determinant factors of firm performances in the …ymine/valizadah.pdfi abstract determinant factors...

Determinant Factors of Firm Performances in the Development of

Traditional Industrial Clusters in Herat City, Afghanistan:

The Application of M.E. Porter’s Diamond Model from the

Social Capital Perspective

by:

Parviz Ahmad Valizadah

Adviser:

Prof. Yoichi MINE

A Dissertation

Submitted to

The Graduate School of Global Studies

Doshisha University

In Partial Fulfilment of the Requirements for the Degree of

Doctor of Philosophy

In

Global Society Studies

May 2016

Kyoto, Japan

i

ABSTRACT

Determinant Factors of Firm Performances in the Development of Traditional Industrial

Clusters in Herat City, Afghanistan:

The Application of M.E. Porter’s Diamond Model from the Social Capital Perspective

Parviz Ahmad Valizadah

Abstract:

More than three decades of conflicts destroyed much of Afghanistan’s infrastructure, as well as its

economy. This turmoil resulted in the brain drain and deprived the country of the knowledge

necessary for its sustainable economic growth. Despite the fact that little attention has been paid to

the growth of private sector, especially, to the development of traditional clusters of micro and small

scale enterprises (MSEs) in Afghanistan, the existing mechanisms of traditional survival methods

at the level of individuals, enterprises, or community have enabled these traditional industrial

clusters to gain ground in today’s complex market and consequently contribute to the economic

growth of country.

The aims of this study are to assess the direct and indirect impacts of social capital on the

performance of MSEs (daily sales revenue of enterprises). The primary data used in this study were

ii

collected through structured questionnaires survey carried out at 204 micro and small scale

enterprises in six sampled traditional clusters in Herat City. The hypotheses derived from the

literature review were tested in order to examine the direct and indirect impacts of social capital on

the performance of MSEs.

The results of the analysis conducted in the framework of Porter’s Diamond Model have revealed

that social capital plays a significant role in promoting the performances of MSEs and cooperation

within the traditional clusters in Herat City. The quality of social capital of enterprises has dynamic

positive and negative effects on their performances. It has been confirmed that social capital

dimension has direct and indirect impacts on the performance of MSEs mediated through other

factors in the Diamond Model.

The components of social capital such as trust and networking seem to play significant roles in

facilitating and synergizing the activities of MSEs, by means of improved access to, and sharing of,

the information on product design, input materials, prices, and other market-related issues.

The findings of this study indicate that such cooperation and competition can be practically

achieved by working on those factors related to social capital and other dimensions in the

framework of Porter’s Diamond Model.

Complex relationships exist among human capital, social capital, and the performance of MSEs in

these traditional industrial clusters. Findings show that the level of trust in the informal networks

such as family, relative and neighbor were much higher than the level of trust in the formal

organization such as local and national government officials and municipality officials in these

clusters. The size of entrepreneurs’ social networks and groups are found to have positive influences

on the performance of enterprises, whereas participation in religious activities has negative effect

on the performance of enterprises in these traditional clusters.

The level of trust in neighbors is found to have a positive association with the cooperation in sharing

information, machinery and tools among the cluster members. The findings have demonstrated that

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entrepreneurs who have a family member in the same cluster are more effective in the process of

decision-making and cooperation in price bargaining methods. The findings have also revealed that

charitable activities are more common among enterprises with higher performance. Furthermore,

the participation of entrepreneurs in the informal social networks (such as the local and cultural

associations) has a positive correlation with the size of sources of investment from relatives.

Findings from regression analysis indicate that about 45% of the variations in the performance of

MSEs are explained by thirteen variables representing the social capital and other dimensions within

the conceptual model in this study.

The outcome of regression analysis with the path diagram models reveals that, in addition to its

direct impacts on the performance of MSEs, social capital also has significant indirect impacts on

the performance of enterprises mediated through other dimensions in the conceptual framework of

this study. Therefore, based on the regression analysis conducted in this thesis, all of the constructed

hypotheses were tested and it is statistically accepted that social capital has both direct and indirect

impacts on performance of MSEs.

Finally, the findings of this study indicate that, given that the major role of social capital has been

identified in the framework of Porter’s Diamond Model, a set of policies can be implemented by

policy-makers of the Afghan Government to promote social capital in the process of evaluating and

upgrading activities of the clusters of micro- and small-scale industries.

Keywords: Traditional Cluster, MSE’s Performance, Social Capital, Porter’s Diamond, Herat City,

Afghanistan

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ACKNOWLEDGMENT

First of all, I would like to acknowledge my deep gratitude and praise to God

(Almighty Allah) for the blessings that he gave me to pursue and complete this dissertation.

The completion of this dissertation would not have been possible without the support,

friendship, and love from many people and organizations who deserve to be acknowledged

and to receive my hearty gratitude.

I would like to especially thank my supervisor, Professor Yoichi Mine, for his

professional and valuable knowledge, guidance, constant support, patience, his great

personality and always-positive attitude during this research project. His insights and

wisdom will remain with me throughout my academic career.

I would also extend my gratitude and appreciation to each member of my dissertation

committee: To Professor Hisae Nakanishi, for providing great advice and invaluable

support throughout my doctoral course. Without her encouragement, questions and

suggestions the completion of this dissertation would have been extremely hard. To my

committee member, Professor Eiji Oyamada, for providing intellectual guidance, great

assistance, and valuable recommendations. To Professor Mitsuaki Ueda, for taking the time

to be one of the readers of this dissertation. For his invaluable comments, guidance, and for

providing technical assistance in the process of data analysis.

I would like to express my appreciation and gratitude to all of those individuals who

have contributed to my accomplishments. To professor Yoshio Kawamura, Emeritus

Professor of agriculture and rural economics at Ryukoku University, for offering precious

advice and recommendations on early stages of my work. Special thanks to Dr. Sultan

Ahmad Salehi and Dr. Casper Wits for their worthy technical comments on my dissertation.

I would like to acknowledge all those individuals who participated in in-depth

interviews, particularly Dr. Hitoshi Suzuki, a senior research fellow at Institute of

Developing Economies (IDE-JETRO). Mr. Ferda Gelegen, deputy head of United Nations

Industrial Development Organization (UNIDO-ITPO), Tokyo. Mr. Ahmad Zia Sayed

Khaili, director of SME Development in the Ministry of Commerce and Industries, Kabul.

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Mr. Abdul Samad Katawazy, senior researcher at American University of Afghanistan

(AUAF).

I would like to thank Japan International Cooperation Agency (JICA) and especially

the PEACE-Project for awarding me a scholarship; without which, I would not have had

the opportunity to get my doctoral degree in Japan.

I would like to especial thanks to administrative staffs both from JICA-PEACE project

and the Graduate School of Global Studies at Doshisha University for their always eager to

support me in the educational and daily life during my stay in Japan.

I would like to thank the Ministry of Higher Education (MoHE), as well as Herat

University in Afghanistan for introducing me to this program. I am also extremely grateful

to my colleagues at the Faculty of Economics and Management at Herat University for their

assistance and constructive support.

I would like to thank the entrepreneurs interviewed for this dissertation in Herat City.

I found these interviews extremely insightful in helping me answer the research questions,

and also for personal inspiration. In addition, I also would like to thank my friends and

students at Herat University for their assistance, support, and endeavours in the course of

data collection.

Last but not least, in the memory of my late father (Abdul. Jalil Valizadah), whose life

journey and insight into “social responsibility” inspired me to weave his wisdom into this

dissertation, as an invisible chapter. In addition, I extend my gratitude to my other family

members, specially my mother, my wife, and children (Rayan and Arsh) for their prayers,

patience, support, and unconditional love throughout my stay in Japan. And,

To all those who care for a better tomorrow for Afghanistan

vi

Table of Contents Pages

ABSTRACT…………………………………….……………………….………………..i

ACKNOWLEDGEMENT……………………………………………………………….iv

LIST OF TABLES……………………………………………………………………….xi

LIST OF FIGURES……………………………………………………………………...xii

LIST OF ABBREVIATIONS………………………….………………………………..xiv

1. CHAPTER I: INTRODUCTION .................................................................................... 1

Introduction ........................................................................................................... 1

Statement of the Problems ..................................................................................... 4

Research Objectives .............................................................................................. 6

Research Questions ............................................................................................... 7

Significance of the Study ....................................................................................... 8

Organization of the Study ...................................................................................... 8

2. CHAPTER II: LITERATURE REVIEW ..................................................................... 10

Introduction ......................................................................................................... 10

Industrial Agglomeration and Economic Development ...................................... 11

Cluster Initiative and Its Contribution to the Industrial and Economic

Development in Developing and Transition Economies ............................................ 13

The Application of Porter’s Diamond Model in Industrial Development Through

the Cluster Initiative ................................................................................................... 16

vii

2.4.1. The Dimensions of the Porter’s Diamond ................................................... 18

2.4.1.1. Factor Conditions ............................................................................ 18

2.4.1.2. Demand Conditions ......................................................................... 18

2.4.1.3. Related and Supporting Industries .................................................. 19

2.4.1.4. Firm Strategy, Structure and Rivalry............................................... 20

2.4.1.5. The Roles of Chance and Government ............................................ 20

2.4.2. The Dynamics and The Critics of Porter’s Model .................................... 21

The Role of Social Capital on Firms’ Performance within Porter’s Diamond

Model .......................................................................................................................... 24

Conceptual and Hypothetical Framework ........................................................... 31

2.6.1. Hypothetical Formulation .......................................................................... 32

3. CHAPTER III: RESEARCH DESIGN AND METHODOLOGY ............................... 35

Introduction ......................................................................................................... 35

Operationalization of Variables ........................................................................... 36

3.2.1. Independent and Intermediate Variables ................................................... 37

3.2.2. Dependent Variable ................................................................................... 41

Sample and Data Collection ................................................................................ 42

3.3.1. The Profile of Sample Areas ..................................................................... 42

3.3.2. Sampling and Data Collection ................................................................... 43

Limitations of the Study ...................................................................................... 45

Analysis Methods ................................................................................................ 45

viii

3.5.1. Descriptive Statistics ................................................................................. 46

3.5.2. Correlation Matrix ..................................................................................... 46

3.5.3. General Multiple Regression with Path Analysis Method ........................ 47

4. CHAPTER IV: TRANSITION ECONOMY AND ENTERPRISE DEVELOPMENT

IN AFGHANISTAN ..................................................................................................... 50

The Profile and Socio-Economic Indicators of Afghanistan ............................... 50

Post-2001 Agendas and Transitional Economy in Afghanistan .......................... 52

Enterprise Development and Policy Discourse in Afghanistan .......................... 54

Preliminary Findings from Traditional Clusters in Herat .................................... 57

4.4.1. Dried Fruits and Nuts Cluster .................................................................... 58

4.4.2. Tailoring Cluster ........................................................................................ 64

4.4.3. Carpenter Cluster ....................................................................................... 70

4.4.4. Shoemaker Cluster..................................................................................... 75

4.4.5. Ironmonger Cluster ................................................................................... 81

4.4.6. Tinwork Cluster ......................................................................................... 86

5. CHAPTER V: FACTORS ASSOCIATION WITH MSEs’ PERFORMANCES IN

HERAT CITY ............................................................................................................... 93

Introduction ......................................................................................................... 93

Identification of Factors Associated with MSEs’ Performances ......................... 94

5.2.1. Association between Social Capital and the Performances of MSEs ........ 95

ix

5.2.2. Association between the Social Capital, Factor Condition, and

Performance of MSEs.......................................................................................... 98

5.2.3. Association of Social Capital, Related and Supporting Industries of the

MSEs, and their Performances .......................................................................... 102

5.2.4. Association between MSEs’ Social Capital, Demand Conditions, and

Performances ..................................................................................................... 104

5.2.5. Association between MSEs’ Performance and Social Capital, Strategy,

Structure, and Rivalry ........................................................................................ 106

5.2.6. Association between the Performance of MSEs and Social Capital, and the

Role of Government Policies and Chance ......................................................... 108

6. CHAPTER VI: THE IMPACT OF DETERMINANT FACTORS ON CLUSTER

DEVELOPMENT IN HERAT CITY ......................................................................... 113

Introduction ....................................................................................................... 113

The Impact of Significant Factors on MSEs’ Performance in Traditional Cluster

in Herat City ............................................................................................................. 114

The Dynamic of Social Capital through the Porter’s Model on the MSEs’

performances in Traditional Clusters ....................................................................... 120

6.3.1. The Impacts of Social Capital on MSEs’ Factor Conditions and

Performance ....................................................................................................... 121

6.3.2. The Impact of Social Capital on the Performances of MSEs and on

Industries that are Related to and Supporting Them ......................................... 124

x

6.3.3. The Impact of Social Capital on MSEs’ Demand Conditions and

Performance ....................................................................................................... 127

6.3.4. The Impact of Social Capital on MSEs’ Characteristics and Performance

........................................................................................................................... 130

6.3.5. The Impact of Social Capital on MSEs’ Firm Strategy, Structure, Rivalry,

and Performance ................................................................................................ 133

6.3.6. The Impact of Social Capital on The Government Policies and MSEs’

Performances ..................................................................................................... 136

6.3.7. The Impact of Social Capital on the Role of Chance and MSEs’

Performances ..................................................................................................... 139

Results and Discussions .................................................................................... 142

Conclusion ......................................................................................................... 147

Policy Recommendations .................................................................................. 152

Future Studies .................................................................................................... 155

7. REFERENCES: ........................................................................................................... 156

8. APPENDIXES ............................................................................................................. 164

xi

List of Tables Pages

Table 3.1. Frequency distribution of sample of clustered MSEs ....................................... 44

Table 3.2. Value of Association and Appropriate Phrase ................................................. 47

Table 3.3. Criteria for Fit-Indices of Model Test .............................................................. 49

Table 5.1. Correlation Matrix of Between Social Capital and MSEs Performance ......... 96

Table 5.2. Correlation Matrix of Between MSEs Social Capital, Factor Conditions and

Performance ....................................................................................................................... 99

Table 5.3. Correlation Matrix of Between MSEs Social Capital, Related, Supporting

Industries and Performance ............................................................................................. 103

Table 5.4. Correlation Matrix of Between MSEs Social Capital, Demand Conditions and

Performance ..................................................................................................................... 104

Table 5.5. Correlation Matrix of Between Social Capital, MSEs Strategy, Structure, and

Performance ..................................................................................................................... 107

Table 5.6. Correlation Matrix of Between Government Policies, MSEs Social Capital,

and Performance .............................................................................................................. 109

Table 5.7. Correlation Matrix of Between Chance, MSEs Social Capital, and Performance

......................................................................................................................................... 110

Table 6.1. Model Summery of Significant Variables with Direct Impact on MSEs’

Performances ................................................................................................................... 115

xii

List of Figures Pages

Figure 2.1. Conceptual/Hypothetical Framework ............................................................. 32

Figure 3.1. Map of Sample Area in Herat City, Afghanistan ............................................ 42

Figure 4.1. Trust and Networking - Dried Fruits and Nuts Cluster .................................. 59

Figure 4.2. Cooperation and Collective Action - Dried Fruit and Nuts Cluster ................ 61

Figure 4.3. Benefits of Belonging to a Cluster- Dried Fruit and Nuts Cluster .................. 61

Figure 4.4. Trust and Networking- Tailoring Cluster ........................................................ 65

Figure 4.5. Cooperation and Collective Action - Tailoring Cluster .................................. 66

Figure 4.6. Benefits of Belonging to a Cluster - Tailoring Cluster ................................... 67

Figure 4.7. Trust and Networking - Carpenter Cluster ...................................................... 71

Figure 4.8. Cooperation and Collective Action - Carpenter Cluster ................................. 72

Figure 4.9. Benefits of Belonging to a Cluster - Carpenter Cluster .................................. 73

Figure 4.10. Trust and Networking - Shoemaker Cluster .................................................. 76

Figure 4.11. Cooperation and Collective Action - Shoemaker Cluster ............................. 78

Figure 4.12. Benefits of Belonging to a Cluster - Shoemaker Cluster .............................. 78

Figure 4.13. Trust and Networking - Ironmonger Cluster ................................................. 82

Figure 4.14. Cooperation and Collective Action - Ironmonger Cluster ............................ 83

Figure 4.15. Benefits of Belonging to a Cluster - Ironmonger .......................................... 84

Figure 4.16. Trust and Networking - Tinwork Cluster ...................................................... 88

Figure 4.17. Cooperation and Collective Action - Tinwork Cluster ................................. 89

Figure 4.18. Benefits of Belonging to a Cluster - Tinwork Cluster .................................. 90

xiii

Figure 6.1. Path Diagram for Impact of Social Capital on MSEs’ Performance and Factor

Conditions ........................................................................................................................ 122

Figure 6.2. Path Diagram for Impact of Social Capital on MSEs’ Related, Supporting

Industries and Performance ............................................................................................ 125

Figure 6.3. Path Diagram for Impact of Social Capital on MSEs’ Demand Conditions and

Performance ..................................................................................................................... 129

Figure 6.4. Path Diagram for Impact of Social Capital on MSEs’ Characteristics and

Performance ..................................................................................................................... 131

Figure 6.5. Path Diagram for Impact of Social Capital on MSEs’ Strategy, Structure,

Rivalry, and Performance ................................................................................................ 134

Figure 6.6. Path Diagram for Impact of Social Capital on The Role of Government

policies and MSEs’ performances ................................................................................... 137

Figure 6.7. Path Diagram for Impact of Social Capital on The Role of Chance and MSEs’

Performance ..................................................................................................................... 141

Figure 6.8. Summary of Analysis and Test of Hypothesis .............................................. 143

xiv

ABBREVIATIONS

AISA Afghanistan Investment Support Agency

AMOS Analysis of Moment Structures

ANDS Afghanistan National Development Strategy

AREU Afghanistan Research and Evaluation Unit

CBN Cost of Basic Needs

CFI Comparative-Fit index

CMIN/DF Minimum Discrepancy/Degrees of Freedom

CSO Central Statistics Organization

DFID U.K. Department for International Development

EIU Economist Intelligence Unit

GDP Gross National Product

GFI Goodness-of-Fit-index

GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit

GMRA General Multiple Regression Analysis

GNP Gross National Product

JICA Japan International Cooperation Agency

MDGs Millennium Development Goals

MOCI Ministry of Commerce and Industry

MRRD Ministry of Rural Rehabilitation and Development

MSEs Micro and Small Enterprises

NATO The North Atlantic Treaty Organization

xv

NRVA National Risk and Vulnerability Assessment

NSS National Surveillance System

PRSP Poverty Reduction Strategy Paper

RMSEA Root Mean Square Error of Approximation

SMEs Small and Medium Enterprises

SPSS Statistical Package for Social Science

UNIDO United Nations Industrial Development Organization

USAID U.S. Agency for International Development

WB World Bank

1

1. CHAPTER I

INTRODUCTION

Introduction

The destructive effects of more than three decades of wars on the infrastructure and

financial system of Afghanistan are very obvious. Another detrimental effect of these

conflicts is the deprivation of knowledge needed for sustainable economic development

of the Afghan people.

After 2001, the United States, its NATO allies, and other partners in Afghanistan

have been struggling to lay the foundation of a long-lasting stability in this country;

however, the outcome is not so promising in spite of their continuous efforts. The

outcomes of these efforts reveal that the private sector has often been overlooked in the

policy discourse (Cusack and Malmstrom 2011), despite that people still hope for

potential positive changes through their own efforts.

One of the most ignored areas within the private sector is the role of traditional

industrial and economic clusters of micro- and small-scale enterprises (MSEs). These

enterprises include those engaged in selling dried fruits and nuts, tailors, carpenters,

shoemakers, ironmongers and tinwork.

2

There are two conflicting views about the contribution of MSEs to the economic

development of a country. One view, maintained by Biggs, Grindle, and Snodgrass (1988)

in a study from a sample of different countries suggests that the contribution of small

firms to the economy of countries included in their study is not significant. Other scholars

such as Pyke and Sengenberger (1992) have emphasized that MSEs are in fact capable of

playing an important role in the process of development.

The micro, small, and medium enterprises are prevalent in both developed and

developing countries. For instance, the percentage of small and medium enterprises in the

total number of enterprises in developed countries such as Japan and Germany is very

high, more than 99%, and their contribution to the total employment and to the value

added in 2007 accounted for over 60% and 53% respectively (EIU 2010). On the other

hand, the contribution of small and medium enterprises to the employment in developing

countries varies from less than 5% in countries such as Azerbaijan, Belarus and Ukraine

to 80% in Chile, Greece, and Thailand (Meghana, Thorsten, and Asli 2003). In the case

of Afghanistan, for a number of reasons and despite facing continuous economic and

political instability, some of these traditional clusters of MSEs have survived, provided

livelihoods for the people and thereby contributed to the country’s economy. For instance,

some of the enterprises included in our study reported that they have been operating

continuously for more than 100 to 300 years.1

Since these clusters of MSEs play an important role in providing livelihood for the

people and contributing to the economic development of several countries, the public

1 Based on the data collected through author’s interviews with some of MSEs’ owners during fieldwork

from January 5 to February 25, 2014.

3

sector needs to intervene through policy initiatives to create a supportive environment for

the survival and development of these clustered MSEs. However, according to the

author’s interviews with policy-makers in Kabul, traditional clusters of MSEs in

Afghanistan have often been neglected in the policy and decision-making process. Studies

suggest that creating MSEs’ clusters and supporting them in increasing their access to

skills, training, and information on markets, networks, and infrastructures would help

these small enterprises to overcome production and marketing obstacles and bolster them

to compete with other big enterprise in the competitive globalized market (Puppim de

Oliveira 2008).

Since the consequences of prolonged conflicts were catastrophic, the situation in

Afghanistan incited the Afghan government and the international community to take

action and reconstruct the country. They committed themselves to achieve the indicated

goals of the Afghanistan National Development Strategy Plan (ANDS) especially the

reconstruction of the infrastructure and the recovery of the economy of Afghanistan.

To accomplish the developmental goals in a proper way, it requires implementing

various programs that would mainly focus on the empowerment of Afghan citizens’

economy, particularly that of the private sector, so that to achieve a better living standard

and more productive economic activities. In short, to find a solution for the problems of

unemployment, low productivity, insecurity, poverty and other major socioeconomic

challenges that exist in the Afghan community. In recent years, the efforts of the

government of Afghanistan and the international community have been focused on the

economic development agenda and fundamental regulatory reforms across different

4

sectors in the country. These efforts mainly pinpoint on the development of the private

sector.

Both in developing and developed countries, the development of private sector and

entrepreneurial activities, have widely been acknowledged for their contribution to the

creation of job opportunities and economic growth.

Statement of the Problems

The very prolonged war in Afghanistan has created many social, political and

economic challenges such as poverty, inequality, political instability and extensive

insecurity. These protracted conflicts, natural disasters, and political instability, have

created many problems that have inflicted unbearable suffering on Afghans. Nonetheless,

the people of Afghanistan have endured these agonies and have survived. They have

overcome these challenges primarily through their own efforts, using their own resources,

such as subsistence farming, wage labour migration, strategic family alliances and

negotiation with armed forces. Furthermore, they have mostly relied on their own

livelihood systems during disasters, conflicts, and crises.

Challenges that the private sector, particularly the traditional clusters of MSEs face in

Afghanistan can be summarized in the following order.

Poor infrastructure seems to be very common and very serious among developing

countries, however, in Afghanistan, these constraints can be more crucial compared to

other developing countries. In the area of traditional industries, much effort has not been

made during the last 15 years to tackle the issues of investment in vital areas such as

electricity, warehouse facilities, establishment of coordinating body for enhancing the

5

interactions between these clusters and related organizations and industries, and creating

framework for legal protection of enterprises. Lack of government intervention and

constraints regarding tenure and land ownership for business activities of enterprises in

the traditional cluster in Herat City and other regions of Afghanistan is yet another serious

challenge. Financial problems and access to credit sources is another challenge that

seems to be one of the main constraints for the increase of the performance, upgrading of

productivity and competitiveness of enterprises in the traditional clusters in Afghanistan.

The lack of effective strategy plans for the development of enterprises seems to

be another challenge for the industry sector, especially for the traditional clusters of MSEs

that are more vulnerable to the negative competitive challenges in the domestic and

international markets than their competitors in modern industry. In addition, the lack of

knowledge base or insufficient information in the areas of the enterprises development

and particularly the absence of studies on traditional clusters, seem to be another problem

that shows the gap between existing knowledge in context of policy discourse and the

uncertain challenges in the traditional clusters that need to be addressed through a

comprehensive policy intervention with consideration of needs of private sector in these

tradition clusters in Herat City as well as other regions of Afghanistan.

The total population of Afghanistan was estimated to be around 28.1 million (CSO

2015), of which almost 71.5% live in rural areas, 23.1% live in urban areas and 5.4% of

them are nomadic people. The regional and seasonal differences, as well as the lack of

infrastructure and low productivity profile among entrepreneurs in Afghanistan, are

important aspects of poverty. In addition, traditional industries in Afghanistan in general

and in Herat City in particular mostly depend on the livelihoods of other sectors that have

similar low levels of productively. The nationwide Cost of Basic Needs (CBN) estimate

6

of poverty reveals that 39.1% of the Afghan population are not able to meet its basic needs

(CSO 2016, p. 110).

The issue of obtaining loans is one of the major challenges for micro and small

enterprises because they often cannot meet the criteria for obtaining loans from the

modern banks for the reason of their inability of providing collateral or paying interest

due to the Islamic financial restrictions.

Another major challenge that these traditional industrial clusters face is the lack of

a capacity building framework. The government also lacks policies for improving the

level of competitiveness and boosting productivity of these clusters.

Research Objectives

The aims of this study were to assess the impact of social capital on the

performance of MSEs through the other determinants in the Porter’s Diamond Model in

the traditional clusters in Herat City, Afghanistan. Here, the MSEs’ performance is

conceptualized in terms of firms’ daily sales revenues. Therefore, in order to examine and

to address the above issues of the performance of MSEs in traditional clusters in Herat

City, we set the following specific objectives:

To determine the impact of social capital and other factors on the performance of

MSEs within Porter’s Diamond framework in the traditional clusters in Herat

City.

To examine the direct and indirect impacts of social capital on the performance of

MSEs through other dimensions in the Porter’s Diamond Model.

To construct causal models for each of the dimensions within the conceptual

framework in this study.

7

To identify the significant association between MSEs’ performances, social

capital and other dimensions in the Porter’s Diamond Model.

To explain the characteristic of entrepreneurial practices in traditional clusters of

MSEs in Herat City.

Furthermore, the study seeks to provide policy-makers with recommendations to

enhance the performance and productivity of the traditional clusters of micro and small

scale enterprises in Herat City and possibly in other regions of Afghanistan.

Research Questions

In order to analyze and examine the impacts of determinant factors such social

capital and other dimensions of Porter’s Diamond on the performance of MSEs in

traditional clusters in Afghanistan, a set of questions were formulated for this study. The

principal research question in this study is:

Does the social capital dimension of enterprises have any impact on MSEs’

performances based on Porter’s Diamond framework in traditional clusters in Herat

City?

In addition to that, the following sub-questions are also designed:

Does social capital determine the performance of MSEs in the traditional cluster

of Herat City?

How much social capital and which factors in other dimensions of Porter’s

Diamond can determine the performance of the enterprises in traditional clusters

in Herat City?

8

What types of casual relationship exist among the performance of MSEs, social

capital and other dimensions in the conceptual framework of this study?

What types of association does exist among the performance of MSEs social capital

and other dimensions in the conceptual framework of this study?

Significance of the Study

The studies show that the contribution of micro- and small-scale enterprises to the

economy of many developing and developed countries counts for a significant proportion

of share in the Gross Domestic Products (GDP) and employment. This also implies in

Afghanistan where large number economic activities are based on the contribution of

MSEs. However, on one hand, the significance of the contribution of these micro and

small enterprises were often excluded from the attention of policy discourses in this

country. On the other hand, the lack of adequate knowledge about the economic activities

regarding the traditional cluster of enterprises was another reason for the lack of attention

to this specific sector in Afghanistan. Therefore, this study is an attempt to fill the gap

between these challenges and to address the issues related to entrepreneurial activities in

clusters of micro and small enterprises in Herat City. This study aims to explore the nature

of collaboration and competition among the enterprises and to address the challenges they

have been facing within the traditional clusters in Herat City, Afghanistan.

Organization of the Study

This study is structured as follows. The first chapter comprises the introduction,

research problems, research objectives, questions and a general overview on Afghanistan.

Chapter 2 is composed of the review of literature on the cluster approaches, the

9

performance of MSEs and entrepreneurship development, followed by an explanation of

the conceptual framework based on the literature review. Chapter 3 contains research

methodology including the sampling method and the method for the analysis of the

collected data. Chapter 4 provides a review of the enterprise development experience and

indicators of economic progress in Afghanistan, followed by the description of

preliminary results of the interview survey of the sampled clusters of enterprises in Herat

City. Chapter 5 comprises the results of the structural correlation analysis for each of the

dimensions in Porter’s Diamond Model. Finally, Chapter 6 consists of the efforts of

identification of the factors that are supposed to have significant impacts on MSEs’

performances, the testing of the hypothesis, and the construction of a causal model in each

dimension of the conceptual framework of this study. The last section of this chapter

provides discussion, conclusion, and policy recommendations combined with suggestions

for future studies.

10

2. CHAPTER II

LITERATURE REVIEW

Introduction

In the scholarly literature, the concept of a cluster is defined as the economic and

geographic concentrations of interconnected people or firms to create collaboration and

competition (Porter 2000). In a narrower definition with the emphasis on growth

processes of small firms, Schmitz (1995) describes a cluster as a sectoral and geographical

concentration of small firms. He argues that such clustering provides efficiency gains

which individual small firms can rarely attain. Clustered firms benefit from proximity and

geographic concentration through collective efficiency, defined as the competitive

advantage derived from external economies and joint action. In addition, clusters are

thought to affect competition in at least three ways: first, by increasing the productivity

of firms within a cluster; second, by providing an environment for innovation and future

productivity growth; and third, by stimulating the formation of new firms in the cluster

itself (Porter 1998).

The present mainstream scholarship of cluster studies emerged since the 1990s, and

major works have focused more on the role of cluster dynamics for the development of

industrial policies in both developed and developing countries. To some extent, a number

of those studies provide insight into the significant role of clustered firms and their

contribution to increasing industries’ competitiveness (Porter 1990). Other studies like

11

the one conducted by Nadvi and Barrientos (2004) provide us with intensive critical

knowledge of cluster development initiatives as a capable means for the alleviation of

poverty.

The aims of this study are to examine the impact of MSEs’ capital factors and

characteristics on its growth and competitiveness using Porter’s Diamond Model as a

framework. This chapter covers the literature review on the topics related to theories of

industrial development and the implementation of cluster initiatives with an attempt to

explain the causal relationship between social capital, Porter’s Diamond Model, and firm

performances.

In the literature related to business networks, social bonds have been identified as a

dimension of buyer-seller relationship, however, few studies have actually focused on

this issue. In the context of traditional clusters of firms, this interaction and exchange of

information can be vital for enhancing the performance of a firm and increasing the level

of its competitiveness. The characteristics of such interactions can vary from one cluster

to another. Stam, Arzlanian, and Elfring (2014) argue that the social capital-performance

link depends on the age of small firms, the industry, and institutional contexts in which

they operate, and on the specific network or performance measures that are in use. In

addition, the importance and the role of proximate interactions among the firms in clusters

and industrial districts have been studied by numerous scholars such as Saxenian (1996)

and Pyke, Becattini, and Sengenberger (1990), as well as in the case study of furniture

makers in Mississippi and apparel makers in Northern Italy by Rosenfeld (Rosenfeld

1997).

Industrial Agglomeration and Economic Development

12

A large bulk of literature emphasizes on the existence of a relationship between

industrial agglomeration and the economic development both in the developing and

developed countries. However, in most of this group of literature, the concept of industrial

agglomeration and economic progress has been related to the cases of developed

economies of the western countries. On the other hand, East Asian economies also had

experience of agglomeration and economic development similar to those or Western

countries. Despite the fact that the industrial agglomeration and cluster approaches played

a significant role in economic performance in both East Asian and Western countries,

however, studies show that there are some factors which are unique in the case of East

Asian economies.

The experience of agglomeration and fast economic growth rate in East Asian

countries indicate that there are other factors such as the role of government policy

interventions in the economy through the implementation of constant industrial plans. In

addition, the experiences of industrialization process in the case of Asian economies

shows that they also benefited from other factors such as regional development as well as

socio-economic and cultural factor, such as the existence of formal and informal

organizations. This indicates that the industrialization in most countries was interpreted

primarily in terms of economic theories and experiences which originated earlier from

the Western societies.

Moreover, the recent experiences of industrial development in East Asian countries such

as China show that for many years industrial development was based on central planning.

However, recently, a transition took place in China which created complications like

socioeconomic and cultural differences among the population. When those economic

13

theories are to be applied in further stages of industrialization in countries with conditions

similar to China, these complications should be considered in the development plan.

In their study, Fan and Scott (2003) found that there was a positive association between

economic performance and the industrial clustering in some of these Asian economies.

However, this positive association was more common among industrial clusters that were

more deeply influenced by a proper policy reform that was often adopted with a market-

oriented approach.

Cluster Initiative and Its Contribution to the Industrial and Economic

Development in Developing and Transition Economies

Recently, the cluster initiative in industrial development, mainly in developing

countries, have attracted a high level of attention from the public sector such as policy-

makers for their planning of economic development in these countries. These types of

industrial policy can provide opportunities for the promotion of small and medium

enterprise (SMEs) in these countries. Moreover, the concept of cluster initiative seems to

be very important in the context of industrial policies in developing countries. Further,

clusters have also the potential to assist enterprises, especially the small size firms, to

cope with their constraints related to size, low productivity, technological upgrading, and

adapting to competitive environment of domestic and international markets.

Parto (2008) suggests that co-locating of firms in proximity with other suppliers

and supporting institutions in a cluster often leads to a higher level of coordination and

increases the trust among firms. He argues that a successful firm can be found where it

makes economic sense, provided the knowledge about its products or services, the labour

pool, and other input materials in the market. On the other hand, such coordination and

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14

collaboration among firms is informal and depends on the quality of interaction among

stakeholders as a means of information exchange among personnel from different

enterprises. Arndt and Sternberg (2000) suggest that, despite numerous network

relationships on the national and international levels, small enterprises are most likely to

cooperate with others in their vicinity.

A study conducted by UNIDO (2004) indicates that geographical concentration of

enterprises can enhance the local collaboration and cooperation such as joint action

between enterprises and other local institutions. Schmitz (1995) indicates, that

concentration of enterprises in a geographical proximity can provide collective efficiency

to the enterprises within a cluster and benefit these enterprises to distinguish between the

passively acquired advantages as outcome of specialized concentration of enterprises

such as skills, knowledge and inputs, and the generated gain from the joint action among

all actors in the clusters.

