Content uploaded by Katherina Kuschel
Author content
All content in this area was uploaded by Katherina Kuschel on Jul 17, 2018
Content may be subject to copyright.
Women-Led Startups and Their
Contribution to Job Creation
Katherina Kuschel, Juan-Pablo Labra, and Gonzalo Díaz
Abstract Purpose: Given the scant literature of female founders in technology
ventures, and scarce evidence of Latin American startups, we have examined gender
similarities and differences between male and female-led teams regarding their
business stage, growth expectations, strategic vision skill, and team composition.
Design/methodology/approach: A unique online survey was sent to male and
female founders via email and social networks, out of which a total of 199 responses
were analyzed using tools for descriptive analysis and mean comparisons. We delib-
erately sought for a greater proportion female founders from Latin American coun-
tries. The respondents were surveyed on their individual and startups characteristics.
Findings: We have found (1) no significant gender difference in business stage,
(2) slightly fewer growth expectations among women, compared to men, although
not significant, (3) slightly less strong skill of strategic vision among women,
compared to men, (4) female-led teams are smaller than male-led teams, both diverse
teams in terms of employees’gender, and (5) male- (73%) and female- (55%) led
teams create further jobs than the minimum team size.
Originality/value: This study discusses the findings on gender differences
(growth expectations, strategic vision, team-building) in relation to the discussion
on whether startups create employment or not. According to our results, both male
and female-led startups create jobs.
Keywords Startups · New high-technology ventures · Gender differences · Team
size · Team composition · Latin America
JEL Codes L26 entrepreneurship · M13 new firms and start-ups · L1 firm strategy
K. Kuschel, PhD (*)
Lazaridis School of Business and Economics, Wilfrid Laurier University, Waterloo, ON,
Canada
Universidad Tecnológica Metropolitana, Santiago, Chile
e-mail: kkuschel@wlu.ca
J.-P. Labra · G. Díaz
Universidad Nacional Andrés Bello, Santiago, Chile
©Springer International Publishing AG, part of Springer Nature 2018
A. Presse, O. Terzidis (eds.), Technology Entrepreneurship, FGF Studies in Small
Business and Entrepreneurship, https://doi.org/10.1007/978-3-319-73509-2_7
139
1 Introduction
“Business creation contributes to economic development”is a statement that has
been often mentioned in the entrepreneurship literature. The general assumption is
that small businesses create value and jobs (Acs & Audretsch, 1988; Schumpeter,
1934). Moreover, it was found that opportunity entrepreneurship has a positive
significant effect on economic development, whereas necessity-driven entrepreneur-
ship has no effect (Acs & Varga, 2005). Aligned to this statement, many countries
have implemented policies for fostering an entrepreneurial culture for encouraging
business creation and economic development. Accordingly, to the increasing female
participation in entrepreneurship (Brush, 1992; Minitti, Arenius, & Langowitz,
2005), there is growing research interest in the dynamics and economic impact of
female entrepreneurship (Zinger, Lebrasseur, Robichaud, & Riverin, 2007). Latin
American countries have higher rates of entrepreneurial activity among women.
Terjesen and Amorós (2010), using GEM data, demonstrate the role that formal and
informal institutions have in improving the quality and quantity of female
entrepreneurs.
In spite of their many contributions, SMEs are characterized by high failure rates
and poor performance levels (Jocumsen, 2004). Startups share similar characteris-
tics. Shane (2009) argued that the typical startup is not innovative, creates few jobs,
and generates little wealth. According to Isenberg (2016), a large majority of startups
fail, and only some of them survive and create jobs in this increasingly competitive
international environment. On average, a startup in the UK reaches only $180,000 in
revenues after its sixth year, barely enough to pay salaries (Coutu, 2014). Moreover,
two thirds of the jobs created by startups in Denmark are low-skilled service jobs
(Kuhn, Malchow-Møller, & Sørensen, 2016).
And for the last couple of years, there has been a discussion whether “startup
creation contributes to economic development”. Are startups creating value? Are
startups creating jobs? Do women-led technology ventures perform differently than
men-led startups?
This study explores the characteristics of male- and female-led new high tech-
nology ventures, shedding light to the question: are women-led startups creating
jobs?
First, our work reviews the relevant literature related to a particular measure of
performance that is important for economies development: job creation.
Our study contributes to the evidence on job creation of new high-technology
business. This study discusses the findings on gender differences (growth expecta-
tions, strategic vision, team-building) in relation to our results: both male (73%) and
female-led (55%) startups create jobs.
140 K. Kuschel et al.
2 Literature Review
This literature review summarizes previous research that informs this study, on the
topics of gender differences on startup performance and growth expectations, and
job creation.
