· Web viewRepublic of Macedonia. University 'St. Kliment Ohridski-Bitola' Faculty of...

Republic of Macedonia

University 'St. Kliment Ohridski-Bitola'

Faculty of Economics-Prilep

Doctoral dissertation:

BARRIERS TO THE GROWTH OF SMALL BUSINESSES IN KOSOVO

Candidate: Mentor:

Shaip GASHI Prof. Dr.Vasilika KUME

File no. 3046

Prilep

2017


Dedication:

To myparents:

”…to my mother deceased,for persistence and commitment to do things a reality and to my father for wisdom and maturity which I have inherited from him.”

Prepared by: Shaip Gashi Supervised by: Prof. Dr. Vasilika Kume Page 1


Acknowledgment

The realization of a Ph.D. level work requires support from other people. Therefore, it is a great pleasure to thank at least some of them, who assisted until the end. A special thank you goes to mentor Prof. Dr.Vasilika KUME, University of Tirana – Economic Faculty, for the brilliant guidance and continual cooperationuntil the completion of the dissertation.

I deeply appreciate all the help offered bythe team of professors to thedepartment for doctoral studies of the Faculty of Economics in Prilep, especially to the coordinator of the program Prof. Dr. Marika Baseska and her assistant Marija Midovska for the great contribution and commitment to the DOCSMESstudies.Also, thanks to Prof. Dimitar Nikoloski and Prof. Olivera Kostoska for the importantadvice to prepare the Ph.D. theses proposal.

A warm recognition goes to Department for doctoral studies of theUniversity of Tirana, in particular to Prof. Dr. Fatmir Memaj for excellent cooperation and facilitation to accomplish the mobility study required, who enabled using of all infrastructure and research resources of thefaculty.

Also,for the support received from professors engaged in the program from universities: UB, UNICE, UAB, UT, and others, are deeply appreciated.

Thanks to my family as well for the assistance and patience, while in particular,I thank my son Arbias Gashi, astudent in Computer Engineering for computer maintenance at all time.

Sincerely,

Shaip Gashi



Abstract

Understanding of the phenomenon of the growth of Small Businesses (SB) has been

asignificant and continuous subject of academics and scholars of economy and management

over the world since thesecond half of the last century, increasingly last three decades. The

interest has been increased for investigation in transition countries with a challenging

environment for doing business, which facedmany obstacles in their activities: administrative

barriers, limited access to finance, lack of technologies, skills of entrepreneurship and so on.

In a transition economy, entrepreneurs faced with numerous formal and informal difficulties.

Even significant contribution to this issue there is enough space to accomplish this

phenomenon.The aim of this study was to identify the main barriers that hinder small

business growth in Kosovo and try to find and suggest solutionsto survive and overcome

these barriers. This research is precededby several years of the entire research work and

development of the surroundingsrelatedto the barriersto small businessgrowth. Furthermore,

dissertation research pays particular attention to the negative impact of several barriers to

small business growth and entrepreneurship development in Kosovo. This dissertation joins

these efforts, to give a modest contributionexamining an empirical study on the barriers to

thegrowth of SBs in Kosovo. Knowing that, the important role of SBs in improving the

fragile stance of economic development of the country, contribution on this issue is abenefit

for the inside and beyond. The purpose of the study is a better understanding ofbarriers that

hinder thegrowth of SBs and how to overcome them by owners/managers of the

businesses.Further on to examine an empirical study, why these barriers impede business

growth, estimate the perceptions of the managers regarding the phenomenon, to understand

better why some businesses grow and some don’t.

This dissertation workis not based on a single theory. To achieve the objectives of the

research firstly are reviewed the theories of the growth of the firm, particularlythe new

growth theory which represent the authors of last decades to see the conceptualization of the

phenomena and understand general scope of the topic. Since the barriers are mostly

correlated to human behavior in one side and institutions on the other, the focus is greater to

human capital theory and institutional theory.This comprehensive approach is chosen to

answer to many questions related to the growth of SBs.This uniqueness becomes even more

evident in the context of countries in transition process or recently faced the conflict and not



sustainable economic development, with inadequate and often unfriendly institutional

environment; lack of the power rule of law; lack of institutional support for small

businessesand characteristics of countries in transition like Kosovo.It was argued, in such

situation, the growth of the firmsmight be substantially influenced by the external

environment in general and the institutional context in particular.

Based on literature review and three years research in this issue are generated many ideas,

raised the knowledge to build a comprehensive questionnaire for the main study in the field.

Are interviewed 20 managers/owners of the businesses face to face and distributed more than

200 surveys to ensure the feedback of 157.The interviews were conducted face to face with

small business owners/managers from which are derived detailed narrative information about

general barriers faced on doing thebusiness process. The results obtained much information

to generate the ideas for the main research of barriers to small firm growth. Seeing the

multidimensional character of the barriers have been made the classification in some groups

of barriers (internal and external) and on this basis is preparedan inclusive questionnaire to

proceed with main research. The research conductwas followed by the distribution of

questionnaire providing information from 157 small businesses which are processed through

SPSS and are extracted significant statistical data. The data collected in the field are analyzed

through SPSS 20.0 software using multiple linear regressions and interpreted accordingly.

Additionally, is identified that:

a) It exists a significant correlation between barriers and small business growth;

b) Barriers are constantly challenging the SBs to growth;

c) Perceptions of the managers for them arereal, and they need additional efforts to plan

their activities.

Up to the identified gaps from the previous studies, the research questions havebeen raised.

Based on the findings, it is concluded that barriers that hinder business growth in Kosovo

exist and needs several masses to survive and overcome them.Further, on that base, the

conclusions and recommendations are made. Limitations and recommendations for future

researchers are listed as well.

Keywords:Entrepreneurship; small business; growth; barriers; institutions; internal barriers;

external barriers; human capital.



Table of Contents

Dedication: 1

Acknowledgment 2

Abstract 3

Abbreviations 9

List of Tables 10

List of Figures 12

CHAPTER 1 13

1. INTRODUCTION 13

1.1. PHILOSOPHICAL APPROACH 15

1.2. PROBLEM STATEMENT 16

1.3. IMPORTANCE OF THE STUDY 17

1.4. THE OBJECTIVES OF THE RESEARCH 20

1.5. RESEARCH QUESTIONS AND HYPOTHESES 21

1.6. DESIGN AND ORGANIZATION OF THE STUDY 23

CHAPTER 2 26

2. THEORETICAL BACKGROUND 26

2.1. The Entrepreneur and Entrepreneurship 28

2.2. Historical Evolution 30

2.3. Entrepreneur and Entrepreneurship – Some concepts 31

2.4. Multidisciplinary Concept of Entrepreneurship 33

2.5. Institutional theory 34

2.6. Entrepreneurship in Transition Economies 35

2.7. Current Challenges - Problems Facing 37

2.8. WHAT IS THE FIRM GROWTH? 38

2.8.1. Firm Growth and Job Creation 42

2.8.2. Firm Growth with Positive or Negative Effect 44

2.8.3. Is the Firm Growth Profitable? 45

2.8.4. Entrepreneurs’ Strategies as a Determinant of Growth 46

2.8.5. Integrated Models of Growth Determinants 48

2.9. BARRIERS, CHARACTERISTICS, AND STAGES OF GROWTH 49



2.9.1. Barriers to growth 50

2.9.2. Barriers to Entrepreneurship in Transition Economies 52

2.9.3. Access to finance as a barrier to small firm growth 54

2.9.4. Negative phenomena, barriers to SBs growth 55

2.9.5. Unfair Competition 56

2.9.6. Market Barriers 57

2.9.7. Access to foreign markets 58

2.10. Internal factors 59

2.10.1. Organizational Determinants 60

2.10.2. Lack of Qualified Human Resources 61

2.10.3. Individual Determinants62

2.11. External and Internal Determinants 63

CHAPTER 3 66

3. METHODOLOGY 66

3.1. Secondary data 66

3.2. Primary data 67

3.3. Pilot research work 67

3.4. Determination of sample size 67

3.5. The conceptual model of data processing 70

3.6. Population and Sample of the Research 71

3.7. Sample size in regression 72

3.8. The questionnaire design scheme 74

3.9. Procedures and instruments for gathering data 76

3.9.1. Interviews 76

3.9.2. The Survey 77

3.10. Critical Incident Technique (CIT) 77

3.11. Standardization and ethics in the research 78

3.12. Analysis and data processing 79

3.13. Dependent and independent variables 80

3.14. Dependent Variable 81

3.15. Independent Variables 82

CHAPTER 4 83

4. BARRIERS TO SMALL BUSINESS GROWTH IN KOSOVO – CURRENT SITUATION83

4.1. Short profile of Kosovo evolution 83

4.2. Data Analysis and findings – secondary data 84

4.3. Environment of Doing Business in Kosovo 85



4.4. General situation 88

4.5. Barriers to Growth of SB in Kosovo 90

4.6. External Factors 92

4.6.1. Institutional barriers 92

4.6.2. Access to Finance 93

4.6.3. Rule of Law 93

4.6.4. Public Procurement Process 94

CHAPTER 5 96

5. FIELD RESEARCH DATA AND INDICATORS MEASURED 96

5.1. Measurement of indicators and variables97

5.3. Procedure of data analysis 99

5.3.1. Reliability and availability of statistical data 101

5.3.2. Conceptual model of variables and coding 103

5.3.3. The main findings of the study - Results and interpretation 104

5.3.4. Descriptive statistics related to profile of surveyed companies 105

5.4. Hypothesis Testing 109

5.5. Correlation analysis 116

5.6. Multiple Linear Regression analysis 117

5.6.1. Regression Model and Regression Equation 117

5.6.2. Estimated Multiple Regression Equation 118

5.6.3. Least Squares Method (LSM) 119

5.6.4. Models Building 120

5.6.5. Analysis Barriers related to Institutional support – Model 1 120

5.6.6. Data sorting to obtain qualitative outputs 121

5.6.7. Multiple Coefficient of Determination 122

5.6.8. Assessing the assumption of multicollinearity 127

5.6.9. Casewise diagnostics – Model 1 128

5.6.10. Checking assumptions – Model 1 129

5.7. Regression Analyses of Barriers related to Rule of Law – Model 2 131

5.7.1. Collinearity Diagnostics of the variables related to rule of law 138

5.7.2. Residuals Statistics – Model 2 139

5.7.3. Checking assumptions 139

5.8. Multiple regression of market-related barriers – Model 3 141

5.9. MR of barriers related to infrastructure – Model 4 149

5.10. Internal Barriers related to capacities of human resources (MLR) – Model 5 155

5.11. Finding the most significant variables 161



CHAPTER 6 164

6. ENDINGS – DISCUSSIONS, CONCLUSIONS, AND RECOMMENDATIONS164

6.1. Results and discussions 164

6.2. Results from hypotheses testing and regression analysis (the statistical support of the research) 165

6.3. Theoretical contributions 170

6.4. Conclusions and discussions 173

6.5. Recommendations 179

6.5.1. Practical Recommendations related to financial barriers 181

6.5.2. Practical Recommendations related to Rule of Law barriers 182

6.5.3. Practical Recommendations related to market barriers 183

6.5.4. Practical Recommendations related to infrastructure barriers 185

6.5.5. Practical Recommendations related to HR barriers 185

6.6. Limitations of the research 187

6.7. Advice for future research 189

Bibliography 191

Annexes 210

Annex 1. Some of the statistical table210

Annex 2. Questionnaire 221



Abbreviations

ARBK Agency for Registration of Businesses

BSCK Business Support Center of Kosovo

CBK Central Bank of Kosovo

CBK Central Bank of Kosovo

CE Central Europe

CEEC Central East European Countries

CIT Critical Incident Technique

CVR Covariance ratio

EO Entrepreneurial orientation

EU European Union

FDI Foreign Direct Investment

GDP Gross Domestic Product

HR Human resources

IMF International Monetary Fund

LSM Leas Square Method

MLR Multiple Linear Regression

MTI Ministry of Trade and industry

MF Ministry of Finance

MEST Ministry of Education, Science, and Technology

OECD Organisation for Economic Development and Co-Operation

PPRC Public Procurement Regulatory Commission

SAP Stabilization and Association Process

SB Small Business

TE Transition Economies

UNMIK United Nations Mission in Kosovo

USAID United States Aid for International Development

VIF Variance Inflation Factor

WB World Bank



List of Tables

Table 1. Aspects of the research philosophy - paradigm elements of our assumptions ..........16Table 2. Share of businesses in the population and the sample by size and sector..................68Table 3. Sample structure by sector and size...........................................................................69Table 4. Regional Distribution of Enterprises..........................................................................69Table 5. Sample structure by region (in percent).....................................................................70Table 6. Doing business ranking position of Kosovo and neighboring countries....................88Table 7. Doing business topics ranking position......................................................................89Table 8. Barriers related to the environment............................................................................91Table 9. Case processing summary of statistical data............................................................101Table 10. Reliability of statistical data...................................................................................102Table 11. Conceptual model of variables and coding............................................................103Table 12. Level of education of Owners / Managers surveyed..............................................105Table 13. Regions of businesses location...............................................................................106Table 14. Type of businesses surveyed..................................................................................107Table 15. H1 - Correlations....................................................................................................110Table 16. H2 - Correlations....................................................................................................110Table 17. H3 - Correlations....................................................................................................111Table 18. H3.1 – Correlations................................................................................................111Table 19. H3.2 – Correlations................................................................................................112Table 20. H3.2 - Correlations.................................................................................................112Table 21. H3.3 – Correlations................................................................................................113Table 22. H3.4 - Correlations.................................................................................................114Table 23. H3.4 – Correlations................................................................................................114Table 24. H3.5 - Correlations.................................................................................................115Table 25. H3.6 - Correlations.................................................................................................115Table 26. H3.7 - Correlations.................................................................................................116Table 27. Descriptive Statistics Model 1...............................................................................121Table 28. Correlation Model 1...............................................................................................122Table 29. Model Summary – Model 1...................................................................................123Table 30. ANOVA – Model 1................................................................................................124Table 31. Coefficients – Model 1...........................................................................................125Table 32. Casewise Diagnostics – Model 1...........................................................................129Table 33. Descriptive statistics – Model 2.............................................................................132Table 34. Correlation – Model 2............................................................................................133Table 35. Model summary – Model 2....................................................................................134Table 36. ANOVA table– Model 2........................................................................................135Table 37. Coefficients – Model 2...........................................................................................137Table 38. Casewise Diagnostics – Model 2...........................................................................139Table 39. Descriptive statistics – Model 3.............................................................................142



Table 40. Correlations – Model 3...........................................................................................143Table 41. Model summary – Model 3....................................................................................144Table 42. ANOVA – Model 3................................................................................................144Table 43. Coefficients – Model 3...........................................................................................146Table 44. Casewise Diagnostics – Model 3...........................................................................147Table 45. Descriptive Statistics – Model 4............................................................................150Table 46. Correlations – Model 4...........................................................................................150Table 47. Model Summary - Model 4....................................................................................151Table 48. ANOVA – Model 4................................................................................................151Table 49. Coefficients – Model 4...........................................................................................152Table 50. Casewise Diagnostics – Model 4...........................................................................153Table 51. Descriptive Statistics – Model 5............................................................................156Table 52. Correlations – Model 5...........................................................................................156Table 53. Model Summary – Model 5...................................................................................157Table 54. ANOVA – Model 5................................................................................................157Table 55. Coefficients – Model 5...........................................................................................158Table 56. Casewise Diagnostics – Model 5...........................................................................159Table 57. The most Significant Variables of all groups.........................................................162

ANNEXES

Annex 1. Tables 1 – 13.

Table 1. Correlation Matrix of Dependent Variable and Independent Variable....................210Table 2. Longevity of businesses surveyed............................................................................214Table 3. Number of employees of companies surveyed........................................................214Table 4. Collinearity Diagnostics – Model 1.........................................................................215Table 5. Residuals Statistics – Model 1.................................................................................215Table 6. Collinearity Diagnostics – Model 2.........................................................................216Table 7. Residual Statistics – Model 2...................................................................................216Table 8. Collinearity Diagnostics – Model 3.........................................................................217Table 9. Residual Statistics – Model 3...................................................................................217Table 10. Collinearity Diagnostics – Model 4.......................................................................218Table 11. Residuals Statistics – Model 4...............................................................................218Table 12. Collinearity Diagnostics – Model 5.......................................................................219Table 13. Residuals Statistics – Model 5...............................................................................219



List of Figures

Figure 1. General process of the research................................................................................23Figure 2. A model of strategic entrepreneurship......................................................................47Figure 3. Levels of barriers (internal and external)..................................................................63Figure 4. Small business growth barriers (Storey, 1994).........................................................64Figure 5. The conceptual model of data processing.................................................................71Figure 6. Number of Predictors................................................................................................73Figure 7. The questionnaire design scheme.............................................................................75Figure 8. Relationship between SB Growth and barriers.........................................................85Figure 9. How Kosovo and comparator economies rank on the ease of doing business.........87Figure 10. Doing business topics ranking position..................................................................90Figure 11. Research process...................................................................................................100Figure 12. Level of education of Owners / Managers............................................................105Figure 13. The regions of business location...........................................................................106Figure 14. Type of business...................................................................................................107Figure 15. Longevity of businesses surveyed........................................................................108Figure 16. Number of employees of businesses surveyed.....................................................108Figure 17. Multiple regression variables................................................................................117Figure 18. The Estimation Process for Multiple Regression.................................................119Figure 19. Histogram of normally distributed residuals - Model 1........................................130Figure 20. P–P plots of normally distributed residuals – Model 1.........................................131Figure 21. Histogram – Model 2............................................................................................140Figure 22. P–P plots of regression normally distributed residual – Model 2.........................140Figure 23. Scatterplot – Model 2............................................................................................141Figure 24. Histogram – Model 3...........................................................................................148Figure 25. P–P plots of regression normally distributed residual – Model 3.........................149Figure 26. Scatterplot – Model 3............................................................................................149Figure 27. Histogram - Model 4.............................................................................................154Figure 28. PP Plot of regression – Model 4...........................................................................154Figure 39. Scatterplot – Model 4............................................................................................155Figure 30. Histogram – Model 5............................................................................................160Figure 31. Normal P-P Plot of Regression – Model 5...........................................................160Figure 32. Scatterplot – Model 5............................................................................................161



CHAPTER 1

1. INTRODUCTION

A permanent question nowadays to scholars of economics, government leaders, managers of

institutions for economic issues and others is why small businesses do not grow, or some of

them fail to grow. One of the possible ways of explaining this question is through the notion

of the barriers to growth. These obstacles (barriers) are generally defined as internal factors

or conditions that restrict the external growth potential of the firms that want to grow (Storey

D. J., 1994a).

The main objective of this study is to recognize the barriers to small business growth.1The

challenge on creation and growth of new businesses is acentral topic that paced the process of

transition. This is because the economic system change from communism to capitalism

means a redistribution of resources, a process in which new firms are the main factors of

economic growth. As a period of economic and institutional changes, the transition creates

countless opportunities for entrepreneurs, incentives for innovation and efficiency(Hayek,

1945).

One of the countries with aspecial process of movement over these stages is Kosovo,under

conditions of damaged almost the entire former economic enterprises. Small businesses are

the engine of the entire economy of the country and a key factor of development. Especially,

in transition countries, they are faced with many obstacles in their business activities:

administrative barriers, limited access to finance, lack of technologies, skills of

entrepreneurship and so on (Kume, V. et al., 2009). In a transition economy, entrepreneurs

are faced with numerous formal and informal difficulties(North D., 1990).

1In this doctoral thesis, the notion “small business” used, is the European Union Commission Recommendation’s 2003/361/EC as published in the Official Journal of the European Union L 124, p.36 of 20 May 2003, Micro enterprises are included in the definition of SMEs. According to this recommendation, micro firms have up to ten employees; small enterprises are those with less than 50 employees; and firms with employees from 51 up to 250 are classified as medium-sized. This classification has been adapted from Kosovo’s institutions as well.



Even significant contribution to this issue there is enough space to accomplish this

phenomenon. Existing researches offer us an incomplete representation of small business

development by neglecting extensive studies about the process of growth and barriers in

transition countries. The main objectives of the research are to identify the barriers which

have impact on small business growth and to understand clearly the correlation of different

environmental factors which influence the growth.

This dissertation is an extension of many scholars’ efforts to contribute in the identificationof

the many obstacles and barriers that hinder business growth and giving recommendations for

the better solutions. Is evident the fact that there are a lot of studies related to this topic but

most of them failed to explore the main factors that hinder business growth in transition

countries. Many authors gave asignificantcontribution, but the approach of many of them to

the phenomenon waspartial not and inclusive. The study takes modest steps to address these

shortcomings by identifying critical factors from the perspective of small businesses

owners/managers, approaching to an empirical study and also to the qualitative research

based on newest literature andinvestigation of therealcurrent situation in Kosovo. This study

is based on what (Davies, C.D. et al., 1985)are referred to the method of "course of study," in

which the results from the first study will influence how to proceed with the second study.

Small businesses have a very significant role ingeneral economic development of any country

and for Kosovo as well. This study will explore the basic theoretical concepts of the business

growth and barriers that hinder the growth. Some of the existing theories of small firm

growth fail to explain the determinants of small firm growth that underline economies in the

transition and economies in thedowngraded and risky environment. They do not sufficiently

take into account the institutional features and business environment, and their impact on

entrepreneurship and SME growth, which usually involves high transaction costs of doing

business (Krasniqi, 2007). Barlett (2002)emphasizes the large entry of businesses in the

market in transition countries, and therefore barriers are more present due to the existing

spaces of negative action.Many researchers have identified numerous barriers to the growth

of thesmall firm sector in those countries. Institutional commitment on the reforming the

business environment is one of essential areas in the global development agenda. But our

institutions and policy makers of economic development not succeeded to builda healthy

environment of doing business.



The intention is to present the perceptions of the owners/managers of enterprises to barriers

that hinder the business growth. The study is also distinctive in the following aspects. It

focused in particular on theKosovo’s growth obstacles of businesses as a special one and

explored the main key factors that make a business environment turbulent with a very high

level of uncertainty.To learn about the real barriers to small businesses in Kosovo, are

conducted 20 interviews face to face with selected business owners/managers. Initially,

important information was gained in generating ideas for access to the main study and clear

definition of objectives. The same ones served to improve the questionnaire for the main

study survey and the questionnaire has been drafted.In order to achieve the objectives of the

studyis prepared the research planto proceed with data collection. The comprehensive

questionnaire survey including 51 questions related to main factors that are considered as the

obstaclesto small business growth in Kosovo has been distributed.To develop econometric

analyses used the SPSS software, multifactorial analyses through multiple regression models

and then from data refined are obtained statistical outputs (details will see in chapter 5).

The importance of this study consists of thehigh necessity of Kosovo society and beyond,

policy makers, institutions, academics and other groups of interests.The research questions

are organized in comprehensive level, exploring what are the most important results -

changes happen through the last five years and describe the “critical incidents” that

influenced the growth or hinder the growth. Additionally, are developed the interview

questionnaires referring the internal and external (legal and regulatory issues) factors that

hinder the small business growth in Kosovo.

1.1. PHILOSOPHICAL APPROACH

The main purpose of the research is the investigation and discoversreliable facts based on

objectivity and impartiality as the fundamental principles on which can be drawnrelevant

conclusions and recommendations. In view of this objective, the research modelof the

positivist approach is presented in Table 1.

As can be seen in Table 1, the philosophical basis of research paradigm is the theory of

logical positivism where there is an objective of reality. In this case ontology paradigm is

objectivity. But in terms of epistemology or the results of previous scholars, we are actually

approaching the facts, how the problems impartially are handled and the assumption that we

can draw from them as a credible knowledge that we can apply to our problem of research.



The methodological approach is heuristic search, therefore having the general theory as a

starting point and going to focus on the specific problem, we do propose theoretical

propositions or research issues that we test for the existence of causal connections.

Table 1. Aspects of the research philosophy - paradigm elements of our assumptions used in the research

Elements of paradigm Assumptions In our search use:

Ontology Our assumptions about reality Objectivism. The author assumes that there is a reality that does not depend on him, and that can be recognized tentative.

Epistemology Our assumptions on the report seekers - reality

Positivism. The author assumes that the observable facts in the objective reality of the above constitute reliable knowledge.

Methodology Our attitude toward the working approach

Deductive approach - from theory to theoretical propositions to be tested.

Methods Our attitude towards techniques and combining them

Quantitative methods of collecting data and econometric models to test hypotheses.

Source: Author's reasoning based on – Skreli, (2015).

Through this approach, we seek to recognize and explain the objective reality. We

accomplish this methodological approach using appropriate quantitative methods and

econometric models, such as multiple linear regressions assisted by descriptive statistics. A

detailed presentation of the methods used for navigation and justification made in the

following sections of this chapter(seeSkreli, 2015).

1.2. PROBLEM STATEMENT

The Recentliterature explains that the active growth of the private sector, especially SBs, has

been one of the main driving forces for economic improvement in transition economies.

Moreover, expanded research into the experience of other transition economies, notes that the

promotion of entrepreneurship and SBs remains the only and the best to encourage economic

development of any country.



It is generally accepted that the problems of doing business are evident in Kosovo and need

prompt action from scientific motivated to study this topic and to disseminate the quality

studies in benefit of governments, policy makers, and all other affected institutions. In

particular, this is in the interest of SBs and wider society. This will motivate

owners/managers of businesses as well.

Small businesses, particularly growing small businesses, are considered by academics and

policy makers as theveryimportant basis of wealth creation, employment generation, and

innovation.The benefits of small business growth are wide-ranging. The greatest economic

benefits are well known and include the creation of wealth, jobs, and innovation (Janssen F.,

2003); (Carter, S. et al., 2000); (Carter. S. and Jones-Evans, D., 2006).

Yet, few small businesses grow. One potential way of explaining why so many businesses do

not grow is through the notion of 'barriers.' Previous studies on barriers typically identify and

predict what kinds of barriers affect business growth, rather than attempt to explain the

details how or why these barriers exist and their implication to specific cases and in general

(Rachel, 2009).

Unlike most transition economies, SBs in Kosovo are not sufficiently developed; they

continue to face an unfriendly environment. Kosovo now faces the challenges of

theintegration process and economic development through the process of improving

institutional capacities and a more favourable business environment. In this regard,

entrepreneurship in risky contexts is an under-researched topic(Naude, 2007). Kosovo still is

in the last stageof consolidation and transition process. The path started with thecreation of

small businesses proceeding continuously. Today they are the most contributors to economic

growth. Therefore, in relation tofirm growth,the theory can be more inclusive, and

theresearches should be directed toward small business growth in transition and under

limitedconditions. The inadequate and often hostile institutional environment in countries in

transition was frequently mentioned as playing a major role in constraining small business

development (Smallbone, D. and Welter, F., 2001).

1.3. IMPORTANCE OF THE STUDY

The perception about small firms and their role in the economy has changed evolutionary

over time.Predominantperception through the early part ofthe20th century of many scientists



headed the attention of researchers to investigate the growth of large firms, due to the general

perception that large firms grow faster than small firms and consequently they are the benefit

of the general economy.The conventional wisdom in economic theory has long held that, due

to economies of scale and scope, the growth of firms is positively related to their size. Large

firms were typically expected to have advantages over small firms and so grow faster. This

process was expected to lead concentration into growing of industry(Barlett, W., Bukvic, V.,

2001).Nowadays, this perception has changed, and researchers have started to recognize the

role of SBs in job creation, innovation and general economic development of anycountry.

Currently, many countries see SBs as the main source of job creation and consequently

contributors to the growth trends process.Now widely is accepted that SBsare the main

engine of new job generation and improve the general economy of any country. SMEs are the

main pillar of the economy of each country. They are a key source of employment,

cultivation of entrepreneurial spirit and innovation and are therefore crucial to strengthening

competitiveness and employment. SMEs are most vulnerable to changes in the business

environment. They should be considered as key drivers for innovation, employment as well

as social and local integration in Europe. In all member states of the European Union, there

are about 23 million SMEs, representing about 99% of all enterprises in the EU, employing

about 75 million people and accounting for about 65% of sales and 67% of employment in

the European Union(EU, 2005).

Understanding the importance of SBs in general, researchershave understood very well the

importance of exploring factors that affect their growth on both sides, factors coming from

inside the firm and external factors. Some regions and countries remain behind on

thisattention, especially faced with a lack of data. One of these countries is Kosovo as the last

countrywhich faced the transition process from a central controlled to a free market economy

in the Western Balkans. Kosovo as a country emerging from conflict makes a unique case

with a difficult environment for entrepreneurship development (Solymossy, 2005).

The population of Kosovo is 1.8 million. Around 30.5% of Kosovo’s labor force is

unemployed(ASK, 2017). This has resulted in extensive social and political tensions, with

poverty, migration, and crime being presentthreats in Kosovo. The incapacity of the SBs

sector to create new jobs, largely as a result of the hostile economic environment, has

significantly handicapped the perspective ofthe economic development in Kosovo. According

to MTI, SMEs in Kosovo, like to other countries are evaluatedto be for more than 99% of



total firms, and they have the main role in economic growth and overall development of the

country.

The main purpose of this thesis is to conduct empirical research that will analyze thebarriers

and factors that impact small business growth in Kosovo. Understanding these factors and

their respective intensity can help on developmentsof strategies and policy recommendations

that will serve topolicy makers to provide a more sustainable economic environment.

The importance of this dissertation consists as well, on thehigh necessity of Kosovo society

and beyond, policy makers, institutions, academicians and other groups of interests. There are

a lot of studies on this topic but in this thesis are explored more factors that hinder the

business growth in Kosovo. The main course on small business development is the

investigation of the barriers to the growthand proposal of strategies and policies how to

overcome those (Sanders, M. et al., 2009). Is evident the fact that many authors hesitate to

disclose the institutional barriers to business growth, those which are related politics and

people of power. As of owners/managers,there are a lot of arguments that some of the

barriers come from people in power, butstill,some owners hesitate to declare on this issue.

There are many studies related to business growth in transition countries but, still, there are

the gaps that extensively need to be accomplished with anew idea, especially related to

thespecificationof Kosovo’s path and rhythm of development. They do not sufficiently take

into account the institutional features and business environment, and their impact on

entrepreneurship and SME growth, which usually involves high transaction costs of doing

business(Krasniqi, 2007). A more serious potential problem which may hold up transition is

barriers to the growth of firms, especially the potentially dynamic fast growth firms that will

provide the largest part of future employment growth and be the seeds of the successful large

firms of the future economy” (Barlett, W. and Bukovic, V., 2001, p. 2).Various researchers

have identified some barriers to the growth of small and the medium firm sector in countries.

Therefore, more contribution to this topic will step forward to explore key barriers that

seriously are hindering the business growth in Kosovo.

1.4. THE OBJECTIVES OF THE RESEARCH

The general objective of this research is to elaborate the understanding of barriers to small

business growth exploring the best literature and selecting the adequate scientific theories that

explain the phenomenon. In particular, the objectives are:



To examine the main barriers to small business growth from the perspective of the

individual - owner/manager, highlighting the meaning of barriers and how they

are correlated to small business growth.

To identify the main barriers internal and external through interviews and surveys

conducted from the first hand directly from businesses as they are perceived by

owner/managers of small businesses.

To explore and study how the firm can survive in an unfavorable business

environment in which the barriers are evident and to find the modalities how to

overcome the barriers.

To identify benefits from the growth of businesses.

To explore the internaldeficiencies of the SBs and identify the factors that hinder their

growth.

To make a resourceful research of the barriers and make recommendations and draw

conclusions for businesses and policymakers in order to reduce barriers and to

build a strategy for improving government policies and the general situation.

Additionally, the objective of the research is to see how new entrepreneurs can contribute to

economic growth throw creation of new mindset of entrepreneurship development, based on

contemporary models of doing business.

The main purpose of this research is the examination of the barriers to SBs growth and their

impact on the sustainable development and finding the appropriate strategies to overcome

these barriers.

The analysis of the current situation of small enterprises, in order to identify problems,

obstacles and barriers that suffocate development of their activities, the needs, and

requirements of these enterprises will find the modalities of “healthy” developments and

reduce their daily concerns. According to Welter F. (2011) the situation that provides

individuals with entrepreneurial chances by setting borders for their actions, and it is essential

for understanding when, how, and why business actions happen and who is involved. This

means on the general factors that are surrounding the small businesses; this is the

environment of doing business.



Another objective of this research is to produce relevant results to the current situation,

problems, difficulties, and to measure the trends of the development of small enterprises, to

determine the criteria and recommendations for the support their development. Analysis of

obstacles and difficulties for business development including the main barriers as lack of

skilled staff, lack of training in various areas, the lack of access to the market and cost of raw

materials, use of modern technology, workspace, physical infrastructure, transportation,

finance, legal, economic and fiscal policies, trade policies etc. There are a lot of studies

related to this topic but, still exist a considerable space for critical review from the past and

fulfill the gapes. Critical evaluation of studies investigating barriers to the development of

small businesses in this and similar contexts is required (Doern R. , 2009).

This study will make a clear picture exploring the main external barriers related to

institutional barriers, market-related barriers, infrastructure barriers and internal barriers

including human resources and lack of their capacities to face daily challenges.

1.5. RESEARCH QUESTIONS AND HYPOTHESES

The research questions aim is to explore what are the most important results and changes that

happened through the last five years and describe the “critical incidents” that influenced or

hinder the growth of small businesses. Therefore,the primary questions, with special

attentionare addressedto the negative impact of several barriers to SBs growth in Kosovo.

There are many obstacles and barriers which seriously damage the business climate in

Kosovo. Most of the barriers are related to the institutions and government policies with

impact on the business environment.

The rising of the hypothesis proceeds after possession of some relevant data.To do that there

are identifiedfacts that can be measured and then can beanalyzed. The analysis of the data

should be supported on thetheory thatwill give usthe cause to modify the theory,always being

sure that the processes of data collection and analysis in one side generating theories on the

other are essentially linked. Theories lead to data collection/analysis, and data

collection/analysis informs theories! (Field, 2009).

Hypotheses raised are formulated according to the current situation incontext of barriers to

thegrowth of SBs. The 3 first hypotheses are formulated to investigate the perception of

owners/managers related to the existence of the barriers and how they influence their



behaviour and intention to grow. Others are assumed questions on therelationship between

growth and barriers that hinder the growth: institutional barriers, access to finance, tax

policy;the rule of law barriers, market barriers, infrastructure and skills of HR barriers.

Hypotheses were tested through bivariate “Pearson Correlation” and confirmed by using the

approach of structural equation modeling through MLR (chapter 5).

Based on the literature, the preliminary study and the knowledge about the barriers to the

growth of small business in Kosovo have been raised threemain hypotheses and 7 sub

hypothesis, in order to examine how business environmental barriers affect the growth of

SBs. In this framework the following hypotheses will be tested:

H1 - Barriers of doing business in Kosovo are present (more barriers, less growth of SBs)!

H2 - Despite the existence of barriers, the owners / managersdon’t give up to the growth

intentions!

H3 - Presence of barriers influence owners/managers to cancel their objectives of business

growth!

H3.1 – Difficulties in accessing to finance for small businesses are obstacles to their

development!

H3.2 – Inadequate tax policy influence negatively to small businesses growth!

H3.3– Lack of institutional support is an obstacle to small business growth!

H3.4 – Barriers related to Rule of Law are the most evident obstacle to small business

growth!

H3.5 – Barriers related to the market hinder SBs growth!

H3.6 – Barriers related to infrastructure constrain the SBs growth!

H3.7 – The lack of adequate HR, influence negatively to business growth!

To test these hypotheses are arranged the questions in the survey questionnaire, and the

dataare conducted through SPSS 20.0.Output analyses and interpretation are made properlyin

chapter 5.



1.6. DESIGN AND ORGANIZATION OF THE STUDY

The research is defined as "the systematic process of collecting and analyzing information, in

order to increase our understanding of the phenomenon about which weare concerned or

interested”(Leddy, P. D., and Ormrod, J.E., 2005). Key points in this definition are logical

steps and methods of systematic collection and data analysis of thestudy, which represents the

main research steps that are definedas amethodology. In this way, this thesis investigates the

problem using an organized, systematic exploration and justifies the data, based on a reliable

theoreticalframework, to assist in finding a suitable solution.Thus, this study can be described

as part of theinvestigative search, whichis based on application and conceptual testing of the

theoretical structure, using empirical methods(Figure 1).

Figure 1. General process of the researchdrafted by author

The study is organized into 6 chapters. In the first chapteris included the introductory part of

the research, general description of the subject, problem statement, the purpose of thestudy,

research objectives and research questions and hypotheses.At the end of this chapter is

presented the formulated scheme of the research, objectives, paradigms and the structural

process of the dissertation.

In the second chapter is displayed a depth overview of the literature used. Mainly, this

chapter will include the contemporary literature that clearly explains the main factors of small



business growth. In this chapter, additionally are comprised analyses of the most relevant

publications written for the main purpose of the study: why most of the small businesses do

not grow? What is the firm growth and which barriers mostly hinder the growth? Which are

barriers that hinder the growth; Entrepreneurship and business growth in transition

economies? The concepts of entrepreneur and entrepreneurship, historical evolution of the

phenomenon have been treated as well.Furthermore, will be explored the concept of this

phenomenon in transition countries like Kosovo. There is described the key concept that

explains the phenomenon that specifically corresponded to the Kosovo’s situation on

thefocus of barriers to small business growth.

In the 3rdchapter is disclosed the research methodology. In this chapter are explained in

details the motives for selecting the questionnaire, the principles that served to build

theresearch concept,population, and sample of the research, determination of size selection

and procedures for data collection. There are explained the different qualitative and

quantitative methods of data collection, primary, secondary and others. The exhaustive

questionnaire will be explored which is formulated in accordance with the hypotheses and

research objectives related to main key factors that hinder the business growth, thus, to be

comprehensive and reach the real objectives of the study.

In the 4th chapter are presented some data of the economic situation of Kosovo in general,

underlining the obstacles of growth, exploring main factors that damage the environment of

doing business. This chapter will be dedicated to the business environment in Kosovo,

through prior publications in the field of growth ofSBs. The empirical data are used to

generate anew idea and fill the gaps in this field.

In the5th chapter are shown statistical data from the field research, described the results of

research, interpretation,and data analysis generated by verification the models used and

hypotheses testing through SPSS 20.0 version.

In the 6th chapter are summarized research findings, and their practical

importance,discussions, general conclusions, and recommendations.Furthermore, this section

will show the significant findings and benefits of the research for the businesses, policy

makers, scholars and theentire society. This chapter contains the conclusions retrieved by the

investigation, in the depth analyses of the perceptions of managers/owners for growing in the



future and their behavior related to barriers. At the end of the study will appear restrictions

and recommendations for further research of the phenomenon in the future.



CHAPTER 2

2. THEORETICAL BACKGROUND

The theoretical framework is described as "a group structure related (concepts), definitions

and propositions that present a systemic phenomenon, specifying relationships between

variables in order explaining and predicting phenomena” (Kerlinger, 1979).

Theoretical approaches frames are a collection of theories and models from the literature,

which strengthen positivist research study and determine its factors, which have been

investigated and fully explained in this chapter.

The aim of the theses is mainly the investigation of the firms in thematurity stage of growth.

According to the Veciana (1999),no unique theoretical model is explaining the post-entry

performance of firms. Furthermore, Acs, J.Z.,(2008)says that until now there has been no

theory which explains rapid firm growth. Many authors utilized different theoretical

modalitiesto investigate small business growth and entrepreneurship. In the frame of growth

theory, some models try to explainthis phenomenon. Investigators from all fields of social

sciences (economics, sociology, psychology, and politics, etc.) have been making efforts to

contribute to this area of study. In its simplest form, Gibrat’s law suggests that the expected

growth rate of a certain firm is independent of its size at the launch of the period examined

(Gibrat, 1931). In the beginning, some of the studies are supported by this theory and served

as amodel to promote the further discussions.Moreover,Simon, H. A., and Bonini (1958)

shown that fast growth is unrelated either to size, its prior growth or its age. Later on, most of

the studies conducted within this framework have shown a tendency to reject Gibrat’s Law,

stressing that smaller firms grow faster. There are others models of firm growth, as identified

by Brock and Evans (1986) for small enterprises. They are the stochastic model, the human

capital model, and the learning-by-doing model. A significant contributionfor analyzing the

small business growth has been the resource-based view, which reflects the influential study

of Penrose (1959). She concluded that growing discrepancy was a result of illegal resources

and activities, particularly management skills and behaviors, in addition to strategic

capabilities to identify growth potentials. Penrose's idea of strong growth considers firms to



grow due to "growth economies" that are inherent in theprocess of growth and not due to of

any sizerelated advantage.In the context of stochastic models, the opposite to Gibrat’s legacy

is the Jovanovic’s learning model which explains that new firms gain information about their

effectiveness only post market entry stage and can learn based on previous experiences,

therefore new, and small businesses should grow faster in order to

surviveJovanovic(1982).This influential model of firm growth has been discussed extensively

in the literature (Storey D. J., 1994a); (You, 1995); (Liedholm, C., and Mead, D. C., 1999);

(Barlett, W. and Bukvic, V., 2001); (Krasniqi, 2007);(Vivarelli, 2007).Also, he assumes that

firms have different efficiencies which are not directly observable. The efficiency of the

firmcan only be learned gradually after the company entersinto production. After learning

about its true abilities, a firm will adjust its behaviour. Firmschoose output levels to

maximize expected profits by imperfect information ontheir efficiency concentrations in each

period. They also update their expectations based on theproductivity level. Thus, this learning

model synthesizes the key elements of the human capital model and the stochastic model as

discussed above(Le, 2009).

Psychological theories such as those settled by McClelland (1965)and which focus

specifically on personal traits of the human factor, motives, and incentives of an individual to

engage in entrepreneurship, conclude that entrepreneurs have a strong need for achievement.

Within this similar framework, (Davidsson P., 1989)is underlined achievement motivation as

the most important factor explaining thevariationin growth rates and entrepreneurship.

Furthermore, the “population ecology” or “organizational ecology” perspective comes from

sociology and the seminal contribution of (Hannan, M. and J. Freeman, 1984).

Since the content of the research has a heterogeneous character with many implications to

other disciplines, like economics, finance, human resources, psychology, sociology,

anthropology and so many others, the approach of using different theories will enrich the

value of the research, will be more comprehensive givingto the study a multidimensional

character.

We argue that, because of the integrative nature of strategy research, it is imperative for

research to adopt severalstructures represented by different theories for the progress of the

field.Recently, the role of entrepreneurship has also been highlighted by the “new growth

theory” with its prominence on “knowledge” as a key factor stimulatingis fundamental for

economic growth (Romer, E. 1990, 1994).Also,if we look new theories of growth,many



authors have identified and confirmed that key mechanisms, through shared knowledge

contribute to job creation and overall economic growth (Schmitz, 1989); (Nooteboom, 1994);

(Audretsch D.B. and A.R. Thurik, 2001). The new growth theory has been used and

developed by many authors (see Davidsson, 1989, 1991, 2002, 2010; Storey, 1994a; Hashi,

2001, 2010; Krasniqi, 2007, 2011). So, in this research will be used different theories but

mostly focused on new growth theory based on theinfluence of institutional theory and the

worth of human capital theory.

