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This paper analyses the role of gender of the chief executive officer (CEO) on the propensity to introduce innovations using a sample of 1405 Spanish small- and medium-sized enterprises (SMEs). We examine whether there are significant differences between female- and male-led businesses in terms of their propensity to innovate, and whether these differences may be explained by factors related to the attributes of the CEO concerning risk tolerance, self-confidence, education level and cooperative behaviour. In particular, this study tests if the linkages between these managerial attributes and the propensity to innovate are influenced by the gender of the CEO. Using a multivariate probit model ( triprobit ), we further investigate the role of gender on the CEO decision to simultaneously introduce product, process and organisational innovations. Our main results indicate that there are not significant differences in the propensity to introduce innovations between male- and female-run businesses when considering innovation at an aggregated level, that is, innovating in any of the three types of innovations considered. However, we obtain a higher propensity of male CEOs to introduce process innovations, as compared to their female counterparts. No significant differences by gender are found for product and organisational innovations. Additionally, results of the multivariate probit model indicate that the three innovation decisions are interdependent and should be jointly analysed. This study contributes to the scant literature regarding gender impact on firm’s innovativeness with novel empirical evidence for SMEs.
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CEO gender and SMEs innovativeness: evidence
for Spanish businesses
Alfonso Expósito
1
&Amparo Sanchis-Llopis
2
&Juan A. Sanchis-Llopis
2,3
Accepted: 17 May 2021/
#The Author(s) 2021
Abstract
This paper analyses the role of gender of the chief executive officer (CEO) on the
propensity to introduce innovations using a sample of 1405 Spanish small- and
medium-sized enterprises (SMEs). We examine whether there are significant differ-
ences between female- and male-led businesses in terms of their propensity to innovate,
and whether these differences may be explained by factors related to the attributes of
the CEO concerning risk tolerance, self-confidence, education level and cooperative
behaviour. In particular, this study tests if the linkages between these managerial
attributes and the propensity to innovate are influenced by the gender of the CEO.
Using a multivariate probit model (triprobit), we further investigate the role of gender
on the CEO decision to simultaneously introduce product, process and organisational
innovations. Our main results indicate that there are not significant differences in the
propensity to introduce innovations between male- and female-run businesses when
considering innovation at an aggregated level, that is, innovating in any of the three
types of innovations considered. However, we obtain a higher propensity of male
CEOs to introduce process innovations, as compared to their female counterparts. No
significant differences by gender are found for product and organisational innovations.
Additionally, results of the multivariate probit model indicate that the three innovation
decisions are interdependent and should be jointly analysed. This study contributes to
the scant literature regarding gender impact on firms innovativeness with novel
empirical evidence for SMEs.
Keywords Gender .SMEs .Product innovation .Process innovation .Organizational
innovations .triprobit model
JEL classification C35 .J16 .O30 .M21
International Entrepreneurship and Management Journal
https://doi.org/10.1007/s11365-021-00758-2
*Juan A. Sanchis-Llopis
sanchisl@uv.es
Extended author information available on the last page of the article
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Introduction
Innovativeness is crucial not only for maintaining competitive advantage and survival
of established businesses, but also for improving their performance (Hashi & Stojčić,
2013; Love & Roper, 2015; Lichtenthaler, 2016; Buratti et al., 2017).
1
Existing
literature shows that the decision to introduce innovations is conditioned by multiple
factors, including managers set of motivations, preferences, skills, knowledge and
abilities (Alegre et al., 2013;Donate&dePablo,2015; Sánchez-Báez et al., 2018;
Expósito & Sanchis-Llopis, 2019). Different businesses, given the same economic
environment and external factors, may adopt different innovation strategies due to
different perceptions of managers (Hambrick, 2007; Hambrick & Mason, 1984). This is
especially relevant in the case of SMEs, where the owner is usually the chief executive
officer (CEO), and he/she is responsible for all strategic decisions, such as innovation
adoption/implementation decisions. In this context, managers gender has been con-
sidered as a factor influencing the innovative behaviour of the firm in studies such as
Nissan et al. (2012), Alsos et al. (2013) and Marvel et al. (2015), among others.
However, existing knowledge on whether there is a gender gap in SMEsinnova-
tiveness is still scant and inconclusive (Alsos et al., 2013; Buratti et al., 2017; Busolt &
Kugele, 2009; Carrasco, 2014; Link, 2017; Elam et al., 2019).
2
In particular, the
literature is scarce regarding the differences between male- and female-run businesses
beyond the start-up stage, especially in terms of innovativeness, and on how gender
influences managerial personal characteristics that may affect the propensity to imple-
ment innovations inside businesses. The aim of this paper is to fill this gap. We explore
the role of gender on SMEs innovativeness, considering that gender may influence the
relationship between CEOs managerial traits, such as risk-tolerance, self- confidence,
educational level, and R&D cooperative behaviour, and the propensity to implement
innovations, controlling for other managerial and business characteristics. As reviewed
in next section, these managerial traits have been considered in the gender literature as
gender-sensitive and have been shown to foster businesss innovativeness.
3
Most of existing studies have focused on technological innovations, such as
product and process innovations, in high-technology and manufacturing sectors,
1
The link between innovation and firmsperformance has been extensively analyzed in the literature, both in
the case of mature market economies and in transition economies (see, e.g. Hashi & Stojčić,2013,and
references therein, and also the works of Reçica et al., 2019; Mahmutaj Krasniqi, 2020 and Ramadani et al.,
2017).
2
The underrepresentation of women in entrepreneurship is well documented (Elam et al., 2019). The Global
Entrepreneurship Monitor 2019/2020, one of the most relevant sources of information on entrepreneurial
activity worldwide, reports that most countries included in the survey show a lower engagement of women in
early-stage entrepreneurial activities (Bosma et al., 2020).
3
We acknowledge the distinction between sex, as a biological category, and gender, as a social construction
of feminine and masculine characteristics, which map into male and female individuals (Holmes, 2007). This
distinction has been extensively discussed in some recent studies on entrepreneurship (Harrison et al., 2015;
Gokta & Gupta, 2015). Policy and research interest focus in gender, not sex, but evaluation of data by sex is
the usual method to carry out gender-based analysis. In this study, we use information on the biological sex of
the CEO to capture gender and to analyze gender differences between male and female CEOs. While we are
aware of the complexities of gender issues and the new approaches to analyze gender identity, no additional
information in our dataset allows us to capture gender identity by means of masculine and feminine traits nor
to measure gender stereotypes. Therefore, the use of the CEOs biological sex as a measure of gender in this
study should be considered when interpreting our findings.
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since they are considered to represent fields where innovation occurs. These sectors
are dominated by gender segregation in favour of male entrepreneurs (Nissan et al.,
2012; Strohmeyer & Tonoyan, 2005). Therefore, non-technological innovations,
such as organizational innovations, which usually require less resources and contain
a lower level of technical complexity, are not typically considered when analysing
gender issues (Wajcman, 2010). This approach simply ignores innovation activity in
services and other female-oriented sectors (Nählinder et al., 2012; Pettersson &
Lindberg, 2013; Alsos et al., 2013). Our study also contributes to fill these gaps by
considering both technological and non-technological innovations, as well as all
business sectors, including those female- biased sectors, such as retail and services.
Data used in this study have been drawn from a survey regarding competitiveness of
Spanish SMEs carried out in 2012. The main advantage of this survey is that, although
the information is at the business level, it also includes some basic socio-demographic
characteristics of the CEO, such as gender, age, educational attainment, experience in
running the business, and also some managerial traits, such as her/his willingness to
undertake risky projects and her/his level of self-confidence in running the business.
This information at the individual level is very valuable when it comes to the analysis
of gender differences in the innovativeness behaviour of the firm. Regarding innova-
tiveness, we consider three types of innovation, namely, product innovations, process
innovations and organizational innovations.
Our empirical approach consists of using quantitative methods to explore the role of
CEO gender on the propensity to introduce innovations, taking into account managerial
attributes that have been associated with innovativeness in the existing literature, such
as risk tolerance, self-confidence, educational level, and R&D cooperative behaviour.
First, a probit model is used to estimate the probability of the aggregate decision to
introduce any of the three types of innovations considered in this study (product,
process or organizational innovations). Second, we implement a multivariate probit
model that allows for an estimation of the simultaneous probability to introduce the
three types of innovations in a disaggregated way.
To anticipate our main results, we obtain that there are not significant differences
in the propensity to introduce innovations between male- and female- led businesses
when considering innovation at an aggregated level, that is, innovating in any of the
three types of innovations: product, process and organizational innovations. How-
ever, when analysing the disaggregated decision to introduce the three types of
innovations, we provide evidence supporting a higher propensity of male CEOs to
introduce process innovations, as compared to their female counterparts, after
controlling for a number of personal attributes and business characteristics. No
significant differences by gender are found for product and organisational innova-
tions. In addition, we find that SMEs innovativeness also depends on a number of
personal attributes of the CEO, such as risk- tolerance, self-confidence, educational
level, and R&D cooperative behaviour. The influence of these managerial attributes
on the propensity to innovate is robust to controlling for gender, suggesting that, in
general, the factors underlying SMEsinnovativeness are irrespective of the gender
of the CEO.
In summary, this study contributes to the literature in a number of ways. First, our
findings provide new evidence to the scarce empirical literature dealing with the
comparison between female and male-led businesses in terms of their propensity to
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introduce innovations, and thus, to the understanding of the role of gender in SMEs
innovativeness. This is a relevant issue given the growing awareness that gender
inequality in innovation hinders economic growth by lowering the pool of potential
talents for entrepreneurship and innovation (see, e.g., Elam et al., 2019; OECD, 2006;
IMF, 2013;WB,2011).
