurial innovativeness activity among high-
income European regions; (ii) to uncover key factors leading to appropriate levels of entrepreneurial
innovativeness and (iii) to suggest policies that may enhance the regional level of entrepreneurial innovation.
Design/methodology/approach –A sample of 4,430 nascent and new entrepreneurs from 16 different
high-income European countries drawn from the Global Entrepreneurship Monitor (GEM) Adult Population
Survey (APS) was used in conjunction with macroeconomic indicators. Data were analyzed using a logistic
regression analysis.
Findings –There are significant differences in the conditions that influence entrepreneurial innovativeness in
European regions. These variations in entrepreneurial activity can be explained using contextual factors and
individual characteristics. Although technological novelty increases the probability of innovative entrepreneurship,
the technology effect is significantly greater in Western Europe than other regions across Europe.
Originality/value –This study illustrates how a contextualized view of entrepreneurship enriches the
knowledge of the human and dynamic socioeconomic drivers that motivate innovative entrepreneurial action
in high-income European countries.
Keywords Innovative entrepreneurship, Environmental factors, Human traits, GEM, Europe
Paper type Research paper
1. Introduction
Entrepreneurship promotes the innovation required to boost productivity and generate
employment and also to address some of the challenges posed by the United Nations’
sustainable development goals. For this reason, European society calls for entrepreneurial
behavior and a society that nurtures and rewards entrepreneurship (Bosma et al., 2020). Many
European governments are increasingly focusing on instituting policy frameworks and
mechanisms to drive and promote entrepreneurial activities. In this context, a clearly
differentiated and difficult-to-replicate entrepreneurship in products and services is highly
desirable because it adds value to individual lives and economies. However, entrepreneurs differ
in the degree and type of novelty they create, and this entrepreneurship culture varies across
countries. For example, although high-income economies tend to have lower levels of
entrepreneurship (Bosma et al.,2020), entrepreneurs in highly developed countries are more
likely to engage in innovation than purely imitative activities (Koellinger, 2008). Consequently,
high-income European countries represent an appealing innovation-driven context for analysis.
Innovative
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Europe
The authors appreciate the valuable suggestions and comments of the two anonymous reviewers and
the editor of IJEBR.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1355-2554.htm
Received 17 June 2022
Revised 13 September 2022
14 December 2022
Accepted 2 January 2023
International Journal of
Entrepreneurial Behavior &
Research
© Emerald Publishing Limited
1355-2554
DOI 10.1108/IJEBR-06-2022-0546

Scholars have interpreted entrepreneurship within its institutional, spatial, and social
contexts to understand when, how and why it happens (Welter, 2011). The existence of highly
supportive regional entrepreneurial environments can create entrepreneurs (Fischer and
Nijkamp, 2019). For instance, Urbano et al. (2016) found that a high voluntary spirit (social
factor) has a positive influence on innovative early stage entrepreneurial activity. In contrast,
it is widely acknowledged that entrepreneurial decisions depend on individual factors, which
are often shaped by region-specific factors (Fritsch and Storey, 2014;Dubini, 1989;Yang et al.,
2020) because entrepreneurs’expectations are primarily driven by their internal perceptions
of reality (Poblete, 2018). For example, Bayon and Lamotte (2020) confirmed that older
individuals who are employed full-time are less likely to choose risky innovative
entrepreneurship. Thus, both individual and institutional qualities explain the decision to
become innovative entrepreneurs. As Van Hemmen et al. (2015) showed, participative
leadership styles (organizational factor) and higher education (individual factor) were the
leading explanatory factors of innovative entrepreneurship. Furthermore, innovation
opportunities for entrepreneurship are structured by the nature of technology that prevails
in different spatial contexts. For example, mobile broadband can be an enabling technology
for entrepreneurship (Alderete, 2017). Accordingly, this study investigates the personal
factors influencing innovative entrepreneurship combined with additional contextual
insights, including technology, which help us understand where innovation originates in
high-income European countries. European regions differ in innovative performance owing
to their distinct levels of technological readiness, among other factors (European-
Commission, 2021).
Therefore, this study focuses on three main objectives in the context of high-income
European countries. First, we measured differences in the level of entrepreneurial
innovativeness within high-income European regions. Second, we uncover the key factors
that lead to appropriate levels of entrepreneurial innovation. Third, we suggest policies to
enhance the regional level of entrepreneurial innovation.
For this purpose, the Global Entrepreneurship Monitor (GEM) conceptual model is
suitable, as it reflects a wide range of factors associated with national variations in
entrepreneurial activity and major contextual features (Acs et al., 2005a, b;Reynolds
et al., 2005). Along with information about individual entrepreneurs from the GEM Adult
Population Survey (APS), this study includes macroeconomic indicators to quantify several
relevant dimensions of the environment in which these innovative entrepreneurs make their
decisions. In this empirical study, data originated from the 2016 GEM APS, yielding a sample
of 4,430 nascent and new entrepreneurs from 16 high-income European countries. Data were
analyzed using a logistic regression analysis.
The results of this study confirm that both individual and regional characteristics
influence the decision to become innovative entrepreneurs in high-income European
countries. Indeed, the strong gross domestic product (GDP) effects shown in the regression
results suggest that entrepreneurial innovation has a factual component rather than being
entirely attached to the entrepreneur’s individual traits and skills. In this sense, the empirical
evidence of this study shows that the availability and quality of objective opportunities for
innovative new businesses vary across European regions. In particular, the Northern and
Western European regions had higher scores for creating innovative entrepreneurship than
the Southern and Eastern regions. The results confirm that recent technology use has
increased the probability of innovative entrepreneurship. However, as the level of
technological readiness varies, even in the context of high-income European countries, the
technology effect is significantly greater in the Western European region.
Therefore, this study extends the existing literature by integrating individual and
environmental factors that may influence entrepreneurial behavior. Although other studies
examine the factors motivating innovative entrepreneurship, the novelty of this study is that
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it conceptualizes the influence of technological novelty on entrepreneurial innovativeness
across the European region. This study provides valuable insights into entrepreneurial
conditions and helps diagnose the strengths and weaknesses of entrepreneurial ecosystems
in high-income European countries. These countries could learn from one another by
considering both their circumstances and the need to tailor successful policies in other
countries according to their national conditions. Furthermore, policymakers and other
stakeholders can use our results to discern where improvements can be made so that they can
derive even greater gains from entrepreneurship.
