UNIVERSITY OF GHANA 
 
 
SMALL AND MEDIUM-SCALE ENTERPRISE (SME) SURVIVAL IN GHANA: A 
SOCIAL NETWORK THEORY PERSPECTIVE 
 
 
 
 
BY 
 
GEORGE ACHEAMPONG 
 
 
 
 
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN 
PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF 
DOCTOR OF PHILOSOPHY MARKETING DEGREE 
 
 
 
 
JULY 2015 
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DECLARATION 
I, George Acheampong, the author of the thesis titled “Small and Medium-Scale Enterprise 
Survival in Ghana: A Social Network Theory Perspective” do hereby declare that, except 
for references to studies duly cited, this thesis is the result of my original work. This thesis 
has never been presented in whole or in part to another university for any other degree. 
 
 
 
 
 
Signature:………………………………  Date:…………………………….. 
George Acheampong 
(10328765) 
PhD Candidate 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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CERTIFICATION 
I hereby certify that this thesis was supervised in accordance with procedures laid down by 
the University. 
 
 
 
 
____________________    ________________________ 
Professor Robert E. Hinson    Date 
(Supervisor) 
 
 
____________________    ________________________ 
Dr. Bedman Narteh     Date 
(Supervisor) 
 
 
____________________    ________________________ 
Professor John Rand      Date 
(Supervisor) 
 
 
 
 
 
 
 
 
 
 
 
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ABSTRACT 
Small and medium-scale enterprises (SME) are significant contributors to the development 
efforts of developing nations like Ghana. Yet their high failure rate continues to intrigue 
academics, policy makers, SME owners and managers. Scholars have used many 
approaches and their associated theories to understand the phenomenon yet one theory that 
has received little attention regarding the survival of SMEs is the social network theory. 
This thesis focuses on four network mechanisms of resource access, structural capital, 
isomorphism and diffusion and how they serve as antecedents to SME survival. Data was 
collected from the Dormaa poultry cluster in the Brong Ahafo region of Ghana in 2014 and 
2015. A network survey using multiple name generators was used to solicit information 
relating to financial, distribution and cluster networks from which network effects were 
generated. Other firm level attributes such as owner characteristics, SME characteristics, 
managerial orientations and capabilities, technical competencies and investment climate 
constraints was also collected. Survival is modelled as persistence from 2014 to 2015 
using lagged independent network effects and their corresponding attributes as covariates. 
The results indicate that approximately forty percent of the SMEs failed. Specifically, 
market resource access through distribution ties are seen to lead to negative survival 
outcomes and when moderated with SME characteristics the negative effects still persist 
for age and resource partition but not for size. Structural capital in financial networks in 
the form of prestige of financiers is associated with positive survival outcomes. When the 
specific type of financier is considered, universal banks and cooperative credit unions lead 
to positive outcomes while savings and loans companies are associated with negative 
outcomes. Descriptively, SMEs with prestigious financiers and ties to universal banks 
were less likely to be credit constrained. Network isomorphism measured as structural 
equivalence is seen to be positively linked to SME survival and useful in overcoming the 
general investment climate constraints. However, the benefits of this isomorphism reach a 
threshold and returns negative results. Finally, SME alter attributes in both technical and 
market forms are seen to be useful for SME survival. The direct and indirect mechanisms 
through which the resources reach SMEs are both associated with positive outcomes. The 
study makes a contribution to the issue of SME survival by attempting an explanation with 
relational network effects instead of the attribute-based approach that has been adopted by 
earlier scholars.       
 
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DEDICATION 
 
This thesis is dedicated to my parents, Kwaku Otuo Acheampong and Vida Sarfo-Yeboah 
and my dear wife, Linda.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ACKNOWLEDGEMENTS 
I thank the Almighty for His Grace and Mercy in the course of the PhD research. I wish to 
express my profound gratitude to my advisors: Prof. Robert E. Hinson, Dr. Bedman Narteh 
and Prof. John Rand for their time and guidance that has resulted in the completion of the 
thesis project. I am very grateful to the DANIDA Building Strong Universities - Growth 
and Employment Platform (BSU/GEP) for funding the PhD research. In this direction, I 
express my gratitude to the administrative team at Office of Research, Innovation and 
Development (ORID) especially, Prof. Adjei, Mr. Amofa, Miss Baryeh and Mrs Agamah. 
I am thankful for the varied advice of senior members of the Department of Marketing of 
the University of Ghana Business School. I wish to express my appreciation to the 
members of the Global Development Unit and Department of Economics all of University 
of Copenhagen especially Christian Lund, Henning Tarp Jensen, Thi Minh Thai, Neda 
Trifkovic and Snorre Ralund for their support. I am also grateful to Finn Tarp, Smriti 
Sharma, Andy McKay, Bruck Tadesse and Lay Poh Allonen for their support during my 
PhD internship at United Nations University- World Institute for Development Economics 
Research (UNU-WIDER). I am also thankful to the executives of the Dormaa Poultry 
Farmers Association (DPFA) especially Mr. Deku, Mr Ampofo, Nana Atosi, Adama 
Musah and Charity for their support during the data collection exercise. I am very grateful 
to members of my data collection team (Prince, Foster, Daudi, Clement, Vera, Felix, David 
and the late Nana Yaw Kyeremeh) for their effort. Finally, I want to thank my wife, Linda, 
for the tireless nights we spent entering, cleaning and checking the data.  
 
