Different motive, different process?
Analysing the engagement process of social entrepreneurs and
the influence of perceived external factors
[TYP DE BEDRIJFSNAAM]
September 27, 2012
Author: Qushánnick R.P. Thodé, 289716
Supervisor: Dr. P.W. Van der Zwan
Coreader: Dr. B. Hoogendoorn
Master: Entrepreneurship
and Strategy Economics
Erasmus School of Economics
Abstract
This thesis contributes to the field of social entrepreneurship by investigating to what extent
socially motivated entrepreneurs are hampered in their advancement in the entrepreneurial
engagement process. Furthermore, this thesis investigates to what extent perceived external
factors are hindering factors in the advancement of socially motivated entrepreneurs. Data
from the Flash Eurobarometer Survey 2009 (No. 283) on Entrepreneurship are used, which
covers 26,168 individuals in 36 countries. A series of ordered logit regressions is performed.
The results first show that perceived administrative complexities have a negative
relationship with the advancement in entrepreneurial process for all individuals. Secondly,
the results indicate that socially motivated entrepreneurs are less likely to advance in the
entrepreneurial process than less socially motivated entrepreneurs. Finally, the results
suggest a stronger negative influence of perceived lack of sufficient information about
running a business on this advancement for the more socially motivated individuals.
Although there are limitations to this research, a first indication is given for the possibility
that socially motivated entrepreneurs have stronger perceptions of external factors, and
that this can be a hinder in their entrepreneurial advancement. In the end this thesis creates
room for further investigation on this topic using more extensive data.
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Table of contents
............................................................................................................................................................. 0
Abstract ................................................................................................................................................... 1
1.Introduction.......................................................................................................................................... 3
2. Literature review ................................................................................................................................ 6
2.1 The definition of social entrepreneurship ..................................................................................... 6
2.2 Entrepreneurial engagement process ........................................................................................... 7
2.2.1 Challenges for the social engagement process ..................................................................... 8
2.3 Perceived external factors ........................................................................................................... 10
2.3.1 Lack of financial support...................................................................................................... 10
2.3.2 Administrative complexities ................................................................................................. 11
2.3.3 Lack of sufficient information............................................................................................... 12
2.4 The Hypotheses ........................................................................................................................... 13
3. Data and Methodology ..................................................................................................................... 14
3.1 Data ............................................................................................................................................. 14
3.1.1 Dependent variable .............................................................................................................. 14
3.1.2 Independent variables .......................................................................................................... 15
3.1.3 Control variables................................................................................................................... 16
3.2 Methodology ............................................................................................................................... 17
4. Results ............................................................................................................................................... 19
4.1 Descriptive analysis ..................................................................................................................... 19
4.2 Empirical analysis ........................................................................................................................ 22
4.3 Additional analysis ....................................................................................................................... 25
5.Discussion and Conclusion ................................................................................................................. 27
5.1 Discussion .................................................................................................................................... 27
5.2 Limitations ................................................................................................................................... 28
5.3 Conclusion ................................................................................................................................... 29
Tables .................................................................................................................................................... 31
References ............................................................................................................................................. 38
Appendix................................................................................................................................................ 42
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1.Introduction
Social entrepreneurs are widely appreciated for their determination to create social change
for the better. This group of people distinguish themselves from commercial entrepreneurs,
firstly by their primary motive to address social or ecological needs that are often unmet by
the public sector or established firms (Zahra et al., 2009; Dacin et al., 2010). Examples are:
improving the quality of life in poorly-developed parts of the world and bringing innovative
solutions to rising environmental problems. Secondly, social entrepreneurs set themselves
apart from closely related organizations like social service providers and social activists by
the effectiveness and efficiency with which they need to operate their business in order to
survive and maintain growth (Martin & Osberg,2007; Weerawardena et al., 2010). This
implies a complex entrepreneurial engagement process of constant balancing between
economic and social value creation that encompasses several levels ranging from infancy to
maturity. According to scholars, The entrepreneurial engagement process of a social
entrepreneur differs to the process of a commercial entrepreneur (Moizer & Tracy,2010;
Mair & Marti, 2006; Dorado, 2006).
Scholars have also found that a movement through the stages of engagement can be
encouraged or hindered by the individual’s perceptions of internal and external factors
(Grilo & Thrurik, 2005; Areniuns & Minniti, 2005; Van der Zwan et al., 2010). Perceptions of
internal factors can include an individual’s fear of failure, one’s ability to recognize new
opportunities and the confidence in one’s own skills. Regarding external factors, one’s
perception of the availability of financial support, one’s perception of the complexity of
administrative processes, or one’s perception of the availability of information on starting
and running a business can be mentioned. The important difference between perceived
internal and external factors is the possibility that external factors can be targeted by policy
makers, especially when the promotion of entrepreneurial activity is prominent in the
agenda. Thus that is the main reason why this thesis focuses on these external factors.
Earlier research has suggested that social entrepreneurs are mainly represented in the early
stages of the entrepreneurial engagement process (DiDomenico et al., 2010; Peredo &
McLean, 2006). However, this has hardly been investigated on the micro level. Therefore,
this thesis will investigate how the entrepreneurial engagement process is influenced by the
3
social motivation of entrepreneurs. Furthermore, this study investigates whether perceived
external factors have a differential impact on the advancement in the entrepreneurial
process for socially motivated individuals and individuals and less socially motivated
individuals. Research done on the perceived external factors, in particular the perceived lack
of financial support, perceived administrative complexities, and perceived insufficient
information on running a business, has found significant relationships with the
entrepreneurial engagement process in general (Grilo & Thrurik, 2005 ; Van der Zwan et al.,
2010). However, a more precise investigation of whether perceived external factors weigh
more heavily on the advancement for socially motivated individuals than for less socially
motivated individuals is lacking. Thus, this thesis raises the following questions:
What is the relationship between the social motivation of individuals and their position in the
entrepreneurial process? And do the influences of perceived external factors on the position
in the entrepreneurial process depend on the social motivation of individuals?
The Flash Eurobarometer 2009 (No.283) Survey on Entrepreneurship by the European
Commission has been specifically chosen for this research because it is one of the few
datasets to contain information about the social motivation of the respondents while
starting a business. The questionnaire divides the individuals in four categories based on the
importance of addressing unmet social or ecological needs while starting their business.
Furthermore, the survey contains information about perceptions of external factors that
may influence the start-up of a business. The present thesis contains three levels that are
naturally ordered regarding the involvement in the entrepreneurship process: “taking steps
to start a business”, “having a young business” and “having an established business”,
because information on the social motivation of individuals is available for these
engagement levels only. Because of the natural order of the engagement levels in terms of
