a look at the characteristics of Qredits-entrepreneurs · a look at the characteristics of Qredits-entrepreneurs ... programmes for effective SME succession, ... ~ 15 ~ Gender Previous

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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

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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).