a look at the characteristics of qredits-entrepreneurs · a look at the characteristics of...
TRANSCRIPT
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a look at the characteristics of Qredits-entrepreneurs
comparative research and growth ambitions
Alija Ibrahimovic
PhD Researcher (Corresponding author)
Lex van Teeffelen
Professor Financing and Firm Acquisition
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a look at the characteristics of Qredits-entrepreneurs
comparative research and growth ambitions
Alija Ibrahimovic
PhD Researcher (Corresponding author)
Lex van Teeffelen
Professor Financing and Firm Acquisition
MA
R-2
83
Dit onderzoek is mede mogelijk gemaakt door de Citi Foundation.
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No differences in entrepreneurial motivating factors
between men and women.
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Table of content
About the authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Motivations to become an entrepreneurIntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Representativity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Barriers for starting up a business . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Necessity vs. opportunity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Motivating factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Conclusions and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Growth ambitions and loan approval ratesIntroduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Growth ambitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Key variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
The data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Representativity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Pre-existing vs. start-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Company size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Main source of income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Analytic results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Growth ambition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Regression analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
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About the authors
Alija IbrahimovicAlija Ibrahimovic MSc. is a PhD candidate and lecturer of finance and research at the HU
business School Utrecht. Mostly publishing on Microcredits, financing and advisory services
he is specialized in impact studies on entrepreneurial performance. Currently he is preparing
his PhD on the social and economic effects of microcredits in developed economies at the
Utrecht University School of Economics.
Lex van TeeffelenDr. Lex van Teeffelen is a Professor of Finance and Firm Acquisition at the HU Business School
Utrecht. He publishes on SME succession, financing, governance and advisory services. He is
involved in the development and execution of national and European educational support
programmes for effective SME succession, (crowd)financing, matching platforms and co-cre-
ation between business schools and entrepreneurs.
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Motivations to become
an entrepreneur
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Introduction
Worldwide regularly and on a large scale, research is done on the motivations of entrepreneurs
to start a business. The most commonly used tools to examine the motivations of entrepreneurs
are the Global University Entrepreneurial Spirit Student Survey (GUESSS) and the Global Entre-
preneurship Monitor (GEM). The research results of these two studies are open and available to
the public and can serve as good benchmarks.
Where the GUESSS monitor is primarily focused on the motivation, attitude, intention and be-
haviour of students in the age group of 18-26 years, the GEM focuses on the entire working po-
pulation of 18-65 years.
This report provides the motives of young entrepreneurs in the Netherlands that are funded (up
to €50K loan) by Qredits, a Microfinance Institution.
Past studies tend to focus their research on the dichotomy of opportunity and necessity. By fo-
cussing on the individual entrepreneur and their motivations a whole range of motivation can
be unveiled.
For this study the authors have used the meta-study by Stephan, Hart and Drews (2015) as a
benchmark for the data to be collected and compared to. The first chapter of this research will
therefore focus on the literature at hand, what is currently known and commonly accepted. In
the second chapter I will focus on the data, what methodology was used in choosing the data to
be collected, and what way of collecting the data was conducted. The third chapter will focus on
the results; a comparative result analysis will be conducted using the data from the Stephan et
al. (2015) study as a basis. The fourth chapter will be the concluding chapter tying the study and
its results together.
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Literature review
This research is mainly based on the meta-study conducted by Stephan et al. (2015). They set
a basis for what is already known regarding entrepreneurs and their motivation to start a
business, by selecting 51 relevant papers that provide answers on three key questions regar-
ding entrepreneurial motivation, namely:
1. What typologies exist to describe entrepreneurial motivation?
2. What influences and shapes entrepreneurial motivation?
3. What consequences have different entrepreneurial motivations for entrepreneurial per-
formance?
‘Classic’ researches into the motivations of entrepreneurs focus on the difference between
‘opportunity’ and ‘necessity’ motivated entrepreneurs. Stephan et al. (2015) however argue
that seven more dimensions need to be taken in to consideration when speaking of entrepre-
neurial motivation. The following five dimensions are identified as the most relevant by the
authors and will serve as a basis for this study as well:
1. Achievement, challenge & learning• This dimension focuses on the intrinsic motivation of entrepreneurs to better themselves.
Operationalized this translates into four items namely:
• Make use of an existing skill
• Challenge myself
• Fulfil a personal vision
• Achieve something, get recognition
• Make a positive difference
• Achieve a higher position
• In the (Stephan, et. al., 2015) study this dimension is considered as one of the main mo-
tivating factors for entrepreneurs to start-up a business and therefor this dimension is
included in this study as well. It is combined with the fourth dimension recognition and
status.
2. Independence & autonomy• To what extend do independence from work and freedom/flexibility in regard to work
play a role? This dimension is operationalized in the following three items:
• Freedom to set my own time
• More flexibility in my work
• Have better work
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3. Income security & Financial success• This dimension speaks towards the proportionate importance that is assigned by the en-
trepreneur to income security and financial success. Operationalized this translates into
the following three items:
• Financial security
• Larger income
• Increase change of wealth
4. Recognition & Status• What role do Recognition & Status play in an entrepreneur’s choice to start-up a busi-
ness? This dimension is combined with the first dimension: achievement, challenge and
learning, because of their similarities and internal consistency.
5. Family & Roles• This dimension measures the importance of family and legacy related motivating factors
entrepreneurs might have. Operationalized this translates in the following three items:
• Build inheritance
• Follow an example
• Continue family tradition
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Access to finance is perceived as
the largest barrier among entrepreneurs.
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Data
Data collectionThe data collection was conducted by means of online surveys. During the period September –
December 2016 in four different rounds, start-up entrepreneurs were invited to participate in
the study. In this period a total of 474 people were invited to participate of whom 126 people
responded resulting in a response rate of 26.6%.
For the study only start-up entrepreneurs in
the age group 18-35 were considered. The fo-
cus of this study is on ‘young’ entrepreneurs, as
‘young’ is a relative term the cut of point of 35
years was taken.
RepresentativityA general representativity comparison is made between the sample group and the total po-
pulation (all questioned applicants).
GenderIn our sample 59% of the respondents is male vs. 41% being female. When looking at the total
population of Qredits borrowers the figures are 65% male vs. 35% being female. When apply-
ing a mean comparison t-test the difference is not statistical significant at a p-value of 0.16.