The remainder of this paper is organized as follows. The second section presents the
conceptual framework and outlines the research questions. The third section describes the
methodology of data collection, data analyses and results. This paper concludes with a
discussion of the results and implications for entrepreneurship research, practice and public
policy.
2. Conceptual framework
Most entrepreneurship definitions refer to using available resources creatively, creating
value, taking risks, looking for opportunities and innovating (Medeiros et al., 2020).
Schumpeter (1934) highlighted the importance of innovation in business and economics.
Moreover, Brem (2011) considered innovation a central aspect of entrepreneurs, while
Nonaka and Takeuchi (1995) saw novelty as a crucial element for business success.
Therefore, innovation is considered a fundamental component of entrepreneurship.
Although most definitions focus on novelty, there is no consensus on how to define
innovation (Johannessen et al., 2001), which should not be equated with change. As
Slappendel (1996) pointed out, although innovation presupposes change, not all change
presupposes innovation.
Entrepreneurs are agents who create companies with new products or production
methods in response to market imbalances (Schumpeter, 1934). Currently, national and
international markets are characterized by increasing openness and competitiveness.
To respond to customers’new needs and lifestyles, organizations continually innovate by
making use of technological advances, thus taking advantage of new business opportunities
(Baregheh et al., 2009;Martin, 1994).
Entrepreneurial companies differentiate themselves to the extent that they introduce
innovation into the market (Wiklund et al., 2011). Some start-ups are more innovative than
others, and the factors that contribute to the level of innovation are both individual and
contextual (Autio et al., 2014;Block et al., 2017;Cohen, 2010;Galende, 2006).
2.1 Individual factors contributing to innovative entrepreneurship
The literature on entrepreneurship dedicates a section to new company innovation,
specifically in the innovative entrepreneurship literature. This type of entrepreneurship
emphasizes that the sources of opportunity and the characteristics of entrepreneurs are
critical components (Autio et al., 2014;Shane, 2003). Hoogendoorn et al. (2020) highlighted that
the entrepreneur’s specific personal traits make new companies more likely to be innovative.
As age is another key factor in entrepreneurial decisions (Minola et al., 2014), age
distribution is a key factor in determining the rate of entrepreneurship in a region (Bonte et al.,
2009;Lamotte and Colovic, 2013). From an individual perspective, the probability of being an
entrepreneur is highest when individuals are young and then decreases over time
(Blanchflower, 2004;Levesque and Minniti, 2006). The relationship between age and
entrepreneurship is an inverted U-shaped relationship (Bonte et al., 2009). Empirical evidence
shows that the proportion of people trying to start a business is highest between the ages of
Innovative
ventures in
Europe

25 and 35 (Reynolds et al., 2003;Reynolds, 2007). Mueller (2006) found that the desire to start a
business peaks at the age of 41.
Innovative entrepreneurship involves taking more risks than imitative entrepreneurship
(Hyytinen et al., 2015), as the inherent risk of entrepreneurship is compounded by the nature of
innovation. Risk preferences differ across an individual’s lifetime (Backman and Karlsson,
2018) and the likelihood of pursuing innovative entrepreneurship gets lower as people age
because of the considerable risk attached to this kind of entrepreneurship (Bayon and
Lamotte, 2020). Thus, the age of an individual entrepreneuris a principal factor when choosing
entrepreneurship (Bayon et al., 2016;Rietzschel et al., 2016). Furthermore, attitudes towards
risk could influence the choice of innovative entrepreneurship, especially in the introduction of
new products, as older people tend to be more reluctant to use innovative products (Colovic
et al., 2019) and slower to adopt innovative tools (Bertschek and Meyer, 2008). For example,
using data from French companies, Behaghel and Greenan (2010) showed that workers over
the age of 50 are more reluctant to adapt to new technology. Moreover, the mindset and
routines that become established over time leave less room for recognition of entrepreneurial
opportunities or creativity, which inhibits the decision to create an innovative firm (Bonte
et al., 2009). However, various empirical studies found a positive and significant relationship
between age and innovative entrepreneurship, concluding that older entrepreneurs are, in
general, more innovative (Bonte et al., 2009;Fuentelsaz and Montero, 2015).
Gender is another factor that is often studied in the field of entrepreneurship. This is
because female entrepreneurship has increased in recent decades (Cabrera and Mauricio,
2017;Kickul et al., 2008;Mart

ınez-Rodr

ıguez et al., 2022;Ratten and Dana, 2017). However, the
participation rate of men in entrepreneurship is still higher than that of women (Arenius and
Minniti, 2005;Kwong et al., 2009;Minniti et al., 2004;Vracheva and Stoyneva, 2020). This
difference is explained by certain psychological traits, which previous studies have
considered predictors of entrepreneurship (Kickul et al., 2008;Langowitz and Minniti, 2007;
Mueller and Dato-on, 2013). However, the factors influencing female and male
entrepreneurship tend to be equalized (Langowitz and Minniti, 2007).
In economic theory, human capital is a proxy for education and is considered a driver of
innovation (Arenius and Minniti, 2005). Highly educated individuals possess a broader
knowledge base, which contributes to the ability to recognize opportunities (Arenius and De
Clercq, 2005) and a higher probability of undertaking an activity (Arenius and Minniti, 2005;
Davidsson and Hoing, 2003). The innovativeness of entrepreneurs is influenced by individual
characteristics and enhanced by the quality of the education system (Schott and Sedaghat,
2014;Wegner et al., 2020). Innovative entrepreneurial activity is positively related to national
levels of scientific and technological entrepreneurship education and training, with training
being the basis for knowledge that facilitates technological innovation (Levie and Autio,
2007). Countries with highly developed educational systems have a positive relationship with
innovation (Koellinger, 2008). This is similar in rich countries that have a high rate of income,
as they have higher rates of high-tech company creation (Blanchflower, 2004).
Another factor influencing the choice of innovative versus imitative entrepreneurship is
the employment status of the individual, whether employed or unemployed (Koellinger, 2008).