the level of engagement in the entrepreneurial process, ordered logistic regressions are
performed. This method was used by Van der Zwan et al. (2010) to investigate the influence
of a wide range of factors, including perceived obstacle variables on entrepreneurial
engagement in general.
Policy makers, especially those in Europe have directed their policies for the last decades
towards improvement and stimulation of entrepreneurial activity in order to create
economic growth (European commission, 2002). Recently, the European Commission stated
4
that there are emerging needs of the society that are to be addressed by social ventures
(European Commission, 2011). In addition, the European Commission‘s policy towards social
enterprises emphasizes the importance of a market where social entrepreneurs can
compete effectively and on equal terms with other forms of entrepreneurship without
regulatory discrimination. However, in order for European policy makers to invest in social
entrepreneurship, they must gain a great understanding of the external factors that are of
influence and in what way these factors are related to the social entrepreneurial process.
Therefore, contributing to the empirical research field of social entrepreneurship is the key
objective of this thesis.
This paper is organized as follows: The following section will elaborate on background
literature surrounding social entrepreneurship regarding its definition and engagement
process. Thereafter, the influences of perceived external factors are discussed for
entrepreneurship in general. The hypotheses will be provided followed by an explanation of
the data as well as the statistical methods used to perform the descriptive and empirical
analysis. Finally, the results will be given, followed by the discussion, limitations, conclusion
and implications for further research.
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2. Literature review
The literature review of this thesis is divided into four subsections. Due to the knowledge
that there are many definitions of social entrepreneurship in existing literature, the first
subsection will indicate how this term is defined throughout this research. The second
subsection will elaborate on the current state of existing literature with respect to the
entrepreneurial engagement process, followed by the third subsection which will elaborate
on the perceived external factors. These three subsections will form the basis on which the
hypotheses in the fourth subsection are formed.
2.1 The definition of social entrepreneurship
As mentioned before, social entrepreneurship generally distinguishes itself from commercial
entrepreneurship in not pursuing the primary goal of maximizing profit, but that of
increasing social wealth (Mair & Marti, 2006). Various scholars have dedicated their
research in trying to define the term social entrepreneurship. However, no uniform
definition has been established so far (Christie & Honig, 2006; Weerawardena &Mort, 2006).
Martin & Osberg (2007) defined social entrepreneurship by three main components: 1.
identifying a stable but unjust equilibrium which excludes, marginalizes or causes suffering
to a group of people who lack the means to transform the equilibrium, 2. identifying an
opportunity in this unjust equilibrium and developing a new social value proposition trough
creativity, inspiration, courage and fortitude to challenge the unjust stable state, and 3.
forging a new, stable equilibrium to alleviate the suffering of the targeted group through
imitation and creation of a stable ecosystem around the new equilibrium to ensure a better
future, not only for that group but for the whole society.
Throughout the years scholars have adapted their definition to the changing times and
importance of social entrepreneurship. Zahra et al. (2009) provide the following definition:
“Social entrepreneurship encompasses the activities and processes undertaken to discover,
define, and exploit opportunities in order to enhance social wealth by creating new ventures
or managing existing organizations in an innovative manner” (Zahra et al., 2009: p.520). This
definition tries to emphasize the importance of pursuing social motives when setting up or
operating a business. Moreover, recent research have found that the nature of
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entrepreneur’s motives is a key distinctive characteristic in the overlapping fields of social
and commercial entrepreneurship (Dacin et al.2010). In accordance with the study of Dacin
et al. (2010), the following definition of social entrepreneurship will be applied:
Social entrepreneurs are individuals who are motivated to enhance social wealth through the
process of new business creation.
For this study, the motivation of social entrepreneurs are defined in terms of the importance
to address social or ecological needs while setting up a business. Examples of social or
ecological needs may be protection of the environment, employment creation in poorlydeveloped parts of the world or the help in areas where people are in need of food, shelter
and medicinal care.
The following subsections will elaborate on the specific factors influencing the
entrepreneurial engagement process.
2.2 Entrepreneurial engagement process
When entrepreneurs are setting up a business, they do not only find themselves in a
decision to be an entrepreneur or not. Rather, they engage in a process which can be divided
in stages such as conception, gestation, infancy, adolescence, maturity and decline
(Reynolds, 1997). Research by Reynolds (1997) provides evidence that much more of these
processes are active in the United States than in the rest of the world. This entrepreneurial
engagement process has been studied by many other scholars and it is believed that there is
a difference between the engagement process of a social entrepreneur and that of a
commercial entrepreneur (Moizer & Tracy,2010 ; Mair & Marti, 2006 ; Dorado, 2006 ; Zahra
et al., 2009). According to these scholars, social entrepreneurs face specific challenges
because their value creation process consists of a combination of both economic and social
values. This leads many scholars to believe that social entrepreneurship is an early stage
phenomenon, which means that social ventures are not expected to survive the stages of
starting and operating a business (DiDomenico et al., 2010; Peredo & McLean, 2006).
Evidence has been found by Hoogendoorn et al. (2011) that social entrepreneurs are mainly
engaged in the infancy levels of entrepreneurial engagement.
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2.2.1 Challenges for the social engagement process
An explanation many scholars give for social entrepreneurs finding it more difficult to thrive
than commercial entrepreneurs is that they differ in some aspects. One of the key aspects
they differ in is resource mobilization (Austin et al., 2006). The resource mobilization activity
encompasses the process of deployment and mobilization of resources needed to be
successful as an entrepreneur. According to scholars this process can be affected by
regulatory, political and technical institutions (Desa, 2008; Hit et al., 2004; Thornton&
Ocasion, 2008). Firstly there is the human capital resources. Starting social entrepreneurs
find it more difficult to attract the best talented employees because most of the time they
cannot pay market rate salaries that commercial entrepreneurs are accustomed to pay
(Austin et al., 2006). The social entrepreneur is left to rely on his own networking skills and
inspiring abilities to attract volunteers and work with a variety of people with different
purposes (Zahra et al., 2009; Vidal, 2005).
Another problem comes with financial capital. According to scholars, attracting funding is a
difficult matter for social entrepreneurs on the one hand because in their ‘mission’ to solve
social problems they find themselves in countries and markets that do not function
optimally (DiDomenico et al., 2010, 2006; Zahra et al., 2009). The resources available for
support are limited as well as the potential to capture and measure the economic value
created (Mair & Marti, 2006). On the other hand, there might be difficulties in attracting
investors because of the “motivation”. Motivation of investors in entrepreneurship depend
on the expectations of the business. Funders who expect to run a competing profitable
business, most of the time choose to invest in commercial ventures. The legal forms in which
social enterprises operate normally have restrictions with respect to profit distribution
(Weerawardena & Sullivan Mort, 2006). This leaves social entrepreneurs to rely on sources
like government funds, individual contributions and foundation grants.
Furthermore social entrepreneurs most of the time do not have the flexibility to change
product or market because their whole existence is tied to the social or ecological need they
address in that particular market.
Even though there are lots of constrains for social entrepreneurs, there are significant cases
of success where social enterprises thrive in their engagement process and eventually reach
their humanitarian goals.
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To understand how social ventures can manage to survive in constrained environments
where there is lack of institutional support, it is important to understand the different
perspectives in which the term “resource” is seen. According to the resource dependency
theory (Pfeffer& Salancik, 1978) firms are dependent on an unpredictable environment for
their resources. This theory assumes that resources are objective and independently
definable for all firms and environments. So in order to survive firms can only lower their
dependency on their environment by ensuring the needed resources (Yitshaki et al., 2008).
On the other hand the resource-based view (Penrose, 1959) argues that resources can
include labour and skills and that the importance lie on the services firms provide with the
resources rather than the resource itself. Therefore every firm is unique in its relation to the
environment, and resources that may be worthless to one firm can be valuable to another.
This forms the basis for the “Bricolage” phenomenon. Creative firms work with resources at
hand in combining them for new purposes (Baker &Nelson, 2005). This process of bricolage
serves not only as a tool for survival in constraint environments. Rather, because of the
combination of resources normally not used by other firms, it enables firms to embrace new
problems and new opportunities. These creative firms are not limited to the commonly
accepted definitions, practices and standards. Desa (2012) studies 202 social ventures in 45