This means that there is no significant difference between the gender-means of our sample
group and the population it was drawn from.
AgeThe average age of our sample group is 29.61, which is in line with the average of the total
population of applicants being 29.38. A one-sample t-test shows a p-value of 0.602, which in-
dicates that there is no significant difference between our sample group and the population.
Seeing as only a specified age group was eligible for participation in this study, no differences
in the mean age were expected.
EducationWhen comparing the four educational levels
that have been identified for this research of the
sample group to the population, no major diffe-
rences are found. Table 1 shows the comparison
between the sample group and the population of total approved applicants by Qredits in the
period September – December 2016, that were considered for this research:
start-upentrepreneurs
18-35 yrs
26,6%
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Table 1: Educational level representativity
Level Sample Population
VMBO 6% 8%
MBO 48% 52%
HBO 39% 33%
WO 8% 7%
SectorWhen comparing the sectors in which our sample group is active to the sector in which the
population of applicants is active, again no major differences are found, Table 2 shows the
proportional percentage of entrepreneurs active in the services and non-service sector in our
sample group compared to the total population.
Table 2: Industry comparison representativity
Sample Population
Service sector 49% 45%
Non-service sector 51% 55%
A t-test gives a p-value of 0.101, which means there is a no significant difference in industry
division between our sample and the population it was drawn from.
Concluding on the representativity:
When looking at the four variables selected to test the representativity of our sample no vari-
able seems to differ in our sample from the population it was drawn from. This indicates that
in this sample there appears to be no selective dropout and the sample is likely to be repre-
sentative of the total population of start-up entrepreneurs that were granted a micro credit in
the period October – December of 2016.
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76% of the entrepreneurs would have been unable to start
their business at this time wERE it not for a Qredits loan.
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Results
The results of this study have been divided in to two parts, namely:
• Descriptive results
• Analytic results
First the descriptive results and their implications are discussed:
AgeThe average age of a start-up entrepreneur in the Netherlands is 35 years of age, as only en-
trepreneurs in the age group 18-35 years are applicable for this study the average age of our
sample is expected to be significantly lower. This has turned out to be the case as table 3 de-
picts. Graph 1 shows an overview of respondents by age showing that the bulk of the start-up
entrepreneurs in sample are between the ages of 28-32 years.
Table 3: Average age
Qredits Dutch
Average 30 35
Graph 1: Respondent’s age
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GenderPrevious studies (GEM monitor 2015/2016) have
shown that the gap in distribution of male and f e -
male entrepreneurs grows smaller amongst
younger entrepreneurs. Seeing as the avera- g e
age of our sample group is lower than the na-
tional average a relatively smaller gap in the
distribution between male and female entrepreneurs is expected. Table 4 shows that these
expectations have been met, compared to the Dutch and UK standards our sample shows a
more even distribution between male and female entrepreneurs. A more even distribution
between male and female entrepreneurs is favourable, as this will diminish the probability of
one group steering the outcomes of this research.
Table 4: Gender distribution comparison
Qredits UK Dutch
Male 59% 61% 66%
Female 41% 39% 34%
EducationEducation has proven in past studies to be a sig-
nificant projector of opportunity vs. necessity en-
trepreneurs (Ismail, A. Z. B. H., Zain, M. F. B. M., &
Ahmed, E. M.; 2011). In addition an evenly distri-
buted sample population is favourable for com-
parison and representativity of the sample. Table
5, shows that the vast majority of the entrepreneurs are MBO (A-level professional education or
equivalent) or HBO (bachelors or equivalent) educated, where the lowest and highest tiers of edu-
cation seem underrepresented in the sample. Compared to the level of education of start-up entre-
preneurs in Great Britain a smaller percentage of our sample group has finished a masters/doctoral
degree. When looking at the start-up entrepreneurs in the Dutch economy a relatively higher per-
centage of entrepreneurs are VMBO educated, but again the masters/doctoral percentage exceeds
that of our group. Even though there is a difference in the distribution of the level of education
between our sample and the UK start-up community, this is mainly due to a lower number of WO
(Master degree) graduates in our sample.
Table 5: Level of education
Level Qredits UK Dutch
VMBO • B-level prof edu 6% 7% 15%
MBO • A-level prof edu 48% 40% 47%
HBO • BA/Bsc 39% 32% 21%
WO • MA/MSc 8% 21% 17%
VMBO MBO HBO WO
41%59%
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Entrepreneurs in the familyIn trying to understand the background of our sample group we asked the entrepreneurs the
following question: ‘Do you have any entrepreneurs in your family?’. 56% of the start-up en-
trepreneurs in our sample answered positive on this question as shown in Table 6.
Table 6: Entrepreneurial family
Percent
Yes, both parents 11%
Yes, one or both parents 24%
Yes, another family member 21%
No 44%
Prior entrepreneurial experienceIn addition to the entrepreneurial family background it is also important to know of any prior
entrepreneurial experience as this might influence the entrepreneurs motivation to start up
a business. From our sample we gathered that 27% of entrepreneurs have previous entrepre-
neurial experience. Table 7 shows the distinction between the forms of experience.
Table 7: Prior entrepreneur
Have you been an entrepreneur before? Percent
Yes, and still am 13%
Yes, but not for a period 14%
No 72%
Prior situationIt is of importance to know what effects funding of a start-up have on the personal situation
of the applier. Are they giving up a paying job to fully commit to their start-up or is this a low
risk project on the side while they remain working at a firm. In this context the question: “Are
you giving up a paying job to start this firm?” was asked. Almost half of the respondents (47%)
confirmed that they were completely giving up a paying job to start up this firm. In addition to
47% of the respondents giving up a paying job, 21% of the respondents show that they did not
have another job before starting this firm. This indicates that the being granted of a microcre-
dit directly reduces unemployment. Table 8 gives a complete overview of the given answers.
Table 8: Prior situation of the entrepreneur
Are you giving up a paying job to start this firm? Percent
Yes, completely 47%
Yes, partially 18%
No, I will continue working at a firm 13%
No, I did not have another job 21%
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SupportTo what extend can entrepreneurs count on friends, family and fan’s for financial support?
Because of this the following question was asked: “Have you borrowed money from friends
or family to start up your business?” Entrepreneurs that apply for microcredits are not ap-
plicable for bank loans, because of the relative small size of the loan (van der Veen, M., van
Teeffelen, L., Ibrahimovic, A., & Lentz, M.; 2015).