The literature shows that entrepreneurship is reduced when individuals are employed
because of a common cognitive bias; employed individuals are reluctant to lose favorable
situations (Horodnic and Williams, 2020;Tversky and Kahneman, 1974). In other words,
employees are reluctant to start an innovative venture, as they are averse to a negative
business outcome that puts their economic well-being at risk (Bayon and Lamotte, 2020).
However, Bergmann and Sternberg (2007) found that employed individuals (self-employed or
gainful employment) have a higher level of entrepreneurial propensity than unemployed
individuals do. While there are significant differences in gainful employment (e.g. the type of
entrepreneurship and the period analyzed), self-employed people always present positive and
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significant results (in all types of entrepreneurship and in all the periods analyzed). This
result may be because of their experience and acquired skills.
Thus, we suggest the following hypothesis:
H1. There is a positive relationship between individual characteristics, such as gender
(male), high education level, being self-employed and innovative entrepreneurship.
Specifically, the inverted U-shaped relationship between age and innovative
entrepreneurship reaches its peak later than general entrepreneurship.
2.2 Contextual factors influencing innovative entrepreneurship
Contextual factors that can influence innovative entrepreneurship include GDP per capita
(macroeconomic factor), the rate of self-employment in each region, the region to which the
entrepreneur belongs and the use of modern technologies (see, e.g. Bergmann and Sternberg, 2007).
Traditionally, economic growth has been measured based on variations in per capita
income. However, the literature does not establish a clear relationship between
entrepreneurship and economic growth. Empirical studies have suggested that the levels
of business creation differ significantly across countries (Galindo-Mart

ınet al., 2020) and over
time (Reynolds et al., 2003). Van Stel et al. (2005) found a positive relationship between
entrepreneurship and growth in advanced economies and a negative relationship in less-
developed economies. This trend can be explained by the relationship between the GDP per
capita and family income. Arenius and Minniti (2005) found a relationship between family
income and the probability of starting entrepreneurial activity. This relationship was
represented by a U-shaped curve. For low-income groups, the possibility of accessing high
returns increases the probability of starting a new business. Conversely, for high-income
levels, the increase in the probability of starting an activity is due to the reduction of financial
barriers, thanks to the individuals’family income.
Another contextual factor that can influence innovative entrepreneurship is the self-
employment rate in each country. Minniti (2004) found that the presence of role models
increases individuals’confidence. Observing and meeting other entrepreneurs reduces the
uncertainty and ambiguity associated with entrepreneurial initiatives and stimulatesnascent
entrepreneurship (Minniti, 2005). Bergman and Sternberg (2007) analyzed the German context
and found that regions with a high proportion of self-employed people have a different
entrepreneurial culture than regions with a low percentage of self-employed people (with a
positive relationship between self-employment and the propensity to start a business).
However, the entrepreneurial culture or climate of the region (measured in terms of self-
employment rate) becomes less important when the country is experiencing an economic crisis
because people must focus on profitability rather than their preferred mode of employment.
Empirical studies by Acs and Audretsch (1993) suggest that country variables
significantly affect business decisions. In line with Sulkunen and Malin (2018), they found
that a crucial factor for innovation and entrepreneurship is cultural and social norms and,
more specifically, the percentage of the adult population with higher education. The most
innovative regions are those with the highest levels of economic development and education
(Makkonen and Inkinen, 2013;Medeiros et al., 2020). However, Pinillos and Reyes (2011)
observed that countries with similar levels of wealth have distinct levels of entrepreneurship
and innovation. If we focus on Europe, these differences are due to cultural differences across
regions, even though it is a continent with relatively similar cultures (Li~
n
an and Fern
andez-
Serrano, 2014).
Thus, we suggest the following hypotheses:
H2. There is a positive relationship between GDP per capita growth and innovation
entrepreneurship.
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H3. There is a positive relationship between self-employment rates and innovative
entrepreneurship.
Globalization and rapid technological changes have made the business environment more
competitive (Jones et al., 2000). Therefore, organizations that differentiate themselves and
become more competitive carry out the innovation process, transforming their ideas into new
or improved products to respond to the needs of current and future markets (Baregheh et al.,
2009;Geels, 2004;Miller, 1983). Companies with a prominent technological component will
have higher levels of innovation and success than those with a lower technological level
because innovation depends on the type of technological components that the company has
(Mart

ın-Rojas et al., 2017). Thus, innovation and technology have become a factor of global
competitiveness in most advanced countries (Buesa et al., 2010), and are especially important
in economic development (Drucker, 2014). The relationship between technology and
entrepreneurship has also been analyzed in several studies; for example, Audretsch
and Belitski (2017) found a strong relationship between the use of information and
communication technology (ICT) and entrepreneurship. The authors concluded that the use
of modern technologies and faster access to information can lead to higher levels of
entrepreneurial activity and innovation. Colovic and Lamotte (2015) pointed out that
technology has a positive impact on the advancement of entrepreneurship. Alderete (2014)
presented results indicating that countries with higher ICT development indices achieve
higher levels of entrepreneurial activity. Similarly, Arabiyat et al. (2019) found a high and
robust level of association between ICT and entrepreneurship.
Therefore, technology and innovative entrepreneurship are closely related to each other.
The development of modern technologies and the alignment of information technology (IT)
with business goals are crucial in the development of innovative entrepreneurship
(Audretsch and Belitski, 2017). A country endowed with optimal infrastructure can
promote higher levels of entrepreneurial activity and innovation (Dahlman, 2007).
Technological skills are positively related to a company’s corporate entrepreneurship and
favor product innovation (Chen et al., 2015). Technological capabilities are thus crucial for
determining the level of innovation in companies (Lynskey, 2004).
It should not be forgotten that the basic decision to start a business relies on the individual,
but when the person takes this decision, their environment and the opportunities in their
region are decisive factors (Frank et al., 2007). Regional differences in entrepreneurship can be
explained by several specific economic attributes, such as market opportunities, available
technology, groups of individuals with a high entrepreneurial spirit or behavior, national or
regional values, and national or regional norms. In Europe, the regions present differences in
various aspects; the different values in the Global Innovation Index and Global
Entrepreneurship Index are relevant examples of interest to our current research. Ionescu
et al. (2020) used a hierarchical clustering analysis to classify European Union (EU) countries
into four groups and identified the common characteristics and differences between the
groups. Four clusters were obtained. In Cluster 1, they placed the Central and Baltic countries,
which have average European values for the overall innovation and entrepreneurship
indices. Cluster 2 was located in eastern countries with low average values for the two indices.