countries and finds evidence that in the international perspective, social enterprises are
moving towards bricolage activity to deal with problems of resource mobility.
Summarizing, the entrepreneurial engagement process is by many scholars believed to be
more difficult for social entrepreneurs than for commercial entrepreneurs. Resource
mobilization is believed to be a key aspect in this difference.
Other researchers have tried to analyse the success in entrepreneurial engagement in
general by investigating the influence of people’s perceptions. Perceptions of factors that
can contribute positively or negatively to one being a successful entrepreneur. These
perceptions can be divided into perceived internal factors and perceived external factors.
The perceived internal factors are specific for each individual and they can include fear of
failure, alertness to new opportunities and confidence in one’s own skills and abilities
(Arenius & Minniti, 2005; Koellinger et al., 2004; Baron, 2000). The perceived external
factors are dependent on the environment and will be elaborated on in the following
subsection.
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2.3 Perceived external factors
Going through existing literature it becomes clear that a few scholars have investigated the
relationship between entrepreneurship in general and the perceived external factors: lack of
financial support, administrative complexities and lack of sufficient information on running a
business. Most of these studies are directed to the research field of commercial
entrepreneurship. The research field of social entrepreneurship is left under researched
with respect to these obstacle variables and this creates the opportunity for this thesis to
investigate the relationship of these variables with the engagement process of socially
motivated entrepreneurs. The following subsection will provide a review of what is currently
investigated with respect to these perceived external factors prior to going further on to the
formulation of the hypotheses.
2.3.1 Lack of financial support
Financial support plays a vital role in setting up a business. According to the financial
constraints theory by Evans and Jovanovic (1989), the decision to become an entrepreneur is
influenced by the assets of an individual. Blanchflower and Oswald (1998) have found
empirical evidence for this in the UK. The inheritance of gifts received by surveyed
individuals showed a positive impact on their probability of becoming an entrepreneur.
The role of financial support is even bigger for social entrepreneurship because especially in
the early stages of engagement social entrepreneurs derive their funds from grants whether
from foundations, individuals or from the public sector (Sharir & Lerner, 2006). Unlike
business ventures, social ventures are not familiar with the venture capital infrastructure in
the start-up stages (Bygrave et al., 1996).
Research has been done regarding the influence of financial support in a more subjective
perspective. Grilo and Thurik (2005b) conducted a research on perceived obstacle variables
and their results show no significant results for the influence of perceived lack of financial
support on entrepreneurial engagement levels. Suggesting that relative to never having
thought about starting a business, a perceived lack of financial support has no effect on an
individual’s entrepreneurial position. Also according to Van der Zwan et al. (2010), the
probability of moving up the entrepreneurial ladder is not affected by perceived lack of
10
financial support. However analysing entrepreneurs with different social motivations,
Hoogendoorn et al. (2011) have found that social entrepreneurs are more likely to perceive
a lack of financial support when starting up a business than their commercial counterparts.
2.3.2 Administrative complexities
For a sustainable growth it is important that social ventures receive also non-financial
support. Non-financial support such as venture philanthropy tools can help social
entrepreneurs overcome certain infrastructure hinders that come from excessive regulations
that are present in some countries. These infrastructure hinders are called administrative
complexities. An OECD paper has described administrative complexities as paperwork and
administrative formalities used by governments to collect information and interfere with
individual economic decisions (OECD, 2000). Scholars consider this interference as a possible
hinder for entrepreneurship in general because it works as a discouragement potential
entrepreneurs. On the aggregate level Krauss and Stahlecker (2001) show that government
restrictions and administrative burdens had a negative impact on the development of the
biotechnological industry in Germany
Concerning the role of people’s perception of administrative burdens on entrepreneurship,
researchers have found a negative relationship. Firstly regarding the actual occupational
choice, studies by Van Stel and Stunnenberg (2001) Have found a negative influence of
perceived administrative complexities on the business ownership rate. A Study by Grilo and
Irigoyen (2006) confirm this result for the preference of being an entrepreneurship. On the
entrepreneurial engagement perspective, the results of Grilo and Thurik (2005b) show that
when it comes to actually having a business, perceived administrative complexities play a
clear disturbing role on entrepreneurship. Meaning the probability of “having given up” , of
“considering” or of “taking steps to start a business’ relative to “never having considered
setting up a business” does not show a significant effect . While the probabilities of the more
active entrepreneurial positions are significantly negative affected by perceived
administrative complexities. These results have been confirmed by Van der Zwan et al.
(2010) for the ordered context of entrepreneurial engagement. Meaning that perceived
administrative complexities have a negative influence on the probability of an individual
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being in a higher level of entrepreneurial engagement rather than staying at the current
level.
Hoogendoorn et al., (2011), using flash eurobarometer data (no.283) on entrepreneurship
have found surprisingly that perceived administrative complexities have a positive effect on
the probability of becoming social entrepreneurs rather than commercial entrepreneurs.
2.3.3 Lack of sufficient information
According to Leeming (2002), a good advisory support is a key element in the growth of
social entrepreneurs. Social entrepreneurs need skilled advisors that can give them
information of the market they are entering and on the best ways to carry-out their
particular business in this mostly “unknown“ environment. This so-called information
support is believed to have an influence on social entrepreneurship by scholars (Sharir &
Lerner, 2006; Leeming, 2002).
Regarding commercial entrepreneurship , the perception of lack of available information on
how to start a business, Interestingly enough showed positive significant effect for the
categories “taking steps” and “less than 3 years owning a business ” (Grilo and Thurik ,
2005b) .For the explanation of this surprising finding Grilo and Thurik ( 2005b) suggest a
possible information bias of individuals who find themselves in these two phases of the
entrepreneurial process. ( the information needed to start a business is not necessarily far
away in their memory). However, the lack of sufficient information does not affect the
probability for an individual to climb the entrepreneurial ladder according to Van der Zwan
et al.( 2010). Concerning the influence of perceived Lack of sufficient information on
entrepreneurs with different social motivations, Hoogendoorn et al., (2011) have found that
social entrepreneurs are more likely to perceive a lack of information support when starting
up a business than commercial entrepreneurs.
Summarizing this subsection, scholars have found primarily a negative influence of perceived
administrative complexities on entrepreneurship. Regarding the social motivation of
entrepreneurs, a positive influence has been found for administrative complexities on the
probability of being a social entrepreneur rather than a commercial entrepreneur.
Furthermore it has been found that social entrepreneurs are more likely to perceive a lack of
12
financial support and a lack of sufficient information on running a business than commercial
entrepreneurs. These findings together with the findings of the previous subsection raise the
suggestion that the entrepreneurial engagement process is negatively influenced by the
social motivation and that has to do with socially motivated individuals perceiving different
or more obstacles of external factors than commercial entrepreneurs.
2.4 The Hypotheses
Based on the research question stated in the previous section and the current knowledge on
existing literature surrounding social motivation, perceived external factors and the
entrepreneurial engagement process, this study will test two Hypotheses. Hypothesis 2 will
be tested using interaction terms between the social motivation and the perceived external
factors.
H1: Socially motivated individuals are less likely to be in high levels of entrepreneurial
engagement than are less socially motivated individuals.
H2a: The influence of perceived lack of financial support on the position in the
entrepreneurial process is more negative for socially motivated individuals than for less
socially motivated individuals.
H2b: The influence of perceived administrative complexities on the position in the
entrepreneurial process is more negative for socially motivated individuals than for less
socially motivated individuals.
H2c: The influence of perceived lack of sufficient information on the position in the
entrepreneurial process is more negative for socially motivated individuals than for less
socially motivated individuals.
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3. Data and Methodology
3.1 Data
The dataset used for this research comes from the Flash Eurobarometer 2009 (No.283)
Survey on Entrepreneurship. The interviews were conducted by telephone or door-to-door
at the request of the European Commission. The survey for this particular dataset was
carried out with 26,168 respondents in 36 countries (27 Member States of the European
Union, 5 other European countries, the United States, and 3 Asian countries).
3.1.1 Dependent variable
The level of entrepreneurial engagement of an individual could be captured by using the
question: “Have you ever started a business or are you taking steps to start one?” The
individuals could choose between the following answers:

I am currently taking steps to start a new business.

I have started or taken over a business in the last three years that is active today.

I started or took over a business more than three years ago, and it is still active.

I once started a business, but currently I am no longer an entrepreneur because the
business has failed.

I once started a business, but currently I am no longer an entrepreneur because the
business was sold, transferred or closed.
However, this research analyses the advancement in the entrepreneurial engagement
process. Therefore, the two last categories will excluded from the analysis, leaving three
categories ordered in increasing levels of entrepreneurial engagement:

“Taking steps” (value 1)

“Young business” (value 2)

“Established business” (value 3)
This information was used to create the variable “engagement level” that is used as the
dependent variable for the empirical analysis.
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3.1.2 Independent variables
The first Hypothesis states that socially motivated individuals are less likely to be in high
levels of entrepreneurial engagement than less socially motivated entrepreneurs. As
previously mentioned, the ability to capture one’s social motivation was primarily the reason
why data from the Flash Eurobarometer (No.283) 2009 survey was chosen for this research.
The social motivation of the individuals was measured as follows: For “addressing an unmet
social or ecological need” , The individuals were asked whether it was very important, rather
important, rather not important or not important at all for making them to take steps to
start a new business or take over one.
The answers were valued in the following order :

“Not important at all” (value 0)

“Rather not important” (value 1)

“Rather important” (value 2)

“Very important” (value 3)
The second Hypothesis states that the influence of perceived external factors on the position
in the entrepreneurial process is more negative for socially motivated individuals than for
less socially motivated individuals. This analysis includes three perceived obstacle variables.
Each, in its own dimension represents the individual’s perception of whether or not (s)he
feels hindered by the infrastructure when starting a business. The respondents had to
answer to what degree they agree or disagree with the following statements:

It is difficult to start one’s own business due to lack of available financial support
(“Lack of financial support”).

It is difficult to start one’s own business due to the complex administrative procedures
(“Administrative complexities”).