Last decades the interest in the research for barriers have been increased due to system

management transformation. During thetransitionprocess, many countries failed to build the

consolidated institutions and faced with many problems. Recently, the relationship between

growth and barriersis a great interest for the young scholars.Different kind of obstacles tends

to prevent SBs to achieving their full potential(Gruda, S., and Milo, L., 2010). Mostly, the

obstacles are related to the business environment. The business climate is a multi-

dimensional concept representing the fundamentalissue of economies in transition. In

transition countries, lack of legislation, limited access to entrepreneurship, lack of

infrastructure and financial resources are major obstacles to policy-making for SME

development. Most transition countries are aware that SMEs have a critical role in industrial

restructuring and have formulated national SMEs development policies, enterprise

development programs (Zahra, Sh. A. and Ellor , D., 1993); (Lumpkin, G. T. and Dess, G. G.,

1996); (Hashi, 2001); (McMillan, J and Woodruff, C., 2002); (Pissarides, F, Singer, M., and

Svejnar, J., 2003);(Smallbone, D. and F. Welter, 2006) and(Iraj, H. and Krasniqi, 2011).

2.1. The Entrepreneur and Entrepreneurship

Entrepreneurship is an ancient concept that is both simple and complex at the same time.

Conceptualizations, definitions, understandings of the phenomenon, havechallengedmany

scholars over the time allover the world. As quoted by Thomas M. C. (2012) “It is still a

topic of much debate whether entrepreneurs are born or made. While it is generally

acknowledged that there are natural ‘born’ entrepreneurs, there are also researchers who

believe that entrepreneurship is a skill that can be learned”. Drucker (1985) argued that

entrepreneurship is a practice and that “most of what you hear about entrepreneurship is all

wrong. It’s not magic; it’s not mysterious, and it has nothing to do with genes. It’s a

discipline and, like any discipline, it can be learned.” If one agrees with Drucker’s concept of



entrepreneurship, then it follows that education and training can play a key role in its

development. In a traditional understanding, entrepreneurship was strongly associated with

the creation of business, and therefore it was argued that the skills required to achieve this

outcome could be developed through training. More recently entrepreneurship is being

viewed as a way of thinking and behaving that is relevant to all parts of society and the

economy, and such an understanding of entrepreneurship now requires a different approach

to training. The educational methodology needed in today’s world is one which helps to

develop an individual’s mindset, behavior, skills, and capabilities and can be applied to create

value in a range of contexts and environments from the public sector, charities, universities

and social enterprises to corporate organizations and new venture start-ups. Lichtenstein and

Lyons (2001) argued that it is important for service providers to recognize that entrepreneurs

come to entrepreneurship with different levels of skills and therefore each entrepreneur

requires a different ‘game plan’ for developing his or her skills. Furthermore, they suggested

that skill development is a qualitative, not quantitative, change which demands some level of

transformation on the part of the entrepreneur (Thomas M. C., 2012).

Entrepreneurship research has a long tradition, and since the 1980s the field has grown

significantly. Landstrom H.et al., (2012) in their study identified the ‘knowledge producers’

who have shaped the field over time and their core entrepreneurship research works. Creating

a unique database consisting of all references in twelve entrepreneurship ‘handbooks’ (or

state-of-the-art books) has been developed by them. The chapters in these handbooks were

written by experts in the field, and it can be assumed that the most frequently cited references

represent ‘core knowledge’ with relevance to entrepreneurship research. They concluded

“From our analysis, it appears that entrepreneurship is a rather changeable field of research,

closely linked to disciplines such as ‘management studies’ and ‘economics.' Over time, the

field has become more formalized with its own core knowledge, research specialities and an

increasing number of ‘insider works.' However, it is still based on some fairly old theoretical

frameworks imported from mainstream disciplines, although during the last decade we have

seen the emergence of a number of new field-specific concepts and theories. We argue that

successfully develop entrepreneurship research in the future, need to relate new research

opportunities to earlier knowledge within the field, which calls for a stronger ‘knowledge-

based’ focus. We would also like to see greater integration between the fields of

entrepreneurship and innovation studies in the future”.



2.2. Historical Evolution

The concept of entrepreneurship has been around as long as man has existed. But, serious

studies and documentation for this issue, arefound only a couple hundred years ago, and the

field of entrepreneurship studies has only been seriously coalescing over the last fifty years.A

handful of academic programs focused on entrepreneurship by the mid-1970’s but has grown

to universal acceptance in the curriculum, research and practice. Let’s go back in time to

reference some of the more critical writings on entrepreneurship chronologically. Throughout

this journey, are recognized the many nuances of the concept, causation and

person/environment controversies that contribute to the fuzziness of the understanding of this

concept. If we analyze the concept of entrepreneurship,we understand that is a visionary

discipline and sensitive which require the comprehensive approach in order to improve the

theory of organizational of modern systems. We lead to the application of open systems

theory (some other similar theories that we discuss that are closely aligned to open system

thinking a complex adaptive theory, systems theory,emergent theory and learning

organization theory). Illustrating how systems theory has emerged as a point of light from

this mass of literature; and how the future study and practice of entrepreneurship might

proceed along the time we conclude that systems based on some logical assumptions arising

from systems theory that helps synthesize a definition that is more operational, capturing

behaviors that constitute entrepreneurial events and subsystems epitomized by the American

socioeconomic system (Falcone, Th. and Osborne, S., 2005).

As per historical evolution of Veciana(1999)and continued later on by Rialp, A. et al., (2004)

are divided some stages of the entrepreneurial development and approaches:

1st stage(until Marshall): Authors tried to define the entrepreneur and the entrepreneurial

function to explain the entrepreneur’s profit as a distinctive rent (fourth production factor).

2nd stage (first half XX- Century): entrepreneurs, firms, and their functions are considered as

agents and factors promoting economic development often adopting a historical perspective

(biographies, business development courses and so on).



3rd stage(50s-70s): theinitial stage of development as a unique scientific research program in

two main streams: a) research on new firms and SMEs management and b) the scientific

analysis of the entrepreneur and the business creation process (Veciana, 1999).

During the post - World War II era, the importance of entrepreneurship and small businesses

seemed to be fading away (Audretsch, 2003).The role of entrepreneurship in the economy has

changed dramatically over the last half-century.

2.3. Entrepreneur and Entrepreneurship – Some concepts

The word entrepreneur assumed dates from the 12th century, and is related to the French

word “entrepreneur" who means “Doing something different, undertaken, and act a little

differently." Among of many definitions will be mentioned Richard Kantilon’s,

at“Entrepreneurship as an economic activity under conditions of uncertainty.” Peter Drucker

(1985)presents entrepreneurship as a business that is pre-arranged and carried out

systematically and effectively. He treats entrepreneurship as part of their business activities

and tasks leading, as an entrepreneurial management part. According to Drucker

entrepreneurial economy is a cultural and psychological phenomenon, as much as it is an

economic and technological phenomenon. Entrepreneurs we see as rule changes as something

that is acceptable, welcomed and necessary. Harvard’s Professor Jeff Timmons thinks that

entrepreneurs must have “helicopter tune." They should have the ability to deal with

everything in detail (Solymossy, M. and Merovci, S., 2006).

The first author that endowsthe meaning of entrepreneurship with a more precise economic

connotation was Richard Cantillon in his “Essaisurla Nature du Commerce en Général”

(1755/1999), in which he outlined the principles of the early market economy based on

individual property rights and economic interdependence. In the mid-eighteenth century,

theclassicaleconomic theory was developed based on Adam Smith’s critical work “Inquiry

into the Nature and Causes of the Wealth of Nations,” first published in 1776. To a large

extent, this work laid the foundation for the analysis the way of the market economy

functions, but it also influenced the view of the entrepreneur in the economy, who more or

less disappeared from economic theory for a considerable time(Landstrom, H. et al., 2012).

Although interest in entrepreneurship among economists seems to be shrinking, we can

identify a few exceptions. In this respect, Joseph Schumpeter is probably the best known of



the economists with interest in entrepreneurship in the early part of the 20 th century

(Schumpeter, 1912, 1934). Schumpeter’s idea was to build a new economic theory based on

change and newness. His basic realization was that economic growth resulted not fromcapital

accumulation, but from innovations or ‘new combinations’ that create a disequilibrium in the

market. Another view of the entrepreneur in economic theory was to be found in the Austrian

School of economic thought, represented by Carl Menger in the 19th century and further

developed by Ludwig von Mises and Friedrich Von Hayek in the 20th century(Landstrom,

H., et al., 2012). Formal definitions as ofAhmad, N. and R. G.,Seymour (2008)drawing on the

above analysis and arguments related to the entrepreneur,entrepreneurship, and

entrepreneurial activity are proposed:

Entrepreneurs are those persons (businessowners) who seek to generate value,

through the creation or expansion of economic activity, by identifying and exploiting

new products, processes or markets.

Entrepreneurial activity is the enterprising human action in pursuit of the generation

of value, through the creation or expansion of economic activity, by identifying and

exploiting new products, processes or markets.

Entrepreneurship is the phenomena associated with entrepreneurial activity.

As quoted by Shahidi, M. and Smagulova, A., (2008) following thirty years of intensive

study of the phenomenon, the research community still spends much energy on the definition

of the concept of entrepreneurship. This shows the complexity of the area as well as the

process, and that this could and should be exploited from several different concept of

understanding (Blenker, P. et al., 2006). Even though the definition of entrepreneurship has

been discussed among scholars, educators, researchers, and policy makers since the concept

was first established in the early 1700’s, there is still no complete consensus on the definition

of this field of study(Morales G, S.T., and Roig, S., 2005). Bygraveet al., (1991)defined the

entrepreneurial process as “involving all the functions, activities, and actions associated with

perceiving of opportunities and creation of organizations to pursue them.”

According toKirzner (1973), the entrepreneur is as a person who is alert to imperfections in

the market and can coordinate resources more effective thanks to information about the needs

and resources of different actors. Finally, we should mention the work of Frank Knight, who

in his thesis “Risk, Uncertainty and Profit (1916, revised 1921)” made an important

distinction between insurable and non-insurable risk.Arguing that uncertainty of



entrepreneurial activity isa result of activities that cannot be predicted and that

entrepreneurial competence is the individual’s ability to deal with uncertainty(Knight, 1971)

cited by Landstrom, H. et al., (2011).According to (Kirzner, 1973), entrepreneurship has two

most important aspects. First, entrepreneurship is the “alertness” to new opportunities.

Entrepreneurs are alert; this is what they like. Second, entrepreneurship is seizing an

opportunity by taking further innovative actions. Entrepreneurs are innovative; this is what

they do. According to his theory, alertness leads to the discovery of new possibilities. If the

opportunity identified is an actual one, the entrepreneur will take action. Consequently, as we

can assume, alertness necessarily leads to innovativeand creativityactions(Landstrom, H. et

al., 2012).

2.4. Multidisciplinary Concept of Entrepreneurship

In general meaning freely we can say that entrepreneurship concept is a multidisciplinary

discipline in which are implicated many other sciences. The eclectic and pervasive benefits of

entrepreneurship are generating research questions thatinterest scholars in a variety of

disciplines. These issues have been primarily examined within the context of a scholar’s

home discipline while ignoring insights from other disciplines. This approach has left

entrepreneurship research as a widely dispersed, loosely connected domain of issues. In this

point of view, the authors explore entrepreneurship research in accounting, anthropology,

economics, finance, management, marketing, operations management, politicalscience,

psychology, and sociology. They seek to identify shared interests that can serve as a bridge

for scholars interested in using a multi-theoretic and multi-methodological lens to design and

complete entrepreneurship studies(Duane I.R., and Webb W., J., 2007).

Small firms are more risky than large due to substantially to the riskiness their environment

imposes rather than their organizational structure (Storey, D. and Cressy, R., 1996). This

issue is not related only to institutional barriers, but it is multifaceted and complicated

influenced by other factors, such as the behavior of the individual and the society. This is the

necessity that agenda of research on new phenomena in economics like innovation and

growth should look beyond the firm's level and use the new economic theories. (Castells,

2000).

In transition countries, the influence of the business environment is significant due to the lack

of rule of law and lack of the consolidated institutions. These factors have comprehensive



impact with branched stretch. In different point of view in the context of multifactorial

implications on the firm growth many authors tried to explain the phenomenon (Lumpkin, G.

T. and Dess, G., 1996); (Hashi, 2001); (Smallbone, D. and Welter, F., (2001a);; (McMillan, J

and Woodruff, C., 2002).Multidimensional concept of business environment is explained by

many authors, of course, a representing the normal institutional framework, the controlling

mechanism, macroeconomic equilibrium, technological opportunities, and industry growth,

including the rising demand for new products (Storey D. , 1999); (Tsai, H., et al., 1991);

(Zahra, Sh. A. and Ellor , D., 1993); (Smallbone, D. and Welter, F., (2001b); (Pissarides, F, et

al., 2003); (Clement, K., et al., 2004), (Hashi, I. and Krasniqi, B., 2011).

2.5. Institutional theory

As we stated earlier in this chapter, it was helpful the investigation on the transition literature

to see what kinds of theories, have been used to explain barriers to small business growth.

According to Meyer, K. E. and Peng, M. W., (2005) existing theories in international

business and management studies, such as related to economics theories, resource-based

theories, and institutional theories, may contribute to our understanding of key issues small

firm growth. Based on the literature review from previous studies to the same subject and

understanding the multidisciplinary characteristics of the phenomenon the new growth theory

focusing mostly oninstitutional theory and human capital theory have been used in this

research.

The institutional theory has proven to be a significant theoretical foundation for exploring a

wide variety of topics in different domains ranging from institutional economics and political

science to organization theory. The application of institutional theory has proven to be

particularly useful to entrepreneurial research (Bruton et al., 2010); (Welter, F., and

Smallbone, D., 2011b). Recent studies in the field of entrepreneurship support the idea of

growing prominence and explanatory power of the institutional theory. Particularly, the

institutional theory appears to provide a valuable theoretical framework in an environment

characterized by institutional volatility, social change, and transformation. In this context,

Bruton et al., (2008) and Henrekson, M., and Johansson, D., (2009) have argued that very

little is recognized about the institution's impact on the entrepreneur's behavior. They added

that in either transition or mature market economies, and that in terms of theory development

and extension, researchers employing institutional theory have focused extensively on

culture, and have largely ignored the impact of other institutions. Moreover, Henrekson, M.



and Johansson, D., (2009) argue that the literature specifically addressing the effects of

institutions on fast growing firms is scarce. Additionally, the institutional perspective has

served as theoretical path analyzing the impact of external barriers to new venture

development in emerging and transition economies (Aidis, 2005); (Meyer, K. E., and Peng,

M. W., 2005); (Wright, 2005).

The concept of “institutions” refers to the “the rules of the game in society” (North 1990),

which when stable can assist in reducing uncertainty and risk for individual behaviour, as

well as the transaction costs connected with conducting the entrepreneurial activity. These

rules include ‘formal’ institutions, such as the constitutional, legal and organizational

framework for individual actions, but also ‘informal’ institutions, which refers to culturally

transmitted codes of conduct, values, and norms (North,1990). Informal institutions embrace

unmodified attitudes, which are embedded in society, and which act as regulators on

individual behavior.In any context, institutions represent both sides of the process,

constraining and enabling forces concerning the entrepreneurship. However, in the initial

stages of transition, their role is often mainly a constraining one, as the environment is

characterized by a high level of uncertainty, associated with rapidly changing external

conditions and major institutional deficiencies. Such conditions can result in significant

additional operating costs for businesses seeking to comply (Smallbone, D. and Welter, F.,

2001a, 2010).

2.6. Entrepreneurship in Transition Economies

In this section, we will discuss the unique character of entrepreneurship in transition and analyze

how this might change over the time parallel to the transition process move forward.In one of

their study for entrepreneurship in transition economies of the Central and Eastern European

economies Karaye, M., and Çiftçi, (n.d), emerged the main characteristics of the

entrepreneurship in transition in these countries.In thestudy,they analyze transition

economies, CE(Central Europe), including the eight most advanced countries:(Czech

Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovak Republic and Slovenia) and

South-Eastern Europe, including Albania, Bulgaria, Croatia, Macedonia, and Romania. The

transition from controlled to a market economy is a lengthy process contained various

spheres of economic activities. New institutional processes are of key importance for

successful transformation. A market economy requires not only liberal regulation and private



ownership, but also adequate institutions. For this reason, thetransition can be executed only

in a gradual mannerbecause institutions building is a process based on new structural

organisms with various economic entities.Economic, historical, geographical and cultural

pasts of transition economies caused different performances during thetransition period. For

instance Center and East European countries, as different from other transition economies,

they had the advantage of having short central planning period because of their geographic

closeness to the European Union. For this reason, these countries applied transition reforms

more easily.Center and East Europe countries, except Albania, Bulgaria, Romania and former

Yugoslavia countries, made faster reforms according to other transition economies (Karaye,

M., And Ciftci, M., n.d.).

The transition economies have lower rates of entrepreneurship development than in most

developed and developing market economies as are observed. The difference is even more

marked in the countries of the former Soviet Union than those of Central and Eastern

Europe. We link these differences partly with the legacy of communist planning, which

needs to be replaced with formal market-supporting institutions. But many of these

developments have now taken place, yet entrepreneurial activity still remains low in many

places. To analyze this longer term issue, we highlight necessarily the slow pace of

development of new informal institutions and the corresponding social attitudes, notably

rebuilding the generalized trust. We argue that changes are even slower in the former Soviet

Union than Central and Eastern Europe because the communist rule was much longer,

leading to a lack of institutional memory. We posit that changes in informal institutions may

be therefore delayed until after full generational change,Saul Estrin and Tomasz Mickiewicz

(2010) in their discussion paper edited by IZA2.Entrepreneurship as a new discipline of

management during the last few decades has expanded extraordinarily on a world wide scale

considering a sustainable source of new employment, innovation and economic growth

(Morales S.T. and Roig S., 2005). Nevertheless, it is observed that knowledge of firm

growth is still limited (Davidsson, P. and Wiklund, J., 2000); (Wiklund, J. and Shepherd, D,

2003). The existing literature is highly fragmented. Small businesses are the engine of the

entire economy of the country and a key factor in the development of the Kosovo. But, in a

transition countries economy is very difficult to stand up and to develop without proper

support from the government institutions. Entrepreneurship as a creative soul is an essential

2Saul Estrin, Department of Management London School of Economics &Tomasz Mickiewicz University College London. IZA is Institute for the Study of Labour, Bon – Germany.



source of jobs, and a fundamental base for economic growth, create entrepreneurial spirit

and innovation and crucial role in fostering competitiveness and employment. Small

businesses are an individual key to entrepreneurial spirit and innovation and thus crucial to

ensure competitiveness and local economic growth (Gashi, Sh., 2015b). “The growth and

survival prospects of new firms will depend on their ability to learn about their environment,

and to link changes in their strategy choices to the changing configuration of that

environment”(Geroski P., 1995).

The influence of small firm growth to the general economic development of any country is

uncontested. “It is, incorrect to speak of small enterprises as a uniformly expanding and

active group. In transition countries, especially those how passed from central economy to

the free market economy there are a lot of problems on contest of doing business. Some of

them are still facing of understanding and applying the new mindset of entrepreneurship on

the free market competition economy and behaving on different uncertainty circumstances.

McGrath et al., (2000), understand an entrepreneurial mindset as a way of thinking about

business that focuses on and captures the benefits of uncertainty. In the transition countries

with low level of development uncertainty caused due to lack support from institutions and

missing of information for future behave in the business environment. “Uncertainty is a

perceptual phenomenon derived from an inability to assign probabilities to future events,

mostly because of a lack of information about cause/effect relationships,” (Hoskisson, R. E.,

and Busenitz, L. W, 2002). Adapting and applying the new mindset of entrepreneurship in

practice, including the segments with the impact of business growth is an issue of mandatory

for these countries.

2.7. Current Challenges - Problems Facing

The first assumption identified earlier is that individuals are motivated to form and grow

small businesses. It is therefore important for researchers to establish whether or not owner-

managers are actually motivated to grow and develop the business. The second assumption is

that barriers exist and there is a direct and strong link between perceived and actual

barriers,i.e., perceived barriers are the same as actual barriers. The third assumption of

previous research is that barriers perceived to be important prevent people from forming and

growing their business. However, judgments of barrier importance alone are insufficient to

predict growth.Doern(2009) and Davidsson (1991) argue that perceptions of phenomena are



significant in their own right. In his research on determinants of growth in Swedish SMEs, he

acknowledges the significance of subjective perceptions and their relationship to objective

measures. Subjective perceptions contain growth relevant information that is not captured by

objective measures, and subjective factors have their own objective effects.

Over the last three decades, the interest of scholars in SBs growthisincreased rapidly, and

studies on small business growth are not considering in lack of literature. However, this is not

sufficient reason that we know everything about the phenomenon. Definitely, a coherent in

detailed view on SBs growth still needs to be completed. Although the increase in the number

of studies on the growth of small firms still lags far behind in the shed light of this

phenomenon (Wiklund, J. et al., 2009).

The entrepreneur is the individual who undertakes some efforts and acts to start a business,

realize an idea in practice. The entrepreneurship environment from day to day becomes more

attractive phenomena for new scholars and other stakeholders. The recent literature such as

Smallbone (2003), Bartlett and Bukovic (2001) on this issue address several barriers which

can be classified as institutional barriers, internal organizational and resource barriers,

external market barriers, financial barriers and social barriers. Barriers are conditions that lie

in any operational situation making it quite difficult to make progress or achieve an objective

following the system and environment of practice (OECD, 2008).

2.8. WHAT IS THE FIRM GROWTH?

There are many scholars that gave a significant contribution in this field. But, discussing what

thefirm growth is we find to consult the opinions consideredthe only true of classic theory in

this area. Edith Penrose, in her seminal book, characterizes the phenomenon of growth as

follows: “The term ‘growth’ is used in ordinary discourse with two different connotations. It

sometimes denotes merely increasein amount; for example, when one speaks of ‘growth’ in

output, export, and sales. At other times, however, it is used in its primary meaning implying

an increase in size or improvement in quality as a result of a process of development, akin to

natural biological processes in which an interacting seriesof internal changes leads to

increases in size accompanied by changes in the characteristics of the growing object”

(Penrose, 1959). Within-industry studies, even more, specialized measures are conceivable,

such as the number of seats for restaurants or theaters, and the number of vehicles for taxi or

car rental companies (Bolton, 1971). Investigating from the perspective process of the change



in amount, growth can be measured with a sort of different indicators; the most frequently

mentionedto be sales, employment, assets, physical output, market share and profits

(Ardishvili, A., et al., 1998); (Delmar, 1997); (Weinzimmer, L., et al. 1998); (Wiklund J.,

1998). Among available alternatives the researcher has the choice to a) create multiple

indicator indexes; b) use alternative measures separately, and c) find the one best indicator. If

growth is conceived of as a latent construct with common causes but alternative

manifestations the multiple-indicator index makes sense(Davidsson P., 1991). The underlying

theory here is that the same explanatory factors facilitate or hinder growth across firms, but

that growth for some firms manifests itself. For example, radically increased sales turnover

without much change in assets or employment, whereas for other types of thefirm the result is

moderate and balanced growth across, e.g., in assets, employment and sales. The sum of

standardized versions of all three indicators would then be a better representation of the

theoretical growth concept. If only one indicator were used, results would be weak and

possibly distorted(Davidsson, P. et al., 2007).

Small firms always are considered as generators of economic development, job creation and

the engine of the ever-expanding economic development (Schumpeter, 1942),(Galbraith,

J.K., 1956), (Galbraith, J.K., 1967). This conventional wisdom was challenged by Birch D.

L., (1979) who in an empirical investigation claimed small firms to be the main job

generators. Birch‘s results and his conclusions have been questioned and sparked up a

debate(see Kirchoff, B. A., & Phillips, B. D. (1988) and Kirchhoff et al., (1998) for a review

of the discussion). This critique asserts that growth has to be understood in a broader

perspective entailing considerable churning and restructuring(Haltiwanger, John and C. J.

Krizan, 1999). In fact, the rapid growth of some firms implies that they attract factors of

production from other firms. Therefore, grouth requires contraction and exit of some firms to

free up resources that can be reallocated to expanding firms. Entry and expansion are flip

sides to exit and contraction. The process through which the factors of production are put into

different use defines structural transformation(Magnus H. and Dan J., 2010).

What is the growth? Many scholars in different points of view tried to conceptualize the

growth of business. There doesn’t exist any definition all accepted regarding of this theory.

This is amultidisciplinary theory with malty implications. But,in narrowmeaning, the growth

means thecreation of new values of business succeeded by many indicators that expressed the

growth and success in a certain period. The OECD defines HGFs as: “enterprises with



average annualized growth in employees or turnover greater than 20 percent per annum, over

a three year period, and with more than 10 employees atthe beginning of the observation

period”(OECD, 2007).The benefit of using the standard OECD definition is that it enables

both longitudinal and international comparisons (OECD, 2007). Further, the growth can be

achieved in different ways and with varying degrees of regularity, and it manifests itself

along with several different dimensions such as sales, employment and accumulation of

assets(Davidsson, Leona A. and Lucia N., 2010).

On focus of this concept mainly is the interest to be concentrated on thegrowth of business of

the firms which has been newly established. “Gazelles” or young growth firms are estimated

to be a subset of high growth enterprises: “they are the high growth enterprises born five

years or less before the end of the three year observation period”(Moran, P., and Ghoshal, S.,

1999). On the growth of the firm process is avery dominant factor of ‘the human resources

capital,' particularly in terms of the care that they take with recruitment and the degree of

employee empowerment, which is reflected in higher productivity.Wealth creation and firm

growth are interrelated. In general, effective growth is expected to help firms create wealth by

building economies of scale as well as market power. These outcomes provide additional

resources and contribute to achieving a competitive advantage. Likewise, additional wealth

makes it possible for firms to allocate resources to stimulate further growth. This relationship

is especially critical to new venture firms, firms that often create wealth by growing rapidly.

Our work assumes the importance of firm growth while examining wealth creation as an

outcome of the effective use of entrepreneurship and strategic management(Ireland, R.D. et

al., 2003).

Evidence of the practices from different countries, suggests that an entrepreneurial mindset

may support the growth of an entire economy, as well as the growth of individual firms, this

might be a very good experience for transition countries, (Whitley, 1992). This includes

intervention to encourage more entrepreneurial attitudes, support for training, improving

access to finance, promoting exports and internationalization, supporting innovation and

developing business networks and clusters (Kume, V.et al., 2009).

Another important factor that might affect growth is the willingness of the owner to keep

control over the firm. It is argued that owners of most businesses deal with day-to-day

management tasks as well. As firms start to grow, the willingness of the owner to keep

control of the firm and not to delegate decision-making to employed managers might inhibit



the growth of the firm.On the other hand, as the firm gets larger, it faces a managerial

challenge as those owners who wish to keep control of the firm will not be able to manage a

larger firm than one they initially established. The literature suggests that small business

owners sometimes are reluctant to grow even though they may have found opportunities to

expand. In this case the desire of the owner to keep decision-making power and control act as

a barrier to growth (Krasniqi et al., 2008). Firm growth is also uncertain: environmental

conditions such as competition and market dynamics play their roles. For small firms, growth

is also influenced by personal ambition of an entrepreneur. For instance, not every

entrepreneur aims to grow her business (Haibo Zhou and Gerrit de Wit, 2009). The risks

become more often the threats for businesses, and so many businesses rest their activities due

to inability to survive in turbulence conditions. Thus, an entrepreneurial mindset can

contribute to a competitive advantage and is necessary for creating wealth (Mileset al., 2005).

We argue that analyses of firm growth in general, and the economic contribution of HGFs in

particular, benefit from being evaluated in a broader context of structural transformation. The

rapid growth of some firms, on the one hand, requires entry of new firms from which to

recruit high-growth candidates and on the other hand the contraction and exit of other firms

to free up resources for expanding firms (Magnus H. and Dan J., 2010).The perpetual search

for economic actors for profits that exceed the risk-adjusted rate of return available for

passive investors leads to a situation in which entrepreneurship; talent and ownership skills

are channeled to the most promising areas and supplied in the best possible quantities. This

increases the probability that new business opportunities will be developed and exploited to

their full potential. This process creates the organizational and structural capital that is an

indispensable component in all successful enterprises. The potential entrepreneur can always

refrain from developing and using his/her skills and remain an employee with a fixed salary;

the venture capitalist can choose to remain passive instead of supplementing his/her financial

investment by supplying management skills, and so on (Magnus H. and Dan J., 2009,

2010).Employment growth indicates that a change has occurred in the organizational

composition or strategy of the firm (Hanks et al., 1993), which warrants an increase in the

number of persons working for the firm. This change is often due to expansion in the scope of

firm operations or an immediate increase in business. With employment growth, a venture is

equipped with new human capital through which its objectives can be executed. The venture

is also better enabled to assess the external environment to ensure it can compete most

effectively (Box, T. M. et al., 1993). In addition to indicating internal changes occurring in



the firm, employment growth signals the contribution the venture is making to the community

from which it operates (Kirchoff, B. A., and Phillips, B. D., 1988) and(Venkataraman et al.,

1990).

2.8.1. Firm Growth and Job Creation

The growth of the firm is a complex and multidimensional phenomenon, it is clear that a

clean approach, limited to the impact of resources and in particular the determinants

associated with the manager, neglects the potential for prediction of firm-related variables,

objectives, environment and the interactions between these different types of variables

(Weinzimmer, 1993,Janssen F., 2002a).Van Praag, M. C., and Versloot, P. H.,

(2008)analysed the empirical evidence on job creation by small firms, and conclude that it is

a clearoutcome that small firms create more jobs on net than large firms, even when the

methodology suggested by the critics is applied. Fairly small number of high-growth firms

(HGFs for short)on average smaller and younger than other firms contribute the bulk of net

employment (Henrekson, M. and Johansson, D., 2010). Understanding a societal point of

view the creation of new jobsresulting in increased tax revenue and reduced welfare costsis

often the vantage point for an interest in firm growth. The tax system is particularly important

for the issue discussed here. Taxes invariably influence transactions in that they create a

wedge between the net receipt of sellers and gross costs of buyers of a service or a product.

Hence, taxes greatly influence the incentives to acquire and apply creative knowledge as well

as the possibility of reallocating factors of production to more productive areas. Taxes can

create lock-in effects of labor and capital. In Section 2 we identified six distinct categories of

actors crucial for HGFs. However, the tax code only imperfectly acknowledges these

categories; there is no specific tax on income from entrepreneurial effort or inventive activity.

Instead, based on provisions in the tax code, individual income will be classified as labor

income, capital income and/or corporate income, and within each of these categories, there

may be further provisions influencing the effective tax rate. In what follows we will examine

how the incentives for the different categories of key actors are affected by the tax system

(Magnus Henrekson and Dan Johansson, 2010).

The level and progressivity of labor taxation (including mandatory social security

contributions) affect employees directly, by determining the incentives for work effort, labor

supply (on the extensive and intensive margin), occupational choice, career aspirations, and



the propensity to upgrade and learn new skills. Most obviously, high and progressive labor

taxes lower the rate of return on productive skills, and therefore they are likely to reduce the

supply of skilled workers. The incentives to acquire human capital through formal schooling

may be strong thanks to low or zero tuition fees, subsidized student loans and housing

financed by taxes, while high marginal taxes abate the incentives to use and further develop

that kind of capital (Ibid. p. 20).

There is no doubt that the majority of gross new jobs in the economy are the result of

thegrowth of already existing firms, rather than theentry of new firms. In the case of Sweden,

the proportion has been estimated as roughly one-third for entry and two-thirds for expansion,

(Davidsson et al., 1998). This should come as no surprise as there are many more established

firms in an economy than there are new entrants. The more important question concerns

where net additions of jobs come from. As noted by Steven et al.(Steven J. D. 1993, 1996),

this is a trickier issue, as post hoc,a given net addition can be attributed to many different

subcategories of the economy. Studies in the U.S. and U.K. have claimed that a small

minority of rapidly growing firms so called “flyers” or “gazelles”are the real creators of net

new jobs in the economy (Birch, D. and James M., 1994;Birch et al., 1995). As confirmed by

Davidsson(2004), if one follows a cohort of firms over time and there is any outcome

variance at alleven completely stochastic varianceit will always be the case that a small

proportion of firms eventually accounts for a large proportion of the jobs created by that

cohort. The greater the outcome variance and the longer the analysis period, the more marked

will this effect be. However, this does not prove that the elite of high growth firms creates a

large proportion of all new jobs in the economy. In order to establish the latter, the job

creation of all gazelles in the economy has to be compared with total job creation in the

economy. There are also other reasons not to equate employment growth on the firm level

with job creation in the economy at large. As noted above, especially for large and old firms,

growth usually reflects acquisition, that is, transfer of already existing activities of jobs from

one organization to another. Even those firms that grow organically may do so at the expense

of other firms whose employment consequently shrinks. Yet other firms contribute to the

growth of the economy by reducing the need for manpower for a given output. Clearly, head

counting on the firm level is a very narrow sighted analysis for societal purposes. When the

interest truly is in the size of employment in the economy and its changes it seems advisable

to start at a more aggregate level and then try to tease outon region, industry and firm



levelshow the aggregate effects emerge from entry, exit, expansion, contraction, and transfer

of economic activities across borders (Davidsson, P. et al., 2007).

2.8.2. Firm Growth with Positive or Negative Effect

It is interesting the fact that in theacademic and non-academicliterature we find the both sides

of the phenomenon. It depends on what dimension the phenomenon is being researched. But,

in the most situations,we refer the growth on the contest of the positive sense of the

issue,especially,inthe cases of not developed countries and countries in the

transition.Therefore, the growth is the situation of business on the progressive stage of the

increment of the main indicators of the performance, like employment, productivity, annual

turnover, sales, profit, etc.

Both in academic and non-academic literature, firm growth is frequently equated with

success (Baum, J. R.,et al., 2001;Covin, J. G., and Slevin, D. P., 1997 and Low, M. B., and

MacMillan, I. C., 1988). Owners/managers of small firms are generally aware that growth can

have both desirable and undesirable effects, and hence growth is something of a dilemma for

them. In researchesare directly addressed small firm owners/managers’ expectations as to the

negative and positive consequences of growth.Therefore, have been found that expectations

of economic gain are not a dominant growth motivators, that almost all respondents expect

both negative and positive outcomes and that negative expectations are overall somewhat

more frequent or pronounced than positive ones (Davidsson, 1989; Wiklund et al., 2003). The

strongest dominance for negative expectations concerned the issue of vulnerability; a

majority believed that increased size would make their firms less able to survive a severe

crisis. According to Wiklund et al. (2003), consistently across threeseparate studies and

various subsample breakdowns, the strongest negativeeffect on overall growth willingness

stems from expectations that growth wouldhave adverse effects on employee welfare, which

they interpret as fearof losing the informal, family-like character of the small organization.

Inthis regard, the research literature lends some support to the owners/manager’sfears: small

firms have certain advantages that risk beinglost if the firm grows larger (Arrow, 1983,

Barker, R. G., and Gump, P. V., 1964). As mentioned above, many owners/managers also

resentthe idea of achieving growth based on thesubstantial influx of external

capital(Sapienza, H. J., et al., 2003). Clearly, then, small firm owners/managers expect

growthto bring both positive and negative outcomes, and they are not all wrong indoing



so.The following section will discuss two outcomes in more detail, namely,profitability and

an increase in a number of employees. Possibly, the former isone of the most important

potential effects of growth for the owners/managersof firms, while the latter represents a key

interest among policy makers.

2.8.3. Is the Firm Growth Profitable?

Regarding the relationship between growth and profitability, Davidsson’s research (1989)

showed that 40% of the small firm owner/managers in his sample did not believe growth

would improve their personal income stream, thus effectively removing one important reason

to pursue growth(Davidsson, P. and Delmar, F., 1998). While fairly strong theoretical

arguments can be put forward both for growth enhancing profits and for profits enhancing

growth, the fact is that the research evidence on the association between growth and

profitability is surprisingly weak and mixed. Growth in assets and sales individually show

positive relationships to performance at both industry and firm/business levels of analysis.”

This is actually evidence against the hypothesis that firms that grow more than their close

competitors become more profitable(Davidsson. et al., 2010).

It is surprisingly difficult to find more recent studies that explicitly examine the growth and

profit relationship. Many authors found the relationship between them, for instance,Chandler,

G. N., and Jansen, E., (1992), Mendelson (2000) and Wiklund J. (1998).All found a positive

association in passing; their main research questions concerned other relationships. A few

recent studies have addressed the growth-profitability as their main research question. Cox et

al. (2002), surveyed 672 members of the Entrepreneurs of theYear Institute and found a

positive relationship between sales growth rate andprofitability growth.

Cowling(2004),investigated U.K. firms across industries and concluded from a series of

regression analyses that profit and growth tended to move together. Further more,

Roper(1999), who studied a large sample of Irish firms, found turnover growth and return on

assets to be very weakly related (r below 0.10 and not statistically significant). Similarly,

Sexton, D. L.,et al., (2000), who analyzed over 75,000 firms in the Kauffman Longitudinal

Financial Statement Database, found a very weak overall correlation between sales growth

and profitability. Markman et al.(2002),used longitudinal data on 500 firms and found that

change in sales and change in employment both had a weak negative correlation with change

in profit.



Therefore, the empirical evidence on the relationship between growth and performance is

inconclusive. In addition, to the extent a primaryrelationship existed, it has not been

determined whether this is because the growth leads to profits orconversely, profitability

drives to growth. This is initiatedby Davidsson, P. et al., (2005),who recently examine

precisely that question. Their results showed thatfirms originating in the high profit/low

growth category were in each analysis about two to three times more likely to end up in the

desirable high growth/high-profit category as were firms originating in the high growth/low-

profit category. The latter category was instead strongly overrepresented among firms

regressing to a low profit/low growth position. This is a strong reason to caution against a

universal and uncritical growth ideology and for small firm owner managers whenever

possibleto secure a sound level of profitability before they go for growth. While perhaps

appropriate under some circumstances, as a general rule the idea of growing in order to

become profitable seems a much more questionable prospect(Davidsson, P. et al., 2007).

2.8.4. Entrepreneurs’ Strategies as a Determinant of Growth

Strategic entrepreneurship (SE) involves simultaneous opportunity-seeking and

advantageseeking behaviors and results in superior firm performance. On a relative basis,

small, entrepreneurial ventures are effective in identifying opportunities but are less

successful in developing competitive advantages needed to appropriate value from those

opportunities. In contrast, large, established firms often are relatively more effective in

establishing competitive advantages but are less able to identify new opportunities. We argue

that SE is a unique, distinctive construct through which firms are able to create wealth. An

entrepreneurial mindset, an entrepreneurial culture and entrepreneurial leadership, the

strategic management of resources and applying creativity to develop innovations are

important dimensions of SE. Herein we develop a model of SE that explains how these

dimensions are integrated to create wealth(Duane I. R. et al., 2003).

Historically, small companies and start-up ventures have been relatively skilled in identifying

entrepreneurial opportunities but less effective at developing and sustaining the competitive

advantages needed to exploit those opportunities over time. In contrast, more established

organizations have demonstrated relatively superior skills in terms of developing and

sustaining competitive advantages but have been less effective in recognizing entrepreneurial

opportunities that can be exploited with their resources and resulting capabilities (Ibid, p.

966).



Thus, entrepreneurial and new venture firms tend to excel at opportunity-seeking behavior

while established companies typically excel in the exercise of advantage-seeking behavior.

Alternatively, firms pursuing SE seek fundamentally new opportunities (i.e., opportunity-

seeking behavior) either to disrupt an industry’s existing competitive conditions or to create

new market spaces (i.e., advantage-seeking behavior).Although useful, early research efforts

to explicate SE as a unique construct do not adequatelydescribe its distinctive dimensions.

Herein, we extend the contributions of priorwork by identifying and critically examining

SE’s underlying dimensions. Several theoreticalbases, including the resource-based

view(RBV) of the firm, human capital, social capital,organizational learning, and creative

cognition are integrated into this work. This integrationis important because it addresses how

combining and synthesizing opportunity-seekingbehavior and advantage-seeking behavior

leads to wealth creation(Duane I. R. et al., 2003).

Figure 2. A model of strategic entrepreneurship.

Source: R.D. Ireland et al. / Journal of Management 2003 29(6), 963–989

Acording to Cowfrey (2012), the characteristics that have been covered so far are necessary

for someone to be able to act in an entrepreneurial way, but there is one other characteristic

for success that seems to be fast being eroded in the western world, and that is the one of hard

work. There seems to be a misapprehension in people’s minds that the true entrepreneur just

happens. Whilst it satisfies the media to sell stories of amazing and instant success, the reality

is that, along with the opportunities that appeared before them and the mindset to grasp those

opportunities, there is a need to get about 10,000 hours of work in to achieve excellence.

It is interesting to read Malcolm Gladwell in his book Outliers, with regard to the way people

as Bill Gates and the Beatles managed their ‘instant success as a result of their 10,000 hours!



So we are now getting close to defining our mindset that we need to develop. According to

Cowfrey (2012), entrepreneurs need to have:

A clear and achievable vision

A vision where all the resources may not be in their control

Self-awareness

Confidence

Self-motivation

A willingness to take calculated risks

A willingness to listen to others

A lack of fear of failure

A willingness to work hard

Understanding of the relationships between entrepreneurship and performance improves as

more contiguous variables are tested and model specification increases(Lyon, D.W.et al.,

2000). For instance, Dess et al., (1997) link entrepreneurial strategy making processes to firm

strategy, the environment, and performance, and Zahra (1993)found that corporate

entrepreneurship influenced firm performance depending on the external environmental

context.Whilst, Covin et al., (1991) and Miller (1983)suggest innovation, risk taking, and

pro-activeness as key dimensions of entrepreneurial activity. Lumpkin, G. T., and Dess, G.

G., (1996) in an effort to focus on an important component of entrepreneurship, identified the

dimensions of an “entrepreneurial orientation” (EO) construct(Lyon, D.W. et al., 2000).

2.8.5. Integrated Models of Growth Determinants

Evidently, many different internal and external factors could undersome circumstances affect

firm growth, and consequently, a very long list of specific growth determinants has been

suggested in the literature. This poses a challenge for studies aiming at approaching afull

explanation of the phenomenon of small firm growth, rather than testing effects predicted by

a particular theory. On the one hand, it has to include a broad range of explanatory variables;

on theother hand, some abstracted sense-making is needed, that is, the grouping of themany

specific variables under a smaller number of overarching themes. While similarly other

individual studies cover a range of factors on different levels,e.g.,Eisenhardt, K. M. and

Schoonhoven, C. B., (1990); Sandberg, W. R., and Hofer, C. W., (1987);Davidsson P.,

(1991)and Wiklund J., (1999) represent some model of growth. In Davidsson’s model, all



low-level specifics are regarded as aspects of three exhaustive factors: ability, need and

opportunity. He further distinguished between objective and perceived versions of these

variables, but as his study was intersectional, only the objective factors could be related to

actual growth in the empirical analysis. He agreed that three factors affect the growth, but the

priority gives the need for growth as most influential (Davidsson P., 1991).