2.1 Women-Led Startups and Women’s Growth Expectations
Women participation in the technology industry remains low. While only 5% of high
technology entrepreneurs who received private funding in 2008 in the U.S. were
women (Robb & Coleman, 2009), recently, the Startup Genome Report (2015)
identified that the global average of startups with female founder was 18% in
2015, and 10% in 2012. This low female participation also exists in emerging
countries. For example, 15% of technology ventures benefited by Start-Up Chile
acceleration program were led by women (Kuschel and Labra, forthcoming). This
number is becoming the “new norm”.
Women are just as likely as men to desire growth, although women seem to have
less prior business ownership experience and less freedom from domestic responsi-
bilities, and are less likely to measure success by the size of their firms (Cliff, 1998).
Research on the dynamics and economic impact of female entrepreneurship is
important because there is a marked difference between men and women in high-
growth businesses (Gatewood, Brush, Carter, Greene, & Hart, 2009) and more
research should be done in developing countries (De Vita, Mari, & Poggesi, 2014;
Kuschel & Lepeley, 2016a).
Despite growing participation of women in the public arena, still studies report
that women have less access to financial resources (De Bruin, Brush, & Welter,
2007; Gatewood et al., 2009; Marlow & Patton, 2005), less quality and diversifica-
tion in their product and services (Bulanova, Isaksen, & Kolvereid, 2016; Costin,
2012), and a less qualified team (Costin, 2012), which determine their business
performance and potential for growth.
Gender differences on access to financial capital has been well studied since the
creation of the Diana Project (Gatewood et al., 2009). According to Eddleston,
Ladge, Mitteness, and Balachandra (2016), capital providers reward the business
characteristics of male and female entrepreneurs differently to the disadvantage of
women. Women normally obtain significantly less financial capital to develop their
new businesses (Alsos, Isaksen, & Ljunggren, 2006), which is critical in early
stages, particularly for technology ventures (Alsos & Ljunggren, 2017; Kuschel,
Lepeley, Espinosa, & Gutiérrez, 2017).
A possible explanation to this “underperformance hypothesis”is the social
expectation. Women are expected to play a primary role as mothers and caregivers.
Consequently, women in business receive little support from the family (Bogren,
von Friedrichs, Rennemo, & Widding, 2013; Venugopal, 2016) and are still doing
Women-Led Startups and Their Contribution to Job Creation 141
most of the household chores (Office for National Statistics, 2016). This fact may
impact on women’s ability and time horizon for strategic planning (Mitchelmore &
Rowley, 2013).
Women have less time available to devote to the business (Marlow & Patton,
2005) and to participate in exhibitions and events (Greene, Hart, Gatewood, Brush,
& Carter, 2003) and networks (Kalafatoglu & Mendoza, 2017; Surangi, 2015; Wing-
Fai, 2016).
2.2 Job Creation: Team Size as a Measure of Growth
There are a number of ways in which we can measure the growth of a company (e.g.,
sales, workforce, market share, book value, cash flow, etc.), and no “best way”to do
so. The most common SME’s measures for growth are sales and number of
employees. However, startups do not necessarily have sales (they have enough
“traction”to get investment), and they might not even have employees (either the
founding team absorbs the entire workload, or they hire “freelancers”), particularly
in their earlier stages. Performance comparison among startups is a tough task.
Often, they require different amounts of investment to develop a product which
highly depends on their level of progress (stage) and industry.
In our exploratory study, we focus on the founding team, because it has been
shown that the team size and composition is critical for a startup success (Baum &
Silverman, 2004; Kaiser & Müller, 2015; Li, 2008).
A cross sectional study among startup teams participating in Start-Up Chile
acceleration program, showed that those teams led by men are bigger than teams
led by women (Kuschel and Labra, forthcoming). Most of them were startups in their
earlier stages, that haven’t still scaled their sales, nor studied in a longer period of
time of 3 or 5 years to explore the survival bias. Kuhn et al. (2016) studied the job
creation and job destruction of startups and established firms in different industries
and job types in Denmark. They developed a “surplus job creation”measure. Based
on the idea that startups and small firms are not identical, although startups are
typically small, and small firms are often young, their results illustrated that new
firms can account for the entire net job creation in the economy, regardless their size
and industry. Similarly, in order to compare job creation by women- versus men-led
startups, we measured jobs that have been created beyond the minimum team size of
a startup.