4
By including a gender perspective, our study contributes to a
better understanding of the gender issues that may arise in entrepreneurship and
management. In particular, by acknowledging the same ability of female and male
CEOs to innovate and thereby help their companies to develop competitive advantages,
we may contribute to recognise the value of women in management practice, and help
to reduce prejudice towards social gender-based stereotypes that may still exist regard-
ing female entrepreneurs. Second, most of the empirical studies use a control
variable for female when undertaking comparative gender analysis (Johnsen &
McMahon, 2005; Koellinger et al., 2013;Alsosetal.,2013; Dohse et al., 2019;
Na & Shin, 2019), whereas in our study we go beyond this by exploring how
gender may influence the linkages between managerial attributes, such as risk-
tolerance, self-confidence, educational level, and R&D cooperative behaviour, and
the propensity to innovate. These managerial traits are usually not available in
business surveys, and therefore have not been sufficiently explored as determi-
nants of SMEs innovativeness. We use this valuable information to empirically
test a number of research hypotheses related to the role of gender on SMEs
innovativeness. Third, our analysis considers both technological (i.e., product
and process innovations) and non-technological (i.e., organizational) innovations,
as well as, all business sectors, including those female-biased sectors, such as
retail and services. This also represents a significant contribution to the existing
literature on innovation and CEO gender in SMEs, since most of the studies focus
only on the technological dimension of innovation and on male-oriented sectors
(i.e., industrial and technological sectors). By considering different types of
innovation and a wide spectrum of business sectors, we aim to offer valuable
information to decision makers, both in public and private spheres, for the design
of the best policy mix to promote an innovative behaviour regardless the CEO
gender and maximize the development of new economic opportunities. Fourth, our
work also contributes to the literature analysing the possible complementarities
among different types of innovation decisions by firms and the convenience to
analyse them jointly (e.g., Martínez-Ros & Labeaga, 2009;Doran,2012; Carboni
&Russu,2018). To the best of our knowledge, none of these studies have
explored the role of the gender of the CEO on the propensity to introduce product,
process and organizational innovations.
The rest of the paper is organized as follows. Section 2reviews the relevant literature
and develop testable hypotheses. Section 3describes the data and the variables to be
used. In Sect. 4we explain our analytical approach and discuss the main results.
Finally, Sect. 5presents concluding remarks.
4
Gender issues have moved forward in national and global agendas. In spite of the general recognition that
women and men should enjoy the same opportunities and be treated equally, they continue to have different
roles in society, different access to resources and different skills. This different role in society, together with
other cultural and institutional factors, imply the existence of a gender gap in many areas, including
entrepreneurship and management, unfavourable to women.
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Literature review and research hypotheses
Though a significant number of studies have documented the impact of gender on
business performance (Watson & Robinson, 2003;Alsosetal.,2013;Marveletal.,
2015; among others), these have generally focused on the start-up stage of the
venture, and have not paid sufficient attention to innovation activities (Alsos et al.,
2013).
5
Studies such as Weber and Geneste (2014), Buratti et al. (2017), Marvel et al.
(2015), Dohse et al. (2019), and Na and Shin (2019), are among the scarce recent
studies that have addressed how the managers gender may affect the innovation
profile of the firm. The work of Weber and Geneste (2014) documents that female
business managers are less interested in financial performance than in personal
welfare and self-fulfilment, what would explain a lower propensity to pursue
strategic choices such as innovation activities. Buratti et al. (2017) show that,
compared to male managers, females had a lower preference for risky strategies,
such as innovation implementation, and a higher propensity to adopt a conservative
approach. Marvel et al. (2015) using a sample of new Korean businesses, document
that male entrepreneurs are more prone to apply for patents and to spend on R&D, as
compared to their female counterparts, but they also find that entrepreneurseduca-
tion, firms cooperation networks and regional location significantly influence the
gender-innovation relationship.
The works of Dohse et al. (2019) and Na and Shin (2019) are two recent studies
analysing gender issues in developing countries. Dohse et al. (2019), using a cross-
country database of more than 100 developing countries, compare the propensity to
innovate of female managers with respect to their male counterparts, and find that the
last, rather than female managers, are more likely to introduce product innovations
(although they neither analyse process nor organizational innovation). Na and Shin
(2019), using a sample of over 30 emerging countries, provide evidence that the gender
of top managers has no significant impact on the probability to introduce either product,
process nor organizational innovations.
6
Therefore, the existing evidence on the role of
CEO gender on business innovativeness is still inconclusive and further research is
needed.
Our study is linked to several strands of the literature. In particular, to develop our
conceptual model we use insights from upper echelons theory (UET) and the resource-
based view (RBV), in combination with feminist theories on gender and entrepreneur-
ship. These different perspectives may help to explain how firmsaccess to different
resources may contribute differently to male- and female-led SMEs innovativeness.
Specifically, according to UET differences in demographic characteristics among
managers (e.g., age and education) reflect differences in their underlying experiences,
5
Within the field of gender entrepreneurship (Alsos et al., 2013) most of the literature has focused on the
barriers faced by women to become entrepreneurs, as compared to men, especially regarding financial and
social obstacles (e.g. Aristei & Gallo, 2016;Robb&Watson,2012; Zolin et al., 2013; among others), as well
as the different preferences and psychological traits of female entrepreneurs when deciding to start a new
business (e.g. Sexton & Bowman-Upton, 1990; Koellinger et al., 2013).
6
These two studies, however, differ from our work not only in that they analyse developing countries,but also
in that they only include a binary variable regarding gender and they do not have information on other
managerial traits.
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cognitions and values, which in turn determine their strategic decisions such as the
introduction of innovations (Hambrick & Mason, 1984;Hambrick,2007;Wangetal.,
2016). Similarly, the RBV suggests that firmsinnovative performance depends not
only on the resources that are available to the firm (e.g., in terms of human capital or
financial capital), but also on the individual-specific resources or abilities of their CEOs
to manage those resources (Barney, 1991; Wernerfelt, 1984;Alvarez&Busenitz,2001;
Wang et al., 2016). However, both the UET and the RBV have paid little attention to
the issue of gender, so we draw on feminist theories and entrepreneurship literature to
establish testable hypotheses on how SMEs innovativeness differ among female- and
male-led businesses. Both liberal and social feminist theories are described as ap-
proaches that allow to identify those factors behind strategic decisions influenced by
gender (Fischer et al., 1993;Ahl,2006). In particular, liberal feminist theories postulate
that men and women are essentially similar, so managers of any gender with similar
resources (and characteristics) are equally able and are expected to have similar
innovative propensities. On the other hand, social feminism theories consider that,
due to the societal hierarchical structure, men and women are different, and have
different perceptions and values, and that these differences will influence their attitudes
and behaviour in running their businesses (Weber & Geneste, 2014).
A third group of theories are social constructionist and poststructuralist feminist
theories, which postulate that similarities and differences between men and woman
are the result of cultural values, education and social interactions (Fischer et al.,
1993;Ahl,2006). Gender is a construct arising from the way society is built, leading
to stereotypes about differences between men and women in attitudes, abilities and
behavioural patterns. Entrepreneurship is therefore a gendered process shaping
individual expectations concerning how females and males should behave
(Eddleston & Powell, 2012). In particular, if the role of women is considered as
linked to the family, and women internalize those values, the approach to business of
male and female entrepreneurs and how to run their businesses may differ (Brush
et al., 2009). Consequently, these theories argue that the decision to undertake
innovative activities is gendered, so that female-led business are less likely to
introduce innovations than male ones.
In summary, according to liberal feminist theory, we should not observe any role of
the CEO gender on SME innovative behaviour, and if differences by gender are
observed, they are due to discrimination or structural barriers, such as unequal access
to resources. However, according to social and constructionist feminist theories, gender
is a socialization construct affecting managerial perceptions, behaviour and business
decisions, and therefore, gender differences in innovation propensities would remain
after controlling for systemic differences in specific attributes at managerial and
business levels. Hence, the following research hypothesis is proposed:
Hypothesis 1: SMEs run by male CEOs will show a higher likelihood of
implementing innovations of any type (product, process and/or organizational
innovations) as compared to SMEs run by female managers.
In addition, we also consider that a number of CEO managerial traits, such as, risk-
tolerance, self-confidence, level of education and R&D cooperative behaviour will be
positively associated with the introduction of innovations, and this association may be
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influenced by gender. In what follows we describe these relationships, how they relate
to the innovativeness of SMEs, and how we expect gender to impact this relationship,
setting our testable hypotheses.
Gender, risk tolerance and innovation
Investment in innovative activities is usually associated to high risk and uncertainty,
so that managerial attitude towards risk is considered to be associated with innova-
tiveness (March & Shapira, 1987). The links between gender and risk tolerance have
been extensively analysed and the bulk of the literature documents that male SME
managers tend to register higher levels of risk taking, compared to their female
counterparts. The works of Sexton and Bowman-Upton (1990), Buratti et al. (2017),
Weber and Geneste (2014) and Mínguez-Vera and Marin (2011), among others,
show that female managers are less willing to get involved in risky projects,
compared to male managers. By contrast, other studies such as Sonfield et al.
(2001), Atkinson et al. (2003) and Croson and Gneezy (2009) show that there are
no significant gender differences in risk preferences between female and male
managers.
However, attitude towards risk may be influenced by other personal or managerial
unobservable traits, such as job personal satisfaction, perceptions of success in running
the business, or external business environment (Weber & Geneste, 2014;Dawson&
Henley, 2015). Job satisfaction and perceptions of success may differ by gender, so that
female managers may be more interested in pursuing conservative and non-risky
managerial strategies that allows them a time allocation to better keep a work-family
balance (Hughes, 2006, Brush et al., 2009), or pursue personal welfare and self-
fulfilment (Weber & Geneste, 2014). Therefore, even with similar levels of risk
tolerance, female CEOs may be less prone to get involved in innovation activities, as
compared to male counterparts. Therefore, we propose the following research hypoth-
eses to be tested:
H1a: A high level of managerial risk tolerance is positively associated with the
likelihood of implementing innovations of any type (product, process and/or
organizational innovations) by the firm.
H1b: The positive impact of risk tolerance on the likelihood to implement inno-
vations is stronger in SMEs run by male CEOs, as compared to those run by
female counterparts.
Gender, self-confidence and innovation
Given the uncertainty and complexity associated with research and development
activities, we might expect self-confidence to play an important role in the decision
to innovate. Some works document that overconfident CEOs are more likely to
innovate (Galasso & Simcoe, 2011; Hirshleifer et al., 2012). On the other hand, studies
such as Díaz-García and Moreno (2010) and Niederle and Vesterlund (2007) argue that
overconfidence does not necessarily lead to better business results, including innova-
tion performance.
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Existing literature has reported evidence on gender differences in self-confidence
levels, in particular regarding the decision to run a business and to innovate (Moore &
Buttner, 1997; Sirec & Mocnik, 2012). Studies, such as Bengtsson et al. (2005), Croson
and Gneezy (2009), Lim and Envick (2013), and Meuleman et al. (2012)findthatmale
managers tend to be more self-confident than their female counterparts. This could be
linked to the fact that female entrepreneurs show lower perceptions regarding their
managerial skills compared to men (Koellinger et al., 2013). In this sense, Casson
(1982) argued that the combination of lower self-confidence and higher risk aversion
reduce the propensity to innovate among female managers. Conversely, the studies of
Belás et al. (2015) and Garwe and Fatoki (2012), among others, do not confirm the
general statement of higher self-confidence levels among male managers. In this line,
the study of Ashourizadeh et al. (2014) finds no evidence about differences in self-
confidence levels between male and female managers, though they do find that
confidence may be turned toward action (e.g., innovation implementation) more easily
for men than for women.