It is difficult to obtain sufficient information on how to start a business (“Insufficient
information”)
In order to facilitate the interpretation in the ordered model, binary variables were created
using the value 1 for “strongly agree” and “agree” in addition to the value 0 for “disagree”
and “strongly disagree”.
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3.1.3 Control variables
The empirical analysis includes several control variables. The variable “Female” is included as
a control variable to control for gender specific effects. Various scholars have found that
males are more likely to become entrepreneurs than females (Grilo & Thurik, 2005a; Grilo &
Thurik , 2005b). Furthermore, Van der Zwan et al. (2010) provides evidence that men have a
higher probability than women of being in high levels of entrepreneurial engagement.
Therefore the gender variable “Female” was created by assigning the value 1 for females,
whereas males were assigned the value 0.
Previous research has also shown that a certain age group is highly represented in the selfemployment. Scholars have found that individuals of the middle-aged group (35-44 years)
are the individuals most likely to become entrepreneurs (Cowling, 2000; Williams, 2004).
Possible explanations for this relationship is presented by Parker (2009). According to this
study, young individuals may lack the necessary capital, knowledge or skills to start a
business, while older individuals might have less time in which they can gain profit from
their initial investments, which may have a negative influence on their incentives to start a
business in the first place. Results from a recent study suggest that even the mildest form of
entrepreneurial engagement is most likely to take place in the mid-thirties, with this
likelihood decreasing as one gets older (Van der Zwan , 2010). Therefore the control
variable “Age” is included. The variable “Age “ includes four categories: “15-24” , “25-39”,
“40-54” and “55+”, respectively.
Education is believed to have an influence on entrepreneurship. Studies on the relationship
between formal education and the preference to become self-employed have found a
negative relationship (Grilo & Thurik, 2005; Grilo & Irigoyen, 2006). These scholars believe
that education increases the value of the outside option of wage- employment when
individuals have the choice. On the other hand, studies on the entrepreneurial engagement
has found that education has a positive effect on the probability of being in a higher level of
engagement (Van der Zwan et al., 2010). Therefore the control variable “Education” is
included, and was created as follows: The individuals were asked how old they were when
they finished full time education. Students that were still in fulltime education were
excluded from the analysis. The variable “Education” was created into a binary variable that
16
includes a value of 1 for people that were 20+ when they finished education and 0 for the
individual that were younger or that never received an education.
Another control variable is “Parentoccupation”, which takes a value of 1 if one or both
parents was/were self-employed, and takes a value of 0 if none of the parents was selfemployed. Whereas it has been shown that the self-employment status of an individual’s
parents has a positive impact on his or her entrepreneurial engagement (Dunn & HoltzEakin, 2000), The variable “Parentoccupation” is included to control for this relationship.
As mentioned in the previous section, scholars have discussed the importance of financial
resources and constraints on the decision to become an entrepreneur (Evans & Jovanovic,
1989). Therefore studies suggest a positive relationship between the household income and
the entrepreneurial engagement (Kihlstrom & Laffont, 1979). However, from the necessityentrepreneurship perspective and on the aggregate level, Bosma and Harding (2007) find
that necessity entrepreneurship is more commonly present in low income countries. To take
the financial situation of the individuals into account, this study includes the control variable
“Income” . To measure this variable, the individuals were asked to describe their feelings
about their current household income. Respondents who answered that they could live
comfortably or could get by on the present income received the value 1 for the variable “
income” and the respondents who found it difficult or very hard to manage their present
income received the value 0.
It is also worth mentioning that 36 country dummy variables are included in the analysis to
control for country-specific effects.
3.2 Methodology
In order to get a first impression of the data, a number of tables including descriptive
analyses will be first presented. A comparison between countries will be provided, followed
by a first impression of the relationship between the engagement levels and the
independent variables.
Thereafter, ordered logit regressions will be performed to test the hypotheses. The ordered
logistic method is specifically chosen for this analysis due to the possibility it provides to
evaluate the engagement levels in an ordered context.
Additionally, binary logit is performed on the probability to go from “taking steps” to “young
17
business” and from “young business” to “established business”. This is done in order to
check whether the effects of the significant independent variables stay significant or not,
and whether they increase or decrease for increasing levels of entrepreneurial engagement.
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4. Results
In this section the results will be given first for the descriptive analysis followed by the
empirical analysis. The descriptive analysis includes tables that describe the data and the
empirical analysis includes several ordered, and binary logit regression models. The
respondents included in the analysis are the individuals who have indicated to be in the
levels of entrepreneurial engagement “Taking steps”, “Young business” or “Established
business”. Thus throughout this thesis the term respondent refers to individuals belonging to
ether one of these levels of engagement.
4.1 Descriptive analysis
Table 1 presents a cross-country comparison of the importance of addressing an unmet
social or ecological need when starting up a business. This table is given in percentages for
each category because as is shown in the last column, the total observations differ widely
per country ranging from 22 to 203. An interpretation in percentages makes it possible to
compare countries with each other in terms of their distribution in social motivation. For
example 21.1 percent of the Austrian respondents think it is very important to address an
unmet social or ecological need when starting up a business whilst 39.5 percent of the
Belgian respondents agree to this. By observing all countries and especially by looking at the
total averages for these four categories it becomes clear that the category “Rather
important” is the category with the highest percentages with an average total percentage of
36.4.
Furthermore table 1 presents a column with the average value of the variable under
investigation. Each category has a number ranging from 0 for “Not important at all ” to 3 for
“Very important”. For each country an average of these numbers is presented in this
column, making this time a comparison of the importance level between countries possible.
The higher the value of this average, the higher the importance level of the country in
general to address an unmet social or ecological need. Given this, it is possible to say that
countries with an average above the total average of 1.8 could be categorized as the
countries with the higher levels of importance to address an unmet ecological or social need.
19
Hence Japan and Turkey, each with an average of 2.4 are the countries with the highest
social motives in this perspective.
Table 2 presents the percentages of respondents that have agreed to perceive either one of
the obstacles when starting their business. This is shown for all countries included in the
analysis. For instance, in the specific case of The Netherlands, 61.5 percent have perceived a
Lack of financial support, 57.7 percent have perceived administrative complexities and 26.9
percent have perceived a lack of sufficient information when starting up or running a
business. These percentages make The Netherlands one of the countries with the lowest
perceived external obstacles on average. Suggesting that these factors have an influence on
the entrepreneurial process, these numbers could give an indication of The Netherlands
having one of the best entrepreneurial climates in the European countries. Furthermore, it is
worth mentioning that this table does not give a distribution between these obstacle
variables, rather it gives a distribution between agreeing and not agreeing for each of these
variables separately. Given this it is possible to say that “lack of financial support” is the
obstacle variable which most of the respondents have perceived.
Table 3 shows the distribution of the categories of social motivation (the importance to
address a social or ecological need) for the levels of engagement. This way a comparison
between the categories can be made to give a first impression of the categories in which the
respondents have a tendency to go to a higher level of entrepreneurial engagement.
Analyzing the category “Not important”, It can be seen that the frequencies have a tendency
to increase for the increasing levels of engagement. The same is noticed for the “Rather not
important” category. However for the categories of respondents who think it is rather
important or important to address a social or ecological need, these percentages drop for
increasing levels of engagement. These observations imply that the frequencies of
individuals in these categories tend to decrease for increasing levels of engagement. With
these percentages this table can already give an indication of what, according to the
background literature, is expected in the empirical analysis: The socially motivated
individuals are better represented in the lower levels of engagement than it tends to be the
case for the higher levels of engagement.
20
Table 4 presents the percentages of respondents agreeing to have perceived an obstacle
variable for each level of the dependent variable “engagement level”. This way a comparison