142 K. Kuschel et al.
3 Theoretical Framework
3.1 Homosociality: An Explanation to Team Composition
and Size
Human capital is a critical factor for young successful companies (van der Sluis, van
Praag, & Vijverberg, 2008), and not only the level of human capital, but also the
diversity among the team members may affect performance positively (Hambrick,
Cho, & Chen, 1996). These studies suggest that there are beneficial outcomes from
skills heterogeneity or diversity for young business success.
A heterogeneous composition of the top management team (TMT) may increase
creativity, which in turn increases the odds of making innovative and strategic
decisions (Bantel & Jackson, 1989; Barkema & Shvyrkov, 2007; Beckman, Burton,
&O’Reilly, 2007; Talke, Salomo, & Rost, 2010; Wiersema & Bantel, 1992). It has
been found that heterogeneous teams behave in a more innovative way, entering to
new markets, compared to homogeneous TMT (Boeker, 1997a,1997b).
New venture team heterogeneity is associated with both cognitive conflict and
affective conflict (Kaiser & Müller, 2015). Education and prior wages heterogeneity
of the team is positively associated to cognitive conflict, as it avoids group think and
leads to a variety of perspectives. Moreover, age heterogeneity is associated with
affective conflict which is negative and leads to problems in communication and
decision making. Age is a relationship-oriented characteristic and hence may lead to
affective conflict, while prior wages and education constitute task-related character-
istics which are more closely linked to cognitive conflict.
Existing empirical evidence does not, however, clearly suggest that there are
beneficial effects of skill heterogeneity for the success of young firms. It rather by
and large shows that there is a weak positive but often a statistically insignificant link
between skill heterogeneity and performance (see the reviews and meta-analyses by
Bell, Villado, Lukasik, Belau, & Briggs, 2011; Bowers, Pharmer, & Salas, 2000;
Horwitz & Horwitz, 2007; Webber & Donahue, 2001; Williams & O’Reilly, 1998).
But according to a study conducted in the U.S. by First Round Capital (2015),
startups with at least one women in the TMT have had 63% more success than those
startups with only men in their TMT.
But contrary to team heterogeneity, the homophily—“the tendency of agents to
associate disproportionately with those having similar traits”(Golub & Jackson,
2012: 1287)—may also play a role during the earlier stage of a company. For
example, new venture’s initial network ties are precisely formed by the entrepre-
neurs’assessments of the resource’s value, but this process is amplified by age and
gender similarity. Copreneurial teams in technology ventures normally met each
other during their undergraduate education, therefore they have the homogeneous set
of skills and knowledge (Kuschel & Lepeley, 2016b). This type of TMT can result in
a less qualified team with lack of organization and planning (Davidsson,
Achtenhagen, & Naldi, 2010) which affect growth potential.
These arguments on the benefits of a heterogeneous team conflict with the
homosociality framework. This paradox raises the question whether women will
Women-Led Startups and Their Contribution to Job Creation 143
tend to look for similar traits rather than pick the best talents for their startups
partners and employees.
4 Methodology
4.1 Design and Procedure
We have built a survey both in English and Spanish using the platform Qualtrics.
That survey was distributed on-line among many accelerators and networks of
entrepreneurs via mailing in Chile (Start-Up Chile, UDD Ventures, Wayra,
ASECH, ONG Emprendedoras de Chile, Girls in Tech Chile, Women who Code
Chile), social networks (Facebook, Twitter and LinkedIn), and some international
networks (Mujeres del Pacífico, business schools, and accelerators abroad, such as
Endeavor Uruguay, IESA Business School in Venezuela, and the Universidad de
San Andrés in Argentina), during 2015.
Both versions of the survey included a consent form at its beginning. Average
time for completing the survey was 26 min. The participation was voluntary and it
has an incentive of participating in a contest of one US$100 Amazon Gift card, if the
survey were fully completed.
4.2 Measures
The survey included questions regarding the participants’characteristics (e.g., skills,
computer language knowledge, country of origin, gender, age, motivation to start up
a business) and the startup characteristics (e.g., team size, team composition, type of
product, industry, business stage, growth expectations).
4.2.1 Growth Expectations
Expectations to grow the business was measured by the question “Does your
business scale?”Five possible answers were:
•No. You add operating costs (sales force, marketing, administrators, R&D) at the
same rate you grow revenue.
•Yes. You add sales (new markets, new lines of product, new businesses), that
requires relatively smaller and smaller additions to operating costs.
•Does not apply.
•No. It’s too soon.
•No. I prefer to have control over the business. If it’s too big, it’s difficult to
manage it.