Additionally experience of finance offers at Qredits is that in most cases entrepreneurs come
to Qredits before they try crowdfunding. Taking this information in to consideration and
looking at the results in Table 9, an astonishing 76% of entrepreneurs seems not to be able to
start their business were it not for the Qredits micro-credit, since friends, families or friends
are unable or unwilling to finance their start-up.
Table 9: Financial support
Have you borrowed money from friends or family to start your business? Percent
No 76%
Yes 24%
Barriers for starting up a businessIn the Stephan et al. (2015) paper, qualitative interviews found six main reasons for entrepre-
neurs to refrain from starting up. These reasons are barriers for starting up a business, in no
particular order:
• Not knowing the sector,
• Not knowing the market,
• Not knowing the customers,
• Insufficient know-how
• Lack of entrepreneurial skills
• Lack of financial access
These barriers were presented to the entrepreneurs as barriers. The entrepreneurs were as-
ked to rate the barriers on a scale from one to seven (not a barrier at all – very much a bar-
rier), for comparison reasons the scale has been calculated back to a five-point scale. Graph 2
shows the results of this question.
Graph 2: perceived barriers
financialaccess
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As the graph shows, one particular barrier stands out as being perceived, by far, as the most
constraining barrier for entrepreneurs to start up a business, namely: financial access. Graph
3 shows the results from our sample group compared to the UK control group.
Graph 3: Perceived barriers comparison
It is evident to see that compared to UK start-up entrepreneurs a significantly higher percen-
tage of our sample group entrepreneurs perceives financial access as a high/very high barrier.
On the other hand UK entrepreneurs seem to perceive not knowing the customers/stability of
demand as the highest barrier for starting up a business.
Necessity vs. opportunityFrequently in start-up entrepreneurial there is the discussion on necessity vs. opportunity
(Stephan, et al, 2015). Even though recent studies have proven that motivating factors for en-
trepreneurs are not necessary as black and white as this dichotomy paints, it is still a useful
start off point for understanding the underlying motivation for entrepreneurs to start up their
business. Table 10 shows the percentage of entrepreneurs that identify themselves as either
an opportunity, necessity, mixed or neither start-up entrepreneur.
Table 10: Opportunity vs. necessity
Label Qredits UK
Opportunity 63% 62%
Necessity 2% 21%
Mix 18% 11%
Other 17% 6%
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Opportunity start-up entrepreneurs answered the theorem: “I started this business because I
saw a chance in the market” with I agree a little/ I agree /I agree completely and “I started this
business because I could not find another job” with I disagree a little/ I disagree/ I disagree com-
pletely. Necessity entrepreneurs answered the theorem: “I started this business because I saw
a chance in the market” with I disagree a little/ I disagree / I disagree completely and “I started
this business because I could not find another job” with I agree a little /I agree / I agree completely.
Mixed entrepreneurs answered a combination of “I don’t agree and I don’t disagree” on both
theorem, and entrepreneurs that do not fit any group answered “I disagree a little / I disagree/I
disagree completely” or “I agree a little / I agree / I agree completely” on both theorem.
From table 10 we can see that 63% of the entrepreneurs view themselves as opportunity entre-
preneurs, which is very comparable to the UK result of 62%. It is the necessity entrepreneurs
results that differ significantly, namely: only 2% of the entrepreneurs in the sample group
identify themselves as necessity entrepreneurs compared to 21% of the entrepreneurs in the
control group. This result can be explained in multiple ways, first being: previous studies have
shown that younger entrepreneurs are generally more opportunity driven, see Graph 4.
Graph 4: Opportunity vs. necessity: United Kingdom
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Seeing as our sample group only covers entrepreneurs until the age of 35 it is expectable
that a larger portion of the sample group considers themselves opportunity entrepreneurs.
Another possible explanation is the better social and welfare safety net in the Netherlands
compared to Great Britain. In addition next to the excellent social safety net in the Nether-
lands, entrepreneurs might still not want to be dependent on social welfare and therefor
‘seek’ opportunities to start their business, and identify rather as opportunity entrepreneurs
than necessity entrepreneurs.
Motivating factorsIn the paper of Stephan et al. (2015) 4 major sets of motivations are researched, autonomy,
challenge, finance and family/legacy, they believe these are the explaining motivating factors
for entrepreneurs. Graph 5 shows the items of the four groups and the relative percentage of
entrepreneurs that consider the given factor to be important/very important.
Graph 5: Motivating factors
From the graph we can see that a few motivating items stand out, namely: challenge myself,
fulfil a personal mission, make use of an existing skill and financial security, all four of these
factors are considered by more than 75% of entrepreneurs to be important/very important.
In the following sections we will show that compared to the UK results, Dutch start-up entre-
preneurs show higher scores on Challenge, Financial Independence and Family Legacy than
their UK peers.
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AutonomyAutonomy is believed to be constructed out of three separate factors: freedom to set my own
time, more flexibility in my work and have better work. Graph 6 shows the respective percen-
tage of entrepreneurs in our sample group that rate these items as important/very important.
Graph 6: Autonomy
First the Cronbach’s alpha is calculated to validate the internal validity of our scale, Table 11
shows the Cronbach’s alpha of the three factors considered to measure autonomy.
Table 11: Cronbach’s alpha autonomy
Internal validity Cronbach’s alpha
Autonomy 0.799
A Cronbach’s alpha higher than 0.7 indicates a significant coherency between the selected
variables, and that the variables measure ‘the same’ output. Next up is the recalculation of
the scale. Because of the acceptable Cronbach’s alpha the three scale results may be added up,
resulting in a new scale output varying from 3 to 21 (3 times a 1 to 7 output). To get a compara-
ble scale number to the UK study, which used a five-point scale, the new added up scale needs
to be divided by 3 and multiplied by 5/7 so that we have a new scale item that measures on a
five-point scale the effect of autonomy. Table 12 shows the comparison of the average of the
new calculated scale compared to the average of the UK results.
Table 12: averages comparison autonomy
Weighted average Qredits UK T-test
Autonomy 3.9 3.8 No (0.238)
An average of 3.9 means that on average out of all respondents on a scale of 1 to five our
sample groups assigns a 3.9 level of importance to autonomy, which in words would be ‘im-
portant’. Additionally a means comparison (t-test) is conducted to measure any possible sig-
nificant difference in the means between the two groups, resulting for autonomy in a ‘no’
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meaning no statistically significant difference between our sample and control group when it
comes to autonomy being a motivating factor.