 
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TABLE OF CONTENTS 
 
DECLARATION .................................................................................................................... ii 
CERTIFICATION ................................................................................................................. iii 
ABSTRACT .......................................................................................................................... iv 
DEDICATION ....................................................................................................................... v 
ACKNOWLEDGEMENTS .................................................................................................. vi 
TABLE OF CONTENTS ..................................................................................................... vii 
LIST OF FIGURES .............................................................................................................. xii 
LIST OF TABLES .............................................................................................................. xiii 
LIST OF APPENDICES ..................................................................................................... xiv 
CHAPTER ONE ..................................................................................................................... 1 
INTRODUCTION .................................................................................................................. 1 
1.1 Background ................................................................................................................... 1 
1.2 Problem Formulation and Research Questions ............................................................. 3 
1.2.1 Research Objectives ............................................................................................... 7 
1.2.2 Research Questions ................................................................................................ 8 
1.3 Literature Review ......................................................................................................... 8 
1.3.1 Defining an SME and Its Survival or Failure ......................................................... 9 
1.3.2 SME Survival: Key Approaches, Issues and Evidence ........................................ 11 
1.4 Conceptual Framework ............................................................................................... 25 
1.4.1 Structural Social Capital....................................................................................... 28 
1.4.2 Resource Access ................................................................................................... 30 
1.4.3 Isomorphism ......................................................................................................... 31 
1.4.4 Diffusion............................................................................................................... 32 
1.4.5 Moderators ........................................................................................................... 33 
1.5 Context of the Study ................................................................................................... 34 
1.5.1 Issues in the Ghanaian SME Sector ..................................................................... 34 
1.5.2 Ghanaian Poultry Sector: A Focus on the Dormaa Cluster .................................. 36 
1.6 Overview of Methodology .......................................................................................... 38 
1.6.1 Philosophical World Views .................................................................................. 38 
1.6.2 Research Approach .............................................................................................. 40 
1.6.3 Research Method .................................................................................................. 42 
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1.6.4 Data Sources and Collection ................................................................................ 43 
1.6.5 Data Analysis Techniques .................................................................................... 45 
1.7 Significance of the Study ............................................................................................ 46 
1.8 Structure of the Thesis ................................................................................................ 47 
CHAPTER TWO .................................................................................................................. 49 
DISTRIBUTION TIES, SME CHARACTERISTICS AND SURVIVAL IN GHANA ..... 49 
Abstract ............................................................................................................................. 49 
2.1 Introduction ................................................................................................................. 49 
2.2 Literature Review and Hypotheses Development....................................................... 52 
2.2.1 Distribution Ties and SME Survival .................................................................... 52 
2.2.2 Distribution Ties, SME Characteristics and Survival .......................................... 56 
2.3 Research Methods ....................................................................................................... 58 
2.3.1 The Research Setting ............................................................................................ 58 
2.3.2 Network Survey.................................................................................................... 59 
2.3.3 Measures and Operationalization ......................................................................... 61 
2.3.4 Model Specification and Estimation .................................................................... 65 
2.4 Results ......................................................................................................................... 67 
2.4.1 Descriptive Analysis ............................................................................................ 67 
2.4.2 Regression Analysis ............................................................................................. 71 
2.5 Discussion of Results .................................................................................................. 73 
2.6 Conclusions ................................................................................................................. 77 
APPENDIX ....................................................................................................................... 78 
CHAPTER THREE .............................................................................................................. 82 
FINANCE NETWORK CAPITAL, CREDIT CONSTRAINT AND SME SURVIVAL ... 82 
Abstract ............................................................................................................................. 82 
3.1 Introduction ................................................................................................................. 82 
3.2 Literature Review ....................................................................................................... 85 
3.2.1 Small Business Credit Constraints ....................................................................... 86 
3.2.2 Financial Network Capital ................................................................................... 89 
3.2.2.1 Closeness to Financial Institutions .................................................................... 91 
3.2.2.2 Prestige of Financiers ........................................................................................ 92 
3.2.3 Financial Institution Type .................................................................................... 93 
3.3 Research Methodology ............................................................................................... 97 
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3.3.1 The Research Setting ............................................................................................ 97 
3.3.2 Network Survey.................................................................................................... 99 
3.3.3 Measures and Operationalization ....................................................................... 101 
3.3.4 Model Specification and Estimation .................................................................. 106 
3.4 Results ....................................................................................................................... 108 
3.4.1 Descriptive Analysis .......................................................................................... 108 
3.4.2 Regression Analysis ........................................................................................... 113 
3.5 Discussion of Results ................................................................................................ 115 
3.6 Conclusions ............................................................................................................... 118 
APPENDIX ..................................................................................................................... 119 
CHAPTER FOUR .............................................................................................................. 121 