However, CEO self-confidence may also be influenced by other personal unobserv-
able traits, so that female managers may hold a preference for running the business in a
conservative non-risky way, whereas men may be more interested in running growth-
oriented and profitable entities, leading to different strategic behaviours by gender
(Brush, 1992;Cliff,1998;Weber&Geneste,2014). Therefore, even with similar levels
of self-confidence, female CEOs may be less prone to undertake innovation activities,
as compared to male counterparts. Hence, literature review shows that the analysis of
how gender influences the relationship between CEO self-confidence and the propen-
sity to implement product, process and/or organizational innovations needs further
research. Therefore, the following hypotheses will be tested:
H2a: A high level of managerial self-confidence is positively associated with the
likelihood of implementing innovations of any type (product, process and/or
organizational innovations) by the firm.
H2b: The positive impact of managerial self-confidence on the likelihood to
implement innovations is stronger in SMEs run by male CEOs, as compared to
those run by female counterparts.
Gender, educational level and innovation
Formal education is one of the critical components of an entrepreneurs human capital,
reflecting the level of managerial know-how and capabilities (Becker, 2009). Existing
literature commonly shows that higher levels of education attained by business man-
agers are usually related to greater cognitive ability to deal with complex problems and
implement innovative solutions (Chen et al., 2010). Therefore, high educated CEOs are
expected to be more prone to get involved in innovation implementation (Bantel &
Jackon, 1989; Barker & Mueller, 2002;Koellinger,2008;Olivari,2016). Studies, such
as Koellinger (2008), Chen et al. (2010)andKoo(2019) argue that higher educational
levels significantly contribute towards the individual ability of the manager to conceive
innovative business ideas, thus facilitating the implementation of innovation of any
type within the firm.
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Regarding gender, women have on average a lower interest in the educational and
training aspects related to natural sciences, technology, engineering and mathematics,
which has been highlighted as a significant barrier for them to access entrepreneurial
activity in high-technology sectors (Carrasco, 2014; Delmar et al., 2003;OECD,2017).
In this same line, Marvel et al. (2015) argue that there are significant differences in
terms of educational background and specialisation between males and females man-
agers. Several studies have shown that women tend to pursue studies in liberal arts to a
greater extent than in technical fields, being the opposite in the case of males
(Strohmeyer & Tonoyan, 2005; Busolt & Kugele, 2009; Walters & McNeely, 2010).
The role played by the formal education of the entrepreneur on the innovative profile of
the firm is more significant in the case of new technology ventures, where male
managers with specialized degrees in technological fields are more common
(Sullivan & Marvel, 2011). Hence, even with similar levels of formal education, if
male CEOs hold degrees in technological fields, SMEs run by male executives may be
more likely to innovate, as compared to male counterparts, or to implement different
innovation strategies (in terms of product, process and/or organizational innovations).
These issues have not been sufficiently investigated in the literature. Therefore, the
following hypotheses will be tested:
H3a: Higher levels of educational attainment are positively associated with the
likelihood of implementing innovations of any type (product, process and/or
organizational innovations) by the firm.
H3b: The positive impact of a higher level of education attained by the manager on
the likelihood to implement innovations is stronger in SMEs run by male CEOs, as
compared to those run by female counterparts.
Gender, R&D cooperative behaviour and innovation
Business networks play an important role in explaining the type and intensity of
innovation in all business sectors, especially in SMEs, which do not usually have
access to all necessary resources (e.g., financial and human capital) to successfully
implement innovations (Teece, 1996; Freel & Harrison, 2006;Carrasco,2014). In this
sense, literature shows that the establishment of external relationships with market
agents, such as customers and suppliers, or with institutional and public agents, such as
public administrations, research centres and universities, help to generate knowledge
and competences to implement innovations within the firm (Rothwell & Dodgson,
1991; Pittiglio et al., 2009), as well as to acquire important complementary resources
(e.g., knowledge, technology) and enhance innovative learning by the manager (Teng,
2007; Carrasco, 2014;Marveletal.,2015).
A variety of studies have found gender differences in individual networks in terms of
variety of actors, gender composition, available resources and capacities, and network
use (McGuire, 2000; Manolova et al., 2007;Dawsonetal.,2011;Watson,2012).
However, evidence on the impact of different cooperative behaviours between male
and female CEOs on the likelihood of implementing different types of innovation
strategies constitutes a gap in the current literature. As postulated by theoretical
arguments from social psychology and social cognitive theory, different gender
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socialization experiences, such as prior professional experience and social network ties,
shape different strategic choices (Bussey & Bandura, 1999). Women may perceive
their businesses as cooperative networksin which business relationships are closely
linked to family, societal, and personal factors, whereas men may be more interested in
running profitable entities, leading to different gender strategic approaches to run the
businesses (Brush, 1992;Cliff,1998). Hence, even with similar levels of cooperative
attitude, SMEs run by male executives may be more likely to implement different
innovation strategies (product, process and/or organizational innovations), as compared
to female counterparts. Therefore, the following hypotheses will be tested:
H4a: Managers cooperative behaviour in R&D activities is positively associated
with the likelihood of implementing innovations of any type (product, process and/
or organizational innovations) by the firm.
H4b: The positive impact of the managers cooperative behaviour in R&D activ-
ities on the likelihood to implement innovations is stronger in SMEs run by male
CEOs, as compared to those run by female counterparts.
An overview of our conceptual framework and hypotheses is graphically represented in
Fig. 1.
Data and descriptive statistics
Data has been gathered from a survey on business competitiveness for Spanish SMEs
carried out in 2012.7 The surveyed sample corresponds to businesses with less than 250
employees, with the exception of those self-employed entrepreneurs without em-
ployees, and a turnover below 50 million euros. The businesses surveyed were located
in six Spanish regions, representing the southern, central and northern regions of Spain,
corresponding to a 41% of Spanish SMEs in 2012. Our initial dataset corresponds to
CEO Gender
H1
H2b
H3b
H4b
Control variables
H5a
R&D cooperation
H2a
Risk-tolerance
H3a
Self-confidence
H4a
Education
Product innovation
Process innovation
Organiz. innovations
H5b
Fig. 1 Conceptual framework and hypotheses
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1425 small and medium-sized enterprises (SMEs), of which we have information
regarding a single major decision-maker of the company (usually the CEO), including
its gender, age, and other personal attributes, such as number of years of managerial
experience, level of studies and his/her level of risk tolerance, and also information
related to the business characteristics and the market where the business operates (e.g.,
main industrial activity, age of business, ownership form, location). Out of these 1425
SMEs, 429 are female- managed SMEs (30,1%), whereas 996 are male-managed
SMEs (69,9%). However, since our focus is on exploring the differences in innova-
tiveness between female- and male- managed established SMEs, following Mukhtar
(2002), we include in our sample only those SMEs that can be considered as
established SME, that is, those that have been operating in the market for at least
5 years and those run by CEOs with at least 5 years of managerial experience. The idea
here is to be able to compare innovativeness of SMEs that have overcome the barriers
and the obstacles associated with the start-up stage, which have been found to be
stronger for female entrepreneurs (Koellinger et al., 2013;Aristei&Gallo,2016). If
nascent businesses run by female are at disadvantage due to higher obstacles, by
excluding these businesses from our sample, we are able to better compare innovative-
ness of SMEs by gender, given that, after a difficult start-up stage, entrepreneurs, both
male and female, have accumulated enough experience and acquired abilities and skills,
and are capable to operate their businesses with sufficient managerial practices
(Mukhtar, 2002).
7
Under these criteria our final sample is composed of 1,148
established SMEs, of which 828 SMEs correspond to male-managed businesses
(72,12%), and 320 SMEs are female-managed (27,88%).
8
Thus, in our final sample
there are 2.58 men for every women who qualifies as CEO of an established SME. This
ratio is in accordance to the average quote reported by official statistics (Spanish
Ministry of Industry, Energy and Tourism, 2013) and similar to the ratio of other
studies.
9
Our focus on SMEs is founded in several factors. First, our analysis is relevant given
the importance of SMEs in terms of value added, employment and the number of
businesses. According to the Annual report on European SMEs 2017/2018 (European
Commission, 2018), the contribution of SMEs to the non-financial business sector in
Spain corresponds to 62.2% of valued added, 72,2% of employment and 99.9% of the
total number of enterprises. These figures are 56.8%, 66.4% and 99.8%, respectively,
for the EU-28 Member States. Second, the control of SMEs is usually centralized and
concentrated at the top of the organization, usually in the person of the CEO. This
centrality enhances our confidence in the relevance and the role that gender plays in
implementing and undertaking innovative activities within the firm.
7
The surveyed population was stratified by sector and size following the criteria of the Central Directory of
Firms (Spanish National Institute of Statistics). A 20.8% response rate was achieved and no bias between
respondents and non-respondents was perceived.
8
The average age of a business in our sample at the time of the survey is 19 years (20 years for male- managed
businesses, and 17 years for female managed ones). Similarly, the average length of time the CEO has been
running her/his business is 19 years (20 for male-managed and 16 for female-managed ones).
9
For instance, the study of Koellinger et al. (2013) analyzing gender differences in entrepreneurial propensity
in 17 countries for the period 20012006 found that the ratio of male established entrepreneurs to female ones
is 2.15.
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The questionnaire was addressed to the person holding the role of the CEO in the
firm, defined as the business decision-maker or the person who performs the main
managerial functions within the company. There is evidence documenting that CEOs of
SMEs are the key decision-makers within their organizations (Van Gills, 2005), and
their decision include the main business choices, such as innovation. As stated in the
existing literature, managerial perceptions shape the business strategies to a great extent
(Alegre et al., 2013; Donate & Sánchez de Pablo, 2015). In addition, self-reported
information by CEOs may be used as an adequate approach to measure business
innovation strategies, as it is the case of the Community Innovation Survey, and has
been used in other studies (see, e.g., Madrid-Guijarro et al., 2013, and Ramadani et al.,
2017).
10
Data obtained from the questionnaire allows us to establish a direct relationship
between gender, as the business decision-maker agent, and the business propensity to
implement innovations.