144 K. Kuschel et al.
The small business literature suggests that women business owners prefer to grow
slowly or remain small (Morris, Miyasaki, Watters, & Coombes, 2006). We expected
similar proportions of the results among men and women but we also expected a
higher proportion of women answering the stereotype: “No. It’s too soon”, and “No. I
prefer to have control over the business. If it’s too big, it’s difficult to manage it”.
4.2.2 Strategic Vision
Strategic vision was measured with a single item: “I can scan the marketplace and
assess potential needs and gaps”. Participants should assess their skill level as “need
improvement, average, or strong”.
4.2.3 Business Stage
The measure of the business stage is the same used by the Global Entrepreneurship
Monitor, GEM. The stage before the start of a new firm is called nascent entrepre-
neurship (0–3 months) and the stage directly after the start of a new firm is called
owning-managing a new firm (4 months to 3.5 years). The distinction between
nascent and new firms is made by GEM in order to determine the relationship of
each to national economic growth. Taken both stages together this phase is defined
as “total early-stage entrepreneurial activity”(TEA). Owner-managers of established
firms have been working in their business by more than 3.5 years, which is the last
category of business stage.
4.2.4 Team Composition
We have measure team composition according to both number of co-founders and
employees, by gender.
4.3 Inclusion Criteria
There was a total of 1177 surveys completed. We have left out from the sample;
(1) 587 participants that did not declare their gender, (2) 216 participants that were
only employees and, therefore are not running an active venture, (3) 151 participants
that run a non-technology business or that do not require a software engineer or
programmer for its operation, and (4) 24 outliers.
1
After that filter, 199 effective
surveys were ready for analysis.
1
We have considered as “outliers”participants who didn’t declare the number of co-founders or
declare 0 co-founders, and/or more than 150 employees.
Women-Led Startups and Their Contribution to Job Creation 145
4.4 Sample Description
4.4.1 Country and Gender
Most individuals that participated in the survey were originally from the following
countries: Chile, Uruguay, Venezuela, and United States. Figure 1shows the country
distribution of the sample, according to gender. Latin American countries represent
the 73.8% of the sample.
According to Fig. 2, 53% of the sample were female (n ¼106) and 47% male
(n ¼93).
These descriptors are the reflection of our active intention to collect data from
female participants, i.e., women-led startups from Latin America.
4.4.2 Industry
According to Fig. 3, the main industry of this sample was IT and Software, for both
male and female founders.
4.4.3 Product
The three main product categories (see Fig. 4) for this sample were: (1) software and
web applications, (2) service and tangible, and (3) software for mobile devices.
Chile
Uruguay
Venezuela
Other LATAM countries
USA
European countries
Rest of the world
45
41
34
27
26
17
9
0 5 10 15 20 25 30 35 40 45 50
Male Female TOTAL
27 18
19 22
13 21
17 10
521
710
54
Fig. 1 Gender distribution by country
146 K. Kuschel et al.
4.5 Analysis
Data was downloaded from Qualtrics and analyzed using Microsoft Excel. First, we
have conducted a descriptive analysis, and then, we have performed mean
comparisons.
Fig. 2 Gender distribution by age
45
45
35
30
25
20
15
10
5
0
41
26
977
13 12
13
3
12
7666
3
7
46
1
7
22 223122 124
12
IT & Enterprise Software
Other
Education
Consulting
E-Commerce & Fashion
Mobile & Wireless
Media & Advertising
Social Enterprise
Social Media /Social Network
Healthcare & Biotechnology
Gaming
Chemical science, biology and biotechnology
Finance
Tourism
Energy & Clean Tech
Natural Resources - mining, food, lumber etc.
Import/ Export
Male Female
Please select the industrial sector that best represents your company
Fig. 3 Industry distribution by gender
Women-Led Startups and Their Contribution to Job Creation 147
This study structured the results in five major topics.
1. Business Stage
2. Growth expectations
3. Strategic vision
4. Team formation
5 Results and Discussion
5.1 Business Stage
Most of the sample report that their business is in the second stage (4 months to 3.5
years), but still not an established stage (more than 3.5 years) (see Fig. 5).
The gender distribution shows similar proportions of ventures for male (55%) and
female founders (56%) in the business stage from 4 months to 3.5 years (Fig. 6).
According to this result, most of men (55 + 12 ¼67%) and women
(56 + 10 ¼66%) are working on an early stage of their business.
What is your type of product?
60
50
40
30
20
10
0
47 52
20 18
10
24
34
911
23
Software- web
apps
Software- mobile Other: (please
specify)
Service-tangible Hardware -
physical product
Hardware- non
physical product
(e.g. operating
system)
Male Female
Fig. 4 Type of product
What stage is your business in?