Wiklund (1999) combined three theoretical perspectives in his model: the resource-based

view, the motivation perspective, and strategic adaptation. In his model,

strategyoperationalized as Entrepreneurial Orientation(EO)Lumpkin, G. T. and Dess, G. G.,

(1996)stated “is assumed to be directly related to growth, whereas resources, motivations,

and characteristics of the environment are assumed indirectly to affect growth via strategic

adaptation.” From the results, we learn that many factors influence growth but variables that

show the correlation of intentionto grow shows that have a greater impact. Subsequent

analyses have shown that the EO performance link increases in strength over time, at least

over periods of moderate length (Wiklund J.,1999). Further on, his results support the notion

that strategy has the strongest and most direct influence on growth. This is an important

addition to conclusions as explicit consideration of strategy was lacking in his

study(Davidsson P.,1991).

An excellent and recent example is thë ‘psychological study of factors of firm growth’

(Baum, J. R., and Locke, E. A., 2004). Confining their study to a population of North

American architectural woodwork firms and including a small number of firms and

environment level control variables, these researchers found strong direct effects of purposes,

communicated vision, and self-efficacy on growth over a six-year period. In line with their

theory, they also found mostly indirect effects of passion, tenacity and new resource skills. In

a less carefully operationalized study, and using a more heterogeneous sample, these

relationships may well have been undetected (Davidsson, P. et al., 2007).

2.9. BARRIERS, CHARACTERISTICS, AND STAGES OF GROWTH

Research on small business growth is often divided into two research streams: 1)

characteristics that predict firm growth, and 2) stages of firm growth. A brief discussion of

these research streams is warranted for three reasons. First, because it is found that barriers

are frequently part of empirical studies,in relation to characteristics and stages of firm

growth. Second, it appears that it is difficult to investigate the current understanding of



barriers and their relationship to growth without drawing on research about characteristics of

growth. And third, research on the characteristics and stages of growth has shaped the way

barriers have been studied empirically.

2.9.1. Barriers to growth

What are barriers exactly, and how they have been described in the literature? The concept of

barriers to growth, has been described in four ways: as factors or conditions 1) that represent

a perceived discrepancy between those conditions for growth that exist and those that are

desirable, 2) have a negative influence on business growth, when growth is desirable, 3) are

internal and/or external to the firm, and 4) can be managed or overcome. While these features

of barriers are discussed separately below for the purposes of clarification, they are in fact

related. While, discussing this phenomenonsome authors (Welter, F. and D. Smallbone,

2003); (Barlett, W. and Bukovic, V., 2001)(Welter, F. and D. Smallbone, 2006) addressed

several barriers which can be classified as institutional barriers, internal organizational and

resource barriers, external market barriers, financial barriers and social barriers.

Many scholars in adifferent point of view tried to conceptualize the growth of businesses. It

does not exist any theory, accepted by all. This is a multidisciplinary theory with many

implications. But in anarrowmeaning, the growth means the creation of new values of

business development, dependent form many indicators that expressed the growth and

success in a certain period. This is the base of the concept and purpose of the theses.

The OECD defines high growth firms (HGFs) as: ‘enterprises with average annualized

growth in employees or turnover greater than 20 percent per annum, over a three year period,

and with more than 10 employees at the beginning of the observation period(OECD, 2007).

On thefocus of this concept mainly is the interest to be concentrated on the growth of

thebusiness of the firms which has been newly established. ‘Gazelles’ or young growth firms

are estimated to be a subset of high growth enterprises: ‘they are the high growth enterprises

born five years or less before the end of the three year observation period’(OECD, 2008). On

the growth of the firm process human resources engaged area very dominant factor,

particularly in terms of the care that they take with recruitment and the degree of employee

empowerment, which is reflected in higher productivity.

Wealth creation and firm growth are interconnected. In general, effective growth can help

firms create wealth by building economies of scale and market power. These effects provide



additional resources and contribute to achieving a competitive advantage(Barney, 1991).

Likewise, additional wealth makes it possible for firms to allocate resources to stimulate

further growth. This relationship is especially critical to new ventures, firms that often create

wealth by growing rapidly.

The influence of small firm growth to the general economic development of any country is

uncontested. In transition countries, especially those have passed from central economy to the

free market economy, faced with a lot of problems in thecontext of doing business. Some of

them are still facing of understanding and applying the new mindset of entrepreneurship on

the free market competition economy and behaving on different uncertainty circumstances.

McGrathand MacMillan, (2000), view an entrepreneurial capability as a way of thinking

about business that focuses on and catches the benefits of uncertainty.

In the transition countries with low level of development, uncertainty caused due to lack of

support from institutions and missing of information for future behaviour in a business

environment.Baumol(1990), argued that entrepreneurial activity could take productive,

nonproductive and even destructive forms, depending on the institutional context. Further,

heargued,“only that at least one of the prime determinants of entrepreneurial behavior at any

particular time and place is the prevailing rules of the game that govern the payoff of one

entrepreneurial activity relative to another.” If the rules are such as to impede the earning of

much wealth via activity A or are such as to impose social disgrace on those who engage in

it, then, other things being equal, entrepreneurs' efforts will tend to be channeled into other

activities, call them B. But if B contributes less to production or welfare than A, the

consequences for society may be considerable (Ibid p. 898).

The risks become more often the threats for businesses, and so many businesses rest their

activities due to inability to survive in turbulent conditions. Thus, an entrepreneurial mindset

can contribute to a competitive advantage (Miles, G. et al., 2000) and is necessary for

creating wealth.

Adapting and applying the new mindset of entrepreneurship in practice, including the

segments with theimpact of business growth is an issue of mandatory for these countries.

“Based on experience, we define an entrepreneurial mindset as a growth-oriented perspective

through which individuals promote flexibility, creativity, continuous innovation, and renewal.

In other words, even under the cloak of uncertainty, the entrepreneurial minded can identify



and feat new chances because they have to explore abilities that allow them to impart

meaning to ambiguous and fragmented situations”(Alvarez, S. A., and Barney, J. B., 2002).

2.9.2. Barriers to Entrepreneurship in Transition Economies

In follow, we intend to review the development of the institutional, social and cultural

environment for entrepreneurs in transition economies, focusingon difficulties on the

consolidation of societies to built a comfortable environment of doing business.The most

important factors in establishing a political environment that supports a strong business and

investment climate are Security; Protection and guarantee of foreign investor rights;

Legislative stability; Transparency; Freedom from corruption; and Good governance. In

general, the low percentage of companies considering the lack of human resources as a major

obstacle is not too surprising. Gelb et al. (2007) found that the basic restrictions (such as

macroeconomic stability, electricity, access to finance), seems to be more important at low

levels of income.

According to Saul E. et al., (2005), institutions affect entrepreneurial endeavours in two ways:

Firstly, they may hinder the creation of firms, thus lowering a total number of entrants

into the market.

Second, they may create obstacles to firm performance, as measured by survival period,

growth or profits.

In transition process are opened many opportunities for entrepreneurship development, the

heritage of the planned area was in many ways not favourable, and many aspectsof the reform

process acted to make the environment even less conducive to entrepreneurs(Boettke, P. and

Christopher J. C., 2006).

The recent literature has begun to pay attention to the role and impact of institutions on

entrepreneurial behaviour(Boettke, P. and Christopher J. C., 2003).In the initial phase of the

transition to a market economy, the introduction of new laws creates new opportunities for

entrepreneurship by allowing private owners to exist legally, but a deficient legal

infrastructure, such as implementation gaps, restricts entrepreneurship

development(Smallbone, D. and F. Welter, 2006). The institutional frame for

entrepreneurship can be considered at different levels of scale (Welter F. , 1997).This issue

concerns not only with institutional barriers but is multidimensional and more complicated,



influenced by the behaviour of the individual and society. This is the reason that agenda of

research on the dynamics of adoption of new economic practices, innovation, and economic

growth necessities to be extended beyond the level of the firm (Castells, 2000). However,

economies in transition started their reforms without a complete legal framework and

institutional, needed to create the basis of the market economy and entrepreneurship (Ingrid

V. et al., 2003;Chilosi, 2001).

Rather, the institutional environment has created numerous barriers to market entry and

further growth of new firms. This prevents entrepreneurs to exploit opportunities created by

the transition. Kosovo is faced with numerous obstacles lack of consolidation of law and

various institutional constraints as well. Specific Kosovo’s way from the past decades

influenced those obstaclesto be more evident. Before identifying ‘what kinds’ of barriers

comprise the focus of the current review, it is useful to consider the classifications of barriers

that have featured both in previous reviews and in previous empirical research.Further on,

learning from preliminary research, the investigation will be oriented on sub-sections based

on the following classifications of the barriers that hinder business growth, as of Rachel

(2011): 1. Financial, 2. Skills related 3. Institutional, and 4. Market-related.

As it is mentioned above, market related issues influence the business growth. Kangasharju

(2000) suggests that the demand for the firm’s products is a major external determinant of

small firm growth. On the other side, the market actions of competitors, the supply of

production factors, and the features of the local business environment have their substantial

impact on business growth.

The firm growth, in theory, remains a multi-faceted phenomenon. For example, (Delmar, F.et

al., 2003) discussed heterogeneity according to what we have to apply specific measure of the

firm growth and also as regards the appropriateness of these different measures in according

to specific theories. They further treated heterogeneity in the regularity or irregularity of

growth over time, and in the type of growth, organic or acquisition based. Empirically, they

show that when the top 10% “high growth firms” in a large sample of firms was singled out

according to six different growth indicators, over 40% were qualified according to at least

one criterion. However, only 16.6% made the hurdle for three or more criteria, and a tiny

2.5% were classified as “high growth firms” regardless of what criterion was used.

Underlying this are very low correlations between some of the growth indicators, as these

researchers also reported. By means of cluster analysis, they draw out seven different types of



“high growth firms” which show markedly different growth patterns and background

characteristics. They conclude that firm growth is a multidimensional rather than one-

dimensional phenomenon and that diverse forms of growth may have different determinants

and effects. In this situation, according to Davidsson and Wiklund(2000), the theoretical

approach might be different to each case, may also need different theoretical explanations.

SMEs development, in most advanced western economies,is considered as a key factor in

economic growth, innovation, and market competition (Acs, Z.J. and Audretsch, D.B., 1990).

Equally, SMEs are considered as a central source of job generation and wealth creation

(Birch, 1979; Storey, 1994b). In transition countries, the role of SBs is even more

highlighted. It is expected that they take the role of renewing the economic state of the

country. They contribute to regional development, and provide social wealth, through

creating a substantial number of jobs and serving as an engine of growth. Still, SBs failed to

be on that level of comfortability due to the barriers, even significant contribution role in

most transition countries. The hostile and adverse environment for doing business, lack of

financial capital and other barriers substantially impeded SMEs growth and development in

transition countries (Smallbone and Welter, 2001); (Aidis, 2005); (Bartlett and Bukvic,

2001). Certainly, firm growth is an important indicator of a thriving economy, and SMEs

development is seen as a key to economic growth, innovation and market competition in most

developed western economies. Moreover, SMEs are considered as an essential source of new

job creation and profit generation (Acs and Audretsch, 1990), (Storey, 1994c), (Janssen F.,

2009).

2.9.3. Access to finance as a barrier to small firm growth

Under communism, individuals were not permitted to accumulate financial assets the state owned

almost all wealth, and this must have been a major constraint on the possibilities for

entrepreneurship (Pissarides, 1999); (Chilosi 2001). More generally, the distribution of income

and wealth may be a major determinant of the level of entrepreneurial activity. According to

Banerji and Long (2001), quoted by Chilosi (2001), “under capital market imperfections due to

moral hazard, the very rich and the very poor do not undertake any risk and became passive

lenders. Only individuals whose wealth lies within the medium range choose to be entrepreneurs”

(p.1). This is because poor people would not have access to risk capital and rich people do not

wish to undertake the extra effort. This situation is when entrepreneurs will largely come from the



middle classes, but this is precisely the group that was largely absent at the start of transition

(Saul E. at al, 2005). Some of the constraints mostly reported by managers of businesses are:

High interest

Credit terms (short terms for returning credits)

Difficulties to take the credit etc.

Zylfijaj, K. and Nikoloski, D., (2016),discussing the access to finance of SMEs in Kosovo

stressed that lack of grants is an obstacle for both formal and informal sector. Though, this is

more obstacle for formal businesses compared to informal businesses. With this respect,

formal businesses differ from informal businesses by several features such as they register

and pay taxes as well as other social contributions; in turn, they expect more institutional

support through grants and access to finance in appropriate conditions. Moreover, formal

businesses in comparison with informal businesses have more expectations for reasons that

are more capable in their organization, skills, and education, preparation of business plans; so

as a result, they find the easierfulfilment of certain criteria for obtaining grants. Therefore,

those businesses assess the lack of grants as an obstacle to their operations and growth.

Finally, as profits decline through the economic transition process, while investments often

become larger and longer term, firms can rely less on retained earnings to grow and

increasingly need access to external finance (McMillan, J., and Woodruff, C., 2002).

2.9.4. Negative phenomena, barriers to SBs growth

Administrative burdens and bureaucracy, not efficiency application of legislation, licensing

procedures, informal economy, fiscal evasion, corruption, organized crime,etc. Corruption as

a phenomenon is associated mostly with countries in transition representing a significant

challenge to the development of open competitive markets and entrepreneurship (Manolova

at al, 2008),(Tonoyan et al., 2010),(Budak, J. and Rajh, E. , 2014),(Griffiths, et al., 2009).

For example, while informal institutions can develop as a result of spontaneous and intended

individual actions, they can also partly result from formal institutions, which they can in turn

modify. In this regard, informal institutions evolve as a culture-specific, collective and

individual interpretation of formal rules. For example, while a specific legal framework

normally contains explicit regulations for implementing laws, over time these regulations are

complemented by unwritten rules, which provide an implicit understanding of their content.



In this sense, informal institutions may fill legal gaps, which may only become apparent

when laws and regulations are applied to daily life (Smallbone, D. and Welter, F., 17 May

2010).

Formal institutions are enforced by coercive mechanisms, which are mainly set down in

government rules and regulations, while informal institutions are enforced by normative and

mimetic mechanisms. Normative mechanisms assist in creating legitimacy, which is of

particular importance in the case of nascent entrepreneurs and entrepreneurs in contexts

where the newness of the concept of entrepreneurship may affect its acceptance in the wider

society. In this context, the way that government deals with entrepreneurs influence the

extent to which involvement in entrepreneurship is an acceptable form of behbehaviour

within the population as a whole, as does the behaviour of entrepreneurs themselves. This

illustrates the relationship between the behavior of formal institutions and informal

institutional change; secondly, between informal institutional change and entrepreneurship

development; and thirdly, the recursive link between entrepreneurship and informal

institutional change (Ibid. p.5).

Before, existed the perception that large firms are better able to protect themselves from

corruption using political power to influence government agents (Hellman, J. and

Schankerman, M., 2000). Now from the practice of the transition economies, the corruption

with implication of the people in power for its political and personal benefits is present.

2.9.5. Unfair Competition

Copying and evasion behaviour can be found in relation to many aspects of the legal and

regulatory framework in the the Commonwealth of Independent States (CIS)3, reflecting

deficiencies in business regulations and/or their implementation. This typically leaves too

much room for interpretation by officials, thereby contributing to corruption(Smallbone, D.

and Welter, F., 2009a).

The most serious barrier of doing business is estimated unfair competition (mainly from tax

evasion). According to the report, tax evasion is considered to be close to 40%. The same

report finds that nearly 36% of workers in businesses in Kosovo are not declared. The most

extreme case is that of the agricultural sector where the percentage of workers who do not

declare up to 70%. Unfair competition in the list is followed by corruption barriers intensity 3 The Commonwealth of Independent States is an association of countries that are former Soviet Republics.



(78). The dominance of this barrier is not surprising, considering that the international

indicators of corruption, regardless of the method of measurement, suggest that Kosovo

continues to be one of the most corrupted countries in the region. In International

Transparency Report (2012), for example, in a rating of 1 to 100, Kosovo ranks in 105th

place. In addition, in third place, are ranked road barriers, crime and theft intensity (74

points). Even international organizations consider this phenomenon as a problem. The US

State Department, for example, finds that the Kosovars, driven by unemployment and other

socio-economic factors, involved in street crime, namely the theft, and so do that Kosovo

ranked alongside other countries not good. The department also documents that businesses,

among other things, are the subject of these thefts. Further, in fourth place, come again issues

related to competition, namely the barrier of anti-competitive practices of competitors

(Riinvest I., 2013).

2.9.6. Market Barriers

In the early years of economic transition, the absence of credit markets, courts, andother

market institutions created substantial impediments to entry. Potential entrants had to find

money with which to purchase equipment and inputs. They had to identify reliable suppliers

and customers when most firms were new and little information was available. The unusually

high profit rates early in the transition provided a strong incentive for entrepreneurs. But what

substituted for the missing formal institutions?Howdid the entrepreneurs succeed in

overcoming the lack of market supporting institutions? Ongoing relationships among firms

substituted for the missing institutions. Firms relied on the logic of the incentives to

cooperate that arise in playing a repeated game. Where courts and laws are unreliable for

settling disputes, firms trust their customers to pay their bills and their suppliers to deliver

quality goods out of the prospect of future business (McMillan, J. and Woodruff, C., 2001).

North’s (1994) approach distinguishes between formal (laws and rules) and informal (social

norms) institutions; the first ones refer to the legal frame that regulates the social relation

produced in a specific society, such as written constitutions, norms, rules, laws, and

governmental procedures. North (1994) suggests it is the interaction between the rules of the

game and organizations that shape the institutional evolution of an economy, in which

organizations and entrepreneurs are players. Informal institutions represent the culture of a

society and include unwritten social norms of conducts that individuals follow in their day-to-

day activities. In culturally homogenous groups such as believes, attitudes, ideas, values,



traditions, perceptions, taboos, all of which are part of the social inheritance and are also

socially transmitted (Sautet, 2005). Tensions derived from the confrontation between the

transformed formal institutions and the persistent informal ones, produce important outcomes

in the way of how economies develop; one of them is that formal rules enrich and encourage

the effectiveness of informal institutions (North, 1990) cited by (Centeno-Caffarena, 2006).

Informally enforced trade rests in the shadow of the future. A firm lives up to its agreements

because it wants to go on doing business with this trading partner. For the future to weigh

heavily enough to induce cooperative behavior, the discounted value of the future profit

stream must outweigh whatever immediate profits could be squeezed from the deal. Some of

the conditions in the transition economies actually worked against cooperation. The scarcity

of credit meant the opportunity cost of capital was high. With high discount rates, firms have

an incentive to take current profits rather than wait for future profits. Moreover, as we saw,

profits tended to decline over time. To the extent that this was predictable, the gains from

forwarding looking behavior were lowered. That firms were nevertheless able to operate

mutually beneficial relationships is striking. Other circumstances of the transition aided

informal contracting. Cooperation is easier to sustain when severing the relationship results in

higher costs. Early in the transition, trading partners were most often located in the same city

or even the same neighborhood. There were usually few firms nearby producing any given

product. When a supplier severed therelationship with a customer, the customer had to incur a

high cost of searching for another trading partner. As a result, trading partners tended to be

locked to each other, inducing them to try to sustain their existing relationships, (Kranton,

1996; McMillanJ., and Woodruff, C., 2002).

Kangasharju (2000) suggests demand for the firm’s products as the major external

determinant of small firm growth, and secondly the market actions of competitors, the supply

of production factors, and the features of the local business environment. Environments vary

along dimensions such as dynamism, heterogeneity, hostility, and munificence (Dess and

Beard, 1984), and these external factors may to a considerable extent determine how much

the firm grows (Davidsson et al., 2010).

2.9.7. Access to foreign markets

Thus, entrepreneurial and new venture firms tend to excel at opportunity seeking behavior

while established companies typically excel in the exercise of advantage-seeking behavior.



Alternatively, firms pursuing SE seek fundamentally new opportunities (i.e., opportunity-

seeking behavior) either to disrupt an industry’s existing competitive conditions or to create

new market spaces, i.e., advantage-seeking behavior, (R. Duane, et al., 2003). SMEs are

faced with increasing competitive pressure stemming from globalization, enlargement and the

opening up of markets spurred by new technologies and innovation. They will need to and

ways to tackle these dificulties because the challenges are likely to persist and to increase in

the future. To survive and win in such a competitive fight, to grow in such an environment,

they must also develop their comparative advantages, and this requires knowledge, financial

resources, and economic flexibility. Public policy at thelocal level can play a significant role

in enhancing entrepreneurship performance by tackling the various market failures that can

occur, for example in the supply of finance, premises, training and business advice, and by

helping to overcome learning failures within local markets by building firm competencies and

networks for knowledge exchange. This includes intervention to encourage more

entrepreneurial attitudes, support for training, improving access to finance, promoting exports

and internationalization, supporting innovation and developing business networks and

clusters. Reform measures have largely contributed to strengthening the private sector

development and supported the SME development (Kume, V., Koxhaj, A., Kume, A., 2009).

2.10. Internal factors

An extensive review of the literature provided by Dobbs and Hamilton suggests that the

deterministic models are more dominant in explaining small business growth. Earlier work of

Storey (1994a) provides a comprehensive framework for analyzing small business growth

which integrates many factors considered by previous research such as entrepreneur, firm and

strategy. Similarly, Dobbs, M. and Hamilton, T.R., (2007) in their survey of the literature also

show that thirty independent variables determining small business growth and they tend to

fall into four broad categories: management strategies, characteristics of the entrepreneur,

environmental/industry specific factors, and finally characteristics of the firm. These set of

components are not mutually exclusive, but moreover frequently have to be combined in the

different ways in order to achieve the high growth. Similar to Dobbs and Hamilton (2007) as

we see below Krasniqi et al., (2008) use a deterministic approach and in their empirical

analysis include three group of factors considered to have an impact on firm growth:

1. The characteristics of the entrepreneur, such as age, gender, education, experience,

entrepreneurial team and motives for start-up a business



2. The characteristics of the firm, such as size, age, legal form, multi plant and

separation of ownership from control

3. The business environment, such as factors related to the different industrial sector and

the growth of the sector in which business operates.

2.10.1. Organizational Determinants

The organizational structure of the firm, like managerial capabilities, team building, labor

delegation tasks to reach an equilibrium of actions, internal communication is very important

factors on firm growth. Thus, organizational determinants should have more direct impacts

on firm growth. “Various empirical studies have been conducted to explore the determinants

of growth concerning this dimension. In summary, the following determinants have

frequently been discussed in previous studies from various disciplines: firm attributes, firm

strategies such as marketorientation and entrepreneurial orientation, firm specific resources

including human capital and financialresources, organizational structure and dynamic

capability” (Haibo Zhou and Gerrit de Wit, 2009).

The concept ‘growth’ denotes both a change in the amount and the process by which that

change is attained. Further, the growth can be achieved in different ways and with varying

degrees of regularity, and it manifests itself along with several different dimensions such as

sales, employment, and accumulation of assets (Per Davidsson et al., 2010).

Factors Related to Structural Characteristics of the Firm - As regards the firm’s structural

characteristics the evidence suggests that firm age and size, as well as its legal form, are

systematically related to growth (Storey, 1994a). Especially the discussion of age and size as

determinants of firm growth has a long tradition, following the formulation of ‘Gibrat’s law’

(Gibrat, 1931). Gibrat’s law states that the rate of growth of a firm is independent of its size

at the beginning of the period and that the probability of a given growth rate during a specific

time interval is the same for any firm within the same industry.

Factors Related to Firm Strategy - As regards strategy variables the evidence is much less

conclusive than for the structural firm variables discussed above. For variables that were

included in five or more of Storey’s (1994a) studies a relatively consistent positive effect

found for technological sophistication, market positioning, and new product introduction.



2.10.2. Lack of Qualified Human Resources

Lack of qualified human resources including managerial stuff, technical stuff, finance,

accounting, IT stuff, field management staff,etc. considered as barriers that hinder SBs

growth in Kosovo. It is interesting that 50% of ICT companies surveyed reported that a lack

of skilled labor is a major obstacle to innovation. Similarly, analysis of the ICT industry in

Kosovo shows that the gap between demand and supply of highly qualified graduates,

especially in the areas of software development and programming, is a significant challenge

in this sector (OECD, 2013).

Human capital – Resourcebased capabilities of firm employees contribute positively to

venture growth by helping the entrepreneurs execute their objectives (Chandler, G. N., &

Hanks, S. H., 1994). However, human resource needs change as the firm progresses from

start-up to an established mature firm (Thakur, 1999). According to Cardon(2003), a start-up

may require more specific expertise and highly skilled workers than a mature firm. As the

firm enters its expansion stage, it may be able to use lower skilled workers to meet

production demands. In the stage of startup to survive the expansion phase Cardon argues, it

is necessary for the entrepreneur to staff for it ahead of time. The majority of research we

examined analyzed the effect of human capital resources on the sales or employment growth

of the venture.

However, most of the studies examined the effect over an aggregate period of time, such as a

3 or 5 year period, when human capital needs could drastically change as strategic direction

changes. According to (Gimeno, J. et al. 1997) the coherent educational of human capital,

enrichment with new knowledge enables the normal development of theenterprise. This stage

achieved by reforming formal education in the country. While, the well-known economist

Gusia, I. (1982) said, "In the knowledge of cadres, much more investment have to be done to

gain intensive experience in an organized way because knowledge more and more becomes

substance in the production price."

As Birley(1987)reported, quoted by Gilbert et al., (2006) “The growth in certain classes of

employees is likely to change with the needs of the firms. The rate of increases for each of

these classes of employees may also inform the field as a great deal about strategic directions

in which the firm is heading and the extent to which the venture is staffed to exploit new

strategic opportunities”.



2.10.3. Individual Determinants

Characteristics of the owner/manager and their influence on growth have been on thefocus of

many researchers over the last decades. Many of them have tended to focus on the

relationship between the characteristics of the owner/manager and firm growth. Within the

broad category of owner/manager’s characteristics Storey (1994a) suggests five elements

which are likely to influence growth, these are age, gender, education, motivation, previous

work experience of the owner/manager. Another important factor that might affect growth is

the willingness of the owner to keep control over the firm. It is argued that owners of most

businesses deal with day-to-day management as well. As firms start to grow, the willingness

of the owner to keep control of the firm, not to delegate decision-making to employed

managers, might inhibit the growth of the firm.On the other hand as the firm gets larger they

face with managerial challenges, those owners who wish to keep control of the firm, they will

not be able sometimes to grow, even though they may have found opportunities to expand. In

this case, the desire of the owner to keep decision-making power and control acts such

barriers to growth is not allways posible (Krasniqiet al. 2008).

According to Janssen (2003), the performance of indivdual inflenced by the need for

achievement andthe growth of a firm is to a certain extent a matter of decisions made by an

individual entrepreneur, as:

Risk taking propensity

Locus of control

Self-Efficacy

Extraversion

Growth motivation

Individual competencies

Personal background

Determinants linked to the manager have categorized the growth determinants relative to the

characteristics of the managerinto five groups: the psychological characteristics of the

manager, his expertise and familybackground, his motivations, his demographic

characteristics and the presence of a team ofmanagers. Research on the link between the

psychological characteristics of the manager and the growth of his firm finds its source in



past studies on "traits" which aim to differentiate entrepreneurs from other professional

groups (Janssen F., 2003).

2.11. External and Internal Determinants

Additionally, the barriers are presented in multidimensional impact:

1. Internal level (firm level)

2. External level (environment)

Or:

1. Micro

2. Macro (level)

Figure 3. Levels of barriers presence (internal and external).

Source: Refined by theauthor based on Smallbone, D. and Welter, F., (2010).

At the macro level, it includes the responsibility for policy making with respect to

entrepreneurship and SME development within government, together with the mechanisms

for policy implementation (Smallbone, D. and Welter, F., 2010). At the mesolevel, it includes

the system of banking and other financial institutions; the training and business support

systems; but also organizations that seek to represent special interest groups in dealing with

government (such as employers’ associations and Chambers of Commerce). At the micro

level, institutional development focuses on the operation of macro and mezzo-level

organizations within the institutional frame at the local/regional level. This includes the

operation and behavior of business development centers but also local offices of regulatory



bodies and those of national agencies and organizations. National policies may exist for these

organizations, but their implementation at a local level may differ. The effectiveness of the

overall institutional frame as an enabling influence on entrepreneurship development is

affected by the relationships between these different levels, as well as by the behavior of

individual organizations within them. Although EU procedures are often criticized for being

overly bureaucratic, a need for transparency, accountability, and auditability has led the EU

to suspend financial transfers to countries where an inadequate system of financial control is

evident, in order to pressurize them to change their practices. While this may not benefit

entrepreneurs directly, there are potential indirect benefits in terms of increased transparency

in funding allocations, representing a form of institutional change. At the same time, there are

constraints on the effectiveness of formal institutional change, particularly in cases where a

substantial culture shift is required in terms of institutional behavior (Ibid, p. 14-16).

Many other theoreticians gave the contribution the growth of SBs, but Storey’s is clearer and

include two dimensions of the scientific explanation. Storey (1994b) defines the determinants

of business growth in two groups, as internal and external factors:

Figure 4. Small business growth barriers (Storey, 1994b), refined by the author.

As of many theoreticians, the determinants of entrepreneurship development are defined as

internal and external. Storey (1994b), organized the evidence in three categories: the

entrepreneur, the firm, and strategy. Support for influence was found in all three categories.

As of factors related to the entrepreneur - according to Storey (1994b), among the variables

related to the individual features of an entrepreneur, enormous of studies found that

motivation, education, management experience, number of founders, and functional skills

influence the growth.The firm’s structural characteristics likefirm age,size, and its legal form



are exclusively related to firm growth. Factors Related to Firm Strategy - The evidence

regarding strategy variables is much less conclusive than for the structural firm variables

discussed above. Internal and external determinants based on Storey’s theory will be used

during this research to examine barriers inside and outside of businesses.



CHAPTER 3

3. METHODOLOGY

The research methodology will include the various data collection methods to fulfill the

objectives of the research. The methodology is described as: "the steps to be taken in order to

give areliable answer and valuable to those questions and determine theeligibility of the tool

set research"(Ellis, T., and Levy, Y., 2008). The research will be supported exclusively from

secondary data and on that base to extend with primary data.

3.1. Secondary data

The recent literature and publications that provide qualitative and quantitative empiric data

related to the topic are used in this study.“Qualitative research includes the studies and

collection of a variety of empirical materials: a case study, personal experience, introspective,

life story, interviews, observational, historical, interactional, and visual texts that describe

routine and problematic moments and meaning in individuals’ lives” (Denzin, N.K. and

Lincoln, Y.S, 1994).Qualitative methods may shed light on these issues and reveal other

important differences between transition countries, such as differences in the institutional

environment (Doern R., 2009). To explore the historical evolution of the phenomenon are

used empirical data fromthe relevant recent literature. Additionally, to see the developments

of the business growth and barriers in the country are investigated a lot of exclusive and

complementary literature.Also are investigated various documents related to the finances of

companies, investments; business magazines, business boks, business associations reports,

government institutions publications, online librariyresearches, annual reports, and

conferences, etc.

Moreover, are used the research publications processed by many national and international

agencies, related to small businesses growth obstacles, government, and non-governmental

organizations. Further, are reviewed in depth the factors that inhibit the growth from the

previous researchers.



3.2. Primary data

To fulfil the objectives of this dissertation it was initially organized a preliminary pilot study,

in order to test the questionnaire, face to face interviews with the owners / managers or other

key persons of 20 businesses. This processof study intended "to find out" and "to generate

ideas” for the main study.The questionnaire survey used has been prepared to proceed with

some changes of small elements for use in the main study. The interviews are conducted in an

open and free atmosphere, talking responsively with owners or managers of those businesses,

exclusively related to barriers and general obstacles they face continuously.

3.3. Pilot research work

In addition, besides face-to-face interviews with 20 owners / managers of businesses, a pilot

study was conducted with 20 companies in the Pristina region to test the procedures in

advance and to better complete the questionnaire for the main research. The pilot phase is

conducted since it is not easy to predict how the target sample will answer and react to survey

questions. In addition, it provides an opportunity toidentify and correct potential problems in

the format of the research questions. A pilot study is described as "a small study, which aims

to test protocol research, data collection instruments, model recruitment strategies and other

research techniques in preparation for a wider study" (Polit D.F. & Beck C.T., 2004, p.196).

Pilot studies are used in different ways in scientific research to serve many purposes,

including the preparation of the main study and special instrument pre-test study (Baker,

1994),Teijlingen, E.R.V and Hundley, V., 2001). One of the main advantages of carrying out

pilot studies in this research was led by Vaus(De Vaus, 1993, p. 54), who said: "To not take

therisk should first pilot test.” In addition, it is used to control full study and resultsof

theanalysis, to assess the adequacy of degree study, to determine the sample design and

framework properly and collect some initial information about the study area and customers

(Teijlingen, E.R.V, and Hundley, V., 2001).

3.4. Determination of sample size

Many authors have proposed different ideas for determining the size of thesample to be taken

in the study. For example,Comfrey, A. L., and Lee, H. B., (1992), suggested instructions

related to determining appropriate sample size: 50 - very poor, 100 - the poor, 150 - 200 – the

right, 300 - well, 500 - very good, in 1000 or so - excellent.



As stated above, the selection of monitoring siteswas made on the basis of completion of

vesting basic statistical analysis that requires the representative sample to be as representative

as possible. But to meet this condition is necessary to determine the size of the sample

accurately.

In our case,the research sample was prepared for 200 businesses. Primary data collection in

the field, directly from businesses, is conducted through questionnaires distribution.

Thedistribution process was repeated several times by e-mail and not reached the determined

number of sample. The personal persistence and commitment and some professional people

engagedinthe processenabled toaccomplish the required rate. It was managed to ensure 157

valid surveys for analyses. The sample was drawn randomly from the business register

obtained from the Ministry of Trade and Industry/Agency for Business Registration and

refined by BSCK. The sample structure contains two categories: size and sectors were

businesses operate.

Table 2 presents more accurate information on the share of enterprises in the population and

the sample, by size and sector.

Table 2. Share of businesses in the population and the sample by size and sector.

Sector size Micro Small Medium Total Share of sector in population (%)

Share of sector in the sample (%)

Manufacturing 95.2 2.4 2.4 100 10.1 23

Services 97 1.7 1.3 100 40 35Trade 98.7 0.8 0.6 100 50 42Share of company size in the population (%) 97.7 1.3 1 100 100 100

Share of company size in the sample (%) 70 25 5 100 - -

Note: Total number of businesses is 100,000.

Source: BSCK, 2016

On the base of the above sample refined by BSCK experts, is constructed the representative

sample which will be used in the research. All small sized firms included in the sample are

defined as following: micro firms, 5 to 9 employees; small firms, 10–49 employees. In this

sample is avoided the category of medium enterprises, since our topic of research is small

businesses. The structure of the sample is presented in table 3.



This randomly stratified sample enables us to draw significant data about the whole population of SBs in Kosovo.

Table 3. Sample structure by sector and size.

Sector Size Micro Small Total Share of sector (%)

Manufacturing 11 4 15 10

Service 42 18 60 40

Trade 52 23 75 50

Total 105 45 150 100

Source: Self-refined.

The sample included SBs across all regions of Kosovo and is stratified by three main sectors

(production, trade, and service).

There are different opinions related to the impact of location in firm growth.Davidsson et al.,

(2002) in several studies advise that regional location of firms may have impacton growth

opportunities. In difference, a study of UK firms by Storey, (1994) suggests the contrary,

while other researchers (Almus, M., and Nerlinger, E., 1999) do not find any association

between location and firms’ growth, based on population density as the primary location

variable.

Table 4. Regional Distribution of Enterprises

Gjakova Mitrovica Gjilan Peja Ferizaj Prizren Prishtina Total

7% 8% 11% 11% 12% 16% 35% 100%

Source: MTI 2012, Private Sector Development Strategy 2013-2017. Refined by theauthor.

Statistical classification will be done in terms of general participation of SBs based on

regional and sectoral alignment in order to ensure the representation of the SBs including all

regions of the country.

In our case in order to provide the comprehensive research according to the percentage of

businesses registered in each region was foreseen the distribution process including 7 regions

overall of Kosovo.The sample structure for distribution through regions is presented in table5:

Table 5. Sample structure by region (in percent)

Gjakova Mitrovica Gjilan Peja Ferizaj Prizren Prishtina Total



10 12 16 16 18 24 54 150

Source: Refined by theauthor.

3.5. The conceptual model of data processing

Fromthe literature review, on the purpose of this dissertation, are seen a set of

determinants that lead to the growth of SBs. But, according toRodriguezet al.(2003), there is

no single theory which provides a generalized framework for SME growth. The barriers or

factors of SBs growth can be broadly distributed into external and internal factors. The main

concerns related to the barriers of growth which are interrelated to the institutions and their

impact are analyzed. There is evidence from many studies of this field that the lack of

institution performance impacts SME growth (North D. , 1990,Aidis, R., and Estrin, S., 2006,

Barlett, W., Bukovic, V., 2001,Hashi, 2001Bartlett, 1995,Krasniqi, 2007).

The general consensus among numerous studies on transitioning economies is that the external

determinants, including legal environment, level of corruption, financial institution, regulatory

and taxes are so far more important for SMEs growth than the internal factors such as individual

firm characteristics. Institutional factors are mainly external indicators, factors out of firms’

control, that can have negative impact on the firms’ growth.

Majority of studies dealing with economies in transition argue that these factors tend to be the

main barriers that hinder firms growth. Institutional constraints are considered those that can be

manifested by corruption, the lack of a judicial system, the level of crime and the weak

functionality of governing bodies. (Hashi, 2001, Storey D. J., 1994b,Smallbone, D., and Welter,

F., 2009b). The decision for this model of research has been made according to the situation and

dimension in which the barriers in Kosovo are present, trying for a wider approach of

investigation.

Below,in Figure 5, is presented the conceptual model and framework which will be used in

the study:



Figure 5. The conceptual model of data processing.


3.6. Population and Sample of the Research

The subject of the population in the research will be the managers or owners of SBs. The list

of businesses is selected from different regions of Kosovo including three sectors:

Production, Trade, and Services.

Since the focus of the study was the perception of barriers to the growth by owners/managers

of small business and no barriers to creating a business, was considered important to



interview those owners/managers who work for consolidated businesses (not start-ups),

owners/managers who have grown their businesses or intend to grow.

A. Firms are included on the base of some specifications, as:

1. The businesses at least 5 years old;

2. Businesses with not less than 5 employees and no more than 50;

3. The businesses independently owned.

B. Specifications of individuals:

1. Respondents primarily are supposed to be the owner or manager;

2. To the participants has been made clear that it was very important to discuss directly

with the owners/managers of businesses who have experience in growing the business

or planning to do so in the future.

In the classification of small businesses, it is used the criterion of “number of employees.”

Based on the classification of enterprises, small businesses are those with less than 50

employees.

3.7. Sample size in regression

According to Field (2009),it is important to determine and use the optimal sample size in

order to keep the currency of the results. The primary questions addressed to usbefore we

start the research are: How much data should be collected? How much is enough? We will

find a lot of rules of thumb floating about, the two most common being that you should have

10 cases of data for each predictor in the model, or 15 cases of data per predictor. So, with

five predictors, it is neededfor 50 or 75 cases respectively (depending on the rule you use). In

fact, the sample size required will depend on the size of theeffect that we are trying to detect

(i.e., how strong is the relationship that we’re trying to measure) and how much power we

want to detect these effects.Sample size 150 to 200 seems to be enough (Spector, 1992). The

reason is that the small size samples are not consistent with the assessment, the maximum

likelihood (ML) models of covariance structure. However, Fornell(1992) reported that

maximum opportunity might be justified when the sample size minus the number of

parameters to be estimated exceeds 50.



The simplest rule of thumb is that the bigger sample size is the better! The reason is that the

estimate of R that we get from regression is dependent on the number of predictorsk, and the

sample sizeN., In fact, the expected R for random data is k / (N − 1) and so with small sample

sizes random data can appear to show a strong effect: for example, with six predictors and 21

cases of data, R = .6 / (21 − 1) = 3,obviously for random data we want the expected R to be 0

(no effect) and for this to be true we need large samples (to take the previous example, if we

had 100 cases, not 21, then the expected R would be more acceptable .06(Field, 2009).

It’s overall well knowing, that larger is better, but researchers usually need some more

concrete guidelines (much as we would all love to collect is 1000 cases of data but, it isn’t

always practical!).Green(1991) makes two rules of thumb for the minimum acceptable sample

size, the first based on whether you want to test the overall fit of our regression model (i.e.,

test the R2), and the second based on whether you want to test the individual predictors within

the model (i.e., test b-values of the model). If we want to test the overall model, then he

recommends a minimum sample size of 50 + 8k, where k is the number of predictors.

Figure 6. Number of Predictors

Source: Field, (2009)

So, with five predictors, we need a sample size of 50 + 40 = 90. If we want to test the

individual predictors, then he suggests a minimum sample size of 104 + k, so again taking the

example of 5 predictors we need a sample size of 104 + 5 = 109. Figure 6 shows the sample

size required in regression depending on the number of predictors and the size of the

expected effect (Field, 2009).



This diagram shows the sample size required to achieve a high level of power depending on

the number of predictors and the size of theexpected effect. To summarize the graph very

broadly:

1. If is expecting to find a large effect, then a sample size of 80, will always

suffice (with up to 20 predictors), and if there are fewer predictors then can afford to

have a smaller sample;

2. Ifis expecting a medium effect, then a sample size of 200 will always suffice

(up to 20 predictors), should always have a sample size above 60, and with six or

fewer predictors it is fine with a sample of 100; and

3. Ifis expecting a small effect size then just don’t bother unless is disposable the

time and resources to collect at least 600 cases of data and much more if we have six

or more predictors(Field, 2009).

In our case, to reach a high level of reliability, selection of the sample size and number of

predictors,according to the above advice mentioned we would run the regression dividing the

predictors into five (5)groups, from 4 to 8 predictors per group with a sample of 157.

3.8. The questionnaire design scheme

In order to obtain accurate information and coverage of a wide range of problems was

devoted specialattention toprepare the questionnaire draft. Questions were formulated clearly,

into a meaningful order and formatand some questions were repeated twice in different ways,

to assure accurate answers. We have followed up the procedure of questionsinteractively

linked to each other giving the systemized form to achieve the purpose of the research.Also,

the questionnaire is organized in order to develop the questions in the correct format to be

easily understandable for the managers/owners surveyed and enable to extract the required

information and accomplish the research objective.In the beginning, the questionnaire has

been pre-tested and at the end has been completed the final questionnaire form. Mostly,

multiple choice of answer selection is applied. Five points of Likert – scale from‘strongly

disagree’to ‘strongly agree’ or very unsatisfied to very satisfied have been implementedin

thequestionnaire, in order to examine the level of satisfaction of managers/owners. The

combined structure of the questionnaire has been appliedto ensure that respondents fully

understand the questions and ensure to obtain themostqualitative information. It was

arranged with short and clear questions that easly enable hypotheses testing and analysis of

the data and interpretation to be possible.Figure7 shows the scheme of the questionnaire.