In Cluster 3, they placed Southern European countries, which had the second lowest values
for the two indices, and in Cluster 4, they included Western and Nordic countries, which are
characterized as having the highest average values in the innovation and entrepreneurship
indices. According to the literature, there is a clear separation of countries’innovation levels
according to where they are located in Europe, such that Central European countries have
higher potential for innovation and entrepreneurship. Additionally, Nordic countries have an
industry structure with a high concentration of companies, training clusters, intensive
knowledge and advanced technology (Cavallini et al., 2016). However, Southern European
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countries are characterized by companies with low levels of knowledge intensity and
innovative technology (Capello and Lenzi, 2017).
Thus, we suggest the following hypotheses:
H4. The level of innovative entrepreneurship differs across European regions.
H5. The use of modern technology drives innovative entrepreneurship.
H6. The European region positively moderates the association between the perception of
modern technology use and innovative entrepreneurship, such that the association is
stronger when the EU region’s innovative performance is high and weaker when it
is not.
Figure 1 summarizes the research model and hypotheses.
3. Data and variables
The data used in this study were obtained from the GEM Europe APS. The GEM is the
only global research source that collects data on entrepreneurship directly from
individual entrepreneurs. The GEM assesses the level of business activities in countries
worldwide on an annual basis. The GEM began in 1999 as a joint research project between
Babson College (USA) and London Business School (UK) and has since become the richest
source of reliable information on entrepreneurship and entrepreneurial ecosystems
worldwide. A detailed description of the methodology and data used by the GEM can be
found in Reynolds et al. (2005).
The GEM APS provides estimates of the adult population’s participation in the creation of
a new business. The APS is administered to a minimum of 2000 adults in each economy,
ensuring that it is nationally representative. The GEM defines early stage entrepreneurs as
Personal traits
Gender
Age
Educational level
Self-employed
GDP per capita
Rate of self-employment
European region
New Technology
Innovative
entrepreneurship
H1(+)
H2(+)
H3(+)
H4(+)
H6(+)
H5(+)
Regional indexes
Figure 1.
Research model and
hypotheses
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nascent entrepreneurs and new business owners as new entrepreneurs. Those who have paid
wages and salaries for more than three months and less than 42 months are considered new
business owners. The sum of nascent entrepreneurs and new entrepreneurs is what the GEM
calls “Total entrepreneurial activity”(TEA) (Pe~
na et al., 2017).
This study uses both individual- and aggregate-level data. GEM microdata is
advantageous as it allows us to combine the individual characteristics of the founder of a
company with information on the country where the founder lives in a single analysis.
Individual data are combined with regional (country) data from official statistics.
This study focuses on innovative entrepreneurship, which is the dependent variable in our
econometric analysis. The endogenous variable is defined as a dichotomous variable that
takes the value of one if the entrepreneur creates both a product that is new to all or some
customers and a new market (few/no businesses offer the same product), and it takes the
value zero otherwise. Creating a new product and market is a specific concept of the GEM that
provides an updated picture of new business activities. The GEM has information about
entrepreneurs’predisposition to launch new innovative businesses. This entrepreneurship is
considered entrepreneurial innovation, and it is the percentage of those involved in TEA, who
indicate that their product or service is new to at least some customers and that few or no
other companies offer the same product. This entrepreneurship is considered to be by
opportunity as opposed to by necessity, in which the lack of employment opportunities is the
main reason for starting a business. Opportunity-driven entrepreneurship represents 70.7%
of the primary motivation for undertaking a new business (Pe~
na-Legazkue et al., 2019).
This study analyzes the innovative technology used to create a new business using a
complementary model. National innovation systems are repositories of various resources that
firms rely on in their innovation activities and are composed of various institutions that
influence them. Empirical research demonstrates how successful innovation depends on
several factors, such as knowledge, skills, technology, financial resources and demand, which
are available within the nation (Edler and Fagerberg, 2017;Edler and Georghiou, 2007).
The high-income countries analyzed in this study comply with the necessary
characteristics according to the European Innovation Scoreboard (EIS) to provide optimal
environments for innovation. Countries with higher innovation scores tend to have more
advanced economies with higher GDP per capita. The EIS introduces a synthetic innovation
index (Summary Innovation Index –SII) composed of 27 indicators (25 indicators until the
2017 edition) that characterize the innovation systems of each of the territories. According to
EIS, the higher the value of the indicators, the higher the SII and therefore, the better the
innovation performance. Countries with higher innovation scores tend to have more
advanced economies with higher GDP per capita.
In summary, we examine a particular type of entrepreneurship—innovative
entrepreneurship—which represents a percentage of those involved in TEA who indicate
that their product or service is new to at least some customers and that few or no companies
offer the same product. The information used covers the year 2016. We analyzed 16 high-
income European countries, in accordance with the World Bank classification. This
classification is relevant, because the entrepreneurial needs of each economy vary depending
on the stage of development (Wennekers et al., 2005). Context data were obtained from the
World Bank.
The variables used to explain the probability of innovative entrepreneurship are:
(1) Personal characteristics:
Gender. The entrepreneur’s gender is quantified through a dichotomous variable
that takes the value of one for a man and zero for a woman.
Age (in years).
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Age squared: The squared value of age (in years) is included as a separate
variable in the models to identify nonlinear relationships between age and
entrepreneurial activity.
Higher Education (Uneduc) (yes/no). It takes the value of 1 if the entrepreneur
holds a university or postgraduate degree, and 0 otherwise.
Self-employed worker (Occuself) (yes/no): This takes the value of 1 if the person
works for themselves or has their own business, and 0 if otherwise.
(2) Economic characteristics, regional variables:
Unemployment rate: Unemployment rate in 2015 (the year before the one included
in the analysis).
Density: Regions with a density greater than 32 inhabitants/2 km or more have a
high density (according to the European average).
Change in GDP per capita: Change (in %) in GDP per inhabitant from 2015 to 2016.