11
In particular, the survey includes questions regarding the
innovative activities carried out by the business during the 3 years previous to the
survey (period 20092011), as well as about other attributes at the CEO and business
levels. The survey follows the OECD (2010) guidelines for the definition of innova-
tions and distinguishes between three types of innovations: product, process and
organisational innovations. Product and process innovations are usually considered as
technological innovations, since technological changes generate new or transformed
product/services and processes (Freeman, 1974). Organisational innovations are usu-
ally related to changes in the organisational and administrative structures of the firm.
This classification of innovations has been used in other studies, such as Lichtenthaler
(2016), Madrid-Guijarro et al. (2013), and Ramadani et al. (2017). Thus, using the
information provided by the CEO regarding the introduction of these three types of
innovations, we will construct three yes/no dummy variables to investigate gender
managerial differences in innovativeness.
12
In order to test the different hypotheses stated in the previous section, we
use the information provided by the questionnaire to build a number of
variables. In relation to personal attributes of the CEO, the questionnaire
includes not only the gender, but also information regarding some psycholog-
ical traits, such as the level of risk-tolerance, self- confidence perceptions on
their skills in running the business, and R&D cooperation attitudes towards
other business agents and/or institutions. The questionnaire also includes other
socio-demographic characteristics of the CEO, such as the level of education,
age, and managerial experience. All these attributes are considered to be key
factors in business decision making (Storey, 1994).
10
Research performed with Global Entrepreneurship Monitor (GEM) surveys, covering a high number of
countries, has also been carried out using information from self-reported personality traits of entrepreneurs and
adult population, such as self-perceptions of having the skills and experience to start a business, and the fear of
failure as a barrier to become an entrepreneur (Reynolds et al., 2005). Some of these self-reported traits have
been analysed in the gender literature by Koellinger et al. (2013) and Alves et al. (2017). The work of Liñán
et al. (2011), also using GEM data, documents the importance of self-perceptions to become entrepreneur.
11
Unfortunately, the dataset lacks information regarding the number of owners or the composition of the
board of directors. Therefore, we cannot analyze gender diversity among owners or within the board of
directors nor gender issues regarding entrepreneurial top management teams.
12
See Table A1 in Appendix for a description of all variables used in our analysis.
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Starting with psychological traits, and in contrast to most of existing empirical
analysis on business innovativeness, we have information regarding the CEOsattitude
towards risk: they are asked to answer their willingness to undertake projects of high
risk and high expected returns. This is a valuable piece of information regarding the
preferences of the CEO, given the importance of this attribute in determining invest-
ments, especially uncertain and risky investments such as those related to innovation,
and also given the high number of studies on gender that has established differences in
risk aversion as one of the main differences between male and female business
managers. In fact, gender differences in risk attitudes have been explored mainly in
field and laboratory experiments, with subsamples of the general population or univer-
sity students. These samples may be not appropriate for drawing conclusions about
gender specific-attitudes of business managers.
The questionnaire also asked CEOs several questions regarding their perceptions
about their own abilities and skills to run their businesses. From this information we
construct an indicator of high self-confidence which is a dummy variable taking the
value of one when the CEO declares that it could expand the business with a high
probability of success. In addition, CEOs are also asked about their attitudes regarding
R&D cooperation with other businesses (e.g., suppliers, customers and competitors)
and also with institutions and innovation centres (e.g., universities, laboratories). From
this information we construct two binary variables indicating that the CEO reports to
actively cooperate in R&D activities with other businesses and with institutions,
respectively. There is a broad literature documenting the positive association between
R&D cooperation and the introduction of innovations by firms (see, e.g. Becker &
Dietz, 2004, and references therein). In addition, we also consider whether the CEO
reports participating in business exhibition fairs, since this could be understood as a
way to expand business opportunities and to build networking collaboration, facilitat-
ing the introduction of innovations.
In relation to other personal attributes, the questionnaire asks about the level of
education attained by the manager, since as discussed in Sect. 2,higherlevelsof
education may be related to higher propensities to introduce innovations in SMEs
(Koellinger, 2008;Koo,2019). In this respect, our analysis includes a binary variable to
register if the manager has attained tertiary (university) education or not. Additionally,
the age of the CEO is also a very relevant factor, since younger CEOs may be more
prone to pursue innovation strategies, as compared to older ones (Barker & Mueller,
2002). According to Chen et al. (2010), there are three reasons to expect this. First,
younger managers register higher capabilities for learning and adapting their business
decisions (Taylor, 1975). Second, younger managers have received their education
more recently, so that their technological knowledge may be superior to the older
managers, inducing them to innovativeness (Bantel & Jackson, 1989). Third, younger
managers may be more willing to undertake risky projects since their concerns about
career security and stability are projected towards a long time horizon (Barker &
Mueller, 2002). Further, the experience accumulated by managers affects their willing-
ness to undertake risky actions, such as innovative activities (Barker & Mueller, 2002).
The ability to introduce innovations depends positively on previous experience in a
particular field (Shane, 2000). Previous work experience implies greater opportunities
to acquire the necessary managerial knowledge for business success in general, and for
innovativeness in particular (Koellinger, 2008).
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Finally, a set of other control variables has been also included in our analysis in
relation to the business and market characteristics. First, involvement of the firm in
innovative spending is usually shown to be directly related to innovation outcomes
(Baker & Mueller, 2002;Olivari,2016;Koo,2019). Although expenditure in R&D
is a common indicator for measuring innovation input, this type of expenditure is
often low or absent in SMEs, therefore investment in innovation activities seems
more appropriate. Hence, a variable indicating whether the firm has made spending
in innovation activities during the last 3 years has been considered in our analysis.
Second, we include the age of the business, measured as the number of years elapsed
since its constitution (with a minimum of 5 years). Third, we control for the size of
the firm by including dummy variables that account for micro, small and medium
businesses according to their number of employees. This is important when
analysing gender differences in innovativeness since female managers show a
preference for businesses of a smaller size (Cliff, 1998). Fourth, to capture the
influence of external or environmental factors, we include sectoral and regional
dummy variables. The literature documents that innovative behaviour differs sig-
nificantly across sectors. Regarding the industry sector, we include binary variables
accounting for manufacturing, real estate and construction, commercial, and ser-
vices, respectively. As for regional dummies, they correspond to the six Spanish
regions included in our analysis. We rely on these sectoral and regional dummies to
capture differences in the levels of technology among sectors, and also differences in
the availability of resources, such as infrastructures, and policy and industrial
regulations among regions. A definition of all the variables used in this study is
offeredinTable6in the Appendix.
In what follows we present some descriptive statistics for our sample of SMEs.
Table 1presents the mean and standard deviation for all variables used in our
analysis, distinguishing between those businesses run by a male CEOs (72.12%)
and those run by a female ones (27.88%). It emerges that the percentage of CEOs
that introduce an innovation of any type (either product, process or organizational) is
around 71% both for male and female CEOs. We also observe that product innova-
tions is by far the most common type of innovation for both male and female CEOs
(54.6% and 52.8%, respectively), followed by organizational innovations (32.3%
and 29.7%, respectively). Regarding process innovation, Table 1also reports that
male CEOs introduce process innovations in a higher proportion, as compared to
their female counterparts, (30.8% and 22.2%, respectively) and this difference is
statistically significant. We may also observe that there are statistically significant
differences between male and female CEOs in some attributes: male CEOs are older,
have more years of experience in their businesses, and report a higher participation
in business exhibitions than female counterparts.
Table 1also reports the sectoral distribution of SMEs in our sample, following the
general distribution of SMEs by sector and size at national level (Spanish Ministry of
Industry, Energy and Touris, 2013). Almost half of the observations in our sample
correspond to the services sector, where there is a higher participation of female CEOs,
as compared to males. In the case of manufacturing and real estate and construction, we
observe a higher proportion of male CEOs, whereas for the commercial sector the
participation of male and female CEOs is similar. Regarding businesses size, most of
SMEs in our sample are micro-businesses, with number of employees between 1 and 9
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employees (85.63% in the case of male CEOs, and 90% in the case of female
counterparts).
Finally, Table A2 in the Appendix presents the Pearson correlation matrix to test for
multicollinearity. By inspection, correlations between the pairs is low and not signif-
icant. We observe that none of the correlation coefficients is higher than 0.8, so that we
may conclude that our empirical approach is free of multicollinearity concerns. In
addition, Table 7also provides information regarding the Variance Inflation Factor
(VIF) test. All VIFs values are lower (or equal) than 2, indicating that the results are not
biased due to multicollinearity.
Table 1 Means and standard deviations for all variables
Male Female
Mean s.d. Mean s.d. Difference
Business introduction of innovations
Innovation in either product, process or organization (any
of the 3 types).
0.7077 0.455 0.7094 0. 454 0.0017
Product innovation 0.5459 0.498 0.5281 0.499 0.0178
Process innovation 0.3080 0.461 0.2219 0.416 0.0861***
Organizational innovation 0.3237 0.468 0.2969 0.457 0.0268
CEO personal traits
High risk-tolerance 0.1196 0.324 0.1094 0.312 0.0102
High self-confidence 0.4155 0.493 0.4031 0.491 0.0119
Tertiary education 0.4698 0.499 0.4844 0.500 0.0146
R&D cooperation with other businesses 0.2415 0.428 0.2219 0.416 0.0196
R&D cooperation with institutions 0.1510 0.358 0.1281 0.334 0.023
Participation in business exhibitions 0.7621 0.426 0.6750 0.469 0.0871***
Age of the entrepreneur 49.328 9.568 45.759 8.477 3.569***
Managerial experience 20.294 9.600 16.240 7.996 4.054***
Business characteristics
Involvement in innovative spending 0.7246 0.446 0.7218 0.448 0.0028
Age of Business 20.268 14.345 17.658 11.292 2.61***
Micro business (19 employees) 0.8563 0.351 0.9000 0.300 0.0437**
Small business (1049 employees) 0.1244 0.330 0.0844 0.278 0.0404*
Medium business (50250 employees) 0.0193 0.137 0.0156 0.124 0.0037
Business sector
Manufacturing 0.1184 0.323 0.1062 0.308 0.0122
Real estate and construction 0.1655 0.371 0.1344 0.341 0.0311
Commercial 0.2681 0.443 0.2688 0.444 0.0007
Services 0.4481 0.497 0.4906 0.500 0.0425
Number of observations 828 320
Note: ***, ** and * indicate that the difference between male and female mean is significant at the 1%, 5%
and 10% levels, respectively
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Model specification and results
Model specification
In order to test our proposed hypotheses, we proceed in two steps. In the first step, a
probit model specification estimates the determinants of the aggregate decision to
introduce innovations of any type, i.e., product, process or organizational innovation,
by the SME. This specification is presented as follows,
Innovationi¼1ifβ0þβ1Xiþβ2Ziþui0
0otherwise
ð1Þ
where irefers to the SME; the variable Innovation is a binary variable that takes value 1
if the CEO has introduced any of the three types of innovations in the last 3 years, i.e.,
either a product, a process or organizational innovation; Xi is a vector of personal traits
that includes the gender of the CEO and other managerial attributes (risk-tolerance,
self- confidence, tertiary education, R&D cooperation with other business or institu-
tions, participation in business exhibitions, experience in managerial activities and age),
and Zi is a vector business characteristics that may have an effect on the decision to
innovate, including the business age, size, industry and region.