Clusters tend to evolve on the basis of geographical concentrations of economic

and interrelated sectors along the value chain. Developing over time, they boost

competition and collaboration, resulting in innovation and the ability to create greater

economic success through higher productivity, better knowledge exchange and

management, and entrepreneurial opportunities. Clusters seem to have the tendency to

generate both higher incomes and higher rate of employment growth (Chuluunbaatar et

al. 2014, Campbell-Kelly et al. 2010). Besides, clusters in developing countries provide

livelihood and job opportunities, while policy intervention for enhancement of their

performance may result in an exit of other vulnerable enterprises from the market. To


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15

minimize or avoid this, a better understanding of the dynamic of the linkage among

clustered firms and their relations with external linkages is required.

It is important to consider cluster initiative in industrial development policy in

transitional and developing countries. Precisely speaking, because of the influence of

globalization on the structure of world economies, value chain in the world no more

depends only on local factors, instead, it depends on the relationship between local

enterprises and their global buyers in most of the transitional economies (UNIDO 2004).

Thus, it can be stated that industrial development through the cluster initiative can have

positive influence on the efforts of countries regarding poverty reduction, through

enhancing the creation of constant and sustainable job opportunities and incomes to

improve the livelihood of destitute citizens in developing and transitional economies.

Nadvi and Barrientos (2004) state that for effective studies of cluster initiatives, a

number of features and processes need to be considered. Those features are the

geographical location of the cluster; the type of industry in the cluster; the type of the

employment that cluster generates; the processes that are affected by the nature of links

to external economies (skills, markets, knowledge, and information); joint or collective

capabilities; and social capital.

Parrilli (2007) indicates that major challenges that small and medium enterprises

in transitional and developing economies are facing originate from the difficulties in

accessing production input materials, technology, finance, human and social capital, and

the lack of supportive policies. These challenges have emerged rapidly because of

globalization and market liberalization, jeopardizing the present and future of vulnerable

enterprises in developing countries. However, Parrilli suggests that, based on an analysis


16

of clusters capacity for survival, some form of policy intervention, together with careful

consideration of different dimensions of economic, governance-related, and social

linkages, could provide to some extent the clusters with opportunities to persist and to

survive in challenging environments.

The Application of Porter’s Diamond Model in Industrial Development

Through the Cluster Initiative

Porter (2008) asserts that the concept of clusters in any form needs to be recognized

and explored within a broader theory of competition and with the consideration of the

influence of the location in the global economy. In addition, the concept of the cluster

represents a new approach to thinking about the economic and industrial competitiveness

of a country at different levels (local, regional, and national economies), and refers to a

new role for firms, government, and other organizations that strive to enhance their

competitiveness and economic performances. One of the most cited models in assessing

the performance of clusters and their competitiveness within an economy is Porter’s

Diamond Model that was introduced by Michael E. Porter in one his famous work “The

Competitive Advantage of Nations” in 1990. In his work, he provides a complete

analytical framework (see Figure 2.1) for assessing the competitiveness of a nation’s

industries and the performances of the industrial sector at the firm level.

17

Porter’s Diamond Model can cover a range of actors (government, firms, related

organizations) that are involved in the interaction within the industrial clusters. In

addition, in comparison to other existing models, Porter’s Diamond Model has more

applicability in assessing the industry sectors both in developing and developed

economies. Thus, in this study, we use Porter’s Diamond Model in the presence of social

capital at firms’ level to assess the competitiveness of traditional clusters in Herat City.

Therefore, in this section we provide a brief introduction for each of the dimensions

within Porter’s Diamond.

Firm strategy, structure

and rivalry

Demand conditions

Related and

supporting industries

Factor conditions

Chance

Government

Figure 2.1: Michael. E. Porter’s Diamond Model

Source: M E. Porter (1990, p.127)

18

2.4.1. The Dimensions of the Porter’s Diamond

2.4.1.1. Factor Conditions

The factor condition within Porter’s Diamond refers to the production factors in its

classical term. This concept is also based on the availability of natural resources, capital,

land, labour, and infrastructure. In addition, in this model, Porter’s definition of

production factors stands for the perspective of trade theories, where the firms’

comparative advantage and its performance in the market are highly dependent on the

availability or the lack of those production factors. In other words, he divided the factors

into two categories (basic factors and advanced factors). According to this model, the

quality of these factors is different from one industry to another. Firms in a country can

gain a high level of competitive advantage and perform well if they can access low cost

or very high-quality factors of the specific types that are very important for their

competition in the market. In general terms, he also categorized the factors into these five

types: human resources; physical resources; knowledge resources; capital resources; and

infrastructure.

The definition of factor condition can be applied to these industries in Afghanistan

that highly depend on their raw materials which are produced domestically, have a

relatively higher comparative advantage and can compete in local, regional and

international markets.

2.4.1.2. Demand Conditions

The second dimension within Porter’s Diamond Model is the demand conditions

which cover the demand side of firms’ competitive advantages. Porter (1990) emphasizes

19

the sophistication and the nature of local consumers as a pushing force for firms for

constant upgrading, adapting to the newer technology, and anticipating the desire of

consumers for their products in the international markets. On the other hand, Porter’s

model considers the influence of the home demand on competitive advantage to be most

important. This indicates that the structure of home demand shapes how firms interpret,

consider, and respond to buyers’ needs. The influence of the needs of home buyer for the

quality of goods is important for the competitive advantage of the industries of a country.

It often seems that the structure of home demand is less significant in the process of

globalization and competition, whereas the fact is that firms that are more able to

perceive, interpret, and act upon buyers’ needs in their home market are presumed to be

more confident and successful in the international markets. The case of the experience of

some Japanese companies in competing in the world arena is a good example.

2.4.1.3. Related and Supporting Industries

The role of domestic suppliers that are internationally competitive is also another

factor that can determine and reinforce the emergence of more competitive firms in the

domestic and international markets. Therefore, the location of firms within a cluster, its

proximity to sophisticated buyers, and the presence of competitive suppliers can often

lead to the success of firms in both domestic and world markets. Furthermore, the

availability of other supporting industries is also an important factor for the enhancement

of firms’ competitiveness and their performances in the markets. The existence of well-

equipped infrastructure such as trading logistics, financial institutions, a legal framework

for supporting the designated industry can contribute to the development of industry,

particularly, the performances of firms in markets. In addition, the presence of such

20

supporting and related industries in a cluster can create more advantages to other firms

through achieving more efficiency, access to the most cost-effective materials, tools and

machinery. Porter (1990) argues that the domestic suppliers that have achieved world

class standards can positively collaborate to increase firms’ competitiveness and

performances even if they do not compete internationally.

2.4.1.4. Firm Strategy, Structure and Rivalry

The fourth dimension within the Porter’s Diamond Model is composed of the

strategy on which the firms are established, managed, organized; and the environment of

domestic rivalry in the market. However, the vision, strategies, and methods for managing

the firms vary from nation to nation. The nature of rivalry among firms plays a significant

role in shaping strategies of firms and contributes remarkably to the innovation of these

firms and their methods of organizing in a challenging environment in markets.

In addition, Porter argues that the ability of firms to compete internationally is partially

the outcome of the performances, nature and characteristics of rivals in the domestic and

international markets However, he still considers the domestic rivals to be superior in

importance than the ones in the international markets. He considers the presence of very

strong local rivals to be the most powerful stimulus and motivating power for successful

competition in domestic and international markets.

2.4.1.5. The Roles of Chance and Government

In addition to other four dimensions in Diamond Model, Porter includes two

more dimensions (The role of chance and government policies) in this model. Porter

21

(1990) indicates that, besides the aforesaid four dimensions, the two dimensions, i.e., the

role of chance and government policies, also play a significant role in shaping the

environment for firms to compete and perform in specific industries. He argues that the

influence of chance is often beyond the control of firms and the performance and strategy

of firms cannot determine the chance events such as political decisions by foreign

governments, wars, technological changes and changes in financial markets and supply

of raw materials. In addition, the chance events, in some cases, can create a situation for

firms of a country that will force them to adopt at the early stages of completion and deal

with such new environmental changes in the markets.

The role of government policies is more effective in the early stages of industrial

and economic development, especially in the case of industrial progress in developing

countries where the government has a very strong influence on the protection and

empowerment of emerging firms with potential of competing in the international markets.

In addition, the government can also influence other dimensions of this model. Porter

(1990) emphasize that one of the most important roles that governments can play is the

creation of factors of production such as qualified human resources, financial and

physical infrastructures, and facilitating technological advancement.

2.4.2. The Dynamics and The Critics of Porter’s Model

Porter’s works on firms’ competitiveness also confirm the necessity to ensure the

interdependency, reinforcing functions of elements of his diamond model. However, the

main conclusion that can be drawn from Porter’s research and other empirical evidence

is the fact that geographic concentration and domestic rivalry have stronger potential to

transform this model into a functioning system. Therefore, the systematic nature of the

Porter’s Diamond Model leads to the emergence of clusters which can reinforce and

22

synergize the system both horizontally (customers and distribution chain) and vertically

(buyer-seller) and bring about a synergy in the relationship of the horizontal and vertical

elements. Thus, the cluster development approaches are a new element in the mechanism

of the diamond model, through generating a jointly supportive association of industries

that establishes a specific and hard-to-change source of constant national and

international competitive advantage for both developing and developed countries.

In addition to “Diamond” model, Porter (1990) has developed a process of

national competitive development through dividing the nations’ efforts for achieving the

industrial development into four stages: factor driven, investment driven, innovation

driven, and wealth driven. According to his definition, Afghanistan can be placed in the

first category of economic development of factor-driven stage. This means that

Afghanistan at this stage can focus on industries that are more factor driven and have the

potential to compete in domestic and international markets. Therefore, this study focuses

on the performance of industries within the traditional clusters that can fit in the

characteristic of Afghanistan’s process of economic development (factor driven). Porter

(1990) suggests that every nations’ industries have their own unique characteristic of

advantage or disadvantage that need to be considered in the national strategy for industrial

development in order to be able to compete in national and international markets.

Besides being very popular among scholars in management and economics

since 1990, the success of Porter’s Diamond Model has also been criticized whether it

can be used as a proper model to be used for assessing the competitiveness of a country’s

industries. The first criticism on his model was about the composition of countries

included in his work; and his anticipation about the future of countries such as South

23

Korea and Singapore, the experiences of both countries turned to be different from his

results of analysis. The second criticism is that this model is mostly based on firms’

comparative advantage in its home country and there was limit information on these

advantages in other countries. This resulted in reconsideration of his model by work from

other scholars such as Rugman (1992) and later Rugman and D'cruz (1993) who expanded

his model to a double-diamond model. Another criticism on Porter’s Diamond Model was

that it was not applicable to some very small open economies (Rugman and D'cruz 1993).

Therefore, based on the characteristic of Porter’s Diamond Model explained

earlier in this chapter, in this study we embrace the basic framework of Porter’s Diamond

to assess the performances of firms and its possible competitive advantages in traditional

clusters. The reason for this selection is because traditional clusters in Afghanistan are at

their very early stages of development (factor driven) and most of them still do not

perform in the international market, so it cannot be considered within a double-diamond

model as developed by other scholars. In this study, the Diamond Model is used as a

framework to analysis the performances of firms at the firm level rather than macro level.

In addition, since this study focuses on micro-level analysis from the firms’ perspective,

and the interactions between all dimensions of Porter’s Diamond were not assessed in

previous literature to figure out how social capital can contribute to the firms’

performances and facilitate these interactions among each of the dimensions in this

model. Therefore, in this study, we attempt to assess the contribution of additional

dimensions of social capital and the explanatory dimension of firms’ characteristics on

the performances firms.

24

The Role of Social Capital on Firms’ Performance within Porter’s Diamond

Model

“We of the West have all the rudiments of civilization, all the dividends of a

mounting standard of living. But the Afghans—one thousand years behind us

in many respects—have a warmth of human relations that is often missing all

the way from New York City to San Francisco” (Douglas, 1952).

The dimension of social capital in this study refers to the extent to which the

entrepreneurs in traditional clusters feel they can rely on relatives, neighbours, colleagues,

acquaintances, actors within a cluster, and strangers, either to assist them or receive

assistance from them. In addition, sufficiently defining “trust” and “network” in a given

social context function as a precondition for exploring and understanding the

complexities of entrepreneurs’ relationships in traditional cluster of enterprises. However,

sometimes trust is a choice; in other cases, it reflects a necessary dependency based on

the established contacts or familiar networks (Dudwick et al. 2006, p.16). Nair and Salleh

(2015) argue that at organizational level, it is well recognized that trust can play a catalyst

role in facilitating the relationship among organizations, however at individual level little

has been discovered on how trust can influence the performance of an organization. In

the context of clusters of enterprises, it well recognized that social capital provides the

glue that can facilitate cooperation, exchange of information, resources, and lead to

innovation (OECD 2001).

The concept social capital and its components (trust, network, and cooperation)

for a long time have been in the center of scholars’ discussions on how social capital can

be defined. Putnam (2000) refers to social capital as characteristic of a society, as an

indicator of the network’s quality, relationships, and connections that enable individuals

25

to cooperate and act as a collective. This definition of Putnam considers social capital the

result of a high degree of mutual trust and the trustworthiness of public institutions and

the rule of law, facilitating the creation and safety of exchanges.

Bourdieu (1986) defines social capital as mostly the cognition and the

characteristic of the individual, that achieves self-interest goals through the mobilization

of available networks and connections. In this context social capital represents the private

good, in which individuals mobilize to achieve their own personal goals.

In addition, in this study, social capital is basically defined as the “norms and

social relations, which are included in the social structures, and can facilitate and enable

people to coordinate in joint actions to achieve their desired goals” (Narayan-Parker 1999,

234-256). Narayan and Pritchett argue that the rise of the social capital, on one hand, can

improve the quality of government’s functions; on the other hand, increase in social

capital can lead to an increase in the community cooperative action and, as a result, it can

facilitate solving common problems in the community.

Many scholars argue that the level and characteristic of social capital can be

defined based on a community and individual’s behavior (Bourdieu 1986, Fukuyama

2001, Halpern 2005, Fukuyama 1999). This indicates that the attribute of social capital

varies from society to society. Especially, when other social factors (religious values) are

involving and influencing the quality and quantity of social capital in a society. Therefore,

the presence of religious values can positively or negatively determine the nature of social

capital and the interactions among individuals or communities. The relation between

religious values and social capital has been well documented by many studies, yet, there

is little known about the role of religions such as Islam in entrepreneurial activities. On

26

the other hand, the studies found that the status of social entrepreneurship growth in

Islamic societies like Indonesia seems to be depend on factors such as perceived degree

of economic empowerment, Islamic identity, and the level of social activism (Idris and

Hijrah Hati 2013).

In addition, the Islamic conception of economic development highly emphasizes

the need to focus the human’s energy on achieving social solidarity and unity. On the

other hand, from a social and economics perspective, Islam aims to utilize cooperation

and competition in achieving and structuring the ideal but somehow useful forces at every

level of social organization. This is often emphasized by the Quran and throughout the

traditions of the Prophet Mohammad (sawa) 2 that competition and cooperation always

must be utilized in probity and piety rather than evil and enmity (Q5:2). In a more social

perspective, the communal spirit also applies in worship. In which, Islam encourages

congregational prayers that are considered far better than prayers performed individually

(Mirakhor, Ng, and Ibrahim 2015, p.32).

The people’s manifestation is in social interaction. Every action-decision, no

matter how significant or apparently mundane, becomes an act of worship and is

sanctified so long as it is done while fully conscious of the Creator and initiated in His

Name. This is particularly true in economic interactions. Since every human has a dual

nature of matter and sprit, the society must be cognizant of these two dimensions of

human nature; neither can be neglected if the society is to progress and develop.

Therefore, the fundamental objective is to create a society in which the individual

becomes cognizant of all of their capabilities, including spiritual issues (Mirakhor, Ng,

2 S.A.W. Salallahu Alaihi Wa Salam. (Peace be upon him)

27

and Ibrahim 2015).

Besides cooperation and solidarity, trust is another important component of social

capital which by some scholar itself as social capital. In all transactions, trust is an

important component of social capital that permits voluntary participation in production

and exchange and a proper functioning of the market and social solidarity. Trust is an

important part of economic grown as also described by (Arrow 1971). In his work, Arrow

argues that every transaction within itself has an element of trust. According to him, it

can be plausibly argued that much of the economic backwardness in the world can be

explained by the lack of mutual confidence.

Trust in the context of Islamic values is also emphasized in different ways. The

Muslim believers are often reminded not to break a covenant or a peace treaty between

them and even their enemies, in order to be trusted on the promises a believer makes. For

instance, The Prophet Mohammad (sawa) was once asked: “Who is a believer? He

replied: “A believer is a person to whom people can trust their person and possession.”3

The in-depth mixture of Islamic values when emphasizing the importance of

solidarity, cooperation, and trust among each other can synergize the capability of

Muslims to enhance their capabilities and tackle the challenges in many aspects of their

life through collaboration and collective action. Thus, it is important to note that to utilize

and enhance the quality of social capital with religious values, it is required that Muslim

communities integrate and improve these two factors in a similar proportion to achieve

proper outcomes in social and economical life.

3 Hadith (Saying of Prophet Mohammed) Tirmidi: 2570, Ahmad: 8731

28

In the context of traditional clusters, greater levels of social cohesion usually mean

that social capital of members in a cluster allows for greater synergies in industrial

clustering (Fisher and Reuber 2000). The contribution of social capital to the

performances of firms was recognized in numerous studies. The studies by Maskell

(2000) and Huber (2009) found that the dominant view is that “social capital” can enable

enterprises to enhance the level of their innovation capability and benefit from this

transaction and social relationship through having access to sufficient information and

achieve a higher level of economic performance. In addition, Cooke, Clifton, and Oleaga

(2005) found that there was a significant correlation between social capital and firms’

competitiveness. However, their finding reveals that this link between social capital and

competitiveness was weak, but at the firm level, findings show that social capital strongly

impacts innovation within the clustered firms.

The significance of social capital within the traditional economies seems to be

traceable from the early medieval era in Italy to contemporary era which can explain why

some communities are more capable than others to manage and deal with collective life

and improve or sustain the effectiveness of their institutions in Italy (Putnam, Leonardi,

and Nanetti 1993, p. 121). In addition, the presence of higher trust and social capital at

individual or community level is found to be significantly associated with self-

employment in comparison to individual or communities with a low level of social capital

(Kwon, Heflin, and Ruef 2013).

Research examining the connection between social capital and the outcome of

public programs goes back to the works of established scholars such as the influential

writings of Pierre Bourdieu (Bourdieu 1986) and those of James S. Coleman who

29

affirmed that social capital could serve to meet certain public policy goals such as the

improvement of performance in the educational sector (Coleman 1988). On the other

hand, studies in developed countries also suggest that a strong positive association exists

between educational outcomes and social capital measures (Putnam 2000). Coleman

(1988) introduced the concept of social capital as a parallel to the concepts of other forms

of capital such as physical capital, financial capital, and human capital. In his study,

Coleman provided evidence of the effect of social capital on the formation of human

capital in the family and community.

From the perspective of the effect of social and human capital on firm

performances, some researchers have examined components of social capital with regard

to educational outcomes, and suggest that trust and voluntary action at the individual level

improve the students’ performances, while it has a diverse effect on parental networks

depending on income levels (John 2005). Wang and Chang (2005) have found that the

components of intellectual capital directly affect the quality of business performances,

with the exception of human capital elements. However, this study argues that human

capital has an indirect effect through other types of capital, namely customer capital and

innovation capital.

Even though some studies have found that the causal relationship between social capital

and human capital, as well as the relationship between social and human capital and firms’

performance, is a positive one, some other studies claim that the nature of this relationship

could be negative. The work of Batjargal (2007) on the internet ventures in China found

that social capital elements and the experience of an entrepreneur living abroad have a

30

positive effect on the survival of the firms, whereas the interaction among those social

and human capital components has a negative effect on firms’ performance.

A more recent study argues that social capital benefits some groups more than

others and that it often interacts with the management to improve performance (Meier,

Favero, and Compton 2014). Another study has found that both human capital and social

accumulation affect the equilibrium growth rate (Dinda 2008). In addition, some scholars

argue that social capital is embedded in human capital and education fosters its

accumulation (Becker 2009). To connect these arguments, it must be emphasized that

there is evidence indicating that human capital affects social capital and that experience

and cognitive ability influence personal relations. On the other hand, numerous studies

confirm significant association and causal relationships between human capital and social

capital concerning organizational performance (Augusto Felício, Couto, and Caiado

2014).

Therefore, the consideration of social capital in assessing the performances of firms

through the implementation of Porter’s Diamond framework is assumed to play an

important role in the process of industrial development in traditional clusters in

Afghanistan. Especially, where the traditional or modern industrial clusters are

recognized by some scholars as “social communities” and in addition to that, majority of

economic activities in those clusters operated mainly by human resources (Morosini

2004, p. 307). Therefore, the structure and characteristics of those economic activities can

highly depend on the quality and the behaviour of human and social capitals that exist in

those traditional clusters, especially in the case of Afghanistan.

31

Conceptual and Hypothetical Framework

The aims of this study were to examine the impacts of determinant factors on the

performances of MSEs using Porter’s Diamond Model as a theoretical framework. As

mentioned earlier, this study mainly attempts to assess the direct impact of social capital

dimension (X1) on the performance of MSEs and its indirect impacts through other

dimensions of Porter’s Diamond Model adapted for this study, namely, factor conditions

(X2), related and supporting industries (X3), demand conditions (X4), firm strategy,

structure and rivalry (X6), government policies (X7), chance (X8), and also another

additional dimension of firm characteristics (X5) in traditional clusters of micro and small

scale industries (MSEs) in Herat City.

Therefore, based on the literature cited in previous sections, as well as in order to

achieve the objectives and to answer the research questions of this study, a number of

hypotheses were formulated. Figure 2.2 shows the conceptual and the hypothetical

framework of this study.

32

2.6.1. Hypothetical Formulation

Figure 2.2 shows the conceptual framework of this study that was drawn based on

the literature review in this chapter. In order to achieve the objectives of this study, this

section provides formulation of hypothesis based on Figure 2.2. As it is shown in this

figure, there are two categories of hypotheses that were formulated and tested in this

study. The first category of hypothesis refers to only one main hypothesis that is assessing

the direct impact of social capital and other determinant factors on the performance of

MSEs in the traditional cluster. The second category of hypotheses that was formulated

and tested in this study was to assess the indirect impact of social capital on the

performances of MSEs mediated through other dimensions of Porter’s Diamond. In the

second category, eight hypotheses were formulated to test the social capitals significant

Note: a) An arrow indicates a significant direct impact path

b) An arrow indicates a significant indirect impact path

Figure 0.1. Conceptual/Hypothetical Framework

33

indirect impact of social capital on the performance of MSEs. Therefore, the following

hypotheses were formulated and tested in this study.

H11: There is a possibility that social capital (X1) with other dimensions within Porter’s

Diamond

have significant direct impacts on MSEs’ performances (Y) in traditional clusters.

H01: β=0, and H11: β ≠ 0, for X=1, 2, 3,4,5,6,7,8………………… (i)

H12: There is a possibility that social capital (X1) has significant indirect impacts on the

performances of MSEs through the dimension of factor conditions (X2) in traditional

clusters.

H02: β=0, and H12: β ≠ 0, for X=1, 2……………………………… (ii)


performances of MSEs through the dimension of related and supporting industries

(X3) in traditional clusters.

H03: β=0, and H13: β ≠ 0, for X=1, 3……………………………… (iii)


performances of MSEs through the dimension of demand conditions (X4) in

traditional clusters.

H04: β=0, and H14: β ≠ 0, for X=1, 4……………………………… (iv)

34


performances of MSEs through the dimension of firm characteristics (X5) in


H05: β=0, and H15: β ≠ 0, for X=1, 5……………………………… (v)


performances of MSEs through the dimension of firm strategy, structure and

rivalry (X6) in traditional clusters.

H06: β=0, and H16: β ≠ 0, for X=1, 6……………………………… (vi)


performances of MSEs through the dimension of government policies (X7) in


H07: β=0, and H17: β ≠ 0, for X=1, 7……………………………… (vii)


performances of MSEs through the dimension of chance (X8) in traditional clusters.

H08: β=0, and H18: β ≠ 0, for X=1, 8……………………………… (viii)

35

3. CHAPTER III

RESEARCH DESIGN AND METHODOLOGY

Introduction

The aims of this chapter are to explain the research setting and methodological

approaches, which enabled us to analyze the determinant factors of MSEs’ performances

in traditional clusters in Herat City, Afghanistan. In this study, I adopt Porter’s Diamond

Model as a framework for assessing the contribution and the impacts of the social capital

dimension on MSEs’ performances, both directly and indirectly through the other

dimensions within the Porter’s model.

This chapter consists of three sections. The operationalization of variables in each

of the dimensions in the conceptual framework of this study (see Figure 2.2) was drawn

on the literature review in Chapter 2. The sampling and data collection and the final

section describes the statistical methods used for the analysis of data in this study. In

addition, in order to achieve the objectives of this study, some statistical software

packages (MS. Excel, SPSS v.23 and SPSS AMOS v.23) were also used throughout the

analysis process.

36

Operationalization of Variables

As in the hypothesized framework shown in the second chapter (see Figure 2.2),

this study classifies the determining factors of MSEs’ performances into eight

dimensions, namely, social capital (X1), factor conditions (X2), related and supporting

industries (X3), demand conditions (X4), firm characteristics (X5), firm strategy, structure

and rivalry (X6), government policies (X7), and chance (X8).

In addition, based on the second objective (see section 1.3) that examines the

direct and indirect impact of social capital on MSEs’ performances in this study, except

the dimension of social capital (X1), other dimensions in Porter’s Diamond are

functioning as independent and intermediate dimensions in the conceptual framework

(see Figure 2.2). To test the direct impact of the determinant factors (including social

capital) on MSEs’ performances, in the first stage, we included all dimensions in the

regression analysis as independent variables. In the second step, for the regression

analysis with path diagram models in Chapter 6 and to test the indirect impact of social

capital (X1) dimension on MSEs’ performances, we considered other dimensions in

Porter’s Diamond as intermediate variables in this study.

Thus, the following section provides a list of variables from each of these

dimensions that is based on correlations analysis in Chapter 5, identified to have a

significant association with MSEs’ performances (Y) and other dimensions of the

conceptual framework in traditional clusters. In addition, a complete list of all variables

used in this study was attached in the appendix section as well (see Appendix 3). The

MSEs’ performances (Y) is considered a dependent variable, measured through the daily

sales revenue of an enterprise in this study.

37

3.2.1. Independent and Intermediate Variables

The position of an independent variable in a causal relationship framework is a

variable that stands alone and is not changed by the other variables that we seek to

measure. In fact, the independent variable causes some kind of change in other variables

(dependent variables). In other words, the independent variable is a variable that has an

antecedent or causal role and usually appears first in a hypothesis (Knoke, Bohrnstedt,

and Mee 2002, p.12). In addition, the intermediate variables can stand as independent or

dependent variables in a causal relationship framework, in other words; intermediate

variables usually take a position between independent or explanatory variables and

dependent variables.

Within a hypothesized framework, the intermediate variable can be changed or impacted

through an independent variable, and could then act as an independent variable and cause

change or impact on dependent variables, and thus in this study, on MSEs’ performances

(Y). Therefore, except the dimension of social capital (X1) that is hypothesized to have a

direct impact on MSEs’ performances and functions as an independent variable, the other

seven dimensions in the conceptual framework both function as independent and

intermediate variables in this study. In this section, each of these eight dimensions in the

conceptual framework are explained, separately, with the variables that had a significant

association with MSEs’ performances in Chapter 5.

Social capital (X1): The social capital factor has been recognized in a number of studies

on MSEs as one of the important sources for networking, trust, and cooperation, as well

as for sharing and transferring the knowledge and innovative ideas within and between

individuals or communities. We measured social capital in terms of the trust, participation

38

in networks and groups, as well as cooperation, and cohesion within the community and

traditionally clustered MSEs in Herat City. The variables from the dimension of social

capital that had significant association with MSEs’ performances are; having a family

member in same industry (X111), the number of close friends (X112), the number of friends

who can help (X113), trust in family and relatives (X129), help from a stranger (X154), the

frequency of mosque attendance (X155). In addition, the variables from the social capital

dimension that had significant association with the variables from other dimensions are,

namely, meeting with friends (X147), internet usage (X150), joining a loans association

(X16), trust in the police (X143), charity activities (X153), effectiveness in decision making

(X123), joining Senf (X11), joining industries and commerce associations (X13), trust in

neighbors (X133), trust in municipality officials (X139), and the social media index (X152).

The quantity and the characteristic of these social capital components can serve as a good

measure of the social capital factor for MSEs in this study.

Factor Conditions (X2): One of the possibilities to measure MSEs’ factor condition such

as human resource is to identify the age, sex, education and experience level of an MSE’s

owner. Education level is recognized as one of the most important indicators in terms of

human resources. Managing properly the human resources enables MSEs to adjust and to

allocate their human resource through enhancing the performances of each member with

and within the MSEs cluster, and can contribute to increasing their sales volume which

in this study is referred to as the dependent variable. Thus, the quality and the quantity of

these human resources serve as a good yardstick of measurement for the factor conditions

(X2) dimension in this study.

39

In the context of MSEs’ performances, growth and sustainable competitiveness,

access to credit and other financial services is a vital factor. In addition, in the traditional

cluster of MSEs, this factor can be more important due to the limit and unavailability of

collateral documents to be submitted by MSEs to the formal banks and other financial

institutes. Especially for those MSEs that are involved in very basic methods of

production with low profile industries, this obstacle can be a challenge for enhancing their

performances.

The performance of the MSEs is highly depended on its access to sufficient

equipment, venue, machinery and other production tools. The physical assets of the MSEs

can significantly contribute to its performance and competition in the market including

its rivals in the same or different cluster. Therefore, the variables, namely, the

entrepreneur’s age (X21), work experience (X22), level of education (X23), vocational

training (X210), source of investment from saving (X212), MSE’s total funded assets (X217),

total of current assets (X219), venue’s rented status (X224), and car ownership (X227) can

be considered as measurable components of the factor conditions (X2) dimension in this

study.

Related and Supporting Industries (X3): The role of related and supporting industries

in a cluster is vital to the competitiveness and performances of MSEs. As described in

Chapter 2, the location of the firms within a cluster, its proximity to sophisticated buyers,

and competitive suppliers in the domestic market can contribute to the MSEs’

performances and its competitiveness. Therefore, in this study, the variables of enterprise

location (X35) from the dimension of related and supporting industries (X3) is considered

as measurement variable in this dimension. Indicating the proper location of an enterprise

within a cluster can provide enterprises with sufficient access to market information,

40

buyers, raw materials, and other benefits from existed infrastructures in a particular

traditional cluster.

Demand Conditions (X4): Porter’s Diamond Model emphasizes the significant role of

the domestic buyer’s characteristic as a major factor influencing an industry’s competitive

advantage. Thus, in this study, the variables, namely, customer feedback (X46), customer

preference for quality (X44), customer preference for price (X43), and changes in sales

volume (X42) are considered to be the measurement of the dimension of demand

conditions (X4) in the conceptual framework of this study.

Firm characteristics (X5): The characteristic of the MSEs in this study is an exploratory

dimension that refers to an entrepreneur’s character, attitude, and optimism about their

economic activities in a cluster. Thus, the variables, namely; prosperity achievable by

endeavor (X528), being abroad (X511), council with the employee (X512), and the changed

varieties of products (X520) are considered to be measurement variables of a firm’s

characteristics (X5) dimension in this study.

Firm Strategy, Structure, and Rivalry (X6): This dimension found to have the largest

number of variables that are classified as significant factors from the dimension of firm

strategy, structure and rivalry (X6). These variables are, namely; manager status (X61),

investment in employees training (X616), business card (X623), and expansion of enterprise

(X631) are considered to be the measurement factor from this dimension in this study.

The Role of Government Policies (X7), and Chance (X8): Porter (1990) argues that

both government policies and chance can play a significant role in increasing the

competitive advantage and performance of the enterprise within an industry. However,

the character of these two dimensions often seems to be influenced by an enterprise’s

41

strategy and performance. Thus, the dimension of government policies (X7) can be

measured by the variable of the government marketing in international markets (X714). In

addition, the two variables of threat from suppliers (X83) and the improved economic

status (X89) are considered to be a good measurement of chance (X8) dimensions in this

study.

3.2.2. Dependent Variable

A dependent variable is one that has a consequent, or affected, role in relation to

an independent variable (Knoke, Bohrnstedt, and Mee 2002, p.12). In other words,

dependent variables can be influenced or affected by other independent or explanatory

variables in a causal relationship, or the change in the dependent variables is the result of

change in independent variables. There are various approaches for measuring a firm’s

performance such as the MSE’s size, profits, sales, and market shares. Some studies have

identified the sales revenue as an indicator of the MSEs’ performances within a cluster

(Augusto Felício, Couto, and Caiado 2014). This study classified the MSEs’

performances (Y) as a dependent variable in this study. Although there are various

approaches to measuring MSEs’ performance, scholars mainly use sales and its changes

to measure performances at MSEs level. Thus, in this study, we used the total amount of

daily sales (Y) to identify the performances of micro and small scale enterprises in

traditional clusters in Herat City in Afghanistan.

42

Sample and Data Collection

3.3.1. The Profile of Sample Areas

Herat province is located in the Western Region of Afghanistan. Herat is bordered

by three provinces, namely: Badgis, Ghor, and Farah, and also borders with Iran to the

west and Turkmenistan to the north. It covers an area of 55,869 km2 and represents 8.6%

of the total Afghan territory. It is the second largest province in the country after Helmand.

The province is divided into 16 districts, including its provincial center that is called Herat

City. Given that Herat province is home to 7.8% (approx. 1,762,157 inhabitants, 2015) of

the total population of Afghanistan, it is also ranked as the second most populous province

in the country after Kabul.

Source: Adapted from the maps of AIMS office in Herat City

Figure 0.1. Map of Sample Area in Herat City, Afghanistan

43

Herat is routinely portrayed as an economic powerhouse but has seen a significant

slowdown of economic activities, along with other provinces, in the wake of the political

impasse that followed the presidential election in 2014 and reduced international

investment that had been made through assistance programs by foreign military forces.

Herat province’s annual output in 2011 was estimated at $1.2 billion, $325 million in

agriculture, $465 million in the service sector, and $425 million in industrial enterprises

including mining (Leslie 2015).

3.3.2. Sampling and Data Collection

In this study, we used the primary data obtained through a structured questionnaire

as well as through in-depth interviews with individuals from different private and public

organizations. In addition, in this study we also used secondary data which were collected

from various departments and research institutes during fieldwork in Afghanistan. Thus,

in this section, we provide a description of the data coverage for this study.

There were three main reasons for selecting Herat City as a sample area in this study.

First, Herat is the second largest province in terms of inhabitants as well as land size.