Figure 7. The questionnaire design scheme refined by theauthor.

The binary model of answers with Yes or No for same questions is applied as well.

The questionnaire contained three groups of questions, 51 questions at all:

1. Questions related to the owner’s/manager’s and companies profile according to the

anticipated sample of research;

2. General questions related to the external and internal barriers, as Institutional

barriers, barriers related to the market, to the rule of law, barriers related to the

infrastructure, and internal barriers mainly including the human resources skills.

3. The growth of businesses in last five years in percentage (indicators: Annual

turnover; Employment increase; Sales turnover and Investments)up to the declaration

of owners/ managers.



3.9. Procedures and instruments for gathering data

3.9.1. Interviews

As mentioned earlier were conducted 20 interviews with the managers/owners of the

businesses selected to generate ideas and learn more about the problem of research. Taking

their thoughtscarefully, facilitatedthe questionnaire preparation with structured and semi-

structured questions with aspecific narrative expression of the issues. To ensure the quality of

data collection, the interviews are conducted face to face with owners/managers of selected

businesses. The importance of the interviews for quality research is high; the interviews

allowed respondents considerable flexibility in their responses on a broad topic, with minimal

prompting from the researcher (Robinson, 2002).

Conducted interviews are based on the questionnaire well enough prepared and have included

questions that meet the objectives of the research. The mainquestions related to:

• Personal background – education, work history and roles/responsibilities within the

business at present;

• Company profile – number of employees, years in operation, sector (production,

service, and trade).

• Discussion about the company’s past, present, and future developments; critical

incidents with influence to the future of the company.

• Attitudes towards the business climate - resources, competition, regulation andkey

barriers which hinder business growth.

• Growth average in a period of last five years, thegrowth of annual turnover, sales,

investments, and employment,etc.

When designing an interview schedule, it was foreseen to ask questions to yield as much

information as possible about the study phenomenon and also be able to address the aims and

objectives of the research. Typically,trying the best, starting with questions that participants

could answer easily and then proceed to more difficult or sensitive topics. This is done in

order tofacilitate the respondents, build up confidence and rapport and often generate rich

data that subsequently develops the interview further.

Before an interview takes place, respondents have been informed about the study details and

given assurances about ethical principles, like as anonymity and confidentiality. This gives



respondents some idea of what to expect from the interview, increases the value of honesty

and is also a fundamental aspect of the informed consensus process.

3.9.2. The Survey

Specialattention is given to well-designed questionnaire survey to be well formulated, easy to

understand so that their completion as much as correct and should meet the research

objectives. The intention was to encourage the respondents to fill out all survey to do not

leave the questions unanswered in order to avoide the need for further researchon the same

issues.The objective of good questionnaire design is to minimize theproblems that damage

the quality of the research. This may seem obvious, but many research surveys omit

important aspects due to short preparatory work and do not adequately investigate particular

issues due to poor understanding. It should achieve the most complete and accurate

information possible. In this way has been taken care that the questionnaire to be designed to

ensure that respondents fully understand the questions and are not likely to refuse to answer,

lie the interviewer or try to conceal their attitudes. The questionnaire is organized and worded

in order to encourage respondents to provide accurate, unbiased and complete information.

A well-drafted questionnaire maked it easy for respondents to give the essential information

and for the interviewer to record the answerseasily. It was arranged so that sound analysis and

interpretation are possible. This would help to keep the interview short at that point that the

interviewees remain interested throughout the interview process.

3.10. Critical Incident Technique (CIT)

The process of identifying critical events was initially applied in the relationship between

business and consumer, explaining events that have left ‘traces’ of further business life,

whether for good or as a bad event for the future.But not only in that relation, but it was

alsovery important to learn if the different events are influencing positively or negatively to

the future of “business life.”In our case this technique is applicable, and many of

owners/managers described the different situationswhich have influenced their behaviour in

relation to the barriers. As mentioned by Keaveney(1995), CIT is appropriate when the

purpose of the analysis includes the benefit theory and application management and

development. The method of critical incidents described bySenge, P. M., et al., (1994)

explained it succinctly: “As a natural mechanism to cultivate meaning, people learn to divide



the world into categories and distinctions in our thoughts. We then tend to become

hypnotized by these distinctions, forgetting that we created them. ‘The economy is falling

apart,’ or ‘the people are corrupt,' becomes our reality, with a seemingly independent power

over us”. This is anappropriate way to investigate the perception of managers related to

barriers of doing business (negative phenomena) and their behaviourin different situations.

This is because, CIT may refer to the general history or behaviour discrete part of the

story(Kearveney, 1995). The method was developed by Flanagan(1954),and it is similar to

the analysis of complaints and compliments. It classifies variable services into three types:

basic, exciting and performance. The basic principle ofthis procedure is that the core

variables are never associated with pleasure. Exciting variables enabledto declare the

happiness or unhappiness, and finally, performance variables can be associated with both

pleasure and displeasure. In such situation, when we investigate the barriers to growth in

challenging environments those stories are present. Managers and owners are asked to show

the ancestors of frustration and satisfaction, for specific benefits from institutions or the

feeling of being forgotten by them. If the process of data collection occurs after the incidents

(of good experience or bad), then the perception of the respondents may be modified.

However, this method is complementary and sometimes helps to argue for an issue or

phenomenon with influence to the objectivity of the study. In our case, one of the questions to

the respondents was:Did they have any experience (positive or negative) that someone has

threatened in one way or another regarding business activities? Or any action has damaged

his business and on the other hand, has helped? Certainly, there were cases both positive and

negative, but most were negative, and they are explained by this research.

3.11. Standardization and ethics in the research

Adhering to ethical criteria is the fundamental principle of any academic research. The

information collection processes have been carried out based on the highest ethical standards

to protect and respect the rights of affected individuals. No data have been collected or stored

that would in any way jeopardize a business safety, individuals and others subject implicated.

In particular, direct interview methods with business managers/owners and others are used if

the required information will be very confident and certainty.There are two main issues

concerning the ethical considerations, which need more explanation on this point: anonymity

and confidentiality. This is because of the anonymity and procedures to privacy impact on the

response rates for different studies is greatly different (Armstrong, J., and E. Lusk, 1987).



The following is a rough and general summary of some ethical principles that various code

addresses:

Honesty and objectivity

Integrity and competence

Carefulness, confidentiality and human subjects protection

Openness, and legality

Respect for Intellectual Property

Respect for colleagues

This research deals with human behaviour, so all the necessary ethical procedures, including

instructions and regulations of the "KlimentOhridski" University,Ph.D. studyare taken into

consideration.

This study is conducted according to ethical procedures, promises of theauthorregarding

anonymity of respondents, and that the data from the survey are used only for research

purposes. According to Fuller(1974), "An anonymous survey is one in which no one can

identify data provided on questionnaires completed,"and the lack of anonymity reduces the

rate of responses. Some participants feel threatened if their identity could become known,

particularly when they are asked about some critical issues(Klein, S., J. Mahler, and R.

Dunnington., 1967). An anonymous study is one in which nobody (not even the study

directors) can identify who provided data on completed questionnaires" (Berdie, 1973).

Details of the respondents were strictly confidential, and their privacy is guaranteed.

The purpose of the study is explainedto all participants and anonymity, confidentiality,

storage and use of the data was conducted carefully. Also, the purpose of the survey was

explained previously, distributing questionnaires and the subjects are informed that the

process for completing the surveyforms was voluntarily (Armstrong, J., and T. Overton.,

1977). Also, participants are announced that any information derived from interviews and

surveys will be saved and any participants verbally agreed to participate.

3.12. Analysis and data processing

Why have we selectedSPSS to Work with?There is no doubt that business, education

institutions, and all fields of science have come to rely heavily on the computer. This

dependence has become so high that it is no longer possible to understand social and health

science research without substantial knowledge of statistics and at least some rudimentary



understanding of statistical software. The number and types of statistical software packages

that are available continue togrow each year. In this dissertation, we have chosen to work

with SPSS or the Statistical Package for the Social Sciences. SPSS was selected because of

its popularity within both academic and business circles, made it the most widely used

package of its type(Arkkelin, 2014). According to Arkkelin(2014),SPSS is also a

multipurpose package that allows many different types of analyses, data transformations, and

forms of output - in short, it will more than adequately serve our purposes. The SPSS

software package is continually being updated and improved, and so with each major revision

comes a new version of that package.Data analysis is done based on theassessment indicators

provided and key research findings. Data obtained from the survey was recorded in a special

database for relevant computer analysis, interpretation, and argumentation of the main

studyfindings.

The data will be processed by SPSS 20.0 exclusively, for any inappropriate or accuracy

trouble will use alternating methods. Based on the distribution of data, we will focus mainly

on the analysis applying Multiple Linear regressionmodels, and other models as needed.

Multiple Linear regression analysis is used to assess the statistical dependence between

variables. In correlation analysis, the goal is to determine linearstrength connection between

variables and coefficients that indicate the average of Y change when relevant variable for a

unit differ substantially, and other factors are held constant. In some cases, it may happen that

the regression calculation isdone without the constant presence. In these regressions, the

graphic will have the origin, while the value of constants is zero. Multifactorial model

variables can contain positive values which present the correlation between variables.

Multifactorial analysis can be given a better understanding of elements in interpretation and

therefore analyze the impact of independent variables on the dependent variable, holding

constant other variables included in the model.

3.13. Dependent and independent variables

In the classification of variables, we will have very few dummy variables we use to test main

hypotheses; others will be tested based on five points Likert scale data obtained.Linearity is

not the only possible ways to present the relationship between dependent variable (growth)

and independent variables (barriers).Our econometric model selection is made on several

criteria of econometric analysis that will be performedthrough:

a. Graphs and tables



b. Coefficient of determination,

c. Distribution of error,

d. Statistical significance,etc.

There is used a multiple linear regression model to test the influence of the determinants

listed in the table above on firm growth and the relationship between dependent variable

(firm growth) and independent variables (barriers).

Hereby, growth denotes variables of relative growth in employment; annual turnover, sales,

investments, and determinants include variables/factors of individual features, organizational

and environmental determinants; barriers cover variables/factors of growth barriers. To

construct factors from the questions corresponding to these determinants, we have used

Factor Analysis (FA). We test for reliability using the reliability coefficient and calculate the

correlations between the variables.

3.14. Dependent Variable

The dependent variable in this study is firm growth. Firm growth as of many authors

estimated can be measured by several indicators such as annual turnover, sales, employment,

assets, market shares, profits, etc. In particular sales, turnover and employment are widely

used indicators for firm growth (Davidsson P., 1991); (Delmar, 1997); (Ardishvili, A.et al.,

1998). Previous studies differ enormously in relation to the period included inthe research.

According to Weinzimmer, L. G. et al., (1998), to raise the correctness, to avoid short-term

tendencies and to enable for a reliable estimation of organizational performance, the time

period explored should be at least 5 years. The prototypical growth firm we have in mind,

unless otherwise stated, is one that experiences relatively stable growth in sales over

considerable time, and where this growth in sales is at least to some extent accompanied with

accumulation of employees and assets, so that organizational and managerial complexity

increases with growth (Davidsson, P. et al., 2007).

Our data will contains several (4 four) indicators of firm growth such as employment,

turnover, sales, and investments for last five years.The data from questionnaireare merged

and calculated the average for each case as representative of growth.



3.15. Independent Variables

The independent variables based on the theory discussed above aredivided into5 groups,

which include factors and determinant variables representing the correlationand impact to

thedependent variable (growth). Independent variables mostly are correlated to environmental

barriers (the rule of law, market-related barriers, and infrastructure related barriers) on one

side and barriers related to human resources capabilities like lack of professional staff,

growth motivation of owners/managers andother individual determinants that include

personal traitson the other hand.We used the Ordinary Least Squares method (OLS), which

according to Gujarati(2004) is the most commonly used method in the regression

econometrics analysis. The average of growth in5 yearsperiod was 4.3% orthemaximum of

growth 18%.This will not determine the level of barriers influence to the growth.Managers

and owners might be declared for high presence of the certain barriers, but the business still

has growth.However,resultsclearlyshow the correlation between variables. According to

Gujarati (2004), applying OLS method in cases when the dependent variable has two values

is inappropriate.To avoid this is applied one value for each instance (calculated the average of

growth for the given period). Moreover, the most appropriate approach in such situations are

Probit and Logit models. Both models, Probit and Logit, can be written as:

Y = β0 + β1x1 + β2x2 +…..+ βpxp + ε

Herewith, Y is the dependent variable, X’s are independent variables, and ε is the error term.

According to inclusive literature, there is no clear difference between these both models. For

instance, Gujarati (2004) argues that Logitmodel, compared to Probit has a distribution that is

a little fatter in tails and others donot withdraw any crucial difference.

In this chapter, after analyzing and deciding on the research paradigm which is positivist, we

have analyzed and set the methods to be used for the collection and processing of data. As it

ispresented in detail, is decided to use the Multiple Linear Regression model to refine the data

and withdraw the results from econometric models. Also in this chapter are discussed and

analyzed the scope of research regarding data collection. Based on the literature review, are

also set procedures for the operationalization of theoretical concepts into measurable

variables.For each group of variables are conducted econometric modelswhich are presented

in detail in chapter 5.



CHAPTER 4

4. BARRIERS TO SMALL BUSINESS GROWTHIN KOSOVO – CURRENT

SITUATION

4.1. Short profile of Kosovo evolution

The Republic of Kosovo (spelled “Kosova’, Kosovë” in Albanian and Kosovo

internationallytaken from the denominations of Serbia and the former Yugoslavia) is

theyoungest European country located in the south-eastern part of Europe, situatedon the

Balkan Peninsula. With its strategic position in the Balkans, it serves as a vital link between

central and south Europe, the Adriatic Sea, and Black Sea(Wikipedia, 2016). Its capital and

largest city is Pristina, and the other main urban areas include Prizren, Pejë,and Gjakova.

Kosovo is abundant with natural resources. Kosovo is mainly rich in lignite and mineral

resources such as coal, zinc, lead, silver and chromium but also productive agricultural land.

Kosovo is also rich in forests, rivers, mountains and soil; it is among the richest countries

regarding natural resources in Europe, based on surface. Kosovo is especially rich in coal,

being aligned among European countries as the third with the largest coal reserves. Kosovo

possesses around 14,700 billion tons of lignite in reserves, which aligns Kosovo has an area

of 10,908 square kilometres and an estimated population of 1.8 million (Albanians contain

92% and Serbs 4% othersare other communities). Before 1990, Kosovo was one of the eight

constitutive units of the Socialist Federation of the Republic of Yugoslavia (SFRY). Before

the failure and the breakdown of former Yugoslavia, Kosova was the less developed entity,

with a per-capita output of only 28% compared to average per-capita output in Yugoslavia.

In the case of Kosovo, the advancement of entrepreneurial development must be considered

within the broader spectrum of social and political changes, taking into account the period of

war that has had a tremendous impact on overall entrepreneurial activities. As Stilhoff S.

(2006, p. 319) states,“A central theoretical departure point in the present analysis is that the

economic sphere cannot be isolated from its political and social context, but that the economy



is embedded in social relations, informal and formal institutions.”This is the implication in

the concept of ‘political economy’, which rejects ‘pure economic’ factors, as opposed to the

non-normative and non-political conceptualization of the economy, as a single and separate

unit of analysis, which was brought from development of neoclassical economics,Business

Environment and Development of entrepreneurship.Under these conditions, we cosideras a

more special case compared to other countries in the region.Currently, theeconomyof Kosovo

has made progress in transitioning to a free market-based system and keeping

macroeconomic stability, but it still needs too much. It is highly dependent on the

international community assistances and the diaspora for financial and technical

assistance.With per capita GDP estimated of close to €3,000, Kosovo is one of the poorest

countries in Europe. Average per capita income is about one-tenth that of EU levels, and the

incidence of poverty remains high. Standardized poverty lines used by the World Bank

defined by a threshold of US$5 per person per day (at purchasing power parities) lead to

poverty rates of about 80 percent. Using the national poverty line of €1.72 per day (2011

data) as defined by the Kosovo Agency of Statistics, 29.7 percent of its population of 1.8

million are considered poor (World Bank, 2016).

There are many opportunities from many branches and economic sectors from which SMEs

in Kosovo can be established, mostly based on Kosovo's natural assets. They are a powerful

foundation for the development of the extractive, processing, metallurgy, handicrafts and

chemical industries.

Specific historical and institutional context in Kosova visualizes the opportunities and threats

in development of the private sector dominated by SMEs. The development path begins with

so-called “small economy” during 70’s and 80’s of the last century, with a mixture of the

elements of planned and market economy system within “self-management socialism” - a

specific feature of former Yugoslavia. Private ownership of land, farms, handicrafts, and

small firms was something more pronounce in Kosova as compared to the other TEs in CEE

(Krasniqi and Mustafa, 2016).

4.2. Data Analysis and findings – secondary data

Nowadays, it is worldwide recognized that entrepreneurship is a phenomenon that fosters the

economic development of any country. This research pays special attention to the negative

impact of several barriers to SBs growth in Kosovo. There are many obstacles and barriers



which seriously damage the business climate. Most of the barriers are related the institutions

and government policies that arrange the business environment in Kosovo.

Figure 8. Relationship between SB Growth and barriers

Source: Self refined.

If we look in general, the barriers that hinder business growth in Kosovo are:

Barriers related to markets, selling, products, customs regulations, infrastructure,

import and export, unfair competition, etc.

Access to foreign markets

Limited capacity of local market

High shipping costs

The quality of the transport services

Cost of transactions

Access to finance

Access to raw materials and equipment

Administrative burdens and bureaucracy, legislation, licensing procedures, informal

economy, fiscal evasion, corruption, organized crime, etc.

Supply, equipment, and machinery.

Workspace: offices, building,etc.

Lack of qualified Human Resources

4.3. Environment of Doing Business in Kosovo

Economies in transition started their reforms without a complete legal framework and

institutional, needed to create the basis of the market economy and entrepreneurship(Verheul



et al. 2001, Chilosi, 2001). Rather, the institutional environment has created numerous

barriers to market entry and further growth of new firms. This has prevented entrepreneurs to

fully exploit the opportunities created by the process of transition. Kosovo is faced with lack

of consolidation of the rule of law and various institutional obstacles as well. Specific

Kosovo’s way the past decades influenced that obstacles are more expressed.

Kosovo lags behind other countries in the region, based on the statements of some

international organizations, despite the progress achieved. The progress is significant to all

sectors of theeconomy. These differences are important not only when more mature market

economies are comparedwith transition economies (TEs) but also when (TEs) are compared

with each other. It is generally accepted that the environment is a crucial factor in any success

of entrepreneurs. Baumol (1993) gives some examples that the economies in question are

interested mainly in the relations between the object created (an enterprise and innovation)

and the economic environment. Begley et al. (2005) have argued that different business

environment conditions may influence the appearance of the various types of entrepreneurs.

Their goal is not to penetrate the “black box,” or to understand or predict the entrepreneurial

event (Julien 1989). What they are trying to do is to understand the impact of this “black

box,” with its specific attributes or behaviours, on the economic environment or, conversely,

to establish the environmental characteristics that are favourable or unfavourable to the

phenomenon. Different kind of barriers tends to prevent SBsobjectives of growth(Gruda, S.

and Milo, L. 2010). Mostly, the obstacles and barriers are related to the business

environment.

The policy makers, trying to improve their economy’s regulatory environment for business,

find out as a good place to start how it compares with the regulatory environment in other

economies. Doing Business provides an aggregate ranking on the ease of doing business

based on indicator sets that measure and benchmark regulations applying to domestic small to

medium-size businesses through their life cycle. Economies are ranked from 1 to 190 by the

ease of doing business ranking. Doing Business presents results for 2 aggregate measures: the

distance to frontier score and the ease of doing business ranking. The ranking of economies is

determined by sorting the aggregate distance to frontier scores, rounded to two decimals. An

economy’s distance to frontier score is indicated on a scale from 0 to 100, where 0 represents

the worst performance and 100 the frontier. The ease of doing business ranking compares

economies with one another; the distance to frontier score benchmarks economies with



respect to regulatory best practice, showing the absolute distance to the best performance on

each Doing Business indicator.

The 10 topics included in the ranking of Doing Business 2017 are: starting a business, dealing

with construction permits, getting electricity, registering property, getting credit, protecting

minority investors, paying taxes, trading across borders, enforcing contracts and resolving

insolvency. The labor market regulation indicators are not included in this year’s aggregate

ease of doing business ranking, but the data are presented in the economy profile

(WorldBank, 2017).4

Figure 9. How Kosovo and comparator economies rank on the ease of doing business.

Source: World Bank, (2017).

External determinants of SME growth - It is widely recognized that the external environment

plays a crucial part in SME growth. The recent line of research investigating the impact of

4 Note: The rankings are benchmarked to June 2016 and based on the average of each economy’s distance to frontier (DTF) scores for the 10 topics included in this year’s aggregate ranking. The distance to frontier score benchmarks economies with respect to regulatory practice, showing the absolute distance to the best performance in each Doing Business indicator. An economy’s distance to frontier score is indicated on a scale from 0 to 100, where 0 represents the worst performance and 100 the frontier. For the economies for which the data cover 2 cities, scores are a population-weighted average for the 2 cities. Source: Doing Business database, World Bank, 2017.



external environment on thegrowth of small firms forms the focus of the so-called “barriers to

growth” literature. This literature maintains that while only a proportion of small businesses

are growth-oriented, the ability of this group to achieve their growth potential is impeded by

the external business environment, (Hashi, I. and Krasniqi, B., 2011).

4.4. General situation

The institutions that are responsible for maintaining macroeconomic stability have done an

admirable job in ensuring that Kosovo remains one of the most fiscally and financially stable

countries in the region(USAID, 2013). Significant progress has been made in the World

Bank’s Doing Business Index, with Kosovo’s ranking improving from 128th in 2009 to 119th

in 2011, 98th in 2012(WorldBank, 2013). This progress changed Kosovo ranked on 74thplace

in 2014. However, looking at its closest neighbors, it is clear that Kosovo still has some way

to go; Macedonia ranks 23rd, Montenegro 51st, Albania 85th, and Serbia 86th in 2012. To see

the progress achieved we compare the data of 2017th as it is presented in Table6:

Table 6. Doing business ranking position of Kosovo and neighboring countries

Country Year 2011 Year 2012 Year 2017

Macedonia 38 23 10

Montenegro 66 51 51

Albania 82 85 58

Serbia 89 86 48

Kosovo 119 98 60

Last position 183 183 190

Source: World Bank, 2013, World Bank, 2017, refined by author

The challenge now is to ensure that more improvements are made in the overall regulatory

environment so that Kosovo can realize a robust private sector and attract foreign direct

investment(USAID, 2013).

The path of entrepreneurship in Kosovo is unique compared to other countries in the region,

due to large damages from the recent war. Kosovo remains the poorest economy in the region

and struggles with high levels of poverty, massive unemployment (estimated at an average of

45% compared, for example, to 37% in Macedonia and 24% in Albania), and over-

dependence on imports combined with a very small export sector, and energy shortages. The



country’s Gross National Income (GNI) per capita is estimated at $3,520, ranked 93rd

worldwide and behind Macedonia, Albania, Serbia and Bosnia and Herzegovina, as assessed

from World Bank, 2013;(USAID, 2013).

The economy has maintained a growth rate of 3-5% for about a decade up to 2014. The

Kosovo Agency of Statistics (KAS) estimates that during the three-year period (2013-2015),

the economy was grown with an average of 4.7%, the highest average estimated growth rate

among Southeastern European economies. However, the pace of growth is not nearly enough

to have notable effects on poverty and unemployment. For example, the World Bank

estimates that Kosovo would need to more than double its growth rate to 12% per year for an

entire decade to reach Montenegro’s current Gross Domestic Product (GDP) per capita level.

In short, the income gap between Kosovo and other countries in Southeast Europe is likely to

remain large despite higher growth.

Table 7. Doing business topics ranking position

Indicator Kosovo DB2017

Kosovo DB2016

Albania DB2017

B. H. DB2017

Bulgaria DB2017

Croatia DB2017

Czech Republic DB2017

Slovenia DB2017

Starting a Business

13 27 46 174 82 95 81 49

Dealing withConstruction Permits

129 125 106 170 48 128 130 80

Getting Electricity

114 111 156 123 104 28 13 16

Registering Property

33 33 106 99 60 62 31 34

Getting Credit

20 19 44 44 32 75 32 133

Paying Taxes

43 77 97 133 83 49 53 24

Trading across Borders

51 59 24 36 21 1 1 1

Enforcing Contracts

44 43 116 64 49 7 68 119

Resolving Insolvency

163 164 43 41 48 54 26 12

Source: World Bank, Doing Business 2017, refined by theauthor.5

5 Note: DB2016 rankings shown are not last year’s published rankings but comparable rankings for DB2016 that capture the effects of such factors as data revisions and changes to the methodology. The global best performer on time for paying taxes is defined as the lowest time recorded among all economies in the DB2017 sample that



Kosovo is making steady and significant macroeconomic progress in both first-stages (price

liberalization, trade and foreign exchange reforms, and privatization) and second-stage

(competition policy, and enterprise, banking, infrastructure, and non-bank financial reforms)

restructuring. In Table 7, we can see World Bank doing business ranking – Kosovo and other

regional countries.

The data presented (Fig. 11) are seen in different positions depending on certain criteria of

the states that present the business climate. In the 2016 Doing Business survey, Kosovo

ranked 64 overall, with the subcategories on starting business, registering properties, and

enforcing contracts being, respectively, 13, 33, and 44. Kosovo also records steady

improvement in Distance to Frontier with overall 68.79 percentage points in the 2016 Doing

Business, 1.64 percentage points improvement compared to previous year (World Bank,

2017).

Figure 10. Doing business topics ranking position

Source: World Bank, 20117.

4.5. Barriers to Growth of SB in Kosovo

Some of the barriers with impacton the business growth are macroeconomic environment,

legal and regulatory environment, unfair competition, informal economy and corruption,

financial obstacles, tax burden (Krasniqi, 2007). Below we will see the list of barriers derived

from one of most wide research report conducted by MTI of Kosovo. Theresearch based on

the business survey conducted by the MTI of Kosovo with 800 businesses in all regions of

levy the 3 major taxes: profit tax, labor taxes and mandatory contributions, and VAT or sales tax.



Kosovo in 2011 have identified barriers that mostly hinder doing business in Kosovo(MTI,

2011). The sample was designed to investigate the general stance and the development trends

of the SMEs, including all regions of Kosovo and covered all sectors of business

activities.This study explored many obstacles related to environment character which helped

for our research orientation(Table8).

Unfair competition (arising from tax evasion and under-declaration of workers) is referred to

as higher barriers in doing business (from a list of 22 barriers) from the respondents. Given

the level of public services, respondents believe that they should pay 60 percent less taxes

than they pay now. In a hypothetical case, if there is a tax increase of 10 percentage points,

78 percent of respondents have shown readiness to evade taxes. Potential tax rate increases to

82.5 percent, if taxes go up to 20 additional percentage points. Managers and owners

inerviewed believe that businesses in their respective industries reported an average 63

percent of their employees. This means that on average 37 percent of the total workforce is

not reported (RIINVEST, 2013).

Table 8. Barriers related to the environment

Financial barriers Access to financeHigh interest Credit terms (short terms for returning credits)Difficulties to take the credit etc.

Economic policy and general situation

Political instabilityGeneral economic policy, Trade policy etc.

Power Rule of Law Not the efficiency of the judiciary, corruption in courts, policy implications in justice,etc.

Fiscal policy High rate of VAT,Refund of VAT

Infrastructure Lack of electricity,Telecommunications, Lack of water, Network undeveloped, Lack of roads

Access to raw materialsand equipment

High customs tariffs for import of raw materialsLack of raw materials in country

Negative phenomenon Administrative burdens and bureaucracy, not efficiency, application of legislation, licensing procedures, informal economy, fiscal evasion, corruption, organized crime,etc.

Market barriers Unfair competition,



Access to foreign markets, Limited capacity of local marketHigh shipping costs The quality of the transport services

Supply, equipment, and machinery

Lack of machinery and other equipment

Workspace Insufficient work space, offices, buildings,etc.Location inappropriate

Lack of qualified Human Resources

Managerial stuff, technical stuff, finance accounting, IT stuff, field management staff,etc.

Source: Report - Research of 800 Small and Medium 2011 - MTI, refined by author

4.6. External Factors

4.6.1. Institutional barriers

In general,there are two main factors that constrain theeconomic development of the country:

Political instability

General economic policy

These factors are present due to theincapacity of institutions to build the sustainable

mechanisms to support the private sector development. In Central and Eastern European

countries (CEECs) newly joined in the EU, the state has become a positive agent of

institutional change concerning entrepreneurship development. At the same time, it may be

argued that entrepreneurs have contributed to creating a demand for institutional change

through the key role they played in the development of the economies in the first decade

oftransition. However, the process of accession to the EU has contributed to institutional

development and administrative reform, driven partly by the need for accession countries to

meet the conditions for entry laid down by the EU, and partly by their desire to have the

institutional conditions in place that would enable them to successfully access EU, Structural

Funds. This has affected the institutional structures, including the development of sub-

national institutional frameworks (in some countries), policy processes as well as thepolicy

itself(Smallbone, D. and Welter, F., 17 May 2010).

Kosovo institutions achieved a goodoverall progress, but still need improvements in order to

be more consolidated, to reach the rhythm of development through increasing the support

capacities to SB.



4.6.2. Access to Finance

The Small and Medium Enterprises Support Agency (SMESA) considers that SMEs in

Kosovo are experiencing several barriers to financeaccess. This realization led to the

inclusion of“Improving SMEs access to finance” as “Strategic Goal 2” in the “SME

Development Strategy for Kosovo 2012-2016” (MTI, 2012). Mainlythey use personal

resources, borrowing from relatives, selling something from ownership and other alternatives.

In such conditions, the commencement of businesses was challenging and do not proceed

with growth. Regarding the origin of financial resources (investment) in Kosovo, emerges as

they are provided by different sources. Thus, the survey of 600 enterprises conducted by MTI

notes that 57% of funding, comes from internal sources, while 43% of the funds are from

external sources. Furthermore, bank loans are 32.1%, loans from individuals and households

9.8%, financial funding without return 0.3%, and 0.7% other sources (Gashi, 2014).Also, due

to the aforementioned institutional difficulties and the rule of law issues, specifically in the

area of economic courts and contract enforcement, Kosovo is faced with a very high cost of

finance.

4.6.3. Rule of Law

Lack of transparency in the judiciary creates more opportunities for corruption, which is not

limited to bribes or misuse of funds allocated to the judiciary but goes to the bias of the

highlighting of decisions and judgments in litigation, arising as a result of politicization

judiciary and partisan bias of judges. Among many other issues, an ongoing judicial

challenge in Kosovo is the lack of transparency (Riinvest, 2014).

Organized crime, which is interfaced with mentioned barriers, is also considered as serious

barriers, being ranked on ahigh level in the overall list of barriers.There are also other

indicators that highlight these barriers as problematic. So, unfair competition, corruption,

non-competitive practices, debt collection, the lack of political stability and non-functioning

of the judiciary system are perceived as serious barriers. All these have one thing in common

- the lack of rule of law.

In lacking legal infrastructure, there is room for the appearance of negative phenomena.These

phenomena hinder the normal development of the enterprises(Polishchuk, 1997). Sometimes,

informal institutions are adopted because the formal rules are inefficient and individualsfind



it is less costly to make informalarrangements than depending on the formal rules (Feige,

1997); (Pejovich, 1999);(Smallbone, D. and Welter, F, 2003).

Based on a survey conducted by the RIINVESTInstitute, themostserious barrierof doing

business is estimated unfair competition (mainly from tax evasion). Since this barrier is very

serious, it reflected in the report as the main cause of informality (RIINVEST, 2013).

According to the report, tax evasion is considered to be close to 40%.

Such conditions are present with what Baumol W. J., (1990) defines as unproductive and

destructive entrepreneurship. It is only in situations where formal and informal institutions

combine to form a coherent framework that formal regulations and the ‘rule of law’ will

positively shape individual behbehaviour. By contrast, in fragile settings where the rule of

law is largely absent, ‘noncompliance with the formal rules becomes pervasive,'(Feige, 1997)

(Smallbone, D. and Welter, F., 17 May 2010)and(Andrew Atherton & David Smallbone,

2012).

4.6.4. Public Procurement Process

According to RIINVEST research, there have been many violations in procurement

procedures in Kosovo. The lack of a non-discriminatory procurement system not only

endangers the provision of public services of poor quality but also undermines fair

competition. Therefore public procurement is an area of government where it is very

important to have transparency as it constitutes the bulk of public expenditure and also has

implications in the form of market operation (Riinvest, 2014).

According to the Public Procurement Regulatory Commission (PPRC), the annual value of

public contracts in Kosovo is around 800 million Euro. Almost all contracts are awarded

using the criterion of lowest price, and only a small fraction of them using most economically

advantageous tender. Losses of paperwork, bureaucratic procedures and lack of efficiency of

the above two forms of contracting are high.

Lack of e-procurement is one of the biggest threats to transparency in the procurement of

public funds. Actually, PPRC website publishes almost all public contracts; however, the

process up to the award of thecontract is hardly followed by all stakeholders.

Contractsnotices also are not always posted on the website of the PPRC.Although current

legislation guarantees that the process should be transparent, yet there is a lack of



mechanisms to ensure easy access to information. Electronic procurement solves many of

these issues. Application instructions are located on the system; so that the problems

associated with the application process are less costly. Electronic procurement makes the

whole process more transparent, making the information more easily accessible, and leaving

less discretion to procurement officials.



CHAPTER5

5. FIELD RESEARCH DATA AND INDICATORS MEASURED

As is discussed above in chapter two,it is still necessary contribution to complete deficiency

of a discussion of an appropriate measure of firm growth in the literature. Theoreticians have

used different measures of growth, ranging from employment to profits, value-added,

turnover, and total assets. Most studies have been onedimensional, looking at singleor few

indicators only. Many models of firm growth exist in the literature, some of them identified

to be most applicable to small enterprises. They include many models in different

dimensions.In this relation,the research shows the complexlandscape of the question of firm

growth. When weapproach to study small firm growth, it is important to pay attention to the

multi-dimensional nature of growth as we stated previously in this study. Thus, it is useful to

combine or modify existing models and respecting theories of firm growth and studies about

small firm growth.Initially, it is intended for the model and comprehensive approach to the

problem in order to comply with our case and provide a wide-ranging overview of the

information to enable easer todraw conclusions. This study has reviewed and discussed many

models of small firm growth and the determinants of small firm growth. Also, the literature in

this area is often fragmented,many studiesfocus only on acertain perspective. Hence, there is

a lack of an inclusive model to measure the small firmgrowth. When it comes to the

discussion about the determinants of growth, firm size and firmage, have always been

dominant(Davidsson, P., Delmar, F., & Wiklund, J., 2002b). Is this enough and should we

stop with that? Of course not! We need to get more variables and expand the research,

buttonot overcome the premises of a dissertation, we should choose and classify the most

important only.

Why multidimensional approach to research barriers tofirm growth? Liedholm and Mead

(1999, p. 20-21) list a number of other variables that influence small firm growth in addition

to the key variables of age and size. According to thesector of operation, location, gender,

and human capital can influence growth. Also, relevant macro variables such as the aggregate

level of economic activity, policy, and constraints can have a direct effect on firm growth.

But we do not forget that our focus is in SBs, and we cannottakein account the size as afactor



of growth. New growth theory based on comprehensive approach is finding the support by

many new scientists.Wiklund, J.et al.(2009),referring the indicators of firm growth,attempted

to build an integrative model. They identified four groups of factors to build the growth

model. These include entrepreneurial orientation, environment, resources and growth attitude.

However, they note that these perspectives are not necessary independent of each other. For

example, they view entrepreneurial orientation as the central construct mediating the impact

of resources, environment, and attitude on growth. However, based on this model and so

many others mentioned in this dissertation (Storey, 1994, Barlet and Bukvic 2001, Rachel,

2011, Davidson 2006, and so on),the question is: Whether or not these models can be applied

to Kosovo case? We decided to select the most important factors that hinder business growth

in Kosovo that mostly has been considered by the others as well. Or, to merge and adopt the

main factors that influence the SBs growth in Kosovo, almost taking on consideration the

research of perception of owners / managers.

The main field research of this study based on perceptions of the owner/managers has been

focused on two main directions:

1. Institutional barriers (external barriers)

2. Barriers inside of the businesses (internal barriers)

5.1. Measurement of indicators and variables

Referring the indicators that influence the growth of small firms the permanent question will

be, what kind of factors to be measured? Let see what the different authors say. The growth

of SB can be measured by many different indicators, most common are sales, employment,

assets, physical production, market share and profits (Ardishvili et al., 1998); (Delmar, 1997);

(Weinzimmer, L. G. et al., 1998); (Wiklund J., 1998).

Among available alternatives the researcher has the choice to a) create multiple indicator

indexes; b) use alternative measures separately, and c) find the one, best indicator. If growth

is conceived of as a latent construct with common causes but alternative manifestations the

multipleindicator indexes makes sense (Davidsson P., 1991). The fundamental theory here is

that the same illustrative factors facilitate or hinder growth of the firms, but that this growth

for some firms manifests itself as, e.g., radically increased sales turnover without much

change in assets or employment, whereas for other types of firm the result is moderate and

balanced growth across, e.g., assets, employment, and sales. The sum of standardized



versions of all three indicators would then be a better representation of the theoretical growth

concept. If only one indicator were used, results would be weak and possibly

distorted(Davidsson P., 1989). Alternatively, the underlying theory predicts that certain

antecedents would be related to, e.g., growth in sales and market share while other predictors

are believed to influence growth in employment and profits, respectively. If so, the sensible

course of action is to include and analyze different growth indicators separately (Delmar,

1997). The theoretical and empirical evidence is leaning in favor of this other notion. For

example, Chandler, G. N., et al.(2005), successfully used transaction cost theory to explain

when growth in sales and employment do and do not move closely together.

Another defensible alternative would be to confine the study to the one growth indicator that

is the best match with the theory in question. If only one indicator is used, and the study has a

cross industry design, there is growing consensus that sales growth should be the preferred

choice(Ardishvili et al., 1998); (Hoy, F. et al., 1992); (Weinzimmer, et al. S. J., 1998);

(Wiklund J., 1998). It is the most general of the alternatives, as all commercial firms need to

have sales to survive. According to Barkham, Gudgin, Hart, and Hanvey (1996), it is also the

indicator small firm owner/managers use themselves. Additionally, it may be argued that

sales often precede the other indicators; it is the increase in sales that necessitates increases in

assets and employees and results in rising profits or market share (Flamholtz, 1986). These

favorable aspects of sales as anindicatoris reflected in that with 30.9 percent of the studies it

is the most used in theresearch reviewed by (Delmar, 1997). It is always so popular that the

employment growth was widespread used with 29.1% of the studies reviewed, considered as

relevant to policy makers for some purposes, the interest in speeding up employment growth

and for the reason of data availability(Davidsson, P. & Wiklund, J., 2000).Very few

managers see growth in employees as a goal in itself (Gray, 1990); (Wiklund J., 1998);

(Robson, P. J.A. & Bennett, R. J., 2000)and because some growing firms outsource

employment growth heavily is not always highly correlated with sales growth (Delmar, F. et

al., 2003). Furthermore, we see the different opinions regarding the selecting indicators for

measuring are presented in literature, but the measuring growth based only a single indicator

is not sufficient, as much as indicators including complete resultswould be.

Different studies have used a range of diverse theoretical concepts of the firmgrowth to

measure the number within any given economy(Henrekson, M. and Johansson, D., 2010).

More broadly discussed and frequently arefound in theory three groups of indicators to



measure the growth. E.g., according to some authors(Garnsey, E., Stam, E. and Heffernan, P.,

2006); (Moran, P., and Ghoshal, S., 1999)firm growth can be measured in three different

ways:

a) inputs (investment, employees);

b) value (assets, market capitalization) and

c) outputs (sales turnover, profits)

However, the overlap between these different measures of growth is relatively weak; hence a

firm may be identified as high growth in terms of sales turnover but not employment, or vice

versa(Delmar, F. et al., 2003). While,recognizing that a single metric will not capture all

elements of firm growth (Janssen F. , 2009), turnover will be used as the main indicator of

high growth during this study(Mason, C. and Brown, R. , 2010). OECD(2007) defines HGFs

as: ‘enterprises with average annualized growth in employees or turnover greater than 20%

per annum, over a three year period, and with more than 10employees at the beginning of the

observation period’.

5.3. Procedure of data analysis

In this chapter are presented the procedures and results of data processing by SPSS 20.0.

Initially, was startedwith conducting the data analysis on the base ofsmall business growth

literature and variables used in the study. In the following, the analysis are conductedstep by

step of statistical topics, ensuring that the model outputs shows the data of normality,

linearity, and multicollinearityare logical, showing correlation between variables, considering

that data arerelevantto be interpreted. Econometric models showing the dependent variable

and independent variables and the structural equation for each model including all groups of

variables arebuilt.Before starting analyzing the data, the discriminatory analysis was

performed from surveys data related to barriers, declared by various managers/owners of

businesses to identify changesin responses.Build of the structure is done after confirmatory

factorial analysis, in which not only were defined factors affecting the variables in the study

but using multiple linear regression stepwise analyses,assessing the parametersthat confirm

links between the model and constructs. Furthermore, were conducted tests required by the

above regression to confirm the importance of the model construct and applying the analysis

step-by-step was extended on the selection of the most significant variables. Rather, through

the comparison of statistical indicators and the data of descriptive statistics, are identified



theadditional impact of variables in the model. Thus, is identified and argued the effect of the

barriersand relations to small business growth.

The first part of the survey was processed to fill in the data related to the profile of surveyed

businesses, such as the level of education of managers/owners, business location, longevity,

number of employees and activity.The second part is preceded with questions regarding the

hypotheses testing raised in this study, through Pearson’s correlation.The third part contains

questions that have to do with the creation of statistic models for data processing. Five groups

of independent variables have been created, and through the data entry in the system (SPSS

20.0), the correlation between each single independent variable with dependent variables

(growth) is measured.

Figure 11. Research process

Source: Field,(2009).

Statistical analysisis based on a chronological link of preliminary research work, empirical

and theoretical analysis, raising research questions and hypotheses to continue with data

collection, data analysis and output of results. The conduct of the research process lookalike

as in Figure 11.



5.3.1.Reliability and availability of statistical data

Testing the validity and reliability of the survey data is a prerequisite for data analysis and

conclusions. Theprocessis performed in two steps. In thefirststep is tested the reliability of the

questionnaire and in the nextstep is done factor analysis, which aims to establish the

questionnaire. Reliability was handed over by parameters through the coefficient of

Cronbach's alpha, standardized for each distinct construct, which flowed from thefactorial

analysis (Cronbach, 1951). First, it was concluded that a respondent must answer the

questionnaire in the same way at different times. Secondly, two respondents with the same

attitude towards related to one variable to be able to respond to the survey in an identical

manner. Thus, the degree of reliability is a necessary prerequisite for the study of test validity

(Carmines, E. G., and Zeller, R. A., 1979).