80
60
40
20
0
511 12 12
29 29
57
67
Does not apply 0 - 3 months More than 3.5 years 4 months - 3.5 years
Male Female
Fig. 5 Business stage by gender
148 K. Kuschel et al.
5.2 Growth Expectations
Although female founders have high levels of commitment, they have slightly lower
(52%) expectations to scale their business than male founders (58%) (Fig. 7).
Altogether, the proportion of women that don’t have growth expectations
(6 + 8 + 27 ¼41%) is higher than men (13 + 6 + 15 ¼34%). We perform a mean
comparison analysis to assess whether this difference was significant (Table 1),
resulting in non-significant difference between means (p >0.05).
5.3 Strategic Vision
Strategic vision is a skill that has been measured with the question whether the
participant can scan the marketplace and assess potential needs and gaps. Partici-
pants self-assessed their level of development (need improvement, average, or
strong) of that skill (Fig. 8).
More than the half (54%) of male founders report that their level of this skill is
strong, compared with a 42% of women (Fig. 9).
5.4 Team Composition
There are differences on how men and women build their teams, both at the
management level (co-founders), and their employees (or freelancers). In our
study, 199 startup founders answered the survey, indicating their team size and the
Male Female
5%
12%
28% 55%
9%
10%
25%
56%
4 months - 3.5 years
0 - 3 months
More than 3.5 years
Does not apply
Fig. 6 Proportion of business stage by gender
Women-Led Startups and Their Contribution to Job Creation 149
number of co-founders that participated in the team. A preliminary analysis of team
composition yielded the following results:
Our sample had an average of 8.5 team members, having on average 2.3 founders
and 6.2 employees. A view per gender of the founder revealed that males had larger
team sizes, in terms of number of co-founders and number of employees, versus
female founders.
To analyze job creation as a variable, we excluded from the analysis those teams
whose total number of members was equal to the number of co-founders (that is, a
Male Female
8%
15%
6% 13%
58%
27%
8% 6%
52%
7%
No. You add operating costs (sales force, marketing, administrators, R&D) at the same
rate you grow revenue.
Yes. You add sales (new markets, new lines of product, new businesses), that requires
relatively smaller and smaller additions to operating costs.
Does not apply.
No. It’s too soon.
No. I prefer to have control over the business. If it’s too big, it’s difficult to manage it.
Fig. 7 Distribution of scaling expectations by gender
Table 1 Mean comparison
analysis Male Female
Mean 2.4231 2.5918
Variance 1.1785 0.9966
Observations 104.0000 49.0000
Hypothesized mean difference 0.0000
df 151.0000
t Stat 0.9200
P(T t) one-tail 0.1795
t Critical one-tail 1.6550
P(T t) two-tail 0.3590
t Critical two-tail 1.9758
150 K. Kuschel et al.
team of five members comprised of five cofounders does not generate jobs). We also
excluded those cases where the total number of members in the team was three or
less than three, considering that independently of the fact that a team of three may be
comprised of one founder and two employees, or two founders and one employee,
we established three members as the minimum size required to make a startup work
(for example considering the typical positions of CEO—Chief Executive Officer,
COO—Chief Operating Officer and CMO—Chief Marketing Officer).
This filter left us with a total of 126 startups that created jobs. That is 63% of the
teams in our sample created jobs, ranging from 1 to 50 employees in each company.
The table below that the creation of jobs is associated more predominantly to male-
founded startups.
I can scan the marketplace and assess potential
needs and gaps
120
100
80
60
40
20
0
14
30
44
Need Improvement Average
113
67
101
Strong
Male Female
Fig. 8 Strategic vision by gender
Male Female
11%
54% 35%
42%
12%
46%
Need Improvement Avera
g
e Stron
g
Fig. 9 Proportion of strategic vision by gender
Women-Led Startups and Their Contribution to Job Creation 151
6 Discussion and Conclusion
6.1 Contribution
Our study contributes to the evidence on job creation of new high-technology
business. Both male (73%) and female-led (55%) startups create jobs. According
to our results, female founders have similar (or slightly lower levels of, but not
significant) grow expectations, strategic vision, and were in similar business stage
than their male counterparts. The analysis shows that the big difference between
male- and female-led teams is how they build their teams, both regarding to size and
gender diversity. This finding is consistent with the evidence found in SMEs and
startups (Kuschel and Labra, forthcoming) literature (see Fig. 10).