A first specification of the proposed probit model only includes the gender of the
CEO to capture the isolated impact of gender on the propensity to innovate. In a second
model specification the rest of covariates (CEOs attributes and business characteristics)
are added. Finally, a third specification accounts for interactions of the gender with the
rest of attributes, thus allowing to test the hypotheses we have established in Sect. 2(in
particular, regarding risk-tolerance, self-confidence, tertiary education, R&D coopera-
tion with other businesses and institutions, and participation in business exhibitions).
In a second step we explore the possibility that CEOs may consider simultaneously
the different types of innovation and therefore we jointly analyse the three types of
innovation decisions: introducing new products into the market, new processes of
production and organizational innovations. In order to do so, we use econometric
techniques based on a multivariate probit specification that allows simultaneous inno-
vation decisions by the CEO. In particular, a triprobit model is implemented, thus
allowing for an estimation of the joint probability to introduce these three types of
innovations considered in our analysis.
More formally, we estimate the following trivariate discrete choice model for the
three types of innovations:
Producti¼1if βproduct
0þβproduct
1Xiþβproduct
2Ziþeproduct
i0
0othersiwe
Processi¼1if βprocess
0þβprocess
1Xiþβprocess
2Ziþeprocess
i0
0othersiwe
Organizationali¼1if βorgan
0þβorgan
1Xiþβorgan
2Ziþeorgan
i0
0otherwise
ð2Þ
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where the three dependent variables are binary variables taking value one when the
business CEO reports to have introduced product, process and organizational innova-
tions, respectively, during the last 3 years, and cero otherwise. As covariates, we
introduce the same set of variables described in Eq. (1).
The multivariate probit specification allows for systematic correlations between the
choices of the different types of innovation.
13
These correlations may emerge from
complementarities or substitutabilities between the three forms of innovation. The
existence of significant correlations would indicate that estimating separate probit
specifications for each of the three innovation decisions would be inefficient and also
the convenience to analyse the three innovation decisions jointly. This specification is
estimated using the simulated maximum-likelihood three-equation probit models using
the Geweke-Hajivassiliou-Keane (GHK) smooth recursive simulator to compute the
maximum likelihood.
Estimation results
In this section we report the estimation results for the two specifications
explained above. First, in Table 2we report the probit estimation results for
the first specification, where we analyse the aggregate decision to introduce any
of the three types of innovations: either product, process or organizational. In
column (1) we report the estimates of a basic specification that only considers
the gender of the CEO, and in column (2) we expand our specification to
consider the rest of attributes. Further, columns (3) to (8) correspond to
specifications that parsimoniously include interaction terms of the managers
gender with the rest of attributes that, according to the literature, may differ by
gender (as explained in Sect. 2).
TheresultsshowninTable2suggest that gender plays no role as a determinant in
the propensity to introduce innovations, since its coefficient is not statistically
significant in all columns. From a feminist theoretical perspective, this result is
consistent with liberal feminist theories, claiming that gender differences would
vanish after controlling for differences in CEO and business attributes. In addition,
our results reveal that a number of other managerial attributes are associated with a
higher propensity to introduce innovations, as hypothesized in Sect. 2.Inparticular,
regarding psychological traits, CEOs with a high risk tolerance to undertake risky
projects and showing a high level of self-confidence regarding their managerial
skills are significantly and positively associated with a higher propensity to intro-
duce innovations. Thus confirming hypotheses H1a and H2a, respectively. As
regards to the considered socio-demographic traits, results show that holding a
university degree, collaborating in R&D with other businesses and participating in
business exhibitions are all significantly and positively related to the propensity to
introduce innovations, therefore confirming hypotheses H3a and H4a. Conversely,
the age of the CEO is significantly and negatively associated to the innovation
13
It is worth noting that the specified models do not impose that the three decisions are necessarily
interrelated, but allows all possible decision combinations in the sense that some firmsimplement different
innovation decisions.
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Table 2 Probit estimates of innovating in product, process, or organization (any of the 3 types)
Interaction specifications with gender
Dependent variable (1) (2) (3) (4) (5) (6) (7) (8)
Gender 0.005 0.002 0.009 0.040 0.003 0.007 0.012 0.130
(0.087) (0.094) (0.099) (0.118) (0.126) (0.104) (0.100) (0.165)
High risk-tolerance 0.317** 0.409 0.322** 0.317** 0.318** 0.316** 0.318**
(0.140) (0.275) (0.140) (0.140) (0.140) (0.140) (0.140)
High self-confidence 0.242*** 0.240*** 0.169 0.242*** 0.241*** 0.241*** 0.236***
(0.085) (0.085) (0.161) (0.085) (0.085) (0.085) (0.085)
Tertiary education 0.203** 0.204** 0.201** 0.212 0.203** 0.203** 0.207**
(0.087) (0.087) (0.087) (0.159) (0.087) (0.087) (0.087)
R&D cooperation with other businesses 0.488*** 0.489*** 0.490*** 0.488*** 0.526** 0.490*** 0.487***
(0.118) (0.118) (0.118) (0.118) (0.215) (0.118) (0.118)
R&D cooperation with institutions 0.089 0.088 0.087 0.089 0.088 0.024 0.086
(0.142) (0.142) (0.142) (0.142) (0.142) (0.267) (0.142)
Participation in business exhibitions 0.155* 0.155* 0.154 0.155* 0.156* 0.155* 0.026
(0.094) (0.094) (0.094) (0.094) (0.094) (0.094) (0.166)
Gender#High risk-tolerance ––0.124 –––––
(0.318)
Gender#High self-confidence –– 0.101 ––––
(0.190)
Gender#Tertiary education ––– – 0.012 –––
(0.184)
Gender# R&D cooperation with other businesses ––– ––0.051 ––
(0.244)
Gender# R&D cooperation with institutions ––– ––– 0.084
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Table 2 (continued)
Interaction specifications with gender
Dependent variable (1) (2) (3) (4) (5) (6) (7) (8)
(0.296)
Gender#Participation in business exhibitions ––– ––– – 0.187
(0.198)
Managerial experience 0.076 0.048 0.081 0.076 0.078 0.076 0.071
(0.117) (0.116) (0.117) (0.117) (0.117) (0.117) (0.117)
Age of the entrepreneur 0.578** 0.692*** 0.580** 0.578** 0.578** 0.577** 0.585**
(0.280) (0.277) (0.280) (0.280) (0.280) (0.280) (0.280)
Involvement in innovative spending 0.440*** 0.441*** 0.437*** 0.440*** 0.440*** 0.439*** 0.440***
(0.091) (0.091) (0.091) (0.091) (0.091) (0.091) (0.091)
Age of business 0.060 0.061 0.061 0.060 0.060 0.060 0.057
(0.083) (0.083) (0.083) (0.083) (0.083) (0.083) (0.083)
Size small 0.266* 0.267* 0.269* 0.267* 0.267* 0.267* 0.265*
(0.146) (0.146) (0.146) (0.146) (0.146) (0.146) (0.146)
Size medium 0.281 0.282 0.277 0.280 0.279 0.278 0.283
(0.371) (0.370) (0.371) (0.371) (0.371) (0.371) (0.370)
Constant 0.552*** 2.419** 2.401** 2.465*** 2.415** 2.412** 2.416** 2.527***
(0.074) (0.948) (0.949) (0.952) (0.949) (0.949) (0.948) (0.955)
N 1147 1147 1147 1147 1147 1147 1147 1147
Notes
1. Gender is a dummy variable taking value of 1 when the CEO is male, and value of 0 when the CEO is female
2. ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively
3. Standard errors given in parentheses. Sectoral and regional dummies included in all specifications except in column 1
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propensity in SMEs. With respect to the variable measuring if the business has been
involved in innovation spending in the last 3 years, estimated coefficients show a
significant positive impact on the propensity to introduce innovations by the firm.
Regarding the size of the business, the propensity to innovate is higher for small
businesses (those with 1150 employees), as compared to medium or to micro-
businesses. To further investigate if these entrepreneurial attributes differ by gender,
in columns (3)-(8), we report the results of the interactions of the managersgender
with each one of the attributes that we have assumed as gender sensitive, following
our set of hypotheses in Sect. 2. It emerges that the results obtained in column (2)
regarding these managerial attributes are robust to the inclusion of the different
interaction terms with gender, as presented in columns (3) to (8). Our results indicate
that neither of these interaction terms, nor the variable capturing the gender of the
CEO are statistically significant in explaining the aggregate decision of the CEO to
introduce innovations. These results would reject hypotheses H1b, H2b, H3b and
H4b.
Subsequently, the three innovation decisions are analysed separately, but
allowing for the potential correlations among them using a triprobit specification
as explained above. In Tables 35we present the results of the triprobit model for
the joint decision to introduce the three types of innovations: introducing new
products into the market, new processes of production, and organizational innova-
tions. In all estimations we obtain that the correlation coefficients among the three
innovation decisions are positive and statistically significant (we can never reject
the null that the three correlation coefficients are jointly equal to 0). On the whole,
these results indicate that it is appropriate to jointly analyse the three decisions. The
coefficients for the covariates estimated in each decision also differ sensibly across
the equations, indicating the existence of heterogeneity in the three innovations
decisions and suggesting that the analysis of the disaggregated innovation types is
more appropriate than the aggregated innovation decision. These results are
consistent with the study of Carboni and Russu (2018) who found complementar-
ities among product, process and organisational innovations for a sample of
European firms, and also with the findings of Doran (2012)forasampleofIrish
firms data.
As before, the first specification in Table 3, column (1), includes only the variable
capturing the managers gender, and in the second specification, column (2), we add the
rest of variables. Results show that gender is positive and statistically significant for the
decision to introduce process innovations (both in specification 1 and 2), but non-
significant for the decision to either introduce product or organizational innovations.