The same line of reasoning has been applied to the other three groups of motivating factors.
Challenge Challenge is believe to be constructed out of six separate factors: make use of an existing skill,
challenge myself, fulfil a personal vision, achieve something, make a positive difference and
achieve a higher position. Graph 7 shows the respective percentage of entrepreneurs in our
sample group that rate these items as important / very important.
Graph 7: Challenge
First the Cronbach’s alpha is calculated to validate the internal validity of our scale, Table 13
shows the Cronbach’s alpha of the six factors considered to measure Challenge.
Table 13: Cronbach’s alpha challenge
Internal validity Cronbach’s alpha
Challenge 0.716
Again a Cronbach’s alpha higher than 0.7 is acceptable meaning these six items measure
the same output. Table 14 shows the recalculated average of the scale compared to the UK
average and the result of the mean comparison test.
Table 14: averages comparison challenge
Weighted average Qredits UK T-test
Challenge 3.9 3.3 Yes (0.000)
~ 23 ~
An average of 3.9 means that on average out of all respondents on a scale of 1 to five our
sample groups assigns a 3.9 level of importance to autonomy, which in words would be ‘im-
portant’. Additionally a means comparison (t-test) is conducted to measure any possible sig-
nificant difference in the means between the two groups, resulting for challenge in a ‘yes’
meaning a statistical difference between our sample and control group when it comes to
challenge being a motivating factor. As our average factor is higher than the UK average for
challenge this means that on average the entrepreneurs in our sample group rate challenge
as a higher motivating factor compared to UK control group.
Financial IndependenceFinancial independence is believed to be constructed out of three separate factors: financial
security, larger income and increase chance of wealth. Graph 8 shows the respective percen-
tage of entrepreneurs in our sample group that rate these items as important/ very important.
Graph 8: Financial
First the Cronbach’s alpha is calculated to validate the internal validity of our scale, Table 15
shows the Cronbach’s alpha of the three factors considered to measure financial.
Table 15: Cronbach’s alpha financial
Internal validity Cronbach’s alpha
Financial 0.732
Again a Cronbach’s alpha higher than 0.7 is acceptable meaning these three items measure
the same output. Table 16 shows the recalculated average of the scale compared to the UK
average and the result of the mean comparison test.
Table 16: averages comparison financial
Weighted average Qredits UK T-test
Financial 3.6 3.0 Yes (0.000)
~ 24 ~
An average of 3.6 means that on average out of all respondents on a scale of 1 to five our sam-
ple groups assigns a 3.6 level of importance to autonomy, which in words would be between
‘neutral’ and ‘important’. Additionally a means comparison (t-test) is conducted to measure
any possible significant difference in the means between the two groups, resulting for finan-
cial in a ‘yes’ meaning a statistical difference between our sample and control group when
it comes to financial being a motivating factor. As our average factor is higher than the UK
average for financial this means that on average the entrepreneurs in our sample group rate
financial as a higher motivating factor compared to UK control group.
Family/legacyFamily/legacy is believed to be constructed out of three separate factors: Build inheritance,
follow an example and continue family tradition. Graph 9 shows the respective percentage of
entrepreneurs in our sample group that rate these items as important/ very important.
Graph 9: Family/legacy
First the Cronbach’s alpha is calculated to validate the internal validity of our scale, table 17
shows the Cronbach’s alpha of the three factors considered to measure family/legacy.
Table 17: Cronbach’s alpha family/legacy
Internal validity Cronbach’s alpha
Family 0.850
Again a Cronbach’s alpha higher than 0.7 is acceptable meaning these three items measure
the same output. Table 18 shows the recalculated average of the scale compared to the UK
average and the result of the mean comparison test.
~ 25 ~
Table 18: averages comparison family/legacy
Weighted average Qredits UK T-test
Family 2.2 1.9 Yes (0.001)
An average of 2.2 means that on average out of all respondents on a scale of 1 to five our
sample groups assigns a 2.2 level of importance to autonomy, which in words would be ‘not
very important’. Additionally a means comparison (t-test) is conducted to measure any pos-
sible significant difference in the means between the two groups, resulting for financial in a
‘yes’ meaning a statistical difference between our sample and control group when it comes to
family/legacy being a motivating factor. As our average factor is higher than the UK average
for family/legacy this means, despite the motivating factor not being ‘very important’, that
on average the entrepreneurs in our sample group rate family/legacy as a higher motivating
factor compared to UK control group.
~ 26 ~
Challenging yourself and financial independence are the strongest motivating factors
for young entrepreneurs to start a business.
~ 27 ~
Conclusions and limitations
ConclusionsWhen looking at our results and the comparison to the UK results a few conclusions can be
drawn namely:
• 76% of the entrepreneurs would have been unable to start their business at this time was
it not for a Qredits loan*
• Access to finance is perceived as the largest barrier among entrepreneurs in our sample group.
• Qredits entrepreneurs do not consider experience/skills/knowledge to be significant barriers.
• Autonomy is not a significantly higher or lower motivator for Qredits entrepreneurs com-
pared to UK entrepreneurs
• Challenge is a significantly higher motivator for Qredits entrepreneurs compared to UK
entrepreneurs
• Financial motives are significantly more important for Qredits entrepreneurs compared
to UK entrepreneurs
• Legacy motivations, although not particularly high motivating factors, are still more im-
portant for Qredits entrepreneurs compared to UK entrepreneurs
• No differences in entrepreneurial motivating factors were found between male and fe-
male respondents in our study, which deviates from the UK study.
When looking at all these results it may be fair to conclude that microfinance is as important
in developing economies to overcome financial barriers to start-up. Also previous studies (Van
der Veen, et. al,. 2015) have shown that the initial barrier to finance is very high for start-up
entrepreneurs in the Netherlands. Start-ups lack collateral and banks consider the amounts
of start-up loans too low and cost inefficient. Qredits micro funding definitely helps overcome
this barrier, since three quarter of the start-up entrepreneur say start-up would have been
impossible without a Qredits loan.
Less than in developing economies, micro funding helps prevent unemployment, but rather
helps applicants to switch careers from employee (full or part-time) into fulltime entrepre-
neur. To the surprise of the authors the motive of financial independency is much higher
among Dutch than UK start-up entrepreneurs. Generally the assumption is that a well-orga-
nised social welfare system, inhibits entrepreneurial activities (GEM, 2014). This study shows
the contrary, since the UK social welfare system is less generous than the Dutch. An explana-
tion for this finding could be that being on the dole in the Netherlands is a far from attractive
perspective. Regulations and control have become tight and severe in the past five years. Also
many municipalities demand community services from the unemployed.