Rate of self-employment. Percentage of self-employed to all gainfully employed
persons in the region.
(3) Technology indicators
(4) NewTech measures the number of years in which the technology needed to produce
the product is available. It is coded by taking the values of 1, 2, or 3. Less than one year
takes the value of 1, between one and five years takes the value of 2, and more than
five years takes the value of 3. NewTech is quantified through three dichotomous
variables that take the value one and zero, indicating “technology novelty.”
In addition to the regional variables, we also include distinctions between the four
European areas.
(1) European areas
Eastern: Poland and Slovakia.
Southern: Greece, Spain, Italy, Cyprus, Croatia and Slovenia.
Western: Netherlands, France, Austria, Germany and Luxembourg.
Northern: United Kingdom, Sweden and Ireland.
The classification of the four areas was performed following the division of sub regions
provided in the United Nations Geoscheme for Europe. The scheme subdivides the continent
into Eastern Europe, Northern Europe, Southern Europe and Western Europe. The United
Nations Statistical Division notes that the assignment of countries or areas to specific groupings
is for statistical convenience and does not imply any assumption regarding the political or other
affiliation of countries or territories. The European areas are quantified through four
dichotomous variables that take the values one and zero, indicating belonging to the area.
Table 1 presents the characteristics of the sample in relation to the explanatory variables
for technology and geographical areas. We show the distribution of innovative entrepreneurs
according to geographical area and innovative technology. In this study, technology
indicators represent the use of technologies or procedures required in the entrepreneurial
process, which have been available for less than one year or between one and five years and
are then considered as modern technology. The use of technology available for more than five
years is considered old technology.
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For example, of 4,430 entrepreneurs, 45% carried out an innovative venture and 55% carried
out another type of venture. As shown in Table 1, 59.41% of entrepreneurs offered innovative
products using modern technology. In comparison, 40.59% of entrepreneurs using modern
technology do not offer innovative products. We found that a small percentage (37.78%)
carried out an innovative undertaking with the use of old technology. The behavior is the
same across European geographical areas. The percentage of entrepreneurs who use modern
technology is higher in the case of innovative entrepreneurship; however, the greatest
difference was found in the western zone. Here, 73.73% of those who use modern technology
carry out innovative undertakings. Other European countries did not present such a
significant difference, although the use of modern technology is associated with a higher
percentage of innovative entrepreneurship.
4. Methodology
The dependent variable is innovative entrepreneurship. This dichotomous variable is
measured at the individual level based on one question from the GEM survey asking
entrepreneurs whether they provide a new product or service to the market (value 1) or not
(value 0). The logit model is suitable for analyzing binary-dependent variables.
First, logit Model 1 is specified and estimated, in which the probability of innovative
entrepreneurship is estimated using the following characteristics: gender, age, education,
previous work experience and education. The unemployment rate and change in GDP per
capita of the country are included as proxy variables for the economic cycle. We then included
four European geographical areas. Model 2 includes the technology variables. We analyzed
whether the use of advanced technology influences the probability of innovative
entrepreneurship. Finally, Model 3 included the moderation; this moderation implies that
European regions moderate the association between perception of modern technology use
and innovative entrepreneurship.
To compare the goodness of fit of the three models, we use the criteria proposed by Akaike
(1973),Hannan and Quinn (1979) and Schwarz (1978).
Overall
Technology novelty
New Old
Non-innovative 40.59% 62.22%
Innovative 59.41% 37.78%
Northern
Non-innovative 35.52% 59.12%
Innovative 64.48% 40.88%
Southern
Non-innovative 49.06% 68.18%
Innovative 50.94% 31.82%
Eastern
Non-innovative 44.14% 52.97%
Innovative 55.86% 47.03%
Western
Non-innovative 26.27% 60.10%
Innovative 73.73% 39.90%
Note(s): Data source. GEM APS 2016
Table 1.
Sample characteristics
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5. Results and discussion
Tables 2 and 3show the results of the logit estimate for creating new products or innovative
entrepreneurship and the calculation of the marginal effects for each model.
5.1 Individual characteristics
Our study clearly shows that gender is not significant in Models 1, 2 and 3. Therefore, gender
is not a relevant characteristic of innovation entrepreneurship. These results are in line with
those of previous studies that show that gender does not have a significant effect on
entrepreneurship (in general) (Br€
uderl and Preisend€
orfer, 1998;Kalleberg and Leicht,
1991;Oberschachtsiek, 2008). In contrast, various studies have shown that there is a
positive differential effect on the probability of men becoming entrepreneurs over women
(Cabrer-Borr
as and Belda, 2018), or others who claim that these differences can be explained
by contextual conditions (Arenius and Minniti, 2005;Ventura and Quero, 2013;Wagner and
Stenberg, 2004), structural barriers in society (Poggesi et al., 2016), or factors related to the
competitive environment (Du Rietz and Henrekson, 2000;Goldin, 2006;Leoni and Falk, 2010;
Sappleton, 2009;Watson, 2002).
When considering the age variables (age and age
2
), the significance of one of the two
variables should not be interpreted alone. The existence of a meaningful relationship between
age and innovative entrepreneurship can only be investigated using a joint test of both age
variables (Bergmann and Sternberg, 2007). Wagner (2007) considered that the effect of age on
entrepreneurship is an empirical question that determines which of the two effects is most
dominant. In the literature, age is considered to influence the decision to become an
entrepreneur in a non-linear way; therefore, it is common to include it in quadratics in
econometric models (Leoni and Falk, 2010). As shown in Model 1, age has no significant
influence on innovative entrepreneurship. However, in Models 2 and 3, age is statistically
significant, and the positive value of the beta coefficient indicates that, as age increases,
individuals are more likely to create innovative ventures. However, the negative sign of the
squared age coefficient indicates that the innovative entrepreneurship rate increases with
age, but at a decreasing rate of change, which implies a reversed U-shaped relationship. The
age at which the probability of innovative entrepreneurship reaches its peak is 45 (see
Figure 2).
Age is a key factor in starting an entrepreneurial process. Over time, people develop
experience and accumulate knowledge that allows them to exploit opportunities. This is
higher according to the degree of progress in each phase of the entrepreneurial process. Our
results are similar to those of Cabrer-Borr
as and Belda (2018), who found that the survival of
Spanish entrepreneurial firms increases with age, but begins to decrease from 45years of age.