Second, this Herat City is one of the oldest and historical cities in Afghanistan and well

known for its ancient civilization and artisans, and it is located in a corridor of Silk Road

(see Appendix 2). Third, in terms of the implementation of government policies on

industrial and other development programs, Herat province was selected because of its

advantage of having a more proper infrastructure in comparison to other provinces.

This study used the World Bank definition for identifying the size of MSEs, in which

enterprises that employ less than 5 employees are considered as micro enterprises; 5 to

44

19 employees as small enterprises; 20 to 99 employees as medium enterprises; and 100

or more employees as large enterprises.

Since included as the target clusters of MSEs for this study were different type of

industries, the author used the stratified purposeful random sampling methods to divide

the survey population into smaller groups and to select proportional representatives of

MSEs from each of those six clusters. The survey was conducted in Herat City in August

and September of 2015. A structured questionnaire was developed (see appendix 4 and

5) in English and translated to the local language of Dari (Persian), and then elaborated

in advance through a preliminary test with some of the concerned MSEs. Then, the author

interviewed a total number of 118 micro and small scale enterprises (MSEs) with a

structured questionnaire. Except for 14 questionnaires which were not included in the

analysis process, because those questionnaires were missing information or were not

completed during fieldwork in Herat City. Thus, the results of interviews with 204 MSEs

were included in this study and the Table breakdown is shown in Table 3.1.

Table 0.1. Frequency distribution of sample of clustered MSEs

No. Type of Cluster MSEs

MSEs

No. Type of Cluster MSEs

MSEs 1 Dried fruit and nuts 21 4 Shoemaker 33

2 Tailor 42 5 Iron monger 27

3 Carpenter 49 6 Tinwork 32

Sub-total: 204 MSEs

45

Limitations of the Study

As in any study that implementing the case method, qualitative or quantitative

research methodology, there are often limitations for interpreting and coming to a

conclusion, as well as for the generalizations of results for the future studies. Thus, there

are at least three major limitations in this study. First, because of possible security threats

and time limits, as well as limits of financial and logistical resources, in this study we

were able to conduct one-off interviews with micro and small scale enterprises in only

one of the provinces (Herat) in Afghanistan, instead of obtaining a larger sample size and

covering more geographical areas in Afghanistan for this study. Second, there is very

little literature on the incorporation of the social capital dimension within the framework

of Porter’s Diamond Model in case of both developing and developed countries. Third,

there are serious constraints and lack of sufficient information available about the

economic performances of Herat City as well as Afghanistan as a whole.

Analysis Methods

In this study, we used Porter’s Diamond Model as the main analytical framework

of MSEs’ clusters. The scope of this study was at the three different levels (Micro, Meso

and Macro). The unit of analysis in this study was at the firm level. Meanwhile, this study

mainly focuses on the analysis of MSEs’ performances and its determinant factors as

developed in the hypothesized framework in this study. In addition, in order to achieve

the objectives of this study, during the process of data analysis, we applied different

statistical methods such as descriptive statistics, correlation matrix, multiple general

regression analysis as well as the path analysis methods. Each of these statistical methods

is briefly described in the following sections.

46

3.5.1. Descriptive Statistics

In this study, we used descriptive statistics analysis as the primary and most useful

statistical method in order to explore the basic information and to explain the nature of

data, as well as the variables by calculating minimum (min), maximum (max), and mean

as well as variance and standard deviation. The aim of executing those descriptive

statistical methods is to identify the characteristics of collected data and to describe their

range, central tendency, and deviations. Therefore, in order to do so, first we dropped

those variables that yielded a standard deviation of zero before including those variables

into further analysis.

3.5.2. Correlation Matrix

One of the most general meanings of the concept of a relationship between a pair

of variables is that knowledge with regard to one of the variables carries information

about the other variable (Cohen et al. 2003).

To measure the association between two continuous variables estimating the direction

and strength of linear relationship, in this study, the Pearson product moment-correlation

coefficient (r) was used to measure the association4 between two variables. In addition,

Pearson product-moment correlation can be used to estimate the direction and strength of

linear association between a pair of variables (Knoke, Bohrnstedt, and Mee 2002).

In principal terms, correlation coefficient (r) ranges from (-1.00) to (+1.00). The value of

-1.00 represents a perfect negative correlation and the value of +1.00 represents a perfect

positive correlation between two variables. In this study, we consider the significance

4 In this study, correlation and relationship are synonyms for association.

47

level5 at (α ≤ .05) as a significant variable (criterion 1 for correlation analysis). In addition,

in order to avoid the problem of multicollinearity6 in regression analysis, if the variables

were associated with correlation coefficients of (r ≥ 0.8), we selected only one of those

paired variables for further steps of analysis (criterion 2 for correlation analysis). In

addition, in order to gain a better interpretation of the degree of strength of the association

between two variables in correlation analysis, we used the following conventions7

presented in Table 3.2.

Table 0.2. Value of Association and Appropriate Phrase

Value of Association Appropriate Phrase

+ 0.600 or higher A very strong positive association*

+ 0.300 to + 0.599 A moderate positive association

+ 0.100 to + 0.299 A low positive association

+ 0.010 to + 0.099 A negligible positive association

0.000 No association

- 0.010 to - 0.099 A negligible negative association

- 0.100 to - 0.299 A low negative association

- 0.300 to - 0.599 A moderate negative association

- 0.600 or lower A very strong negative association

3.5.3. General Multiple Regression with Path Analysis Method

General multiple regression analysis (GMPA) examines the joint relationship

between a dependent variable and two or more independent, or predictor, variables

5 It is, the level of probability at which it is agreed that the null hypothesis will be rejected (Everitt 1998,

p.303). 6 A term used in regression analysis to indicate situations where the explanatory variables are related by a

linear function, making the estimation of regression coefficients impossible (Everitt 1998, p.219). 7 This convention were adopted from James A. Davis (1971, p.49).

48

(Knoke 2002, p. 235). Therefore, a multiple regression equation is a linear model

constructed from a dependent variable and a set of independent (explanatory) variables

(Kawamura 1978, p. 228). In addition, the term multiple linear regressions is usually

applied to models in which a continuous response variable, Y (dependent variable), is

regressed on a number of explanatory variables, X1, X2, X3….Xn (independent variables).

The aims of using general multiple regression analysis in this study are to achieve our

objectives8 by testing the hypothesized conceptual frameworks in which the social capital

with other dimensions within Porter’s Diamond have direct impact on MSEs’

performances in traditional clusters.

In addition, the execution of multiple regression analysis in this study utilized the

Statistical Package for Social Science (SPSS v. 23). Thus, in order to examine the

hypothetical model, we set the criteria of beta coefficient (β ≠ 0) and the significance

level of (α ≤ 0.10) for multiple regression analysis in order to calculate the casual

relationship, and test the hypothesis for the impact of social capital and other determinant

factors on the MSEs’ performances.

The second objective of this study is to determine whether social capital has

indirect impact on MSEs’ performances throughout the other dimensions of the

conceptual framework in this study. Thus, we utilized the software package of SPSS with

Analysis of Moment Structures (SPSS AMOS v.23) with Structural Equation Modeling

(SEM) to test the direct and indirect impact of social capital on MSEs’ performances. The

Bootstrap method is used in order to achieve more reliable results in testing mediation in

the analysis of the path diagram model in Chapter 6. In order to examine the hypothetical

8 See, Chapter I for detail of objectives (2).

49

model, we set the criteria of beta coefficient (β ≠ 0) and the significance level of (α ≤

0.10) for the path analysis. In addition, to attain a reliable test of Model-Fit for path

analysis models, we set the criteria that are often recommended and used for the testing

of the structural model as shown in Table 3.3.

Table 0.3. Criteria for Fit-Indices of Model Test

Fit Indices Recommended by Recommended Criteria

X2 Meyers, Gamst, and Guarino (2006) P-value >.05

CMIN/DF Marsh and Hocevar (1985)

Hair et al., (2009)

< 5.0

<3.0

CFI

Bentler, (1990)

Hatcher, (1994)

>.90

>.90

GFI Chau, (1997);

Segars and Grover, (1993)

>.90

>.90

RMSEA Byrne, (2001);

Hu and Bentler, (1999)

<.08

<.05

50

4. CHAPTER IV

TRANSITION ECONOMY AND ENTERPRISE

DEVELOPMENT IN AFGHANISTAN

The Profile and Socio-Economic Indicators of Afghanistan

Afghanistan is a land-locked and mountainous country in South Asia with the area

of approximately 650,000 km2. The country is bordered with Pakistan in the east and the

south, Iran in the west, Turkmenistan, Uzbekistan, Tajikistan and China in the north (see

Appendix 1). Afghanistan is also called the heart of Asia, and long ago it was a center of

commercial and economic activities because of its strategic and geographical location in

the region. The topography of the country is a mix of central highlands and peripheral

foothills and plains; and has an arid continental climate in which summers are dry and

hot, while winters are cold with heavy snowfall in the highlands.

Afghanistan is comprised of thirty-four provinces including Kabul as the capital of the

country. The government in Afghanistan has a presidential system and is controlled by

the central government in Kabul. The country is rich in a variety of natural resources such

as iron, silver, garnets, copper, natural gas etc. Most of these natural resources have not

been unearthed due to the civil war, recent political instability, lack of capacity and human

resources, and the absence of required technologies, especially in the last three decades.

51

Afghanistan’s economy is highly dominated by agriculture and more than 65% of the

population of the country depends on agriculture and related industries for their livelihood

(CSO 2016). The data about the economy of Afghanistan in year 2014-15 were more

hopeful in comparison to year 2013-14. The value of the Afghan GDP reached 21.0

billion and shows a 1% decrease over the value of GDP in last year. The growth rate of

GDP in year 2014-15 was about 2.1% and the GDP per capita was $747 in this year. The

share of agriculture in GDP in 2014-15 was 24.32%, services 51.30%, and the sector of

industry was 20.92% as the lowest share in GDP. In addition, the total value of the

industrial products of both government and private sector in year 2014-15 was Afs 7315

million (exchange rate Afs 57=$1), which means it decreased by 29.6 % compared to the

previous year. The consumers price index (CPI) shows that the level of inflation in

Afghanistan was -0.7% that indicates a relative decrease in comparison to the last year.

The data on foreign trade in Afghanistan shows that in 2014-15 the value of imports was

$7729 million and the value of exports was $571 million. The data shows that in

comparison to the previous year, imports decreased by 11.4% and exports increased by

10.9% (CSO 2015).

Regarding the land, about 12% of the country’s total land is arable, 3% is under

forest cover, 46% consists of permanent pastures, and the remaining 39% is mountainous

and inhabitable (CSO 2015).

The population of Afghanistan was estimated to be 28.1 million in 2014-15 (CSO

2015). The population of men was 51.3% (14.4 million), and women was 48.7% (13.7

million). About 71.5 % (around 20.1 million) of the populations lives in rural areas, 23.1

% (6.5.0 million) lives in urban areas and 5.3% (1.5 million) are classified as Kuchi

(Nomadic). The data on educational attainment of government officials shows that there

52

are 305 people with a Ph.D. 3848 with an MA/M.Sc., 46307 people with a BA/B.Sc. and

nearly 242909 people have a BA/B.Sc. In addition, in 2014-15, the total number of

students at public and private education institutions was 253161 people, which shows a

23.6 % increase compared to the previous year. The data of CSO 2014-15 shows that in

these years there were 126 public and private universities, which means an increase of 16

private education institutions.

Afghanistan is a multicultural and multi-ethnic country, composed of many ethnic

groups such as Pashtun, Tajik, Hazara, Uzbek, Turkmen and others. Many languages are

spoken, with Pashto and Dari being the two main ones and the majority of Afghans9 can

speak one of the two main languages10.

Post-2001 Agendas and Transitional Economy in Afghanistan

Almost three decades of war have created the innumerable challenges that the

people of Afghanistan are facing today. The results of those civil wars were the

destruction of core institutions of the state and a heavily war-torn economy, which led to

an unrivaled level of absolute poverty, national ill health, large-scale illiteracy and an

almost complete disintegration of gender equality. In spite of the intensive reconstruction

efforts in recent years of reconstruction at a cost of billions of dollars, the path to

prosperity from extreme poverty remains as distant as ever. In addition to insecurity,

poverty, and corruption. there are several other wide-ranging challenges that Afghan

people face. Poverty reduction is one of the three key objectives of the Afghanistan

National Development Strategy (ANDS).

9 Refers to the nationality of a person in Afghanistan. 10 These two languages (Pashto and Farsi-Dari) are the official languages in Afghanistan.

53

In 2002, Afghanistan was a thoroughly devastated country in virtually every

respect. The political, social and economic structures of the country had been severely

damaged or completely destroyed. Massive numbers of Afghans had left the country and

lived as refugees. Moreover, uncountable numbers either died or were severely disabled

in these conflicts. Every family has paid a price, and many had to cope with the loss of

their main breadwinner. This caused the disruption of education for youth, and in the case

of girls, it was totally terminated. Today Afghanistan has the highest rate of illiteracy in

the world. Despite these desperate conditions, since 2001 the country has had some

remarkable achievements. The progress that has been made should be measured against

the desperate conditions that prevailed during the last few years.

While Afghanistan still faces many enormous challenges, the achieved progress

has led to the optimism that with determination Afghans can rebuild their lives and their

country. In addition, significant political, social, and economic achievements have been

made in recent years

Recently, many development plans were implemented in Afghanistan, some of which

focused on poverty reduction. The most comprehensive and detailed plan developed and

implemented in order to reduce poverty was the Afghanistan National Development

Strategy plan (ANDS 2007). The ANDS plan represents the combined efforts of the

Afghan people and the Afghan government with the support of the international

community to address major challenges facing the country. This plan, ANDS, was drafted

to comprehensively address security, governance, and development needs of Afghanistan.

Therefore, the overriding objective of the ANDS plan is to substantially reduce poverty,

improve the lives of the Afghan people, and create the foundation for a secure and stable

country. This requires building a strong, rapidly expanding economy able to generate

54

employment opportunities and adequate income essential for the reduction of poverty. In

fact, the ANDS plan serves as the country’s Poverty Reduction Strategy Paper (PRSP).

Thus, it establishes the joint Government/international community commitment for

reducing poverty and it also describes the extent and patterns of poverty that exist in

Afghanistan. In general, the ANDS’s plan lays out the strategic priorities and policies,

programs and projects for achieving the Government’s development objectives. These

are organized under three pillars: first, security; second, governance, rule of law and

human rights; and third, economic and social development (ANDS 2007).

Enterprise Development and Policy Discourse in Afghanistan

“A society of hope and prosperity based on a strong, private-sector led market

economy, social equity, and environmental sustainability” (ANDS 2007, p. i).

The status of business environment in Afghanistan shows that this country needs

to solve a number of obstacles to achieve a higher level of prosperity and domestic and

international market. The overview of economy in Afghanistan shows the lack of strong

infrastructure that can enhance the competitiveness of its economy and create an efficient

business environment for the enterprises to growth and development in this country

(World Bank 2014). It is well known that the efficient infrastructure can properly connect

the enterprises to their suppliers, customer, and can provide them with access to

information in market and also opportunity for adapting to the new methods and modern

technologies for their production system. On the other hand, the lack of this infrastructure

55

in the business environment of Afghanistan means that this country cannot perform

properly in an international scale. According to an enterprise survey conducted by the

World Bank, the status of business environment in Afghanistan ranked as 177 out of 189

economies across the globe (World Bank 2016, p. 6).

With respect to the status of business environment in Afghanistan, there have been

many efforts made by the government of Afghanistan and its partner organizations from

the international community such as international organizations to promote the private

sector in this country since 2001. A number of NGOs and international organizations have

been working in Afghanistan to promote the role of the private sector side by side in the

transitional economy of this country. These efforts from the international organizations

were provided directly to the private sector or indirectly through the government channels

such as Ministry of Commerce and Industry (MOCI) and Afghanistan Investment Support

Agency (AISA). In addition, there have been another national and international NGOs

that contributed to these efforts during the past 15 years. The international NGOs such as

U.S. Agency for International Development (USAID) took initiatives to provide

assistance to the private sector in terms of technical, financial and business development

services. On the other hand, these NGOs have also assisted the government organizations

with technical assistants that focus on capacity building, organization and legal reforms,

and providing expertise. Similarly, other international organizations such as World Bank,

Department for International Development (DFID), Deutsche Gesellschaft für

Internationale Zusammenarbeit (GIZ) also have been among the major contributors of the

agenda for development of private sector in Afghanistan.

Despite all these efforts that have been made by the Afghan government and the

assistants from the international community, and even though successful cases were

56

reported in some sectors, still there are fundamental challenges that need to be addressed

in order to enable the enterprises to grow and attain sufficient levels of competitiveness

in domestic and international markets.

One of those challenges that was not considered in the policy discourse of private

sector development and especially SMEs’ development in Afghanistan is the lack of legal

framework for assessing the achievement and obstacles for the private sector in this

country.

The findings from the interview with expertise and government officials during

the fieldwork for this study at the MOCI in 2015 indicates that there was a lack of

coordination between Afghan government and those national and international

organizations for implementing the programs that aims to promote the enterprises

development in Afghanistan. For instance, there were large scale programs for enterprise

development that designed by international organization such as USAID and spent a

budget of about $200 million, and the duration of this program was for about 5 years. As

part of program, it aimed to provide a national strategy plan for the development of

enterprise. But, at the end of this program, the ministry of commerce and industry (MOCI)

in Afghanistan did not received any strategy plan or any document contains lesson learned

from this program. The only document that the ministry has received in 2009 was a MS.

PowerPoint Slides’s document (consist of 84 pages) and the ministry were not provided

with any additional sources or references that used for in the contents of this document.

In addition, this document that titled: “Afghanistan SME Development Strategy” has not

considered as strategy plan this ministry but have been used by many other organizations

intentionally or unintentionally and referenced as the only available strategy plan for SME

development in Afghanistan, said Mr. Ahmad Zia Sayed Khaili, director of SME

57

development in the ministry of commerce and industries, April 4, 2015, Kabul. The fact

is that, even though official at ministry of commerce and industry ignored and questioned

the validity of such a strategy plan, due to its unrealistic anticipation and data within

mentioned document, but the ministry of commerce and industry in Afghanistan in its

online website refer to it as MOI’s SMEs strategy.

Another challenge that especially micro scale enterprises in traditional clusters are

facing in Afghanistan is the definition that have been given to these type of enterprises.

Finding from the interview at MOCI indicate that there was not agreed definition for the

scale of enterprises between this ministry of other international organizations (World

Bank) that welling to assist the government in the course of SMEs development. In

addition, it seems that in most programs and agendas relevant to development private

sector in Afghanistan, the prioritize were given to medium or large scale enterprises, and

often there were not any emphasis on micro scale enterprises in the policy discourse in

Afghanistan. In which, such situation can undermine the importance and the challenges

of the micro and often small scale enterprises to perform properly in the traditional

clusters in Afghanistan.

Preliminary Findings from Traditional Clusters in Herat

This section describes the results of preliminary analysis and its findings from the

sampled clusters in Herat city. Even though the main objective of this study is to analyze

the group of traditional clusters as a whole, in this section, for a better understanding of

the characteristics of every single cluster, we used a descriptive analysis to understand

the role of social capital (trust, networking, cooperation and collective action) in the

contexts of each of these clusters.

58

4.4.1. Dried Fruits and Nuts Cluster

This cluster is one of the very old traditional industries in Herat City and

Afghanistan; a country with an agriculture-based economy. The firms of this cluster are

mainly located between Chahar Su, Darb Malik and Shahr-e Naw streets (see

Appendixes). Some cluster members reported that they had run their industry for more

than 60 years, whereas nearly 45% of the cluster members reported that they did so for

less than 10 years. A random sample of 21 enterprises was selected for this study. In

addition, findings in this and the following two chapters (5 and 6) are the results of

investigations conducted in MSEs in Herat City.

Herat province has been famous for its land potential to grow up a variety of

agricultural products such as delicious and cheaper fruits (Malleson 1880, p. 37).

Therefore, the potential of growth in this cluster is high due to its long tradition of being

a part of the Afghanistan economy. On the other hand, it is important to take into account

the location of this industry. Herat City is one of the most productive regions for this

industry in Afghanistan equipped with the potential to grow and possibly to compete in

other regional and international markets. In addition, despite the fertility of land and the

geographic location of Herat City in a corridor adjoining with other regional markets

mainly in Central Asia, the needs for a good and beneficent administration were always

a crucial factor to enable it to attain and sustain a higher degree of prosperity mainly in

the agriculture sector (Malleson 1880, p. 56).

On the one hand, in terms of production and creation of employment, the dried fruit and

nuts industry in the domestic market plays a significant role. On the other hand, in the

59

composition of the balance of trade in Afghanistan, this industry with a proportion of 37%

of export to the total export value is the largest contributor to the economy in the country.

The preliminary findings from the collected data during the field survey in Herat

City in this study indicate that a high level of trust exists among the family members and

relatives 76.2%, neighbors 52.4% and in other members of this cluster 71%. The data

shows that entrepreneurs’ trust in the police, the municipality officials, and the national

government officials tends to be low: 28.6%, 19% and 14.3% respectively. The structure

of trust within this cluster reveals that there is a huge difference in trust between family,

relatives and other entrepreneurs within the cluster on the one hand and the official in

formal organizations such as police, municipality and government on the other (Figure

4.1). The study has also found that a higher level of trust exists in regard to suppliers of

raw input materials 42.9%, teachers and professors 47.6%, followed by their local

representatives 19% (Wakil and Arbab11).

11 Wakil and Arbab: traditionally refers to a village and community representative or leader who acts as a

link between the rural or urban population and the district and wards chief in Afghanistan (Adamec

2003).

0.0%

14.3%

14.3%

14.3%

61.9%

14.3%

19.0%

28.6%

71.4%

52.4%

76.2%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%

Join Credit association (X16)

Join industries and trade chamber (X13)

Join sport group (X17)

Join ethnic group (X18)

Join asnaf (X11)

Trust in national government staff (X142)

Trust in municipality staff (X140)

Trust in police (X144)

Members are trustful (X116)

Trust in Neighbors (X134)

Trust in family and relatives (X130)

Figure 4.1. Trust and Networking - Dried Fruits and Nuts Cluster

60

Figure 4.1. shows that within this cluster, the networking and membership in

groups and associations vary from 61.9% of the enterprises in this cluster that have

membership in their industry association (Senf12) to 14.3% that have membership in

ethnic groups, sports groups and associations for industries and trade chambers. The data

from interviewed enterprises in this study reveals that no entrepreneur in this cluster has

any type of membership in cooperatives and associations or in any credit and loan

association. In addition, the data from this cluster indicates that 33.3% of the enterprises

are reported to have membership in other types of groups or associations than those

mentioned above. Therefore, the data for entrepreneurs’ membership in groups or

associations shows that 66.7% of the enterprises in this cluster have at least one type of

membership while less than 10% of them do not have membership in any group or

association.

Figure 4.2 shows a very high level of cooperation and collective action within the

dried fruit and nuts cluster. This indicates that 76.2% of the enterprises do cooperate with

each other to share the machineries for the production purposes. The data shows that

61.9% of the enterprises in this cluster often share information among them about their

production methods and the market status. The data also indicates that there is high-level

cooperation to give and receive support to and from others among the enterprises in this

cluster.

12 Senf refers to an association for a specific industry in urban area of Herat City.

61

The presence of collective action among enterprises in this cluster is also reported

at a very high level. In this data, more than 65% of the entrepreneurs have reported that

they are effective in the decision-making process of their cluster. 76.2% of the

interviewed entrepreneurs said that they participated and voted in the last presidential

election 2014. In addition, the entrepreneurs in this cluster reported that in the past they

participated with different types of clusters’ members in some of the demonstrations to

protest against the municipality or the government regarding policies issues relevant to

their business activities in Herat City.

76.2%

61.9%

66.7%

66.7%

61.9%

76.2%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%

Vote in presidential election (X128)

Friends asked help (X146)

Friends can help (X114)

Effective in cluster decision-making (X127)

Enterprises share infomations (X120)

Enterprises share machinaries (X122)

Figure 4.2. Cooperation and Collective Action - Dried Fruit and Nuts Cluster

9.5%

23.8%

28.6%

38.1%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%

Other benefits (X55)

Benefit customer market awearness (X54)

Benefit access market info (X52)

Benefit increase cooperation unity (X53)

Figure 4.3. Benefits of Belonging to a Cluster- Dried Fruit and Nuts Cluster

62

Figure 4.3 shows the entrepreneurs’ responses about the main benefits of

belonging to a cluster based on the four different types of possible benefits that are shown

in this figure. The finding indicates that 38.1% of the entrepreneurs in this cluster believe

that their belonging to a cluster can increase the chance of unity and cooperation among

members. This figure is followed by 28.6% of the entrepreneurs who agreed that being in

a cluster could provide them with better access to information on prices, production

methods and only 23.8% of them thought that it could increase the awareness of the

customers’ needs and changes in the market for their products.

The sampled enterprises in the dried fruits and nuts cluster in this study consist of

17 micro-scale enterprises and 4 small-scale enterprises. The structure of firms’ place

ownership within this cluster shows that 42.9% are privately owned and that 43% of

firms’ place are rented and the remaining 15% are inherited or mortgaged.

In the interview, the entrepreneurs were asked to evaluate the competitive position

of their firms in the market in comparison to other firms in the same cluster. Their

responses varied from 23% who believed that their position compared to other firms is

weaker, 52% who thought similar, 19% who thought stronger, and 5% who evaluated

themselves as much stronger in this cluster. In addition, they asked the question of

whether they planned to establish or expand their firms during last three years. The data

shows that 62% of them have been considered the plan for the expansion or the

establishment of new firms in this cluster.

The data shows main priorities for additional investment in this cluster: for 43%

of the respondents it is to invest in a better location for their firms, 33% to invest in raw

63

input materials, 14% to invest in machineries, 10% to invest in additional storage, and

5% to invest in more employment and tools.

The traditional cluster of enterprises often uses less sophisticated technology and

methods in their production process, but the firm strategies such as innovation in the

changing market’s environment is a very important factor for their survival and

continuing its operation properly. Findings from the dried fruit and nuts cluster indicate

that about 85.7% of the enterprises in this cluster have innovated or changed their

products during the last two years. The major sources of innovation for the enterprises in

this cluster are like this: 81% are from customer’s feedback and 19% are by imitation of

domestic or imported products.

The enterprises in this cluster often apply different strategies to their customer

base. As for the major customer base strategies in this cluster, 71.4% employed a strategy

through discount, 43% through improvement in the quality of the product, and 14%

through marketing.

The business satisfaction level among interviewed entrepreneurs in this cluster

has been evaluated based on the five-point Likert scale question (very satisfied, satisfied,

neither satisfied nor dissatisfied, dissatisfied and very dissatisfied). The findings indicate

that about 66.7% of them stated that they were satisfied with their business activities.

In relation to the public-private partnership discourse, the perspective of

entrepreneurs on the government and its policy implementation are evaluated in this

study. The findings indicate that only 38% of the entrepreneurs in this cluster believe that

the government considers them when it makes decisions, whereas 62% of them think that

64

they often have been neglected in the policy-making processes. In addition, about 71.4%

of entrepreneurs in this cluster believe that the government does not follow any specific

strategy to be supportive of their business and economic activities. In terms of priority on

the part of the government policies and initiatives, the major concerns of the entrepreneurs

in this cluster are that the government should provide them with subsidies and the access

to the international market and vocational training, impose import quota to similar goods,

and increase the access to market information.

The existence and survival of traditional clusters in the future, especially in this

city and eventually in Afghanistan, can be one of the key issues regarding the

development of an industrial strategy plan in this country. The understanding of

challenges and threats that these clusters are facing can facilitate the efficiency of such a

plan. The findings show that 57% of the entrepreneurs in this cluster consider imported

products as the major threat to the future of their survival in the market, 43% consider the

suppliers and the raw input materials as a threat, and 29% of them consider the rivals as

the main threat to their business survival in the future.

4.4.2. Tailoring Cluster

This cluster has a history of more than 35 years and is one of the traditional

industries in Herat City, though nearly 60% of the enterprises in this cluster are less than

10 years old. The tailoring cluster is mainly located in the areas stretching from Masjid-

e-Jame Herat along to the Jada-e-Lilami and the areas from Chawki Shahr-e-Naw to the

Pai Hisar street. The enterprises in this cluster are mainly located in plazas such as small

markets in those areas.

65

In comparison to other clusters in this study, the tailoring cluster seems to be

younger and more dynamic in adapting to the new technology and machineries in their

new design and production methods across time, whereas this flexibility gives an

advantage to the enterprises in this cluster to be innovative, being one of expanding

industries in Herat City and eventually in the other parts of this province.

The findings from the field survey in this study show that a very high level of trust

exists among cluster members 64.3% as well as among family members or relatives

92.2%. On the one hand, more than 83% of the entrepreneurs believe that most members

in their cluster are trustworthy. On the other hand, Figure 4.4 shows that the

entrepreneurs’ trust in the police, municipality, and national government official is at a

lower level 35.7%, 14.3%, and 4.8% respectively. The data shows that about 50% of the

entrepreneurs trust in suppliers of raw input materials, 57% of them trust in teachers and

professors, and only 31% of them do trust in Wakil and Arbab in the area where they

work or live.

9.5%

4.8%

33.3%

21.4%

35.7%

4.8%

14.3%

35.7%

83.3%

64.3%

92.2%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%





Join asnaf (X11)







Figure 4.4. Trust and Networking- Tailoring Cluster

66

The findings about this cluster indicate that, in comparison to dried fruit and nuts

cluster, there is a slightly different pattern of membership rate and networking practices

observed among the entrepreneurs. In this cluster, only 35.7% of the enterprises often

participate in the tailoring association (Senf), and about 33.3% of the enterprises join

some types of sports activity group. A low level of 4.8% of the enterprises participate in

the association of industries and the trade chamber. In contrast to the dried fruit and nuts

cluster, nearly 10% of the enterprises in this cluster have stated that they are members in

the loan and credit associations. The data shows that only 7% of the entrepreneurs in this

cluster have some membership in cooperatives and associations, 9.5% of them participate

in the local council, and nearly 12% of them do participate in other types of groups or

associations.

Figure 4.5 shows the level of cooperation and collective action among the

enterprises in this cluster. There is high-level cooperation among the enterprises in this

cluster in sharing the machineries and information related to production methods and

design. The findings from the field survey reveal that enterprises within this cluster often

benefited from the cooperation among them in order to adapt themselves to the new

designs and machinery in a competitive environment and market changes.

81.0%

73.8%

54.8%

57.1%

73.8%

85.7%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%







Figure 4.5. Cooperation and Collective Action - Tailoring Cluster

67

The findings show that there is a high level of cooperation and collective action among

enterprises in this cluster. More than half of interviewed entrepreneurs in this cluster

stated that they can receive some types of support from friends and relatives whenever

they needed. Nearly 74% of these entrepreneurs reported that they gave support and help

to their friends and other cluster members in past three months. In addition, nearly 57%

of the entrepreneurs described themselves as effective in the process of decision-making

in this cluster. About 81% of the entrepreneurs participated in the national-wide decision-

making events such as voting in the last presidential election.

Figure 4.6 shows the main benefits of belonging to a cluster. The data in this

figure indicates that about 36% of the entrepreneurs in this cluster believe that being part

of the cluster can increase the chance of cooperation and unity among cluster members.

Only 21.4% of the interviewed entrepreneurs have agreed that it can provide them with

the access to information on design, production methods, prices and customer in the

market, as well as other types of benefits.

21.4%

21.4%

21.4%

35.7%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%





Figure 4.6. Benefits of Belonging to a Cluster - Tailoring Cluster

68

The sample of the enterprises in the tailoring cluster in this study consists of 24

micro-scale enterprises and 18 small-scale enterprises. The forms of firm ownership in

this cluster are private 45.2%, rent 47.6%, inherited 4.8% and mortgage 2.4%. The status

of management of the enterprises in tailoring cluster shows that 83% of the enterprises

are managed by the owners while the remaining 17% of the enterprises are run by the

managers other than the owners in this cluster.

The entrepreneurs in this cluster evaluated their competitive positions compared

to other firms in the same cluster and 4.8% of them believe that they are in weak positions,

40% of them stated that their positions are similar to those of other firms, 46% stated that

theirs are stronger, and 14.5% evaluated that they have very strong positions in the

market. In terms of the plan for firms’ expansion, 45.2% of the entrepreneurs have

planned to expand or to establish new enterprises in this cluster during last three years. In

addition to the expansion of the enterprises, the data shows that, for the entrepreneur’s

major priority of investment in their firms, 50% of them stated the need for investment

are strong in machineries, 31% had needs for investment in employment and a better

location for the firm, 19% expressed the need for investment in tools, followed by 9.5%

who reported the need for investment in storage and raw input materials, and about 7.1%

who stated the need for investment in vocational training for the employees.

The findings from this cluster indicate that innovation often occurs within this

cluster due to the nature of changing and competitive environments of production

methods and design in this industry. 78.6% of the enterprises in this cluster stated that

they had changed their product variety or design during the last two years. As for the main

sources for the innovation in this cluster, 66.7% are based on the customer’s order or

feedback, 19% by imitation from similar products made by others, 11.9% based on the

69

internet sources, and 2.4% based on other sources. In addition, popular strategies for the

customer base in this cluster are by way of product quality 62%, by discount 7.15% and

through the current customers and marketing 2.4%. Therefore, the presence of a strong

connection between customers’ feedback and relationship with an enterprise’s strategies

for the customer base in this cluster indicates that the customer’s demand and feedback

on products is a very important factor for innovation within the tailoring cluster.

The level of business satisfaction among the interviewed entrepreneurs in this

cluster shows that only 11.9% of the entrepreneurs in this cluster have stated that they are

dissatisfied or very dissatisfied with their business, 2.4% of them reported neither

satisfied nor dissatisfied, and the majority of 59.5% and 26.2% of the entrepreneurs stated

that they are satisfied or very satisfied with their business activities, respectively.

In regard to the entrepreneurs’ perspectives on the government and its policy intervention

in the economy, 62% of them in this cluster believe that the government does not take

into account their needs when making policy decisions and only 38% think it gives

consideration. When the entrepreneurs in this cluster were asked the question of whether

they agree or disagree on the statement that the government does follow any strategy

related to their economic activities, nearly 83.3% of the respondents stated that they

somewhat or strongly disagreed, and only 16.7% of them stated that they agreed or

somewhat agreed that the government followed any economic strategy. The data shows

that the major concern of the entrepreneurs in this cluster is that the government should

take initiatives and provide them the opportunities for vocational training, impose import

quota on the similar imported goods, subsidies, and facilitate the access to information on

domestic and international markets. In addition, in terms of survival and resistance of

traditional clusters of micro and small enterprises in the challenging environment of

70

markets in Afghanistan, more than 40% of the entrepreneurs in this cluster indicated the

threat of import of similar goods from some of the neighboring countries, while 38%

mentioned the threat of rivals in domestic market, and about 31% mentioned the suppliers

of raw input materials as the major threats to the survival of their business in the future.