~ 28 ~
In addition to the above mentioned conclusions a few interesting points can be highlighted
from the data as well, namely:
• 56% of the entrepreneurs that participated in this study come from an entrepreneurial family.
• Only 27% of the entrepreneurs that participated in this study have prior entrepreneurial
experience.
• 21% of financed entrepreneurs in this study were previously unemployed.
• 63% of Qredits entrepreneurs identifies as an opportunity entrepreneur.
• Only 2% of Qredits entrepreneurs identifies as necessity entrepreneurs, which is a signifi-
cantly lower percentage, compared to the UK percentage of entrepreneurs.
These points may indicate both positive and negative biases from Qredits financial officers.
That most of the financed applicants are without prior entrepreneurships experience, is de-
finitely a positive bias, since banks rather like to finance experienced entrepreneurs (Van
Veen et al., 2015). That only few necessity entrepreneurs pass the application might be seen
a potential negative bias. Since the far majority of the Dutch potential entrepreneurs is op-
portunity driven (GEM, 2016/2017).
LimitationsThe results in this study may be biased because of selection bias. Only applicants that were
approved entered this study, so unknown positive and negative biases of the Qredits person-
nel selecting the entrepreneurs influence the outcomes of the study in a more favourable
manner.
Incomplete applications though were not a criteria for denial of the microcredit, since incom-
plete applications cannot be submitted.
The age restriction may skew the results due to younger entrepreneurs being higher educated,
better distributed and higher motivated (opportunity driven).
~ 29 ~
References
Bosma, N., Schott, T., Terjesen, S. A., & Kew, P. (2016). Global Entrepreneurship Monitor 2015 to
2016: Special Report on Social Entrepreneurship. Global Entrepreneurship Research Association.
Hart, M., Levie, J., Bonner, K., & Drews, C. C. (2015). Global Entrepreneurship Monitor United
Kingdom 2014 Monitoring Report, Global Entrepreneurship Monitor.
Ismail, A. Z. B. H., Zain, M. F. B. M., & Ahmed, E. M. (2011). A study of motivation in business
start-ups among Malay entrepreneurs. International Business & Economics Research Journal (IBER),
5(2), p 103-112.
Sieger, P., Fueglistaller, U., & Zellweger, T. (2011). Entrepreneurial intentions and activities of
students across the world (International Report of the GUESSS Project 2011), St. Gallen, Uni-
versity of St. Gallen.
Singer, S., Amorós, J. E., & Arreloa, D. M. (2014). Global Entrepreneurship Monitor 2014 Glo-
bal Report. New York: Global Entrepreneurship Research Association (GERA) (Complete report
downloaded, March 17, 2016 at http://strathprints.strath.ac.uk/57319/1/Levie_Global_Entre-
preneurship_Monitor_Scotland_2014.pdf).
Stephan, U., Hart, M., Drews, C. (2015). Understanding motivations for entrepreneurship: A
review of recent research evidence, Enterprise Research Center, University of Sheffield, UK.
Van der Veen, M., van Teeffelen, L., Ibrahimovic, A., & Lentz, M. (2015). MKB financiering: be-
hoefteonderzoek en analyse. Kamer van Koophandel/Hogeschool Utrecht, Utrecht.
~ 30 ~
Growth ambitions and loan approval
rates
~ 31 ~
Introduction
Growth ambitionsIn recent years much has been written about the con-
nection between growth and ambition. The retur-
ning question being: “are more ambitious entre-
preneurs faster growers?” this case study aims at
answering this question through a comparative
case study amongst applicants for a Qredits mi-
crocredit. As a basis for this comparative paper
the Wiklund and Shepherd 2003 study titled: “As-
piring for, and achieving growth: the moderating
role of resources and opportunities” was taken. In
this study Wiklund and Shepherd take Ajzen’s theory
of planned behaviour (1991) as a predictor of entrepre-
neurs’ stance on growth. It is then theorized that the more
positive an entrepreneur’s attitude towards growth is the stron-
ger their ambition to grow will be which in return results in greater growth. An interesting
question is if the same logic applies to debt financing. Are entrepreneurs with higher growth
ambitions more successful in terms of acquiring debt financing (the micro credit)?
Key variablesWiklund and Shepherd (2003) emphasize in their paper that the relationship between growth
ambitions and actual growth is far more complex than one might originally expect. Key vari-
ables determining and predicting this actual growth are, according to their study, level of edu-
cation, experience, and the dynamic of the environment in which the entrepreneur operates.
Each of these variables enlarges (acts as moderator) the eventual effect of growth ambitions
on actual growth.
Are more ambitious
entrepreneurs faster
growers?
~ 32 ~
MethodologyWiklund and Shepherd (2003) measure growth ambitions using four questions, aimed at
quantifying an individual entrepreneurs ideal situation. These four questions are:
Two five point scale questions ranging from very negative to very positive:
• Would a 25% increase in the number of employees in five years be mainly negative or
mainly positive?
• Would a 100% increase in the number of employees in five year be mainly positive or
mainly negative?
And two open questions:
• What would be your ideal turnover in 5 years?
• What would be your ideal number of employees in 5 years?
This method of measuring and determining
growth ambitions finds its origins in previous
studies on entrepreneurial growth intentions
and actual realized growth (Davidsson, P., 1989;
Delmar, F., 1996). For this study it has been de-
cided to only use the two open questions, sin-
ce with the standard information provided by the entrepreneurs their stance on employee
growth can be measured without them answering scale questions. Since the entrepreneurs in
our population are applying for microcredits and not regular bank loans their overall payback
period is significantly smaller (5 years at the most). Therefor the open questions have been
adjusted to reflect this shorter time span and changed into measuring an entrepreneurs ideal
number of employees and turnover in three years.
Risk assessors where unaware of the growth ambitions of the applicants, having had no ac-
cess to their answers on these growth ambitions items.
5 » 3 yrs
~ 33 ~
ConclusionsThe most surprising and unexpected conclusion is definitely that growth ambitions do not
seem to have a significant impact on the receiving of a credit or the percentage that is gran-
ted. The receivers of a microcredit have significantly lower growth ambitions. This could im-
ply that Qredits attracts more modest/realistic entrepreneurs as opposed to the high growth
entrepreneurs.