These authors argued that “entrepreneurship should not be encouraged indiscriminately, the
authorities should promote business training and help entrepreneurship to those over
45years of age”(Cabrer-Borr
as and Belda, 2018, p. 277).
Education level, as an approximation of human capital, influences the entrepreneurial
profile. In our three models, higher education qualifications positively influence innovative
entrepreneurship. Entrepreneurs with higher education levels are approximately 7% more
likely to undertake innovative entrepreneurship. Therefore, having higher studies positively
affects innovative entrepreneurship compared with having a primary or secondary school
education level. A higher educational level is correlated with higher intelligence, abstract
thinking, curiosity and a strong interest in finding general solutions to problems. Thus, we
can expect that higher educational attainment is associated with creativity, as well as with a
higher probability of perceiving innovative business ideas. Individuals with high educational
attainment are more likely to start innovative businesses. Countries with highly developed
educational systems exhibit a higher share of innovation (Koellinger, 2008). Our results are in
Innovative
ventures in
Europe

Innovative entrepreneurship
Model 1 Model 2 Model 3
Coefficient z Sig Coefficient z Sig Coefficient z Sig
const. 2.1089 3.3112 *** 3.1423 4.7585 *** 2.9824 4.4730 ***
Personal characteristics
Age 0.0152 0.8622 0.0304 1.6817 * 0.0304 1.6799 *
Age squared 0.0002 1.0561 0.0004 1.7506 * 0.0004 1.7402 *
Gender 0.0197 0.2884 0.0639 0.9101 0.0640 0.9103
Occuself 0.3391 4.8460 *** 0.2783 3.8634 *** 0.2858 3.9578 ***
Uneduc 0.2883 4.2877 *** 0.2792 4.0371 *** 0.2812 4.0580 ***
European regions
Western 0.7476 2.8341 *** 1.0235 3.7547 *** 0.8860 3.1786 ***
Eastern 0.1838 0.8490 0.2781 1.2437 0.2349 1.0109
Northern 0.6174 2.7269 *** 0.8369 3.5726 *** 0.7326 3.0497 ***
Regional variables
GDP Change 15.1738 2.4849 ** 18.6687 2.9759 *** 17.5519 2.7569 ***
Unemployment 0.0038 0.2726 0.0001 0.0073 0.0032 0.2161
Density 0.0012 2.8225 *** 0.0013 3.0893 *** 0.0013 3.0096 ***
Self-employment rate 0.0323 2.3963 ** 0.0336 2.4342 ** 0.0323 2.2934 **
Technology indicators
NewTech high 0.9332 9.4470 *** 0.7521 5.2178 ***
NewTech medium 1.1198 13.7759 *** 1.1101 13.6697 ***
Interaction terms
Western* NewTech 0.4934 2.0102 **
Eastern* NewTech 0.0127 0.0356
North * NewTech 0.3089 1.2354
N 4,430 4,430 4,430
Corrects predictions 70.0% 71.7% 71.4%
Prob>F 0.0000 0.0000 0.0000
Crit. de Schwarz 5,424.872 5,214.024 5,234.379
Crit. de Akaike 5,341.722 5,118.082 5,119.248
Crit. de Hannan–Quinn 5,371.044 5,151.915 5,159.848
Note(s): Data source. GEM APS 2016. *Significant on 10%-level; **Significant on 5%-level; ***Significant on 1%-level
Table 2.
Results of the logit
estimation for
innovative
entrepreneurship
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line with those obtained by others concerning the decision to become entrepreneurs (Leoni
and Falk, 2010), or the survival of entrepreneurship (Block and Sandner, 2009;Haapanen and
Tervo, 2009;Joona, 2010;Millan et al., 2012). However, our results are contrary to those that
showed that the level of education is negatively related to entrepreneurship (Georgellis et al.,
2007;Nafziger and Terrell, 1996;Nziramasanga and Lee, 2002). For example, entrepreneurs
with high levels of education may have more opportunities for salaried employment than
those with low levels of education, which can reduce their likelihood of business creation
(Cabrer-Borr
as and Belda, 2018).
Outcome
Model 1
Outcome
Model 2
Outcome
Model 3
Predicted probabilities 0.2960 0.1667 0.8936
Marginal effects
Age 0.0032 0.0042 0.0029
Age squared 0.0001 0.0001 0.0000
Gender 0.0041 0.0089 0.0061
Occuself 0.0707 0.0387 0.0272
Uneduc 0.0601 0.0388 0.0267
Western 0.1558 0.1422 0.0843
Eastern 0.0383 0.0386 0.0223
Northern 0.1287 0.1163 0.0697
GDP Change 3.1622 2.5937 1.6693
Unemployment 0.0008 0.0000 0.0003
Density 0.0002 0.0002 0.0001
Self-employment rate 0.0067 0.0047 0.0031
NewTech high 0.1297 0.0715
NewTech medium 0.1556 0.1056
Western* NewTech 0.0469
Eastern* NewTech 0.0012
Northern * NewTech 0.0294
N4,430 4,430 4,430
Corrects predictions 70.0% 71.7% 71.4%
Note(s): Data source. GEM APS 2016
Table 3.
Marginal effects
Figure 2.
Relationship between
age and innovative
entrepreneurship
Innovative
ventures in
Europe

In all three models, self-employment negatively influences the probability of creating
innovative entrepreneurship. A self-employed person is 7% less likely to start an innovative
business. Our results are in line with other studies that concluded that employment (workers or
self-employed) has a negative effect on entrepreneurship (Br€
uderl et al., 1992;Haapanen and
Tervo, 2009;Roberts et al., 2013;Van Praag, 2003). Previous experience, either as a self-employed
worker oras an employee, negatively affects the probability of starting a business. Munasinghe
and Sigman (2004) showed that people who regularly change their employment status could
have lower human capital, which is an essential prerequisite for acquiring skills and knowledge
that could be useful when starting a new business.
However, other studies have shown a positive relationship between self-employment and
entrepreneurship. For example, Georgellis et al. (2007),Mill
an et al. (2012) and Taylor (1999)
found that previous work experience positively influences the survival rate of entrepreneurship.