4.4.3. Carpenter Cluster

This cluster, more than 65 years old, is one of the very old traditional craft

industries in this city. Compared to the tailoring cluster, enterprises in this industry tend

to be older; close to 60% of them have been operating for 13 years or longer within the

cluster. In contrast to the other clusters in this study, carpenter enterprises are located

within areas outside of the old city of Herat. These enterprises are mainly located within

the areas from Chahar Rahi Mustufiyat to Jakkan, Saraki See Mitra to Jada-e- Baghi

Azadi, in 64-Mitra street, and the Darb Qandhar. This cluster is geographically more

scattered in comparison to the other five clusters in this study, which indicates the

potential for expansion of this industry in across the city over time.

Figure 4.7 shows the level of social capital such as trust and networking among

enterprises in the carpenter cluster. The data shows that 83.7% of the entrepreneurs in this

cluster trust in family members and relatives, 49% of them trust in neighbors, and 83.7%

of entrepreneurs believe that most of the entrepreneurs in the same cluster are trustworthy.

This data shows a slightly lower level of entrepreneurs’ trust in the police, municipality,

and national government officials. About 38.8% of the entrepreneurs trust in the suppliers

of the raw input materials, followed by 57.1% who trust in teachers and professors, and

more than 40% of them has trust in Wakil and Arbab in the work or residential area.

71

The data in Figure 4.7 indicates the level of participation of entrepreneurs in

groups or networks connected to the carpenter cluster. Nearly 71.4% of the entrepreneurs

in this cluster participate in the cluster association (Senf). However, the participation in

ethnic associations and sports groups is 20.4% and 16.33% respectively. Around 4.1% of

the entrepreneurs in this cluster participate in loan and credit associations as well as in

the association of industries and the trade chamber. Close to 16.3% of the entrepreneurs

in this cluster participates in a cooperative or association, local council, and associations

other than those mentioned above.

Figure 4.8 shows the cooperation and collective actions that exist among cluster

members. The data from this figure indicates that 81.6% of the enterprises in this cluster

share the machineries among themselves, and about 80% of them share information

related to the prices, design and production methods with other enterprises in the same

cluster. In terms of collective action, the data shows that there is a high level of confidence

among entrepreneurs in the effectiveness in decision-making on the issues related to their

cluster. 81.6% of them believes that they are effective in the decision-making process

4.1%

4.1%

16.3%

20.4%

71.4%

6.1%

12.2%

20.4%

83.7%

49.0%

83.7%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%





Join asnaf (X11)







Figure 4.7. Trust and Networking - Carpenter Cluster

72

with their cluster, and nearly 86% of the entrepreneurs participated in the nation-wide

decision-making process by voting in the last presidential election in 2014.

Nearly 69.4% of interviewed entrepreneurs stated that they have someone who

can provide them with the support whenever help is needed, also the entrepreneurs

reported that they also have provided help to the friends who sought their support during

the past three months.

The entrepreneurs believe that belonging to a cluster is beneficial to them. Around

28.6% of the entrepreneurs in this cluster believe that belonging to a cluster provides them

with the benefit of access to information on production methods, design, and prices.

Nearly 22.4% of the entrepreneurs consider it can increase the cooperation and unity

among cluster members, and 26.5% of them think this can be beneficial in regard to the

awareness of customer needs and changes in the market.

85.7%

69.4%

69.4%

81.6%

79.6%

81.6%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%







Figure 4.8. Cooperation and Collective Action - Carpenter Cluster

73

The sampled cluster of carpenters in this study consists of 44 micro-scale

enterprises and 5 small-scale enterprises. The structure of firms’ place ownership in this

cluster is slightly different from the other five clusters in this study. In this cluster, around

75.5% of firms operate in the rented place, 12.2% of them are private owned, 10.2% are

inherited, and 1% of the ownership of firms’ place is a mortgage. The data for status of

an enterprise’s competitive position compared to other enterprises in the same market

reveals that close to 10% of entrepreneurs evaluated the competitive position of their

firms as weaker than firms in the cluster, 65% as the same position, 19% as strong

position, and only 6% believes that their firm is in a stronger position than the other firms

in the same cluster.

The data shows that close to 45% of the enterprises in this cluster are planning or

considering to establish or expand their business during the past three years. In addition,

the major priorities for additional investment in the enterprises mentioned by

entrepreneurs are: the need for the investment in more machineries 51%, the investment

in employment of more employees 40.8%, the investment in raw input materials 38.8%,

22.4%

26.5%

28.6%

22.4%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%





Figure 4.9. Benefits of Belonging to a Cluster - Carpenter Cluster

74

better firm location 30.6%, additional storages in the firm 22.4%, and finally, close to 9%

indicated the need for the investment in vocational training for employees.

Innovation often occurs within this cluster and about 79.8% of enterprises have changed

the variety of their products during the last two years. During this time, the main sources

of innovation within the carpenter cluster are from customer’s feedback or design orders

61.2%, use of the internet as source of innovation 26.5%, imitation as a source for

introducing new designs or products 10.2%, and other sources 1%. The main strategies

for the customer base that practices by enterprises in this cluster are 61.2% that wants to

achieve higher product quality, 26.5% who want to provide a price discount, 24.5%,

through the current customers, 4.1% by marketing, and around 8.2% by other customer

base methods.

The level of business satisfaction among entrepreneurs in this cluster reveals that

close to 2.2% of entrepreneurs consider their business activity as dissatisfying or very

dissatisfying, 16.3% consider it as neither satisfying nor dissatisfying, 53.1% consider it

satisfying, and around 18.4% of the entrepreneurs stated that they are very satisfied with

their business activities within carpenter cluster.

In term of government policy intervention in the economy and the consideration of the

private sector, especially the traditional clusters of micro and small enterprises, close to

86% of the entrepreneurs from this cluster stated that the government does not consider

the interest of their business activities in its decisions and policies discourse. The data

also reveals that around 92% of the entrepreneurs from this cluster believe that the

government does not follow any specific strategies related to the interest of their business

environment in Herat City. From the entrepreneurs’ point of view on their major

priorities, more than 53% of the entrepreneurs in this cluster believe that the government

75

should provide protection by imposing import quota on similar products, 20.4% believes

that the government should facilitate the marketing of their product in the international

markets, 18.4% believes that the government should provide them with access to

vocational training for their employees, and 16.3% consider that government should

provide them with access to information on prices, raw input material, and market

changes. In addition, the major threats for the future survival of the carpenter enterprises

ranked from the entrepreneurs’ perspective are for 75.8% the threat from imported

products, for 18.2% the threat from rivals in the domestic market, for 12% the threat from

suppliers of raw input materials, and for 15.2% the threat from customer negotiation and

demand.

4.4.4. Shoemaker Cluster

This cluster is more than 40 years old, and close to 50% of the enterprises in this

cluster have been operating for around 5 years in this industry. The cluster is located in

the areas between Pai-e-Hasar to Jadah-e- Bank Khon, and Darb Qandhar. The shoemaker

cluster is one of the very old traditional industries in Herat City. This cluster has achieved

a high level of sophistication in production methods and design over time in Afghanistan.

This study found that the shoemaking industry is expanding in the recent years in

Afghanistan and particularly in Herat City. During the field survey in this study, it

reported that there are a few large size shoemaking enterprises in this city and that their

products have reached into the markets in other provinces in Afghanistan.

76

Figure 4.10 shows the level of trust and networking among shoemaker cluster in

Herat City. The findings from this cluster indicate that nearly 82% of the entrepreneurs

from this cluster trust in their family members and relatives, and a very high level of 91%

of the entrepreneurs trust in the other members found within this cluster. On the one hand,

33.3% trust in neighbors and only 9.1% trust in the police and municipality officials. This

shows a very large difference in the structure of social capital among entrepreneurs in this

cluster. On the other hand, the figure shows that there is a very high level of mistrust in

the national government officials among the entrepreneurs in the shoemaker cluster in

Herat City. Similarly, about 15.2% of the entrepreneurs in this cluster trust in Wakil and

Arbab. The level of entrepreneur’s trust in the suppliers of raw input materials and trust

in teachers and professors indicates a slightly higher trust level of 42.4% and 60.6%

within this cluster, respectively.

Networking among enterprises in the shoemaker cluster was found to be a slightly

lower in comparison to the previously described clusters in this chapter. More than 51%

3.0%

9.1%

21.2%

15.2%

51.1%

0.0%

9.1%

9.1%

90.9%

33.3%

81.8%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%


Join industries and trade chamber

(X13)



Join asnaf (X11)

Trust in national government staff

(X142)






Figure 4.10. Trust and Networking - Shoemaker Cluster

77

of the enterprises reported the participation in the association of shoemakers (Senf) in this

cluster. The networking and participation of cluster members in ethnic associations,

sports groups, and associations of trade and chamber are 15.2%, 21.2%, and 9.1%,

respectively. The data from this cluster shows that only 3% of members in this cluster are

members of any loan and credit associations, indicating a very low level of enterprises in

financial institutes in the shoemaker cluster. The findings from this study indicate that

around 24.2% of enterprises participate in cooperatives and associations, and only about

3% participate in the local council of their community.

Figure 4.11 shows the level of cooperation and collective action among

shoemaker enterprises in this study. About 84.8% of enterprises in this cluster share the

machineries among themselves, and 57.6% of enterprises cooperate by sharing the

information related to prices, production methods, product designs, and raw input

materials in this cluster. The study also found that there is supportive cooperation among

shoemaker enterprises in this cluster. More than 72% of entrepreneurs in this study stated

that they had provided some types of support to the other cluster members and friends

during the past three months.

This study found that there is collective action taken by the entrepreneurs in this cluster.

More than 72% of the entrepreneurs stated that the process of decision-making within

shoemaker cluster was effective and nearly 79% of the entrepreneurs in this cluster

reported that they participated in the nation-wide events such as voting in the presidential

election in 2014. The study found that some members of this cluster also participated in

the other collective action events in this city such as the parades related to their business

activities.

78

Figure 4.12 shows the entrepreneurs’ perspectives on belonging to a cluster. The

data shows that 36.4% of the entrepreneurs believe that belonging to a cluster can increase

the access to market information on products prices, design, and production methods.

More than 24% of them consider that it can increase the cooperation and unity among the

enterprises to handle issues relevant to their business interests. 30.3% of them consider

that belonging to a cluster can provide them with better access to awareness on customers

and market changes, while only less than 10% of the entrepreneurs consider that it will

provide them with the other type of benefits.

The shoemaker cluster in this study consists of 19 micro-scale enterprises and 14

small-scale enterprises. The data shows that close to 82% of enterprises in this cluster is

78.8%

72.7%

36.4%

72.7%

57.6%

84.8%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%







Figure 4.11. Cooperation and Collective Action - Shoemaker Cluster

9.1%

30.3%

36.4%

24.2%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%





Figure 4.12. Benefits of Belonging to a Cluster - Shoemaker Cluster

79

operated by its owner and only 18% of them operated by managers other than the owners

in the shoemaker cluster. The ownership of firms’ places in this cluster is about 57.6% in

a rented place, 39.4% in private places and about 3% in places which are inherited.

Evaluation of the competitive position of an enterprise in comparison to the same

enterprises of this cluster in the market as evaluated by the entrepreneurs is that 21%

stated their enterprise as being in a weaker position, 51.5% in the same position, 24% in

a stronger position, and 3% in a very strong position in this study. In addition, the findings

from this cluster show that more than 27% of enterprises in this cluster had planned to

expand or establish a new firm during the past three years. The data shows that the main

priorities of the enterprises for additional investment are: 63.6% of the enterprises stated

the need to invest in additional machineries, 48.5% stated the need to invest in

employment of more employees and raw input materials, 30% the need to invest in a

better location for the enterprise, and about 18.2% in vocational training for the

employees of their enterprise.

Findings from this cluster show that innovation in product design is close to 79%

within the enterprises in the shoemaker cluster. The main sources of the innovation and

the design changes of products are 48.5% by the feedback and order from customers,

27.3% through the internet sources, 21.2% by imitation from products that were either

imported or produced in domestic markets, and only 3% of the enterprises use other

sources. In addition, the enterprises in this cluster use different types of strategies to keep

their customer bases. The most popular strategies for increasing the customer base that

apply to the enterprises in this cluster are higher quality products 57.6%, by providing a

discount to customers 45.5%, through current customers 21.2% and other methods of

marketing 3%.

80

The findings of this study on the level of business satisfaction within enterprises in this

cluster indicate that only 18.5% of the entrepreneurs in this cluster stated the level of their

satisfaction as dissatisfying or very dissatisfying, 7.4% stated neither satisfying nor

dissatisfying, and the majority of 74.1% of entrepreneurs in this cluster stated the

satisfaction level of their business activities as satisfying or very satisfying within this

cluster.

The entrepreneurs’ perspective on government policies and decisions in this

cluster indicates that only 12% of entrepreneurs in this cluster believe that the government

considers the interests of their business activities in its decisions. Whereas the majority

of 88% of them believes that they have been neglected in the government’s decisions,

close to 85% of entrepreneurs in this cluster consider that the government does not follow

any specific strategy related to their business activities at all. The data from this cluster

indicates that more than 81.8% of the entrepreneurs in this cluster believe that the

government should consider the protection for their business by imposing import quota

on similar products, 21.2% stated that the government should facilitate the access to the

foreign markets, and about 12.1% of the entrepreneurs stated that the government should

provide the opportunity for vocational training to their employees within this cluster. In

addition, the data shows that close to 75.8% of enterprises in this cluster consider

imported products to be major threat to the future of their industry, 18.8% considers the

revivals in the market to be the threat, and only 12.1% of them consider the supply of raw

input materials the major threat to the survival of shoemaker industry in the future.

81

4.4.5. Ironmonger Cluster

This cluster, which is nearly 300 years old, is one of the most traditional industries

in this city and in Afghanistan as a whole, and about 60% of the enterprises have been

operating for more than 15 years in this cluster. The cluster is mainly located in the area

between Darb Khush along with Saraki Bazari Misgar Ha running toward the Darbi Iraq.

The cluster produces a variety of products used for home consumption, other industries,

and in the agriculture sector. The findings from the field survey indicate that the

ironmonger cluster has the potential to expand and to survive within the competitive

environment of markets in Afghanistan.

Figure 4.13 shows the level of trust and networking among ironmonger enterprises

in this cluster. Data in this figure shows that 92.6% of the entrepreneurs in this cluster

have a very high level of trust in their family members and relatives, more than 74% of

them have trust in neighbors, and around 70.4% of the entrepreneurs consider most

members of the same cluster are trustful. Findings from this study show around 40.7% of

the entrepreneurs have trust in the suppliers of raw input materials, more than 81% of

them trust in teachers and professors, and about 52% of entrepreneurs trust in Wakil and

Arbab in their work and residential area. Trust in the formal organizations in this cluster

indicates a very similar pattern with other clusters that described before. More than 40%

of the entrepreneurs have trust in the police, 22.2% of them trust in municipality official

and only 7.4% of the entrepreneurs have reported that they have trust in the national

government official.

82

Networking through participation in groups and associations in this cluster

indicates that nearly 71% of entrepreneurs within this cluster participate in the association

of ironmongers (Senf), 18.5% of them join in ethnic associations, around 15% of them

join in sport groups, and there is no one from this cluster who participates in the

association for industries and trade chambers, mainly because of the major parts of the

value chain in this industry are located only within the domestic market in Afghanistan.

The data shows that only 3.4% of the enterprises within this cluster participate in loan

and credit associations. Findings from this study show that around 15% of the

entrepreneurs in this cluster are a member of types of cooperative and associations, 11%

participate in the local council in their community, and only 7.4% of them participate in

other types of groups and associations.

3.7%

0.0%

14.8%

18.5%

70.4%

7.4%

22.2%

40.7%

70.4%

74.1%

92.6%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%





Join asnaf (X11)







Figure 4.13. Trust and Networking - Ironmonger Cluster

83

Figure 4.14 shows the cooperation and collective action among the members of

the ironmonger cluster. There are 81.5% of the enterprises in this cluster that cooperate

by sharing the machineries among each other, nearly 67% of the enterprises share

information on prices, raw input materials and product designs. Around 55.6% of the

entrepreneurs in this cluster stated that they have someone who can help them whenever

there is a need related to their business, and entrepreneurs themselves have provided the

assistance to other friends and members from the same cluster during the past three

months.

Participation in collective action within this cluster shows that nearly 52% of

entrepreneurs consider themselves effective in decision-making, and around 81.5% of

entrepreneurs have reported of participation in nation-wide decision-making such as the

presidential election in 2014. The findings from this cluster also indicate the collective

action of cluster members through participation in demonstrations such as the resistance

against the implementation of tax raises and the municipality plan related to the relocation

of some of these clusters from current locations to other areas outside the old city of Herat.

81.5%

55.6%

55.6%

51.9%

66.7%

81.5%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0%







Figure 4.14. Cooperation and Collective Action - Ironmonger Cluster

84

In terms of the benefits that a cluster can provide, more than 40.7% of the

entrepreneurs in this cluster consider that being in a cluster can increase the cooperation

and unity among enterprises and close to 30% of them believe that they can benefit from

the access to information on prices, raw input materials, and products design. Only 3.7%

of the entrepreneurs consider that the cluster can provide them with awareness of

customer demand as well as market changes, and around 26% of the entrepreneurs

consider other types of benefits to be the advantages of belonging to a cluster.

The sampled ironmonger cluster in this study consists of 24 micro-scale

enterprises and 3 small-scale enterprises. The structure of firms’ place within this cluster

is that 55.6% of them operates in a rented place, 33.3% of them operates in privately

owned place, and only 11.1% of enterprises operate in an inherited place within this

cluster. Findings from this cluster indicate that a majority of 74% of the clustered

enterprises is operated by their owners, and around 26% of them are run by managers

other than the owners in this cluster. Considering the entrepreneurs’ perspective on the

competitive position of their enterprise in comparison to other enterprises within the same

cluster; more than 70% of the enterprises described their competitive position as similar

to the others, 11% consider their enterprise in a weak position, and about 18.5% of them

25.9%

3.7%

29.6%

40.7%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0%





Figure 4.15. Benefits of Belonging to a Cluster - Ironmonger

85

stated its as strong or very strong position in comparison to the other enterprises in this

cluster.

The findings from this cluster indicate that there is potential for expansion of

enterprises within this cluster. Around 30% of the entrepreneurs from this cluster reported

of plans to expand or to establish new enterprises during the last three years. The main

priorities for additional investment in the enterprises within this cluster are found to be

the need to invest in raw input materials (52%), the need to invest in machineries 44%, in

tools and better location for the enterprise (33%), in employment of additional employees

29.9%, and in vocational training for employees in their enterprise 7.4%.

Innovation occurs often within this cluster. More than 66.7% of enterprises from

this cluster reported that they have changed the varieties and the design of their product

in the past two years within enterprises in this cluster. The main sources for innovation

are perceived by the enterprises in this cluster as follows: 74% based on customer

feedback and orders, 15% by imitation from imported and domestic products, 4% through

internet sources, and around 7% of enterprises use other types of sources for innovation.

In addition, popular strategies for the customer base that is implemented by enterprises in

this cluster are 70% by providing discount on the price of products, 56% through better

quality of products, 7% through the current customers of an enterprise, and only 4% of

the enterprises uses other types of marketing methods for the customer base in their

enterprise within this cluster.

The findings from this cluster show that there is a very high level of business

satisfaction within enterprises in this cluster. About 18.5% of the entrepreneurs stated the

level of their satisfaction in business activities as dissatisfying or very dissatisfying, only

7% of them stated it as neither satisfying nor dissatisfying with their business, and the

86

majority of 74% of the entrepreneurs consider they are satisfied or very satisfied with

their business activities within ironmonger cluster. In addition, the perspective of

enterprise on the contribution of the government policies and decisions to the overall

economy indicates that about 78% of the entrepreneurs from this cluster believe the

government does not consider business interests in its policies and decisions discourse.

On one hand, more than 85% of enterprises somewhat or strongly disagrees that

the government does not follow a specific strategy that can favor their business activities.

On the other hand, regarding the priorities of the initiatives that government should

provide to the enterprises within this cluster, it is revealed that 55.6% of the enterprises’

owners in this cluster stated that the government should protect their business by

imposing import quota on similar products coming mainly from neighboring countries,

48% of enterprises stated that the government should protect them by providing subsidies

to these enterprises, and around 14% of them stated that the government should take

initiatives to provide them with facilities such as vocational training for their employees

as well as increase their access to the information on prices, designs and other markets

within Afghanistan. In addition, concerning the survival of their ironmonger industry,

56% of the enterprises in this cluster consider the power of customers’ negotiation and

demand to be a major threat to their industry, 44% of them consider imported products to

be a threat, and more than 48% of the enterprises in this cluster consider the rivals the

major threat to their business and the survival of the ironmonger industry in future.

4.4.6. Tinwork Cluster

The Tinwork cluster is 65 years old and is one of the traditional clusters in Herat

City. In comparison to the Ironmonger cluster, this cluster seems to be a younger industry,

87

and close to 50% of enterprises in this cluster have been running for less than 12 years.

The cluster is mainly located together with the Ironmonger cluster in the old city of Herat.

In addition, a small proportion of enterprises in this cluster are also located in the area

outside of the old city between Chahar Rahi Mustufiyat to Jakkan, and Falaka-e- Bikrabad

toward the Posta-e- Number Yak. The sampled enterprises of tinwork cluster in this study

has few similarities with the previous cluster of ironmongers such geographical location,

in some case connected through supply side of raw input materials, and the potential for

the expansion of this industry in the future.

Figure 4.16 shows the level of social capital such as trust and networks among the

tinwork enterprises in this cluster. Around 90.6% of entrepreneurs within this cluster have

stated that they have trust in their family members and relatives, only 50% of them

reported to have trust in neighbors, and close to 88% of enterprises believes that most of

the members within their cluster are trustful. The findings from this cluster indicate that

around 44% of the entrepreneurs have trust in the suppliers of raw input materials, 59.4%

of entrepreneurs have trust in teachers and professors and only 34.4% of entrepreneurs

described that have trust in Wakil and Arbab in their communities.

88

The findings in this study show that the tinwork enterprises have a similar pattern

of trust in the governmental body other than their neighbors, close friends, and family

members. The data in Figure 4.16 shows that more than 34% of the entrepreneurs in this

cluster have trust in the police, more than 9% have trust in municipality officials, and only

12.5% of the entrepreneurs described to have trust in national government officials. In

addition, in the same figure the data indicates that close to 72% of entrepreneurs often

participate in the association of tinworks (Senf), a low proportion of 15.6% of

entrepreneurs participate in the ethnic groups, only 12.5% of entrepreneurs from this

cluster have stated that they participate in any sport groups, and there is no one from

tinwork cluster who participate in the association of trade chambers or is a member of

any loan and credit groups in this cluster. The enterprise membership in the cooperative

is reported to be about 6%, and around 9.4% of enterprises participate in the local

community council and other types of groups or associations.

0.0%

0.0%

12.5%

15.6%

71.9%

12.5%

9.4%

34.4%

87.5%

50.0%

90.6%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%





Join asnaf (X11)







Figure 4.16. Trust and Networking - Tinwork Cluster

89

This data in Figure 4.17 shows the cooperation and collective action among the

tinwork enterprises within this cluster. This study found that around 68.8% of the

enterprises in this cluster cooperate with each other by sharing the machineries among

themselves. More than 62% of enterprises in this cluster share information about the

prices, production methods, product design, and bulk purchase of raw input materials

within this cluster. There is cooperation and trust between members of this cluster and as

well as their friends and relatives. More than 40% of the entrepreneurs in this cluster

stated that they have at least one person in their network who can receive support from

them, and around 50% of entrepreneurs stated that they also provided support or other

types of assistance to the members in the same cluster or friends.

The findings from this cluster indicate that more than 53% of the enterprises

consider themselves as effective in the decision-making process within this cluster. There

are more than 87% of the entrepreneurs within this cluster that participate in a type of

collective action event such as the vote in the presidential election in 2014, as well as

participated in demonstration regarding their business activities in the past in this city.

87.5%

50.0%

40.6%

53.1%

62.5%

68.8%

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%







Figure 4.17. Cooperation and Collective Action - Tinwork Cluster

90

The Figure 4.18 shows that around 22% of the entrepreneurs from this cluster

believes that belonging to a cluster can increase the cooperation and unity among tinwork

enterprises, around 25% of entrepreneurs consider that the cluster facilitates their access

to information on prices, design, production methods, and raw input materials within this

cluster. Nearly to 22% of the entrepreneurs believes that the cluster can increase the

awareness of customers and changes in the market, and more than 31% of entrepreneurs

in this cluster believes that being in a cluster has other types of benefit to their business

activities within this cluster.

The composition of enterprises within the tinwork cluster in this study consists of

29 micro-scale enterprises and 2 small-scale enterprises. The structure of enterprises’

management within this cluster indicates that more than 62% of the enterprises are

operating by their owner, and only about 38% of the enterprises are operated by managers

other than the owners in this cluster. The majority of 53% of the firms’ places are rented

31.3%

21.9%

25.0%

21.9%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%





Figure 4.18. Benefits of Belonging to a Cluster - Tinwork Cluster

91

places, and only 47% of firms operate in the private and heritage places within tinwork

cluster in this study.

The competitive position of an enterprise, when compared to the other cluster

members in the market as evaluated by the entrepreneurs from the tinwork cluster, is that

15.6% of entrepreneurs ranked their enterprise as being in a weak position in comparison

to other members of this cluster, while more than 37% consider their enterprise as being

in the same position with others, and about 47% of entrepreneurs evaluated their

enterprise as having a strong or very strong position in the market, compare to another

member of this cluster. There is a potential of the expansion of this cluster. More than

37% of the enterprises in this cluster have planned to expand or to establish a new

enterprise in the past three years. The priority of the enterprises for additional investment

in this cluster indicates that around 47% of the enterprises consider the need to invest in

more machineries, 34% need to invest in additional raw input materials, 25% stated the

need to invest in employment of more employees, and about 6% of them stated the need

to invest in vocational training for their employees and additional storages as enterprise

priority for investment in this cluster.

Innovation often occurs within this cluster. There are more than 78% of the

enterprises within this cluster that introduced new products during the past two years. The

main sources for innovation in this cluster are through customer feedback or order 72%,

by imitation of similar products that were imported or produced by domestic producers

16%, the internet as a source of innovation 9%, and other types of sources 3%. In addition,

the enterprises in this cluster apply different strategies for the customer base in their

enterprise. The finding from this cluster indicates that 75% of the enterprises apply quality

92

improvement of their products as the main strategy, and around 47% apply a discount on

products as the major strategies of their enterprise.

There is a high level of business satisfaction found among entrepreneurs from this

cluster. More than 75% of entrepreneurs in the tinwork cluster reported they were

satisfied or very satisfied with their business activities in this cluster, 9.4% stated they

were neither satisfied nor dissatisfied with their business and only 15% of them reported

to be dissatisfied or very dissatisfied with their business activities in this cluster. The

entrepreneur’s perspective on the government role in the economy within this cluster

indicates that more than 82% of entrepreneurs believe that the government does not

consider the interest of their industry in it decisions and policies. 81% of the entrepreneurs

within this cluster that somewhat or strongly disagree that the government does follow

any specific strategies related to their economic activities. In addition, close to 41% of

the enterprises in this cluster believes that the government should protect their industry

by imposing import quota on similar imported products, 31% believes that the

government should provide subsidies to their enterprise, and more than 15% of

entrepreneurs consider that the government should provide the facilities such as

vocational training for their employees.

The findings from the tinwork cluster in this study indicates that more than 53%

of enterprises considers the revival to their enterprise as major threat, 50% considers the

threat of imported products, and about 25% of enterprises consider the power of customer

negotiate and demand condition as the major threat to the survival of their enterprises in

the future.

93

5. CHAPTER V

FACTORS ASSOCIATION WITH MSEs’

PERFORMANCES IN HERAT CITY

Introduction

The aims of this chapter are to explore the structural relationship between the

social capital dimension and the performance of enterprises through other dimensions

within the conceptual framework of this study. In this chapter, I used correlation matrix

methods to analyze and explain these structural relationships between enterprises’ social

capital, performance, and other dimensions. In addition, the implementation of correlation

analysis method allows us to identify the variables that have a significant association with

MSEs’ performances in each of dimension within the conceptual framework in chapter

2. In order to test the causal relationship among enterprise’s performance, social capital

and factors from other dimensions, only the identified variables with the significant

association from the analysis in this chapter were considered to enter into the succeeding

stages of analysis such as regression with path diagram model in Chapter 6 of this study.

94

Identification of Factors Associated with MSEs’ Performances

Understanding the structure of association between enterprise’s performance,

social capital, and other dimensions in Porter’s Diamond framework, can help us to

explore the nature and characteristics of the overall relationships among micro and small

scale enterprises within the traditional clusters in Herat City. Based on the main claim of

the hypothesis in this study, that social capital plays important direct and indirect roles in

the traditional clusters in Herat City (see Chapter II). Beside the identification of factors

having significant association with the performance of enterprises, this section also aims

to analysis the relationship between MSEs’ performances and every other dimension in

the presence of social capital. In each of the following sections, the study described the

structure of the relationship between enterprise’s performance, social capital, the

significant variables from other dimensions, namely, factor conditions (X2), related and

supporting industries (X3), demand conditions (X4), firms’ strategy, structure and rivalry

(X6), government backing policies (X7), and chance (X8) in the conceptual framework of

this study on the traditional clusters of micro and small scale enterprises (MSEs) in Herat

City.

In this chapter, the method for choosing each of the variables in the following

tables of correlation matrix was based on the number of paired cells with a significant

association between different variables in the same dimension or other dimensions within

the conceptual framework of this study. The same method for choosing and considering

variable for further analysis was applied to all other section in this chapter. In addition,

in each of the following sections, the findings from other variables were also described

in relation to the variables that were included in each table of Chapter 5.

95

5.2.1. Association between Social Capital and the Performances of MSEs

The social capital dimension in this study consists of thirty-five variables in this

study. Table 5.1 shows the matrix of fifteen variables from the dimension of social capital

(X1) that were identified to have the highest number of paired cells of significant

association with MSEs’ performances (Y).

Findings in Table 5.1 show that there was a moderate positive association between

the entrepreneur’s number of close friends (X112) and MSEs’ performances in traditional

clusters with correlation coefficients of (r=.225, p<0.01). This finding points to the fact

that entrepreneurs with a higher number of close friends have more chance to increase the

performance of their enterprise through the sales of their products within the traditional

clusters in Herat City.

The results for correlation matrix reveal that the number of friends who are willing

to provide support to an entrepreneur (X113) with (r=.226, p<0.01) has positive association

with enterprise’s performance. This means when an entrepreneur has a wider network of

friends who can provide assistant whenever needed, has a positive association with

enterprise’s performance. In addition, the results of correlation matrix in Table 5.1 show

that these type of assistance from the entrepreneur’s friends (X113) positively associate

with the number of close friends (X112), level of trust in neighbors (X133), and how much

influence in decision-making (X123) an entrepreneur has in a tradition cluster with (r=.168,

p<0.05), (r=.183, p<0.01), and (r=.161, p<0.05), respectively. In addition, the findings

indicate that there was a positive association between the variable of entrepreneur helping

a stranger (X154) and MSEs’ performances with (r=.227, p<0.01). This means that the

cooperation with and supporting of others occurs more often in the enterprises that have

better performances within the traditional cluster in Herat City.

96

Table 0.1. Correlation Matrix of Between Social Capital and MSEs Performance

(Y) (16) (17) (18) (111) (112) (113) (123) (129) (133) (136) (138) (139) (151) (154) (155)

MSEs’ performances (Y) 1

Join Loans Association (X16) -.019 1

Join sport group (X17) .069 .282** 1

Join ethnic group (X18) .003 .167* .184** 1

Family in Same Industry (X111) -.138* .041 -.098 -.087 1

Number close friends (X112) .225** -.059 .096 .267** -.011 1

Friends Can Help (X113) .226** -.003 -.006 .096 .126 .168* 1

Effective in Decision Making (X123) -.008 .007 .139* .079 .146* .160* .161* 1

Trust Relatives (X129) -.147* -.015 .026 .038 .007 .035 .053 .037 1

Trust Neighbors (X133) .003 -.109 -.104 .062 -.004 .069 .183** -.077 .209** 1

Trust suppliers (X136) .041 .028 -.006 .104 -.063 .179* .059 -.017 .071 .279** 1

Trust teachers/professors (X138) .025 .112 -.079 .096 -.010 .004 .046 -.140* .255** .230** .181** 1

Trust Municipality Officials (X139) .023 -.037 -.186** -.008 -.003 .071 .053 -.139* .005 .161* .118 .087 1

Facebook account (X151) .066 .147* .196** .087 .119 .108 .087 .036 -.068 -.243** -.128 -.158* .037 1

Help a Stranger (X154) .227** -.077 -.005 .067 -.065 .171* .065 .183** .045 .006 .092 -.170* -.074 -.012 1

Attended Mosque (X155) -.171* .022 .010 -.148* .033 -.111 .042 -.044 .080 .176* -.017 .026 .079 -.172* -.068 1

*. Correlation is significant at the 0.05 level (2-tailed), **. Correlation is significant at the 0.01 level (2-tailed).

The results of correlation matrix in Table 5.1 show that there was a negative

association between the enterprise’s performance, the entrepreneur’s trust in relatives

(X129), and an entrepreneur having a family member in the same industry (X111) in a

cluster with coefficients of (r= -.147, p<0.05) and (r= -.138, p<0.05) respectively. This

indicates that trust in family and relatives (X129), a family member in the same cluster

exist more among the enterprises with the lower level of performance within the

traditional clusters. The result also indicates that there was a positive association among

entrepreneurs that have a family member in the same industry and the frequency of

supporting their friends within these traditional clusters with correlation coefficients of

(r=.211, p<0.01).

The results of correlation matrix in Table 5.1 show that the number of times an

entrepreneur attended the mosque (X155) has a negative association with MSEs’

performances with (r= -.171, p<0.05). This indicates that practicing a religious activity

such as going to mosque for prayers by entrepreneurs, has a negative association with the

enterprise’s performance within the traditional clusters in Herat City. In addition, the

finding shows that there was a negative correlation between entrepreneur’s attending the

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Mosque (X155), participation in the ethnic group association (X18), and the use of social

media such as having a Facebook account (X151). On the other hand, findings reveal that

there was positive correlation between the use of Facebook and entrepreneur’s

participation in social network or groups such as sports groups and credit or other

financial networks with coefficients (r=.196, p<0.01) and (r=.147, p<0.05), respectively.