A few additional conclusions that can be drawn from the data:
• There are no or hardly effects of age and education on acceptance rates and obtained
percentage of funding.
• The sources of income do not discriminate in acceptance and total percentage of ob-
tained funding in the end.
• It is evident that women are more often accepted in the overall sample than man.
• Start-up entrepreneurs have a higher probability compared to pre-existing entrepreneurs
to be accepted for financing.
• The number of employees is by far the strongest predictor for acceptance.
• Yet having more employees also predicts a lower obtained amount than asked for.
The lower obtained, than asked for amounts are probably related to the ceiling in guarantees
of 20K for Qredits loans, making it more risky to grant higher amounts of loans.
Growth ambitions do not seem to have a significant impact on the receiving of a credit or the percentage that is granted.
~ 34 ~
Higher success-rates for young female entrepreneurs.
~ 35 ~
The data
DataThe data was collected amongst all Qredits
credit applicants in the period December 2016
through May 2017 (6 months). During this pe-
riod a total of 4057 applicants filled out a com-
plete application and were considered for a
Qredits credit (group 1), of these 4057 a total of
648 (16%) was accepted into the program and
3409 (84%) was rejected. For the remainder of
the paper however another distinction is made,
namely: between the applicants for a micro-
credit (loan up to €50.000.-) and applicants for
a business credit (loan between €50.000,- and €100.000,-) (group 2). In addition to the respec-
tive loan size a second criteria was applied where the applicants are divided in two groups,
‘young’ entrepreneurs, in the age range of 18 to 35, and ‘mature’ entrepreneurs, aged 36 and
older, (group 3).
RepresentativityRepresentativity tests yield no significant differences between the sub groups for gender, age,
sector or level of education. Due to the large scale of the research and the inclusion of all ap-
plicants, this study might be considered representative for all microcredit applicants so far
of Qredits.
Total applicationsMicrocreditYoung MCAccepted Young MC
N = 4057 Total applicationsN = 3152 MicrocreditN = 1229 Young MCN = 210 Accepted Young MC
~ 36 ~
Qredits doesn’t discriminate on source of income, age
or level of education.
~ 37 ~
Descriptive statistics
GenderOut of the total population of 4057, 3118 applicants were male (76.9%) and 939 applicants were
female (23.1). When only taking into consideration the applicants for the microcredit (a total
of 3152 applicants) 2402 were male (76.2%) and 750 (23.8% were female. If we then filter by age
we get a total of 1229 applicants younger than 35 years of age of which 954 (77.6%) is male
and 275 (22.4%) is female. Table 1 shows the differences between males and females for the
determined subgroups.
Table 1: Gender
Qredits Total
applicants
Microcredit
applicants
Young MC
applicants
Accepted Young
MC Applicants
Dutch
Male 76.9% 76.2% 77.6% 71.9% 66%
Female 23.1% 23.7% 22.4% 28.1% 34%
AgeThe average age of a start-up entrepreneur in the Netherlands is 35 years of age (Kamer van
koophandel, 2016), as only entrepreneurs in the age group 18-35 are applicable for this study
the average age of our sample is expected to be significantly lower. This has turned out to be
the case as table 2 depicts. Table 3 shows an overview of respondents by age showing that the
bulk of the start-up entrepreneurs in the sample are between the age of 25 and 35.
Table 2: Average age
Young MC applicants Accepted Young MC Applicants Dutch
Average age 28 28 35
Table 3: Respondents age
~ 38 ~
A means comparison test (t-test) shows no significant differences in acceptance rates based
on age for young entrepreneurs as table 4 depicts.
EducationWiklund and Shepherd (2003) theorize that level of education is a key determinant of what
an entrepreneurs growth ambitions will turn out to be. They even hypothesize that higher
educated entrepreneurs will have a higher growth ambition. They base this reasoning on
the finding that educated individuals are more likely to run faster-growing small businesses
compared to those who are less educated (Sapienza H. J. and Grimm C. M., 1997; Storey D. J.,
1994). For this study this would mean that higher educated entrepreneurs are more likely to
receive a microcredit and on average should have a higher ideal future turnover and number
of employees. Table 5 shows the division of educational levels amongst the sample.
Table 5: Level of education
Level Total
applicants
Microcredit
applicants
Young MC
applicants
Accepted Young
MC Applicants
Dutch
VMBO 12% 14% 12% 10% 15%
MBO 48% 51% 54% 50% 47%
HBO 31% 28% 28% 33% 21%
WO 9% 7% 6% 7% 17%
The educational level more or less resembles the total Dutch population, given that HBO is
bachelors and WO is masters education. Summated HBO and WO are quite on par with the
national average.
A means comparison test shows that the group of young entrepreneurs that received a micro-
credit has a slightly higher mean compared to the group of young entrepreneurs that did not.
This would indicate that a slight preference towards higher educated entrepreneurs exists.
~ 39 ~
SectorThe variable sector has been added as a control variable to see if any significant differences
exist between the sector in which the accepted group of entrepreneurs and rejected group of
entrepreneurs are active. From the data gathered, a slight overrepresentation of the industry
sector is noticeable. When looking at the difference in sector for the accepted it is noticeable
that both sectors are exactly evenly represented indicating that no overrepresentation or in-
fluence from either can be assumed. Table 7 shows the percentage distribution of entrepre-
neurs amongst the two distinct sectors.
Table 7: Sector
Total applicants Microcredit
applicants
Young MC
applicants
Accepted Young
MC Applicants
Service sector 46.7% 47.9% 47.8% 50%
Industry sector 53.3% 52.1% 52.2% 50%
A means comparison test confirms no significant difference in sector between the financed
and not financed entrepreneurs. Table 8 shows the results of the t-test.
Pre-existing vs. start-upEntrepreneurial experience is considered a significant determinant of growth ambitions by
multiple studies (Hessels, J., Van Gelderen, M., & Thurik, R., 2008; Wiklund, J., & Shepherd, D.,
2003; Gundry, L. K., & Welsch, H. P., 2001). Some of these researches claim opposing points
of view, where in one-instance start-up entrepreneurs seem to be more growth oriented,
whereas other papers claim the entrepreneurs of existing firms to be the more ambitious
ones. It is for this reason that it is important to measure and monitor the difference between
start-up entrepreneurs applying for credit and entrepreneurs of existing firms. Table 9 shows
the percentage division of start-up entrepreneurs vs pre-existing entrepreneurs amongst the
predetermined groups.