Therefore, having previous experience as a self-employed person increases the probability of
carrying out entrepreneurial activity (Cabrer-Borr
as and Belda, 2018).
5.2 Regional characteristics
First, among the regional influential factors investigated, the change in GDP per capita
proved to be significant for all three models. The estimated coefficient of the change in
GDP per capita shows that innovative entrepreneurship is significantly more likely to
occur in highly developed European countries. These results suggest that entrepreneurial
innovativeness cannot be fully explained by individual factors. Instead, this shows that
regions with higher levels of GDP per capita offer a range of opportunities for the creation
of a new product, which makes launching an innovative new company more attractive.
However, if entrepreneurship is analyzed in its broadest sense, an increase in GDP
might negatively affect entrepreneurship (Cabrer-Borr
as and Belda, 2018). When
analyzing individual characteristics, we found that higher education positively
influences innovative entrepreneurship. Our results show that in regards to educational
level, innovative entrepreneurship is more likely in the case of higher education than
intermediate or primary education. In addition, this observed difference becomes greater
as GDP grows. Similarly, we observe that innovative entrepreneurship is less likely in the
case of the self-employed, and this difference is greater for higher GDP values (ranging
between4and7%).
Second, our results show that the percentage of unemployed people is not relevant in
explaining innovative entrepreneurship in any of the three analyzed models. However,
Dawson et al. (2009) and Schuetze (2000) found a positive relationship between
unemployment and general entrepreneurship because difficulties in finding work may
push individuals towards self-employment. In contrast, Blanchflower (2000) found a negative
relationship between the unemployment rate and the general entrepreneurship rate.
Third, although the question of whether someone is self-employed is already considered
at the individual level among the person-related influential factors, the regional rate of
self-employment proved to be significant for all three models. Specifically, an increase in the
rate of self-employment at the country level positively affects innovative entrepreneurship.
Regions with a high proportion of self-employment have a different entrepreneurial culture or
climate than those with a low percentage of self-employed people. In this sense, the positive
relationship between self-employment and innovative entrepreneurship may be due to prior
accumulation of entrepreneurial culture.
5.3 European regions
The Northern, Eastern and Western European areas had higher scores in creating innovative
entrepreneurship than the southern region. In Model 3, we found that the Western European
IJEBR

region increases the probability of innovative entrepreneurship by 22% compared with the
Southern area. In general, the Eastern zone is the one that presents the least difference from
the Southern one (except that in the Eastern area, a person is 5% more likely to be an
innovative entrepreneur). As our results show lower rates of innovative entrepreneurship in
Southern Europe, we believe it is necessary to continue advancing fiscal policies to simplify
the tax burden. We suggest that a review and simplification of administrative processes is
required to finance entrepreneurship in future, as well as greater coordination of
administration, since many of the incentives currently offered to promote entrepreneurship
are territorial or local.
Southern European countries have a social and political infrastructure that is different
from other EU countries. The power of the central and local administrations presents
limitations in the articulation of agreements oriented toward entrepreneurship and
innovation. Governments in Southern European countries should be more concerned with
creating policies that allow for tax cuts and bureaucracy (Chorianopoulos, 2003). Investment
in universities and incentive policies will allow for greater cooperation between companies
and increase technology transfer and innovation. The implementation of these policies will
allow Southern European countries to increase economic development, thus reducing their
differences from Nordic countries (Medeiros et al., 2020).
5.4 Technological component
Model 2 includes a variable indicative of the newness of the technology used in innovative
ventures. In general, product or service innovation includes significant alterations in
technical specifications, components, materials, software, or other functional characteristics
compared with process or organizational innovation. Solow (1956) proposed a model to
measure productivity growth based on two explicit factors, physical capital and labor capital,
and an additional implicit factor, technological advance. Solow argued that productivity
factors (that is, capital and labor) do not necessarily explain the variation in growth, since
most of this variation is explained by technological progress. In our study, the newness of
technology use is a relevant variable for explaining the variation in the probability of
innovative entrepreneurship. Our results confirm that the use of modern technology
increases the probability of innovative entrepreneurship by 15%. As in Solow’s growth
theory, the conceptualization of the influence of technology novelty is a key aspect in
explaining innovative entrepreneurship. Technological progress can be defined as advances
in knowledge related to the art of production (new and better ways of doing things), which can
lead to new products or services and new modes of organization that are stimulated by
innovation (Braunerhjelm et al., 2010). Innovations in new products or processes require
entrepreneurs to be willing to take the risks involved in such a launch. Recent changes in
innovation policies have transformed innovation entrepreneurship in Europe.
5.5 Moderator effect results
In Model 3, we analyze the moderating variable’s effect on innovation entrepreneurship.
These four European geographical areas are important for innovative entrepreneurship.
Southern Europe is the baseline where innovative entrepreneurship from other regions is
confronted. The Western area has the highest probability of innovative entrepreneurship,
and technological innovation is particularly important in this area. The formation of an
ecosystem that is conducive to innovation drives the birth of innovative companies. Our
results show that entrepreneurs from Western European who use modern technology are
12% more likely to create innovative ventures.
The ecosystem that favors innovation has an unknown configuration, but it is
characterized by favoring Science, Technology, Engineering and Mathematics. The
Innovative
ventures in
Europe

existence of innovative business opportunities is influenced by environmental factors such as
changes in technology, politics, regulation, demographics, and other societal trends such as
changes in culture, fashion and urbanization (Shane and Venkataraman, 2000). These factors
vary across countries, and significant changes in one or more of them are likely to generate
opportunities for entrepreneurship (Eckhardt and Shane, 2003). New knowledge and
technological opportunities generated by research and development (R&D) are also likely to
stimulate innovative entrepreneurship (Acs et al., 2005a,b;Shane, 2003). According to the
grouping presented in 2016 by the EIS, European countries located in the Western and
Northern regions are considered to be either leaders or followers in innovation. Conversely,
countries in Southern Europe are considered modest or moderate innovative ecosystems.
Less innovative regions are becoming increasingly specialized, thus laying the groundwork
for possible increases in innovation performance in future (European Commission, 2017).