As it can be inferred from Tables 2and 3, male and female co-founders tend to
“homosociality”(i.e., same-sex relationships), being this effect stronger in the
management team. More research is needed to explore if the decision of women to
get a female business partner is made using the criteria of “trust”—as has been
explicitly expressed by female founders of technology ventures in Latin America
(Kuschel and Labra, forthcoming; Kuschel & Lepeley, 2016b)—or, on the other
hand, is that men do not want to have female partners or female leaders in the
Fig. 10 Average team size and diversity, according to co-founder gender
Table 2 Team composition, according to founder gender
Gender of the
founder
Number of
teams
Average number of
co-founders
Average number of
employees
Average number of
team members
Male 93 2.6 8.7 11.3
Female 106 2.0 4.1 6.1
Total 199 2.3 6.2 8.5
152 K. Kuschel et al.
technology sector, which is a highly male-dominated industry. A common underly-
ing assumption of the studies on top management teams is that new venture teams
are stable over time, i.e., the studies (including this exploratory study) emphasize the
initial team characteristics and do not account for changes in the team as the venture
grows. However, there is evidence that supports the fact that women-led TMTs do
not change significantly over time (Kuschel & Lepeley, 2016b). Future research can
explore the TMT restructuring and the opinion of male partners and employees on
female founders’leadership styles.
6.2 Implications for Entrepreneurship Research and Policy
The entrepreneurial ecosystem in Chile is relatively young and still underdeveloped.
This ecosystem doesn’t provide sufficient resources for women entrepreneurs, in
comparison to more developed ecosystems.
Our findings suggest that a continuing development and investment into the
ecosystem will strengthen women-led teams in high technology by providing net-
works, support, mentors and role models. This current lack of support might be
adding obstacles and leading entrepreneurs, particularly women, into the so called
“business failure or underperformance”.
We suggest that there is a need for affirmative actions for women in Latin
American entrepreneurial ecosystems. To build strong, sustainable companies and
fill the growing talent gap, we need more qualified women in leadership roles within
our tech community. The Chilean agency of development (CORFO) is supporting
pre-acceleration programs (e.g., The S Factory from Start-Up Chile, ADA Academy
from Girls in Tech Chile) intended for women in technology ventures. We believe
that this is a way other Latin American countries can follow, as well.
Although special acceleration programs and workshops for relevant skills devel-
opment are key elements for women founders, other ingredients are needed too. For
the ecosystem to be sustainable and growth-oriented, it has to address the need for
strong mentorship and effective role models (Kuschel and Labra, forthcoming), as
well as cultivating in our society more flexible and inclusive HR practices and
raising awareness of the advantages of a diverse workforce. This suggestion is
particularly relevant for countries where women have had a traditional role outside
Table 3 Job creation, according to founder gender
Gender of
the
founder
Number
of teams
% of teams
that created
jobs
Average
number of
co-founders
Average
number of
employees
Average number
of team members
Male 68 73 2.6 11.8 14.3
Female 58 55 2.3 6.9 9.2
Total 126 63 2.5 9.5 12.0
Women-Led Startups and Their Contribution to Job Creation 153
the public spheres. All these elements will assist women in tech in maximizing their
careers and in general industry development.
References
Acs, Z. J., & Audretsch, D. B. (1988). Innovation in large and small firms: An empirical analysis.
American Economic Review, 78(4), 678–690.
Acs, Z. J., & Varga, A. (2005). Entrepreneurship, agglomeration and technological change.
Small Business Economics, 24(3), 323–334.
Alsos, G. A., Isaksen, E. J., & Ljunggren, E. (2006). New venture financing and subsequent
business growth in men- and women-led businesses. Entrepreneurship Theory and Practice,
30(5), 667–686.
Alsos, G. A., & Ljunggren, E. (2017). The role of gender in entrepreneur–investor relationships: A
signaling theory approach. Entrepreneurship Theory and Practice, 41(4), 567–590.
Bantel, K. A., & Jackson, S. E. (1989). Top management and innovations in banking: Does the
composition of the top team make a difference? Strategic Management Journal, 10(1),
107–124.
Barkema, H. G., & Shvyrkov, O. (2007). Does top management team diversity promote or hamper
foreign expansion? Strategic Management Journal, 28(7), 663–680.
Baum, J. A., & Silverman, B. S. (2004). Picking winners or building them? Alliance, intellectual,
and human capital as selection criteria in venture financing and performance of biotechnology
startups. Journal of business venturing, 19(3), 411–436.
Beckman, C. M., Burton, M. D., & O’Reilly, C. A. (2007). Early teams: The impact of team
demography on VC financing and going public. Journal of Business Venturing, 22(2), 147–173.
Bell, S. T., Villado, A. J., Lukasik, M. A., Belau, L., & Briggs, A. L. (2011). Getting specific about
demographic diversity variable and team performance relationships: A meta-analysis. Journal of
Management, 27, 709–743.