N = Number of questionsCov = covariance / average questions

S = variancebetween questions

In this micro-tease, "α" is used as a measure of consistency on the internal scale, using the

SPSS (Statistical Package for Social Sciences). According to Field (2009), values between

0.7 and 0.8 of "α" are acceptable. Cronbach’s α indicates the overall reliability of a

questionnaire and values around 0.8 are good (or 0.7 for ability tests and such like).

Reliability is the consistency of a measure. Reliability analysis can be used to measurethe

consistency of a questionnaire. Any value less than these will be considered as untrustworthy.

In our search, the final alpha Cronbach coefficients of all elements ranged around and not

below 0.8,respectively 0.767 and 0.873,as we see on the table below:

Table 9. Case processing summary of statistical data

Case Processing Summary N %

CasesValid 155 98.7Excluded 2 1.3Total 157 100

a. Listwise deletion based on all variables in the procedure.

Source: SPSS output, processed by author



Field (2009) advice tomeasure thereliability obtained by splitting of items into two halves (in

some random fashion) and obtaining a score from each half of the scale. We did so; we could

do more subscales of measure but, since the splitting up in two parts obtained the parameters

of Cronbach's alpha, it was not necessary to proceed further.

Table 10. Reliability of statistical data

Reliability Statistics

Cronbach's Alpha

Part 1Value 0.767N of Items 21a

Part 2Value 0.873N of Items 21b

Total N of Items 42

Correlation Between Forms 0.206

Spearman-Brown Coefficient

Equal Length 0.341

Unequal Length 0.341Guttman Split-Half Coefficient 0.32


As with the previous two subscales, the overall α is around .8, which indicates

goodreliability.The simplest way to do this in practice is to use split-half reliability. As of this

method splits the data set into two values. A score for each participant is then calculated

based oneach half of the scale. If a scale is very reliable,the value of the first part on one half

of the scale should be the same (or similar) to the value on the other half: therefore, across

several participants, scores from the two parts of the questionnaire should correlate perfectly.

The correlation between the two halves is the statistic computed in the split-half method, with

large correlations being a sign of reliability. The problem with this method is that there are

several ways in which a set of data can be split into two and so the results could be a product

of the way in which the data were split. To overcome this problem, Cronbach (1951) came up

with a measure that is loosely equivalent to splitting data in two in every possible way and

computing the correlation coefficient for each split (Field, 2009).

The average of these values is equivalent to Cronbach’s alpha, α, which is the most common

measure of scale reliabilitywhich may look complicated, but actually isn’t. The first thing to

note is that for each item on our scale we can calculate two things: the variance within the

item, and the covariance between a particular item and any other item on the scale. Put



another way; we can construct a variance–covariance matrix of all the elements. In this

matrix, the diagonal elements will be the variance within a particular item, and the off-

diagonal elements will be covariancebetween pairs of items. The bottom half is just the sum

of all the item variances and item covariance (i.e., the sum of everything in the variance–

covariance matrix). However, (Cortina, 1993) notes that such general guidelines need to be

used with caution because the value of α depends on the number of items on the scale. We

notice thatthe top half of the equation for α includes the number of elements squared.

Therefore, as thenumber of the issues on the scale increases, α will increase. Therefore, it is

possible to get a large value of α because we have many items on the scale, and not because

the scale is reliable!For example, Cortina reports data from two scales, both of which have α

= .8. Thefirst scale has only three items, and the average correlation between items was a

respectable.57; however, the second scale had 10 items with a meancorrelation between

theseitems of a less respectable .28. The internal consistency of these scales differs

enormously,yet according to Cronbach’s α,they are both equally reliable! (Field 2009).

5.3.2. Conceptual model of variables and coding

Due to a large number of questions we have separate the variables in 5 groups in order

tofacilitate the process of data analyses and interpreting. The outcomes derived will be

correct as well.Dividing the questionnaire into parts in such cases, running separate liability

analyses for all subscales of thequestionnaire is allowedand raises the correctness according

to the Field, 2009. The table below shows groups of independent variables and thedependent

variable (growth – last five years 2010-2015 in %) and coding.

Table 11. Conceptual model of variables and coding

Group Variables (x, y) Coding Independent variable

(x)

Dependent variable

(y)1. Barriers related to Institutional support

Current tax policy(X1); Access to finance (X2); Administrative procedures Economic policy

TaxPoIciyBarrAccFinAdminProcEcPolicy

X1X2X3X4

2. The rule of law Nonefficiency of justice system Corruption in Courts Politics interruption The informaleconomy Tax evasion obstructs Organized crime Corruption in government institutions

CourtEfiCorrupCourtPoliInterrInformEcTaxEvasOrgCrimCorrupInst

X5X6X7X8X9X10X11



3. Barriers related to the market

Customs fees on imports of raw materials Lack of raw materials in country Unfair competition in the market Access to foreign markets The limited capacity of the local market High transport costs are a barrier to your business

CustFeeImpRawMatMarkCompetAccForeiMarkCapLocalMarkTranCos

X12X13X14X15X16X17

4. Barriers related to infrastructure

Lack of electricity Lack of internet Lack of water Lack of roads Insufficient space of workThe location of your business is an obstacle

LackElectricLackInternetLackWaterLackRoudSpacWorkBussLocat

X18X19X20X21X22X23

5. Internal barriers

Lack of motivation Lack of experience in managementInsufficient level of professional skills Lack of management staff A lack of technical staff Lack of staff for accounting and financeThe lack of IT staff hinders your business

MotivationManExperProfSkillManagStaffTechniStaffAccFinStuffITStaff

X24X25X26X27X28X29X30

Growth Growth – Last Five Years (2010-2015) in %

Gr y


Later on the data analysis process, we will measure the correlation between business growth

and the each independent variable, throw multiple linear regression.

5.3.3. The main findings of the study - Results and interpretation

This chapter will deal with the results obtained from the field and interpretation, focusing

mainly on descriptive statistics, hypothesis testing results (research issues),identify the most

significant variables, building statistical models and their econometric analysis.As outlined in

Chapter 3, various statistical methods are used for processing the results and hypothesis

testing. Hypotheses are raised according to the objectives of the study, including some

general hypotheses to confirm the existence of the barriers and on the base of groups of

variables created as in Table 11. Before selecting the model, correlation analysis is initially

performed, where only independent variables that correlate with the dependent variables are

introduced into the model for testing. This rule applies to all models. The econometric tests

are applied for model normality as well, as we can see in following by the indicators.



5.3.4. Descriptive statistics related to profile of surveyed companies

This part will introduce tabular and graphical methods commonly used to summarize both categorical and quantitative data of the questionnaire. The descriptive statistics below will show some data related to the profile of the businesses surveyed, such as level of education of owner/managers’, longevity of businesses, number of employees and type of businesses.

To have a clearer picture of the data obtained from the survey questionnaire for the level of

education of the owners/managerssurveyed in Table 12and Fig. 13is presented descriptive

statistics expressed in frequency and percentage. As seen in the table of participants' the level

of education in the survey was: with elementary education 21 (13.4%), secondary 84 (53%)

and university education 52 33.1%. It is dominated by high school education as it is at the

country level.

Table 12. Level of education of Owners / Managers surveyed

Frequency Percent Valid Percent

Cumulative Percent

Valid

Elementary 21 13,4 13,4 13,4

High school 84 53,5 53,5 66,9

University 52 33,1 33,1 100,0

Total 157 100,0 100,0

Source: SPSS output, processed by theauthor.



Figure 12. Level of education of Owners / ManagersSource: SPSS output

In table 13, we see the frequency of the regions of businesses surveyed as predicted according

to sample research mentioned in chapter 3, on thebase of percentages that are registered

businesses on thelevel of country. We see that 54,or 34% of businesses surveyed are in

Prishtina region and lowest number is in Gjakova 16 or 10.2%.

Table 13. Regions of businesses location


Cumulative Percent

Valid

Pr (1) 54 34.4 34.4 34.4Pz (2) 27 17.2 17.2 51.6Pe (3) 15 9.6 9.6 61.1Mt (5) 12 7.6 7.6 68.8Gl (6) 16 10.2 10.2 79Fr (7) 17 10.8 10.8 89.8Gj (8) 16 10.2 10.2 100Total 157 100 100

SPSS output, processed by the author.

Figure 13. The regions of business location.

SPSS output, processed by the author.



On the survey data are included three sectors: production, trade, and services, respectively, 25

(15.9%) production sector, 73 (46.5%) trade and 59 (37.6%) from the services sector.

This percentage is adjusted according to the participations of sectors in thelevel of the

country based on the register for registration of businesses from MTI.

Table 14. Type of businesses surveyed


Cumulative Percent

Valid

Production 25 15.9 15.9 15.9

Trade 73 46.5 46.5 62.4Services 59 37.6 37.6 100Total 157 100 100

Source: SPSS output, processed by the author.

Figure 14. Type of business

SPSS output, processed by author

Longevity of businesses included is 5years’ experience until 22. Since our objective is the

study of growth barriers for 5 last year’s, we could not include businesses less than 5years’

experience. The averagelongevity of businesses surveyed is around 10 and themajority of

businesses included were with more than 10years’ experience, see Figure 15 and Annex 1,

Table 1.



Figure 15. Longevity of businesses surveyed

Source: SPSS Output, processed by the author

Our objective of theresearch was to investigate barriers to small business growth (number of

employees 5 – 50) as we can see inAnnex 1, Table 3. The minimum number of employees in

companies’surveyed was 5 and maximum 50. But, as we see from the frequency data (Annex

1, Table 3) there are included businesses with a different number of employees; the

distribution was wide, and themajority of businessessurveyed were with more than 10

employees.

Figure 26. Number of employees of businesses surveyed




5.4. Hypothesis Testing

Based on empirical research, face to face interviews with business owners/managers and pilot

research work have been raised 3 main hypotheses and 7 sub hypotheses to investigate this

topic. The first threehave ageneral character and confirm the existence of the barriers, while 7

others represent hypotheses related to the groups of variables selected, which will be

analyzed separately through certain statistical models (Multiple Linear Regression). As

ofDavid R. A. et al.(2011), it is not always obvious how the null and alternative hypotheses

should be formulated. Care must be taken to structure the hypotheses appropriately so that the

hypothesis testing conclusion provides the information the researcher or decision maker

wants. So, we want totest the hypothesesto ppproceed with further with data analyses. In the

context of the situation is very important to determine how the hypotheses should be

stated(David R. at al., p.346), and all hypothesis tested involve data analysesand using the

sample results to provide evidence for drawing a conclusion.After formulating the null and

alternative hypotheses, we selected a sample and computed the value of a statisticaltest and

the associated p-value.We then compared the p-value and r – value, bivariate correlation

between variables to see the level of significance. If p-value <0.05 and r=-1 to 1, we reach the

conclusion “reject H0” and declared the results significant; otherwise, we reached the

conclusion “do not reject H0.”

SPSS Output provides a matrix of the correlation coefficients for the all variables included

for hypotheses testing. Underneath each correlation coefficient both the significance value of

the correlation and the sample size (N) basedare displayed. Growth is negatively or positively

related to the barriers with a Pearson correlation coefficient of r = −1 to +1, and the

significance value is less than .001 (as indicated by the double asterisk after the

coefficient).Main hypotheses and sub hypotheses raised are:

H1 - Barriers of doing business in Kosovo are present (more barriers, less growth of SBs)!

Testing of this hypotheses has been madethrough SPSS, selected the so called model Pearson

Correlation that means measuring the degree of the linear relationship between growth and

barriers of doing business. The outputs of table15show that a straight line can well

characterize therelationship. Pearson correlation(r) ranges from -1.0 to +1.0, i our case is: r=

-0.318 andp=0.00(< 0.01)stating that r=-0.318 we can conclude that there is a moderate to

strong negative relationship between growth and barriers.Each variable is perfectly correlated

with itself (obviously) and so r = 1 along the diagonal of the table. Therefore, we have



enough statistical evidence to reject the H0 (the null hypotheses) and except the

H1(alternative hypotheses) that confirms the correlation between barriers and growth of SBs.

Table 15. H1 - Correlations

Growth – Last Five

Years (2010-2015)

Barriers of doing business in Kosovo are

present

Growth – Last Five Years (2010-2015)

Pearson Correlation 1 -.318**

Sig. (1-tailed) 0N 157 157

Barriers of doing business in Kosovo are present

Pearson Correlation -.318** 1Sig. (1-tailed) 0 N 157 157

**. Correlation is significant at the 0.01 level (1-tailed).


H2- Despite the existence of barriers the owners/managersdo not give up to the growth

intentions!

Correlation is significant, r=-.412 and p=0.00 (<0.01), H0is rejected. The test gives us

sufficient statistical evidence to conclude that the parameters of therelationship between two

variablesshow that the testis significant. On the other hand, we do not reject the H1,as of

perception of owners/managers they do not cancel the intentions to grow their businesses

even why the barriers are present.



Years (2010-2015)

Do you have theintention

to growthyour business?



Sig. (2-tailed) 0N 157 157

Do you have theintention to growthyour business?

Pearson Correlation -.412** 1

Sig. (2-tailed) 0 N 157 157





H3 - Presence of barriers influence owners/managers to cancel their objectives to grow their

businesses!

On table17, the test shows that value of Pearson Correlation r =-0.082 and p=0.305, in this

case, we fail to reject the null hypotheses H0and we reject the alternative hypotheses H1.

So,no evidence to keep the alternative hypotheses we reject the alternative hypotheses since

the test is not significant.



Years (2010-2015)

Presence of barriers influence the

managers to cancel growth objectives


Pearson Correlation 1 -0.082

Sig. (2-tailed) 0.305N 157 157

Presence of barriers influence the

managers to cancel growth objectives

Pearson Correlation -0.082 1

Sig. (2-tailed) 0.305 N 157 157


H3.1 - Difficulties in access to finance for small businesses are obstacles to their development!

We see the correlation is significant at the 0.05 level (2-tailed), p < 0.05 and Pearson

Correlation -0.173 (r=-1 to 1level). Hence, we consider that there is enough evidence we

reject the H0 hypotheses and except the H1. Our criterion for significance is usually .05, so

SPSS marks any correlation coefficient significant at this level with an asterisk.

Table 18. H3.1 – Correlations

Growth – Last

Five Years (2010-2015)

Access to finance for SBs

is difficult


Pearson Correlation 1 -.173*

Sig. (2-tailed) 0.03N 157 157

Access to finance for SB is difficult

Pearson Correlation -.173* 1

Sig. (2-tailed) 0.03



N 157 157

*. Correlation is significant at the 0.05 level (2-tailed).


H3.2 – Inadequate tax policy influence negatively tosmall businesses growth!

As of the data below we see the negative correlation between tax policy and business growth,

r=0.214 and p=0.007. Correlation is significant at the 0.01 level. We accept the H3.2.



Years (2010-2015)

Current tax policy is an obstacle to

growthyour business



Sig. (2-tailed) 0.007N 157 157

Current tax policy is an obstacle to growthyour business


Sig. (2-tailed) 0.007 N 157 157



In table20,we can seethe test of opposite question related to tax policy when respondents are

unsatisfied withlevel of the taxes. Therefore, we reject the H1, and the parameters show the p

> 0.05 respectively 0.694.

Table 20. H3.2 - Correlations

Growth – Last


Level of taxes is satisfied to

growthyour business


Pearson Correlation 1 0.032

Sig. (2-tailed) 0.694N 157 157

Level of taxes is satisfied to growth your business

Pearson Correlation 0.032 1

Sig. (2-tailed) 0.694 N 157 157




H3.3 - Lack of institutional support is an obstacle to small business growth!

As we see, the smaller value of r the higher is significance. The data below shows that

correlations exist between the institutional support and business growth. Correlation is

significant as of r=-0.311 and p=0.00. Hence, we have enough support to reject H0 and accept

the H1.



Years (2010-2015)

Lack of institutional support is an

obstacle to growth



Sig. (2-tailed) 0N 157 157

Lack of institutional support is an obstacle to growth


Sig. (2-tailed) 0 N 157 157



H3.4 – Barriers related to Rule of Law are the most evident obstacle to small business

growth!

Barriers related to Rule of Law mostly hinder the small business growth. To test this

hypothesis are conducted two questions with owners/managers:

1. Nonefficiency of justice system is an obstacle to growthyour business!

2.Lack of Rule of Law is an obstacle to growthyour business!

In both cases results in a high correlation between business growth and factors related to

thepower of the rule of law. Correlation is significant at the 0.01 level (p=0.00) and r=-0.308.

Like in the other cases, on table 23 Pearson Correlation, r=-0.425 and p=0.00, correlation is

significant, and in both cases, we conclude that there is enough evidence to reject the H0

hypotheses and accept the alternative hypotheses.





Nonefficiency of justice system is an

obstacle to growthyour

business



Sig. (2-tailed) 0N 157 157

Nonefficiency of justice system is an obstacle to growthyour business


Sig. (2-tailed) 0 N 157 157





Years (2010-2015)

Lack of Rule of Law is an obstacle to

growthyour business



Sig. (2-tailed) 0N 157 157

Lack of Rule of Law is an obstacle to growthyour business


Sig. (2-tailed) 0 N 157 157



H3.5 – Barriers related to the market hinder SBs growth!

The outputs on table 24show that the bivariate correlation exists between trade policy and

SBs growth. As of r=-0.576 and P=0.00, then there is evidence to reject the H0 hypotheses

and accept the H1.





Trade Policy is an obstacle to growthyour

business



Sig. (2-tailed) 0N 157 157

Trade Policy is an obstacle to

growthyour business


Sig. (2-tailed) 0 N 157 157


Source: SPSS output, refined by author

H3.6 – Barriers related to infrastructure constrain the SBs growth!

The values below shows that this group of barriers are not significant as p=0.838. We

conclude not to reject H0. Even why we know that if we look separately some of the barriers

related to infrastructure, the significance will result positively (e.g., lack of electricity).


Growth – Last


Barriers related to infrastructure

(Electricity, roads, water, internet,

location,etc.) constrains the SBs

growth!



Sig. (2-tailed) 0.838N 157 157

Barriers related to infrastructure (Electricity, roads, water, internet, location,etc.) constrain the SBs growth!


Sig. (2-tailed) 0.838

N 157 157


With the conclusion “do not reject H0,” the statistical evidence is considered inconclusive and

is usually an indication to postpone a decision or action until further research and testing can

be undertaken (Anderson et al., 2011 p:382).



Later on, we will see the complete stepwise multiple regression of each separate variable

related to infrastructure barriers and level of significance for each single variable.

H3.7 – The lack of adequate HRs influence negatively to business growth!

The same situation is in this case, the correlation is not significant, and we conclude to do not

reject the H0. The statistical evidence is considered inconclusive, and we will keep the

reserve to conduct more comprehensive research related to this hypothesis to see if eventually

some of thevariables related to HRs is significant to business growth.



Years (2010-2015)

Incapacity of HRs hinder

business growth



Sig. (2-tailed) 0.165N 157 157

Incapacity of HRs hinder business growth


Sig. (2-tailed) 0.165 N 157 157


5.5. Correlation analysis

Seeing that we have a large number of explanatory variables and their influence on the

dependent variable some of them may not be significant. Therefore, initially, a correlation

analysis is performed to see the relationship between the dependent variable that is the

business growth and the all explanatory variables. So, we canidentify the significant

variables. The correlation analysis is conducted to see the correlationbetween the predicted

variables X1, X2, X3 ... .X30 and growth variable Y(see Annex 1Table 1).

In the next step, we introduced the variables that showed correlation, and after testing all the

other variables that do not correlate with the dependent variable Y will be excluded from the

next analysis, will not be included in the model because they do not result statistically

significant (P> 0.05).



5.6. Multiple Linear Regression analysis

The multiple linear regression in thedifference of simple linear regression is the modelling of

the data set more than one independent variable and one dependent variable. What is multiple

linear regression? MLR explains how a dependent variable Yis correlated with two or more

independent variable. How much variance in the dependent variable does each independent

variable explain? Multiple regression analysis is the study of how a dependent variable y is

related to two or more independent variables, Fig. 18. In the general cases, we use p to

illustrate the number of independent variables, Fig. 19.

Figure 37. Multiple regression variables

Source: Processed by author

In our case, we see how the dependent variable y is correlated with the independent variables

x1, x2, x3…x30. In this instance, we will write as p number of the independent variable.

5.6.1. Regression Model and Regression Equation

The equation illustrateshow the dependent variable (growth)y is related to the independent

variables x1, x2. . .xp and an error term that is called the multiple regression model(David R.

A. et al., 2011); (Studenmund, 2014). We begin with the assumption that the multiple

regression model takes the following form.

Y=β0+β1x1+β2x2+…..+βpxp+ε (Eq.1)

Y is the outcome variable, b1 is the coefficient of the first predictor (X1), b2 is the

coefficientof the second predictor (X2), bn is the coefficient of the nth predictor (Xn), and εi is



the difference between the predicted and the observed value of Y for the ith participant. That

is, we seek to find the linear combination of predictors that correlate maximally with the

outcome variable. Therefore, when we refer to the regression model in multiple regression,

we are talking about a model in the form of equation (Field, 2009).

In the multiple regression model, Y = β0, β1, β2…….βpare the parameters, and the error term ε

(the Greek letter epsilon) is a random variable. A close examination of this model reveals that

y is a linear function of x1, x2. . .xp(the β0 + β1x1 + β2x2 +…..+ βpxp) plus the error term ε. The

error term accounts for the variability in y that cannot be explained by the linear effect of the

p independent variables.

In the following, we will discuss the assumptions for the multiple regression model andε. One

of the assumptions is that the mean or expected value of yis zero. A consequenceof this

statement is that the mean or expected value of y, denoted E(y), is equal toβ0 + β1x1 + β2x2 +

…..+ βpxp. The equation that describes how the mean value of y is relatedto x1, x2. . .xpis

called the multiple regression equation(David, R.A. et al. p. 644).

EY = β0 + β1x1 + β2x2 +…..+ βpxp (Eq. 2)

5.6.2. Estimated Multiple Regression Equation

If the values of b0, b1, b2, . . . , bpwere known, equation (1) could be used to compute the

mean value of y at given values of x1, x2, . . . , xp. Unfortunately, these parameter values will

not, in general, be known and must be estimated from sample data. A simple random sample

is used to compute sample statistics b0, b1, b2. . .bpthat is used as the point estimators of the

parameters b0, b1, b2, . . . , bp.

These sample statistics provide the following estimated multiple regression equation:

Ў= β0 + β1x1 + β2x2 +…..+ βpxp (Eq. 3.)

• b0, b1, b2, . . . , bp are the estimates of b0, b1, b2, . . . , bp

• Ў=estimated value of dependent variable



Figure 18. The Estimation Process for Multiple Regression

Source: David, R.A. et al. p. 645, refined by theauthor

5.6.3. Least Squares Method (LSM)

We use the least squares method to develop the estimated regression equation that best

approximated the straight-line relationship between the dependent and independent variables.

This same approach is used to establish the estimated multiple regression equation. The least

squares standard is written as in following:

Min 2

Where:

Yi= observed value of the dependent variable for the ith observation

i= estimated value of the dependent variable for the ith observation

The estimated values of the dependent variable are computed by using the estimated multiple

regression equation, ў = β0 + β1x1 + β2x2 +…..+ βpxp .



The least squares method (LSM) uses sample data to provide the values of b0, b1, b2, . . . , bp

that make the sum of squared residuals [the deviations between the observed values of the

dependent variable ( yi) and predicted values of the dependent variable ( )] a minimum.

5.6.4. ModelsBuilding

In the next step, we introduced the depended variable (growth) that present the link to the set

of independent variables and after the testing of both functional, linear and semi logic (log-

lin) forms, using Least Squares Method (LSM), we approached to the most suitable models.

For this function that is logarithm (log-lin) according to the significant results presented in

Table 2. Half-logarithmic forms, from all other variables that correlate with Y, see table 2 and

57, sre not include the variables in the models (X11, X20, X28, and X29) that statistically

shown no correlation with dependent variable (business growth), as of (p> 0.05) are not

statistically significant.

Referring to Gujarati (2004), Studenmund (2014), David, R.A. et al.(2011) to test the validity

of the models have been applied some of the multicollinearity detection tests, "diagnosis of

collinearity ", such as Condition Indexes (CI), Variance Inflation Factor (VIF) and Tolerance.

The tests are conducted step by step for each group of variables as are presented in Table 2,

through multiple linear regression, using SPSS (Field, 2009, pg. 225). The results will be

interpreting giving the explanations for the categorization of multicollinearity according to

the values of the respective indicators, always according to the authors referred to in this

paragraph.

5.6.5. Analysis Barriers related to Institutional support – Model 1

In the following, we will show the relationship between the Dependent Variable which is in

our case – business growth and Independent variables (indicatorsrelated to institutional

support) that influence the growth. In this study we continuallyreferred to two groups of

barriers:

External Barriers(related to institutions) that determine the environment of doing

business and

Internal Barriersrelated to the capacities of the businesses to grow, focusing on human

resources.



For the facilitation purposes and increase of accuracy of results,the data are divided

into5groups.

5.6.6. Data sorting to obtain qualitative outputs

The output described in the following is produced using the MultipleLinear Regression:

Statistics provided from output SPSS,Table 27. This tablerepresents the mean and standard

deviation of each variable in our data set and number of participations which is 156. In

addition to the descriptive statistics, the SPSS produces a correlation matrix too. Other values

show the average of the perceptions of managers regarding the barriers (predictors) that

influence the growth. Further on, the valuation through five points of Likert scale related to

barriers is done and confirmed that barriers exist. The mean over than 4, means that majority

of the respondents are “strongly agree or agree” that these barriers influence to the growth.

The table 27 isn’t necessary for interpreting the regression model, but it is a useful to see

summary of the data, mean and standard deviation. In addition to the descriptive statistics,

selecting this option produces a correlation matrix too.

Table 197. Descriptive Statistics Model 1.

Mean Std. Deviation N

Y Growth 4.2791 3.7177 156

X1 TaxPoIciyBarr 4.0769 1.0868 156

X2 AccFin 4.391 0.76684 156

X3 AdminProc 2.6859 1.44045 156

X4 EcPolicy 3.1474 1.46708 156

Variables

Source: SPSS output processed by author

Table 28 shows three things: First, the table illustrates the value of Pearson’s correlation

coefficient between every pair of variableswith growth. In our case, we have negative

correlation to three cases and positive correlation to administrative procedures to the growth,

r=0.392. Second, the One-tailed test significance of each correlation is displayed (e.g., the

correlation is significant, as of r=+1-1 and p < .001), respectively p<0.05. Finally, the number

of cases contributing to each correlation (N = 156) is presented.

We might notice that along the diagonal of the matrix the values for the correlation

coefficients are all 1.00 as we see in Table 28.Therefore, a perfect positive correlation exists



between predictors and outcome. The reason for this is that the data values denote the

correlation of each variable with itself, so obviously the resulting values are 1.00. The

correlation matrix is extremely useful for getting a rough idea of the relationships between

predictors and the outcome, and for a preliminarycheck for multicollinearity.

If there is no multicollinearity in the data, then there should be no substantial correlations to

one or some cases (r > 0.9) between predictors (independent variables). If we look only at the

predictors, ignore the outcome (growth) then the highest correlation is between them if we

compare the level of p < .001 shows on the table, the highest correlation is r=0.392

(administrative procedures). If we look the p value all cases are less than 0.001, so, we can

conclude that exist the perfect correlation between Y and x1, x2, x3 and x4 exist.Certainly,

we are interested to see the correlation between dependent and independent variables, but we

see that correlation in some cases exists even between the independent variables.

Table 28. Correlation Model 1.

Y X1 X2 X3 X4Growth Y 1 -0.22 -0.172 0.392 -0.336

TaxPoIciyBarr X1 -0.22 1 -0.207 -0.31 0.155

AccFin X2 -0.172 -0.207 1 0.1 -0.046

AdminProc X3 0.392 -0.31 0.1 1 -0.225

EcPolicy X4 -0.336 0.155 -0.046 -0.225 1Growth Y . 0.003 0.016 0 0

TaxPoIciyBarr X1 0.003 . 0.005 0 0.027

AccFin X2 0.016 0.005 . 0.107 0.285

AdminProc X3 0 0 0.107 . 0.002

EcPolicy X4 0 0.027 0.285 0.002 .

N 156 156 156 156 156

Correlations

Pearson Correlation

Sig. (1-tailed)

Source: SPSS output processed by author

5.6.7. Multiple Coefficient of Determination

In multiple linear regressions, the total sum of squares can be partitioned into two

components: the sum of squares due to regression and the sum of squares due to error, the

term multiple coefficient of determinationindicates measuring the “goodness of fit” for the

estimated multiple regression equation. The multiple coefficient of determination denoted R2,

is computed as follows(David, R.A. et al. p. 654, 2011).

Relationship among SST, SSR, and SSE:



SST = SSR + SSE

Were:

SST = total sum of squares = Σ (yi - ȳ) 2

SSR = sum of squares due to regression = Σ (ўi–ȳ)2

SSE = sum of squares due to error = Σ (yi -ў) 2

Here we will focus firstly only on the R square value. Meanwhile, the statistical indicator of

R2understands that the four variables together, explain 28% of their impact on business

growth and emphasize that multicollinearity is existent in this relationship. For an R2of 0.281,

we can say that the model explains 28% of the variations in business growth of these four

predictors among of 30 that the others we will explain in following other groups and so the

model is a “good structured model.”

Let’s see the data from Model Summary Table, R or Pearson R = 0.531 where Pearson R is

equal to R2 square, (R=R2).

(0.531)2 = 0.281

So we can say from both the F value and the Sig value that the 4 variables are indeed

different from each other and they affect the growth (Y)differently.

Table 29. Model Summary – Model 1

R Square Change F Change df1 df2 Sig. F

Change1 .531a 0.281 0.262 3.19282 0.281 14.788 4 151 0

Model Summaryb

Model R R Square Adjusted

R Square

Std. Error of the

Estimate

Change Statistics

Source: SPSS, processed by author

From the ANOVA table sum of squares, we will divide by a total number of squares, we find

the R2:

602.990/ 2142.298 = 0.281

Stein’s formula was given in equation and can be applied by replacing n with the sample size

(156) and k with the number of predictors (5). If n = sample size and p = repressors

(Independent Variables):

Ṝ2=1-(1-R2) n-1/n-p-1



157-1/157-5-1=0.281

From the data on the table we see the value of adjusted R2 is 0.281, converted in percentage

this is 28.1% of the growth of businesses explain the four predictors as are above indicated on

the tables. Or, the influence of these four indicators predicted as independent variable is

28.1%.

According to the Field 2009, the adjusted R2 gives us some idea of how well our model

generalizes and preferablythe value we would like to bethe same, or close to the value of R2.

In this example the difference in the final model is small (in fact the difference between the

values is .281 − .262 = .019 (about 1.9%). This decline means that if the model was derived

from the population rather than a sample, it would account for approximately 1.9% less

variance in the outcome. We can apply Stein’s formula to the R2 to get some idea of its likely

value in different samples as well. Stein’s formula applied by replacing n with the sample

size (156) and k with the number of predictors (4):

This value is very similar to the observed value of R2 (.281) indicating that the cross-validity

of this model is good.

Table 30. ANOVA – Model 1.

Sum of Squares df Mean

Square F Sig.

Regression 602.99 4 150.747 14.788 .000b

Residual 1539.308 151 10.194

Total 2142.298 155

ANOVAa

Model

1


In the ANOVA table we see the columns:

SSR = Sum of squares regression

DF = Degrees of freedom

MSR = Mean Square of regression

F = Frequency

Using multiple regression analysis, the constructs built by structural equation modeling of the

independent variables, and see the Pvalue compare it with the coefficient α (0:05).



In our case, Pvalue = (0.00) < (α) 00:05. This shows that results testingstatistically supports

variables. So variables obtained (considered barriers) to study together are important to

business growth. Respectively, the SPSS outputs for the multiple regression model including

variables: Current tax policy (x1), Access to finance (x2), Administrative procedures (x3) and

Economic policy (x4) as the four independent variables, in the analysis of variance as a part

of the output, we see that MSR = 150.7 and MSE = 10.2 Using equation:

F= respectively F= we obtain the F test statistic F=14.78.

Using α =.01, the Pvalue = 0.00 in the last column of the analysis of variance table indicates

that we can reject H0: β1 =β2 …..β4=0, because the Pvalue is less than α =.01 and conclude

that a significant relationship exists between Y(business growth) and four independent

variables (x1, x2…x4), since the p<.001 (significant).In table31, we see the values of the

different coefficients, we will interpret the meaning of them and see the multiple regression

equation built, interpreting the measures of the association and applying the model for

estimation. The next part of the output is coefficient table containingthe parameters of the

model. We are interested in the final model because this includes all predictors that make a

significant contribution to the model and to see the main indicators that arepredicting the

impact ofthe growth (dependent variable). So, we will look only at the lower part of the table

(Model 1).

Table 31. Coefficients – Model 1.

Standardized Coefficients

B Std. Error Beta Lower Bound

Upper Bound

Zero-order Partial Part Tolerance VIF

11.116 2.19 5.075 0 6.788 15.444

X1 TaxPoIciyBarr -0.453 0.254 -0.132 -1.786 0.046 -0.954 0.048 -0.22 -0.144 -0.123 0.866 1.155

X2 AccFin -1.175 0.342 -0.242 -3.436 0.001 -1.851 -0.5 -0.172 -0.269 -0.237 0.956 1.046

X3 AdminProc 0.821 0.191 0.318 4.305 0 0.444 1.198 0.392 0.331 0.297 0.87 1.149

X4 EcPolicy -0.647 0.18 -0.255 -3.588 0 -1.003 -0.291 -0.336 -0.28 -0.248 0.941 1.062

Collinearity Statistics

1

(Constant)

Coefficientsa

Model

Unstandardized Coefficients t Sig.

95.0% Confidence Interval for B

Correlations




In multiple regression, the model takes the form of an equation that contains a coefficient b

for each predictor. The table shows us estimates for these b values that indicate the individual

contribution of each predictor to the model. The b values present the correlation between the

growth (dependent variable) and each predictor as theindependent variables (x1, x2, x3, and

x4). If the value is positive, we can tell that there is a positive relationship between growth

and predictors. If the value is positive, we can say that there is a positive relationship between

predictor and the outcome while a negative coefficient represents a negative relationship,

according to the (Field, 2009).

In our case, we see that b values that predict the relationship between growth and barriers

considered by owners and managers of businesses, and their influence to the growth. So, if

we compare the data b data coefficients in table31, we see three predictors (variables), have

anegative relationship because the values are negative expect the Administrative procedures

(x3). Even why the administrative procedures have apositiverelationship and this variable

compared to the others but when we see other parameters, this variable remains significant.

This is how the owners-managers surveyed are declared. More concretely, when these values

increased in given period the impact to the growth will be decreased. So, three other variables

have negative b coefficients, and their impact to the growth is bigger.

The regression coefficient for Tax Policy (x1) is -0.453, Economic policy -0.64, and Access

to finance -1.175 with stronger negative relationship than the others and consider that this

variable hinders growth business more than the others. While the administrative procedures

have positive value 0.82, the influence of this is smaller than the others, and this variable has

the value less than 1 we can say this is still importantto our model and further investigation.

The coefficient table highlighted in bold presented the structure of the model. We see the

Constant and four other independent variables as in following:

Y= 0.82 - 0.453 - 0.64-1.17

Applying the model for estimating the regression considering the dependent variable (ў) and

β coefficients respectively β0, β1, β2…..β4the equation will be:

Ў = 0.82 - 0.45 - 0.64-1.17= -1.44



We see that negative value of the average whichpresents the average influence of these four

predictors to the dependent and tells us the impact of these barriers to business growth is

significant.

As we see on table31, each of these data values has one associated standard error indicating

to what extent these values would very across different samples, and these standard errors are

used to determine whether or not the b value differs significantly from zero (using t-statistic.

Therefore, if the t-test associated with a b value is significant, less than 0.05 then that

predictor is making an important contribution to the model. The smaller value of p (sig.) and

larger value of t the greater is the contribution to the model of that predictor.

From the data on table 31 above we see the t and sig. of all variables are very well associated.

Allpredicators are very well associated since the p value is less than 0.05 and these predictors

have ahigh level of contribution significance to the model. Alternatively, the impact of these

variables is high to business growth as they are the perceptions of the owners’/managers’

surveyed.

The standardized versions of the b values are easier to interpret because they are not

dependent on the units of measurement of the variables, according to(Field, 2009). The

standardized Beta valuesprovided by SPSS show us the number of standard deviations that

the outcome will change as a result of one standard deviation in the predictor. In our case, the

standardized beta values are all measured in standard deviation units and are directly

comparable. Therefore, they provide a better insight into the importance of a predictor in the

model. To explain the impact of the predictors respectively the independent variables

comparing each other individually we have extracted data from the main table above to see

the standardized Beta values and in ascending order we see which variable has bigger impact

compared to others, as we can see on Table 31, Tax Policy (-0.132), Access to Finance (-

0.242), Economic Policy (-0.255) and Administrative Procedures (-0.318).

5.6.8. Assessing the assumption of multicollinearity

We use the term independent variable in regression analysis is to refer to any variable being

used to predict or explain the value of the dependent variable. The term does not mean,

however, that the independent variables themselves are independent in any statistical sense.

On the contrary, most independent variables in a multiple regression are correlated to some



degree with one another. In multiple regression analysis, multicollinearityrefers to the

correlation among the independent variables, (David R. et al., 2011).

As we can see the SPSS Output Table,31Coefficients provided some measures of whether

there is collinearity in the data. Specifically, it provides the VIF (Variance Inflation Factor)

and tolerance statistics (with tolerance being 1 divided by the VIF).

According to different authors (Bowerman & O’Connell, 1990; Myers, 1990) if the largest

VIF is greater than 10 then there is a cause for concern. In our case, as we can see VIF in

Table 31 last column on the right no one is not, so no concern in our model.If the average of

VIF is substantially greater than 1, then the regression may be biased. In our case, is not

substantially greater than 1, as we see (1.55+1.046+1.149+1.062=1.201).

Tolerance below 0.1 indicates a serious problem. Tolerance below 0.2 indicates a potential

problem (Menard, 1995). In our case, as we can see the column tolerance in table coefficient

no one case is not below 0.1 neither 0.2, so no problem indicated to appear.

For our current model the VIF values are all below 10 and the tolerance statistics all well

above 0.2; therefore, we can safely conclude that there is no collinearity within our data. To

calculate the average VIF, we simply add the VIF values for each predictor and divide by the

number of predictors (k):

The average VIF is close to 1, and this confirms that collinearity is not a problem for this

model. Therefore, in SPSS Output (Annex1, Table 4) we look at the bottom few rows of the

table (these are the small eigenvalues) and look for any variables that both have high variance

proportions for that eigenvalue. The variance proportions varybetween 0 and 1, and for each

predictor should be distributed across different dimensions (or eigenvalues). For this model,

you can see that each predictor has most of its variance loading on to a different dimension:

Current tax policy 49% of variance in dimension 4, Access to finance has 70% of variance on

dimension 5, Administrative procedures 48% of variance on dimension 2, and economic

policy 75% of variance on dimension 3.6

6Each of a set of values of a parameter for which a differential equation has a nonzero solution (an eigenfunction) under given conditions. He showed that in this case the integral equation had real eigenvalues, and the solutions corresponding to these eigenvalues he called eigenfunctions.



5.6.9. Casewise diagnostics – Model 1

In a usual sample, we can expect 95% of cases to have standardized residuals within about

±2. We have a sample of 156, so it is reasonable to expect about 7 cases less than (5%) to

have standardized residuals outside of these limits (Table 32). From SPSS Output we can see

that we have only 7 cases (4.48%) that are outside of the limits. Therefore, our sample is

within 5% of what we would expect. In addition, 95.52% of cases are in lie within ±2, and so

we would expect only 4.48% of cases to lie outside of these limits. From the cases listed here,

it is clear that 7 cases (4.48%) lie outside of the limits (see column case number). Therefore,

our sample appears to confirm to what we would expect for a fairly accurate model. These

diagnostics give us no real reason for concern except those cases have a standardized residual

greater than 3(only two cases), which probably might be a case for further investigation.

Table 32. Casewise Diagnostics – Model 1.

Case Number Std. Residual

Growth – Last Five Years

(2010-2015)Predicted Value Residual

15 -2.827 -5 4.0274 -9.02744

33 2.181 12 5.0364 6.96358

35 3.375 18 7.2227 10.77728

49 -2.201 -3 4.0274 -7.02744

63 3.001 15 5.4195 9.58053

64 3.37 17 6.2409 10.75909

115 2.122 12 5.2258 6.77417

Casewise Diagnosticsa

Source: SPSS, refined by author.

We can also look at the ‘DFBeta’ statistics to see whether any case would have a large

influence on the regression parameters. Looking the last two rows of Table5 (Anex 1), an

absolute value greater than 1 is a trauble, and thedata in all casesshow the values lie within

±1, which confirm that these cases have no undue influence over the regression parameters.

To demonstrate this, the further step is to continue to use the following criteria, calculate the

CVR:

CVRi > 1 + [3(k + 1)/n] = 1 + [3(4 + 1)/156] = 1.09.

CVRi < 1 − [3(k + 1)/n] = 1 − [3(4 + 1)/156] = 0.90.



Consequently, looking for any cases that deviate substantially from these boundaries, most of

our 7 potential outliers have CVR values within or just outside these limits, or taking the

average of both values (0.99) confirms that data are within boundary +1.

5.6.10. Checking assumptions– Model 1

As a final stage in the analysis, we should test the assumptions of the model. We have already

looked for collinearity within the data and used Durbin–Watson to check whether the

residuals in the model are independent. In thelast stage running the SPSS multiple regression,

we asked for a plot of ZRESID against ZPRED and a histogram and normal probability plot

of the residuals. The graph of ZRESID and ZPRED should look like a random array of dots

evenly dispersed around zero. As we can see below Figure 20 and Figure 21 shows the

histogram and normal probability plot of the data for the current model we have analyzed

above. The histogram looks like a normal distribution (a bell-shaped curve).

Figure 49. Histogram of normally distributed residuals - Model 1.


A final set of plots specified in figures below are the scatterplots of the residuals of the

outcome variable (growth) and each of the predictors (independent variable IV) when both

variables are regressed separately. As it is mentioned earlier that obvious outliers on a partial

plot represent cases that might have undue influence on a predictor’s regression coefficient

and that non-linear relationships and heteroscedasticity. Using these plots as well as showed

in Figure 20 this pattern is indicative of a situation in which the assumptions of linearity and



homoscedasticity have been met. There are no obvious outliers on this plot, and the batches

of dots are evenly spaced out around the line, indicating homoscedasticity.7

Figure 20. P–P plots of normally distributed residuals – Model 1.