The results of correlation matrix in Table 5.1 reveal that effectiveness of

entrepreneurs in the process of decision-making the ability of entrepreneurs in making

effective decisions has mostly positive association with their participation in networks

such as sports clubs or groups (X17), having a family member in the same industry (X111),

cooperation with others in terms of providing or receiving support with (X113) and (X154),

respectively.On the other hand, their effectiveness in decision making within a cluster has

negative associations with the level of their trust in municipality officials (X139) and trust

in teachers and professors (X138) within the traditional clusters of micro and small

enterprises in Herat City.

As shown in Table 5.1, results of correlation matrix disclose that out of the fifteen

variables from the social capital (X1) dimension which were included in this table, only

six of them, namely, having s family member in the same industry (X111), number of close

friends (X112), number of friends who can help (X113), trust in family and relatives (X129),

helping a stranger (X154), and the number of times attended a mosque (X155) were

identified to have significant association with MSEs’ performances and considered for

further analysis in Chapter 6 in this study.

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5.2.2. Association between the Social Capital, Factor Condition, and

Performance of MSEs

The results in Table 5.2 show that seven variables from the dimension of factor

conditions (X2), namely, total of current assets (X219), space is rented (X224), age of

entrepreneur (X21), level of education (X23), vocational training (X210), source of

investment from family friends (X213), and car (X227) were included in the correlation

analysis in this section, beside the variables from social capital dimension.

Findings in this table further reveal that there was a strong positive association

between the total value of current assets of an enterprise and its performance within the

traditional clusters in Herat City. This points out that the enterprises’ performance within

the traditional cluster highly correlates with the value of their current assets that they

possess. The value of an enterprise’s current assets (X219) positively associate were higher

among the enterprises that participate in the association of industries and trade chamber

(X13) and the cluster’s association (X11) with (r=.168, p<0.05) and (r=.145, p<0.05).

Another fact this analysis revealed is that there was a moderate positive

association between the enterprise’s total value of current assets (X219) and the ownership

status of enterprise’s operation space as the inheritance (X223) with correlation

coefficients of (r=.207, p<0.01). This leads to the conclusion that if enterprises inherit the

space where they conduct their business from their ancestors, the possibility of their better

performance and greater value of their current assets highly increases in those six

traditional clusters of micro and small scale enterprises in Herat City which were included

in this study.

Table 5.2 shows a negative association between MSEs’ performances and the

venue for the activity of the enterprise if the space is rented (X224) with (r= -.219, p<0.01).

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This implies that if the venue for the activity of enterprises is not a rented space, the

probability of higher performance increases.

The results of correlation analysis in Table 5.2 show that only two variables from

the dimension of factor conditions (X2) within the conceptual framework of this study

were identified to have a significant association with MSEs’ performances (Y) in this

section.

Table 0.2. Correlation Matrix of Between MSEs Social Capital, Factor Conditions and Performance

(Y) (11) (13) (14) (15) (16) (17) (18) (111) (112) (113) (122) (123) (151) (153) (154) (155) (219) (224) (21) (23) (210) (213) (227)

MSEs’ performances 1

Join Senf (X11) -.046 1

Join Industries Chamber (X13) -.010 .047 1

Join local council (X14) -.010 .092 .141* 1

Join cultural association (X15) .009 -.032 .078 .311** 1

Join Loans Association (X16) -.019 .011 .071 .255** .273** 1

Join sport group (X17) .069 -.023 .174* .187** .211** .282** 1

Join ethnic group (X18) .003 .049 .011 .246** .273** .167* .184** 1

Family in Cluster (X111) -.138* .100 -.057 -.049 -.049 .041 -.098 -.087 1

Number close friends (X112) .225** .058 .012 .145* .092 -.059 .096 .267** -.011 1

Friends Can Help (X113) .226** .002 -.003 .052 .040 -.003 -.006 .096 .126 .168* 1

Share Machineries (X122) .073 .048 .112 .012 .121 .036 .057 .104 .024 .138* .017 1

Decision Making (X123) -.008 .032 .147* .051 .012 .007 .139* .079 .146* .160* .161* .043 1

Facebook account (X151) .066 -.085 .103 .088 .294** .147* .196** .087 .119 .108 .087 .048 .036 1

Charities (X153) -.122 .086 .259** .090 .038 .088 .035 .083 -.103 .027 -.172* .087 .030 .066 1

Help a Stranger (X154) .227** -.047 .039 .011 .176* -.077 -.005 .067 -.065 .171* .065 .156* .183** -.012 .166* 1

Attended Mosque (X155) -.171* .096 -.021 -.093 -.137 .022 .010 -.148* .033 -.111 .042 -.100 -.044 -.172* -.135 -.068 1

Current Assets (X219) .385** .145* .168* -.033 -.053 -.024 .017 -.080 -.110 .126 .093 .068 .036 -.047 -.077 .080 .038 1

Space is Rented (X224) -.219** .061 .012 .076 .118 .123 -.048 .123 .052 .038 .047 -.001 -.019 -.052 .038 -.042 -.028 -.083 1

Age (X21) .032 .063 -.106 .060 -.082 -.081 -.245** -.006 .033 -.027 -.159* .020 -.054 -.330** .013 .140* .131 .090 -.201** 1

Level of education (X23) .040 -.016 .077 -.045 .101 .093 .169* -.090 .099 .141* .003 -.019 .045 .249** .097 -.054 -.113 .009 .127 -.343** 1

Vocational training (X210) -.021 -.216** .073 .036 .007 .136 .035 .052 .183** .080 -.005 .088 .208** .187** .071 .192** -.001 .064 .038 .007 -.040 1

Source Invest relatives (X213) .056 -.056 .115 .246** .204** .102 .277** .224** -.052 .032 .149* .082 .089 .182** .017 -.039 -.054 -.061 .153* -.184** .091 .018 1

Car (X227) .070 .016 .085 .026 -.031 -.054 .011 -.056 .099 .071 .146* .018 .105 .216** -.071 .010 -.023 .064 -.095 -.044 .132 .036 -.037 1


Findings in Table 5.2 show that there was not a significant correlation between

MSEs’ performances and its human capital factors such as the age (X21), work experience

(X22), and level of education (X23) of entrepreneurs.

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On the other hand, previous findings based on the same data collected by the

author indicate that there were a significant association between the factors within human

capital and social capital (Valizadah 2015).

Findings of correlation analysis reveal that there was a very strong positive

association between the entrepreneur’s age (X21) and the work experience of an

entrepreneur in the same industry (X22) with correlation coefficients of (r=.810, p<0.01).

Therefore, in order to avoid the problem of multicollinearity13 in regression analysis in

the next chapter, these associations of higher than (r=.8), and in accordance with our

criterion for correlation analysis in Chapter 3 we dropped the variable of work experience

(X22) from further analysis in this section. On the other, due to very high correlation

among these two variables, statistically, each of these two variables (age and work

experience) can represent each other because of their close statistical correlation. This

finding indicates that the higher the age of entrepreneurs, the higher would be the possibly

of having more working experience in the same industry.

The findings show that there was a strong negative association between the age of

entrepreneurs and the level of their education with (r= -.343, p<0.01). This indicates that

the higher age and experience of the entrepreneurs negatively associate with their level

of education within the traditional clusters of micro and small enterprises in Herat City.

The age and experience of entrepreneurs in the same field also have negative correlation

with their participation in social networks such as sports groups (X17), number of friends

who can help (X113) and meeting with friends (X147) with correlation coefficients of (r= -

13 A term used in regression analysis to indicate situations where the explanatory variables are related by

a

linear function, making the estimation of regression coefficients impossible (Everitt 1998, p.219).

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.245, p<0.01), (r= -.159, p<0.05), and (r= -.142, p<0.05), respectively. There was a

negative association between entrepreneurs’ age and the use of social communication and

IT gadgets such as Facebook and the Internet. This means that younger entrepreneurs

within these traditional clusters in Herat City have higher level of education and higher

tendency for participation in social networks and the use of social communication and IT

gadgets. Even though, the use of Facebook found to be more popular among younger

entrepreneurs with a higher level of education within these traditional clusters. However,

the findings from correlation analysis in this study indicate that there is a positive

association between the use of Facebook (X151) and the entrepreneurs’ participation in

social networks and utilizing these networks and groups as sources of finance and

investment in their business activities.

The findings show that nearly 73% of the entrepreneurs borrow money from their

relatives and friends as the main source of investment for their business activities. The

results of correlation analysis reveal that there was a positive association between the use

of relatives and friends as a source of investment (X213) and the entrepreneurs’

participation in social networks such as local councils (X14), cultural associations (X15),

sports groups (X17), and joining the ethnic associations (X18) with correlation coefficients

of (r=.246, p<0.01), (r=.204, p<0.01), (r=.277, p<0.01), and (r=.224, p<0.01),

respectively. This demonstrates that entrepreneurs’ participation in social networks and

communities significantly associate with their investment from their relatives or friends,

and often their participations in these networks function as a base for providing them

financial resources to investment in the micro and small enterprises within the traditional


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5.2.3. Association of Social Capital, Related and Supporting Industries of the

MSEs, and their Performances

The results of the correlation matrix in Table 5.3 describe the structural

relationship between MSEs’ performances, social capital, and the dimension of

enterprise’s related and supporting industries (X3) within the conceptual framework of

this study. There were two variables, namely, the economic status of helpers who were

willing to provide support to the entrepreneurs (X31) and the current location of enterprise

within the cluster (X35) that represent the dimension of related and supporting industries

(X3) which were included in the correlation analysis in this section.

The results of the correlation analysis in Table 5.3 show that there were significant

negative association between MSEs’ performances and the location of enterprise (X35)

within a cluster with (r= -.167, p<0.05). In order to find out the nature of the above

correlations, entrepreneurs in the traditional cluster in Herat City were asked to evaluate

the location of their enterprise through question with the following answer options: “not

proper location”, “proper location”, and “very proper location” within the cluster. The

finding of the survey revealed that the enterprises with better performance mostly chose

the option “not proper location” within the cluster.

Findings from the correlation analysis reveal that there was a positive association

between the location of enterprise (X35) and the variable of increased sales volume (X42)

from the dimension of demand conditions (X4) with (r=.147, p<0.05). This implies that

those enterprises that considered their location in the cluster to be proper, it is very

possible that their sales volume had increased during the past two years. In other words,

enterprises with a higher performance and increased sales volume rarely complain about

the location of their business activities in these traditional clusters in Herat City. The

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findings further revealed that there was a positive correlation between the location of the

enterprise (X35) and the variable of enterprises’ investment in expansion (X622) with

(r=.201, p<0.01). This finding demonstrates that enterprises with higher performance tend

to invest more in expanding their economic activities and have fewer tendencies to

evaluate whether their enterprises are properly located within a cluster in Herat City. In

addition, findings also revealed there was a positive correlation between the location of

enterprises (X35) and the participation of entrepreneurs in the cluster association (Senf)

with (r=.145, p<0.05).

Table 0.3. Correlation Matrix of Between MSEs Social Capital, Related, Supporting Industries and Performance

(Y) (11) (111) (112) (113) (123) (129) (133) (136) (139) (149) (152) (154) (155) (31) (35)


Join Senf (X11) -.046 1

Family in Cluster (X111) -.138* .100 1

Number close friends (X112) .225** .058 -.011 1

Friends Can Help (X113) .226** .002 .126 .168* 1

Effective in Decision Making (X123) -.008 .032 .146* .160* .161* 1

Trust Relatives (X129) -.147* -.072 .007 .035 .053 .037 1

Trust Neighbors (X133) .003 -.046 -.004 .069 .183** -.077 .209** 1

Trust in suppliers (X136) .041 -.073 -.063 .179* .059 -.017 .071 .279** 1

Trust Municipality Officials (X139) .023 -.033 -.003 .071 .053 -.139* .005 .161* .118 1

Mobile Phone (X149) .020 -.062 .003 -.071 -.075 .023 -.046 -.096 -.067 -.151* 1

Social Media Index (X152) .067 -.075 .111 .055 .047 .057 -.070 -.194** -.150* -.039 .553** 1

Help a Stranger (X154) .227** -.047 -.065 .171* .065 .183** .045 .006 .092 -.074 -.051 -.020 1

Attended Mosque (X155) -.171* .096 .033 -.111 .042 -.044 .080 .176* -.017 .079 -.195** -.214** -.068 1

Helper economic status (X31) .060 .066 .185** .042 -.051 .088 -.038 -.109 .028 -.028 .277** .173* -.205** -.261** 1

Location of Enterprise (X35) -.167* .145* -.030 -.084 -.116 -.022 0.123* .052 -.153* -.008 .036 -.032 -.143* .013 .128 1


Results of the correlation matrix in Table 5.3 show that there were no significant

association between MSEs’ performance and the variable of the economic status of

helpers (X31) who were willing to provide support to entrepreneurs within the traditional

clusters. On the other hand, the variable (X31) has positive associations with the variable

of having family members in the same industry (X111), use of business plan (X629), and

employee startup (X630) with correlation coefficients of (r=.185, p<0.01), (r=.154,

p<0.05), and (r=.140, p<0.05), respectively. This finding implies that when entrepreneurs

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have another family member in the same industry, use the business plan, or have an

employee who established a new enterprise, there are more possibilities that these

entrepreneurs could receive supports even from the people with a higher level of

economic status within traditional clusters in Herat City.

5.2.4. Association between MSEs’ Social Capital, Demand Conditions, and

Performances

The results of correlation analysis in Table 5.4 show that within the demand

conditions (X4) dimension, there were three variables, namely, increased sales volume

(X42), customers’ preference for price (X43), and customers’ preference for quality (X44)

were included in this section. Findings in this table indicate that MSEs’ performances and

the variable of enterprise’s increase in sales volume (X42) have positive association with

correlation coefficient (r=.185, p<0.01). This implies that the increase in the sales volume

associated positively with the increase in the performance level of enterprise (Y) in this

study.

Table 0.4. Correlation Matrix of Between MSEs Social Capital, Demand Conditions and Performance

(Y) (11) (110) (111) (112) (113) (122) (123) (128) (139) (153) (154) (42) (43) (44)

MSEs’ performances (Y) 1

Join Senf (X11) -.046 1

Join in index (X110) -.003 .388** 1

Family in Cluster (X111) -.138* .100 -.023 1

Number close friends (X112) .225** .058 .173* -.011 1

Friends Can Help (X113) .226** .002 .068 .126 .168* 1

Share Machineries (X122) .073 .048 .174* .024 .138* .017 1

Effective in Decision Making (X123) -.008 .032 .173* .146* .160* .161* .043 1

Vote in Election (X128) .095 .040 -.024 -.003 .015 .040 -.034 -.048 1

Trust Municipality Officials (X139) .023 -.033 -.049 -.003 .071 .053 .112 -.139* .030 1

Charities (X153) -.122 .086 .166* -.103 .027 -.172* .087 .030 -.053 -.018 1

Help a Stranger (X154) .227** -.047 .050 -.065 .171* .065 .156* .183** .079 -.074 .166* 1

Sales Volume Increased (X42) .185** -.055 .016 -.159* .115 .069 .066 .100 -.171* .154* .183** .016 1

Customer prefer price (X43) -.119 -.106 -.166* .080 -.204** -.061 -.068 -.103 -.056 .045 .025 -.062 -.092 1

Customer prefer quality (X44) .074 .149* .135 -.032 .130 .172* .013 .107 .028 .020 -.130 .128 .046 -.597** 1


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The results of correlation analysis reveal that there was a positive association

between the increase in sales volume (X42) and the competitive position of an enterprise

in the market (X632) in comparison with other enterprises within the cluster with (r=.139,

p<0.05). In addition, there were also positive association between increase in sales

volume and the entrepreneur’s trust in municipality officials (X139) with (r=.154, p<0.05).

These findings signify that the trust in municipality officials was higher among

enterprises that benefit from an increase in their sales volume, and a better competitive

position in the market. In addition, there was a positive association between the increase

in sales volume and the use of the internet as a source of innovation (X523) with (r=.210,

p<0.01). This means that, it is most likely that those enterprises that have experienced

increase in their sales volume during the last two years use the internet as a source of

innovation for the enterprise.

Within the dimension of demand conditions (X4), there were another two

variables, namely, customers’ preference for price (X43) and customers’ preference for

quality (X44) that were not significantly associated with MSEs’ performances within the

traditional clusters. Based on the findings from correlation analysis, these two variables

that represent the demand side of the Porter’s Diamond Model in the traditional clusters,

have strong negative association with each other with correlation coefficient (r= -.597,

p<0.01). This means that customers of the enterprises in these traditional clusters of micro

and small scale enterprises in Herat City have different preferences for purchasing goods.

For instance, customers who prefer more than the price of products, tend to purchase their

desired products from enterprises that have a business plan and have broader social

network. On the other hand, customers who prefer price over the quality of products are

more likely to purchase products that they need from enterprises with smaller social

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networks, participate less in social networks and have lower level of trust in municipality

officials.

5.2.5. Association between MSEs’ Performance and Social Capital, Strategy,

Structure, and Rivalry

Table 5.5 shows the results of correlation matrix between enterprise’s

performance, social capital, strategy, and structure within the traditional clusters in Herat

City. Besides the variable from the social capital dimension, there were also ten other

variables from the dimension of firm strategy, structure and rivalry (X6) that were ran in

correlation analysis in this section.

Findings embodied in Table 5.5 reveal that within the traditional clusters in Herat

City, the managerial status of the enterprise (X61) has a positive association with its

performance with correlation coefficients of (r=.187, p<0.01). In addition, the manager

status of the enterprise has also positive association with the number of friends who can

support the entrepreneurs (X113) with (r=.154, p<0.05). This indicates that the possibility

of higher performance was more among enterprises which were administered by a

manager other than its owner, and have larger social networks of people who are willing

to provide assistant to entrepreneurs. The managerial status of an enterprise also has a

positive association with the source of investment from credit institutes (X214) with

(r=.163, p<0.05); which reveals that the use of credit institutions as a source of investment

is more common among enterprises which are administered by managers other than its

owner.

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Table 0.5. Correlation Matrix of Between Social Capital, MSEs Strategy, Structure, and Performance

(Y) (19) (110) (112) (113) (122) (136) (139) (147) (152) (153) (61) (616) (623 (631) (69) (611) (613) (620) (629) (632)


Join Other Associations (X19) .019 1

Join in index (X110) -.003 .194** 1

Number close friends (X112) .225** -.076 .173* 1

Friends Can Help (X113) .226** .035 .068 .168* 1

Share Machineries (X122) .073 .109 .174* .138* .017 1

Trust in suppliers (X136) .041 .007 .043 .179* .059 .106 1

Trust Municipality Officials

(X139) .023 -.027 -.049 .071 .053 .112 .118 1

Meeting with Friends (X147) -.005 .050 .106 .329** .069 .107 .077 -.074 1

Social Media Index (X152) .067 .021 .187** .055 .047 .067 -.150* -.039 .154* 1

Charities (X153) -.122 -.011 .166* .027 -.172* .087 -.008 -.018 -.010 .050 1

Manager Status (X61) .187** -.083 -.092 .023 .154* -.169* -.013 .015 -.001 .022 -.177* 1

Invest Training (X616) .208** .158* -.010 .080 .059 .060 .131 .024 -.100 .035 -.048 .018 1

Business Card (X623) -.131* .143* .122 -.034 .012 .103 -.051 -.011 .069 .225** .047 .054 .000 1

Expansion of Enterprise (X631) .135* .086 .055 .113 .069 -.018 .064 -.180* .176* .131 .022 -.037 .003 .101 1

Enterprise size (X69) -.022 .009 -.011 .143* -.069 .036 .017 .036 .166* .101 .196** -.083 -.039 .049 .092 1

Invest in tools (X611) .090 -.140* .033 .000 -.028 -.011 -.050 .036 -.007 -.033 -.051 .047 .163* -.160* .072 .074 1

Invest in storage (X613) .056 -.062 .039 .219** .084 .089 .027 .182** .030 .038 -.049 -.066 .241** -.024 -.040 .086 .243** 1

By journal (X620) .100 -.037 -.029 .174* .055 .008 -.010 -.015 .117 .110 .061 .070 .061 -.093 .093 .177* -.112 .087 1

Use Business plan (X629) .072 .017 .223** .194** .145* .018 .135 -.006 .213** .163* .157* -.014 .091 .067 .138* .167* .112 .079 .215** 1

Positions in market (X632) -.030 -.017 -.028 .141* .012 -.016 .138* .091 .164* .132 .014 -.028 -.038 .118 .106 .242** -.021 -.013 -.001 .174* 1


Findings in Table 5.5 show that there was a positive association between MSEs’

performances, the priority for investing in employees training (X616), and the expansion

of enterprise (X631) with (r=.208, p<0.01) and (r=.135, p<0.05), respectively. These

findings manifest that the enterprises with higher performance have possibly more

tendency to prioritize investing in the vocational training of their employees, and also

consider the expansion of their business activities within the traditional clusters in Herat

City. The enterprise’s investment in employees training has a positive association with

its priority to invest in the warehouse (X613), invest in a better location within the cluster

(X614), and invest in tools and machinery (X611).

The use of business card (X623) has negative association with MSEs’

performances with (r= -.131, p<0.05). This implies that the use of business cards as a

marketing strategy within these traditional clusters was not positively associated with the

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enterprise’s daily sales revenue in this study. However, the findings from correlation

analysis reveal that the use of business cards were more common within the enterprises

with larger size, who plan to expand their enterprises, who presume that the government

should facilitate access to information about the market, who usually bring about

variation in their products, and acknowledge the benefits of belonging to a cluster which

can increase their access to information about prices, product design, and raw materials.

5.2.6. Association between the Performance of MSEs and Social Capital, and

the Role of Government Policies and Chance

The first part of this section describes the results of the analysis of correlation

between enterprise’s performance (Y), social capital, and the role of government backing

policies dimension. The second part explains the results of correlation analysis of the role

of chance dimension that were conceptualized based on Porter’s Diamond Model in

Chapter 2 of this study. Table 5.6 shows that out of six variables from the dimension of

government policies (X7), there were only two variables, namely, government helping in

international marketing (X714) and the ease in obtaining a business license (X716) that have

a significant correlation with MSEs’ performances.

The results of correlation analysis in Table 5.6 reveal that the MSEs’

performances has positive significant association with government initiative to facilitate

the marketing of products from these traditional clusters in international markets with

correlation coefficients of (r=.209, p<0.01). The government initiative in the international

marketing (X714) has a positive association with enterprise investment in additional

storage space (X613), investment on employees training (X616), and other types of threats

(X85) to the survival of an industry in the future with correlation coefficients of (r=.145,

p<0.05), (r=.226, p<0.01), and (r=.240, p<0.01), respectively. This indicates that the

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tendency toward demanding from the government to take initiatives such facilitating the

marketing of products of these traditional clusters were more common among enterprises

that had plans to invest in additional storages and in the training of their employees, and

had considered the presence of different factors as threats to their industry and its survival

in future in Herat City.

In addition, the findings from Table 5.6 imply that there was a negative associate

between the trust in family and relatives (X129) and government support in international

marketing (X714); which means, that the lower is the level of trust of entrepreneurs in their

relatives, the will be their tendency to seek for the government initiative such as

facilitating the marketing of their products in places other than domestic local markets in

Herat City.

Table 0.6. Correlation Matrix of Between Government Policies, MSEs Social Capital, and Performance

(Y) (11) (12) (113) (120) (129) (138) (146) (73) (75) (79) (712) (714) (716)


Join Senf (X11) -.046 1

Join cooperative and association (X12) -.031 .093 1

No. of Friends Who Can Help (X113) .226** .002 .048 1

Enterprises share information (X120) .044 .114 .059 .047 1

Trust Family and Relatives (X129) -.147* -.072 -.006 .053 -.058 1

Trust in teachers and professors (X138) .025 .070 -.002 .046 .013 .255** 1

Friends Ask for Help (X146) .049 .031 .085 .165* .038 .036 .059 1

Elected by some members (X73) .045 -.018 .100 .057 -.195** .067 .002 .024 1

Gov’t. decisions consider enterprises (X75) .070 -.145* -.027 .211** -.031 .041 -.008 -.040 .088 1

Gov’t. follow strategy (X79) .081 -.114 .127 .066 -.115 -.056 -.091 .004 .041 .148* 1

Gov’t. impose import quota (X712) -.049 .080 .183** .101 -.012 .146* .151* .242** -.035 -.031 -.114 1

Gov’t. Marketing in Intl. Markets (X714) .209** .023 .057 .085 .099 -.149* -.086 -.030 .037 .014 -.028 -.092 1

Easy to Obtain license (X716) .176* .032 .060 .030 -.058 .001 .042 -.023 .040 .013 -.017 .110 .045 1


Findings Table 5.6 reveal that MSEs’ performances has significant positive

association with the ease of obtaining business license (X716) with (r=.176, p<0.05), while

it has negative association with the variable of entrepreneur asked to pay bribe (X77) in

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the past 12 months with correlation coefficients of (r= -.203, p<0.01). These findings

indicate that the easing of government policies for obtaining business license is associated

positively with MSEs’ performances more among the enterprises that were not asked to

pay bribe.

The findings from the correlation analysis in this section reveals that there was

positive correlation between the government policy of imposing import quota (X712) and

entrepreneurs’ membership in cooperative and other associations (X12), trust in relatives

(X129), and trust in teachers (X138) with (r=.183, p<0.01) (r=.146, p<0.05), and (r=.151,

p<0.05), respectively. These results reveal that the demand for government’s policy

intervention is more common among enterprises that participate in cooperatives, other

associations, and have more trust in their relatives and teachers or professor in Herat City.

Another fact the results in this section revealed is that there were negative

association between enterprise’s increase in sales volume (X42), age of enterprise (X63),

and the government provides subsidies (X710) with (r= -.151, p<0.05) and (r= -.140,

p<0.05), respectively.

Table 0.7. Correlation Matrix of Between Chance, MSEs Social Capital, and Performance

(Y) (15) (16) (17) (18) (120) (129) (139) (147) (150) (151) (152) (155) (83) (89) (81) (84)


Join cultural association (X15) .009 1

Join Loans Association (X16) -.019 .273** 1

Join sport group (X17) .069 .211** .282** 1

Join ethnic group (X18) .003 .273** .167* .184** 1

Share information (X120) .044 -.061 -.027 .041 .126 1

Trust Relatives (X129) -.147* -.025 -.015 .026 .038 -.058 1

Trust Municipality Officials

(X139) .023 .038 -.037 -.186** -.008 .106 .005 1

Meeting with Friends (X147) -.005 .014 .019 .136 .123 .013 .067 -.074 1

E-mail and Website (X150) .054 .149* -.010 .223** .063 .033 -.034 .000 .169* 1

Facebook account (X151) .066 .294** .147* .196** .087 -.004 -.068 .037 .144* .538** 1

Social Media Index (X152) .067 .271** .075 .244** .097 .001 -.070 -.039 .154* .756** .854** 1

Attended Mosque (X155) -.171* -.137 .022 .010 -.148* .001 .080 .079 .040 -.103 -.172* -.214** 1

Threat of suppliers (X83) .138* .143* .136 .095 .148* .105 .069 -.005 .184** .224** .098 .126 .041 1

Economic Status Improved (X89) .186** -.066 -.032 .015 .080 .142* .026 .164* -.044 .056 -.006 .042 -.142* .013 1

Threat of revivals (X81) -.098 .094 .058 -.039 .121 -.061 -.168* .074 -.040 .014 -.039 -.003 -.003 .017 .059 1

Threat of imported products (X84) .004 .160* .129 .140* .136 -.048 -.048 -.018 -.017 .079 .075 .109 -.089 -.027 -.013 -.274** 1


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Within the dimension of chance (X8) in this section, there were four variables that

were ran into correlation analysis. Table 5.7 shows the results of correlation matrix

between MSEs’ performances, social capital and the role chance dimension based on

Porter’s Diamond framework in this study.

Results of correlation in Table 5.7 disclose that there was significant positive

correlation between MSEs’ performances and the threat from suppliers of raw materials

(X83) and improved economic status of the enterprise (X89) with (r=.138, p<0.01) and

(r=.186, p<0.01), respectively. These findings reveal that the enterprise’s performance

was higher among those that considered the power of negotiation with suppliers of raw

materials as major threats to the future of their industry’s survival, and considered the

improvement of their economic condition in the past two years in these tradition clusters

in Herat City.

The results of findings from correlation analysis in this section also reveal that the

threat from the suppliers of raw materials (X83) were possibly more common among

entrepreneurs who urged the government to protect their industry by providing them with

subsidies (X710), those entrepreneurs who have higher trust in police (X143), and have a

wider social network and cooperate more frequently with others in the traditional clusters

in Herat City. In addition, findings show that improvement of economic condition (X89)

within these clusters was significantly associated with the increase of information sharing

among the enterprises.

Finally, based on the results of correlation analysis in this chapter, it concluded

that within the dimension of factor conditions (X2) there were two variables, namely, total

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of current assets (X219) and the venue of enterprise being a rented space (X224) that were

identified to have significant correlation with MSEs’ performances and were included

into further analysis in next chapter of this study. From the dimension of the enterprise’s

related and supporting industries (X3), there was only one variable of the location of

enterprise (X35) that was identified as having significant association with MSEs’

performances. With the dimension of demand conditions (X4), there was only one

variable of the increase in enterprise’s sales volume (X42) that was identified to have a

significant association with enterprise’s performance. The dimension of firm strategy,

structure and rivalry (X6) is found to have a large number of variables with significant

association with MSEs’ performances after social capital dimension in this study. There

were four variables within this dimension that were identified and included in the further

analysis in chapter six. even though, the correlation matrix for the firm characteristics

(X5) dimension as an explanatory conceptual component within Porter’s Diamond

framework in this study were not included in this chapter, but, the results of correlation

analysis indicate that there was one variable of the prosperity being achievable by

endeavor (X528) in this dimension that was identified with significant association with

MSEs’ performances and was considered for the next stages of analysis. The results of

correlation analysis also indicate that there were two variables from the dimension of

government policies (X7), namely, government support in international marketing (X714)

and ease in obtaining license (X716) in addition to another two variables from the

dimension of chance (X8), namely, threat from suppliers (X83) and improvement of

economic condition (X89) that were identified as variables with significant association

with MSEs’ performances and were included into further analysis in the next chapter of

this study.

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6. CHAPTER VI

THE IMPACT OF DETERMINANT FACTORS ON

CLUSTER DEVELOPMENT IN HERAT CITY

Introduction

The aim of this chapter is mainly to examine the impacts of determinant factors

on the development of traditional clusters of MSEs in Herat City. In addition, through the

implementation of social capital in Porter’s Diamond Model, this chapter seeks to explore

the causal relationship between social capital and other significant factors on the

performance of MSEs in this city. This chapter consists of three sections. In the first

section, we examined the direct impact of social capital, human capital and other

significant factors that are within the conceptual framework of this study on the firms’

performance. This section is partially combined with the findings from the previous

article by the author based on the collected data for this study and published in the peer-

reviewed Journal of Global Studies in 2015. The second section of this chapter, contains

Porter’s Diamond Model applied in this study to explore the contribution of social capital

on firm performances by examining the direct and indirect impacts of social capital

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through other significant factors on MSEs’ performance in traditional clusters. The third

section of this chapter consists of discussion, conclusion, and recommendation for this

study.

The Impact of Significant Factors on MSEs’ Performance in Traditional

Cluster in Herat City

The aim of this section is to examine the direct impact of all significant factors on

MSEs’ performances. These factor dimensions are, namely, social capital (X1), factor

conditions (X2), related and supporting industries (X3), demand conditions (X4), firm

characteristics (X5), firm strategy, structure and rivalry (X6), government policies (X7),

and chance (X8) that represent the components of the conceptual framework in this study.

In order to achieve the objective and to test the main hypothesis of this study (see Figure

2.), we used the general multiple regression analysis (GMRA) with Stepwise Method in

this section. Table 6.1 shows that the variables of the number of friends can help (X113)

and the number of times attended the mosque (X155) are considered as components of

social capital (X1). The space rented by the enterprise (X224) and the total current assets

of the enterprise (X219) are considered as factor condition of the enterprise (X2). Location

of the enterprise (X35) is considered as an indicator of related and supporting industries

(X3). Change in sales volume of an enterprise is considered as an indicator variable of

demand conditions for an enterprise (X4). The variable of prosperity being achievable

through efforts (X528) is considered as the indicator of firm characteristics (X5). The

variable of management status of the enterprise (X61), investment in employee training

(X616), use of the business card (X623), and plan to expand the enterprise (X631) are

considered as firm’s strategy, structure and rivalry (X6). The variable of the need for the

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government marketing in international market (X714) is considered as enterprise’s

perspective on the role of government (X7) and the variable of threat from the suppliers

of raw input materials of an enterprise (X83) are also considered as measurement indicator

of chance (X8) for an enterprise within the traditional cluster of MSEs in this study. Table

6.1 indicates the results of the impacts of all of those 13 significant independent variables

mentioned above on the dependent variable of firm’s performance (Y) that ran into the

regression analysis in this study (see Appendix 6).

Table 6.1. Model Summery of Significant Variables with Direct Impact on MSEs’ Performances

No. Predictors Standardized

β

t-value Sig. F

value

Sig. R2

1 Current Assets (X219) .303****

5.497 .000

11.863 .000 .448

2 Rented Space (X224) -.222**** -3.981 .000

3 Manger of Enterprise (X61) .193*** 3.451 .001

4 No. of Times Attended Mosque (X155) -.189*** -3.404 .001

5 Change in Sales Volume (X42) .185*** 3.256 .001

6 Invest in Employee Training (X616) .156*** 2.787 .006

7 No. of Friend Who Can Help (X113) .135** 2.402 .017

8 Gov Backing in Intl. Markets (X714) .130** 2.293 .023

9 Location of Enterprise (X35) -.130** -2.302 .022

10 Prosperity Achievable by Efforts (X528) -.129** -2.289 .023

11 Plan to Expand Enterprise (X631) .114** 2.072 .040

12 Use Business Card (X623) -.106* -1.905 .058

13 Threat from Suppliers (X83) .097* 1.734 .085

*p<0.10, **p<0.05, ***p<0.01, ****p<0.001

Table 6.1. shows that all of the 13 independent variables have a significant impact

on MSEs’ performances (daily sales revenue). In order to test the main hypothesis

formulated in the second chapter of this study which claims that social capital (X1) has

obvious impact on the performance of firms (Y) within the traditional cluster of MSEs in

Herat City. The results of regression analysis indicate that within the social capital

dimension of theoretical framework in this study, the variable of available number of

friends who can offer assistance to the entrepreneur (X113) with beta coefficients of (β=

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.135) at t-value of (p<0.05) level of significance have a significant positive impact on the

MSEs’ performances within those six sampled traditional clusters for this study in Herat

City. This result indicates that with an increase in the number of friends who are willing

to provide support to entrepreneurs upon their request can increase the performances

(sales volumes) of the enterprises in these clusters. In other words, greater number of

friends in the network of entrepreneurs who can help will lead to the larger volume of

daily sales revenue. The second variable of the number of times that an entrepreneur

attended the mosque (X155) in a week within the social capital dimension of this study

with beta coefficients of (β= -.189) at t-value of (p<0.01) level of significance bears a

significant but negative impact on the MSEs’ performances (Y) within those six sampled

traditional clusters for this study. This finding indicates that with an increase in the

number of times that entrepreneurs attend the mosque for prayers can decrease the

performances of their enterprises, in other words, the more an entrepreneur is away from

his enterprise because of participating in religious activities, the less the enterprises can

perform or sale within these traditional clusters in Herat City.