Thus, we obtain that male CEOs are more prone to introduce process innovations as
compared to female counterparts, but also we find no difference by gender in terms of
introducing product and organizational innovations. From a feminist theoretical
perspective, this result is consistent with social and constructionist feminist theories,
claiming that gender is a socialization construct affecting managerial behaviour, so that
gender differences in process innovations would remain after controlling for differences
in CEO and business attributes. These findings are only partially in line with those of
Na and Shin (2019) for a cross-country data of emerging countries, who show that
female top managers are not significantly more likely to introduce either product,
process nor organizational innovations. Further, our results are in contrast to the higher
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propensity of female managers to introduce product innovations found by Dohse et al.
(2019) for a large sample of developing countries.
14
The rest of the CEOs traits show different impacts on the propensity to introduce
the three types of innovations by the firm, although the obtained results are in
general consistent with those reported in Table 2. Thus, having a high tolerance to
undertake risky projects positively and significantly affects the probability to intro-
duce organizational innovations, whereas having a high self confidence in manage-
rial skills increases the probability to introduce technological (both product and
process) innovations. Having tertiary education increases the probability to intro-
duce organisational innovations, but it does not affect the probability to introduce
technological innovations (product and process innovations). These results are
therefore in line with existing literature documenting that a high level of self-
confidence and a high level of education are positively associated with innovative-
ness (Koellinger, 2008). R&D cooperation with other businesses and participation in
businesses exhibitions positively affect the introduction of both product and process
innovations, whereas R&D cooperation with institutions increases the probability to
introduce process innovations but only at a ten percent of significant level. The
experience of the CEO is associated with the introduction of process innovations,
and his/her age is negatively and significantly associated with a lower propensity to
introduce the three types of innovations. Regarding the involvement of the firm in
innovative spending, results confirm the strong, positive and significant impact on
introducing innovations of any type. On the whole, these results would confirm
hypotheses H1a, H2a, H3a and H4a, offering specific information about the different
impact of managerial attributes on each type of innovation.
In Tables 4and 5we present the results of extending, in a parsimonious way, the
triprobit specification with the inclusion of interaction terms for the variable capturing
the gender with those variables capturing the rest of attributes assumed to differ by
gender, i.e., high risk-tolerance, high self-confidence, tertiary education, R&D cooper-
ation with other businesses, R&D cooperation with institutions, and participation in
business exhibitions, respectively.
Starting with the results reported in Table 4, we observe that the gender variable is
positive and statistically significant for process innovations, in the three specifications,
that is after including gender interaction with high risk tolerance, high self-confidence
and tertiary education, respectively, and non-significant for the decision to either
introduce product or organizational innovations. These results confirm the main result
shown in Table 3: male CEOs are more prone to introduce process innovations as
compared to female counterparts, and no significant differences by gender are found for
the introduction of product and organizational innovations. In addition, we observe that
the interactions terms of gender with risk tolerance, high self-confidence and tertiary
education, respectively are non-significant in any of the three estimations. Further,
when interacted terms are included, the corresponding managerial attribute being
interacted in each specification loses its significance (with the exception of a positive
14
The works of Na and Shin (2019) and Dohse et al. (2019) analyze the impact of gender using a dummy
variable, and do not take into account the possibility of gender influencing the effect of other managerial
attributes on the propensity to introduce innovations. Further, Dohse et al. (2019) only consider product
innovations, and do not analyze the propensity to introduce process nor organizational innovations.
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and significant impact of high risk-tolerance on the probability to introduce organiza-
tional innovations). Estimated results regarding the rest of regressors in Table 4are
similar to those obtained and reported in Table 3.Therefore,noevidenceisfoundto
support hypotheses H1b, H2b, and H3b.
Table 3 Triprobit estimates of product, process and organizational innovations
Specification 1 Specification 2
Dependent variable Product
innovation
Process
innovation
Organizational
innovation
Product
innovation
Process
innovation
Organizational
innovation
Gender 0.042 0.276*** 0.0 58 0.076 0 .232** 0.070
(0.083) (0.088) (0.086) (0.088) (0.097) (0.090)
High risk-tolerance 0.167 0.045 0.276**
(0.123) (0.126) (0.119)
High self-confidence 0.169** 0.1 88** 0.083
(0.079) (0.085) (0.081)
Tertiary education 0.056 0.075 0. 176**
(0.082) (0.088) (0.083)
R&D cooperation with other
businesses
0.353*** 0.565*** 0.164
(0.103) (0.101) (0.101)
R&D cooperation with institutions 0.196 0.232* -0.039
(0.125) (0.122) (0.123)
Participation in business exhibitions 0.262*** 0.249** -0.020
(0.090) (0.101) (0.093)
Managerial experience -0.142 0.233* 0.012
(0.110) (0.121) (0.113)
Age of the entrepreneur -0.538** -0.977*** -0.593**
(0.264) (0.289) (0.269)
Involvement in innovative spending 0.490*** 0.438*** 0.236**
(0.088) (0.101) (0.093)
Age of business 0.033 0.030 0. 099
(0.077) (0.081) (0.078)
Size small 0.252* 0.136 0.309**
(0.129) (0.131) (0.124)
Size medium 0.465 -0.044 0.306
(0.321) (0.312) (0.292)
Constant 0.072 -0.761*** -0.522*** 1.992** 1.864* 1.029
(0.070) (0.076) (0.073) (0.891) (0.954) (0.898)
Observations 1,147 1,147
ρproduct_process 0.370*** 0.297***
(0.043) (0.055)
ρproduct_organization 0.138*** 0.08 4*
(0.045) (0.051)
ρprocess_organization 0.389*** 0.379** *
(0.043) (0.056)
LR test of rho12=rho13=rho23=0:
Chi2(3)
Prob > Chi2
137.218
1.50e-29
80.548
2.342e-17
Gender is a dummy variable taking value of 1 when the CEO is male, and value of 0 when the CEO is female
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively
Standard errors given in parentheses. Sectoral and regional dummies included in Specification 2
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Regarding Table 5, we observe that the gender variable is positive and significant
for process innovations in the specification for R&D cooperation with other busi-
nesses, but is not significant in the specifications for R&D cooperation with insti-
tutions nor for participation in business exhibitions. In addition, we observe that the
interaction of gender is significant in only two cases. In the case of R&D cooperation
with other business, the interaction term is negative and significant for organiza-
tional innovations, indicating that male CEOs that undertake R&D cooperation with
other businesses are less likely to introduce organizational innovations (compared to
their female counterparts). Additionally, in the case of R&D cooperation with
institutions, we obtain that the interaction term is positive and significant for the
introduction of process innovations, suggesting that R&D cooperation with institu-
tions enhances the probability to introduce process innovations in the case of male
CEOs. These results are consistent with existing literature on the importance of
establishing external collaboration with other agents as a factor fostering innova-
tiveness (Rothwell & Dogson, 1991; Teng, 2007; Pittiglio et al., 2009). Moreover, in
each specification the corresponding managerial attribute being interacted loses its
significance level in some of the equations. Thus, results only partially support
hypothesis H4b. Estimates regarding the rest of regressors in Table 5are similar to
those reported in Table 3.
In summary, our findings suggest that managers gender plays no role as a deter-
minant in the propensity to introduce innovations when we consider the aggregate
decision to introduce any of the three types of innovation: product, process or organi-
zational. However, when the three innovation decisions are analysed separately, we
obtain that male CEOs are more prone to introduce process innovations, as compared to
female counterparts, and that there are no significant differences by gender with respect
to the propensity to introduce product and organisational innovations, after controlling
for a number of managerial and business characteristics. The higher propensity of male
CEOs, as compared to female counterparts, to implement process innovations may be
associated with at least two factors. First, there might exist a male education bias, that
is, a men bias to choose fields of study more technically oriented and thus more likely
to provide them the technical sources (know-how and skills) and favourable conditions
to develop and implement process innovations (Strohmeyer & Tonoyan, 2005;Busolt
&Kugele,2009;Walters&McNeely,2010). Females traditionally focus on non-
science fields that put them at disadvantage regarding technological innovations with
respect to their male counterparts (Link, 2017;Link&Link,1999). Second, there is a
gender- specific sectoral or occupational segregation whereby female entrepreneurs are
oriented towards traditional sectors and services instead of technical or technologically
oriented sectors, and process innovations are less often implemented in those female-
oriented sectors/occupations (Strohmeyer & Tonoyan, 2005; Carrasco, 2014). Unfor-
tunately, our data do not allow us to fully control for these two factors, so that exploring
the reason for these results is beyond the scope of this study, and an avenue of further
research. Nevertheless, our results contribute to a better knowledge on the role played
by the CEO gender on the innovative profile of the firm, thus providing valuable
insight for the design of more effective policy-mixes to promote innovativeness in
SMEs. National/regional innovation action plans could introduce different gender
initiatives to promote more innovation potential in female-biased sectors (e.g., retail
and services), maximize economic opportunities due to innovation, as well as to
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Table 4 Triprobit estimates of product, process and organizational innovations. Gender interactions with risk-tolerance, self-confidence and tertiary education, respectively
Dependent variable Gender interaction with high risk-tolerance Gender interaction with high self-confidence Gender interaction with tertiary education
Product
innov.
Process
innov.
Organiz.
innov.
Product
innov.
Process innov. Organiz. innov. Product innov. Process
innov.
Organiz.
innov.
Gender 0.087 0.196* 0.104 0.099 0.198* 0.051 0.077 0.215* 0.031
(0.093) (0.102) (0.096) (0.112) (0.121) (0.116) (0.121) (0.132) (0.126)
Gender#High risk-tolerance 0.107 0.316 0.276
(0.279) (0.309) (0.270)
Gender#High self-confidence 0.059 0.082 0.045
(0.177) (0.194) (0.180)
Gender#Tertiary education 0.003 0.034 0.077
(0.172) (0.190) (0.177)
High risk-tolerance 0.246 0.209 0.482** 0.163 0.050 0.278** 0.167 0.045 0.275**
(0.242) (0.276) (0.233) (0.123) (0.126) (0.120) (0.123) (0.126) (0.119)
High self-confidence 0.167** 0.192** 0.079 0.212 0.126 0.051 0.169** 0.188** 0.083
(0.080) (0.085) (0.081) (0.151) (0.168) (0.154) (0.079) (0.085) (0.081)
Tertiary education 0.057 0.071 0.179** 0.056 0.074 0.175** 0.058 0.050 0.120
(0.082) (0.088) (0.083) (0.082) (0.088) (0.083) (0.148) (0.167) (0.153)
R&D cooperation with other bus. 0.354*** 0.561*** 0.167* 0.353*** 0.564*** 0.164 0.353*** 0.565*** 0.165
(0.103) (0.101) (0.101) (0.103) (0.101) (0.101) (0.103) (0.101) (0.101)
R&D cooperation with institutions 0.195 0.236* 0.042 0.196 0.234* 0.039 0.196 0.231* 0.041
(0.125) (0.122) (0.123) (0.125) (0.122) (0.123) (0.125) (0.122) (0.123)
Participation in business exhibitions 0.262*** 0.249** 0.022 0.263*** 0.248** 0.021 0.262*** 0.250** 0.019
(0.090) (0.101) (0.093) (0.090) (0.101) (0.093) (0.090) (0.101) (0.093)
Managerial experience 0.141 0.232* 0.016 0.140 0.232* 0.010 0.142 0.233* 0.012
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Table 4 (continued)
Dependent variable Gender interaction with high risk-tolerance Gender interaction with high self-confidence Gender interaction with tertiary education
Product
innov.