Boeker, W. (1997a). Executive migration and strategic change: The effect of top manager move-
ment on product market entry. Administrative Science Quarterly, 42, 231–236.
Boeker, W. (1997b). Strategic change: The influence of managerial characteristics and organi-
zational growth. Academy of Management Journal, 40, 152–170.
Bogren, M., von Friedrichs, Y., Rennemo, Ø., & Widding, Ø. (2013). Networking women
entrepreneurs: Fruitful for business growth? International Journal of Gender and Entre-
preneurship, 5(1), 60–77.
Bowers, C. A., Pharmer, J. A., & Salas, E. (2000). When member homogeneity is needed in work
teams: A meta-analysis. Small Group Research, 31, 305–326.
Brush, C. G. (1992). Research on women business owners: Past trends, a new perspective and future
directions. Entrepreneurship Theory and Practice, 17(4), 5–30.
Bulanova, O., Isaksen, E. J., & Kolvereid, L. (2016). Growth aspirations among women entrepre-
neurs in high growth firms. Baltic Journal of Management, 11(2), 187–206.
Cliff, J. E. (1998). Does one size fit all? Exploring the relationship between attitudes towards
growth, gender, and business size. Journal of Business Venturing, 13(6), 523–542.
Costin, Y. (2012). In pursuit of growth: An insight into the experience of female entrepreneurs.
International Journal of Gender and Entrepreneurship, 4(2), 108–127.
Coutu, S. (2014). The scale-up report on UK economic growth. Retrieved from http://www.
scaleupreport.org/scaleup-report.pdf
Davidsson, P., Achtenhagen, L., & Naldi, L. (2010). Small firm growth. Foundations and Trends in
Entrepreneurship, 6(2), 69–166.
De Bruin, A., Brush, C. G., & Welter, F. (2007). Advancing a framework for coherent research on
women’s entrepreneurship. Entrepreneurship Theory and Practice, 31(3), 323–339.
154 K. Kuschel et al.
De Vita, L., Mari, M., & Poggesi, S. (2014). Women entrepreneurs in and from developing
countries: Evidences from the literature. European Management Journal, 32(3), 451–460.
Eddleston, K. A., Ladge, J. J., Mitteness, C., & Balachandra, L. (2016). Do you see what i see?
Signaling effects of gender and firm characteristics on financing entrepreneurial ventures.
Entrepreneurship Theory and Practice, 40(3), 489–514.
First Round Capital. (2015). State of startups 2015. Retrieved September 7, 2017, from http://es.
slideshare.net/firstround/state-of-startups-2015
Gatewood, E. J. C., Brush, G., Carter, N. M., Greene, P. G., & Hart, M. M. (2009). Diana: A symbol
of women entrepreneurs’hunt for knowledge, money, and the rewards of entrepreneurship.
Small Business Economics, 32(2), 129–144.
Golub, B., & Jackson, M. O. (2012). How homophily affects the speed of learning and best-
response dynamics. Quarterly Journal of Economics, 127(3), 1287–1338.
Greene, P., Hart, M., Gatewood, E., Brush, C., & Carter, N. (2003). Women entrepreneurs: Moving
front and center: An overview of research and theory. Coleman White Paper Series.
U.S. Association for Small Businesses and Entrepreneurship, Boca Raton, FL.
Hambrick, D. C., Cho, T. S., & Chen, M. (1996). The influence of top management team
heterogeneity on firms-competitive moves. Administrative Science Quarterly, 41, 659–684.
Horwitz, S., & Horwitz, I. (2007). The effects of team diversity on team outcomes: A meta-analytic
review of team demography. Journal of Management, 6, 987–1015.
Isenberg, D. (2016). Do startups really create lots of good jobs? Retrieved from https://hbr.org/
2016/06/do-startups-really-create-lots-of-good-jobs
Jocumsen, G. (2004). How do small business managers make strategic marketing decisions? A
model of process. European Journal of Marketing, 38(5/6), 659–674.
Kaiser, U., & Müller, B. (2015). Skill heterogeneity in startups and its development over time.
Small Business Economics, 45(4), 787–804.
Kalafatoglu, T., & Mendoza, X. (2017). The impact of gender and culture on networking and
venture creation: An exploratory study in Turkey and MENA region. Cross Cultural &
Strategic Management, 24(2), 332–349.
Kuhn, J. M., Malchow-Møller, N., & Sørensen, A. (2016). Job creation and job types–New
evidence from Danish entrepreneurs. European Economic Review, 86, 161–187.