Table 9: Start-up vs. pre-existing firm
Total applicants Microcredit
applicants
Young MC
applicants
Accepted Young
MC Applicants
Start-up 50.2% 51.9% 59.7% 66.7%
Pre-existing firm 49.8% 48.1% 40.3% 33.3%
A means comparison test shows a significant difference in between start-up entrepreneurs
and pre-existing entrepreneurs. Start-up entrepreneurs are significantly more likely to be fi-
nanced compared to pre-existing firms, as table 10 shows.
~ 40 ~
Company sizeOne of the determinants of growth ambition is the entrepreneurs’ eventual ideal view on
company size in terms of employees (Wiklund, J., & Shepherd, D., 2003). For this reason it is of
importance to measure the current size of the company in number of employees. Relatively
larger companies (companies with more employees) are expected to have a higher absolute
ambition of employee growth, however it is the relatively smaller companies that are expec-
ted to show higher relative growth. Diagram one shows the division of company sizes for the
total population. The question asked was: how many FTE (full time equivalent) are active in
your company beside yourself?
Diagram 1: Companies with only self-employed individuals
From the diagram it is evident that the largest group is that of 0, meaning that this are self-
employed individuals with no other FTE active in their company. Diagram 2 shows the divi-
sion among young entrepreneurs that applied for a microcredit.
Diagram 2: Companies with 1 other FTE in their company a entrepeneur
~ 41 ~
There was no evidence of statistical difference between the overall population and the young
entrepreneurs applying for a microcredit. However when looking within the groups at the size
of the companies and the acceptance rate for a Qredits a statistical difference was found in
favour of larger companies. Table 11 shows the means comparison test on company size in
number of employees for the entrepreneurs that did receive a microcredit compared to the
entrepreneurs that did not.
It is very clear that the group of entrepreneurs that did receive a microcredit are on average
significantly larger in size than the group of entrepreneurs that did not.
Main source of incomeThe entrepreneurs’ main source of income was added as a control variable to see if there is a
difference in the entrepreneurs’ background when determining his or her growth ambition.
The distinction is made between 5 sources of income. Table 13 shows the percentage division
amongst the different sources of income.
Table 13: Main source of income
Source of
income
Total applicants Microcredit
applicants
Young MC
applicants
Accepted Young
MC Applicants
Entrepreneur 57% 56% 54% 50%
Salaried 19% 19% 23% 21%
Social welfare 16% 17% 15% 21%
No income 4% 4% 4% 4%
Other 4% 4% 4% 4%
Means comparison tests were conducted to determine a difference between acceptance rates
for the distinct groups of main income. Table 14 shows the respective t-test values and the
significance of each difference.
From Table 14 it is clear that there is only a significant difference between entrepreneurs who
have given salaried income (other than salaried from their enterprise) as their main source
of income. Entrepreneurs that have a salary from somewhere else as their main source of in-
come are sooner financed compared to entrepreneurs who do not have a salary as their main
source of income.
~ 42 ~
Start-ups have a higher chance
of being financed compared to existing firms.
~ 43 ~
Analytic results
CorrelationsIncluded is a correlation table showing the relationship between the chosen variables. A few
noticeable variables have been highlighted (in bold the significant correlations).
Following is the analysis of the selected variables and possible explanations for their relati-
onship.
Interesting correlations:• Requested amount and receiving a credit, the more you request the less likely you are to
get a credit at all.
This is in line with expectations on risk averseness of credit lenders, since larger credits carry
a higher risk it is expected that entrepreneurs requesting a smaller amount are sooner to be
financed.
• % of requested amount received and micro-credit or business loan, microcredits get a
larger percentage of what they request.
This correlation suggests that the smaller the amount requested, if approved, the higher the
percentage of the requested amount is provided. Since Qredits has certain guarantees in pla-
ce for their loans up to €20.000,- it makes sense that they would feel less threatened by small
loans and sooner provide the entrepreneur with the full requested amount compared to the
more risky larger loans.
• Number of employees by far the strongest predictor of getting finance or not.
The correlation between number of employees and being financed is 0.418 and is significant
at a 5% level, meaning that companies with more FTE are far more likely to be financed com-
pared to companies with less FTE.
Growth ambitionThis paper set out to measure and identify the effect of growth ambitions of entrepreneurs on
their actual growth. One-way of doing so is by measuring the difference between the financed
and not financed entrepreneurs. Previous studies have shown that acquiring external debt is
a show of growth (Wiklund, J., & Shepherd, D., 2003). In Table 16, using a mean comparison
test, the means of the variables ‘ideal number of employees’ and ‘ideal turnover’ in three
years time are measured. These results will give an overview of, if any, group seems to be pre-
ferred by the risk assessors or not.
~ 44 ~
These results imply that if you (as an entrepreneur) have a lower ideal number of employees
and a lower ideal turnover in three years you are sooner to be financed. Which is contra-
dicting the Wiklund, J., & Shepherd, D., 2003 findings.
Regression analysis
Binary logistical regression analysisIn order to detect the effect of growth ambitions on the chance of being financed a logistic
regression analysis is conducted. In this situation the dependent variable is: the receiving of
a credit, independent variables are: Entrepreneurial characteristics, enterprise characteristics
and test variables. This translates in the following hierarchical model:
Where Υ is the dependent variable being either receiving a (micro) credit or not, or in the li-
near regression the received percentage of the requested amount of credit. In this equation
the β is the constant, the different χ variables are the independent variables and ε is the error
term.
If you have a lower ideal number of employees and a lower ideal turnover in three years you are sooner to be financed.