between the engagement levels can be made, in order to see in which levels the
respondents believe to be hindered more by these factors. From panel A which includes all
respondents it can be observed that the percentages have a tendency to decrease for the
increasing levels of entrepreneurial engagement. This decrease could somehow be expected
because the entrepreneurs who progress from an engagement level to another are believed
to be the ones who perceive less difficulties from the obstacle variables when starting and
running their business. That is why a lower percentage of the obstacle variables are
perceived for the increasing levels of engagement. This expectation suggests that the
obstacle variables have some kind of negative influence on the probability of entrepreneurs
being in a higher level of engagement rather than staying where they are.
Panel B of table 4 presents the same distribution, however this time the respondents are
divided into the four categories of importance to address an unmet social or ecological need
(social motivation). This division makes it possible to compare the categories and to see
whether the influence described above differs for the increasing levels of social motivation.
What is expected from this table according to the second group of hypotheses is that the
decrease in percentages from “taking steps” to “established business” is steeper in the
increasing order of the categories of social motivation, suggesting that the influence of these
obstacle variables are stronger for the increasing levels of social motivation. This could give
some kind of indication that for higher levels of social motivation, entrepreneurs find it more
difficult to progress into higher levels of entrepreneurial engagement, because they perceive
more hinders from these obstacle variables. Although this relationship is not clearly visible in
the table, there are some small indications for this; analysing for example the variable for
lack of sufficient information for the first two engagement levels “taking steps” and “young
business”, it can be observed that when the social motivation is 0 there is an increase in
percentage (39.0 to 45.4). In the two following categories of social motivation 1 and 2, a
decrease in percentage is noticed (45.1 to 42.9 and 56.4 to 54.5). However in the highest
category of social motivation this decrease is much steeper ( 66.8 to 50.9).
In summary, there are some indications of possible relationships between the social
motivation and the dependent variable. There is also an indication of an influence of the
21
obstacle variables on the dependent variable and that this relationship can be different for
increasing variables of social motivation. However no conclusions can be drawn about
possible relationships based on a descriptive analysis. Therefore, an empirical analysis with
advanced statistical methods is provided in the following subsection.
4.2 Empirical analysis
In this subsection ordered logit regressions are performed to test if the results of the
descriptive analysis can be verified. Additionally binary logit models are performed to
analyze the transition from the first engagement level to the second separately from the
transition of the second engagement level to the third.
Table 5 contains 3 models (Models A,B and C). Each column of a specific model shows the
coefficients and the corresponding standard errors for all the variables that are included in
the model. It should be noted that in an ordered logit model no conclusions can be drawn
regarding the magnitude of the coefficients, however signs of the coefficients can be
interpreted. A positive coefficient implies that an increase in that particular variable will lead
to an individual being more likely to have a higher value of the dependent variable, in this
particular case being in a higher level of entrepreneurial engagement.
Model A includes the three perceived obstacle variables: “Lack of financial support’,
“Administrative complexities” and “ Insufficient information”. Model B again includes these
perceived obstacle variables, while the “social motive” variable is added to the regression to
test the first hypothesis. Model C again includes the perceived obstacle variables and the
“socialmotive” variable and adds the interaction terms between these two to the regression
to test hypothesis 2 a, b and c.
Each model includes the following control variables: “Age” which is divided in to four groups
of increasing age categories, “Female” which controls for gender effects, “Education” which
controls for the effect of whether the individual has above certain years of education or not,
“Parentoccupation”, which controls for whether one or both of the individual’s parents are
or where self-employed and income which controls for whether the individual gets by on
the present household income or not. Furthermore, each model includes 35 country dummy
variables while the US is used as the reference country. The latter is done in order to control
22
for possible unobserved country-specific effects. Although the country dummies are
included in the analysis, their coefficients are not shown in the tables as they mainly serve as
control variables.
In model A (table 5) the coefficients of the variables lack of financial support and insufficient
information show no significant influence. These factors do not seem to discourage or
encourage the respondents in their entrepreneurial activities. On the other hand, a
significant negative coefficient can be observed for the variable “Administrative
complexities”. This implies that administrative complexities have a negative influence on the
probability of being in high levels of entrepreneurial engagement.
Model B (table 5) gives almost the same results for the three obstacle variables: No
significant results for “lack of financial support”and “Insufficient information”, and a
significant negative result for administrative complexities. A disparity between this model
and the first one is that the “socialmotive” variable is added. This categorical variable
represents the level of social motivation. According to this table this variable has a significant
negative influence on the dependent variable. This could imply that an increase in the social
motivation, decreases the probability of being in the higher levels of entrepreneurial
engagement. Thus, this result supports hypothesis 1.
Model C (table 5) gives fairly different results compared to the two previous models in terms
of significance of the obstacle variables. This is because the interaction terms between the
obstacle variables and the social motive variable is added to model to test if the influence of
the perceived obstacle variables varies significantly for the increasing values of social
motivation. According to this model there is a significant negative coefficient for the
interaction term between “insufficient information” and “socialmotive” (Insuffinf X
Socialm). This could be interpreted as follows: For each individual who have perceived a lack
of sufficient information on running a business (dummy variable “Insufficient
information”=1), the influence of insufficient information on the dependent variable is
dependent on the coefficient of the variable” Insufficient information” (β1) and the
coefficient of its interaction term with socialmotive (β3) multiplied by the category of social
motivation in which he is (0,1,2 or 3). This means that for someone who is not socially
motivated at all (value of social motivation =0), the influence of insufficient information is
23
the coefficient β1, which according to the table has the value of 0.3 at a significance level of
10 percent. However, for increasing values of social motivation a coefficient of -0.177
(coefficient β3) is constantly added to this value turning this influence into a negative
influence by the third category of social motivation and increases further in the fourth
category. This could imply that the more socially motivated the individuals are the stronger
the negative influence of insufficient information is on the probability of advancing to higher
levels of entrepreneurial engagement. This result gives support to Hypothesis 2c. Additional
Wald tests are applied to check if the influence of insufficient information stays significant
for the increasing values of social motivation. The results show that the influence of
insufficient information at “Rather not important” (socialmotive=1) is 0.123 (Insignificant), at
“Rather important” (socialmotive=2) this is -0.054 (insignificant), and at “Very important”
(socialmotive=3) it is -0.231 (significant at 10%).
The variable “Administrative complexities” shows again a significant negative influence in
this table. However, the coefficient for the interaction term “Admin.comp X Socialm” is not
significant . As a consequence, Hypothesis 2b is not supported. Furthermore Hypothesis 1 is
supported again in this model with a significant negative coefficient for the variable
“socialmotive”.
In summary, model A finds a significant negative influence for the obstacle variable
“Administrative complexities”. Model B finds a significant negative influence of the
“socialmotive “ variable, supporting Hypothesis 1. And lastly, model C finds a significant
negative coefficient for the interaction term between insufficient information and the social
motivation (Insuff X Socialm) supporting Hypothesis 2.
After analyzing the control variables, it is found that for the variable “Age”, a positive
significant influence exists for all the age categories. In addition, according to the binary logit
models (tables 6, 7, and 8) an increasing marginal effect could be found for the increasing
age categories as opposed to the reference category which is from 15 till 24 years of age.
This could imply a positive linear effect of this control variable on the probability of
advancing into higher levels of entrepreneurial engagement.
For the gender variable “female”, a negative significant effect is found for all the models.
This suggests that being a female has a negative effect on the probability of advancing in
entrepreneurial engagement.
24
Education also yields a negative significant effect for all the models. This result could imply
that individuals with more years of education are less likely to advance in entrepreneurial
engagement compared to those with less years of education.