Source: SPSS output

5.7. Regression Analyses of Barriers related to Rule of Law – Model 2

The interpretation of the data in this group of variables will be shorter since all procedures

detailed are made in the first group interpretation and the model will be the same. The data

outputs show the normal distribution of the data and the model is goodfit to explain. The

output described in this model is produced using the MultipleLinear Regression: Statistics

provided from output SPSS (see table 33). This table tells us the mean and standard deviation

of each variable in our data set and number of participations which is 157.

In addition to the descriptive statistics, have been produced a correlation matrix too. Other

values show the average values related to owners/managers’ perceptions regarding the

barriers (predictors) that influence the growth, and on the five point of Likert scale, these

values confirmed that barriers exist.The mean values,from 3.7 to 4.3, show that majority of

the respondents are ‘strongly agree or agree’ that these barriers have impactto the

7Homoscedasticity, or equal (homo) spread (scedasticity) or equal variance. The word comes from the Greek verb skedanime, which means to disperse or scatter. Stated differently, means that the Y populations corresponding to various X values have the same variance. Put simply, the variation around the regression line (which is the line of average relationship between Y and X) is the same across the X values; it neither increases or decreases as X varies. (Gujarati, 2004, p.62).



growth.This group of variables (barriers) resulted that have the highestinfluence on growth

compare to the others.

We see the mean of growth in certain period is 4.27%, and the mean for independent

variables is higherthan the other groups of barriers.

Table 33. Descriptive statistics – Model 2.

Descriptive Statistics

Mean Std. Deviation NGrowth Y 4.2710 3.70717 157

CourtEfi X5 4.3694 1.15629 157

Corruption X6 4.3312 1.12881 157

PoliInterr X7 3.9554 1.15661 157

TaxEvas X8 3.8854 1.44545 157

OrgCrim X9 3.7389 1.49420 157

InformEc X10 3.8089 1.33077 157

Source: SPSS, refined by author

Table 34 shows three things: First, the table ilustrates the value of Pearson’s correlation,the

coefficient between every pair of variables,and the correlation with growth. In our case, we

see the strong correlation between growth and 6 independent variables. Second, the One-

tailed significance of each correlation is displayed (e.g., the correlation is significant, p

< .001). Finally, are presented the number of cases that influenced to each correlation (N =

157).


coefficients are all 1.00 as we see highlighted in Table34, thus, a perfect positive correlation

exist. The reason for this is because these values represent the correlation of each variable

with itself, so obviously, the resulting values are 1.00. The correlation matrix is extremely

valuable for getting a rough idea of the relationships between predictors and the outcome, and

for a pilot look for multicollinearity.


one or some cases (r > 0.9) between predictors (independent variables). If we look only at the

predictors, ignore the outcome (growth) then the highest correlation is between them if we



compare the level of p < .001 shows on the table, the highest correlation is r=0.912is around

of boundary (Nonefficiency of thejustice system). If we look the p value, all cases are less

than 0.001 except one case p=0.065 (Politics interruption hinder your business), not high

correlation compare to the others.

Table 34. Correlation – Model 2.

Y X5 X6

Politics interruptio

n hinder your

business

Tax evasion

obstructs your

business

Organized crime

hinders your

business

The informal economy prevents

your business

Y 1.000 -.308 -.391 -.494 -.365 .231 -.540

X5 -.308 1.000 .912 .751 .589 -.396 .517

X6 -.391 .912 1.000 .812 .585 -.427 .618

X7 -.494 .751 .812 1.000 .637 -.344 .819

X8 -.365 .589 .585 .637 1.000 -.459 .642

X9 .231 -.396 -.427 -.344 -.459 1.000 -.399

X10 -.540 .517 .618 .819 .642 -.399 1.000

Y .000 .000 .000 .000 .002 .000

X5 .000 .000 .000 .000 .000 .000

X6 .000 .000 .000 .000 .000 .000

X7 .000 .000 .000 .000 .000 .000

X8 .000 .000 .000 .000 .000 .000

X9 .002 .000 .000 .000 .000 .000

X10 .000 .000 .000 .000 .000 .000

N 157 157 157 157 157 157 157

Pearson Correlation

Sig. (1-tailed)

Correlations

Source: SPSS output, refined by author.

If we focus firstly only on the R square value, we see the statistical indicator of R2understand

that six variables together, explain 30.6% of their impact on business growth and emphasize

that multicollinearity is existent in this relationship. For an R2of 0.306, we can say that the

model explains around 30.6% of the variations in business growth of these six predictors.

Let’s see the data from Model Summary Table:R or Pearson R = 0.554 where Pearson R is


(0.554)2 = 0.306


different from each other and they affect the growth (Y)in different manners.Furthermore,Sig

value 0.00 (pvalue=0.00) less than 0.05 confirm the model is significant.



Table 35. Model summary – Model 2.

R Square Change F Change df1 df2

Sig. F Change

1 .554a .306 .279 3.14852 .306 11.045 6 150 .000 1.022

Model Summaryb

Model R R SquareAdjusted R Square

Std. Error of the

Estimate

Change StatisticsDurbin-Watson


Sum of squares from the ANOVAtable36,we will divide by a total number of squares we find

the R2:

656.949/2143.923 = 0.306




Ṝ2=1-(1-R2) n-1/n-p-1=1-(0.306)

157-1/157-6-1=0.902

Furthermore, we see the difference for the final model is small (in fact the difference between

the values is .306 − .279 = .027 (about 2.7%). This decline means that if the model is derived

from the population rather than a sample, it would account for approximately 2.7% less




This value is very similar to the observed value of R2 (.279) indicating that the cross-validity

of this model is good.

The ANOVArepresent whether the model overall results in a significantly good degree of

prediction of the outcome variables. However, the ANOVAdoesnot tell us about the

individual contribution of variables in the model. In this group, there are 6 variables in the

model, and so we can infer that all variable are good predictors.



Table 36. ANOVA table– Model 2.

Sum of Squares df

Mean Square F Sig.

Regression 656.949 6 109.491 11.045 .000b

Residual 1486.975 150 9.913

Total 2143.923 156

ANOVAa

Model1

Source: SPSS, Refined by author

As we can see above, Pvalue = (0.00) < (α) 00:05, this means that variables are statistically

supported by results testing. So, variables obtained (considered barriers) as a group all

together isimportant to business growth. Respectively, the SPSS outputs for the multiple

regression model with (x5, x6, to x10), as the six independent variables, in the analysis of

variance part of the output, we see that MSR = 106.8 and MSE = 8.7. Using equation:

F= respectively F= we obtain the F test statistic F=11.06.

Using α =.01, the p-value = 0.000 in the last column of the analysis of variance table

indicatesthe Pvalue is less than α =.01 and conclude that a significant relationship exists

between Y business growth and six independent variables (x5, x6…x10), since the p<.001

(significant).

The next part of the output is coefficient table concerned with parameters of the model. We

want to see the final model because this includes all predictors (seven independent variables)

that make a significant contribution to predicting the growth (dependent variable). Firstly, we

will look only at the lower part of the table.

The table shows our estimates for these b values indicate the individual contribution of each

predictor to the model. The b values tell us about the relationship between the growth

(dependent variable) and each predictor as the independent variable. If the value is positive,

we can tell that there is a positive relationship between growth and predictors. If the value is

positive, we can say that there is a positive relationship between predictor and the outcome

whereas a negative coefficient represents a negative relationship, (Field, 2009). In our case,

we see the positive and negative values of the predictors, and we say there is positive and

negativerelationship between growth and some predictors.In the certain period if the values



are increased the impact positivelyto growth will be decreased, and if the negative valuesare

increased the effect of those predictors to the growth will be increased.

The coefficients in the table presented the structure of the model. We see the Constant and six

other independent variables. In the equation below there are several unknown quantities (the

b values)the first part of the table gives us assessments for these b-values and these values

indicate the individual contribution of each predictor to the model. If we replace the b-values

in equation we see that we can define the model as follows:

Y= b0, b5, b6…..b10

Growth (Y) = b0+ b5(Non efficiency of justice system) - b6 (Corruption) - b7 (Politics

interruption) – b8 (The informal economy) – b9 (Tax evasion) + b10 (Organized crime)

=10.293+0.659-0.638-0.549-1.022-0.053+0.036 (Eq. 3)

Applying the model for estimation the regression considering the dependent variable (ў) and

β coefficients respectively β5, β6…..β10the equation will be:

Ў = 0.659-0.638-0.549-1.022-0.053+0.036=-1.567 (Eq. for regression test. 4)

The b-values tell us about the relationship between growth and each predictor. If the value is

positive, we can tell that there is a positive relationship between the predictor and the

outcome, while a negative coefficient shows a negative relationship, in our case we have

positive and negative relationship of the predictors to the growth.

The b values tell us to what degree each predictor affects the outcome if the effects of all

other predictors are held constant:

Nonefficiency of the justice system(b5= 0.659, see table 37): This value indicates that if

nonefficiency of justice system increases by one unit, growth increased by 0.659 units. Or, if

the value ofnonefficiency of the justice system increase by one unit the growth will increase

for 6%, more than actual growth. This interpretation is true only if the all other predictors are

held constant.

Corruption (b6= - 0.638): We see the value of corruption is negative and we can say that if

this value statistically will be increased for one unit the growth will increase by6.3% from the

actual growth. This is thru if all other independent variables (predictors) we keep constant,



including the no efficiency ofthejustice system as well. The interpretation is the same for all

other predictors’ relationship to the growth.

Standard errors are used to determine whether or not the b value differs significantly from

zero (using t-statistic). As we can see to all predictors, they are different from zero.


predictor is making animportant contribution to the model. In some cases where b is less than

0.05 – organized crime and corruption are under the value of 0.00, and their significance is

higher than others. While the politic interruption 0.62 and informal economy 0.88 are close to

the boundary and the significance to the model is less.The smaller the value of p (sig.) and

larger value of t the contribution to the model of that predictor is greater. If we compare these

coefficients in the table below, we see that both t and sig.p are well associated,the predictors

above mentioned have greater significance while three others less.Or, we can say that

organized crime;corruption; politic interruption and the informal economy as per perception

of owners/managers have more impact on business growth or these barriers hinder business

growth more than others of this group of variables.

To explain the impact of the predictors (the independent variables), comparing each other

individually we have used the data from the table below to see the standardized Beta values,

and in ascending order, we see which variable has a bigger impact compared to the others.

As: Non efficiency of justice system,(0.206), - (Organized crime) .015, x5, x6 (Corruption)-

0.194, x7 (Politics interruption) -0.171 x8 (The informal economy) -0.367, x9 (Tax evasion) -

0.021, as we can see on the Table 37.

Table 37. Coefficients – Model 2.

Standardized

Coefficients

B Std. Error BetaLower Bound

Upper Bound


(Constant) 10.293 1.648 6.245 .000 7.036 13.550

X5 .659 .569 .206 1.159 .248 -.465 1.783 -.308 .094 .079 .147 6.805

X6 -.638 .631 -.194 -1.012 .313 -1.885 .608 -.391 -.082 -.069 .125 7.979

X7 -.549 .539 -.171 -1.018 .310 -1.614 .516 -.494 -.083 -.069 .163 6.117

X8 -1.022 .374 -.367 -2.731 .007 -1.762 -.283 -.540 -.218 -.186 .256 3.907

X9 -.053 .255 -.021 -.207 .836 -.556 .451 -.365 -.017 -.014 .468 2.135

X10 .036 .200 .015 .180 .857 -.359 .431 .231 .015 .012 .713 1.403

1

Coefficientsa

Model

Unstandardized Coefficients

t Sig.

95.0% Confidence Interval for B Correlations Collinearity Statistics




For our current model the VIF values are all good - below 10, if the largest VIF is greater

than 10 - then there is thereason for concern (Bowerman, B. L., & O’Connell, R. T., 1990);

(Myers, 1990). In our case, as we can see VIF in table 37, no one is not, so no reason for

concern in our model.

While, the tolerance statistics above 0.2 therefore, we can safely conclude that there is no

collinearity within our data. According to Menard (1995) tolerance below 0.1 indicates a

serious problem and tolerance below 0.2 indicates a potential problem. To calculate the

average VIF, we simply add the VIF values for each predictor and divide by the number of

predictors (k):

The average VIF is 4.72, and this confirms that collinearity is not a problem for this model,

even why this value is high. If the average VIF is substantially greater than 1, then the

regression may be biased (Bowerman & O’Connell, 1990). In our case, it is substantially

greater than 1, the value of 4.72, but we know from what this biasing comes. This is due to

high impact of these variables have in business growth, and perceptions of owners/managers

were in the majority‘strongly agree’ and ‘agree’ that barriers related to thepower of the rule

of law.

5.7.1. Collinearity Diagnostics of the variables related to rule of law

Therefore, in SPSS Output (Annex 1,Table 6) we look the variance proportions vary between

0 and 1, and for each independent variable (IV) assumed should be distributed across

different dimensions (or eigenvalues). For this model, we can see that each predictor has most

of its variance loading on to a different dimension (Non efficiency of justice system 84% of

variance on dimension 7, Corruption 91% of variance on dimension 7, Politics interruption

90% of variance on dimension 6, The informal economy 59% of variance on dimension 6 and

organized crime 51% and dimension 5.8

8Each of a set of values of a parameter for which a differential equation has a nonzero solution (an eigenfunction) under given conditions. He showed that in this case the integral equation had real eigenvalues, and the solutions corresponding to these eigenvalues he called eigenfunctions, Field, 2009.



From SPSS Output table 38 we can see that we have only 2 cases (1.26%) that are outside of

the limits (>3).Therefore, our sample is within 5% of what we would expect or permitted.

Also, 98.74% of cases are in lie within ±3, and so we would expect only 1.26% of cases to lie

outside of these limits. Therefore, our sample appears to conform to what we would expect

for a fairly accurate model. These diagnostics give us no real cause for concern except those

cases has a standardized residual greater than 3 (only two), which probably might be a case

for further investigate but not to dispute the model.

Table 38. Casewise Diagnostics – Model 2.

Std. ResidualGrowth – Last Five Years (2010-2015) Predicted Value Residual

1 3.416 18.00 7.2452 10.75480

105 3.246 17.00 6.7786 10.22139


Case Number

a. Dependent Variable: Growth – Last Five Years (2010-2015)

Source: SPSS, refined by author.

5.7.2. Residuals Statistics – Model 2

If we see last lines of the (Annex 1,Table7),Cook's Distance and Centered Leverage Valueis

not present,no one absolute value greater than 1. This is good, in all cases, the values lie

within ±1, which shows that these cases have no undue influence over the regression

parameters, Field, 2009.

If we compare the covariance values we saw in this section the calculation is:

CVRi > 1 + [3(k + 1)/n] = 1 + [3(6 + 1)/157] = 1.13

CVRi < 1 − [3(k + 1)/n] = 1 − [3(6 + 1)/157] = 0.86.

Consequently, we are looking for any cases that deviate substantially from these boundaries.

Most of our 2 potential outliers have CVR values within or just outside these boundaries,

which is not a case for concern.

5.7.3. Checking assumptions

As a final stage of the model analysis, we can see the assumptions of the model. We have

already checked for collinearity within the data and used Durbin–Watson to check whether

the residuals in the model are independent.



In thelast stage running the SPSS multiple regression, we asked for a plot of ZRESID against

ZPRED and a histogram and normal probability plot of the residuals. The histogram looks

like a normal distribution (a bell-shaped curve) as it is and figure21 shows the full linearity of

plots distributions around the diagonal line, which confirms very normal distribution of data

and statistical accuracy.

Figure 21. Histogram – Model 2


Figure 22. P–P plots of regression normally distributed residual – Model 2




The graph of ZRESID and ZPRED should look like a random array of dots evenly dispersed

around zero as per (Field, 2009). As we can see Figure 21 and Figure 22 shows the

histogram and normal probability plot of the data for the current model, we have analyzed

above.

All predictors show the same, the normality of data distribution within parameters of the

model, the plots are well distributed as we see in figure 23.

Figure 53. Scatterplot – Model 2

Source: SPSS output

5.8. Multiple regression of market-related barriers – Model 3

As we can see from the table below this group of barriers,have the influence to business

growth some of the predictors more and some less. The table tells us the mean and standard

deviation of each variable in our data set and a number of participations which is 157. Other

values show the average of the perceptions of managers regarding the barriers (predictors)

that influence the growth, and on the five point of Likert scale, the valuation related to

barriers is confirmed that barriers exist.Thus, that majority of the respondents are ‘strongly

agree’ or agree, means that these barriers influence the growth. This group of barriers is

confirmed that have effect on growth but compare to the others seems that perceptions of

owners/managers are lower than to other groups of barriers.




variables is lower than the other groups.Unfair competition in the market is the first one, lead

in this group of barriers, mean is 3.81,i.e. most of the respondentsdeclared‘strongly agree and

agree’ related to this obstacle thathinders the business growth, following with Access to

foreign markets is difficult and Thelimited capacity of the local market (mean 3.75)and so on.

Table 39. Descriptive statistics – Model 3

Mean Std. Deviation NGrowth – Last Five Years (2010-2015) Y 4.271 3.70717 157

Customs fees on imports of raw materials X11 3.5414 1.50423 157

Lack of raw materials in country X12 2.8854 1.6407 157

Unfair competition in the market X13 3.8153 1.30493 157

Low demand for your services, products X14 2.9809 1.63092 157

Access to foreign markets is difficult X15 3.7516 1.47915 157

The limited capacity of the local market X16 3.7516 1.40355 157

High transport costs X17 3.7006 1.48697 157


Source: SPSS output, processed by theauthor.

Table 40 shows the value of Pearson’s correlation coefficient between every pair of variables

with growth. In our case, we see the strong correlation between Growth and 7 independent

variables. Second, the One-tailed significance of each correlation is displayed (e.g., the

correlation is significant, p < .001). Finally, the number of cases that participated in the

model has been presented (N = 157).


coefficients are all 1.00 as we see highlighted in table 40, thus, a perfect positive correlation

exist. These values show the correlation of each variable independently, so obviously, the

resulting values are 1.00. The correlation matrix is extremely valuable for having theidea of

the relationships between predictors and the outcome, and as randomly for a preliminary look

for multicollinearity.


one or some cases (r > 0.9) between predictors (independent variables). On the 46 below we

see that no one of the predictors does not exceed that parameter (r > 0.9), so no

multicollinearity on the data exists. If we look only at the predictors, ignore the outcome

(growth),then the highest correlation is between them if we compare the level of p < .001



shows on the table, the highest correlation is r=0.833is below of boundary (High transport

costs). If we look the p value in some cases,show the correlation between independent

predictors some less and somedoes not havehigh correlation to the others.

Table 40. Correlations – Model 3

Y X11 X12 X13 X14 X15 X16 X17

Y 1 -0.581 -0.207 -0.688 -0.384 -0.424 -0.468 -0.443

X11 -0.581 1 0.212 0.557 0.412 0.248 0.131 0.125

X12 -0.207 0.212 1 0.086 0.509 -0.099 -0.113 -0.24

X13 -0.688 0.557 0.086 1 0.459 0.627 0.601 0.549

X14 -0.384 0.412 0.509 0.459 1 0.168 -0.033 -0.142

X15 -0.424 0.248 -0.099 0.627 0.168 1 0.433 0.473

X16 -0.468 0.131 -0.113 0.601 -0.033 0.433 1 0.833

X17 -0.443 0.125 -0.24 0.549 -0.142 0.473 0.833 1

Y . 0 0.005 0 0 0 0 0

X11 0 . 0.004 0 0 0.001 0.051 0.06

X12 0.005 0.004 . 0.143 0 0.109 0.08 0.001

X13 0 0 0.143 . 0 0 0 0

X14 0 0 0 0 . 0.018 0.341 0.038

X15 0 0.001 0.109 0 0.018 . 0 0

X16 0 0.051 0.08 0 0.341 0 . 0

X17 0 0.06 0.001 0 0.038 0 0 .

N 157 157 157 157 157 157 157 157

Correlations

Pearson Correlation

Sig. (1-tailed)


If we focus firstly only on the R square value, we see the statistical indicator of R2understand

that seven variables together, explain 58% of their impact on business growth and emphasize

that multicollinearity is existent in this relationship. For an R2of 0.582, we can say that the

model explains around 58% of the variations in thebusiness growth of these seven predictors.

Let’s see the data from Model Summary Table, R or Pearson R = 0.763, where Pearson R is

equal to R2 square (R=R2).

(0.763)2 = 0.582


different from each other and they affect the growth (Y) in different manners.

Let’s see the ANOVA table sum of squares we will divide by total number of squares we find

the R2:

1248.033 / 2143.923 = 0.582



Table 41. Model summary – Model 3

R Square Change F Change df1 df2 Sig. F

Change1 .763a 0.582 0.562 2.45208 0.582 29.652 7 149 0 1.334

R Square Adjusted R Square

Std. Error of the

Estimate

Change Statistics Durbin-Watson

Model Summaryb

Model R

SPSS output




Ṝ2=1-(1-R2) n-1/n-p-1=1-(0.582)

157-1/157-7-1=0.391

Furthermore, we see the difference for the final model is small (in fact the difference between

the values is .582 − .562 = .020 (about2%). This decline means that if the model is derived

from the population rather than a sample, it will account for approximately 2% less variance

in the outcome. We can apply Stein’s formula to the R2 to get some idea of its likely value in

different samples as well. Stein’s formula applied by replacing n with the sample size (157)

and k with the number of predictors (7):

This value is close to the observed value of R2 (.582) indicating that the cross-validity of this

model is good as well.

Table 202. ANOVA – Model 3

Sum of Squares df Mean

Square F Sig.

Regression 1248.033 7 178.29 29.652 .000b

Residual 895.89 149 6.013

Total 2143.923 156

ANOVAa

1

Model


As we can see above, P-value = (0.00) < (α) 00:05,shows that results testing statistically

supports variables. So variables obtained (considered barriers) to study together are important

to business growth. Some of them are more and some less as we saw from the table of



descriptive statistic as well.Respectively, the SPSS outputs for the multiple regression model

with Unfair competition in the market(x13);Access to foreign markets (x15); limited capacity

of the local market(x16); and so on….(x17) as the seven independent variables, in the

analysis of variance, we see that MSR = 178.3 and MSE = 6.01, using equation:

F= where: F= we obtain the F test statistic F=29.66.

Using α =.01, the p-value = 0.000 in the last column of the analysis of variance table

indicates the p-value is less than α =.01 and conclude that a significant relationship exists

between Y business growth and seven independent variables (x11, x12…x17), since the

p<.001 (is significant) for all variables.

The next part of the output is the coefficient table concerned with parameters of the model.

We want to see the final model because this includes all predictors (seven independent

variables) that make a significant contribution to predicting the growth (dependent variable).

Firstly, we’ll look only at the lower part of the table.

The table shows us estimates for these b values indicate the individual contribution of each

predictor to the model. The b values tell us about the relationship between the growth

(dependent variable) and each predictor as independent variable. If the value is positive, we

can say that there is a positive relationship between predictor and the outcome whereas a

negative coefficient represents a negative relationship(Field, 2009). In our case, we see the

positive and negative values of the predictors, and we say there is positive and negative

relationship between growth and some predictors. In the certain period of time if the values

are increased positively the impact to growth will be decreased and if the negative value are

increased the impact of those predictors to the growth will be increased.

The dependent variable Growth in one side independent variables(Predictors): High transport

costs; Customs fees on imports of raw materials; Lack of raw materials in country; Access to

foreign markets; Low demand for your services and products; The limited capacity of the

local market; Unfair competition in the market hinders your business, respectively (x12, x13

…..x17) written in equation in following:

Y= b0, b1, b2…..b7 or Y=b0, b12, b13…..b17



Y=-0.800-0.304-0.730-0.201-0.102-0.279-0.496

Applying the model for estimation the regression considering the dependent variable (ў) and

β coefficients respectively (without constant) β1, β2…..β7 the equation will be:

Ў = 0.800-0.304-0.730-0.201-0.102-0.279-0.496=-2.912

We see that negative value present the average influence of these seven predictors to the

dependent variable (growth) and the impact of these barriers to business growth are

significant.

Standard errors are used to determine the b value to check if fluctuates significantly from

zero or not (using t-statistic). As we can see to all predictors, they are different from zero.


predictor is making a significant contribution to the model. All t-s cases are less than 0.05 and

well associated withb values. To explain the impact of the predictors (the independent

variables) comparing each other individually we have extracted data from the table43 to see

the standardized Beta values and in ascending order we see which variable has bigger impact

compared to others, (-.325Customs fees on imports of raw materials, -.257, Unfair

competition in the market -.135,High transport costs0.199 and so on).

Table 43. Coefficients – Model 3

Standardized Coefficients


Upper Bound Zero-order Partial Part Tolerance VIF

(Constant) 14.63 0.821 17.812 0 13.007 16.253

X11 -0.8 0.168 -0.325 -4.757 0 -1.132 -0.468 -0.581 -0.363 -0.252 0.603 1.659

X12 -0.304 0.145 -0.135 -2.096 0.038 -0.591 -0.017 -0.207 -0.169 -0.111 0.681 1.469

X13 -0.73 0.315 -0.257 -2.319 0.022 -1.352 -0.108 -0.688 -0.187 -0.123 0.228 4.378

X14 -0.201 0.179 -0.088 -1.12 0.264 -0.555 0.153 -0.384 -0.091 -0.059 0.451 2.215

X15 -0.102 0.177 -0.041 -0.577 0.564 -0.453 0.248 -0.424 -0.047 -0.031 0.56 1.785

X16 -0.279 0.275 -0.106 -1.015 0.312 -0.822 0.264 -0.468 -0.083 -0.054 0.259 3.865

X17 -0.496 0.262 -0.199 -1.895 0.06 -1.013 0.021 -0.443 -0.153 -0.1 0.255 3.925

1


Coefficientsa

Model



Source: SPSS output, refined by theauthor

While, the tolerance statistics all well above 0.2, therefore, we can safely conclude that there

is no collinearity within our data. Tolerance below 0.1 indicates a serious problem and



tolerance below 0.2 indicates a potential problem according to (Menard, 1995). To calculate

the average VIF as in previous cases, we simply add the VIF values for each predictor and

divide by the number of variables (predictors) (k):

The average VIF is 2.75 is greater than 1, confirms that collinearity is not a problem for this

model. Therefore, in SPSS Output (Annex 1, Table 8) we look the variance proportions vary

between 0 and 1, and for each variable (predictor) should be distributed through different

dimensions (or eigenvalues). Each predictor has most of its variance loading on to a different

dimension (High transport costs81% of variance on dimension 8, Unfair competition in the

market 0.69% of variance on dimension 8, The limited capacity of the local market 0.59% of

variance on dimension 5, Customs fees on imports of raw material on dimension 4 and so on.

Output table 44shows that we have only 6 cases (3.8%) that are outside of the boundaries.

Therefore, our sample is within 5% of what we would expect. Also, 96.2% of cases are in lie

within ±2, and so we would expect only 3.8% of cases to lie outside of these limits.

Table 44. Casewise Diagnostics – Model 3


Case Number

Std. Residual


Predicted Value Residual

1 3.811 18 8.6545 9.3454511 2.039 8 3.0013 4.9987332 -2.162 2 7.3023 -5.30227

105 2.861 17 9.9834 7.01656129 -2.068 -5 0.0711 -5.0711134 -2.278 -4 1.585 -5.58499



Therefore, our sample appears to conform to what we would expect for the accurate model.

These diagnostics give us no one reason for concern except because only one case has a

standardized residual greater than 3, we consider no reason for further investigation.

If we see last lines of the (Annex 1,table 9)Cook's Distance and Centered Leverage Valueif

appear an absolute value greater than 1 is a problem. We see the summarize dialog box in all

cases the values lie within ±1, which shows that these cases have no undue influence over the

regression parameters.




CVRi > 1 + [3(k + 1)/n] = 1 + [3(7 + 1)/157] = 1.15

CVRi < 1 − [3(k + 1)/n] = 1 − [3(7 + 1)/157] = 0.85.

Therefore, from the results above we are looking for any cases that deviate substantially from

these boundaries. Most of our 6 potential outliers have CVR values within or just outside

these limits, which is not a case for concern. The average of CVR values 1.15 and 0.85 is

1.00, within the boundary (+1).

As a final stage of the model analysis, we can see the assumptions of the model. We have

already looked for collinearity within the data and used Durbin–Watson to check whether the

residuals in the model are independent. The graph of ZRESID and ZPRED should look like a

random array of dots evenly dispersed around zero (Field, 2009).

As we can see below Figure 24 and Figure 25 shows the histogram and normal probability

plot of the data for the current model we have analyzed above. The histogram look like a

normal distribution (a bell-shaped curve) as it is and figure 24, also shows the full linearity of

plots distributions around the diagonal line (Figure 25). This confirms very normal

distribution of data and statistical accuracy. All predictors show the normality of data

distribution within parameters of the model, see the plots distribution figure 26.





Figure 75. P–P plots of regression normally distributed residual – Model 3




5.9. MR of barriers related to infrastructure – Model 4

From the hypotheses testing this group of variables resulted not significant, but since some of

the variables assessed with high of importance as evaluated from the respondents.

Chekingindividually, each single variable the results show three variables with high

significance.Therefore, decided to run regression analysis for this group of three variables as

well, excluding other not significant variables.



The table 45 below shows the mean of growth at the appointed time is 4.27%, and the mean

for independent variables is lower than the other groups. Lack of electricity is the first one

lead in this group of barriers, mean is 4.55,means most of therespondent declared with

strongly agree and agree related to this obstacle that hinders the business growth, following

with Insufficient space of work (mean 3.45)and the location of your business (mean 2.19).

Table 45. Descriptive Statistics – Model 4


Mean Std. Deviation N

Growth – Last Five Years (2010-2015) Y 4.271 3.70717 157

Lack of electricity X18 4.5541 0.61389 157Insufficient space of work X19 3.4586 1.37043 157

The location of your business X20 2.1911 1.23586 157


On the correlation table, we see the value of Pearson’s correlation coefficient between every

pair of variables shows the correlation with growth. In our case, we see the strong correlation

between Growth and 3 independent variables. Second, the One-tailed significance of each

correlation is displayed (e.g., the correlation is significant, p < .001).


Correlations Y X18 X19 X20

Pearson Correlation

Y 1 -0.223 -0.546 -0.259

X18 -0.223 1 0.039 -0.191

X19 -0.546 0.039 1 0.497

X20 -0.259 -0.191 0.497 1

Sig. (1-tailed)

Y . 0.003 0 0.001

X18 0.003 . 0.314 0.008

X19 0 0.314 . 0

X20 0.001 0.008 0 .

N Y,X 157 157 157 157


All independent variables are perfectly correlated to dependent (growth) as we see the

diagonal matrices 1.00, and most of the independent variables are correlatedwith each other



(those where the p<0.001). Finally, is presented the number of cases contributing to each

correlation (N = 157).

Let’s see the data from Model Summary Table:R or Pearson R = 0.583, where Pearson R is


(0.583)2 = 0.34

So we can say from both the F value and the Sig value 0.00 shows that the 3 variables are

indeed different from each other and they affect the growth (Y) in different manners.

Table 47. Model Summary - Model 4

Model Summaryb

Model RR

Square

Adjusted R

Square

Std. Error of

the Estimat

e

Change Statistics Change Statistics Durbin

-Watso

n

R Square Chang

e

F Chang

edf1 df2

Sig. F Chang

e

1 .583a 0.34 0.327 3.04148 0.34 26.253 3 153a 0 0.973


From the previous cases, we have seen how we did the calculation some of squares and other

indicators from ANOVA table we are not going to repeat in this case. Furthermore, we see the

difference for the final model is small (in fact the difference between the values of R2and

adjusted R is 0.34 − 0.32 = 0.02 (about 2%). It is accounted for approximately 2% less




This value is close to the observed value of R (.583) indicating that the cross-validity of this

model is good as well.


ANOVAa

Model Sum of Squares df Mean

Square F Sig.

1

Regression 728.578 3 242.859 26.253 .000b

Residual 1415.345 153 9.251 Total 2143.923 156




As we stressed above this group of variables as we saw from data outputs appears with lower

significance than other groups as we can see the indicators from the coefficients table 49.

The higher negative value is (-1.271)Lack of electricity (x18)with highersignificance to

business growth or, more concretely this kind of barrier as per perception of owners

managers is the real obstacle that hinders the growth from this group of barriers, following to

others with less influence to the growth x19 (Insufficient space of work) and x20 (The

location of your business).

If we compare other indicators all are very well associated with Sig. The sig. coefficients

shows that 2 predictors from this group are significant (p<0.05): Lack of electricityand

insufficient space of work, are verygood contributors to the model, while thethird variable

(the location of your business)is not (sig=0.588 is not less than 0.05).

Collinearity Statistics (tolerance and VIF) are under predicted parameters, tolerance between

the values of 0 and 1, VIF with the value of average no more than 10.

The average VIF is 1.27 is not substantially greater than 1, and this confirms that collinearity

is not a problem for this model since the valuesare not greater than 10.


Standardized

Coefficients


Upper Bound


(Constant) 15.173 1.994 7.61 0 11.234 19.112

X18 -1.271 0.409 -0.211 -3.107 0.002 -2.08 -0.463 -0.223 -0.244 -0.204 0.94 1.064

X19 -1.398 0.207 -0.517 -6.742 0 -1.807 -0.988 -0.546 -0.479 -0.443 0.735 1.361

X20 -0.127 0.234 -0.042 -0.543 0.588 -0.589 0.335 -0.259 -0.044 -0.036 0.709 1.411

Coefficientsa


Model


1


Therefore, in SPSS output (Annex 1, Table 10)we look the variance proportions vary between

0 and 1, and for each predictor should be distributed across different dimensions (or

eigenvalues). For this model, we can see that each predictor has most of its variance



represented to a different dimension, e.g., Insufficient space of work 99% of

thevarianceondimension 3, Lack of electricity 0.97% of thevariance on dimension 4 and

location of your business 0.58 on dimension 2.

Table 50 shows that we have only 2 cases (1.27%) that are outside of the boundaries.

Therefore, our sample is within 5% of what we would expect.Furthermore, 98.73% of cases

are in lie within ±3, and so we would expect only 1.27% of cases to lie outside of these limits.

Consequently, our sample appears to conform to what we would expect for a properly

accurate model. These diagnostics give us no real cause for concern only two cases have a

standardized residual greater than 3; we consider no reason for further investigation.



Case Number

Std. Residual



1 4.482 18 4.3693 13.6307

4 2.091 12 5.6407 6.3593

22 2.049 12 5.7669 6.23308

57 2.113 8 1.5741 6.42594

58 -2.069 1 7.2915 -6.29153

104 2.576 15 7.1653 7.83469

105 3.234 17 7.1653 9.83469

129 -2.161 -5 1.5741 -6.57406


If we see last lines of the (Annex 1,Table 11) Cook's Distance and Centered Leverage Value,

if appear an absolute value greater than 1 is a problem, and the summarize cases dialog box in

all cases the values lie within ±1, which shows that these cases have no undue influence over

the regression parameters, all values are less than 1.

If we compare the covariance values, the calculation procedure is as in follaw:

CVRi > 1 + [3(k + 1)/n] = 1 + [3(3 + 1)/156] = 1.07

CVRi < 1 − [3(k + 1)/n] = 1 − [3(3 + 1)/156] = 0.92

Consequently, we are looking for any cases that is outside from these boundaries (1.07 and

0.92). Most of our 8 potential outliers have CVR values within or just outside these

boundaries, or if we take the average value is 0.99 not greater than 1 which is not a case for

concern.



Finally, we can see the assumptions of the model. We have already looked for collinearity

within the data and used Durbin–Watson to check whether the residuals in the model are

independent.

Figure 97. Histogram - Model 4


Figure 108. PP Plot of regression – Model 4


The graph of ZRESID and ZPRED should look like a random array of dots evenly dispersed

around zero as of Field(2009). As we can see below Figure 27 and 28 shows the histogram

and normal probability plot of the data for the current model we have analyzed above. The



histogram looks like a normal distribution (a bell-shaped curve) shows the full linearity of

plots distributions around the diagonal line. This confirms very normal distribution of data

and statistical accuracy. All predictors show the same the normality of data distribution

within parameters of the model, see the plots distribution (figure 29).



5.10. Internal Barriers related to capacities of human resources (MLR)– Model 5


variables is lower than the other groups. Lack of IT staffis the first one, lead in this group of

barriers, mean is 4.05, (up to 5 Likert scales) i.e. most of the respondents declared with

strongly agree and agree related to this obstacle that hinders the business growth, following

with Insufficient level of professional skills of staff (mean3.91)and Lack of motivation to

increase the business(mean 3.70)and so on.

On the correlation table, we see the value of Pearson’s correlation coefficient between every

pair of variables shows the correlation with growth. Some of the independent variable shows

the correlation as well. In our case, we see the strong correlation between Growth and 5

independent variables. Second, the One-tailed significance of each correlation is displayed



(e.g., the correlation is significant, p < .001). All independent variables are perfectly

correlated to dependent (growth) as we see the diagonal matrices 1.00 and most of the

independent variables are associated with each other (where the p<0.001). Finally, the

number of cases contributing to each correlation (N = 157) is displayed.

Table 51. Descriptive Statistics – Model 5


MeanStd.

Deviation NGrowth – Last Five Years (2010-2015)

Y 4.2710 3.70717 157

Lack of motivation to increase business

X21 3.7070 1.21032 157

Lack of experience in management

X22 2.4459 1.23722 157

Insufficient level of professional skills of staff

X23 3.9108 1.14565 157

Lack of management staff X24 2.6561 1.25939 157

Lack of IT staff X25 4.0510 .93915 157


Let’s see the data from Model Summary Table, R or Pearson R = 0.681, where Pearson R is


(0.681)2 = 0.464


Correlations Y X21 X22 X23 X24 X25Pearson Correlation

Y 1.000 -.503 -.392 -.533 -.191 -.396

X21 -.503 1.000 .328 .462 .468 .464

X22 -.392 .328 1.000 .214 .029 .135

X23 -.533 .462 .214 1.000 .414 .183

X24 -.191 .468 .029 .414 1.000 .172

X25 -.396 .464 .135 .183 .172 1.000

Sig. (1-tailed) Y .000 .000 .000 .008 .000

X21 .000 .000 .000 .000 .000

X22 .000 .000 .004 .359 .046

X23 .000 .000 .004 .000 .011

X24 .008 .000 .359 .000 .016

X25 .000 .000 .046 .011 .016

N 157 157 157 157 157 157


So we can say from both the F value and the Sig value 0.00 shows that the 6 variables are



indeed different from each other and they affect the growth (Y) in different manners.

Table 53. Model Summary – Model 5

Model Summaryb

Model R

R Squar

e

Adjusted R

Square

Std. Error of

the Estimat

e

Change StatisticsDurbin

-Watso

n

R Square Chang

e

F Chang

e df1 df2

Sig. F Chang

e1 .681a .464 .446 2.75961 .464 26.105 5 151 .000 1.148


From the previous cases, we have seen how we did the calculation some of thesquares and

other indicators from ANOVA table.Furthermore, we see the difference for the final model is

small (in fact the difference between the values is .464 − .446 = .018 (about 1.8%). This

decline means that if the model were derived from the population rather than a sample, it

would account for approximately 1.8% less variance in the outcome. We can apply Stein’s

formula to the R2 to get some idea of its likely value in different samples as well. Stein’s

formula applied by replacing n with the sample size (157) and k with the number of

predictors (5):

This value is similar to the observed value of R2 (.44) indicating that the cross-validity of this

model is very good as well.From the ANOVA, table 54 we see the Sig value (0.00) is less

than 0.05, means that model is significant.


ANOVA

ModelSum of

Squares dfMean

Square F Sig.1 Regression 993.993 5 198.799 26.105 .000b

Residual 1149.930 151 7.615

Total 2143.923 156


As we stressed above this group of variables as we saw from data outputs appears with lower

significance than other groups as we can see the indicators from the coefficients66 below.



The higher negative value is -1.860Lack of management staff(x26) with higher significance to

business growth; theInsufficient level of professional skills -0.081 (x27), others have less

influence on the growth x28…x32. If we compare other indicators all are very well

associated with Sig. The sig.coefficients show that only one predictor (Lack of management

staff, x24) from this group is not significant (p<0.05).

Collinearity Statistics (tolerance and VIF) are under predicted parameters, tolerance between

the values of 0 and 1, VIF with the value of average more than 10due to not significance of

most predictors, only two of them appearsignificant.

The average VIF is 1.43 is not substantially greater than 1, and this confirms that collinearity

is not a problem for this model, no one of the values is not greater than 10.


Standardized

B Std. Error BetaLower Bound

Upper Bound


(Constant) 15.831 1.166 13.572 .000 13.526 18.135

X21 -.604 .253 -.197 -2.388 .018 -1.104 -.104 -.503 -.191 -.142 .520 1.922

X22 -.643 .193 -.215 -3.342 .001 -1.024 -.263 -.392 -.262 -.199 .860 1.162

X23 -1.299 .226 -.401 -5.740 .000 -1.746 -.852 -.533 -.423 -.342 .726 1.377

X24 .330 .209 .112 1.582 .116 -.082 .742 -.191 .128 .094 .708 1.413

X25 -.874 .266 -.221 -3.284 .001 -1.400 -.348 -.396 -.258 -.196 .781 1.281

1

Coefficientsa

Model

Unstandardized Coefficients

t Sig.



Therefore, SPSS Output (Annex 1,Table 12) shows the variance outcomes vary in the middle

of 0 and 1, and for each predictor should be distributed across different dimensions (or

eigenvalues). For this model, we can see that each predictor have its variance loading on to a

different dimension, shows with values more close to 1, e.g.,Lack of IT stuff(0.75).

Casewise Diagnostics table 56shows that we have only 7 cases (4.4%) that are outside of the

boundaries. Therefore, our sample is within 5% margin of error of what we would expect.

Also, 95.6% of cases are in lie within ±2, and so we would expect only 4.4% of cases to lie

outside of these limits. Therefore, our sample appears to conform to what we would expect

for a fairly accurate model. These diagnostics give us no real cause for concern only two



cases has a standardized residual greater than 3; we consider no reason for further

investigation.



Case Number

Std. Residual



1 3.427 18.00 8.5432 9.45683

22 2.157 12.00 6.0479 5.95205

25 2.179 9.00 2.9856 6.01441

53 2.065 11.00 5.3014 5.69855

58 -2.971 1.00 9.1989 -8.19894

104 2.530 15.00 8.0193 6.98073

105 3.254 17.00 8.0193 8.98073


If we see last two lines of the (Annex 1,Table 13),Cook's Distance and Centered Leverage

Value, if we see any absolute value greater than 1,it is a problem.However, in all cases the

values lie within ±1, which shows that these cases have no undue influence over the

regression parameters, all values are less than 1.


CVRi> 1 + [3(k + 1)/n] = 1 + [3(5 + 1)/157] = 1.11

CVRi< 1 − [3(k + 1)/n] = 1 − [3(5 + 1)/157] = 0.88

Consequently, we are looking for any cases that deviate substantially from these boundaries.