Kuschel, K., & Labra, J. P. (forthcoming). Developing entrepreneurial identity among start-ups’
female founders in high-tech: Policy implications from the Chilean case. In P. G. Greene & C.
G. Brush (Eds.), Construction of social identity: The case of women entrepreneurs. Boston:
Edward Elgar.
Kuschel, K., & Lepeley, M. T. (2016a). Women start-ups in technology: Literature review and
research agenda to improve participation. International Journal of Entrepreneurship and Small
Business, 27(2/3), 333–346.
Kuschel, K., & Lepeley, M. T. (2016b). Copreneurial women in start-ups: Growth-oriented or
lifestyle? An aid for technology industry investors. Academia Revista Latinoamericana de
Administración, 29(2), 1–19.
Kuschel, K., Lepeley, M. T., Espinosa, F., & Gutiérrez, S. (2017). Funding challenges of
Latin American women start-up founders in the technology industry. Cross Cultural &
Strategic Management, 24(2), 310–331.
Li, J. (2008). Top management team restructuring in pre-IPO high technology startups: The
influence of TMT characteristics and firm growth. The Journal of High Technology Manage-
ment Research, 19(1), 59–69.
Marlow, S., & Patton, D. (2005). All credit to men? Entrepreneurship, finance, and gender.
Entrepreneurship Theory and Practice, 29(6), 717–735.
Minitti, M., Arenius, P., & Langowitz, N. (2005). Global Entrepreneurship Monitor 2004: Report
on women and entrepreneurship. Babson College, Babson Park, MA.
Mitchelmore, S., & Rowley, J. (2013). Growth and planning strategies within women-led SMEs.
Management Decision, 51(1), 83–96.
Women-Led Startups and Their Contribution to Job Creation 155
Morris, M. H., Miyasaki, N. N., Watters, C. E., & Coombes, S. M. (2006). The dilemma of growth:
Understanding venture size choices of women entrepreneurs. Journal of Small Business Man-
agement, 44(2), 221–244.
Office for National Statistics. (2016). Women shoulder the responsibility of ‘unpaid work’. London.
Robb, A., & Coleman, S. (2009). Characteristics of new firms: A comparison by gender. Kansas
City, MO: Ewing Marion Kauffman Foundation.
Schumpeter, J. A. (1934). The theory of economic development. Cambridge, MA: Harvard Univer-
sity Press.
Shane, S. (2009). Why encouraging more people to become entrepreneurs is bad public policy.
Small Business Economics, 33(2), 141–149.
Startup Genome Report. (2015). The 2015 global startup ecosystem ranking. Retrieved September
7, 2017, from http://blog.compass.co/the-2015-global-startup-ecosystem-ranking-is-live/
Surangi, H. (2015). The role of female entrepreneurial networks and small business development:
A pilot study based on Sri Lankan migrant entrepreneurs of tourism industry in London.
The Business and Management Review, 7(1), 267.
Talke, K., Salomo, S., & Rost, K. (2010). How top management team diversity affects innovative-
ness and performance via the strategic choice to focus on innovation fields. Research Policy, 39,
907–918.
Terjesen, S., & Amorós, J. E. (2010). Female entrepreneurship in Latin America and the Caribbean:
Characteristics, drivers and relationship to economic development. European Journal of Devel-
opment Research, 22, 313–330.
van der Sluis, J., van Praag, C., & Vijverberg, W. (2008). Education and entrepreneurship selection
and performance: A review of the empirical literature. Journal of Economic Surveys, 22(5),
795–841.
Venugopal, V. (2016). Investigating women’s intentions for entrepreneurial growth. International
Journal of Gender and Entrepreneurship, 8(1), 2–27.
Webber, S. S., & Donahue, L. M. (2001). Impact of highly and less job-related diversity on work
group cohesion and performance: A meta-analysis. Journal of Management, 27(2), 141–162.
Wiersema, M. F., & Bantel, K. A. (1992). Top management team demography and corporate
strategic change. Academy of Management Journal, 35(1), 91–121.
Williams, K., & O’Reilly, C. (1998). Demography and diversity in organizations: A review of
40 years of research. Research in Organizational Behavior, 20,77–140.
Wing-Fai, L. (2016). The strengths of close ties: Taiwanese online entrepreneurship, gender and
intersectionality. Information, Communication and Society, 19(8), 1046–1060.
Zinger, J. T., Lebrasseur, R., Robichaud, Y., & Riverin, N. (2007). Stages of small enterprise
development: A comparison of Canadian female and male entrepreneurs. Journal of Enterpris-
ing Culture, 15(2), 107–131.
156 K. Kuschel et al.