The results of multiple regression analysis in Table 6.1 indicates that within the

factor conditions (X2) of clustered MSEs in this study, the variable of total current assets

of the enterprises (X219) with beta coefficients of (β= .303) at t-value of (p<0.001) level

of significance have a significant positive impact on the MSEs’ performances in the

traditional clusters in this region. This result portrays the total assets of an enterprise

(X219) as one of the most important determinant factors for the firms’ performances within

the traditional cluster in this study in Herat City. The variable of rented space of an

enterprise (X224) with beta coefficient of (β= -.222) at t-value of (p<0.001) level of

significance bears a marked, but negative impact on SME’s performance in this study.

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This shows that if the possibility of the venue for the activity of the enterprise being a

rented space is high, it lowers the performance of the firm within the traditional clusters.

In other words, when the space for operation of an enterprise is not a rented space can

increase the volumes of sales of the enterprises in these clustered MSEs in Herat City.

Within the dimension of related and supporting industries (X3), the variable of the

proper location of an enterprise within a cluster (X35) with beta coefficient of (β= -.130)

at t-value of (p<0.05) level of significance has a significant negative impact on the MSEs’

performances in the traditional clustered enterprises. This indicates that entrepreneur with

higher tendency toward the belief that the current location for their enterprise is more

proper within the cluster can lead to lower the performances of their enterprises within

the traditional clusters in this study.

The results in Table 6.1 indicate that within the dimension of demand conditions

(X4) for an enterprise in this study, out of five variables, the variable of changes in sales

volume of an enterprise (X42) with beta coefficient of (β= .185) at t-value of (p<0.01)

level of significance has a positive impact on MSEs’ performances within traditional

clusters enterprises in this study. This finding shows that increase in the demand for

products of each of these traditional industries can lead to an increase in enterprises

performance or sale revenues of enterprises in this region.

The results of regression analysis in Table 6.1 shows that in the dimension of firm

characteristics (X5) in the conceptual framework of this study, the variable of

entrepreneur in the belief that prosperity can be achieved by efforts in this country (X528)

with beta coefficients of (β= -.129) at t-value of (p<0.01) level of significance has a

significant but negative impact on MSEs’ performances in the traditional cluster of

enterprises. This indicates that if the entrepreneurs tend to believe that prosperity can be

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achieved by their efforts, this can lead to the decrease in the performances of their

enterprise within a cluster, in other words, if an entrepreneur relies less on the common

belief in Afghanistan that prosperity is achievable by endeavour, it can lead to higher

level of performance of an enterprise and increased sales revenue in the traditional


Based on multiple regression analysis in Table 6.1, with the firm strategy,

structure and rivalry (X6) dimension of an enterprise in the conceptual framework in this

study, there are four variables that have significant impacts on MSEs’ performances

within the traditional cluster in Herat City. The managerial status of an enterprises (X61)

with beta coefficients of (β= .193) at t-value of (p<0.01) level of significance has a

positive impact on the MSEs’ performances (Y). This finding indicates that if an

enterprise is run by a manager other than its owner can increase the performance of this

enterprise, in other words if an enterprise is operated by its owner, it can decrease the

enterprise’s performances or the amount of sales revenue within traditional clusters of

MSEs in this region.

The variable of enterprise’s priority for investment in vocational training for its

employees (X616) with beta coefficients of (β= .156) at t-value of (p<0.01) level of

significance has a positive impact on the MSEs’ performances. This indicates that

increases in enterprise’s investment in its employees can increase the employee’s

productivity through making products with higher quality which eventually leads to the

increase in the performances of an enterprise in those six traditional clusters. The variable

of enterprise plan for expansion (X631) with beta coefficients of (β= .114) at t-value of

(p<0.05) level of significance has a positive impact on the MSEs’ performances (Y) in

this study. This finding indicates that enterprise’s tendency toward expansion of its

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activities as well as the establishment of another enterprise within the same cluster can

increase the possibility of raising the volumes of sales revenues of the enterprises in these

clusters. In addition, the variable of using a business card (X623) with the beta coefficients

of (β= -.106) at t-value of (p<0.10) level of significance has a striking negative impact on

the MSEs’ performances. This result indicates that the use of a business card as an

instrument of marketing strategy for enterprises in traditional clusters cannot contribute

to the increase of the sales volume of the enterprises in the traditional clusters of MSEs.

Table 6.1 shows the results of regression analysis of government policies (X7) and

chance (X8) dimensions within the conceptual framework of this study. These findings

indicate that the variable of government taking initiative to provide marketing facilities

for the enterprises in the international markets (X714) with the beta coefficients of (β=

.130) at t-value of (p<0.05) level of significance has a positive impact on the MSEs’

performances. This finding indicates that the government’s initiatives in providing

marketing facilities for clustered enterprises can enhance the chances of the improvement

of enterprise’s performances through introducing domestic products in the international

market. The variable threat from suppliers of raw input materials (X83) with the beta

coefficients of (β= .097) at t-value of (p<0.10) level of significance has a positive impact

on the MSEs’ performances (Y) within traditional cluster in this study. This shows that

the fear of entrepreneurs of the survival of their industry in the future can increase the

performance of enterprises, or in other words, the less the enterprises consider their

suppliers of the raw input materials as a threat, the lower will be the level of their

performance with those traditional clusters of MSEs in Herat City.

The results of multiple regression analysis in Table 6.1 indicate that out of all

independent variables ran into the analysis in this section; thirteen variables have a

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significant direct impact on firm performances (Y) in this study. In addition, F-test value

of (11.863) at significance level of (p<0.001) indicates that there was at least one

independent variable that has significant impact on firm performances in this study.

Therefore, it is concluded that on the basis of the results of the F-test value

(11.863) in the regression model in Table 6.1, the first null hypothesis (H01) is statistically

rejected in favor of alternative hypothesis (H11), in which there were direct impacts from

at least two variables from the social capital dimension on firm performances in the

traditional clusters of MSEs in Herat City in this study. In addition, the regression model

resulted in an R-square value of (R2=.448). This means that about 45% of the variation in

the dependent variable of enterprise’s daily sales revenue (Y) statistically can be

explained by these thirteen independent variables with significant impacts.

The Dynamic of Social Capital through the Porter’s Model on the MSEs’

performances in Traditional Clusters

The aim of this section is to examine the indirect impact of social capital (X1)

through other dimensions within the framework of Porter’s Diamond Model on the

MSEs’ performances in this study. In order to achieve another objective of this

investigation (Refer to Chapter 2) and to statistically test the second hypothesis of this

study, in this section, we used regression with path analysis method to identify the direct

and the indirect impacts of the social capital dimension on firm performances through the

variables that were identified as significant determinant factors on firm performances in

the previous section of this chapter. In other words, in this section by applying the Porter’s

Model, we examined the dynamic impact of the social capital on MSEs’ performances

through the assessment of every other dimension within the conceptual framework of this

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study. Furthermore, in order to test the second category of hypothesizes (H2) that social

capital (X1) has significant direct and indirect impact on MSEs’ performances through

another enterprise’s dimensions within Porter’s Diamond framework in traditional

clusters of MSEs in Herat City as described in the following sections of this chapter.

6.3.1. The Impacts of Social Capital on MSEs’ Factor Conditions and

Performance

In the dimension of social capital (X1) in this study, there were six variables that

had significant direct and indirect impact on MSEs’ performances. The hypothesis (H12)

tested the fact that there is at least one variable in the social capital with a significant

indirect impact on MSEs’ performances through enterprise’s factor conditions (X2).

Figure 6.1 shows the variables with significant impact on enterprise’s performance,

namely, joining the two main business associations, i.e. the chamber of commerce and

industries (X13) and Senf (cluster’s association) (X11), charitable activities (X153), number

of times attended mosque (X155), number of friends who can help (X113), and helping a

stranger (X154). Within the dimension of factor conditions (X2), only two variables,

namely, the total of current assets (X219) and the rented space (X224) have significant

impacts on MSEs’ performances. Path diagram in Figure 6.1 indicates that out of six

variables in factor conditions dimension (X2), only three variables, namely, business

associations, i.e. the chamber of commerce and industries (X13) and Senf (cluster’s

association) (X11) and charitable activities (X153) have significant indirect impacts on

MSEs’ performances through the variable of total of current assets (X219).

The findings demonstrate that the participation of enterprises in the above

mentioned two business associations, have a positive impact on MSEs’ performances

through the enterprise total current assets (see Appendix 7). In other words, higher

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participation of enterprises in these two association can increase the MSEs’ performances

by an increase in the enterprise assets within the traditional cluster in Herat City. The

variable of charitable activities (X153) in this path diagram model inflicts a significant

negative impact on the total amount of enterprise’s assets. This means, even though the

total current assets (X219) has a significant positive impact on MSEs’ performances, an

increase in enterprise charitable activities can lead to a decrease in the total assets, and as

a result through this variable can eventually lead to a decrease in the MSEs’ performances

within traditional clusters. This finding also indicates that the enterprises with higher

performances and ample current assets are less likely to be involved in charitable

activities in these traditional clusters, and vice versa.

Note: a) An arrow indicates a causal relationship

b) An arrow indicates a correlation relationship

Figure 6.1. Path Diagram for Impact of Social Capital on MSEs’ Performance and Factor Conditions

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Figure 6.1 shows that another three variables, namely, the number of times

attended the mosque (X155), the number of friends who can help (X113) and helping a

stranger (X154) have not any significant indirect but direct impact on MSEs’ performances

through factor conditions (X2) dimension in the above path diagram model. The above

findings show that the number of times an entrepreneur attended mosque (X155) has

significant negative impact on MSEs’ performances within these clusters. On the other

hand, cooperation in terms of the number of friends who can help the entrepreneur when

needed (X113) and the help that an entrepreneur provide to others in need (X154) can

increase the possibility of MSEs’ performances.

The findings in Figure 6.1 clearly demonstrate that the variable of the space an

enterprise rented for its business (X224) with beta coefficients of (β= -.20) at t-value of

(p<0.001) level of significance insert a negative impact on MSEs’ performances within

this path diagram model. This means that when the space where the enterprise operates is

a rented place, it can negatively affect the MSEs’ performances within these traditional

clusters. In contrast, the variable of total of current assets (X219) within this model is

identified as the most significant determinant factor with a positive impact on MSEs’

performances with beta coefficients of (β= .34) at t-value of (p<0.001) level of

significance in this regression model. Therefore, the results of regression analysis of the

above path diagram model in Figure 6.1 imply that at least one of the variables from the

social capital (X1) dimension has a significant indirect impact on MSEs’ performances

through the mediation of factor conditions (X2) dimension in this study. This means, that

besides its direct impact, social capital (X1) also bears a significant indirect impact on

MSEs’ performances through another dimension within Porter’s Diamond framework in

this study. The regression model resulted in an R-square value of (R2=.281). This

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means that, as shown in the path diagram model in Figure 6.1, statistically there is more

than 28% of the variation in the MSEs’ performances explained by these eight

independent and intermediate variables that have a significant impact. Therefore, from

the results of our path diagram analysis in the above figure with Chi-square of (Chi2

=22.071) and the degree of freedom of (df=25) at p-value of (p<0.632) of confidence

level (see appendix…) it is evident that there is no significant difference between our

constructed conceptual model in Figure 6.1 and the perfect possible model based on the

data from those six sampled traditional clusters of MSEs in this study.

6.3.2. The Impact of Social Capital on the Performances of MSEs and on

Industries that are Related to and Supporting Them

The path diagram in Figure 6.2 shows the results of regression analysis to test the

hypothesis (H13) that there is at least one variable within the social capital dimension with

a significant indirect impact on MSEs’ performances through the dimension of industries

that are related to or supporting the enterprise within a cluster (X3). In other words, the

dimension of social capital (X1) in this study has an indirect impact on the enterprise’s

performance (Y) through the dimension of industries that are related to or supporting the

enterprise (X3) in this study. Figure 6.2 shows the variables with significant impact on the

enterprise’s performance. These variables are namely, joining Senf (X11), trusting family

and relatives (X129), helping a stranger (X154), the number of times attending a mosque

(X155), and the number of friends who can help (X113).

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Based on the findings, the variable of the location of enterprise (X35) from the

related and supporting industries (X3) dimension has significant impact on MSEs’

performances. Therefore, the results of the regression in this path diagram model show

that the two variables from social capital dimension namely, trusting family and relatives

(X129) and joining Senf (X11) have a significant positive indirect impact on MSEs’

performances through the related and supporting industries (X3) dimension in this study

(see Appendix 8). The entrepreneur’s participation in the cluster association (Senf) and

profound trust in the family can lead to an increase the enterprise satisfaction with its

current location within the traditional cluster in this city. This finding signifies the fact

that belonging to an association and trusting close relatives increase the degree of

satisfaction with the current location of the enterprise in the market and that eventually

can lead to lower performance of the enterprises within the clusters.



Figure 6.2. Path Diagram for Impact of Social Capital on MSEs’ Related, Supporting Industries and Performance

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The variable of entrepreneur helping a stranger (X154) bears a significant partial

effect on MSEs’ performances through the variable of the location of enterprise (X35).

This proves that if entrepreneurs help a stranger, the possibility of the rise in the volume

of enterprises’ sales increase, however, at the same time, this might decrease the level of

satisfaction of entrepreneurs with the current position of their enterprises, thus leading to

the increase of the performance of enterprises.

The two variables of the number of times an entrepreneur attended the mosque

(X155) and number of friends who can help (X113) have only significant direct impact on

MSEs’ performances with beta coefficients of (β= -.21) and (β= .21) at t-value of

(p<0.001) level of significance, respectively.

The results of the regression analysis in Figure 6.2 reveal an R-square value of

(R2=.142) in the path diagram. This means that there is more than 14% of the variation in

the MSEs’ performances that can be explained by these significant independent and

intermediate variables in this path diagram model. Thus, the results of regression analysis

of the above path diagram model in Figure 6.2 indicate that in this model also, at least

one of the variables from social capital (X1) dimension has significant partial indirect

impact on MSEs’ performances through the mediation of related and supporting

industries (X3) dimension of Porter’s Diamond framework. This means that in addition

to the direct impact of social capital (X1) dimension, it also bears a significant indirect

and partial impact through related and supporting industries (X3) dimension on MSEs’

performances. In addition, the results of path diagram analysis in Figure 6.2 with Chi-

square of (Chi2 =15.911) at p-value of (p<0.319) of confidence level can be accepted as

the constructed model for the traditional clusters of micro and small scale enterprises.

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6.3.3. The Impact of Social Capital on MSEs’ Demand Conditions and

Performance

The hypothesis (H14) that there is at least one variable from the social capital (X1)

dimension that has a significant indirect or partial impact on MSEs’ performances through

the demand conditions (X4) dimension in this study was tested. The results revealed that

the variables of efficiency in decision making (X123), voting in presidential election

(X128), trusting municipality officials (X139), charitable activities (X153), having family

members in the same industry (X111) within the social capital dimension with a significant

indirect and partial impact on MSEs’ performances. These impacts were mediated

through the variable of increased sales volume (X42) within the demand conditions (X4)

dimension (see Appendix 9).

The variable of entrepreneur voting in the presidential election (X128) with beta

coefficients of (β= -.16) at t-value of (p<0.05) level of significance induces striking

negative impact on the increase of sales volume (X42) from the demand conditions (X4)

dimension. However, this variable has an overall positive indirect impact on sales volume

within Porter’s Diamond framework in this study. This means that the participation of

entrepreneurs in collective activities contribute positively to the performance of the

enterprise.

The two variables, namely, charitable activities (X153) and having family members

in the same industry (X111) have significant partial impact on MSEs’ performances. The

variables of cooperation in term of charitable activities (X153) with beta coefficients of

(β= .157) at t-value of (p<0.05) level of significance and having family members in the

industry (X111) with beta coefficients of (β= -.163) at t-value of (p<0.01) level of

significance have partially impact on the performance of enterprises in the traditional

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clusters in Herat City. This finding demonstrates that doing charitable activities

and helping others in these clusters has definite positive influence on the performance of

enterprises through the increase of sales volumes. The presence of a family member in

the same industry within the cluster, which was reported by many entrepreneurs in

clusters included in this study, has a significant negative impact on the sales volume of

enterprises in these traditional clusters in Herat City. Moreover, the findings from the

survey interviews revealed that the presence of family members in the same industry in

some cases leads to a sort of secret alliance between enterprises that belong to same

family, cooperating with each other in “price fixing” in terms of indirectly encouraging

customers to fall in a range of different prices and to buy the products from one of them

by bargaining method, which is common among customers in traditional clusters in Herat

City who tend to search for products with lower prices. Thus, the presence of a family

member in the same industry (X111) can lead to such vile cooperation which inserts

negative impact on the performance of the performance in the traditional clusters in Herat

City.

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The final results of regression analysis in Figure 6.3 indicate that there was a

significant indirect or partial impacts from the social capital (X1) dimension on MSEs’

performances through demand conditions (X4) in this path diagram. Therefore, the

analysis of regression model resulted in an R-square value of (R2=.209). This indicates

that there were more than 20% of the variation in the MSEs’ performances explained by

those nine independent and intermediate variables in the path diagram in this study.

Figure 6.3 shows that the variable of having a family member in the same industry

(X111) has a positive correlation with the variable of effectiveness in decision making

(X123) with coefficients of (r= .14) at p-value of (p<0.05) level of significance. This means

that entrepreneurs with a family member in the same cluster were more effective in the

process of decision-making in their cluster which has positive impact on sales volume of



Figure 6.3. Path Diagram for Impact of Social Capital on MSEs’ Demand Conditions and Performance

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an enterprise and eventually increase the performance of the enterprise within the

traditional clusters in Herat City.

The test of path diagram model in Figure 6.3 resulted in a Chi-square of (Chi2

=30.543) at p-value of (p<0.387) level of significance. Therefore, the path diagram model

in this section can be accepted because statistically there were no significant differences

between this model and the exact model, based on the data collected from the field survey

in this study.

6.3.4. The Impact of Social Capital on MSEs’ Characteristics and Performance

The results of regression analysis in Figure 6.4 show the variables with significant

indirect or partially impact on MSEs’ performances are namely, trusting neighbors (X133),

trusting family and relatives (X129), voting in presidential election (X128), using mobile

phone (X149), helping a stranger (X154). Based on the regression analysis in this section,

we tested the hypothesis (H15) that social capital has indirect impact on MSEs’

performances through the firm characteristics (X5) dimension in this section.

The path diagram in Figure 6.4 shows that the entrepreneurs’ belief that

“prosperity being achievable by efforts” (X528) is the only variable of the firm

characteristics (X5) dimension that has significant impact on enterprises’ performance in

this study (see Appendix 10).

Figure 6.4 shows that variables of entrepreneur’s trust in neighbors (X133) with beta

coefficients of (β= .183) at t-value of (p<0.01) level of significance, and trust in family

and relatives (X129) with beta coefficients of (β= .124) at t-value of (p<0.10) level of

significance have outstanding indirect impact on MSEs’ performances through the

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variable of prosperity is achievable by efforts (X528) in this path diagram model in this

study.

This finding shows that higher trust in close ties such as neighbours, family and

relatives has positive effect on the perspectives of entrepreneurs who have faith in the

common belief that “prosperity can be achieved through endeavour” in Afghanistan. On

the other hand, the variable of prosperity is achievable by endeavour (X528) bears a

significant negative impact on MSEs’ performances. This means that the more an

entrepreneur believes that prosperity is achievable by efforts the less likely to run an

enterprise with lower performance or low daily sales revenue. In other words,

entrepreneurs who run an enterprise with a higher level of performance are more likely

to consider factors and means other than making effort as the main determinant of

achieving prosperity in Afghanistan.



Figure 6.4. Path Diagram for Impact of Social Capital on MSEs’ Characteristics and Performance

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The variables of entrepreneur voting in the presidential election (X128) and an

enterprise use of mobile phone (X149) bear a negative indirect impact on MSEs’

performances through prosperity being achievable by efforts (X528) in the above path

diagram model. This indicates that the participation in collective activities such as general

election and the use of social communication devices such as mobile phone increases the

probability of an entrepreneur to believe that there are other means except endeavour for

achieving prosperity in Afghanistan. In addition, the findings in this figure indicate that

there was a negative association between the use of communication means such as mobile

phone and the number of times an entrepreneur attended a mosque (X155). This means

that the more entrepreneurs tend to use communication means such as mobile phone the

less they tend to participate in the religious activities and places such as mosques within

the traditional clusters of MSEs in Herat City.

The results of regression analysis in Figure 6.4 show that social capital through

the dimension of firm characteristics (X5) has a significant indirect and partially impact

on the enterprise’s performance in the path diagram model in Figure 6.4 in this study.

The regression analysis of path diagram model in this section resulted in an R-

square value of (R2=.216). This implies that statistically there are more than 21% of the

variation in the MSEs’ performances explained by these ten independent and intermediate

variables that have a significant impact.

Findings in Figure 6.4 show that variable of helping strangers (X154) from the

social capital dimension in this study has a significant partial impact on MSEs’

performances through the prosperity achievable by efforts (X528) in this path diagram

model. This indicates that cooperation among entrepreneurs and helping each other can

increase their faith in the belief that they can achieve more prosperity by their endeavors

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within these traditional clusters in Herat City. The variables of charitable activities (X153),

the number of times attended a mosque (X155), family in the same industry (X111), and the

number of friends who can help (X113) have significant but only direct impacts on MSEs’

performances.

Therefore, the results of this path diagram analysis in the above figure with Chi-

square of (Chi2 =37.221) at p-value of (p<0.551) level of significance demonstrates that

there was no significant difference between our constructed conceptual model in Figure

6.4 and the perfect model in this section.

6.3.5. The Impact of Social Capital on MSEs’ Firm Strategy, Structure,

Rivalry, and Performance

This section tested the hypothesis (H16) that social capital has significant impact on

MSEs’ performances through the dimension of firm strategy, structure and rivalry (X6)

in this study.

The results of path diagram analysis in Figure 6.5 show that the variable of the

status of manager (X61) with beta coefficients of (β= .150), investing in employees

training (X616) with beta coefficients of (β= .161), and the desire to expand the enterprise

(X631) with beta coefficients of (β= .124) from the firm strategy, structure and rivalry (X6)

dimension have significant positive impact on MSEs’ performances in this path diagram

model. This denotes that when an enterprise is administered by a manager other than its

owner, the possibility of the increase of its performance in term of sales revenue is

augmented. Findings from this analysis also indicate that enterprise’s tendency toward

priority for investment in employees’ further training together with plans to expand the

enterprise or establish new branches during last two years have also contributed positively

to the higher performance of the MSEs.

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Furthermore, the variable of using business card (X623) from the same dimension

with beta coefficients of (β= -.180) at t-value of (p<0.01) level of significance bears a

significant negative effect on enterprise’s performance in this regression model. This

indicates that the use of business cards as a marketing strategy seems to have a negative

effect on MSEs’ performances in traditional clusters in Herat City.

In the social capital dimension in this study, the variables of charitable activities

(X153), sharing machine tools among enterprises (X122), and meeting with friends (X147)

have significant indirect impact on MSEs’ performances through another significant

variable from firm strategy, structure and rivalry (X6) dimension within Porter’s Diamond

framework in this study.



Figure 6.5. Path Diagram for Impact of Social Capital on MSEs’ Strategy, Structure, Rivalry, and Performance

135

These findings indicate that cooperation in terms of sharing machinery and giving

charitable activities takes place more often when an enterprise is administered by its

owner rather than by a manager other than its owner.

The variable of social media index (X152) with beta coefficients of (β= .222) has a

significant impact on the use of business card and significant indirect impact on MSEs’

performances within traditional clusters. This means that the use of business plan as a

marketing strategy is most likely practiced by enterprises that have access to means of IT

communications such as Facebook and other types of communication instruments gadgets

such as e-mail and mobile phone.

The results of regression analysis in Figure 6.5 show that from the social capital

dimension, the variable of the number of friends who can help (X113) has a significant

partial positive impact on MSEs’ performances through the manager status (X61) in this

study. This signifies that when an enterprise is governed by a manager other than its

owner, the possibility of the creation of a stronger network of friends who could provide

help and support to the enterprise is greatly enhanced in the traditional clusters of MSEs

in Herat City. In other words, a stronger network of people who are willing to provide

support to entrepreneurs can increase the enterprise’s performance through the

managerial status of the micro and small enterprises. Findings in the above figure indicate

that the number of times entrepreneurs meeting friends can increase their desire for

expanding the current business and improving the enterprise’s overall performance.

In addition, similar to the previous model in this chapter the variable of the number of

times enterprises attending a mosque (X155) with beta coefficients of (β= -.207) at t-value

of (p<0.01) level of significance has very obvious indirect effect on MSEs’ performances.

Whereas, the variable of helping a stranger (X154) with beta coefficients of (β= .172) at t-

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value of (p<0.01) level of significance bears a noticeable indirect positive impact on

enterprise’s performance. Thus the regression model in Figure 6.5 found an R-square

value of (R2=.208) which means that statistically there is more than 20% of the variation

of the enterprise’s performance which could be explained by these significant

independent and intermediate variables from the social capital (X1) and firm strategy,

structure and rivalry (X6) dimensions in this study (see Appendix 11).

Therefore, the results of regression analysis of the path diagram in Figure 6.5

indicate that at least one of the variables from the social capital dimension have significant

indirect or partial impact on MSEs’ performances through the firm strategy, structure and

rivalry (X6) dimension of an enterprise within Porter’s Diamond framework in this study.

This means that, besides its direct impact, social capital has a significant indirect or partial

impact on enterprise’s performance within the traditional clusters of micro and small scale

enterprises. Therefore, the results of the path diagram model in above figure with Chi-

square of (Chi2 =56.824) at p-value of (p<0.889) level of significance reveal that

statistically, the constructed model in the above figure were significantly similar to the

possible prefect model in this section. In other words, the conceptual model that

developed based on the theoretical reviews in this section was not different from the

actual possible model in this study.

6.3.6. The Impact of Social Capital on The Government Policies and MSEs’

Performances

Figure 6.6 shows the variable from social capital dimension with significant

indirect impact on MSEs’ performances namely, trust in family and relatives (X129). In

addition, other variables from the same dimension that were found to have significant but

only direct impact on MSEs’ performances are namely, helping a stranger (X154), having

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family members in the same industry (X111), charitable activities (X153), number of times

attended a mosque (X155), and the number of friends who can help (X113).

On the other hand, within the government’s policy (X7) dimension in this study,

there was only one variable namely, government help in international marketing (X714)

with beta coefficients of (β= .181) at t-value of (p<0.01) level of significance has positive

impact on MSEs’ performances. This means that an increase in the government initiative

can promote higher performance of the enterprises within the traditional clusters (see

Appendix 12).

From the social capital (X1) dimension, only variable of entrepreneur’s trust in

family and relatives (X129) with beta coefficients of (β= -.149) at t-value of (p<0.05) level

of significance inserts indirect impact on MSEs’ performances through the variable of the

Gov. facilitate marketing in Intl. markets (X714). This means, that the more entrepreneurs

trust their family and relatives, the less they tend to ask the government to support them



Figure 6.6. Path Diagram for Impact of Social Capital on The Role of Government policies and MSEs’ performances

138

in facilitating marketing of their products in the regional or international markets which

could contribute to the improvement of enterprise’s performances in term of increase in

sales revenues.

The results of regression analysis in Figure 6.6 shows that within social capital

dimension there were two variables namely, entrepreneur helping a stranger (X154) with

beta coefficients of (β= .202), and the number of friends who can help when needed (X113)

with beta coefficients of (β= .199) at t-value of (p<0.01) level of significance that have

only direct positive effect on MSEs’ performances. Within the same dimension there were

other three variables namely, family members in the same industry (X111) with beta

coefficients of (β= -.160), charitable activities (X153) with beta coefficients of (β= -152),

and the number of times entrepreneurs attended a mosque (X155) with beta coefficients of

(β= -.233) that bear significant direct negative impact on MSEs’ performances within the

traditional clusters of micro and small scale enterprises in Herat City.

Thus, we tested the hypothesis (H17) that the social capital has significant indirect

or partial impact on MSEs’ performances through the role of government policy (X7)

dimension in this study. The analysis of regression model in Figure 6.6 that yielded an R-

square value of (R2=.217), indicates that there were more than 21% of the variation in

enterprise’s performance can be explained by those significant independent and

intermediate variables in this path diagram model in this section.

The results of regression analysis in the above path diagram in Figure 6.6 indicate

that there was at least one significant variable in social capital (X1) dimension that has an

indirect impact on MSEs’ performances through government policies (X7) dimension

within the conceptual framework of this study. The results of regression analysis for the

path diagram in above figure with Chi-square of (21.277) at p-value of (p<0.322) level of

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significance indicate that there was marked with no difference between the constructed

conceptual model in Figure 6.6 and the perfect possible model based on the data from

those traditional clusters of micro and small scale enterprises in this study.

6.3.7. The Impact of Social Capital on the Role of Chance and MSEs’

Performances

Within the conceptual framework of this study, the regression analysis for path

diagram in Figure 6.7, this section tested the hypothesis (H18) that there is at least one

variable from the social capital dimension with significant indirect or partial impact

through the dimension of chance (X8) on MSEs’ performances.

The results of regression analysis in Figure 6.7 show the variables from social

capital namely, meeting friends (X147), having access to e-mail and the internet (X150),

joining loans associations (X16), trusting police (X143), joining Senf (X11), trusting

municipality officials (X139), number of times attended a mosque (X155) that have

significant indirect or partial impact on MSEs’ performances in this section (see

Appendix 13).

In the dimension of chance (X8) within the conceptual framework of Porter’s

Diamond Model for this study, there are two variables namely, threat from suppliers (X83)

with beta coefficients of (β= .111) at t-value of (p<0.10) level of significance and

improvement of economic status (X89) with beta coefficients of (β= .156) at t-value of

(p<0.05) level of significance have striking positive effect on MSEs’ performances within

the traditional clusters in Herat City. These findings indicate that both variables from the

dimension of chance (X8) have positively contributed to the increase of enterprise’s

performance. This can be interpreted as, if entrepreneurs consider the suppliers of raw

material as a major threat to the future of their industry, this can lead to the heightening

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of the performance of their enterprise. Moreover, if entrepreneurs assume that their

economic status has improved in the past few years, it can result in the increase in their

sales volumes.

Figure 6.7 shows that three variables from social capital (X1) dimension, namely,

meeting friends (X147), having access to e-mail and the internet (X150), and joining loans

associations (X16) have a significant impact on MSEs’ performances through threat from

suppliers (X83) within Porter’s Diamond framework in this study. These results

demonstrate that an enterprise with larger network in terms of an entrepreneur frequently

meeting with their friend positively affects the enterprise’s performance when they tend

to consider the supplier of raw materials as a major threat to the future of their industry.

The use of communication media instruments such as e-mail and the internet; and

participation in loan associations also can increase the enterprise’s performance more if

enterprises consider the suppliers of raw materials as a major threat to the future of their

industry in traditional clusters.

The regression analysis of path diagram in this section resulted in an R-square

value of (R2=.247). The regression analysis of path diagram model in Figure 6.7 indicates

there were more than 24% of the variation in the MSEs’ performances explained by these

fourteen independent and intermediate variables that have a significant impact.

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The variables of entrepreneur joining Senf (X11) and trusting municipality

officials (X139) have significant indirect impacts on MSEs’ performances through the

variable of improvement in economic status (X89) in the regression analysis of path

diagram in Figure 6.7. This means that the more entrepreneurs participate in the

associations of a cluster, the less they will believe that their economic status has

improved. At the same time, entrepreneurs’ trust in municipality positively contributes to

the belief that their economic condition has improved and eventually it can increase their

enterprises’ performance within the traditional cluster in Herat City. The variable of the

number of times attended a mosque (X155) within the path diagram in the above figure

indicates a significant partial impact on MSEs’ performances through the variable of

economic status improved (X89) in these traditional clusters of micro and small scale

enterprises.



Figure 6.7. Path Diagram for Impact of Social Capital on The Role of Chance and MSEs’ Performance

142

Findings from regression analysis of path diagram in Figure 6.7 shows that there

were three variables, namely, charitable activities (X153) with beta coefficients of (β= -

.159), family members in same industry (X111) with beta coefficients of (β= -.157), and

trust in family and relatives (X129) with beta coefficients of (β= -.163) have significant

direct negative impact on MSEs’ performances. In addition, there were other two

variables in the same dimension, namely, number of friends who can help (X113) with

beta coefficients of (β= .205) and helping strangers (X154) with beta coefficients of (β=

.216) that have significant direct positive impact on the enterprise’s performance within

the traditional clusters of MSEs in Herat City.

The results of the regression of path diagram in Figure 6.7 indicates that at least

one of the variables from the social capital (X1) dimension have a significant indirect or

partial impact on MSEs’ performances through the mediation of chance (X8) dimension

within Porter’s Diamond framework in this study. Therefore, the results of our path

diagram analysis in the above figure with Chi-square of (52.005) at p-value of (p<0.998)

level of significant indicates that there is no significant difference between our

constructed conceptual model in Figure 6.7 and the perfect possible model based on the

data from those six sampled traditional clusters of MSEs in this study.

Results and Discussions

The status of traditional clusters of MSEs in Herat City and in general in other

regions in Afghanistan can be described through various methods. In this study, we

implemented social capital factors within Porter’s Diamond framework to analyze and

describe the contemporary status of economic activities of the traditional clusters of

MSEs in the Herat City in the western region of Afghanistan.

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The results of various methods of analysis, such as correlation and regression

analysis in this and previous chapters in this study, yield the understanding that within

the traditional clusters of MSEs in this country, the factor of social capital plays a

significant role and contributes directly and indirectly to the MSEs’ performance and

eventually to the development of the industrial sector in Afghanistan. Figure 6.8 shows

the findings of the statistical analysis and test of the conceptual framework in this study.

The results of correlation analysis in the previous chapter indicate that there is

significant association among the social capital and other dimensions within Porter’s

Diamond framework. In the results of correlation analysis in the previous chapter indicate

that there is significant association among the social capital and other dimensions within

Porter’s Diamond framework in this study. Therefore, those results allowed this study to

Note: a) An arrow indicates a significant direct impact path

b) An arrow indicates a significant indirect impact path

Figure 6.8. Summary of Analysis and Test of Hypothesis

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assay the causal relationship between the social capital (X1) dimension and other

dimensions within the conceptual framework of this study.