Most of our 5 possible outliers have CVR values within or just outside these boundaries, or if

we take the average value is 0.99 not greater than 1 which is not a cause for concern.

Finally, we can see the assumptions of the model. As we can see below Figure 30 and Figure

31 shows the histogram and normal probability plot of the data for the current model we have

analyzed above. The histogram reprezent a normal distribution, a bell-shaped curve as it is

and figure 31 shows the full linearity of plots distributions around the diagonal line.



This confirms very normal distribution of data and statistical accuracy. All individual

predictors show the same the normality of data distribution within parameters of the model,

see the plots distribution figure 31 and 32.



Figure 31. Normal P-P Plot of Regression – Model 5






5.11. Finding the most significant variables

The models conducted above through Multiple Linear Regression filtered the significant

variable that mostlyinfluences the SBs growth. We saw the hypotheses testing the last two

sub hypotheses that represent groups a barriers resulted to infrastructure and HR resulted not

significant.

After further analyses, we identified variables which are significant and well associated with

SBs growth. So, we decided to conduct further research through the MLR to see which

variables are significant from those groups of the variables. Table 57 shows the mean values

of the variables, ordering from largest to lowest that mostly hinder the growth up to the

declaration of the owners/managers.

As seen from the table 57 the significant variables were reduced from 30 to 25, this derivate

from the selection process. After hypotheses testing and descriptive statistics obtained has

been tested the correlation analysis to all variables (see Annex 1, Table 1- Correlation

matrix). Variables which resulted not significant are excluded from all models on further

analysis process. On further process of analysis, were conducted tests required by the above

regression to confirm the importance of the model's constructs. Using the analysis step-by-

step was reached on the construction of a model obtaining the confirmation for variables

significance. Through the comparison of statistical indicators and the data of descriptive



statistics, was identified the additional impact of variables in the model (e.g.,b data values).

Thus, we have identified and argued the effect of the barriers and relations to small business

growth of each predictor (variable).

Table 57. The most Significant Variables of all groups


No. NMinimu

mMaximu

m Mean

Std. Deviatio

n1 Non efficiency of justice system 157 1.00 5.00 4.8280 3.24665

2 Unfair competition in the market 157 2.00 5.00 4.6433 .62031

3 Organized crime 157 2.00 5.00 4.6115 .58452

4 Lack of electricity 157 3.00 5.00 4.5541 .61389

5 Corruption 157 2.00 5.00 4.5287 .70311

6 Tax evasion 157 1.00 5.00 4.5223 .69417

7 Access to finance for SB is difficult 157 2.00 5.00 4.3949 .76592

8 Politics interruption 157 2.00 5.00 4.3312 .87258

9 The informal economy 157 1.00 5.00 4.3121 .95979

10 Current tax policy 157 1.00 5.00 4.0637 1.09592

11 Lack of IT staff 157 1.00 5.00 4.0510 .93915

12Insufficient level of professional skills of staff

157 1.00 5.00 3.9108 1.14565

13 Lack of technical staff 157 1.00 5.00 3.7707 .97318

14 Access to foreign markets 157 1.00 5.00 3.7516 1.47915

15 The limited capacity of the local market 157 1.00 5.00 3.7516 1.40355

16 Lack of motivation to increase business 157 1.00 5.00 3.7070 1.21032

17 High transport costs 157 1.00 5.00 3.7006 1.48697

18Customs fees on imports of raw materials

157 1.00 5.00 3.5414 1.50423

19 Insufficient space of work 157 1.00 5.00 3.4586 1.37043

20 Economic policy 157 1.00 5.00 3.1529 1.46395

21 Lack of raw materials in country 157 1.00 5.00 2.8854 1.64070

22Administrative procedures related to SB Services

157 1.00 5.00 2.6879 1.43604

23 Lack of management staff 157 1.00 5.00 2.6561 1.25939

24 Lack of experience in management 157 1.00 5.00 2.4459 1.23722

25 Location of your business 157 1.00 5.00 2.1911 1.23586

Valid N (listwise) 157


In closing of this chapter, we did the data analysis and examined the results whether the

hypotheses raised were verified or not. Moreover, a comprehensive empirical approach

through econometric models and descriptive statistics provided answers to each of the

hypotheses separately. The econometric models were tested for their sustainability and were

found to be functional. Also, a particular emphasis on model testing was devoted to



multicollinearity problems, whereby choosing the right selection of variables to introduce

models, multicollinearity problems were minimized. Interpretation of the results, pave the

way for further discussion and drawing of consistent conclusions that will follow in Chapter

VI.



CHAPTER 6

6. ENDINGS – DISCUSSIONS, CONCLUSIONS, AND RECOMMENDATIONS

6.1. Results and discussions

The study ‘Barriers to SBs Growth’ has pushed me to explore a large number of empirical

studies. However, the focal point of this work has been the comprehensive approach in this

study,including a wide number of barriers that hinder the growth or mostly influence to SBs

growth in Kosovo. This is preceded by the exploration of quality literature in this field,

providing sufficient theoretical knowledge as a strong groundbreaking support for the field

research.Moreover, the choice of such a method is chosen in order to fill a part of the gap by

the lack of comprehensive studies of this phenomenon. On the other hand, the inclusion of a

wider range of study variables will make this research more complete.

Taking into consideration the lack of researches in theentrepreneurshipfield on emerging

economies especially in transition countries with fragile institutions, we consider this study as

a modest contribution in this field.Furthermore, we consider this study as an extension of the

existing frame of understanding of barriers to small business growth from specific context as

Kosovo. Additionally, taking on consideration the research necessary in this field in

Kosovo,it was the intention to offer a more particular view of the phenomenon of barriers to

business growth,better understanding of the phenomenon and raise the attention for further

researches.

Firstly, are tested the hypotheses separately in order to have a prediction to the relationship of

variables and their implications to further research. We have tried to fill this gap and have

tested the potential influence on growth, including 30 variables that present barriers

according to perceptions of the owners and managers. To reach the level of accuracy the

variables are divided into 5 groups: barriers related to institutions; barriers related to the rule

of law; market-related barriers; infrastructure and barriers related to HRs capacities. Initially,



our methodological choice was hypothesis testing to provide a preliminary overview for

further analysis.Hypothesis testing questions have been generalized, and some of them have

been rejected.But, it was expected that some variables within the groups would be important

for further research. Initially,those variables are not excluded but through multiple

regression,they are analyzed separately,and some of them are qualifiedwithpotential

contribution to the models and resulted ‘significant’ for further research. The case was in last

two groups of the variables related to infrastructure (Lack of electricity)and HRs capabilities

(Lack of IT staff and insufficient level of professional skills of staff).Herewith,these two

variables from hypotheses testing the association resulted not significant, further on from

regression analyses few variables from both groups resulted in being significant.

In the hypothetical question, do the barriers exist or not? It is quite sure, and freely we can

conclude that barriers exist and there is a direct and vigorous link between perceived and

actual barriers. A second underlying assumption is that barriers exist in the same time of the

study. As we saw in table 15 testing the hypothesis H1 - Barriers of doing business in Kosovo

are present,correlation analyses resultedsignificantly. This has been confirmed by other

studies as well (Hashi, I. and Krasniqi, B., 2011); (Hoxha, 2009); (Krasniqi, 2007); (Krasniqi

et al., 2008); (RIINVEST, 2013); (World Bank, 2017).

6.2. Results from hypotheses testing and regression analysis (the statistical support of the research)

Hypotheses are tested using separate variables, before running the models to all groups of

variables,in order to have a prediction for further analyses. Below are discussing in detail the

outcomes for each raised hypothesis to see what the outcomes say, are they verified or not.

H1 - Barriers of doing business in Kosovo are present!Freely we can conclude that barriers

of doing business in Kosovo are present as we sow the correlation test of this hypothesis. The

so-calledmodel Pearson Correlation measures the degree of linear relationship between

growth and barriers of doing business. As it is seen in the outputs of table 15, the values of

Pearson correlation(r) ranges from -1.0 to +1.0, i our case is:r= -0.318 and p=0.00(< 0.01)

stating that r=-0.318. It was concluded that there was a strong negative relationship between

growth and barriers. Even, further analysis conducted, including all groups of variables,

confirms this. When we see the values of the parameters obtained through the regression

analysis, all indicators show a strong correlation between barriers and growth.



H2 - Despite the existence of barriers the owners/managersdo not give up to the growth

intentions! Even high presence of the barriers, the intention to grow among of

owners/managers exist and in some cases not only to survive in such conditions, but they put

extra efforts to grow their businesses. As we can see in the table 16, the correlation is

significant: Pearson Correlation (r=-.412) and p (sig) =0.00 (p<0.01). Furthermore, as

ofperception of owners/managers, they do not cancel the intentions to grow their businesses

even why the barriers are present. While the present understanding of the relationship

between barriers and growth intentions and behaviours is insufficient, there is some evidence

to suggest that certain factors or barriers such as unfair competition or lack of the rule of law

may stop owner and managers from intending to grow. In some cases,existsevidence that

owners/managers do not have the intention to grow their businesses (ABSDT and Indu,

1991). Alternatively, in some cases, this should be understood as the subjectto the change in

thebehavior of managers (Cliff, 1998).

How barriers are perceived to influence the past or current growth intentions and behaviours,

and how, if at all cases barriers were managed or overcome, this is a matter that differs from

one country to another as of the specifications that they have. Thisalso differs from developed

countries to transition countries. During the process of interviewingofowners/managers,we

understood the meaning of barriers in different cases and the practices wherebarriers may

influence to growth. The situation in Kosovo differs from some other countrieson the context

of perception by owners and managers. For instance, they often feel ‘frightened’from the

barriers,and they mobilized to overcome them.

Looking for (H3) “Presence of obstacles influence managers to cancel their objectives to

grow their businesses”!This hypothesishas been raised to have more evidence and to prove

the hypothesis 2. On table17, the test shows that value of Pearson Correlation r =-0.082 and

p=0.305, not less than 0.01 result not significant, meansthat even presence of the barriers

managers/ownersdo not cancel their objectives of growth. In some circumstances, barriers

might influence to stop the growth. For example, it has also been suggested, that barriers

which cannot be managed or overcome may stop owner/managers from acting on intentions

to grow (Krueger, 2003).

On the question of the difficulties accessing to financialresources,as we have seen from the

table 18, the correlation was significant at the 0.05 level (2-tailed), p < 0.05 and Pearson

Correlation -0.173 (r=+1 level). This means that difficulties for access to finance as of



perception of owners/managers are present. As will see later on results discussion the access

to finance is an important barrier that influences growth, but in the case of Kosovo is not the

first one. There are some difficulties to credit access, and lack of funds, declared from

owners/managers, but there are some other barriers that hinder the growth more than this.

Financial barriers (e.g.,high cost of capital, access to capital) were among the most important

barriers identified in majorityof studies referring the countries in transition. Therefore, we can

conclude that this is a very important factor that influences the growth but this is not the most

important in the case of Kosovo up to the perception of owners and managers. This is not for

the reason that owners/managers undervalue this factor, but compare to the others for

instance factors related to the rule of law this remain behind.

As of the correlation outcomes in table19 we see the negative correlation between tax policy

and business growth, r = 0.214 and p = 0.007. The correlation was significant at the 0.01

level. As we can see from the test of different question related to tax policy, respondents are

unsatisfied with the level of the taxes.

The smaller value of r (Pearson C.), the significance is higher. The data in table 21 shows that

correlations exist between the institutional support and business growth. The correlation was

significant as of r=-0.311 and p=0.00.

On the regression analyses of the first group of variables (barriers related to institutions x1,

x2, x3, and x4) LSM applied, and the multiple coefficient of determinationindicates that we

have measured the goodness of fit for the estimated multiple regression equation (see Eq. 1).

The multiple coefficient of determination denoted as value of R2that present the relationship

among SST, SSR, and SSE is computed. For an R2= 0.281, we sad that the model expressed

as a percentage explains that 28% of the variation in the businessgrowthdepending

(accounted for) by these four predictors.

If we look the data in table 31 (Coefficients) to compare the contribution of each variable, the

column B in ascending order, we see that ‘Access to finance’ is the first one (-1.175)

followed by ‘economic policy’ (-0.647), tax policy (-0.453),and admin procedures (0.821) is

the last one. Even data in the same table shows that all variables are significant (table 31sig).

Barriers related to Rule of Law mostly hinder the small business growth. To test this

hypothesis were conducted two questions with owners/managers: 1)Nonefficiency of the

justice system is an obstacle to growyour business? 2) Lack of Rule of Law is a barrier



togrowyour business? In both cases results a high correlation between business growth and

factors related to the power of the rule of law. As of first question, correlation was significant

at the p <0.01 level (p = 0.00) and r = - 0.308 (r = +1) and the second p = 0.00, r = - 0.425 as

we can see the tables 22 and 23.

Further on through regression analysis is confirmed the strong correlation between growth

and the rule of law factors. This we can see for each predictor separately.If we look the

values in table 34 (Correlation), confirms the strong correlation between growth and 6

predictors.The data outputs of all coefficients show the normal distribution of the data and the

model is a good fit to explain. The output described in this section is produced using the

options in the MultipleLinear Regression: Statistics provided from output SPSS, (see table

35), and this confirms the reliability and normality of the model data distributionhave been

reached. If we focus firstly only on the R square value, we see the statistical indicator of

R2understand that six variables together explain 30.6% of their impact on business growth or

an R2of 0.306, we can say that the model explains around 30.6% of the variations in business

growth is accounted for by these six predictors.This confirms that this group of variables or

the rule of law factors are highly correlated to business growth. Also from the data obtained

from the interviews with owners/managers, they were concerned with high presence of this

group of barriers consideringwith the highest impact on business growth. If we see the

descriptive statistics in table 57, the factors related to the rule of law are on the top of the

table, means that up to the perceptions of owners/managers those mostly hinder the business

growth. Therefore, we can conclude that barriers related to Rule of Law are the most evident

obstacle to small business growthin Kosovo. So, in this context, the question: as more

incomplete/inadequate the legal environment is, the higher constraints exist for SBs in

Kosovo have been statistically supported.

The 5 models conducted above through Multiple Linear Regression (MLR), the data values

of all predictors’, different coefficients confirmed the reliability and normal distribution of

the data. So, models were well constructed and good fitted.

Analyses of market-related barriers resulted as very important to small business growth as

well, following two last groups, model 4 and 5 which resulted with less significance to the

growth. We saw thattwo last sub-hypotheses that represent groups of barriers related to

infrastructure and HR resulted not significant. After further analyses through multiple linear

regression, we identified variables separately, which are very important and well associated



with SBs growth. So, we decided to conduct further research through the MLR to see which

variables are significant from those groups of the variables. For instance, lack of the

electricity resulted to be a significant variable to business growth, among of variables related

to infrastructure. Lack of IT staff, insufficient level of professional staff, and lack of technical

staff from the group of factors related to HR capacities have been discovered that are

significant.

There are a lot of diagnostic statistics that should be examinedafter a regression analysis, and

it is difficult to summarize this wealth of material into a short conclusion. Considering that

too much statistical data represents different meaning to the issues in various situations, the

point is that diagnostics are tools that enable us to see how right or wrong our model is

regarding fitting the sampled data. They are a way of assessing our models. However, seeing

the most significant indicators of measuring and proper fit of all models to all groups of

variables conducted, the decision becomes easier. In our cases, if we compare the values of

all Correlation Tables, Model Summary tables and ANOVA tables) of all models 1 to 5, we

see that correlation resulted in being significant to the most of the variables at level of

Pearson C. ( +1) or sig (Pvalue<0.01). Using multiple regression analysis for constructions of

models by modeling the structural equation with the multiple variables, we see the Pvalue and e

compare it to the coefficient α (0.05). In our cases, Pvalue = (0.00) < (α) 0.05 models we have

addressed all the variables together, to identify their significance and the impact of each of

them. This shows that all models are good and statistically supported by the results of testing.

So, the variables taken in the study together are significantin relation to small business

growth.

As we can see from the stepwise analyses of all 5 groups, in all cases parameters from our

SPSS outputs regarding the cases that might be influencing the regression model according to

the Field, 2009, we can conclude:

1. If we look at standardized residuals and check the criteria,no more than 5% of cases

have absolute values above 2 and that no more than about 1% have absolute values above 2.5.

In our cases to all groups of variables analyzed and to all models, we did not have outliers’

cases with the value above 3 that might be a case for concern. Very few cases evidenced

could be outliers which can’t damage or have influence in our general models because of the

low percentage of participation in the model.



2. If we look in the data editor for the values of Cook’s distances: any value above 1

indicates a case that might be influencing the model. So, we do not have a value above 1 in

the models, and our data fit the models.

3. If we calculate the average leverage (the number of predictors plus 1, divided by the

sample size) and then look for values twice or three times greater than this mean value, for

each model, the results are within the boundaries.

4. If we compare the Mahalanobis distance, a crude check is to look for values above 25

to great samples (500) and values above 15 in smaller samples (100), according to the Field,

2009. In our case, we have the maximum values 19.5, and if we consider that our case size

157 the value above 20, we are within parameters.

5. If we calculate the higher and lower limit of acceptable values for the covariance

ratio(CVR), the upper limit is 1 plus three times the average leverage, whereas the lower limit

is 1 minus three times the average leverage. Cases that have a CVR that fall outside of these

limits may be problematic. Our cases are within boundaries ±1, just around of it which does

not violate the models.

6.3. Theoretical contributions

In the summary of this study, in the light of the multidimensional nature of the problem and

the diversity of topics, we can conclude that we have managed to give a logical understanding

of the problem and have paved the way for further research. From the literature review, we

found that different approachesexist in the various researchesand differentconclusions are

drown depending on the country for which the study was conducted. However, thisstudyhas

examined, among others, a particular manner to identify barriers and explain how barriers

actually impact SBs.

Alternatively, if we compare findings from their study (Barlett and Bukvic, 2001) on barriers

to SME growth in Slovenia the level of the impact of various factors were different compared

to Kosovo. Therefore, it was concluded that in the situation of Kosovo is different and the

environmental barriers declared by respondents had a significant impact on SBs growth and,

as a result, they constrained the growth of the SBs sector seriously. Most of the factors

remain the same with high impact; even in theperiod, we study.The impact of those factors



are associated with some other internal factors related to the lack of capacities of the firms to

survive in the competitive market.

In particular, this doctoral thesis explored the importance of some HR factors related skills of

entrepreneurs and human capital in transitional environments, deriving the barriers related to

skills of staff in ICT as we have presented above in our statistical analyses. The factor of

‘lack of IT staff’ in particular, technical staff, managerial skills, and motivation. Our results

have shownthe not so robust impact of determinants related HR of the firms,owners’ and

managers’ features to the growth of SBs, compare to other factors,e.g.,the rule of law,

market-related and other institutional factors.The same assessment with regard to these

factors is seen in other studies (Krasniqi, Mustafa, 2016) for example, where the

entrepreneurs rank the employee knowledge and managerial abilities as the lowest barriers

for their operation and growth.Although in transition countries, firms often require additional

training for staff to supplement the poor level of knowledge gained in educational

institutions.

An additional contribution is the analysis of the role that entrepreneurs have on growth

processand theirbehaviouron the context of intentions to growthey have in the overall firm

growth process. We have found that despite the conditions in the environmental background

for doing business in Kosovo, intentions to grow by owners/managers are a good indicator of

the growth and as we saw from analyses results it was very well statisticallysupported.

Some authors make a confusion related to the impact of institutionsBruton et al. (2008) have

argued that very little is known about the impact of institutions on firms behave in new

market economies or transition. Based on empirical evidence, and findings of this study is

proved that informal factors hinder the growth of the firm. The institutional obstaclesto

entrepreneurship development, characterized by many deficiencies, influence chaotic forms

of entrepreneurship, creatingsuch situation that entrepreneurs by itself to usethe ‘new rules of

the game.'This means that the presence of corruption and other negative entrepreneurial

behavior are present in the absenceof an efficient legal system. Therefore,a favorable

environment of doing business is necessary more than ever in Kosovo.

There are some implications learned from this dissertation. Primary, the new growth theory

does not differentiate between the contribution of growing the SBs in developed countries in

one side and contribution of growing firms in transitions countries. As we have seen from the



different theoretical findings related, including the case of Kosovo as well, the differences

regarding the contribution of growing firms are essential. This is naturalbecause the

economic structure and business environment differ from country to country. These

conclusions havetheoretical implications because they involve a quite separate focus, which

definitely should take into account the contextual factor of the business environment. Second,

the studies examining the growth of the firms use awide spectrum of the methodologies and

growth indicators making rather difficult to compare the results. Therefore, representing any

growth patterns is almost impossible. Furthermore, these studies have been mainly focused

on some countries in transition stage while the empirical informationregardingto the difficult

environmentare evident. Therefore, the extension of exploring of the firm growth in general

and in particular in underdeveloped, downgraded or transitional countries still are necessary.

As we have seen from theoretical researches, our results of research found the interrelation

between growth and entrepreneurs skills. This shows several implications for entrepreneurs,

like necessity improvement of their profile. The first implication for entrepreneurs as a focal

point and primary in the managementcontext is the necessity of proper level of education,

sufficient experience, and trainings.Also, other staff of the firm as well considered as the

specific human capital are indispensable to attend from time to time relevant business

trainings. Investments in these specific trainingare highly recommended. The second and

very important is that entrepreneurs to be ready and well trained to be able to cooperate with

different partners outside of the country, through various forms of cooperation.

Negative phenomena like law enforcement, fiscal evasion, unfair competition, and corruption

remain highly significant that hit SBs. They are followed by administrative burdens, high

taxes, and others mainly addressed to formal institutional services.Though, the ranking of the

factors based on the perception of managers from the country to another changing,in the case

of Kosovo as well. In the case of Kosovo predominate the barriers related to the rule of law.

Considering that this study is an extension of the previous ones which somehow treated this

topic, we say that those others are made in different time and in more marginalized

circumstances. Also considering the nature of the subject as a process that evaluates from

time to time the updated information was necessary. The time between studies is different

thinking on the necessity of the researching as a process in continuity. The progress achieved

in Kosovo, the gradual transformation from the pre-development to the developmental stage

will change the approach to the topic as well.



6.4. Conclusions and discussions

Based on the literature review and other materials used to accomplish this dissertation and

data provided from the field research, many conclusions have been reached regarding the

barriers to SBs growth in Kosovo. Completion of this study includes a series of remarks,

suggestions, advices, and recommendations. The development of SBs is considered a

strategic orientation in the country's future development, with a special role in transforming

existing businesses, developing new manufacturing businesses and creating new export

structures. The experience of many other countriesconfirms the important role of SBs in

economic growth.

From the data analysis through the multiple linear regressions of the 5 groups of variables we

have foundenough results thatevidenced the strong correlation between business growth and

barriers that hinder the growth. The linear link was proved from the data outputs. The results

showed that as more barriers are present, the lower growth will result. In previous studies,

there have been reservations about the connection of perceived and actual barriers to the way

and the possibility of verification. However, this is not difficult to testifythat our investigation

through interviews with business managers/owners following with empirical evidence and

statistical analysis conducted, clearly confirms that there is a reality that majority of barriers

perceived are real barriers that hinder the growth.

In this study, we have investigated barriers to SBs growth in the case of Kosovo, which

illustrates a distinctive way of barriers that impede the business growth in Kosovo on the

context of the transitional and hard environment of doing business. While most of the

priorresearchers in this area the problem have treatedselectively, we tried to give

multidimensional approach, meanwhile in this study are included general, factors, internal

and external that influencing SBs growth in Kosovo. The theories and theoretical lessons

have been drawn from the most qualitative studies of this field and have served as a guide to

finish this dissertation.Moreover, have been examined institutional factors based on the

institutional theory (North 1994; Welter 2006; Aidis et al. 2008; Smallbone and Welter

2009;Saul Estrin et al. 2013). Also, factors related to the power of the rule of law, market-

relatedfactors, infrastructure, and HRs factors (Morrison et al. 2003; Haber and Raichel 2007;

Doern R., 2009; Wiklund et al., 2009).



Thus, our results show that specific trainingsin case of ‘lack of skills’has a strong positive

impact on the growth of the firm. Hence the findings suggest entrepreneurs’ specific

knowledge, used more directly in the process of SBs development, appears to be more

important in determining the growth than their general knowledge of managers gained

through formal education.

Moreover, our findings point out the importance of intentions to explain barriers related to

HR skills and theirs impact to growth. The statistical results show that 65% of the

entrepreneurs surveyed have the intention to grow, which is considerably higher than results

reported in highly developed countries, for instance, Davidson et al. (2010) found that in

Sweden the intention of the entrepreneurs to grow was much lower. Even this high

motivation to growth could be endorsed to young entrepreneurs who challenge to reach

something more to their career of business and avoid acts which damage the environment for

entrepreneurial activities. Nevertheless, intention to grow has been found to be a strong

determinant of firm growth in the multivariate analysis. One conclusion from such findings is

that intentions to grow in transitional contexts such as Kosovo are more evident than in some

other countries. These intentions to growconsiderably influence positively to SBs growth

since the majority of surveyed owners/managers declared positively.

Contrary to our investigation, perceptions of owners/managers related to some institutional

barriers are not with major influence on the SBs growth. This contrasts with prior evidence

that identify a negative influence of perceived external barriers in transition countries mostly

have been prioritized. The factors related to the rule of law resulted in being more evident,

therefore the presence of negative phenomena like corruption, fiscal evasion, informal

economy and unfair competition are significantly and strongly correlated with firm

growth.This finding may be related to the fact that informal factors might have prevented

formal rules from operating with even a modicum of efficiency.The both groups of barriers

are related to each other and one group influence the other, e.g., if the justice institutions are

not acting properly if the due to the implication of persons who lead the institutions on those

negative phenomenon.

Currently, the barriers to small business growth are evident;alsothishasbeen supported by

national and international research institutions and many authors(EU Commission 2014;

Riinvest, 2013).This study provides a unique criticism aspect of policymakersas key persons



responsible for this which play a fundamental role inimproving the general climate of doing

business in Kosovo.

In the following, learning from the general findings, we summarize some conclusions:

1. There are many barriers related to institutions and implications of policymakerswho

lead institutions frequently stressed by the owners/managers of businesses. Institutional

barriers are crucial that hinder the business growth. Lack of the rule of law, organized crime,

corruption and so many others mentioned above, are seriously damaging the business growth

in Kosovo. Of the hurdles related to informality, it turns out that businesses often confronted

with corruption and unfair competition. The breakdown of outcomes from across the country

shows that they suffer most of these informal phenomena. Among the legal barriers, the

inefficient judicial system seems to be the biggest challenge for businesses.The law

enforcement and fair competition still will remain key challenges in establishing

entrepreneurship development and friendly business environment.

2. Additionally, results of our research suggest that there are several factors such as lack

of IT staff, technical staff, professional staff in general, experience and management

capabilities of the owners/managers, general education that areimportant factors that

haveimpact on SB growth. However, owners/managers rank the employee capabilities and

managerial skills as the lowest barriers after infrastructure barriers for their operation and

growth. Although this assessment exists among business owners/managers, the importance of

these factors remains undisputed.In general, the private sector of Kosovo and SMEs are

assessed with large and difficult constraints to benefit from good management and efficiency,

improvement in this fieldis considered a key development factor for Kosovo(Riinvest,

2013).Staff with the proper level of education enables to use strategies to gain competitive

advantage, to apply organizational culture and values; to perform regulatory and legal

framework which are preconditionsfor the growth. This group of the barriers in one or

another way is connected to institutions but there are barriers which are inside businesses,

and the businesses need to make more improvement related to their behavior in general

competitive environment. Some of the obstacles were related to organizational weaknesses,

not qualified managers and staff. Human resources not prepared to survive in today’s

challenges of doing business in Kosovo, lack of vision and creativity for new ideas among

young people, impede businesses to adapt to new situations. Time management is one

handicap that personnel of the firms are facing. Using holidays of the owners or managers in



particular to small businesses (5 employees) with no replacement and often rest the all

business activities in thesame time is a handicap. Other defects of ineffective time

management are evident as well.

3. It is important to reduce the regulatory burden on SBs, as these enterprises are in

disproportion affected by problems such as complex tax systems, bureaucracy, frequent

inspections by the authorities, and the like. Further on, the responsible institutions should pay

attention to promote the best practices of economic development that assist SBs to have

access to the markets, financing and support services, emphasizing the need for work

knowledge, advises for support programs and incubators that stimulate SBs development,

(Gashi, 2016a).

4. High-interest rates for loans, lack of experience in administration to apply for credits,

missing capabilities to formulate business plans, collateral, ownership problems and others

are the mainchallenges stressed out by the firms. General opinions on most cases that

managers and owners expressed are to release the small firms at the start up the stage from all

taxes and burdens in a future period from 1 year or in specific cases more, to enable them to

proceed with growth in the future.

5. The facilitation procedures by the governments of the country to access in some

relevant sources will improve the situation. Indicated below are the six mandatory

components to which SBs need to have access,as the best chance of survival and to overcome

the barriers. Facilitating access to markets, finance, education, land, technology, and support

services will allow competitive enterprises to survive and grow.

6. The growth of businesses for five years 2011-2015 by respondents is based on the

average growth of annual turnover, sales growth, number of employees and increase of

investments. Most businesses almost 80% of them declare to have growth and a small

percentage pronounce themselves behindor stagnation. In the survey results, the growth was

small and performed in 5 years average of 4.27%. A notable feature of the overall ranking of

barriers, the infrastructure barriers, apart from ‘the lack of electricity,' from the statistical data

we found it not significant. Based on the perception of managers/owners is the fact that there

has been an improvement in infrastructure due to the government’s investments spending in

recent years.



7. Financial barriers, especially the high cost of funding and limited access to finance,

seem to have improved, is less pronounced than other factors. Due to highinterest rates,

among the financial barriers, the high cost of funding is perceived to be the most challenging

in the earliest periods of the last decade. This is due to getting lower interest rates from

banks, even why they are still considered high.

8. In the list of institutional barriers, high taxes are believed to be among ofthe important

barriers for surveyed businesses across the country and throughout the study period. This

study also reflects how businesses consider the services provided by the government,

municipalities, and other government agencies. Results at the national level indicate that

more than 50 percent of them perceive the services as weak or very weak. Findings show that

more than 90 percent of businesses interviewed had stated that they never received any

invitation from their municipalities when business policies were drafted, suggesting that

public debates were rarely organized by their municipalities or never.

9. Barriers related to human recourses (HR) and infrastructure, such ‘lack of motivation

for workers’ and ‘lack of management skills’ are positioned at the bottom of the list, not from

the fact that these factors are not important, but others are more evident. It is worth noting

that, no matter how harsh the barriers are, all are expected to become less problematic in the

next future years. Regarding the HR related barriers, a vast majority of participants,

regardless of region, expressed their concerns about the level of education and skills of

workers. According to them, the workers do not have sufficient qualifications to meet market

demands. Policy-makers need to understand better the complex challenges faced by SBsto

develop appropriate supportive policy frameworks (Rizos et al., 2015). Kosovo ranks on 87th

in the world in UNDP’s rankings using the Human Development Index (HDI). The UNDP

Kosovo Human Development Report for 2012 suggests that Kosovo HDI has increased from

0.700 in 2010 to 0.713, albeit the lowest in the region, lagging behind Serbia, Albania,

Bosnia and Herzegovina, and The Former Yugoslav Republic of Macedonia (UNDP, 2012).

Moreover, several practical implications can be derived from our findings. On the one hand,

given the important role of specific knowledge attained through relevant management

training in the venture development process, entrepreneurs should be encouraged to invest in

such trainings.

10. Broadly is accepted that Kosovo even considerable progress achieved still is in the

earlier development stage to ensure the supportable system of doing business and healthy



business climate. Still are indispensable the general institutional reforms, strategically

orientation, to support the privet sector, stimulate small business to go step by step forward.

Especially, to take off the inherited of bad habits and negative phenomenon which damages

the environment of business development. Despite the significant progress achieved Kosovo

still is challenged to reach the European standards of doing business. In emergency is

required to improve the rule of low to open the road the process of creation of a healthy

environment of doing business. Fighting the negative phenomenon like corruption and

informal economy is the particular challenge for the Kosovo’s government (Gashi, 2016b).

However, SBs to have the desired effect, entrepreneurs must be motivated to start legitimate

entrepreneurship, and their enterprises must survive. The most important issues are general

reforms to stimulate and convincing entrepreneurs to work legitimately; helping them to

survive free market rules, the fair competition of doing business; ensure the prosperity on

small business growth. If the burdens outweigh the potential gains, businesses have little

incentive to leave the informal economy. Even in the best business environments, the

majority of new businesses fail. An unfavorable environment with high taxes, corruption, and

an oppressive bureaucracy further destruct the prospects of success.

Good governance is a crucial factor to reach sustainable development. Rodrik (2000)and

Arin, K.P. et al., (2011) indicate that the better-governed states are less prone to economic

instability. Corruption is one of the most significant impediments to economic growth.

Lastly, corruption is still perceived as very high, which contributes negatively to all areas of

trade and economic activityin Kosovo. Numerous studies have found that corruption reduces

human capital, discourages investment, leads to a misallocation of resources, lowers the

quality of public infrastructure and services, and thus ultimately hampers economic

development (Olken et al., 2012).

Furthermore, as soon as Kosovo reach to reduce negative phenomenon in an economy like

corruption, informal sector, which in according of Statistic Agency of Kosovo (SAK) is

around of 30% or around of 400 million Euros loss per year. Enforcement of the rule of law

is essential to encourage the new business start-ups, domestic new investment, and foreign

direct investments. All of these identified factors can be considered as barriers that lead to a

low level of productivity and, thus, to a weak competitive position in potential export

markets. In the medium-term, to achieve a real growth of about 4.5%, in line with Kosovo’s

average performance of recent years, growth should not only remain driven by domestic



demand but even to strengthened and promote theexport sector. The deficit trade balance is

too high (83% to 17%). Hence, efforts to enhance competitiveness should become of

paramount importance to Kosovo’s institutions to improve the external balance of trade by

increasing exports. The growth rate as of recent years from 3.5 – 4, 5 is not enough to reach

the sustainable development. The growth rate to reach stable economic development must be

a triple. So, despite a considerable progress achieved Kosovo strongly must fight to reach the

main objectives of the standards that ensure the comfortable environment of doing business

in order to create the conditions for business growth.

6.5. Recommendations

The summary of all findings confirmed that the lack of institutional support for small

businesses in Kosovo is evident.As we learned from a broad spectrum of theoretical

explanations in one side and the results obtained from the field research on the other, we will

summarize a series of recommendations for reducing barriers of doing business in Kosovo:

1. More than ever, it is imperative of the time that the Kosovo government take steps to

build a sound strategy for small business support. Starting a wide range of reforms to improve

the business environment, first of all reforming the justice system and in all other areas where

the development of small business is affected. Manolova et al. (2008) found that attitudes

towards the institutional environment for the development of entrepreneurship across three

countries - Hungary, Latvia, and Bulgaria, were less than favorable. Nevertheless, the

reasons given by respondents (i.e., business students) to explain why, differed between

countries, and this finding has implications for how policy reforms should proceed in each

case. Each case has own specifications which should be treated accordingly. Kosovo should

learn from this kind of experiences that passed through transition and practice some of

theapplicable strategies to overcome the situation.

2. The Government of Kosovo should fundingfinancial support policy, compile

thegrants and subsidy schemes in order to encourage the development and growth of small

businesses. Also, it should protect those domestic firms that must compete with firms that

import from other countries and benefit from state subsidies.

3. All administrative barriers to services should be eliminated, increased transparency,

and increased financial support to municipalities where businesses operate. Municipalities in



Kosovo should hire the experts and frequently offer training to businesses staff to provide

them with professional advice according to their needs. Trainings on Information Technology

use are indispensable. Also, municipal workers dealing with businesses need to be constantly

evaluated to be responsible at the right professional level to perform their duties to ensure a

sustainable professional support to small businesses. Municipalities should be more inclusive

when drafting policies for businesses. Municipal meetings may be a way to involve

businesses in drafting policies for them. Opening the offices for SBs support, engaging

experts who evaluate the Kosovo market, identify potential high growth sectors and develop

incentive support schemes for them. In this way, municipalities will be more active in

supporting healthy businesses and growth paths.

3. The government, through a detailed and concrete policy, must improve a development

strategy, similar to the other states in the region. There should be a strategy in the form of an

inclusive framework that takes into account the assessments of businesses, policymakers, and

experts. In the meantime, the Government of Kosovo, and in particular the Ministry of Trade

and Industry, should develop incentive investment schemes that financially support certain

small business activities that plan to increase their businesses. Also, they should provide

stimulating subsidies depending on the growth and progress noted (e.g., increase in the

number of employees, an increasein own investments).

4. Governmental bodies should keep active media campaigns to keep up-to-date

businesses with all the relevant information they need to promote their role to businesses, as

it seems that a large number of the businesses are not notifying about the benefits they can

get from foreign and domestic agencies. Promotion can be done through various media

channels, including social networks.

5. There is a need to increase the consolidation of the inter-institutional communication

of all the central and local organizations on one side, including the universities and the

business community on the other, regarding the institutional assistance for small

businesses.Organization of regular meetings to ensure up to date information. These meetings

should be open to each community resident and to be held in the municipal building.

Participants can share their opinions and discuss the general business developments and

obstacles that can be avoided, meetings with advised officials or professionals who can

benefit too much. Why not even engage experts in the field to teach them about the current

challenges might face businesses.



In order to overcome the current situation and more focused on the promotion of

manufacturing and exporting businesses, it is critical to undertake these measures of

economic policy changes in the country as well as to improve the position of small

businesses. These two sides will elaborate in the following. Furthermore, intensive

communication between the business community and the government needs to be

significantly improved. This can be achieved in various ways, including developing both the

business community and the government of more efficient information mechanisms to inform

about different problems. Some activities related to thegovernment such as approved laws;

encouraging media to report on business problems and economic reforms. Also, the more

effective organization of the business community that would be led by a stronger network of

business associations so that the business would have greater opportunities to communicate

with the government’s bodies about their status.At the end, there is a need for better

institutional and organizational coordination of actors and participants in this field. Export

Promotion Activities of MTI and Foreign Ministry, Chambers of Commerce and Business

Associations and other government bodies are in the initial and largely separate phase of

acting; they should closely cooperate in favor of businesses and stop representing the partial

interest of particular groups.

6.5.1. Practical Recommendations related to financial barriers

Financial barriers characterized by high funding costs and limited access to finance in

Kosovo can be reduced by increasing the counseling credit benefits and from credit

consequences. It is also one of the factors concerning the relatively high interest rates charged

by commercial banks to the private sector (European Commision, 2011). Furthermore,

creating a Guarantee Fund for Small Businesses would facilitate the takeover of loans to

banks and will help too much in the development and growth of these businesses.

The results of the SBs survey conducted show that financing is one of the main obstacles to

SBs growth in Kosovo. This comes from hard bank’s requirements and short terms and

conditions for loans; thisconsidered as anobstacleof SBs development and consequently

constrain their growth. According to Krasniqi(2004) the larger the firm is, the higher

probability of getting a loan and vice versa. Age of the firm also increases the likelihood of

the firm to getting a loan. This analysis confirms that smaller firms are credit rationed in the

financial market. This is because small firms frequently are not able to provide collateral.

Also, banks are biased toward lending larger business because of lower administration costs.



Our econometric results highlight some of the issues that policymakers should address to

easy the SBs access to finance. The relevant authorities should contribute to facilitating and

setting the criteria for licensing new financial institutions to increase competition in the

financial market and thus affect the reduction of interest rates.In the framework of

institutional support policies, it is necessary to create a long-term credit system with more

favorable conditions for manufacturing and service SBs, especially for exporting businesses.

The current credit scheme is "elusive" and disadvantageous for most small businesses,

especially for manufacturing and exporting businesses. Solutions should be required in the

growth of credit supply through increased long-term savings, international financial support

and the good channeling of remittances from the diaspora. Creating a specific credit line for

exporting SBs with export performance would be a viable solution in the case of Kosovo. The

government should work to reduce the overall risk of business environment by improving

courts, combating informal economy, avoiding duplicate books, etc. It should reduce the tax

burden, not necessarily reduce tax rates, but reduce additional costs associated with tax

payments such as cost reporting, bureaucratic costs, and so on. Some of these expenses create

space for corrupt behavior by officials.

Another alternative that institutions may work is to create a credible fund for thesupport of

SBswhich can help small businesses better access to credit. At least to support SBs in

perspective especially those which generate new jobs and operate sustainably in the market.

Kosovo institutions should work more on the quality of products/services with international

standards. Standard harmonization would facilitate cooperation in the flow of goods and

services between businesses in Kosovo and other countries.

6.5.2. Practical Recommendations related to Rule of Law barriers

As we have seen from previous studies (Krasniqi, 2007;Krasniqi et al., 2008;Hoxha,

2009;Riinvest I., 2013; World Bank, 2015, 2016, 2017; EU Commision, 2014) factors related

to rule of low have been predominant and mostly on the top list of the barriers. In this

study,this opinion extended, we saw from research data, as the perception of

owners/managers, lack of the rule of law seriously is damaging the environment of doing

business. Increasing the efficiency of the judiciary system is a priority of the priorities for

respective institutions in Kosovo. All activities related to businesses in the courts is a

necessity to improve, to increase business confidence and provide a more favorable business



climate.In the case of Kosovo, entrepreneurship is developed under special conditions, rarely

seen elsewhere, since it is one of the last countries experiencing transition difficulties toward

a free and open market economy. The business environment and its entrepreneurial

development served as an appropriate setting for Solymossy (2005) for expanding previous

models on entrepreneurship. He suggests that Kosovo presents the complex circumstance of a

fragile climate to develop the entrepreneurship. The circumstances were specific, but the

process will last as in many Balkan countries.

A unified data management system that would be used to improve efficiency, transparency,

and accountability in our judicial system, particularly in commercial court, should be

established. The system would be used to track and monitor cases and disputes related to

property and other relevant data.Increase the capacity of the prosecutorial system to combat

the informal economy and negative phenomena in business. In this case, the action must be

fair, balanced and completely independentfrompolitical influences. The government should

make more efforts in developing a more effective and efficient tax inspectorate. It should also

ensure that this body will function in a transparent and accountable manner. In this way, one-

sided treatment of some businesses would be avoided, which would fuel unfair competition

and informality in general.The Kosovo government needs to adopt and implement serious

anti-corruption policies. Also, ot regulate by the law the independence of judicial system

from politics and the accountability of the people of power. These policies would reduce

fiscal evasion by increasing voluntary cooperation and enforcement mechanisms of the

performance.

6.5.3. Practical Recommendations related to market barriers

More evident on this group of obstacles resulted in being ‘unfair competition’ than other

factors that are connected to “rules of game” in the market. Institutions should guarantee full

rules of free market economy for all domestic and foreign firms which want to invest in

Kosovo. Several international firms have already begun to operate in Kosovo. They need to

have friendly environment of doing business; institutions much better should understand the

importance and the impact of foreign capital foroverall country development.According to

Janssen F., (2002b), dynamism and complexity of the environment are related to the degree

of the disorders and uncertainty in the market.In the case of Kosovo, managers, and owners

often feel insecure about tackling barriers to business competition, in particular from products

comes from abroad. The import of products from abroadcompete the domestic products.