Investing in research and innovation has been a priority in European countries for several
years. The European Commission observes a good overall trend, but warns that more efforts
will be needed to compete on a global scale. Innovation activities are promoted more easily in
environments with high demand and sufficient resources than in less favorable environments
(Katila and Shane, 2005). Our study shows that there are differences between individual and
regional factors that influence innovative entrepreneurship compared with other studies on
general entrepreneurship (see, e.g. Civera et al., 2020).
The effects of the moderation are shown in Figure 3.
The probability of developing innovative entrepreneurship is greater if the entrepreneur
has a higher education level, is not self-employed, uses modern technology and resides in
Western Europe. Therefore, we calculated the probability of becoming innovative
entrepreneurs by assigning the value 1 to dichotomous variables (male, having a
university degree or being a postgraduate student, self-employed, user of new technology
and entrepreneurship in Western Europe) and assigning the mean value to the rest of the
independent variables. The probability that an individual with these characteristics creates
innovative entrepreneurship is 89%.
6. Conclusions
The goals of this study were threefold: to measure entrepreneurial innovativeness differences
among high-income European countries; to uncover the factors leading to entrepreneurial
Figure 3.
Way in which the
moderator changes
the effect of the
independent variable
on the dependent
variable
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innovativeness and to suggest policies that may enhance the regional level of entrepreneurial
innovativeness activity. We examined this issue for a sample of 16 high-income European
economies using the 2016 GEM data. This study confirms that both individual and regional
qualities influence the decision to become innovative entrepreneurs in high-income European
countries. Indeed, the strong GDP effects revealed in the regressions suggest that
entrepreneurial innovativeness cannot be fully explained by individual-specific factors.
Consequently, entrepreneurial innovation has a factual component rather than being entirely
attached to the creativity of individual entrepreneurs.
In this sense, our empirical evidence shows that the availability and quality of objective
opportunities for innovative new businesses vary across European regions. The Northern
and Western European regions show greater scores for creating innovative entrepreneurship
than the Southern and Eastern regions.
Second, our findings show the considerable influence of various individual-level
characteristics, such as education, self-employment and age. Higher education is positively
related to innovative entrepreneurship in high-income European countries. Moreover,
self-employed people are not as likely to be involved in innovative start-ups as workers.
Although intrapreneurship allows employees to explore new opportunities for business
development, some restrictions imposed on innovation, such as the lack of patenting
initiatives, lack of recognition for innovation in non-core areas and poor change management
initiatives, make entrepreneurship more feasible than intrapreneurship. This emphasizes
employees’privileged position regarding innovative ventures. Furthermore, we show that
age is positively related to innovative entrepreneurship, but this effect changes around the
age of 45, when the likelihood of entrepreneurship starts decreasing.
Third, our results confirm that the use of modern technology increases the probability of
innovative entrepreneurship. However, technological readiness differs, even in the context of
high-income European countries. This implies that perceiving, developing and exploiting an
innovative opportunity in high-income European countries remains an individual act that is
inextricably linked to contextual factors, which influence individuals’decision to become
innovative entrepreneurs.
Some policy implications could be drawn from this paper. All countries in the Western
European region (e.g. the Netherlands, France, Austria, Germany and Luxembourg) are
considered strong innovators according to the SII [1]. Members of this group have innovation
performance that is between 100 and 125% of the EU average (European Commission, 2021).
European regions with low innovation performance should rethink their policies concerning
innovation, especially technological innovation. Some European nations are worried about
transferring existing technological knowledge into new ventures because of the heavy
burden of regulations and bureaucracy that govern business practices. European
governments should strengthen both the local entrepreneurial ecosystem and innovation
capacity by promoting and improving the business environment through reforms and
strategies related to R&D investment and fostering better links between universities and
business sectors. Moreover, addressing social and regional European inequalities should be
considered from an entrepreneurial ecosystem perspective that may facilitate or hinder a new
venture. Furthermore, educational systems should align with industries’innovative goals to
improve human resource qualifications. Policymakers and other stakeholders can use our
results to better understand the triggers for healthy entrepreneurial ecosystems and make
better decisions to promote entrepreneurship as an engine of well-being and prosperity.
Innovation and research are at the core of the EU policy agenda and NextGenerationEU,
which is worth EUR 750 billion, contributes to a more sustainable, digital, resilient and
globally competitive economy (European Commission, 2021). This agenda emphasizes
tailored support to enable SMEs and start-ups to embrace green and digital “twin”
transitions. Significant investment in digital infrastructure is needed across the EU to
Innovative
ventures in
Europe

support the broad-based economic recovery after COVID-19. Europe needs a landscape in
which all innovation actors operate in a flexible, effective and collaborative way, drawing on
the strengths and diversity of national, regional and local innovation ecosystems (European
Commission, 2021).
When considering our findings, some of the study’s limitations should be acknowledged.
First, it relies on subjective measures of innovative entrepreneurship. Since the evaluations
of the survey respondents are necessarily subjective judgments of individuals, the
measurement could confound objective innovativeness with the perceptual biases of self-
confidence and business opportunities of the entrepreneurs. Therefore, the use of an
objective measurement of innovation that considers customer perceptions or additional
insights would be beneficial. Second, entrepreneurship research is predicated primarily on
the idea that an entrepreneur’s decisions are endogenous to their expected goals and
performance implications. This study could suffer from endogeneity problems with cross-
sectional data. Accordingly, future studies could implement methods that correct for
endogeneity or use panel data. Moreover, future studies could examine the impact of modern
technology trends on the entrepreneurial process and assess how new technological
advances provide new windows of opportunity and push entrepreneurship towards new
industries. Future studies could also include additional institutional data (e.g. from the EIS
project) to provide a better understanding of the structural differences between European
countries and regions.
We hope this paper encourages additional research on the drivers of innovative
entrepreneurship.
Note
1. The European innovation scoreboard (EIS) provides a comparative analysis of innovation
performance in EU countries. This innovation performance is measured by the SII, which is a
composite indicator obtained by taking an unweighted average of 32 indicators about human capital,
attractive research systems, digitalization, finance and support systems, firm investments, use of
information technologies, innovative firms, linkages, and intellectual assets, etc., in line with the EU’s
political priorities
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Corresponding author
Mabel Pis
a-B
o can be contacted at: mabel.pisa@esic.edu
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Innovative
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