Υ = β + χGender + χAge+ χEducation+ χSector+ χStartup+ χ#Employees
+ χIncSalaried + χIncEntrepreneur + χIncWelfare + χagefirm
+ χIdealemployees + χIdealturnover + ε
~ 45 ~
Table 17 shows the calculated results for the different groups of this research. These are the
results from the bivariate logistic regression analysis with the ‘dummy’ variable “receiving a
credit or not” being the dependent variable
Table 17: Binary logistic regression analysis
Variables
Dependent:
Received a credit or not
Total
applicants
Microcredit
applicants
Young MC
applicantsN = 3241 N = 2510 N = 960
R2 = 0.389 R2 = 0.518 R2 = 0.627
Constant 0.205*** 0.128*** 0.015***
Gender 0.729** 0.878 0.649
Age 1.011** 1.010* 1.047
Education 0.969 1.111 1.258
Sector 0.886 0.889 0.810
Existing firm 0.410*** 0.434*** 0.376***
#Employees 7.033*** 16.572*** 40.746***
Income salaried 0.846 0.823 1.616
Income enterprise 0.853 0.765 1.796
Income welfare 1.131 0.907 1.196
Age firm 0.989** 0.996 0.922*
Growth ambition employees 0.989 0.972* 0.964
Growth ambition turnover 1.000 1.000** 1.000
* = 10% sign. level, ** = 5% sign. level, *** = 1% sign. level.
From Table 17 it is clear that the number of employees a company has is by far the largest
predictor of that firm receiving a (micro) credit. Through all groups of this research the varia-
ble number of employees remains significant at a 1% confidence level and stays the number
one predictor of receiving a (micro) credit. Next to the number of employees being a strong
predictor the variable existing firm (0 is start-up 1 is existing firm) seems to be a strong nega-
tive predictor of an entrepreneur receiving a micro credit. Young start up entrepreneurs have
approximately a 1.5 times better chance at getting a micro credit compared to existing firms
applying. Growth ambitions hardly increase or decrease the probability of being accepted for
a microcredit.
~ 46 ~
Linear regression analysisFollowing is the linear regression analysis on the percentage received of the requested amount.
This regression analysis will give a clear indication of what characteristics play an important
role in entrepreneurs receiving a higher percentage of their requested amount.
Table 18: linear regression analysis
Variables
Dependent:
% of requested received
Total
applicants
Microcredit
applicants
Young MC
applicantsN = 510 N = 485 N = 183
R2 = 0.114 R2 = 0.130 R2 = 0.170
Constant 1.189*** 1.287*** 1.315***
Gender 0.11 0.013 0.023
Age 0.001 0.001 -0.001
Education -0.10 0.001 0.054
Sector 0.004 0.001 -0.014
Existing firm -0.003 0.014 -0.004
#employees -0.067*** -0.069*** -0.109***
Income salaried -0.017 -0.021 -0.59
Income enterprise -0.097 -0.110 -0.107
Income welfare 0.06 0.035 -0.125
Age firm -0.004 -0.006 0.001
Growth ambition in employees -0.001 0.000 0.000
Growth ambition in turnover 0.000 0.000 0.000
Requested amount 0.000*** 0.000*** 0.000***
Current turnover 0.000*** 0.000*** 0.000
* = 10% sign. level, ** = 5% sign level, *** = 1% sign. level
It is clear from the table that the number of employees is the only constant factor that keeps
having a significant negative effect on the received percentage of the requested amount. This
effect is quiet small though. Next to the number of employees the requested amount and
current turnover have a significant but not measurable (too small) impact on percentage of
amount requested received.
~ 47 ~
Size matters: applicants with employees have
a better chance of being financed.
~ 48 ~
Bibliography
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision
Processes, 50, 179-211.
Ajzen. I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of
planned behavior. Journal of Applied Social Psychology, 32, 665-683.
Davidsson, P. (1989a). Continued Entrepreneurship and Small Firm Growth. Stockholm: Stockholm
School of Economics.
Delmar, F. (1996). Entrepreneurial Behavior and Business Performance. Stockholm: Stockholm
School of Economics.
Gundry, L. K., & Welsch, H. P. (2001). The ambitious entrepreneur-High growth strategies of
women-owned enterprises. Journal of Business Venturing, 5(16), 453-470.
Hessels, J., Van Gelderen, M., & Thurik, R. (2008). Entrepreneurial aspirations, motivations, and
their drivers. Small Business Economics, 31(3), 323-339.
Sapienza, H. J. and Grimm, C. M. (1997). ‘Founder characteristics, start-up process, and stra-
tegy/structure variables as predictors of shortline railroad performance’. Entrepreneurship:
Theory and Practice, 20, Fall, 5–24.
Storey, D. J. (1994). Understanding the Small Business Sector. London: Routledge.
Wiklund, J., & Shepherd, D. (2003). Aspiring for, and achieving growth: the moderating role of
resources and opportunities*. Journal of management studies, 40(8), 1919-1941.
~ 49 ~
Table 15: Correlation table Requested amount Requested
amount
Received amount ,689** Received amount
Existing firm ,062** 0,049 Existing firm
FTE 0,018 0,045 ,063** FTE
% received -,173** 0,066 -0,001 -0,041 % received
Financed -,133** .c -,051** ,418** .c Financed
Micro-or-not ,789** ,695** ,062** 0,026 -,097* -,116** Micro- or-not
Inc salaried ,036* 0,038 -,217** ,053** -0,021 0,024 0,021 Inc salaried
Inc entrepreneur
0,03 0,032 ,458** -,041** -0,042 -,051** ,042** -,557** Inc entre-preneur
Inc welfare -,073** -0,045 -,310** -0,013 0,067 ,036* -,074** -,212** -,499** Inc wel-fare
Age 0,028 0,02 ,163** 0,015 0,015 0,019 0,013 -,127** ,056** ,060** Age
Education ,130** ,094* -0,031 ,034* -0,004 0,005 ,142** ,033* -0,019 -0,004 ,042** Education
Gender ,037* 0,031 ,091** -0,029 -0,027 -,069** 0,029 -,063** ,108** -,042** 0,019 -,066** Gender
Ideal employees
-,107** -,083* 0,009 -0,013 -0,022 -0,029 -,150** 0,009 0,008 -0,002 -0,022 0,008 0,019 Ideal employees
Ideal turnover -,109** -0,007 ,036* -0,025 0,004 -,038* -,172** -0,009 0,014 -0,012 0,023 ,062** ,036* ,253** Ideal turnover
Age firm ,126** 0,07 ,327** ,098** -0,061 -,056** ,102** -,089** ,157** -,117** ,264** -0,014 ,057** -0,034 -0,01 Age firm
Sector ,058** ,083* 0,01 0,024 0,018 0,001 ,046** 0,01 -0,021 0 -0,015 -,064** 0,029 -0,024 -0,009 0,009
Included is a correlation table showing the relationship between the chosen variables.
A few noticeable variables have been highlighted (in bold the significant correlations).