Individuals whose parents are or were entrepreneurs could have a comparative advantage
when it comes to advancing in entrepreneurial engagement according to the significant
positive effect in all the models.
And lastly, The individuals who live comfortably or get by on the present income are less
likely to advance in entrepreneurial engagement than individuals who find it difficult or very
hard to manage with their present income. This could be concluded by the significant
negative effect for the variable income in all the models.
4.3 Additional analysis
Table 6, 7 and 8 contain the results of the binary logit regressions of each of the
aforementioned models. These tables include the coefficients of the variables and the
corresponding marginal effects, thereby making it possible to interpret the results of the
models in terms of sign and magnitude. The first column shows the influence of the
respective factors on the probability of moving from the first engagement level to the
second one( from “taking steps” to “young business”), and the second column shows the
same for the probability of moving from the second engagement level to the third one (from
“young business” to “established business”). This information ultimately provides the
possibility to make a comparison for the same variable between the two columns in orderto
make an assumption about whether an effect becomes stronger or weaker when moving
from engagement level 1 to 2 or from moving from level 2 to 3.
From table 6 it can be seen that the negative influence for administrative complexities in
model A (table5) is stronger on the probability of going from “taking steps” to “young
business” than in young business to “established business”. According to table 7 this
phenomenon is the same in model B (table 5). Furthermore, the negative effect for
“socialmotive” found in this model becomes weaker in the transition from “young business”
to “established business”. Hence what could be implied for administrative complexities and
25
socialmotive is that for both variables, of which a significant negative effect is found in the
ordered logit, the coefficient decreases for the transition to increasing levels of engagement.
Model C (table 5) yields a negative coefficient for the interaction term between insufficient
information and social motivation (Insuff X Socialm). According to table 9 this effect only
exists for the probability of going from the engagement level “taking steps” to “young
business” as this effect becomes insignificant in the second column.
Regarding the obstacle variables , in model C (table 5) there is a negative significant
coefficient for “Administrative complexities” and a positive significant effect for the variable
“Insufficient information”. According to table 8 these coefficients become insignificant in the
transition from “young business” to established business”.
It is worth mentioning that compared to the transition from “taking steps” to “young
business”, there is an increase in the effect of the “socialmotive” variable in the transition
from “young business” to “established business”.
26
5.Discussion and Conclusion
5.1 Discussion
For a long time researchers have tried to understand in what way the engagement process
of a social entrepreneur is unique, in the sense that this group of entrepreneurs face specific
challenges when starting and running a business compared to entrepreneurs in general. By
analyzing the results from the three models that have been investigated through this thesis,
a good first impression can be given of a possible relationship between social motivation and
the entrepreneurial engagement process and of a factor that could be of influence on this
relationship.
In the first model where only the three obstacle variables were included, there was only a
significant negative effect of perceived administrative complexities on the probability of
being in a higher level of entrepreneurial engagement. This result confirms previous findings
of Van der Zwan et al., (2010) where using the 2004 version of the Eurobarometer survey on
entrepreneurship, they have found the same results for the obstacle variables.
The second model finds evidence for a decrease in probability of being in a higher level of
entrepreneurial engagement for the more socially motivated individuals. This finding gives
support to previous suggestions from scholars like Moizer and Tracy (2010), Mair and Marti
(2006) and Zahra et al. (2009), that state that social entrepreneurs have a more difficult
entrepreneurial engagement process compared to entrepreneurs in general.
The third model has three important findings. First it finds evidence again for a decrease in
probability of being in a higher level of engagement for social entrepreneurs. Secondly, it
finds evidence for a positive influence of lack of sufficient information on the probability of
being in a higher level of entrepreneurial engagement. But more importantly, it finds
evidence that this influence becomes negative and stronger for increasing levels of social
motivation. These findings give support to earlier study from Leeming (2002), that suggests
that the lack of support from the infrastructure hampers social entrepreneurs in their
development. The findings of the third model also raise the suggestion that the difficulties
that social entrepreneurs encounter during set up and running of their business are partly
caused by an environmental obstacle, in particular not having sufficient information on how
to start and run a business.
27
5.2 Limitations
A limitation comes from the data of the survey used for this research. As explained
previously in the introduction, this survey was specifically chosen because of its uniqueness
to carry information about the social motivation of the respondents. A downside to this
dataset is that it did not give the possibility to include information about other engagement
levels that could also be analyzed in this ordered context like: “Never thought about starting
a business” and “Thinking about starting a business”. This for the simple reason that these
possibilities were not included in the questionnaire (see appendix q10). To keep the
engagement levels ordered, many observations (possibilities 4 and 5) were left out of the
analysis with the possibility of a loss of valuable information.
Another data limitation comes from the measurement of the control variable education
which was used as a control variable in the empirical analysis. This variable was measured
with the question: “age when finished full time education” which is not a proper
measurement for the education level of an individual. For example if someone drops out of
high school and after a few years decides to finish it at the age of 21, he would be perceived
by this type of measurement as someone with a high education level whilst being a high
school graduate.
Social entrepreneurs were defined in this research for the ease of interpretation by
categories of social motivation. However, in drawing conclusions it must be kept in mind
that the concept of social entrepreneur encompasses groups of people that differ in several
dimensions. Such as: their level of innovativeness, their social goals and in their dependency
on public support.
Another phenomenon that has to be kept in mind when drawing conclusion for this research
is the possibility of reverse causality. The perceived obstacle variables are especially
vulnerable to this problem. These variables have an effect on the engagement in
entrepreneurial activity, however the level of entrepreneurial engagement of a commercial
entrepreneur or social entrepreneur also affect how the individual perceive these variables.
For example an entrepreneur who is at the very beginning of starting up his business is much
28
more dependent on financial support than one who already runs a profitable business. Thus
his perception of a lack of financial support from the environment is much higher than the
established entrepreneur.
5.3 Conclusion
This thesis has made contribution to the empirical research field of social entrepreneurship.
In doing so it started by asking the question:
What is the relationship between the social motivation of individuals and their position in the
entrepreneurial process? And do the influences of perceived external factors on the position
in the entrepreneurial process depend on the social motivation of individuals?
To answer this question, the findings of this research give an indication that the position in
the entrepreneurial engagement process is negatively influenced by the social motivation of
the individual, in the sense that the more socially motivated entrepreneurs have a lower
probability of being in a higher level of entrepreneurial engagement. Furthermore, the
results give an indication that the influence of perceived lack of sufficient information on the
position in the entrepreneurial process is more negative for the more socially motivated
individuals than the less socially motivated. Further research using indirect effects, could test
if the stronger influence of perceived insufficient information for the more socially
motivated entrepreneurs is the reason for them to have a lower probability of
entrepreneurial advancement than less socially motivated entrepreneurs.
The results of this research make a contribution because, they give a good first impression
about some rather interesting relationships that could be further investigated. This could
eventually help policymakers in the European Union redirect their strategy towards
promoting social entrepreneurship. Perhaps by focusing on giving more and much wider
information on how to start and run a business. That is why researchers in the field of social
entrepreneurship should further investigate the relationship between the entrepreneurial
engagement process and perceived lack of available information on running a business and
the relationship of this perceived external factor with the social motivation of
entrepreneurs. Using a dataset that carries information about more engagement levels that
29
can be added to the ordered context and testing for reverse causality will add to the
confidence of the results.
30
Tables
Table 1 Percentages of importance to address a social or ecological need (social motive)
across countries
Country
Austria
Belgium
Bulgaria
China
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungray
Iceland
Ireland
Italy
Japan
Latvia
Lithuania