Kosovo has is abounded with unused natural resources, coal, mines and agricultural land,

which may attract further foreign direct investment. In this context, the main challenge for

Kosovo now is to take off the dilemmas and improve the image related to environment for

encouraging the growth of new firms. Foreign investments are directly connected to the

domestic environment of doing business (Gashi, 2015a). Much work remains to be done by

the state institutions, especially in law enforcement and ensuring equal conditions for all

participants in the marketplace, especially if we have in mind that businesses continue to

suffer corruption and red tape in their daily operation. According to Glancey (1998), the idea

alluded to above that external constraints arise mainly from the competitive conditions. Often

the firms are in the situation that movements in the market are not balanced and the activities

of the firms are one direction oriented, creating the strong competition. On the other hand,

hostility also may arise from different sources including the declining demand or radically

changing technology, which pushes the firm to change its technology or to seek other market

opportunities (Clement et al., 2004). In that situation, the support of institutions should be

oriented on advising services and consultancy, in order to create the diversity of activities and

sectors. Or, on the other side to stimulate the activities in deficit. This will help the creation

of “healthy” new firms, survive and grow the existing firms. The evidenceshows that most of

the transition countries were unable to achieve their pre-transition levels of GDP even after

many years of recovery. However, even now when most economies of the region are more

stabilized and growing, as of our analysis as well, low demand seems to be one of the

obstacles to SBs growth. If we add to this the inadequate market infrastructure, especially the

underdevelopment of financial institutions, we have a clearer picture of the overall economic

conditions for small business growth.

From the data obtained from the interviews previously presented in this dissertation, it is

noticed that few are those enterprises that undertake a business study plan, even less are those

who draft a marketing plan. Without a well-designed business and commercialization plan by

marketing experts, hard-to-handle competitive market challenges, and can easily become in

the firm closure position and never think about growth. These problems are strict of a

managerial nature and that managers or owners of enterprises in Kosovo must have a

management plan in which the marketing plan will be necessarily included (Gashi, 2012).

The design of the pieces will serve to benefit funds and subsidies from the outside as well.

Through the marketing plan, the product is managed, its position in the market, the price, the

distribution, the promotion actions, the sales problems, and growth will come surely.



Therefore, is more than ever necessary that responsible government institutions to create long

term strategy of development, including a strategy for SBs which in near future period can

feedback the investments and gradually become a generator of overall development in the

country. Continuing the process to establish trade relations with countries in the region, based

on mutual interests and reciprocity through participation in EU organized activities,

negotiating free trade agreements are necessary. Through bilateral cooperation and also

through EU bodies, insist on removing existing barriers to the free movement of people and

goods.

6.5.4. Practical Recommendations related to infrastructure barriers

Lack of electricity remains the main obstacle of this group. At the same time, power

shortages, poor transportation infrastructure, and an unstable water supply diminish the

chances for small business development and growth. The firms located in the suburb of the

cities and firms in rural areas are less likely to grow because their counterparts in urban areas

operate with better business infrastructure and have better access to financing and higher

power supply. The government in emergency needs to speed up the procedures to build the

new power plants and uses another possible alternative to accomplish the capacities of power

supply. Successful businesses, those with particular interest which generates new jobs and

ensures sustainable growth should be stimulated with such policies like to guarantee regular

supply with electricity, reducing prices for electricity,reducing taxes, reducing the fuel price

for transportation, etc. Also at the same time needs to stabilize the water supply, finding the

alternatives for additional funds, apply for funds through well-prepared projects to

international agencies which gave a significant contribution in this matter.

6.5.5. Practical Recommendations related to HR barriers

A professional education and training system should be created because the current system is

not in line with the labor market needs. Consequently, vocational trainees will be able to meet

the qualifications required by the labor market. Harmonize study curricula with the new labor

market to reduce the need for additional emergency training immediately after employment.

According to Boyle (2007), there have been multiple calls for educators at all levels to

recognize the challenges and opportunities in today’s economy. Also to make the necessary

changes to educational programs to ensure that students develop ‘21st-century’ skills and

abilities including know-howsskills in problem-solving; innovation and creativity; self-



direction and initiative; flexibility and adaptability; critical thinking and communication and

collaboration skills. The continual supportorganized ontrainings to owners/managers related

to motivating employees, given additional knowledge to deal properly with staff, will help

them carry out daily work operations.

Better coordination needs between all organisms that support SBs. Intercommunication

between ministries (MTI, MF, and MEST) Agencies for support of SMEs, incubators, and

other local offices in order tobe updated with information and benefits might have SBs. There

is a need for proper institutional coordination; governmentinstitutionsfor education so that

business people to bewell prepared to do business. Additionally, to overcome the old

phenomena of tradition that are consolidated from the past and to understand the new "know

how" models of doing business based on professional education and technology. The

possibility of misunderstanding is high in formerly planned economies, in which a lack of

business education or traditions phenomena may lead to narrow understandings of key

concepts of doing business (Michailova, S. and Liuhto, K., 1999).

Nowadays, fierce competition, both inside and outside of the country, it is imperative of the

time that business in Kosovo be accompanied by an increase in awareness and professional

skills. For instance, entrepreneurs with specific knowledge may also be better able to detect

profitable market opportunities that are still unexplored. Specific human capital may also

help organize the business successfully, thus facilitating growth (Wiklund, J. and Shepherd,

D, 2003).

For example, human capital theorists argue that education and experience provide individuals

with valuable skills that make them more productive. Human capital can be developed over

time and transferred between individuals. This differentiates human capital from other

individual characteristics, such as personality traits (Wright, 2005). In this regard, Kosovo

government agencies should promote the close cooperation and support to SBs up to their

needs in staff preparation.

6.6. Limitations of the research

At the end, we consider that this study has a few limitations. We know that this dissertation

will not cover the all wide complexity of the phenomenon of barriers to SBs growth, knowing

that remain other factors which might further influence the growth of the firm. The lack of



longitudinal data, which is frequently mentionedin some studies as one of the main

drawbacks of firm growth studies.The different approach and the various methods used in

several studies has created confusion and difficulties to ensure the same continuity of the

study of this field. In fact, as it is claimed in by Davidsson, P. et al., (2006) growth is a

phenomenon that necessarily might happen anytime and move from time to tome.

Normally, we are aware of some limitations of this study, but at the same time, we consider

that this will open many directions for future researchers. While, the comprehensive approach

ofthis study ensure so much information with direct practical use to SBs barriers, to survive

and succeed invery limited environments such Kosovo’s and beyond. However, we are aware

that there is still need to investigate related to this important topic. In the context of variable

measurement, we may have been limited by the declaration of owners/managers related to

general barriers. In one case when the questions were connected to each other they responded

differently, even why we clarified most of them during interviews. Even though, the use of a

relatively high number of variables to measure various obstacles taken as variables for

measuring were based more on perceptions of owners/managers rather than on any objective

measures. Additionally, the perceived institutional barriers were taken from actual

entrepreneurs. Nevertheless, the focus of this study was on actualentrepreneurs; even the

growth phenomenon is nature of evolution that might change over the time.

Clear explanations about the barriers in three sectors with different activities make

differences are more complicated and need a particular treatment. To determine the

boundaries of the research where are the limits of the investigation because of

multidisciplinary implications of the research topic was a particular difficulty. The main

focus of research was the barriers of doing business and their impact on the business growth

of the different branches of the businesses. Many authors stressed the fact that multiplications

of the issue to many areas as social, economic and politic raise level of presence of various

limitations. The complexity of researchrelated to barriers,state that studies on the business

growth are difficult because ofcomplexity of the issue and differences of

environments(Davidson 2008); (Hashi, I. and Krasniqi, B. , 2011); (Doern R., 2009);

(Gartner et al., 2004). Therefore, it is sensible to reduce the scope of a study to avoid losing

the quality of the study.

Expect the problems that are recognized; some problems were foreseen that are appeared

during the research. Most of the problems are institutional. Some other barriers are not related



to institutions or government. Very often business owners/managers hesitate to explain the

details. So it was necessary the proper level of the mastery to draw what is needed. The other

group of barriers is part of those obstacles associated with the individual and society. In this

case, the entrepreneurs with lack of managerial skills might not provide reliable information

and always for the researcher need further investigation. There are problems regarding the

classification of the barriers which appear as general barriers, but that influence in different

situations. There are also some minor problems that appear during the data collection:

providing qualitative information either by personal perception, lack of time, availability and

so on. This requires doubling such information, even from managers or other actors in the

same business. Another phenomenon is a cultural approach to the firm development, in the

context of individual and society and perceived of the concept of the phenomenology of

business development based on specific tradition. In such situations, it was necessary to apply

an integrative approach and with a high level of mastery to determinea proper explanation.

In addition, there are some of the problems which are detected, as: Some small businesses

feel uncertain because of negative phenomena like corruption, non-fair competition, the

intervention of the policy makers in business lives, etc. and hesitate to share the information

fearing from the reach of institutions. Another subject of barriers research is the comparison

of the level of impact on the growth of businesses, as well as identifying those problems that

are more expressed and have a higher impact on growth,etc.

The approach of the researcher to include as much as variables in the examination of small

firm growth to reflect the multi-dimensional nature of growth of the smallfirm appears some

challenges in this type of model. For example, variables in different groups are not

independent, and it is not always possible to obtain the necessary data at the detailed level

required. This can potentially limit the ability of the researcher to examine small firm growth.

But, in our case, we took on consideration only the role of predictors as independent variable

to dependent variable.

Clear explanations of the barriers in different sectors with various activities are more

complicated and request the concentrated attention. To determine the boundaries of the

research where are the limits of the investigation because of multidisciplinary implications of

the research topic. The main focus of research is the barriers of doing business and the impact

on the business growth of the different branches of the businesses.



6.7. Advice for future research

Despite some limitations indicated above, we think that this study opens several questions

that necessitate further discussion in future research. Our results suggestthat the investigation

attention should be directed toward understanding more specifically how the institutional

environment influences to SBs growth. The barriers to growth even why are presented the

mostly the same to different countries in transition we consider that some distinct features

characterize each country. Therefore, future studies should also pay attention to the specific

environments, such as the transitional and challenging, and specificunfavorable environments

for entrepreneurship and SBs growth and somehow to leave the tendencies of generalization.

So the growth theory of the firm will certainly become more affluent.

A natural extension of this research is to continue the investigation for e.g., the factors

correlated to other phenomena that impact growth, like the strategy of the firm and its

relationship with environmental conditions and other organizational factors by the inside of

the company. The approach in depth to the phenomenon applying alarge spectrum of data

will yield more objective results. As much as data and as a longer period of study will be, the

results of the study will be more fruitful.The inclusion of a longer period of the study for

future, advised by Davidsson et al., (2006) so that research can yield more reliable results.

Therefore, a longitudinal period including might bring more reliable results.

In the future studies recommended to be in focus the HR of the firm, in particular,

psychological and educational background which might have significantimpact on the

behavior of owners/managers and their will to avoid the negative phenomenon of doing

business. This dissertation study is heterogeneous and too wide, for sure will not include the

all problematic issues, but will offer a good base and unique material for extension of the

study for future scholars, and other interested classes. To summarize, the critique provided in

this dissertation suggests that empirical evidence does not adequately explain ‘why' ‘how’ or

indeed ‘if,' certain factors or barriers identified by studies taking the approach outlined, limit

or prevent behaviors that influence small business growth and development. The deeper

approach in these details will exceed the frame of this dissertation but will be a good space

for other future researchers. We tried to include the factors that most influence the growth of

small firms. Future research should take a more process-based approach to the topic, treating

existing barriers, perceived barriers in particular, not as static or discrete events, but instead



as ongoing challenges that affect growth, related decisions and actions taken by

owner/managers, and to which owner/managers must respond.

This can be reached as well in particular case studies where a matter of study will be a firm

which successfully reached to manage with barriers and lead to the growth periodically. The

model of growth and challenges of firms might be a good practice to follow by the others. On

the other side, the exploration of the firms which failed to survive and growth due to the

barriers will be fruitfully for the others; this will be a message to avoid mistakes did from

others.

“Positive effect of barriers” has been mentioned by some authors, as an area that new

researchers might be focusing on asegment of firm growth, in the context of extra efforts of

owners/managers they do to survive and grow their businesses.This is not treated

considerably in this dissertation, will be a good space for new researchers to approach more

of this phenomenon.

In closing, considering the multidimensional character of the topic existing researches on this

issue, freely we can say that still leave a too much space to be examined. Has yet to examine

the potentially complex and subtle ways in which behavior may be influenced by the

perceptions of barriers importance. Further on, this dissertation exclusively trying to adapt to

the circumstances of the business environment that characterize Kosovo, considering that will

serve or to be a good model even for the other countries with same specifics. We hope that

this dissertation has provided some suggestions such as to influence others to move forward

the research in this area.



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Annexes

Annex 1.Some of the statistical table

Table 1. Correlation Matrix of Dependent and Independent Variable

Y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12Y Pearson

Correlation1 -.214*

*-.173* .392** -.337*

*.129 -.234*

*-.273*

*-.378*

*-.061 -.275*

*.294** -.581*

*

Sig. (2-tailed)

.007 .030 .000 .000 .107 .003 .001 .000 .451 .000 .000 .000

X1 Pearson Correlation

-.214*

*1 -.213*

*-.309*

*.146 .048 .439** .052 .133 -.161* .175* -.121 .247**

Sig. (2-tailed)

.007 .007 .000 .069 .549 .000 .522 .096 .043 .028 .130 .002


-.173* -.213*

*1 .101 -.043 -.058 -.057 .014 .032 .196* .116 -.070 .097

Sig. (2-tailed)

.030 .007 .208 .595 .474 .479 .861 .692 .014 .148 .382 .227


.392** -.309*

*.101 1 -.224*

*.034 -.407*

*-.423*

*-.450*

*.061 -.478*

*.305** -.696*

*

Sig. (2-tailed)

.000 .000 .208 .005 .674 .000 .000 .000 .447 .000 .000 .000


-.337*

*.146 -.043 -.224*

*1 .008 .189* .040 .080 -.294*

*.262** -.320*

*.364**

Sig. (2-tailed)

.000 .069 .595 .005 .918 .018 .615 .320 .000 .001 .000 .000


.129 .048 -.058 .034 .008 1 .026 -.116 .005 .063 .023 .019 .059

Sig. (2-tailed)

.107 .549 .474 .674 .918 .746 .150 .951 .431 .775 .817 .466


-.234*

*.439** -.057 -.407*

*.189* .026 1 .267** .257** -.083 .350** -.199* .503**

Sig. (2-tailed)

.003 .000 .479 .000 .018 .746 .001 .001 .303 .000 .013 .000


-.273*

*.052 .014 -.423*

*.040 -.116 .267** 1 .366** -.054 .221** -.211*

*.361**

Sig. (2-tailed)

.001 .522 .861 .000 .615 .150 .001 .000 .503 .006 .008 .000


-.378*

*.133 .032 -.450*

*.080 .005 .257** .366** 1 .016 .235** -.297*

*.499**

Sig. (2-tailed)

.000 .096 .692 .000 .320 .951 .001 .000 .845 .003 .000 .000


-.061 -.161* .196* .061 -.294*

*.063 -.083 -.054 .016 1 .140 .139 .029

Sig. (2-tailed)

.451 .043 .014 .447 .000 .431 .303 .503 .845 .081 .083 .723


-.275*

*.175* .116 -.478*

*.262** .023 .350** .221** .235** .140 1 -.113 .483**

Sig. (2-tailed)

.000 .028 .148 .000 .001 .775 .000 .006 .003 .081 .160 .000


.294** -.121 -.070 .305** -.320*

*.019 -.199* -.211*

*-.297*

*.139 -.113 1 -.364*

*

Sig. (2-tailed)

.000 .130 .382 .000 .000 .817 .013 .008 .000 .083 .160 .000

X12 Pearson -.581* .247** .097 -.696* .364** .059 .503** .361** .499** .029 .483** -.364* 1



Correlation * * *

Sig. (2-tailed)

.000 .002 .227 .000 .000 .466 .000 .000 .000 .723 .000 .000


-.207*

*.040 .006 -.203* -.238*

*-.040 .303** .349** .401** .222** .053 -.040 .212**

Sig. (2-tailed)

.009 .621 .944 .011 .003 .620 .000 .000 .000 .005 .510 .618 .008


-.267*

*-.080 .096 .018 .117 .023 .024 .160* .124 .068 .063 -.066 .091

Sig. (2-tailed)

.001 .322 .232 .821 .145 .771 .769 .045 .123 .398 .431 .408 .254


-.468*

*-.231*

*.199* .168* .340** -.122 -.067 .041 .129 -.058 .173* -.181* .131

Sig. (2-tailed)

.000 .004 .012 .035 .000 .128 .401 .606 .107 .473 .030 .023 .102


-.443*

*-.201* .037 .139 .407** -.120 -.044 -.002 -.024 -.172* .034 -.216*

*.125

Sig. (2-tailed)

.000 .012 .646 .082 .000 .136 .585 .979 .766 .032 .668 .007 .120


-.424*

*-.164* .206** -.031 .139 .076 .004 -.060 -.166* .296** .177* -.031 .248**

Sig. (2-tailed)

.000 .040 .010 .703 .082 .341 .962 .455 .037 .000 .026 .702 .002


-.223*

*.090 .036 -.304*

*-.138 -.158* .208** .289** .390** .262** .219** -.111 .249**

Sig. (2-tailed)

.005 .262 .654 .000 .086 .049 .009 .000 .000 .001 .006 .167 .002


-.022 -.120 -.064 -.243*

*-.194* -.003 -.066 .087 .005 -.083 .093 .128 .133

Sig. (2-tailed)

.781 .134 .424 .002 .015 .974 .413 .279 .950 .304 .248 .109 .096


.001 -.130 -.060 -.095 -.119 .262** -.085 .061 .097 -.027 -.080 .100 .011

Sig. (2-tailed)

.985 .105 .455 .237 .137 .001 .287 .449 .226 .734 .321 .213 .893


-.004 -.094 -.102 -.169* -.108 .129 .106 .029 .001 .058 .083 .042 .115

Sig. (2-tailed)

.964 .242 .205 .034 .180 .108 .184 .722 .988 .470 .303 .606 .150


-.546*

*-.156 .107 .103 .268** -.112 -.080 .071 .046 .113 .104 -.144 .149

Sig. (2-tailed)

.000 .051 .181 .201 .001 .163 .318 .380 .564 .160 .196 .071 .062


-.259*

*-.118 -.006 .099 .391** -.099 -.036 .030 -.132 -.216*

*.070 -.083 .085

Sig. (2-tailed)

.001 .141 .943 .218 .000 .218 .656 .708 .100 .006 .386 .302 .288


-.503*

*-.203* .133 .098 .159* -.117 -.118 .099 -.042 .116 .054 -.162* .126

Sig. (2-tailed)

.000 .011 .098 .221 .046 .143 .141 .219 .600 .147 .505 .043 .115


-.392*

*.007 -.018 -.080 .256** .014 .022 -.013 -.075 .033 .160* -.025 .131

Sig. (2-tailed)

.000 .928 .824 .320 .001 .858 .784 .872 .352 .681 .045 .757 .101


-.533*

*.045 .208** -.146 .020 -.223*

*.131 .222** .148 .312** .123 -.177* .292**

Sig. (2-tailed)

.000 .572 .009 .069 .807 .005 .103 .005 .065 .000 .124 .027 .000


-.191* -.425*

*.168* .203* -.090 -.159* -.257*

*-.082 -.017 .264** -.079 .018 -.050

Sig. (2-tailed)

.017 .000 .035 .011 .265 .047 .001 .305 .836 .001 .324 .827 .534


-.050 .224** -.050 -.116 .250** .054 .094 -.016 .008 -.062 .121 .000 -.002



Sig. (2-tailed)

.534 .005 .536 .149 .002 .499 .242 .846 .916 .437 .130 .999 .978


-.072 -.061 -.057 .099 .313** -.152 -.119 .065 .049 -.540*

*.038 -.066 -.107

Sig. (2-tailed)

.367 .449 .478 .216 .000 .057 .138 .417 .542 .000 .640 .408 .183


-.396*

*-.134 .043 -.116 .171* -.010 -.012 .206** .316** .094 .146 -.186* .303**

Sig. (2-tailed)

.000 .094 .592 .146 .032 .904 .882 .010 .000 .243 .069 .020 .000

X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24 X25 X26 X27 X28 X29 X30-.20

7**-.26

7**-.468**

-.443**

-.424**

-.223**

-.022

.001 -.004

-.546**

-.259**

-.503**

-.392**

-.533**

-.191*

-.050

-.072

-.396**

.009 .001 .000 .000 .000 .005 .781 .985 .964 .000 .001 .000 .000 .000 .017 .534 .367 .000

.040 -.080

-.231**

-.201*

-.164*

.090 -.120

-.130

-.094

-.156

-.118

-.203*

.007 .045 -.425**

.224**

-.061

-.134

.621 .322 .004 .012 .040 .262 .134 .105 .242 .051 .141 .011 .928 .572 .000 .005 .449 .094

.006 .096 .199*

.037 .206**

.036 -.064

-.060

-.102

.107 -.006

.133 -.018

.208**

.168*

-.050

-.057

.043

.944 .232 .012 .646 .010 .654 .424 .455 .205 .181 .943 .098 .824 .009 .035 .536 .478 .592-.20

3*.018 .168

*.139 -.03

1-.30

4**-.24

3**-.09

5-.16

9*.103 .099 .098 -.08

0-.14

6.203

*-.11

6.099 -.11

6.011 .821 .035 .082 .703 .000 .002 .237 .034 .201 .218 .221 .320 .069 .011 .149 .216 .146-.23

8**.117 .340

**.407

**.139 -.13

8-.19

4*-.11

9-.10

8.268

**.391

**.159

*.256

**.020 -.09

0.250

**.313

**.171

*

.003 .145 .000 .000 .082 .086 .015 .137 .180 .001 .000 .046 .001 .807 .265 .002 .000 .032-.04

0.023 -.12

2-.12

0.076 -.15

8*-.00

3.262

**.129 -.11

2-.09

9-.11

7.014 -.22

3**-.15

9*.054 -.15

2-.01

0.620 .771 .128 .136 .341 .049 .974 .001 .108 .163 .218 .143 .858 .005 .047 .499 .057 .904.303

**.024 -.06

7-.04

4.004 .208

**-.06

6-.08

5.106 -.08

0-.03

6-.11

8.022 .131 -.25

7**.094 -.11

9-.01

2.000 .769 .401 .585 .962 .009 .413 .287 .184 .318 .656 .141 .784 .103 .001 .242 .138 .882.349

**.160

*.041 -.00

2-.06

0.289

**.087 .061 .029 .071 .030 .099 -.01

3.222

**-.08

2-.01

6.065 .206

**

.000 .045 .606 .979 .455 .000 .279 .449 .722 .380 .708 .219 .872 .005 .305 .846 .417 .010

.401**

.124 .129 -.024

-.166*

.390**

.005 .097 .001 .046 -.132

-.042

-.075

.148 -.017

.008 .049 .316**

.000 .123 .107 .766 .037 .000 .950 .226 .988 .564 .100 .600 .352 .065 .836 .916 .542 .000

.222**

.068 -.058

-.172*

.296**

.262**

-.083

-.027

.058 .113 -.216**

.116 .033 .312**

.264**

-.062

-.540**

.094

.005 .398 .473 .032 .000 .001 .304 .734 .470 .160 .006 .147 .681 .000 .001 .437 .000 .243

.053 .063 .173*

.034 .177*

.219**

.093 -.080

.083 .104 .070 .054 .160*

.123 -.079

.121 .038 .146

.510 .431 .030 .668 .026 .006 .248 .321 .303 .196 .386 .505 .045 .124 .324 .130 .640 .069-.04

0-.06

6-.18

1*-.216**

-.031

-.111

.128 .100 .042 -.144

-.083

-.162*

-.025

-.177*

.018 .000 -.066

-.186*

.618 .408 .023 .007 .702 .167 .109 .213 .606 .071 .302 .043 .757 .027 .827 .999 .408 .020

.212**

.091 .131 .125 .248**

.249**

.133 .011 .115 .149 .085 .126 .131 .292**

-.050

-.002

-.107

.303**

.008 .254 .102 .120 .002 .002 .096 .893 .150 .062 .288 .115 .101 .000 .534 .978 .183 .0001 .130 -.11

3-.240**

-.099

.369**

.041 .005 .064 -.051

-.255**

-.098

-.155

.158*

.055 -.069

-.251**

-.021

.106 .160 .002 .217 .000 .607 .949 .423 .529 .001 .223 .053 .048 .492 .392 .002 .793.130 1 .332

**.314

**.301

**.102 .015 -.05

5.008 .405

**.240

**.278

**.150 .289

**.260

**.002 -.01

8.273

**

.106 .000 .000 .000 .206 .853 .494 .923 .000 .002 .000 .061 .000 .001 .983 .822 .001-.11 .332 1 .833 .433 -.01 -.11 -.00 -.19 .783 .526 .674 .378 .321 .401 -.06 .440 .433



3 ** ** ** 8 6 9 1* ** ** ** ** ** ** 5 ** **

.160 .000 .000 .000 .825 .149 .916 .017 .000 .000 .000 .000 .000 .000 .416 .000 .000-.24

0**.314

**.833

**1 .473

**-.09

8-.13

9-.02

9-.07

8.804

**.603

**.731

**.362

**.345

**.369

**-.10

5.468

**.488

**

.002 .000 .000 .000 .222 .083 .722 .333 .000 .000 .000 .000 .000 .000 .189 .000 .000-.09

9.301

**.433

**.473

**1 .025 .097 .027 .059 .556

**.335

**.532

**.457

**.475

**.356

**-.14

2-.21

0**.272

**

.217 .000 .000 .000 .751 .229 .739 .460 .000 .000 .000 .000 .000 .000 .076 .008 .001

.369**

.102 -.018

-.098

.025 1 .008 -.003

-.019

.039 -.191*

.073 -.074

.244**

.115 -.097

-.255**

.151

.000 .206 .825 .222 .751 .918 .971 .812 .629 .016 .362 .356 .002 .150 .226 .001 .059

.041 .015 -.116

-.139

.097 .008 1 .268**

.164*

-.056

-.041

-.074

.012 .042 .152 -.101

-.163*

-.015

.607 .853 .149 .083 .229 .918 .001 .040 .485 .612 .356 .885 .604 .058 .210 .041 .850

.005 -.055

-.009

-.029

.027 -.003

.268**

1 .175*

-.075

.010 -.018

.140 -.009

-.041

-.018

-.028

.005

.949 .494 .916 .722 .739 .971 .001 .029 .353 .905 .825 .079 .908 .606 .822 .729 .949

.064 .008 -.191*

-.078

.059 -.019

.164*

.175*

1 -.132

.003 -.069

.147 -.021

-.048

-.039

-.119

.012

.423 .923 .017 .333 .460 .812 .040 .029 .100 .968 .388 .065 .797 .555 .632 .137 .879-.05

1.405

**.783

**.804

**.556

**.039 -.05

6-.07

5-.13

21 .497

**.735

**.336

**.557

**.501

**-.18

0*.246

**.445

**

.529 .000 .000 .000 .000 .629 .485 .353 .100 .000 .000 .000 .000 .000 .024 .002 .000-.25

5**.240

**.526

**.603

**.335

**-.19

1*-.04

1.010 .003 .497

**1 .415

**.380

**.189

*.113 .079 .375

**.268

**

.001 .002 .000 .000 .000 .016 .612 .905 .968 .000 .000 .000 .018 .161 .324 .000 .001-.09

8.278

**.674

**.731

**.532

**.073 -.07

4-.01

8-.06

9.735

**.415

**1 .328

**.462

**.468

**-.19

9*.148 .464

**

.223 .000 .000 .000 .000 .362 .356 .825 .388 .000 .000 .000 .000 .000 .013 .064 .000-.15

5.150 .378

**.362

**.457

**-.07

4.012 .140 .147 .336

**.380

**.328

**1 .214

**.029 .219

**.055 .135

.053 .061 .000 .000 .000 .356 .885 .079 .065 .000 .000 .000 .007 .717 .006 .493 .092

.158*

.289**

.321**

.345**

.475**

.244**

.042 -.009

-.021

.557**

.189*

.462**

.214**

1 .414**

-.243**

-.292**

.183*

.048 .000 .000 .000 .000 .002 .604 .908 .797 .000 .018 .000 .007 .000 .002 .000 .022

.055 .260**

.401**

.369**

.356**

.115 .152 -.041

-.048

.501**

.113 .468**

.029 .414**

1 -.520**

-.011

.172*

.492 .001 .000 .000 .000 .150 .058 .606 .555 .000 .161 .000 .717 .000 .000 .892 .031-.06

9.002 -.06

5-.10

5-.14

2-.09

7-.10

1-.01

8-.03

9-.18

0*.079 -.19

9*.219

**-.243**

-.520**

1 .074 -.120

.392 .983 .416 .189 .076 .226 .210 .822 .632 .024 .324 .013 .006 .002 .000 .356 .133-.25

1**-.01

8.440

**.468

**-.210**

-.255**

-.163*

-.028

-.119

.246**

.375**

.148 .055 -.292**

-.011

.074 1 .141

.002 .822 .000 .000 .008 .001 .041 .729 .137 .002 .000 .064 .493 .000 .892 .356 .079-.02

1.273

**.433

**.488

**.272

**.151 -.01

5.005 .012 .445

**.268

**.464

**.135 .183

*.172

*-.12

0.141 1

.793 .001 .000 .000 .001 .059 .850 .949 .879 .000 .001 .000 .092 .022 .031 .133 .079


*. Correlation is significant at the 0.05 level (2-tailed).



Table 2. Longevity of businesses surveyed

Years Frequency Percent Valid Percent

Cumulative Percent

Valid

5 3 1.9 1.9 1.96 9 5.7 5.7 7.67 10 6.4 6.4 148 19 12.1 12.1 26.19 11 7 7 33.1

10 14 8.9 8.9 4211 11 7 7 4912 16 10.2 10.2 59.213 11 7 7 66.214 7 4.5 4.5 70.715 10 6.4 6.4 77.116 7 4.5 4.5 81.517 7 4.5 4.5 8618 8 5.1 5.1 91.119 7 4.5 4.5 95.520 5 3.2 3.2 98.721 1 0.6 0.6 99.422 1 0.6 0.6 100

Total 157 100 100

Source: SPSS output

Table 3. Number of employees of companies surveyed

Nr. Of employees Frequency Percent Valid Percent Cumulative PercentValid 5 1 0.6 0.6 0.6

6 6 3.8 3.8 4.57 5 3.2 3.2 7.68 8 5.1 5.1 12.79 2 1.3 1.3 14

10 7 4.5 4.5 18.511 10 6.4 6.4 24.812 11 7 7 31.813 4 2.5 2.5 34.414 4 2.5 2.5 36.915 3 1.9 1.9 38.916 6 3.8 3.8 42.717 2 1.3 1.3 43.918 4 2.5 2.5 46.519 2 1.3 1.3 47.8



20 6 3.8 3.8 51.621 4 2.5 2.5 54.122 5 3.2 3.2 57.323 1 0.6 0.6 5824 1 0.6 0.6 58.625 7 4.5 4.5 63.126 1 0.6 0.6 63.727 1 0.6 0.6 64.328 4 2.5 2.5 66.930 3 1.9 1.9 68.831 1 0.6 0.6 69.432 7 4.5 4.5 73.933 4 2.5 2.5 76.434 1 0.6 0.6 77.135 7 4.5 4.5 81.536 1 0.6 0.6 82.237 1 0.6 0.6 82.838 4 2.5 2.5 85.440 3 1.9 1.9 87.341 3 1.9 1.9 89.242 4 2.5 2.5 91.744 3 1.9 1.9 93.645 4 2.5 2.5 96.246 1 0.6 0.6 96.848 3 1.9 1.9 98.749 1 0.6 0.6 99.450 1 0.6 0.6 100

Total 157 100 100


Table 4. Collinearity Diagnostics – Model 1

(Constant) TaxPolicy (x1) AccFin (x2)

AdminProc (x3)

EcPolicy (x4)

1 4.571 1 0 0 0 0.01 0.01

2 0.258 4.206 0 0.01 0 0.48 0.19

3 0.112 6.384 0.01 0.13 0.02 0.2 0.754 0.049 9.66 0.01 0.49 0.28 0.22 0.01

5 0.01 21.828 0.98 0.36 0.7 0.09 0.04

1

Collinearity Diagnosticsa

Model Dimension Eigenvalue Condition Index

Variance Proportions




Table 5. Residuals Statistics – Model 1

Minimum Maximum Mean Std. Deviation N

Predicted Value 1.2091 7.7704 4.2791 1.97237 156

Std. Predicted Value -1.556 1.77 0 1 156

Standard Error of Predicted Value 0.393 1.189 0.558 0.124 156

Adjusted Predicted Value 1.0649 7.7613 4.276 1.97501 156

Residual -9.02744 10.77728 0 3.15135 156

Std. Residual -2.827 3.375 0 0.987 156

Stud. Residual -2.85 3.426 0 1.001 156

Deleted Residual -9.17026 11.10359 0.00305 3.24217 156

Stud. Deleted Residual -2.92 3.556 0.002 1.012 156

Mahal. Distance 1.355 19.488 3.974 2.53 156

Cook's Distance 0 0.071 0.006 0.01 156

Centered Leverage Value 0.009 0.132 0.026 0.016 156

Residuals Statisticsa



(Constant) X5 X6 X7 X8 X9 X101 6.614 1.000 .00 .00 .00 .00 .00 .00 .00

2 .253 5.112 .00 .00 .00 .00 .01 .02 .28

3 .053 11.213 .00 .05 .03 .00 .12 .25 .06

4 .046 11.967 .01 .00 .00 .03 .21 .61 .00

5 .021 17.785 .80 .01 .00 .06 .03 .07 .51

6 .009 27.798 .18 .09 .05 .90 .59 .00 .14

7 .005 36.960 .01 .84 .91 .01 .05 .04 .01



Model EigenvalueCondition

Index


1

Source: SPSS output




Minimum Maximum Mean Std. Deviation NPredicted Value 2.3468 8.3866 4.2710 2.05212 157

Std. Predicted Value -.938 2.006 .000 1.000 157

Standard Error of Predicted Value

.389 1.468 .626 .226 157

Adjusted Predicted Value

2.2300 9.0476 4.2701 2.07558 157

Residual -7.41879 10.75480 .00000 3.08738 157

Std. Residual -2.356 3.416 .000 .981 157

Stud. Residual -2.374 3.558 .000 1.007 157

Deleted Residual -7.53355 11.67006 .00082 3.25906 157

Stud. Deleted Residual

-2.412 3.706 .002 1.017 157

Mahal. Distance 1.383 32.922 5.962 5.574 157

Cook's Distance .000 .154 .008 .019 157Centered Leverage Value

.009 .211 .038 .036 157





(Constant) X11 X12 X13 X14 X15 X16 X17

1 7.217 1 0 0 0 0 0 0 0 0

2 0.379 4.365 0 0 0.16 0 0.06 0.01 0.01 0.02

3 0.145 7.063 0.01 0.12 0.45 0.01 0.13 0.02 0.02 0.01

4 0.097 8.607 0.01 0.57 0.01 0 0.28 0.08 0 0

5 0.074 9.877 0.03 0 0.05 0 0.17 0.59 0.06 0.04

6 0.05 11.969 0.84 0.02 0.29 0.03 0.05 0.06 0.01 0

7 0.021 18.371 0.05 0.05 0.01 0.26 0.16 0.03 0.3 0.81

8 0.017 20.735 0.06 0.23 0.02 0.69 0.14 0.21 0.61 0.12




1





Table 9. Residual Statistics – Model 3



Predicted Value 0.0711 10.9302 4.271 2.82846 157

Std. Predicted Value -1.485 2.354 0 1 157Standard Error of Predicted Value 0.356 1.18 0.541 0.12 157

Adjusted Predicted Value -0.0346 11.0568 4.2665 2.83399 157

Residual -5.58499 9.34545 0 2.39643 157

Std. Residual -2.278 3.811 0 0.977 157

Stud. Residual -2.307 3.919 0.001 1.004 157

Deleted Residual -5.72999 9.88045 0.0045 2.53174 157


Mahal. Distance 2.296 35.102 6.955 4.192 157

Cook's Distance 0 0.11 0.007 0.014 157

Centered Leverage Value 0.015 0.225 0.045 0.027 157a. Dependent Variable: Growth – Last Five Years (2010-2015)





Variance Proportions Variance Proportions

(Constant) X18 X19 X20

1

1 3.735 1 0 0 0.01 0.01

2 0.183 4.523 0.01 0.02 0.01 0.58

3 0.074 7.101 0.01 0.01 0.99 0.33

4 0.008 21.538 0.97 0.97 0 0.08






Predicted Value 1.1931 8.5629 4.271 2.1611 157

Std. Predicted Value -1.424 1.986 0 1 157

Standard Error of Predicted Value 0.317 0.941 0.469 0.125 157

Adjusted Predicted Value 0.9471 8.5815 4.276 2.16213 157

Residual -6.57406 13.6307 0 3.0121 157

Std. Residual -2.161 4.482 0 0.99 157

Stud. Residual -2.184 4.506 -0.001 1.003 157

Deleted Residual -6.71098 13.78021 -0.00507 3.08744 157


Mahal. Distance 0.699 13.925 2.981 2.302 157

Cook's Distance 0 0.06 0.006 0.01 157

Centered Leverage Value 0.004 0.089 0.019 0.015 157




Model Eigenvalue

Condition Index


(Constant) X21 X22 X23 X24 X25

1 1 5.587 1.000 .00 .00 .00 .00 .00 .00

2 .191 5.411 .00 .00 .58 .00 .24 .00

3 .103 7.353 .05 .00 .33 .01 .50 .09

4 .052 10.337 .01 .14 .00 .73 .03 .12

5 .046 11.038 .17 .63 .06 .10 .21 .03



6 .021 16.436 .77 .22 .03 .16 .02 .75




Minimum Maximum MeanStd.

Deviation NPredicted Value -.6149 9.6092 4.2710 2.52423 157

Std. Predicted Value -1.936 2.115 0.000 1.000 157

Standard Error of Predicted Value

.253 .986 .523 .131 157

Adjusted Predicted Value -.7842 9.8044 4.2786 2.53763 157

Residual -8.19894 9.45683 .00000 2.71502 157

Std. Residual -2.971 3.427 .000 .984 157

Stud. Residual -3.057 3.486 -.001 1.007 157

Deleted Residual -8.67754 9.78758 -.00763 2.84324 157

Stud. Deleted Residual -3.145 3.624 .000 1.018 157

Mahal. Distance .316 18.940 4.968 3.058 157

Cook's Distance .000 .091 .008 .015 157

Centered Leverage Value .002 .121 .032 .020 157


Annex 2. Questionnaire

QUESTIONNAIRE

Greetings! In the framework of Ph.D. study, we would be asking you to spend some minutes to complete this questionnaire. The questionnaire does not require your identification. All the data you give will be used only for the

purposes of dissertation study and for scientific papers. Your efforts to this matter is so much appreciated. Thank you in advance!

Section 1.

Position of interviewed personOwner/Manager 1 Other 2

Education level Primary 1 High 2University 3



Longevity of business

Number of employees

Type of business Production 1 Trade 2Services 3

Location of your business

Section 2.

Barriers of doing business in Kosovo are present, what do you think?

Strongly disagree 1

Disagree 2

Neither agree nor disagree 3

Agree 4

Strongly agree 5

Do you have intention to growth your business? No 1 Yes 2

Do the barriers hinder business growth plans and objectives of your business? No 1 Yes 2

Presence of barriers influence the managers to cancel growth objectives No 1 Yes 2

Level of taxes is satisfied to growth your business in Kosovo

No 1 Yes 2

Section 3.

Lack of institutional support is an obstacle to growth!

Strongly disagree 1

Disagree 2

Neither agree nor disagree 3

Agree 4

Strongly agree 5

Access to finance for SB is difficult 1 2 3 4 5Financial assistance and support (access to credits, grants, subventions, etc.) influence positively to business growth

1 2 3 4 5Current tax policy is an obstacle to growth your business 1 2 3 4 5Level of taxes is satisfied to growth your business in Kosovo 1 2 3 4 5

Licensing procedures hinder your business 1 2 3 4 5

Laws for SB are applying successfully in Kosovo 1 2 3 4 5

Incapacity of HRs hinder business growth 1 2 3 4 5

Administrative procedures related to SB Services 1 2 3 4 5

Economic policy is an obstacle to growth your 1 2 3 4 5



business

Section 4.

Lack of Rule of Law is an obstacle to growth your business! 1 2 3 4 5Non efficiency of justice system is an obstacle to growth your business 1 2 3 4 5

Corruption is an obstacle to growth your business 1 2 3 4 5

Politics interruption hinder your business 1 2 3 4 5

Informal economy prevents your business 1 2 3 4 5

Tax evasion obstructs your business 1 2 3 4 5

Organized crime hinders your business 1 2 3 4 5Corruption in government institutions hinders your business 1 2 3 4 5Unfair competition in the market hinders your business

Section 5.1 2 3 4 5

Trade Policy is an obstacle to growth your business! 1 2 3 4 5Low demand for your services, products, is an obstacle for your business 1 2 3 4 5Supply of production or service material is a barrier for your business 1 2 3 4 5

Access to foreign markets is difficult 1 2 3 4 5The limited capacity of the local market is an obstacle for your business 1 2 3 4 5

High transport costs are a barrier to your business 1 2 3 4 5The quality of transport services is a barrier to your business 1 2 3 4 5Customs fees on imports of raw materials are barrier for your business 1 2 3 4 5Lack of raw materials in country prevents your business

Section 6.1 2 3 4 5

Barriers related to infrastructure (Electricity, roads, water, internet, location,etc.) constrains the SBs growth! 1 2 3 4 5

Lack of roads is an obstacle to your business growth 1 2 3 4 5Lack of machinery and difficulties to be equipped with to hinder your business 1 2 3 4 5

Insufficient space of work 1 2 3 4 5

Lack of water hinder your business growth 1 2 3 4 5

Lack of internet hinder your business growth 1 2 3 4 5

Location of your business is an obstacle 1 2 3 4 5

Lack of electricity hinder your business 1 2 3 4 5



Section 7.

Incapacity of HRs hinder business growth! 1 2 3 4 5

Lack of motivation to increase business 1 2 3 4 5

Lack of experience in management 1 2 3 4 5

Insufficient level of professional skills of staff 1 2 3 4 5

Lack of management staff prevents your business 1 2 3 4 5

Lack of technical staff hinders your business 1 2 3 4 5

Lack of staff for accounting and finance 1 2 3 4 5

Lack of IT staff hinders your business 1 2 3 4 5

Section 8.

Growth – Last Five Years (2010-2015) in percentage (%)

Annual turnover %

Sales %

Investments %

Number of employees %

Sincerely,

Shaip GASHI


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