The results of regression analysis in the path diagram model in Figure 6.1 show

that social capital (X1) has significant direct and indirect impact on MSEs’ performances

through factors such as the enterprise’s current assets (X219) and the rented space status

of enterprise (X224) within the factor conditions (X2) dimension based on Porter’s

Diamond in this study. Therefore, the findings indicate that statistically the null

hypothesis (H02) were rejected in favor of the alternative hypothesis (H12) that there was

a significant impact from social capital dimension on MSEs’ performances through factor

conditions (X2) dimension within the conceptual framework of this study.

In the second path diagram model in Figure 6.2, the results of regression analysis

indicate that social capital (X1) dimension has significant direct and indirect impact on

MSEs’ performances through the variable of location of enterprise (X35) from the

dimension of related and supporting industries (X3) in Porter’s Diamond framework in

this study. Thus, the results of the regression analysis indicate that statistically, the null

hypothesis (H03) was rejected in favor of the alternative hypothesis (H13) that there was

significant indirect impact from social capital (X1) dimension on the enterprise’s

performance through the related and supporting industries (X3) dimension in the

conceptual framework of this study.

The results of regression analysis in Figure 6.3 shows that at least five variables

from the social capital (X1) dimension have significant direct and indirect impacts on

MSEs’ performances through a factor such as increases in sales volume (X42) from the

demand conditions (X4) dimension in this study. Therefore, the results of the regression

analysis indicate that statistically the null hypothesis (H04) were rejected in the favor of

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the alternative hypothesis (H14) that there was a significant impact from social capital

(X1) dimension on MSEs’ performances through demand conditions (X4) within the

conceptual framework in this study.

The results of analysis in the fourth path diagram model in Figure 6.4 shows that

there were significant direct and indirect impacts from the social capital (X1) dimension

on MSEs’ performances in this study. Out of nine variables within the social capital

dimension, five of them bear a significant indirect or partial impact on MSEs’

performances through the variable of prosperity being achievable by efforts (X528) from

the firm characteristics (X5) dimension in the conceptual framework of this study. Thus,

the findings from the regression analysis indicate that statistically, the null hypothesis

(H05) can be rejected in favor of the alternative hypothesis (H15) which means that there

was at least one variable from social capital dimension with significant indirect or partial

impact on MSEs’ performances through the firm characteristics (X5) dimension in the

conceptual framework of this study.

Findings of the fifth path diagram model in Figure 6.5 reveal that social capital

(X1) dimension has significant direct and indirect impact on enterprise’s performance

through factors such as manager status (X61), investing in employees training (X616),

business card (X623), and the desire to expand the enterprise (X631) within the dimension

of firm strategy, structure and rivalry (X6) in the conceptual framework of this study. In

addition, the findings from the regression analysis in this study reveal that statistically the

null hypothesis (H06) were rejected in favor of the alternative (H16) which means that there

was at least one variable from the social capital dimension with significant indirect or

partial impact on MSEs’ performances through the variables within the dimension of the

firm strategy, structure and rivalry (X6) in the conceptual framework of this study.

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The results of regression analysis in the sixth path diagram model in Figure 6.6

show that social capital has significant direct and indirect impact on enterprise’s

performance through the variable such as the Government initiation for marketing in

International markets (X714) from the government (X7) dimension in the conceptual

framework that developed bases on the Porter’s Diamond Model in the chapter two of

this study. Thus, the results of regression analysis in the sixth path diagram indicate that

statistically the null hypothesis (H07) were rejected in favor of the alternative hypothesis

(H17) that there was a significant impact from social capital dimension on MSEs’

performances through the government policies (X7) dimension in this study.

The results of analysis in the last path diagram model in this study in Figure 6.7

shows that social capital in this model also has significant direct and indirect impact on

MSEs’ performances through the variables, namely, threat from suppliers (X83) and the

improvement of economic condition (X89) from the dimension of chance (X8) in the

conceptual framework of this study. The findings from this path diagram indicate that

statistically the null hypothesis (H08) can be rejected in favor of the alternative hypothesis

(H18). This indicates that in this path diagram model social capital dimension has also a

significant impact on the MSEs’ performances through the variables, namely, the threat

from suppliers (X83) and economic status improved (X89) within the dimension of chance

(X8) in the conceptual framework of this study.

The results of correlation and regression analysis methods in this study reveal that

the concept of social capital can be a significant determinant factor for the MSEs’

performances in the traditional clusters in Herat City. This finding also indicates that in

the presence of social capital indicators, the performance of the enterprises can be

impacted both positively and negatively. Therefore, the survival of micro and small scale

147

enterprises in the future in traditional clusters and in general throughout Afghanistan

seems to be highly dependent on the consideration of factors such as social capital and its

contribution to the performance of these MSEs in the context of policy intervention and

the implementation of effective plans for the development of industrial strategies in

Afghanistan.

Conclusion

Despite the fact that little attention has been paid to the development of traditional

clusters of MSEs in Afghanistan, the existing traditional mechanisms and methods of

survival at the individual, firm, or community level have enabled the traditional clusters

to gain ground in the market while contributing to the economy of the country. Based on

the findings from fieldwork in Herat City, we can conclude that the modality of

cooperation and competition in the traditional clusters of MSEs in this city has often

provided safety nets and sources of the livelihood for hundreds of households of citizens.

On the other hand, clustered MSEs in Herat City are facing a wide variety of challenges

due to their low productivity profile and lack of access to proper input materials on the

supply side and the market-related challenges on the demand side.

The introduction of the social capital dimension to Porter’s Diamond framework

in this study has revealed that social capital plays a significant role in promoting the

performance of MSEs and cooperation within the traditional clusters in Herat City. The

quality of enterprises’ social capital had very dynamic positive and negative effects on

their performance. The social capital dimension in the Porter’s Diamond Model is found

to have direct influence, as well as indirect influence mediated through other determinant

factors, on the performance of MSEs.

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The lack of social and human capital on the MSEs’ level seems to affect the

market share of clustered MSEs. Therefore, it is important to note that human capital

formation could have a positive impact on the performance of MSEs by way of

consolidation of social capital. On the other hand, the educational level and experience of

human resources within a cluster can have various relationships with the social capital

and MSEs’ characteristics, and eventually, could have visible impact on MSEs’

performance. The dynamic associations that exist among those determinant factors need

to be considered in the policy-making process for the development of the traditional

cluster of MSEs in Afghanistan.

The components of social capital such as trust and networking seem to play

significant roles in facilitating and synergizing the activities of MSEs, by means of

improved access to, and sharing of, the information on products design, input materials,

prices, and other market-related issues. The findings from this study indicate that such

cooperation and competition can be achieved by working on those factors related to social

capital and other dimensions within the framework of Porter’s Diamond Model.

The use of social communication media such as Facebook, cellular phones, and

the internet is found to have a positive correlation with the enterprise’s membership in

social groups and access to loans and other financial associations in the traditional

clusters. The possibility of an enterprise’s access to loans and other credit institutions is

higher among enterprises which are administered by a manager rather than its owner.

Findings in this study indicate that the possibility of cooperation among

entrepreneurs is positively associated with the quality of their trust in neighbours, larger

networks friends, and entrepreneur’s effectiveness in the cluster’s decision-making in the


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Complex relationships exist among human capital, social capital, and MSEs’

performances in these traditional clusters. Findings shows, that the level of trust in

informal networks such as family, relative and neighbors were much higher than the trust

in formal organization such as local and national government officials and municipality

officials. The size of entrepreneurs’ social networks and groups were found to have a

positive influence on enterprise’s performance, whereas participation in religious

activities is found to have negative influence on the performance of enterprises in these


The level of trust in neighbours has positive association with the cooperation in

sharing information, machinery and tools among the cluster members. The entrepreneurs

who have a family member in the same cluster are found to be more effective in the

process of decision-making and cooperation in price bargaining within these clusters. The

findings show that the charitable activities were more common among the enterprises

with the higher performance. The entrepreneurs’ participation in informal social networks

(such as local and cultural associations) is found to have a positive correlation with the

sources for investment from relatives in these clusters.

Findings from regression analysis indicate that about 45% of the variations in the

MSEs’ performances are explained by thirteen variables (see Table 6.1) representing the

social capital and other dimensions in the conceptual model of this study.

Besides the social capital’s direct impacts on MSEs’ performances (see Table

6.1), the results of regression analysis with the path diagram model reveal that social

capital also has significant indirect impacts the performances of enterprises mediated

through other dimensions in the conceptual framework of this study (see Chapter 2).

Based on the regression analysis conducted in this chapter, all of the constructed

150

hypothesizes (see Chapter 2) were tested and it is statistically accepted that social capital

has both direct and indirect impacts on MSEs’ performances. In addition, the findings of

this study indicate that, given that the major role of social capital has been identified in

the framework of Porter’s Diamond Model, a set of policies can be implemented by

Afghanistan policy-makers to promote social capital in the process of evaluating and

upgrading activities of the clusters of micro- and small-scale industries.

Findings indicate that the total value of the enterprise’s current assets is the most

significant positive determinant factor for MSEs’ performance among all factors.

In addition, in the same dimension of enterprise’s factor conditions, the ownership status

of the enterprises’ operating venue (such as rented space) is found to be a vital factor to

determine the performance and its competitiveness.

The level of entrepreneur’s satisfaction with the current location of their enterprise

within the clusters has a negative impact on the performances of MSEs. This seems to

reveal that the mindset and the motive of entrepreneurs plays a significant role in

determining the performances of the enterprises through the way they evaluate their

enterprise’s location in a traditional cluster.

This finding indicates that the urge for upgrading the location of enterprises is possibly

stronger among entrepreneurs who run enterprises with higher performances and vice

versa in those traditional clustered enterprises in Herat City.

In traditional clusters, the characteristics of enterprises such as entrepreneur’s

belief in and respect for endeavors as the major drive of prosperity are found to be another

determinant factor that has a negative effect on the performance of MSEs in these

151

traditional clusters. This fact indicates that there seems to be other factors which possibly

play significant roles in achieving prosperity for enterprise in Afghanistan.

In this analysis, it emerges that firm’s strategy, structure and rivalry are vitally

important with regard to a large number of determinant factor of MSEs’ better

performances. The managerial status of the enterprises, investment in upgrading the

human capital recourses and planning for expansion are all found to have considerable

impact on the performance of enterprises. Although the use of business cards is generally

believed to have to contribute to the improvement of the performance of enterprises,

however, this study has found that this strategy has no positive influence on the

performance of enterprises.

Even though Afghanistan’s economy is in its transitional stages and related

ministries do not have any specific industrial development strategy, but the role of

government policies is found to be a very important determinant factor of MSEs’

performances. The government initiatives for facilitating the marketing of the enterprises’

products in the international market must greatly contribute to the improvement of the

performances of enterprises in these six traditional clusters in Herat City.

Understanding the importance of protection by the government regarding measures such

as imposing import quota are found to be more common among entrepreneurs who more

often participate in cooperatives and associations (including cluster’s association).

Additionally, findings from this study indicate the necessity for increasing the MSEs’

access to human resource training, marketing facilities, and networking opportunities

through policy interventions.

152

The competitiveness of enterprises and their survival in the challenging

environments of domestic and international markets are also found to be significant

determinant factors of the performance of MSEs in the traditional cluster. The fear of

suppliers of raw materials as a possible threat and the improvement of improvement of

economic conditions significantly influence the performance level of enterprises in those

traditional clusters in Herat City.

Policy Recommendations

The application of Porter’s Diamond Model can provide a wider strategic

perspective to industries, especially the traditional clusters of micro- and small-

scale industries. Porter (1990, p.72) argues that the Diamond Model is a mutually

reinforcing system. In addition, he suggested that, even though it is formulated

under ideal conditions, this model illuminates the process of industrial

development in which national competitiveness could be achieved as the result of

coordinated efforts between the business establishment and the government. By

way of conclusion, therefore, this section provides the following

recommendations to policy-makers in the government and those who are involved

in policy formulation in national and international organizations for the

development of strategic plans for industries in Afghanistan. The government

should take the initiative to provide facilities for the enterprises, possibly through

the promotion of social capital components, as this study found it to be an

important determinant dimension within Porter’s Diamond Model in facilitating

cooperation among the cluster members, to enhance their access to information

153

about products’ design, prices of raw input materials, and related information

(including marketing) in the domestic and international markets.

The government with assistance from national or international NGOs should take

initiatives to provide facilities for traditional enterprises to have access to

information on product design, prices of raw materials and related information

including marketing techniques in the domestic and international markets. Such

initiatives must consider or include the promotion of social activities or events

(networking and other opportunities for the exchange of information and

resources) among the enterprises in the traditional clusters in Herat City.

The government should take an initiative to provide training and workshops to the

traditional enterprises on issues such as upgrading the quality and designs of their

products as well as to stakeholders in related sectors that provide raw materials to

these enterprises in the traditional clusters.

The government should make arrangements between the training centers, research

institutes, cluster’s association, and other related governmental organization to

provide the vocation training and other types of relevant skills in order to upgrade

the human resources within the enterprises in the clusters.

The government should provide protection to the potential industries in these

traditional clusters through its policy intervention such as imposing quotas on the

imported goods that are similar to the domestic ones, preventing the smuggle of

similar goods from neighboring countries, and reducing the import tariff on the

raw materials used in the production circle in these clusters.

The government should facilitate the establishment of a “coordination center” for

traditional industrial clusters in order to promote the innovation and encourage

154

entrepreneurship initiatives, disseminate book-keeping methods and provide a

legal framework to achieve a higher level of dynamism within the context of each

industry, and to contribute to the boosting of the social capital components such

as the trust, networking, joint actions, cooperation, and positive competition

within and among these clusters and other supporting industries or organizations

in Herat City.

Under the umbrella of such a “coordination center”, cooperation among the

clusters should be encouraged through the holding of social events and providing

opportunities for socializing with each other, exchanging information and

enlarging social networks for entrepreneurs within the traditional clusters.

Findings from this study show that total value of enterprise’s current assets is a

major determinant factor of MSEs’ performance. Therefore, the government

should take an initiative to provide an effective framework for the enterprises to

facilitate their access to financial sources such as banks, credit institutions and

other local informal sources for the investment in the clusters.

The government should provide the enterprises with access to proper essential

infrastructures such as electricity, additional warehouses, and other logistic

infrastructures in those clusters in Herat City.

The enterprises within these clusters should be provided with the incentives for

investing in collaboration with other cluster members, expanding the enterprises,

and increasing the sophistication of production methods through the

implementation of modern machinery and tools across the value chain in each of

those traditional industries in Herat City.

155

Future Studies

There are certain aspects of traditional clusters of micro- and small-scale

enterprises that future studies should address based on the results and findings of this

study.

There are at least two areas that investigators in the future could explore and expand and

thereby contribute to further industrial development with the scope of the traditional

economy in countries such as Afghanistan. First, since this thesis considered only six

sampled traditional clusters in Herat City to examine the role of social capital within

Porter’s Diamond Model, it is necessary that the future studies explore and test the

conceptual framework of this study in more depth and separately for each of the industries

in the traditional clusters. Second, it must be of significant value to analyze Porter’s

Diamond combined with social capital across the entire value chain of each industry in


156

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8. APPENDIXES

Appendix 1: Map of Afghanistan

Source:http://www.un.org/Depts/Cartographic/map/profile/afghanis.pdf

Appendix 2: Map Clusters Location in Herat City

Source: Adapted from the maps of AIMS office in Herat City

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Appendix 3: List of Variables with Descriptive Results

Variable Min Max Mean Std.D.

Join Senf (X11) 0 1 .60 .492

Join cooperative and association (X12) 0 1 .12 .329

Join Industries and Trade Chamber

(X13) 0 1 .05 .216

Join local council (X14) 0 1 .11 .311

Join cultural association (X15) 0 1 .10 .298

Members are trustful (X116) 0 1 .82 .382

Join Loans Association (X16) 0 1 .04 .195

Join sport group (X17) 0 1 .20 .398

Join ethnic group (X18) 0 1 .18 .386

Join Other Associations (X19) 0 1 .12 .329

Join in index (X110) 0.00 6.00 1.5147 1.32233

Family in Same Industry (X111) 0 1 .52 .501

Number close friends (X112) 0 45 6.00 7.736

No. of Friends Who Can Help (X113) 1 4 2.01 1.125

Most members are trustful (X115) 1 5 4.18 1.068

Everyone must be careful (X118) 0 1 .88 .329

Enterprises share information (X120) 0 1 .69 .465

Enterprises Share Machineries (X122) 0 1 .80 .398

Effective in Decision Making (X123) 1 3 1.97 .818

Vote in Presidential Election (X128) 0 1 .82 .382

Trust Family and Relatives (X129) 1 5 4.34 1.157

Trust in Wakil and Arbab (X131) 1 5 2.82 1.325

Trust Neighbors (X133) 1 5 3.41 1.218

Trust in suppliers (X135) 1 5 2.96 1.299

Trust in teachers and professors (X138) 0 1 .60 .490

Trust Municipality Officials (X139) 1 5 1.97 1.176

Trust in national government official

(X142) 0 1 .07 .253

Trust in Police (X143) 1 5 2.55 1.355

Number of friends asked help (X145) 1 4 2.22 1.106

Friends Ask for Help (X146) 0 1 .65 .478

Meeting with Friends (X147) 0 15 1.82 1.940

Meeting friends (X148) 0 1 .72 .450

Mobile Phone (X149) 0 1 .88 .329

E-mail and Website (X150) 0 1 .14 .350

Facebook account (X151) 0 1 .29 .457

Social Media Index (X152) 0.00 3.00 1.3137 .83613

Charity Activity (X153) 0 40 6.08 4.907

Help a Stranger (X154) 0 1 .50 .357

No. of Times Attended Mosque (X155) 0 35 17.53 9.556

Age (X21) 17 70 34.60 12.449

Work experience (X22) 1 66 19.67 12.888

Level of education (X23) 1 6 3.69 1.495

Illiterate (X24) 0 1 .15 .360

Madrasa (X25) 0 1 .01 .121

Elementary school (X26) 0 1 .25 .437

166

Secondary school (X27) 0 1 .25 .434

High school (X28) 0 1 .23 .419

Higher education (X29) 0 1 .10 .305

Vocational training (X210) 0 1 .54 .500

Sources of invest to expand (X211) 1 4 1.94 1.067

Invest from saving (X212) 0 1 .46 .500

Invest from family friends (X213) 0 1 .27 .447

Invest from credit institutes (X214) 0 1 .13 .334

Invest from other sources (X215) 0 1 .14 .345

Invest from self and relatives (X216) 0 1 .74 .442

Total funded assets (X217) 1000 800,000.00 118,941.67 163,774.61

Total of Current Assets (X219) 10000 5,000,000.00 278,568.63 663,268.98

Machineries are fit to business (X220) 0 1 .82 .382

Types of place ownership (X221) 1 4 2.28 .935

Space is Rented (X224) 0 1 .57 .496

Car (X227) 0 1 .24 .428

Motorcycle (X228) 0 1 .58 .495

Helper economic status (X31) 1 3 2.39 .512

Location of Enterprise (X35) 1 3 2.10 .654

Sales Volume Increased (X42) 0 1 .15 .355

Customer prefer price (X43) 0 1 .61 .489

Customer prefer quality (X44) 0 1 .61 .488

Customer feedback (X46) 0 1 .84 .365

Benefit access market info (X52) 0 1 .28 .450

Benefit increase cooperation unity (X53) 0 1 .29 .457

Benefit customer market awareness

(X54) 0 1 .22 .416

Other benefits (X55) 0 1 .21 .405

Wakil efficiency all (X56) 1 3 1.84 .797

Being abroad (X511) 0 1 .61 .489

Council with employee (X512) 0 1 .79 .409

Feel safety all (X513) 1 5 3.55 1.188

To gain profit (X516) 0 1 .17 .378

To earn Halal (X517) 0 1 .49 .501

To make livelihood (X518) 0 1 .23 .419

To contribute to economy (X519) 0 1 .11 .317

Change varieties of products (X520) 0 1 .78 .416

Internet as source of innovation (X522) 0 1 .15 .360

Customers as source of innovation

(X523) 0 1 .66 .476

Imitation as source of innovation (X524) 0 1 .16 .369

Other sources of innovation (X525) 0 1 .03 .169

Level of business satisfactions (X526) 1 5 3.76 1.076

Business satisfaction (X527) 0 1 .76 .428

Prosperity Achievable by Efforts (X528) 0 1 .75 .306

Manager Status (X61) 0 1 .21 .409

Self-manage (X62) 0 1 .79 .409

Enterprise age (X63) 1 65 14.01 12.407

Self-established (X66) 0 1 .63 .483

Established by family (X67) 0 1 .26 .442

167

Established with Friends (X68) 0 1 .10 .305

Enterprise size (X69) 1 2 1.23 .422

Invest in machineries (X610) 0 1 .48 .501

Invest in tools (X611) 0 1 .24 .428

Invest in more employees (X612) 0 1 .32 .469

Invest in storage (X613) 0 1 .14 .345

Invest in location (X614) 0 1 .29 .455

Invest in raw materials (X615) 0 1 .35 .478

Invest in Employees Training (X616) 0 1 .08 .277

Invest in other (X617) 0 1 .14 .345

Book-keeping techniques (X618) 1 3 1.75 .578

By memorizing (X619) 0 1 .32 .467

By journal (X620) 0 1 .61 .489

By None (X621) 0 1 .07 .262

Invested to expand (X622) 0 1 .46 .500

Business Card (X623) 0 1 .53 .500

Customer base by discount (X624) 0 1 .45 .499

Customer base by marketing (X625) 0 1 .04 .206

Customer base by quality (X626) 0 1 .60 .490

Customer base through customers

(X627) 0 1 .14 .345

Customer base others (X628) 0 1 .08 .277

Use Business plan (X629) 0 1 .62 .487

Employee startup (X630) 0 1 .61 .488

Expansion of Enterprise (X631) 0 1 .41 .492

Current positions in market (X632) 1 4 2.27 .777

Strong position in market (X633) 0 1 .34 .474

Elected by municipality and Senf (X72) 0 1 .22 .412

Elected by some members (X73) 0 1 .21 .409

Elected by members vote (X74) 0 1 .57 .496

Gov. decisions consider enterprises

(X75) 0 1 .23 .422

Ever bribes (X76) 1 3 1.55 .783

Gov. follow strategy all (X78) 1 5 1.71 .915

Gov. follow strategy (X79) 0 1 .16 .365

Gov. Provide Subsidies (X710) 0 1 .25 .431

Gov. provide training (X711) 0 1 .20 .398

Gov. impose import quota (X712) 0 1 .48 .501

Gov. provide access to info (X713) 0 1 .11 .311

Gov. Marketing in Intl. Markets (X714) 0 1 .18 .386

Gov. take other initiatives (X715) 0 1 .14 .345

Easy to Obtain license (X716) 0 1 .52 .501

Threat of revivals (X81) 0 1 .36 .482

Threat of consumers negotiate (X82) 0 1 .30 .459

Threat of suppliers (X83) 0 1 .22 .416

Threat of imported products (X84) 0 1 .56 .498

Other threats (X85) 0 1 .13 .334

Trend in number of enterprises (X87) 1 3 1.91 .934

Number of enterprises increased (X88) 0 1 .39 .489

Economic Status Improved (X89) 0 1 .05 .155

168

MSE's Performance 100.00 30,000.00 1,260.93 2,867.25

N= 204

Appendix 4: Questionnaire in English

Questionnaire (English)

Date: / /

==============================================================

==

This questionnaire aims to gather data and explore opinions about status and production

activities of small enterprises in Afghanistan. Collected data will be used for policy

making and programming in government and non-government organization to enhance

employment, accelerate SME growth and to sustain economic development. The

information gathered through this questionnaire will remain private and only used for this

research purpose.

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-

Age of Entrepreneur: ( ) Years

Sex: Male/Female

Type of Activity:

A. Tailoring

B. Shoemaker

C. Dry Fruits and Nuts

D. Ironmonger

E. Carpentry

F. Tinwork

Position of Interviewee: A. Owner B. Entrepreneur Manager/Team leader

1. How long it is you have established this Enterprise? ( ) Years

2. How long it is you are working in this activity? ( ) Years

3. Type of Ownership: A. Private/Individual B. Shared Enterprise

4. Status of Establishment?

169

A. Personal Initiative

B. Through Family

C. Shared with friends

5. How many people are working in this enterprise?

A. Less than 5 Persons

B. 5 -19 Personas

C. 20-99 Persons

D. 100-More Persons

6. What is your highest education?

A. Illiterate

B. Home Classes/Religious School

C. Primary school

D. Elementary

E. 12th Class

F. University

7. Have you taken Vocational Courses Yes No

8. In which of below groups you have participation?

Yes Groups

Industrial Union/Class

Unions/Associations/Cooperatives

Chamber of Commerce

Development Council/District

Social/Cultural Associations

Associations/Unions of Finance

and Micro Credits

Sport Groups

Tribal Assemblies/Meetings

Others..........................

9. What is the major benefit you get from above mentioned groups?

A. Access to Information about Market (Prices/Raw

Materials/Opportunities......)

B. Expanding relationships and solidarity among entrepreneurs

C. Being Informed about Clients/ Changes in market

D. Others (...............................)

170

10. Other than this activity, is there any member of your family working in this

cluster?

Yes No

11. How is your industry representative selected?

A. Through Municipality /Industrial Union

B. Through a limited number of industry members

C. Through all members’ decision and vote

12. Overall, how do you evaluate your cluster representatives?

A. Not effective at all

B. Somehow effective

C. Very Effective

13. On average, how many friends there are that you count on their support.

( ) Persons

14. If you urgently needed financial support/collaboration to your work progress,

how many people do you think would come forward with such help?

A. No One (>> Question 15)

B. 1 Or 2

C. 3 Or 4

D. 5 Or More

15. In your opinion, are these people economically in better or worse condition as

you are?

A. Lower

B. Equal

C. Higher

16. On bellow sentences, please mention how do you agree?

1. Completely Disagree

2. Partially Disagree

3. Neither Agree nor Disagree

4. Partially Agree

5. Completely Agree

A. Most of Entrepreneurs

that work in your industry

could be trusted.

B. In this Industry, Everyone

must be very intelligent to

avoid being exploited by

others.

C. In this Industry, Most of

Enterprises share

171

information about clines and

market with each other.

D. In this Industry,

sometimes Enterprises use

each other machines and

tools.

17. How is your role in decision making process in your industry? A. Not Effective

B. Somehow Effective

C. Very Effective

18. Did you vote in last presidential election? Yes No

19. Do you have experiences of living abroad? Yes No

20. In your opinion, in which level do you think the government/authorities respect

peoples’ expectation in making decisions?

A. A lot

B. Somehow

C. Not at All

21. How do you evaluate your trust to below mentioned groups?

1. Very Low

2. Low

3. Neither High nor Low

4. High

5. Very High

A. Family/Relatives

B. District Representative

C. Nearby Enterprises

D. Sellers of raw materials

E. Teachers/Lectures of University

F. Municipality/Government officials

G. Central Government officials

H. Police

22. With how many of your friends have you consultant or asked for help during

past 3 months?

A. No One

B. 1 or 2

C. 3 or 4

D. 4 or More

172

23. During past month, how many times have you had informal meetings

(picnic/party) with your friends that are from your industry? (

) Times

24. Do you council with your employees in daily base? Yes No

25. Considering your current work/life condition, how secure do you feel?

A. Very Insecure

B. Partially Insecure

C. Neither Secure nor Insecure

D. Partially Secure

E. Very Secure

26. What was your primary motivation to initiate this enterprise?

……………………………………………………………………………………………

………………………………………………………………..

27. What do you think should be main goal of a good entrepreneur by doing

economic works?

A. Earning Money

B. Gaining lawful/ legitimate income

C. Supplying family needs

D. Being supportive to economy of society

28. In past 2 years, how was your selling rate?

A. Decreased

B. Not Changed

C. Increased

29. On average, how much is your selling revenue?

A. Daily ( ) AFs

B. Monthly ( ) AFs

30. In case you decided to increase your investments, what would be your main

financial source?

A. Personal savings

B. Family and Friends

C. Financial Institutions

D. Others……………..

173

31. If you got extra amounts on hand, which of bellow options would be your

priority to invest on?

A. Machines

B. Tools

C. Recruitment More Staff

D. Storage

E. Better Location for Enterprise

F. Buying extra raw materials

G. Staff Capacity Building

H. Others……………..

32. How do you often make record of your daily work?

A. Memorizing

B. Recording in Journals

C. None

33. In past 12 months, have you faced problems that you were forced to pay

government entities’ extra amounts than what you had to?

A. Yes

B. No

34. While establishing your enterprise, what was value of your investment/capital?

( ) AFs

35. In past 2 years, have you has extra investments buying/Advancement of

machineries/ equipments?

A. Yes

B. No

36. Assume that you are going to sell all of your equipment, how much do you think

you can sale? ( ) AFs

37. How do you evaluate your enterprise location?

A. Not Suitable

B. Suitable

C. Very Suitable

38. Are your current equipments/machineries in according to your production line

needs?

A. Yes

B. No

174

39. What is ownership status of your enterprise?

A. Personal

B. Inherent

C. Rental

40. Which of below items are used in your work place?

A. Phone/Mobile Yes No

B. Email Address/Website Yes No

C. Facebook Yes No

D. Car Yes No

E. Motorbike Yes No

F. Business Card Yes No

41. In order to attract more customers, which of below mentioned approaches do

you undertake?

A. Price Reduction

B. Advertisements through public media

C. Supplying quality products

D. Through customers and advertisements among them

E. Others…………………………

42. What is mostly prioritized by your customers while buying products?

A. Price Yes No

B. Quality Yes No

43. Do your customers give feedbacks and recommendation about characteristics of

your products? Yes No

44. Does government follow any supportive strategy or policy to support your works

and enterprise?

A. Completely Disagree

B. Disagree

C. Naturally

D. Agree

E. Completely Agree

45. In your opinion, which of below approaches should the government undertake to

support industries/enterprises?

A. Subsidy

B. Providing capacity building/vocational opportunities

C. Impose import quota on similar products

175

D. Access to information about buyers and sellers

E. Marketing for products outside the country

F. Others……………………………..

46. Are there any changes in diversity of your products compared to past 2 years?

Yes No

47. From which of below do you get inspiration to change/innovate new products?

A. From Internet

B. Customers’’ order

C. Copying imported or similar products

D. Others……………………………

48. Do you design or operate based on a specific business plan?

Yes No

49. In past 2 years, has any of your employee started new business independently?

Yes No

50. In your opinion, which one of below is a serious threat to your business in

Herat? (Select one)

Yes Structures

A. Intensive Competition with other

entrepreneurs

B. Bargaining Power of customers

C. Bargaining Power of suppliers

D. Import of Similar products

E. Others ………..

51. Overall, how satisfy you are from your work progress and production?

A. Very Satisfy

B. Partially Satisfy

C. Neither Satisfy or Dissatisfy

D. Partially Satisfy

E. Very Satisfy

52. In past 3 years, have you decided establishing new/separate enterprise?

Yes No

53. How is current position of your business in the industry compared to the other

similar enterprises?

A. Very weak

176

B. Weak

C. Similar

D. Strong

E. Very Strong

54. In your opinion, obtaining work license is easy and transparent?

Yes No

55. In past 3 years, have there been any changes in number of enterprises in your

industry?

A. Decreases

B. Unchanged

C. Increased

56. Do you think people in this country would achieve prosperity by effort?

Yes No

57. Currently, how do you think, economic situation of the country has changed?

A. Got Better/Improved

B. Got Worse

58. During past week, how many times have you given charity? ( )

times

59. During past week, have helped anyone stranger/whom you did not know?

Yes No

60. During past week, how many times did you attend to Masjid(Mosque)?

( ) times

Appendix 5: Questionnaire in Persian (Dari)

پرسشنامه

1394/ / تاریخ:

==============================================================

==

نظرات در باره وضعیت فعالیت های تولیدی صنایع کوچک در افغانستان هدف از این سروی جمع آوری معلومات و

میباشد. معلومات جمع آوری شده توسط این سروی در حصه پالیسی سازی و پروگرام های دولت و موسسات زیربط جهت

177

بصورت افزایش سطح استخدام، رشد صنایع کوچک و تقویت اقتصاد مورد استفاده قرار خواهد گرفت. این معلومات

محرمانه حفظ خواهد شد و تنها برای استفاده در این مطالعه مورد استفاده قرار میگرد.

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--

کافرما: مرد / زن جنسیت سن کارفرما: ) ( سال

نوع کارگاه:

کفش سازی خیاطی

مسگری نخود بریزی

حلبی سازی نجاری

ب: کارفرما الف: مالک :در تولیدی شونده موفق مصاحبه

( سال ) چند سال میشود که این کارگاه را تاسیس کرده اید؟ .1

) ( سال چند سال میشود که در این حرفه کار میکنید؟ .2

ب: شریکی الف: شخصی نوع مالکیت کارگاه: .3

چگونگی تاسیس این کارگاه؟ .4

الف: ابداع شخصی

ب: توسط فامیل

ت: مشارکتی با دوستان

تعداد کارگران در این کارگاه چند نفر است؟ .5

نفر 5الف: کمتر

نفر 19تا 5: ب

نفر 99تا 20ج:

نفر یا بیشتر 100د:

بلندترین درجه تحصیلی تان کدام است؟ .6

الف: بی سواد

ب: مدرسه

ت: ابتدائی

ث: متوسطه

ج: لیسه

د: تحصیالت عالی

خیر آیا کدام آموزش حرفوی مرتبط به همین حرفه تان دیده اید؟ بلی .7

178

های ذیل اشتراک مینمایید؟ در کدام نوع از گروه .8

نوع گروه

بلی

اتحادیه/صنف صنعتگران

کوپراتیف ها/انجمن ها/اتحادیه ها

اتاق های تجارت

شورای انکشافی گذر/ناحیه

انجمن های فرهنگی/اجتماعی

انجمن/اتحادیه قرضه دهندگان

گروه های ورزشی

نشست/گردهمایی های قومی

.................( سایر موارد )...