Luxembourg
Malta
Norway
Poland
Portugal
Romania
Slovakia
Slovenia
South Korea
Spain
Sweden
Switzerland
The Netherlands
Turkey
United Kingdom
USA
Total ( average)
Adressing an unmet social or ecological need
Not
Rather not
Rather
Very
important
importan
important
important
14.0
31.6
33.3
21.1
0
23.7
36.8
39.5
13.0
33.3
42.6
11.1
2.6
16.0
52.4
29.1
5.0
20.0
45.0
30.0
6.3
19.0
35.4
39.2
25.7
34.6
25.0
14.7
23.7
39.5
15.8
21.1
14.5
30.7
29.0
25.8
26.7
29.1
37.2
6.7
17.1
18.4
38.2
26.3
16.3
41.5
31.1
11.1
11.8
9.2
27.0
52.0
25.9
32.8
25.0
16.4
14.1
23.9
34.8
27.2
16.4
16.4
29.5
37.7
14.6
18.7
41.5
25.2
1.6
7.8
40.6
50.0
11.9
19.1
33.3
35.7
8.3
23.3
50.0
18.3
13.5
13.5
59.5
13.5
9.1
13.6
45.5
31.8
26..0.
19.5
39.0
15.6
9.3
26.5
46.4
17.9
17.0
20.0
39.0
24.0
17.5
5.3
45.6
31.6
11.1
17.8
46.7
24.4
27.0
24.3
16.2
32.4
0.8
16.7
59.2
23.3
17.0
21.7
33.0
28.3
15.1
33.3
36.4
15.2
27.6
27.6
25.9
19.0
18.9
33.3
27.3
20.5
3.2
11.7
30.9
54.2
17.9
26.4
27.4
28.3
18.7
21.2
25.1
35.0
13.9
22.6
36.4
27.1
Average
motives
Observations
1.6
2.2
1.5
2.1
2.0
2.1
1.3
1.3
1.7
1.2
1.7
1.4
2.2
1.3
1.8
1.9
1.8
2.4
1.9
1.8
1.7
2.0
1.4
1.7
1.7
1.9
1.8
1.5
2.1
1.7
1.5
1.4
1.5
2.4
1.7
1.8
1.8
57
38
54
275
40
79
136
38
62
86
76
135
152
116
92
61
123
128
42
60
37
22
77
151
100
57
45
37
120
106
66
58
132
94
106
203
3261
Source: Flash Eurobarometer Survey on Entrepreneurship (2009), No. 283, European Commission.
31
Table 2 Averages of the three perceived external factors for each country
Country
Austria
Belgium
Bulgaria
China
Croatia
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungray
Iceland
Ireland
Italy
Japan
Latvia
Lithuania
Luxembourg
Malta
Norway
Poland
Portugal
Romania
Slovakia
Slovenia
South Korea
Spain
Sweden
Switzerland
The Netherlands
Turkey
United Kingdom
USA
Total
Lack of Financial
support
67.9
70.0
92.3
80.3
84.1
83.1
71.8
59.1
76.4
52.8
84.8
78.0
94.8
84.8
88.9
84.9
91.1
64.6
91.8
81.5
72.2
72.7
72.0
85.2
94.6
95.4
86.7
78.4
68.6
94.6
78.5
71.2
61.5
87.4
75.2
85.2
80.2
Administrative
complexities
48.2
82.5
73.9
53.4
59.1
67.9
66.4
68.2
45.8
48.6
67.1
66.7
85.4
62.8
62.3
69.7
83.0
46.1
77.1
76.6
70.3
61.9
72.2
57.6
75.2
84.4
65.9
41.0
65.0
78.7
62.3
47.5
57.7
86.2
56.6
64.5
65.0
Insufficient
information
29.8
64.3
65.2
68.0
47.6
68.7
36.8
25.0
30.3
27.6
52.5
46.3
81.5
40.5
44.6
45.3
67.4
45.2
34.7
39.3
58.3
54.6
42.9
51.0
69.4
73.4
45.7
41.0
65.6
58.2
39.1
37.0
26.9
75.5
34.3
43.2
51.1
Source: Flash Eurobarometer Survey on Entrepreneurship (2009), No. 283, European Commission.
32
Table 3 Distribution of social motivation across engagement levels
Adressing an unmet social
or ecological need
Not Important at all (0)
Taking steps
7.9
Engagement level
Young business
16.3
Established business
16.7
Rather not important (1)
19.6
18.8
26.2
Rather important (2)
39.9
38.9
33.1
Very Important (3)
32.6
26.0
24.0
Source: Flash Eurobarometer Survey on Entrepreneurship (2009), No. 283, European Commission.
Table 4 Averages of perceived obstacle variables for each engagement level
Percieved Obstacles
Taking steps
Engagement level
Young business
Established business
81.9
68.2
56.0
78.6
63.2
49.6
79.8
63.9
48.6
80.5
63.6
39.0
79.1
60.6
45.4
75.5
61.5
44.3
81.5
66.3
45.9
68.8
58.1
41.9
77.4
59.5
42.9
83.5
69.1
56.4
79.4
65.8
54.5
81.8
63.5
49.8
79.8
68.6
66.8
81.4
64.3
50.9
83.0
68.5
56.4
Panel A
Lack of Financial support
Administrative complexities
Insufficient information
Panel B
Social motivation (0):
Not important at all
Lack of Financial support
Administrative complexities
Insufficient information
Social motivation (1):
Rather not important
Lack of Financial support
Administrative complexities
Insufficient information
Social motivation (2):
Rather important
Lack of Financial support
Administrative complexities
Insufficient information
Social motivation (3)
Very important:
Lack of Financial support
Administrative complexities
Insufficient information
Source: Flash Eurobarometer Survey on Entrepreneurship (2009), No. 283, European Commission.
33
Table 5 Estimation results ordered logit regression engagement levels (including
coefficients and corresponding standard errors)
Model A
Coeff.
SE
Perc. obstacle variables
Lack of financial support
Administrative complexities
Insufficient information
0.094
-0.281***
-0.015
0.094
0.082
0.080
Socialmotive
Model B
Coeff.
SE
0.127
-0.277***
-0.013
0.097
0.086
0.083
-0.165***
Interactions
Lack of fin.sup X Socialm.
Admin.compX Socialm.
Insuff .inf.X Socialm.
Control variables
Age 15-24 (ref)
25-39
40-54
55+
Female
Education
Parentoccupation
Income
Observations
Pseudo R2
Log-psLikelihood
1.191***
2.018***
2.527***
-0.248***
-0.229***
0.312***
-0.358***
3072
0.10
-2861
0.183
0.182
0.191
0.074
0.077
0.080
0.089
1.207***
2.000***
2.542***
-0.195**
-0.217***
0.310***
-0.354***
2867
0.10
-2676
0.189
0.189
0.199
0.077
0.079
0.082
0.092
Coeff.
Model C
SE
-0.132
-0.370**
0.300*
0.191
0.173
0..171
-0.232**
0.096
0.153
0.052
-0.177**
0.099
0.085
0.083
1.203***
1.997***
2.535***
-0.203***
-0.216***
0.307***
-0.357***
0.192
0.191
0.201
0.077
0.079
0.083
0.092
2867
0.10
-2673
Source: Flash Eurobarometer Survey on Entrepreneurship ( 2009) , No. 283, European Commission
Independent variables include percieved obstacle variables , Social motive variables and interaction terms
between percieved obstacle variables and Social motive variables
*** denotes significance at 1%; ** denotes significance at 5%; * denotes significance at 10%.
34
Table 6 Results from binary logit regression on engagement levels (including coefficients
and corresponding standard errors)
Coeff.
(1) vs >(1)
Effect
<=2 vs (3)
Coeff.
Effect
Perc. Obstacle variables
Lack of financial support
Administrative complexities
Insuficcient information
0.057
-0.336***
-0.047
0.010
-0.057
-0.008
0.129
-0.235**
0.008
0.027
-0.045
0.002
Control variables
Age 15-24 (ref)
25-39
40-54
55+
Female
Education
Parentoccupation
Income
1.180***
1.912***
2.293***
-0.238***
-0.137
0.366***
-0.543***
0.259
0.399
0.458
-0.041
-0.023
0.061
-0.092
1.748***
2.685***
3.209***
-0.270***
-0.309***
0.303***
-0.255
0.248
0.459
0.574
-0.056
-0.064
0.063
-0.053
Observations
Pseudo R2
Log-psLikelihood
3072
0.13
-1585
3072
0.13
-1843
Source: Flash Eurobarometer Survey on Entrepreneurship ( 2009) , No. 283, European Commission
Independent variables include percieved obstacle variables
*** denotes significance at 1%; ** denotes significance at 5%; * denotes significance at 10%.
35
Table 7 Results from binary logit regression on engagement levels (including coefficients
and corresponding standard errors)
Coeff.
(1)vs >(1)
Effect
Coeff.
<=2 vs (3)
Effect
Perc. obstacle variables
Lack of financial support
Administrative complexities
Insuficcient information
0.068
-0.319***
-0.028
0.012
-0.054
-0.005
0.170
-0.237**
0.003
0.035
-0.049
0.000
Socialmotive
-0.209***
-0.036
-0.141***
-0.029
Control variables
Age 15-24 (ref)
25-39
40-54
55+
Female
Education
Parentoccupation
Income
1.210***
1.894***
2.306***
-0.186**
-0.149
0.355***
-0.547***
0.265
0.396
0.459
-0.032
-0.026
0.059
-0.094
1.771***
2.685***
3.238***
-0.213**
-0.288***
0.304***
-0.235**
0.247
0.452
0.575
-0.044
-0.060
0.063
-0.048
Observations
Pseudo R2
Log-psLikelihood
2867
0.14
-1486
2867
0.14
-1714
Source: Flash Eurobarometer Survey on Entrepreneurship ( 2009) , No. 283, European Commission
Independent variables include percieved obstacle variables and Social motive variables
*** denotes significance at 1%; ** denotes significance at 5%; * denotes significance at 10%.
36
Table 8 Results binary logit regression engagement levels (including coefficients and
corresponding standard errors)
Coeff.
(1)vs >(1)
Effect
Perc. obstacle variables
Lack of financial support
Administrative complexities
Insuficcient information
-0.349
-0.533**
0.529**
-0.059
-0.091
0.090
-0.061
-0.284
0.239
-0.013
-0.059
0.049
Socialmotive
-0.322***
-0.055
-0.199**
-0.041
Interactions
Lack of fin.sup X Socialm.
Admin.compX Socialm.
Insuff .inf.X Socialm.
0.234
0.117
-0.306***
0.039
0.020
-0.052
0.139
0.026
-0.136
0.029
0.005
-0.028
Control variables
Age 15-24 (ref)
25-39
40-54
55+
Female
Education
Parentoccupation
Income
1.213***
1.895***
2.303***
-0.199**
-0.145
0.349**
-0.555***
0.264
0.393
0.457
-0.035
-0.025
0.058
-0.095
-1.766***
-2.681***
-3.234***
-0.220**
-0.286***
0.300***
-0.237**
0.247
0.452
0.574
-0.044
-0.059
0.062
-0.048
Observations
Pseudo R2
Log-psLikelihood
2867
0.14
-1480
Coeff.
<=2 vs (3)
Effect
2867
0.14
-1714
Source: Flash Eurobarometer Survey on Entrepreneurship ( 2009) , No. 283, European Commission
Independent variables include percieved obstacle variables , Social motive variables and interaction terms
between percieved obstacle variables and Social motive variables
*** denotes significance at 1%; ** denotes significance at 5%; * denotes significance at 10%.
37
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41
Appendix
Relevant questions from the Flash Eurobarometer Survey on Entrepreneurship (No. 283)
D9.
Which of the following phrases describe best your feelings about your
household's income these days:
- Live comfortably on the present income
- Get by on the present income
- Find it difficult to manage on the present income
- Find it very hard to manage on the present income
Q10. How would you describe your situation:
- You are currently taking steps to start a new business
- You have started or taken over a business in the last three years
which is still active today
- You started or took over a business more than three years ago
and it’s still active
- Once started a business, but currently you are no longer
an entrepreneur since business has failed
- Once started a business, but currently you are no longer
an entrepreneur since business was sold, transferred or closed
Q11. For each of the following elements, please tell me if it was very important, rather
important, rather not important or not important at all for making you take steps
to start a new business or take over one.
- Very important
- Rather important
- Rather not important
- Not important at all
a) Addressing an unmet social or ecological need
Q18. Do you strongly agree, agree, disagree or strongly disagree with the following
opinion?
- strongly agree
- agree
- disagree
- strongly disagree
42
a) It is difficult to start one’s own business
due to a lack of available financial support
b) It is difficult to start one’s own business
due to the complex administrative procedures
c) It is difficult to obtain sufficient information
on how to start a business
43