Process
innov.
Organiz.
innov.
Product
innov.
Process innov. Organiz. innov. Product innov. Process
innov.
Organiz.
innov.
(0.110) (0.121) (0.113) (0.110) (0.121) (0.113) (0.110) (0.121) (0.113)
Age of the entrepreneur 0.538** 0.973*** 0.596** 0.536** 0.980*** 0.594** 0.538** 0.977*** 0.595**
(0.264) (0.289) (0.269) (0.264) (0.289) (0.269) (0.264) (0.289) (0.269)
Involvement in innovative spending 0.491*** 0.434*** 0.238** 0.492*** 0.436*** 0.235** 0.490*** 0.437*** 0.235**
(0.088) (0.101) (0.093) (0.089) (0.101) (0.093) (0.088) (0.101) (0.093)
Age of business 0.034 0.022 0.103 0.033 0.030 0.100 0.033 0.030 0.099
(0.077) (0.081) (0.078) (0.077) (0.081) (0.078) (0.077) (0.081) (0.078)
Size small 0.252* 0.142 0.307** 0.251* 0.136 0.310** 0.252* 0.135 0.308**
(0.130) (0.130) (0.124) (0.129) (0.131) (0.124) (0.130) (0.131) (0.124)
Size medium 0.466 0.034 0.305 0.467 0.044 0.305 0.465 0.043 0.309
(0.320) (0.313) (0.292) (0.321) (0.312) (0.292) (0.321) (0.312) (0.293)
Constant 1.976** 1.893** 0.999 1.962** 1.905** 1.050 1.991** 1.876** 1.060
(0.891) (0.955) (0.899) (0.895) (0.959) (0.902) (0.893) (0.957) (0.901)
Observations 1147 1147 1147
rproduct_process 0.299*** 0.298*** 0.297***
(0.055) (0.055) (0.055)
rproduct_organization 0.084* 0.084* 0.084*
(0.051) (0.051) (0.051)
rprocess_organization 0.379*** 0.379*** 0.379***
(0.055) (0.056) (0.056)
LR test of rho12 = rho13 = rho23 = 0:
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Table 4 (continued)
Dependent variable Gender interaction with high risk-tolerance Gender interaction with high self-confidence Gender interaction with tertiary education
Product
innov.
Process
innov.
Organiz.
innov.
Product
innov.
Process innov. Organiz. innov. Product innov. Process
innov.
Organiz.
innov.
Chi2(3) 81.120 80.595 80.507
Prob > Chi2 1.765e17 2.288e17 2.389e17
Notes
1. Gender is a dummy variable taking value of 1 when the CEO is male, and value of 0 when the CEO is female
2. ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively
3. Standard errors given in parentheses. Sectoral and regional dummies included in all specifications
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Table 5 Triprobit estimates of product, process and organizational innovations with gender interactions with cooperative attitudes
Dependent variable Gender interaction with R&D cooperation with
other businesses
Gender interaction with R&D
cooperation with institutions
Gender interaction with participation in
business exhibitions
Product
innov.
Process
innov.
Organiz. innov. Product
innov.
Process
innov.
Organiz.
innov.
Product
innov.
Process
innov.
Organiz.
innov.
Gender 0.032 0.186* 0.164 0.054 0.156 0.109 0.032 0.177 0.015
(0.100) (0.114) (0.105) (0.095) (0.105) (0.098) (0.160) (0.188) (0.165)
Gender# R&D cooperation with other bus. 0.197 0.146 0.375*
(0.210) (0.211) (0.206)
Gender# R&D cooperation with other bus. 0.158 0.466* 0.276
(0.259) (0.264) (0.255)
Gender#Participation in business exhibitions 0.061 0.074 0.077
(0.189) (0.217) (0.195)
High risk-tolerance 0.162 0.039 0.285** 0.166 0.042 0.278** 0.167 0.046 0.276**
(0.123) (0.126) (0.120) (0.123) (0.125) (0.119) (0.123) (0.126) (0.119)
High self-confidence 0.169** 0.186** 0.084 0.169** 0.189** 0.084 0.167** 0.186** 0.081
(0.079) (0.084) (0.081) (0.079) (0.084) (0.081) (0.080) (0.085) (0.081)
Tertiary education 0.057 0.076 0.172** 0.055 0.071 0.177** 0.057 0.076 0.177**
(0.082) (0.088) (0.083) (0.082) (0.088) (0.083) (0.082) (0.088) (0.083)
R&D cooperation with other businesses 0.208 0.455** 0.441** 0.358*** 0.580*** 0.156 0.352*** 0.564*** 0.164
(0.185) (0.187) (0.182) (0.103) (0.102) (0.101) (0.103) (0.101) (0.101)
R&D cooperation with institutions 0.203 0.234* 0.050 0.077 0.136 0.175 0.196 0.232* 0.040
(0.125) (0.122) (0.123) (0.232) (0.242) (0.231) (0.125) (0.122) (0.123)
Participation in business exhibitions 0.261*** 0.249** 0.021 0.262*** 0.250** 0.020 0.220 0.198 0.073
(0.090) (0.101) (0.093) (0.090) (0.101) (0.093) (0.157) (0.183) (0.163)
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Table 5 (continued)
Dependent variable Gender interaction with R&D cooperation with
other businesses
Gender interaction with R&D
cooperation with institutions
Gender interaction with participation in
business exhibitions
Product
innov.
Process
innov.
Organiz. innov. Product
innov.
Process
innov.
Organiz.
innov.
Product
innov.
Process
innov.
Organiz.
innov.
Managerial experience 0.136 0.240** 0.000 0.141 0.242** 0.008 0.141 0.235* 0.014
(0.110) (0.122) (0.113) (0.110) (0.121) (0.113) (0.110) (0.121) (0.113)
Age of the entrepreneur 0.546** 0.982*** 0.585** 0.535** 0.973*** 0.600** 0.540** 0.978*** 0.594**
(0.264) (0.289) (0.269) (0.264) (0.289) (0.269) (0.264) (0.289) (0.269)
Involvement in innovative spending 0.486*** 0.433*** 0.245*** 0.488*** 0.433*** 0.238** 0.489*** 0.437*** 0.235**
(0.089) (0.101) (0.093) (0.088) (0.101) (0.093) (0.088) (0.101) (0.093)
Age of business 0.033 0.027 0.101 0.032 0.021 0.102 0.032 0.028 0.097
(0.077) (0.081) (0.078) (0.077) (0.081) (0.078) (0.077) (0.081) (0.078)
Size small 0.250* 0.135 0.314** 0.254** 0.145 0.304** 0.251* 0.135 0.308**
(0.130) (0.131) (0.124) (0.130) (0.130) (0.125) (0.130) (0.131) (0.124)
Size medium 0.473 0.034 0.284 0.460 0.040 0.309 0.467 0.041 0.309
(0.320) (0.313) (0.294) (0.320) (0.312) (0.293) (0.321) (0.312) (0.292)
Constant 2.039** 1.909** 0.953 1.991** 1.889** 1.038 1.613* 1.993** 1.271
(0.892) (0.955) (0.900) (0.891) (0.954) (0.898) (0.894) (0.958) (0.903)
Observations 1147 1147 1147
rproduct_process 0.295*** 0.295*** 0.297***
(0.055) (0.055) (0.055)
rproduct_organization 0.087* 0.086* 0.084*
(0.051) (0.051) (0.051)
rprocess_organization 0.382*** 0.382*** 0.379***
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Table 5 (continued)
Dependent variable Gender interaction with R&D cooperation with
other businesses
Gender interaction with R&D
cooperation with institutions
Gender interaction with participation in
business exhibitions
Product
innov.
Process
innov.
Organiz. innov. Product
innov.
Process
innov.
Organiz.
innov.
Product
innov.
Process
innov.
Organiz.
innov.
(0.055) (0.055) (0.056)
LR test of rho12 = rho13 = rho23 = 0:
Chi2(3) 81.108 81.153 80.467
Prob > Chi21.775e17 1.736e17 2.437e17
Notes
1. Gender is a dummy variable taking value of 1 when the CEO is male, and value of 0 when the CEO is female
2. ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively
3. Standard errors given in parentheses. Sectoral and regional dummies included in all specifications
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diminish potential negative impacts on the innovative profile of businesses due to
gender segregation in education and in the job market.
Sensitivity analysis
The results we obtained in the previous section are robust to a number of alternative
selection criteria of SMEs in our sample, as well as to the inclusion of other control
variables. First, since the focus of this study is to explore the differences in innova-
tiveness between male- and female-led businesses, we use two criteria to select the
sample to analyse: we consider those businesses that have been operating in the market
for at least 5 years and that are run by a CEO with at least 5 years of experience. The
rationale for this selection is to focus on those established SMEs that have overcome
the start-up stage. In particular, a number of papers provide evidence on the higher
obstacles faced by women as compared to men when deciding to become entrepreneurs
or start to operate new businesses (Koellinger et al., 2013;Aristei&Gallo,2016).
When we consider the whole sample of SMEs provided by the survey, that is, including
also those businesses in their start-up stage, similar results are obtained, especially in
terms of the role of gender and interaction terms of gender with managerial attributes.
Therefore, our results are robust to including also nascent businesses or new
entrepreneurs.
Second, our results robustness has been tested by introducing a number of other
controls in the estimated models. With this aim, we have included a dummy variable
indicating involvement in R&D spending, instead of the dummy variable indicating
involvement in innovative spending (that includes R&D expenditures and other
spending related to the introduction of innovations). Results do not register any
significant change. The rationale to finally introduce in our estimation the indicator
of innovative spending instead of the one for R&D spending is that we are analysing
not only technological innovations (i.e., product and process innovations), but also
non-technological innovations, that is, organizational innovations. Therefore, a
broader definition of innovative spending seems more appropriate. Additionally,
the inclusion of other attributes of the manager and business, such as a dummy
variable capturing the ownership structure of the SME as a limited liability company
(Ltd.) and a dummy variable controlling for other levels of education (e.g., second-
ary education) rendered non- significant coefficients, thus not altering the estimated
results.