عمده ترین منفعت که از اشتراک در گروه های فوق حاصل میکنید چیست؟ .9

الف( دسترسی به اطالعات بازار در مورد)قیمت ها/مواد خام/فرصت ها....(

ب( افزایش همکاری و همبستگی در بین کارگاه ها

ج( شناخت بیشتر مشتریان/تغییرشرایط بازار

ایر موارد )............................( و( س

به غیر از این کارگاه، آیا کسی از اعضای فامیل تان در این حرفه کارگاه جداگانه ای دارد؟ .10

نخیر بلی

وکیل صنف تان معموال چگونه انتخاب میگردد؟ .11

الف( توسط نهاد های شاروالی/اتحادیه صنعتگران

اعضای صنف ب( توسط یک تعداد محدود از

ج( به تصمیم و رای اعضاء

بصورت کل، عملکرد وکیل صنف تان را چگونه ارزیابی میکنید؟ .12

بسیار مؤثر است ج( ب( کمی مؤثر است الف( هیچ مؤثر نیست

ند نفر باالی همکاری شان حساب باز کنید چ میشودبصورت تخمینی، مجموع تعداد دوستان نزدیک تان که .13

اند؟

) ( نفر

اگر به شکل ناگهانی شما نیاز به پول/همکاری برای پیشبرد امور کارگاه تان پیدا کنید، فکر میکنید چند نفر .14

حاضر به ارائه چنین کمکی خواهند بود؟

( 15 الف: هیچکس )<< سوال

ب: یک یا دو نفر

ج: سه یا چهار نفر

د: پنج نفر یا بیشتر

179

نظر شما، آیا این افراد در وضعیت اقتصادی برابر، بلند تر یا پاینتر از شما اند؟ هب .15

ب: برابر ج: بلند تر الف: پائین تر

در جمالت ذیل، لطفآ برایم بگویید که تا چه حد با من موافق یا مخالف هستید؟ .16

کامال مخالف .1

تا حدی مخالف .2

نه موافق نه مخالف .3

نسبتا موافق .4

کامال موافق .5

الف: اکثر صنعتگران که در صنف تان کار میکنند افراد مورد اعتماد اند.

ب: در این صنف، هر کس باید خیلی هوشیار باشد تا مورد استفاده دیگران

قرار نگیرید.

ندگان ه: در این صنف، کارگاه ها عموماً معلومات در مورد مشتری ها وفروش

موادخام شان را با هم دیگر شریک میسازند.

ح: در این صنف، گارگاه ها در بعضی مواقع از استفاده ماشین آالت/لوازم

کاری یک دیگر استفاده مینمایند.

نقش تان در تصمیم گیری های صنف تان معموال چگونه است؟ .17

بدون تاثیرالف:

ب: کمی اثر گذار

ج: خیلی اثر گذار

در انتخابات اخیر ریاست جمهوری رای داده اید؟آ یا .18

خیر بلی

آیا تا بحال تجربه زندگی کردن در خارج از افغانستان را داشته اید؟ .19

خیر بلی

نظر شما، تا چی حد مقامات/مسئولین دولت خواسته های مردم را هنگام تصمیم گیری های شان در نظر به .20

میگیرند؟

ج: به هیچ وجه ب: تا اندازه ای ادالف: خیلی زی

اعتماد تان نسبت هر یک از گروه های را که حاال برای تان نام میبرم را چگونه ارزیابی مینمایید؟ .21

بسیار کم .1

کم .2

نه کم نه زیاد .3

زیاد .4

خیلی زیاد .5

فامیل/اقارب

وکیل/ ارباب گذر

کارگاه داران همسایه

فروشندگان مواد خام تولید

مین/استادان پوهنتونمعل

مامورین شاروالی/والیت

مامورین دولت مرکزی

پولیس

180

چی تعداد دوستان تان طی سه ماه گذشته به خاطر حل مشکل شخصی شان با شما مشوره و یاهم کمک خواسته .22

اند؟

هیچکس الف:

یک یا دو نفرب:

نفر 4یا 3ج:

نفر 5بیشتر از د:

چند مرتبه با افرادی از صنف خود تان در محیطی غیر از کارگاه تان بخاطر طی یکماه گذشته شما .23

تفریح/مهمانی مالقات نمودید؟ ) ( مرتبه

مینمایید؟ نظرخواهی/کارگاه مشوره درآیا معموال، شما با شاگردان تان در مورد تصمیمات روزمره کاری .24

ب: خیر الف: بلی

امن احساس میکنید؟تا چی اندازه خود را رایط موجود محیط کار/زندگی تان، با توجه به ش .25

الف: کامال نا من

ب: نسبتا نا امن

ج: نه امن و نه نا امن

د: نسبتا امن

ه: خیلی امن

چه انگیزه ای در ابتدا باعث گردید تا به این شغل روی بیاورید؟ .26

.............................................................................................................................................

................................................................................................

هدف عمده یک صنعتکار خوب از انجام فعالیت اقتصادی چه باید باشد؟ .27

ب: روزی حالل لف: کسب منفعتا

جامعهد: کمک به اقتصاد خانواده نفقهج:

در دو سال گذشته میزان فروشات شما چگونه بوده است؟ .28

الف: کاهش یافته است

ب: بدون تغیر مانده

ج: افزایش یافته است

ود(بصورت تخمینی مجموع عواید فروشات کارگاه شما چقدر است؟ )یک گزینه انتخاب ش .29

( افغانی روزانه )

( افغانی ماهوار )

برای تامین این مهمترین منبع در صورتیکه بخواهید سرمایه گذاری در کارگاه تان را افزایش بدهید، .30

؟میباشدکدام سرمایه/پول

پس انداز شخصیالف:

دوستانب: فامیل و

ندهدهج: موسسات قرضه

(...................): سایر موارد د

181

مهمترین اولویت سرمایگذاری تان خواهد رادر اختیار داشته باشید، کدام موارد اضافی در صورتیکه سرمایه .31

بود؟

الف: ماشین آالت

ب: ابزارآالت

ج: استخدام کارمندان بیشتر

د: گدام

برای کارگاه ه: موقعیت بهتر برای فعالیت

واد خام اضافیمخرید و:

برای کاگران ز: آموزش های حرفوی

ه: سایر موارد )....................(

تان را ثبت میکنید؟ روزمره معموال چگونه جریان معامالت .32

به حافظه سپردناز طریق الف:

کتبی/استفاده روزنامچهبا ب:

ج: هیچکدام

را اضافی مبلغیادارات دولتی به یکی از یمشکلبرای حل مجبور شده اید که در دوازده ماه گذشته، آیا .33

ب: بلی، بعضی وقت ها الف: نه خیر ؟کنیدپرداخت

) (افغانی ابتدایی شما چقدر بوده است؟ یه/داراییکارگاه، سرمااین در زمان تاسیس .34

کارگاه خود کدام سرمایه گذاری اضافی خریداری ماشین آالت/لوازم/ا در حصه توسعهدر طی دو سال گذشته آی .35

خیر بلی انجام داده اید؟

فرضآ اگر امروز بخواهید که تمام وسایل و امکانات کارگاه تان را بفروشید، فکر میکنید به ارزش چقدر .36

(افغانی ) ؟خواهید فروخت

ارزیابی میکنید؟ چگونهموقعیت فعلی کارگاه خود را .37

ج:خیلی مناسب مناسب ترینب: نامناسب الف:

تان مطابق به نیاز کاری تان میباشد؟ کارگاه آالت موجود درماشین و ابزارآیا .38

خیر بلی

کارگاه تان چگونه است؟ ملکیت نوع .39

شخصیالف:

میراثیب:

تان استفاده مینمائید؟ کارگاهامکانات ذیل را در موارد از دام ک .40

خیر بلی الف: تلیفون/ مبایل

خیر بلی سایت ب: ایمیل آدرس/ ویب

خیر بلی حساب فسبوک ج:

خیر بلی د: موتر

خیر بلی ه: موتر سایکل

خیر بلی کارت تبلیغات دوکان )بزنس کارت(و:

؟استفاده مینمائید معموآل از چی روش های یشترببرای جذب مشتریان .41

182

قیمت الف: از طریق تخفیف دادن

جمعی ب: آگاهی دهی از طریق رسانه ها

باال ج: ارائه محصوالت با کیفیت

د: از طریق مشتریان وتبلیغات در بین شان

ه: سایر موارد.........................

قرار میدهند؟ اولویتکدام یک از موارد ذیل را در بیشتر معموال مشتریان تان در هنگام خرید .42

خیر بلی الف: قیمت

خیر بلی ب: کیفیت

در مورد خصوصیات محصوالت تان به شما نظر یا مشوره میدهند؟ آیا مشتریان تان در هنگام خرید .43

نخیر بلی

را دنبال میکنید؟ ایویاستراتیژی یا پالیسی حمکدام آیا دولت جهت تقویت و حمایت از کار و فعالیت شما .44

الف: کامال مخالف

مخالفب:

ج: طبعا

وافقد: م

ه: کامال موافق

اتخاذ نماید؟برای رشد و توسعه صنعت را تدابیر/سیاست هاکدام باید به نظر شما دولت .45

الف: کمک هزینه

ی/ و حرفویآموزشفراهم کردن تسهیالت ب:

ج: محدودیت واردات کاالهای مشابه

در مورد شرایط خریداران و فروشندگان در بازار به معلومات د: دسترسی

ه: بازاریابی برای محصوالت در بازارهای خارجی

: سایر موارد )..................(و

تغیراتی در تنوع محصوالت شما نسبت به دوسال گذشته بوجود آمده است؟کدام آیا .46

خیر بلی

ید؟یتولید محصوالت تان استفاده مینما درن منبع نوآوری/ابتکار از کدام یک از موارد ذیل منحیث مهمتری .47

انترنتاز طریق الف:

ب: فرمایش مشتری

د: کاپی برداری از محصوالت وارداتی یا مشابه

ه: سایر ...............

مشخص طرح و اجرا میکنید؟)بیزنیس پالن( پالن بر اساس کدامآیا کارگاه خود را .48

خیر بلی

تاسیس نماید؟ جداگانه ای بصورت مستقالنه کارگاه است توانسته کسی گذشته، از کارگران شماال دوسآیا .49

خیر بلی

آینده کارگاه شما برای جدی، یک تهدید قابل موجود در بازار شهر هراتبه نظر شما کدام یک از شرایط .50

(یک گزینه) ؟ خواهد بود

183

ساختار ها بلی

این صنفسایر صنعتکاران شدت رقابت

قدرت چانه زنی خریداران

فروشندگان مواد خام قدرت چانه زنی

ورود کاال های مشابه خارجی

سایر موارد...................

راضی هستید؟ تولیدی تانفعالیت و چگونگی در مجموع تا چی اندازه از جریان .51

یالف: خیلی ناراض

نا راضیب: نسبتاً

یناراضهم ه ج: نه راضی و ن

ید: نسبتاً راض

ه: خیلی راضی

گرفته اید؟جداگانه ای /آیا طی سه سال گذشته تصمیم به توسعه و یا تاسیس کارگاه جدید .52

خیر بلی

تولیدی تان را نسبت به سایر کارگاه های در همین صنف در چه وضعیتی قرار دارد؟ کارگاهفعلی موقف .53

ضعیفالف: بسیار

ضعیفب:

ج: مشابه

قوید:

قویه: بسیار

به نظر شما گرفتن جواز کار ساده و شفاف است؟ .54

خیر بلی

ه تغییراتی آمده است؟تعداد کسبه کاران صنف تان چبه سال گذشته 3طی آیا .55

الف: کاهش یافته

ب: بدون تغییر

ج: افزایش یافته

آیا مردم در این کشور به تالش شان میتوانند پیشترفت نمایند؟ .56

رخی بلی

در حال حاضر، آیا فکر میکنید که وضعیت اقتصادی کشور در مجموع چگونه تغییر نموده است؟ .57

ب: بدتر شده است الف: بهبود یافته است

در طی یک هفته گذشته چند مرتبه خیرات داده اید؟ ) ( مرتبه .58

مک کرده اید؟در طی یک هفته گذشته کدام شخص بیگانه ای را ک .59

خیر بلی

در طی یک هفته گذشته چند مرتبه برای ادای نماز به مسجد رفتید؟ ) ( مرتبه .60

184

Appendix 6: Summery Results of Regression Analysis of Significant Variables with

Direct Impact on MSEs’ performances

Model Summary

Mo

del

R

R

Square

Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .385a .148 .144 2652.70633 .148 35.164 1 202 .000

2 .446b .199 .191 2578.42002 .051 12.807 1 201 .000

3 .490c .240 .229 2518.25477 .041 10.719 1 200 .001

4 .526d .277 .262 2462.53030 .037 10.154 1 199 .002

5 .564e .318 .301 2397.52080 .041 11.938 1 198 .001

6 .589f .347 .327 2352.82087 .029 8.595 1 197 .004

7 .608g .370 .348 2315.97620 .024 7.318 1 196 .007

8 .625h .391 .366 2282.58675 .021 6.776 1 195 .010

9 .635i .404 .376 2264.60911 .013 4.108 1 194 .044

10 .644j .415 .385 2249.26137 .011 3.657 1 193 .057

11 .654k .427 .395 2230.98772 .012 4.175 1 192 .042

12 .663l .439 .404 2213.42477 .012 4.059 1 191 .045

13 .669m .448 .410 2201.89302 .009 3.006 1 190 .085

a. Predictors: (Constant), Total current assets (X219)

b. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155)

c. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government

marketing in intl. markets (X714)

d. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government marketing in intl. markets (X714), Place is rent (X224)

e. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government

marketing in intl. markets (X714), Place is rent (X224), Manger (X61b)

f. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government marketing in intl. markets (X714), Place is rent (X224), Manger (X61b), Sales increased (X42)

g. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government

marketing in intl. markets (X714), Place is rent (X224), Manger (X61b), Sales increased (X42), Invest in

employee training (X616)

h. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government


employee training (X616), Number friends can help (X113)

i. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government


employee training (X616), Number friends can help (X113), Prosperity achievable by efforts (X528)

185

j. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government


employee training (X616), Number friends can help (X113), Prosperity achievable by efforts (X528), Planed for expansion (X631)

k. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government


employee training (X616), Number friends can help (X113), Prosperity achievable by efforts (X528), Planed for expansion (X631), Current location of enterprise all (X35)

l. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government


employee training (X616), Number friends can help (X113), Prosperity achievable by efforts (X528), Planed for expansion (X631), Current location of enterprise all (X35), Business card (X623)

m. Predictors: (Constant), Total current assets (X219), Number times attended Mosque (X155), Government marketing in intl. markets (X714), Place is rent (X224), Manger (X61b), Sales increased (X42), Invest in

employee training (X616), Number friends can help (X113), Prosperity achievable by efforts (X528), Planed for

expansion (X631), Current location of enterprise all (X35), Business card (X623), Threat of suppliers (X83)

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 247441499.276 1 247441499.276 35.164 .000b

Residual 1421443872.621 202 7036850.855

Total 1668885371.897 203

2

Regression 332587162.837 2 166293581.418 25.013 .000c

Residual 1336298209.060 201 6648249.796

Total 1668885371.897 203

3

Regression 400563952.003 3 133521317.334 21.055 .000d

Residual 1268321419.894 200 6341607.099

Total 1668885371.897 203

4

Regression 462138327.241 4 115534581.810 19.052 .000e

Residual 1206747044.656 199 6064055.501

Total 1668885371.897 203

5

Regression 530760384.747 5 106152076.949 18.467 .000f

Residual 1138124987.150 198 5748105.996

Total 1668885371.897 203

6

Regression 578339461.867 6 96389910.311 17.412 .000g

Residual 1090545910.030 197 5535766.041

Total 1668885371.897 203

7

Regression 617591203.822 7 88227314.832 16.449 .000h

Residual 1051294168.075 196 5363745.755

Total 1668885371.897 203

8

Regression 652895926.270 8 81611990.784 15.664 .000i

Residual 1015989445.627 195 5210202.285

Total 1668885371.897 203

9 Regression 673965214.501 9 74885023.833 14.602 .000j

186

Residual 994920157.396 194 5128454.420

Total 1668885371.897 203

10

Regression 692464266.651 10 69246426.665 13.687 .000k

Residual 976421105.246 193 5059176.711

Total 1668885371.897 203

11

Regression 713242580.196 11 64840234.563 13.027 .000l

Residual 955642791.701 192 4977306.207

Total 1668885371.897 203

12

Regression 733128773.563 12 61094064.464 12.470 .000m

Residual 935756598.334 191 4899249.206

Total 1668885371.897 203

13

Regression 747702127.049 13 57515548.235 11.863 .000n

Residual 921183244.848 190 4848332.868

Total 1668885371.897 203

a. Dependent Variable: MSEs’ performances (Y)

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

B Std. Error Beta

1

(Constant) 797.233 201.517 3.956 .000

Total current assets (X219) .002 .000 .385 5.930 .000

2

(Constant) 1968.497 381.422 5.161 .000


Number times attended Mosque

(X155) -67.106 18.752 -.226

-

3.579 .000

3

(Constant) 1742.157 378.882 4.598 .000



(X155) -69.167 18.325 -.233

-

3.774 .000

Government marketing in intl.

markets (X714) 1500.184 458.209 .202 3.274 .001

4

(Constant) 2422.246 427.574 5.665 .000



(X155) -70.368 17.923 -.237

-

3.926 .000

187


markets (X714) 1497.865 448.070 .202 3.343 .001

Place is rent (X224) -1114.956 349.896 -.193 -

3.187 .002

5

(Constant) 2164.368 422.924 5.118 .000



(X155) -67.678 17.467 -.228

-

3.875 .000


markets (X714) 1485.305 436.257 .200 3.405 .001

Place is rent (X224) -1268.120 343.531 -.219 -

3.691 .000

Manger (X61b) 1435.612 415.497 .205 3.455 .001

6

(Constant) 1907.618 424.178 4.497 .000



(X155) -60.389 17.321 -.203

-

3.486 .001


markets (X714) 1313.264 432.126 .177 3.039 .003

Place is rent (X224) -1387.300 339.569 -.240 -

4.085 .000

Manger (X61b) 1559.458 409.933 .222 3.804 .000

Sales increased (X42) 1404.059 478.924 .174 2.932 .004

7

(Constant) 1748.113 421.678 4.146 .000



(X155) -57.361 17.087 -.193

-

3.357 .001


markets (X714) 1036.176 437.518 .140 2.368 .019

Place is rent (X224) -1367.058 334.335 -.236 -

4.089 .000

Manger (X61b) 1546.469 403.542 .221 3.832 .000


Invest in employee training

(X616) 1635.962 604.753 .158 2.705 .007

8

(Constant) 1117.978 480.959 2.324 .021



(X155) -59.586 16.862 -.201

-

3.534 .001


markets (X714) 972.458 431.905 .131 2.252 .025

Place is rent (X224) -1391.422 329.648 -.241 -

4.221 .000

Manger (X61b) 1381.454 402.744 .197 3.430 .001


188


(X616) 1562.370 596.704 .151 2.618 .010

Number friends can help (X113) 380.050 146.000 .149 2.603 .010

9

(Constant) 1760.275 572.808 3.073 .002



(X155) -56.694 16.790 -.191

-

3.377 .001


markets (X714) 1012.484 428.958 .136 2.360 .019

Place is rent (X224) -1309.522 329.538 -.226 -

3.974 .000

Manger (X61b) 1424.914 400.147 .203 3.561 .000



(X616) 1680.278 594.856 .162 2.825 .005


Prosperity achievable by efforts

(X528) -1085.779 535.685 -.116

-

2.027 .044

10

(Constant) 1568.870 577.664 2.716 .007



(X155) -53.978 16.736 -.182

-

3.225 .001


markets (X714) 1001.192 426.091 .135 2.350 .020

Place is rent (X224) -1322.040 327.370 -.229 -

4.038 .000

Manger (X61b) 1468.156 398.078 .209 3.688 .000



(X616) 1697.708 590.895 .164 2.873 .005



(X528) -1173.695 534.037 -.125

-

2.198 .029

Planed for expansion (X631) 620.769 324.635 .107 1.912 .057

11

(Constant) 2693.971 794.684 3.390 .001



(X155) -52.171 16.624 -.176

-

3.138 .002


markets (X714) 932.213 423.976 .126 2.199 .029

Place is rent (X224) -1312.515 324.744 -.227 -

4.042 .000

Manger (X61b) 1385.311 396.920 .198 3.490 .001

189



(X616) 1686.281 586.121 .163 2.877 .004



(X528) -1221.775 530.221 -.131

-

2.304 .022


Current location of enterprise all

(X35) -506.572 247.932 -.116

-

2.043 .042

12

(Constant) 2972.853 800.487 3.714 .000



(X155) -55.595 16.580 -.187

-

3.353 .001


markets (X714) 996.655 421.853 .134 2.363 .019

Place is rent (X224) -1259.412 323.264 -.218 -

3.896 .000

Manger (X61b) 1406.371 393.934 .201 3.570 .000



(X616) 1647.923 581.818 .159 2.832 .005



(X528) -1118.623 528.533 -.119

-

2.116 .036



(X35) -506.789 245.981 -.116

-

2.060 .041

Business card (X623) -643.423 319.364 -.112 -

2.015 .045

13

(Constant) 3112.719 800.392 3.889 .000



(X155) -56.150 16.497 -.189

-

3.404 .001


markets (X714) 963.319 420.095 .130 2.293 .023

Place is rent (X224) -1281.305 321.827 -.222 -

3.981 .000

Manger (X61b) 1355.982 392.958 .193 3.451 .001



(X616) 1613.938 579.119 .156 2.787 .006


190


(X528) -1209.565 528.389 -.129

-

2.289 .023



(X35) -569.340 247.344 -.130

-

2.302 .022

Business card (X623) -606.586 318.410 -.106 -

1.905 .058

Threat of suppliers (X83) 668.005 385.298 .097 1.734 .085

a. Dependent Variable: MSEs’ performances (Y)

Appendix 7: Results of Path Analysis for the Impact of Social Capital and Factor

Conditions MSEs’ performances

Variable Summary (Group number 1)

Your model contains the following variables (Group number 1)

Observed, endogenous variables

MSEs’ performances

Total of Current Assets (X219)

Observed, exogenous variables

Join Industries and Trade Chamber (X13)

Join Senf (X11)

Charities (X153)

Space is Rented (X224)

No. of Friends Who Can Help (X113)

Help a Stranger (X154)

No. of Times Attended Mosque (X155)

Unobserved, exogenous variables

e1

e2

Variable counts (Group number 1)

Number of variables in your model: 11

Number of observed variables: 9

Number of unobserved variables: 2

Number of exogenous variables: 9

Number of endogenous variables: 2

Notes for Model (Default model)

Computation of degrees of freedom (Default model)

Number of distinct sample moments: 54

Number of distinct parameters to be estimated: 29

Degrees of freedom (54 - 29): 25

Result (Default model)

Minimum was achieved

Chi-square = 22.071

191

Degrees of freedom = 25

Probability level = .632

Regression Weights: (Group number 1 - Default model)

Estimate S.E. C.R. P Label

Total of Current

Assets (X219) <---

Join Industries and Trade

Chamber (X13) .198 .070 2.822 .005

Total of Current

Assets (X219) <--- Join Senf (X11) .148 .068 2.177 .030

Total of Current

Assets (X219) <--- Charities (X153) -.141 .070 -2.012 .044

MSEs’ Performances <--- Space is Rented (X224) -.198 .058 -3.388 ***

MSEs’ Performances <--- Total of Current Assets

(X219) .339 .058 5.791 ***

MSEs’ Performances <--- No. of Times Attended

Mosque (X155) -.223 .058 -3.820 ***

MSEs’ Performances <--- No. of Friends Who Can

Help (X113) .200 .059 3.420 ***

MSEs’ Performances <--- Help a Stranger (X154) .161 .059 2.752 .006

Squared Multiple Correlations: (Group number 1 - Default model)

Estimate

Total of Current Assets (X219) .067

MSEs’ performances .281

Bootstrap Distributions (Default model)

ML discrepancy (implied vs sample) (Default model)

|--------------------

15.237 |*

23.565 |****

31.893 |*************

40.221 |*******************

48.549 |********************

56.877 |**************

65.205 |**********

N = 2000 73.533 |******

Mean = 49.101 81.861 |***

S. e. = .342 90.189 |*

98.517 |*

106.845 |*

115.173 |*

123.501 |

131.829 |*

|--------------------

Model Fit Summary

192

CMIN

Model NPAR CMIN DF P CMIN/DF

Default model 29 22.071 25 .632 .883

Saturated model 54 .000 0

Independence model 18 136.823 36 .000 3.801

Baseline Comparisons

Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .839 .768 1.026 1.042 1.000

Saturated model 1.000 1.000 1.000

Independence model .000 .000 .000 .000 .000

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE

Default model .000 .000 .048 .959

Independence model .117 .097 .139 .000

Appendix 8: Results of Path Analysis for the Impact of Social Capital on MSEs’

Related, Supporting Industries and Performances





Location of Enterprise (X35)


Join Senf (X11)

Trust Family and Relatives (X129)





e1

e2












193



Chi-square = 15.911




Parameter Estimate Lower Upper P

Location of Enterprise

(X35) <--- Join Senf (X11) .148 .044 .265 .023


(X35) <---

Trust Family and Relatives

(X129) .140 .021 .254 .043


(X35) <--- Help a Stranger (X154) -.143 -.255 -.038 .030

MSEs’ performances <--- Location of Enterprise (X35) -.113 -.245 -.023 .031

MSEs’ performances <--- Help a Stranger (X154) .181 .052 .363 .016

MSEs’ performances <---

No. of Friends Who Can Help

(X113) .208 .101 .387 .001


No. of Times Attended

Mosque (X155) -.213 -.451 -.058 .014


Estimate

Location of Enterprise (X35) .062




|--------------------

7.775 |*

14.927 |********

22.080 |********************

29.232 |********************

36.385 |****************

43.537 |*********

50.689 |****

N = 2000 57.842 |**

Mean = 30.901 64.994 |*

S. e. = .251 72.146 |*

79.299 |*

86.451 |*

93.603 |

100.756 |*

107.908 |*

|--------------------

Model Fit Summary

194

CMIN


Default model 21 15.911 14 .319 1.136




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .745 .617 .960 .931 .954



RMSEA


Default model .026 .000 .075 .738



Demand Conditions and Performance





Sales Volume Increased (X42)



Charities (X153)

Vote in Presidential Election (X128)

Trust Municipality Officials (X139)

Effective in Decision Making (X123)

Family in Same Industry (X111)




e1

e2











195




Chi-square = 30.543





Sales Volume

Increased (X42) <--- Charities (X153) .157 .066 2.395 .017 par_2

Sales Volume

Increased (X42) <---

Vote in Presidential

Election (X128) -.162 .066 -2.464 .014 par_3

Sales Volume


Trust Municipality

Officials (X139) .180 .066 2.738 .006 par_4

Sales Volume


Effective in Decision

Making (X123) .136 .067 2.046 .041 par_6

Sales Volume


Family in Same Industry

(X111) -.163 .066 -2.452 .014 par_12

MSEs’ performances <--- No. of Friends Who Can

Help (X113) .191 .063 3.015 .003 par_1


Sales Volume Increased

(X42) .155 .064 2.419 .016 par_7

MSEs’ performances <--- Charities (X153) -.195 .065 -2.990 .003 par_8



(X111) -.136 .063 -2.155 .031 par_9

MSEs’ performances <--- Help a Stranger (X154) .219 .063 3.456 *** par_10



Mosque (X155) -.211 .062 -3.380 *** par_11


Estimate

Sales Volume Increased (X42) .122




|--------------------

22.324 |*

28.547 |**

34.769 |*****

40.992 |*********

47.214 |*******************

53.437 |*******************

59.659 |*******************

N = 2000 65.882 |****************

Mean = 59.688 72.104 |*************

S. e. = .328 78.327 |********

84.549 |*****

90.772 |***

196

96.994 |**

103.217 |*

109.439 |*

|--------------------

Model Fit Summary

CMIN


Default model 36 30.543 29 .387 1.053




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .772 .646 .985 .973 .983



RMSEA


Default model .016 .000 .057 .899



Characteristics and Performance





Prosperity Achievable by Efforts (X528)


Trust Neighbors (X133)



Charities (X153)

Vote in Presidential Election (X128)

Mobile Phone (X149)





e1

e2





197










Chi-square = 37.221





Prosperity Achievable

by Efforts (X528) <---

Trust Neighbors

(X133) .183 .065 2.824 .005 par_1



Help a Stranger

(X154) .198 .065 3.057 .002 par_2



Trust Family and

Relatives (X129) .124 .065 1.920 .055 par_3



Vote in Presidential

Election (X128) -.166 .065 -2.568 .010 par_5


by Efforts (X528) <--- Mobile Phone (X149) -.145 .065 -2.248 .025 par_6

MSEs’ performances <--- No. of Friends Who

Can Help (X113) .226 .063 3.573 *** par_7



by Efforts (X528) -.163 .064 -2.536 .011 par_8


Help a Stranger

(X154) .245 .065 3.792 *** par_9




Mosque (X155) -.214 .062 -3.419 *** par_11


Family in Same

Industry (X111) -.148 .062 -2.375 .018 par_12


Estimate

Prosperity Achievable by Efforts (X528) .139




|--------------------

28.468 |*

198

37.227 |*

45.985 |****

54.743 |*********

63.501 |*****************

72.259 |********************

81.018 |******************

N = 2000 89.776 |************

Mean = 77.172 98.534 |*******

S. e. = .383 107.292 |****

116.051 |***

124.809 |*

133.567 |*

142.325 |*

151.083 |*

|--------------------

Model Fit Summary

CMIN


Default model 38 37.221 39 .551 .954




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .754 .654 1.016 1.026 1.000



RMSEA


Default model .000 .000 .046 .971



Strategy, Structure, Rivalry, and Performance





Business Card (X623)

Expansion of Enterprise (X631)

Invest in Employees Training (X616)

Manger Status (X61)


199



Enterprises Share Machineries (X122)


Charities (X153)

Meeting with Friends (X147)

Social Media Index (X152)

Join Other Associations (X19)



e1

e4

e5

e3

e2














Chi-square = 56.824





Manger Status (X61) <--- Charities (X153) -.140 .069 -2.042 .041 par_7

Manger Status (X61) <--- Enterprises Share

Machineries (X122) -.159 .068 -2.346 .019 par_8

Manger Status (X61) <--- No. of Friends Who

Can Help (X113) .133 .069 1.928 .054 par_9

Invest in Employees

Training (X616) <---

Join Other Associations

(X19) .158 .069 2.276 .023 par_10

Business Card (X623) <--- Join Other Associations

(X19) .138 .068 2.040 .041 par_11

Business Card (X623) <--- Social Media Index

(X152) .222 .068 3.287 .001 par_12

Expansion of

Enterprise (X631) <---

Meeting with Friends

(X147) .163 .068 2.396 .017 par_13

Expansion of

Enterprise (X631) <---

Trust Municipality

Officials (X139) -.168 .068 -2.465 .014 par_14

200




Mosque (X155) -.207 .062 -3.356 *** par_1

MSEs’ performances <--- Help a Stranger (X154) .172 .062 2.781 .005 par_2


Expansion of

Enterprise (X631) .124 .062 2.010 .044 par_15


Invest in Employees

Training (X616) .161 .062 2.616 .009 par_16

MSEs’ performances <--- Business Card (X623) -.180 .062 -2.920 .004 par_17

MSEs’ performances <--- Manger Status (X61) .150 .063 2.392 .017 par_18


No. of Friends Who

Can Help (X113) .182 .062 2.917 .004 par_19


Estimate

Manger Status (X61) .069

Invest in Employees Training (X616) .025

Expansion of Enterprise (X631) .055

Business Card (X623) .069




|--------------------

70.851 |*

80.453 |*

90.055 |*****

99.658 |***********

109.260 |******************

118.862 |********************

128.464 |********************

N = 2000 138.066 |******************

Mean = 125.886 147.668 |**********

S. e. = .458 157.270 |*******

166.872 |****

176.474 |**

186.076 |*

195.679 |*

205.281 |*

|--------------------

CMIN


Default model 48 56.824 71 .889 .800


201




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .698 .613 1.121 1.187 1.000



RMSEA


Default model .000 .000 .020 1.000


Appendix 12: Results of Path Analysis for the Impact of Social Capital on The

Role of Government policies and MSEs’ performances





Gov. Marketing in Intl. Markets (X714)



Charities (X153)






e1

e2












202



Chi-square = 21.277





Gov. Marketing in Intl.

Markets (X714) <---

Trust Family and

Relatives (X129) -.149 .069 -2.147 .032 par_1


Gov. Marketing in Intl.

Markets (X714) .181 .062 2.916 .004 par_4


Help a Stranger

(X154) .202 .063 3.209 .001 par_5


Family in Same

Industry (X111) -.160 .062 -2.582 .010 par_6




Mosque (X155) -.233 .062 -3.761 *** par_8


No. of Friends Who

Can Help (X113) .199 .063 3.153 .002 par_9


Estimate

Gov. Marketing in Intl. Markets (X714) .022




|--------------------

8.440 |*

15.202 |**

21.965 |*********

28.728 |**************

35.490 |********************

42.253 |*******************

49.015 |***************

N = 2000 55.778 |**********

Mean = 41.574 62.540 |******

S. e. = .295 69.303 |***

76.066 |*

82.828 |*

89.591 |*

96.353 |*

103.116 |*

|--------------------

203

Model Fit Summary

CMIN


Default model 25 21.277 19 .322 1.120




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .762 .649 .968 .945 .963



RMSEA


Default model .024 .000 .068 .794


Appendix 13: Results of Path Analysis for the Impact of Social Capital on The

Role of Chance and MSEs’ performances





Threat from Suppliers (X83)

Economic Status Improved (X89)


Meeting with Friends (X147)

E-mail and Website (X150)

Trust Police (X143)

Join Loans Association (X16)


Join Senf (X11)




Charities (X153)




e1

e2

e3



204












Chi-square = 52.005





Threat from Suppliers

(X83) <---

Meeting with Friends

(X147) .129 .068 1.904 .057 par_1


(X83) <---

E-mail and Website

(X150) .200 .067 2.980 .003 par_2


(X83) <--- Trust Police (X143) .146 .067 2.191 .028 par_3


(X83) <---

Join Loans Association

(X16) .134 .066 2.026 .043 par_4

Economic Status

Improved (X89) <---

Trust Municipality

Officials (X139) .172 .068 2.539 .011 par_5

Economic Status

Improved (X89) <--- Join Senf (X11) -.132 .068 -1.953 .051 par_6

Economic Status

Improved (X89) <---


Mosque (X155) -.152 .068 -2.241 .025 par_19


Trust Family and

Relatives (X129) -.163 .061 -2.685 .007 par_7


No. of Friends Who Can

Help (X113) .205 .062 3.311 *** par_8

MSEs’ performances <--- Help a Stranger (X154) .216 .062 3.496 *** par_9




(X111) -.157 .061 -2.579 .010 par_11


Economic Status

Improved (X89) .156 .062 2.527 .011 par_18



Mosque (X155) -.191 .062 -3.107 .002 par_20



(X83) .111 .061 1.825 .068 par_21


205

Estimate

Economic Status Improved (X89) .070

Threat from Suppliers (X83) .110




|--------------------

75.505 |*

87.638 |**

99.772 |********

111.905 |*************

124.039 |********************

136.172 |*******************

148.306 |**************

N = 2000 160.439 |********

Mean = 133.402 172.572 |****

S. e. = .509 184.706 |**

196.839 |*

208.973 |*

221.106 |*

233.240 |*

245.373 |*

|--------------------

Model Fit Summary

CMIN


Default model 51 52.005 84 .998 .619




Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2 CFI

Default model .730 .662 1.295 1.457 1.000



RMSEA


Default model .000 .000 .000 1.000


determinant factors of firm performances in the …ymine/valizadah.pdfi abstract determinant factors...

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