Concluding remarks
Using a sample of Spanish SMEs, we have explored the role of gender on the
propensity to introduce innovations, taking into account other managerial attributes
that may be associated with innovativeness, such as risk tolerance, self-confidence,
educational level, and R&D cooperative behaviour. We have established a number of
hypotheses related to role of gender in influencing the impact of managerial attributes
on firms innovativeness. In order to test our proposed hypotheses, we have proceed in
two steps. First, a probit model has been used to estimate the probability of the
aggregate decision to introduce any of the three types of innovations considered in this
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study (product, process or organizational innovations). Second, a multivariate probit
model have been implemented to estimate the simultaneous probability to introduce the
three types of innovations in a disaggregated way.
Our findings have provided evidence that male CEOs are no more likely to
introduce innovations than their female counterparts when considering the aggre-
gated decision to implement either product, process or organizational innovations.
However, when analysing the disaggregated decision to introduce the three types of
innovations, our results indicate a higher propensity of male CEOs to introduce
process innovations, as compared to their female counterparts. No significant dif-
ferences by gender are found for product and organisational innovations, suggesting
that both male and female CEOs are similar regarding their propensity to introduce
these types of innovations.
In addition, we have also found that SMEs innovativeness depends on a number of
personal attributes of the CEO, such as risk-tolerance, self-confidence, educational
level, and R&D cooperative behaviour. These attributes, that have been less explored in
business surveys due to lack of information, seem to operate in much the same way for
female- and male- led businesses, suggesting that, in general, the factors underlying
SMEsinnovativeness are irrespective of the gender. Finally, our results have indicated
that the decision of the CEO to introduce product, process and organisational innova-
tions are interdependent and should be jointly analysed. These complementarities
among the different types of innovation should be also considered by decision-
makers in formulating overall innovation policy.
By contributing to the understanding of the role played by gender in the innovation
profile of SMEs, our findings may help policy makers and practitioners in designing
instruments to reduce gender inequality in innovation and maximize economic gains
through innovation promotion. In fact, as pointed out by Liberda and Zajkowska
(2017), a gender perspective is still missing in policies towards innovation and entre-
preneurship. Our findings suggest the need of policies aimed at reducing innovation
opportunities losses due to gender segregation in education and in the job market. A
key implication of our research is that future research should focus on investigating
those factors explaining why female CEOs are less prone to introduce process innova-
tions as compared to their male counterparts.
Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
Spanish Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional (project ECO2017
86793-R); Generalitat Valenciana (PROMETEU/2019/095); Spanish Ministry of Economy and Competitive-
ness (ECO201455745-R); and Junta de Andalucía (P09-SEJ-4857).
Data availability Data belongs to a private project and it is not available.
Declarations
Conflicts of interest/competing interests None.
Code availability Non-applicable.
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Appendix
Table 6 Definition of variables
Variable name Definition
Gender Dummy variable taking the value of 1 if the CEO of the business is male, and
the value of 0 if female
Product innovation Dummy variable taking the value of 1 if the business has introduced any new
product or service during the last three years, and 0 otherwise
Process innovation Dummy variable taking the value of 1 if the business has introduced any new
process during the last three years, and 0 otherwise
Organizational innovation Dummy variable taking the value of 1 if the business has introduced any
organizational innovation during the last three years, and 0 otherwise
Innovation in any of the
three typ es
Dummy variable taking the value of 1 if the business has introduced either a
product innovation, a process innovation, or an organizational innovation
during the last three years, and 0 otherwise
High risk-tolerance Dummy taking the value of 1 if the CEO reports a high level of risk tolerance,
and 0 otherwise. Risk tolerance is measured as a self-reported statement of
having a high predisposition to undertake projects of high risk and high
expected re turns
High self-confidence Dummy taking the value of 1 if the CEO reports a high level of
self-confidence, and 0 otherwise. We consider that the CEO shows a high
level of self-confidence when she/he states a high probability of success in
the case that she/he decided to expand the business
Tertiary education Dummy variable taking the value of 1 if the CEO holds a university degree,
and 0 otherwise
R&D cooperation with other
businesses
Dummy variable taking the value of 1 if the CEO reports collaboration in
R&D with other businesses (customer, suppliers or competitors), and 0
otherwise
R&D cooperation with
institutions
Dummy taking the value of 1 if the CEO reports collaboration in R&D with
institutions and technology centres (universities, laboratories, etc.), and 0
otherwise
Participation in business
exhibitions
Dummy variable taking the value of 1 if the CEO reports attendance to sector
business fairs, and 0 otherwise
Managerial experience Log of the number of years of experience of the CEO in managerial activities
Age of the entrepreneur Log of the age of the manager
Involvement in innovative
spending
Dummy variable taking the value of 1 if the business has been involved in
expenditures related to the introduction of innovations (in a broad sense,
including R&D) during the last three years, and 0 otherwise
Age of business Log of the age of the business, measured as the number of years since its
founding
Size Three dummy variables corresponding to three business sizes according to the
number of employees: 1. Micro (110 employees); 2. Small (1150
employees); 3. Medium (20250 employees)
Region Six dummy variables corresponding to six Spanish regions (Autonomous
Communities): 1. Andalusia; 2. Extremadura; 3. Madrid; 4. Murcia; 5.
Navarra; 6. Basque Country
Sector Four dummy variables taking the following values: 1. Manufacturing; 2. Real
estate and construction; 3. Commercial; 4. Services
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Table 7 Correlations of main independent variables
123 4 5678910111213
Panel A: Analysis of pairwise correlation coefficients
1. Gender of CEO 1
2. High risk-tolerance 0.021 1
3. High self-confidence 0.018 0.085*** 1
4. Tertiary education 0.052** 0.048* 0.046* 1
5. R&D coop. With other businesses 0.035 0.02 0.047* 0.078*** 1
6. R&D coop. with institutions 0.019 0.011 0.028 0.161*** 0.421*** 1
7. Participation in business exhibitions 0.076*** 0.022 0.057** 0.04 0.085*** 0.087*** 1
8. Managerial experience 0.208*** 0.053** 0.002 0.177*** 0.038 0.02 0.109*** 1
9. Age of the CEO 0.172*** 0.006 0.006 0.089*** 0.027 0.026 0.040 0.609*** 1
10. Age of business 0.109*** 0.006 0.061** 0.033 0.018 0.024 0.056** 0.468*** 0.305*** 1
11. Involvement in innovative spending 0.019 0.076*** 0.035 0.042 0.111*** 0.115*** 0.141*** 0.035 0.024 0.004 1
12. Size small 0.044* 0.008 0.034 0.088*** 0.117*** 0.107*** 0.037 0.078*** 0.055** 0.154*** 0.041 1
13. Size medium 0.032 0.027 0.034 0.047* 0.077*** 0.107*** 0.012 0.043 0.032* 0.079*** 0.015 0.043 1
Panel B: Multicollinearity diagnostics using variance inflation factor (VIF)
VIF 1.05 1.02 1.02 1.08 1.24 1.27 1.05 1.97 1.61 1.32 1.05 1.05 1.03
Notes
1. Pearsons correlation matrix
2. Variance Inflation Factor (VIF)
3. *** Significant at 1%, ** at 5% and * at 10%
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Article
This paper investigates the role of manager gender in SMEs’ decisions to get involved in exporting and importing activities, using a sample of 1,405 Spanish SMEs. We borrow insights from international entrepreneurship theories and feminist theories to set testable hypotheses regarding how managerial gender and entrepreneurial orientation (proactiveness, risk-taking and innovativeness) may influence SMEs export and import propensities. Using a bivariate probit model and controlling for other managerial and business characteristics, results reveal that there are not significant disparities in exporting propensities between men- and women-run businesses. However, female-led SMEs show a lower importing propensity, in comparison to male-led counterparts. In addition, the three entrepreneurial orientation dimensions (proactiveness, risk-taking and innovativeness) are important drivers for participating in overseas markets, and do not depend upon the manager gender. This work provides new empirical evidence on the comparison between men- and women-run SMEs as regards export/import behavior and thus, it contributes to improve our knowledge on the role of gender in SMEs internationalization. The role of manager’s gender in SMEs import propensity has not been investigated so far, and this is the main novelty of our research.
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Research articles on women’s entrepreneurship reveal, in spite of intentions to the contrary, and in spite of inconclusive research results, a tendency to recreate the idea of women as being secondary to men, and of women’s businesses being of less significance or, at best, as being a complement. Based on a discourse analysis, this article discusses what research practices cause these results. It suggests new research directions which do not reproduce women’s subordination, but capture more and richer aspects of women’s entrepreneurship.
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This study examines the impact of gender at three different positions in a firm’s hierarchy on innovative activities, looking at over 6474 firms in 30 emerging countries. We create a dummy variable for each of the six survey questions on product innovation, process innovation, organizational innovation, marketing innovation, and R&D (Research & Development) spending. Each dummy acts as a dependent variable in a separate logit regression, and the sum of the dummies acts as the dependent variable in another ordered logit regression. We use the female ownership percentage, female top management, and female majority in the workforce as test variables. We use the Heckman two-stage model to address endogeneity concerns with gender. We find that the female ownership percentage is generally positively related to individual innovation measures as well as the composite measure, while female top management is positively associated with marketing innovation only, and a female majority in the workforce is not significantly related to any measure. The results suggest that promoting innovation in emerging countries would involve governments encouraging further market participation by women and supporting female CEOs (Chief Executive Officers) to innovate, and firms fostering innovation among female workers.
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This paper analyzes the effects of entrepreneurs' personal values and their entrepreneurial attitudes, as forms of intellectual capital, on the innovative behavior of small businesses. The influence of personal values is examined through Schwartz's value theory and entrepreneurial attitudes via assessments associated with risk taking and personal autonomy. A model of structural equations using the Partial Least Squares technique was applied to a database composed of 191 small business owners in Paraguay. The results show that personal values of conservation and self-enhancement in the entrepreneur have a direct negative effect on innovation. It is also noted that entrepreneurial attitudes, such as openness to change and self-transcendence, have a mediating role in the positive impact of values on innovation. The article contributes to the literature by showing that some elements of intellectual capital, such as entrepreneurial attitudes and entrepreneurs' personal values, interact to influence innovation in small businesses.
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