ISOMORPHISM, INVESTMENT CLIMATE CONSTRAINTS AND SME SURVIVAL 
IN GHANA ........................................................................................................................ 121 
Abstract ........................................................................................................................... 121 
4.1 Introduction ............................................................................................................... 121 
4.2 Theory and Hypotheses ............................................................................................ 125 
4.2.1 Isomorphism and SME Survival ........................................................................ 126 
4.2.2 Investment Climate Constraints ......................................................................... 130 
4.2.3 The Interaction Effect......................................................................................... 133 
4.3 Research Methods ..................................................................................................... 134 
4.3.1 The Research Setting .......................................................................................... 134 
4.3.2 Data Collection ................................................................................................... 135 
4.3.3 Measures and Operationalization ....................................................................... 137 
4.3.4 Model Specification ........................................................................................... 142 
4.4 Results ....................................................................................................................... 144 
4.4.1 Descriptive Results ............................................................................................. 144 
4.4.2 Regression Results ............................................................................................. 150 
4.5 Discussions of Results .............................................................................................. 151 
4.6 Conclusion ................................................................................................................ 155 
APPENDIX ..................................................................................................................... 156 
CHAPTER FIVE ................................................................................................................ 160 
BENEFITTING FROM ALTER RESOURCES: NETWORK DIFFUSION AND SME 
SURVIVAL ........................................................................................................................ 160 
Abstract ........................................................................................................................... 160 
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5.1 Introduction ............................................................................................................... 160 
5.2 Literature Review and Hypotheses ........................................................................... 163 
5.2.1 The Alter Resources, Variations and SME Survival .......................................... 164 
5.2.2 Direct Ties, Indirect Ties and SME Survival ..................................................... 167 
5.3 Research Methods ..................................................................................................... 169 
5.3.1 The Research Setting .......................................................................................... 169 
5.3.2 Data Collection and Network Survey................................................................. 171 
5.3.3 Measures and Operationalization ....................................................................... 172 
5.3.4 Model Specification ........................................................................................... 178 
5.4 Results ....................................................................................................................... 180 
5.4.1 Descriptive Statistics .......................................................................................... 180 
5.4.2 Regression Analysis ........................................................................................... 182 
5.5 Discussion of Results ................................................................................................ 185 
5.6 Conclusion ................................................................................................................ 188 
APPENDIX ..................................................................................................................... 190 
CHAPTER SIX .................................................................................................................. 192 
SUMMARY, CONCLUSION AND RECOMMENDATIONS ........................................ 192 
6.1 Introduction ............................................................................................................... 192 
6.2 Summary of Findings ................................................................................................ 194 
6.2.1 Empirical Paper 1 ............................................................................................... 194 
6.2.2 Empirical Paper 2 ............................................................................................... 195 
6.2.3 Empirical Paper 3 ............................................................................................... 196 
6.2.4 Empirical Paper 4 ............................................................................................... 197 
6.3 Reflections ................................................................................................................ 199 
6.3.1 Theory ................................................................................................................ 199 
6.3.2 Framework ......................................................................................................... 199 
6.3.3 Methodology ...................................................................................................... 200 
6.4 Contributions of the Study ........................................................................................ 201 
6.4.1 Synthesis of Literature ....................................................................................... 201 
6.4.2 Framework ......................................................................................................... 202 
6.4.3 Measurement ...................................................................................................... 202 
6.4.4 New Evidence .................................................................................................... 203 
6.5 Conclusion ................................................................................................................ 203 
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6.6 Practical Implications ............................................................................................... 204 
6.7 Future Research ........................................................................................................ 206 
REFERENCES ................................................................................................................... 208 
APPENDICES: ................................................................................................................... 230 
 
 
  
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LIST OF FIGURES 
 
Figure 1.1: Basic Conceptual Framework ............................................................................ 28 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF TABLES 
Table 1.1: Structure of Thesis............................................................................................ 48 
Table 2.1: Distribution Network Characteristics ............................................................... 60 
Table 2.2: Survival Rate .................................................................................................... 63 
Table 2.3: Means (M), Standard Deviations (S.D.) and Correlations ............................... 69 
Table 2.4: Cross Tabulation of Ties, Moderators and Survival ........................................ 70 
Table 2.5: Distribution Ties and SME Survival ................................................................ 71 
Table 3. 1: Finance Network Characteristics .................................................................. 100 
Table 3. 2: Survival Rate ................................................................................................. 102 
Table 3. 3: Descriptive Statistics ..................................................................................... 111 
Table 3. 4: Cross-Tabulation of Network Capital, Financial Constraint and Survival ... 112 
Table 3. 5: Finance Network and SME Survival ............................................................. 113 
Table 4. 1: Collaborative Network Characteristics ......................................................... 136 
Table 4. 2: Exit and Entry Rate ....................................................................................... 139 
Table 4. 3: Operationalization of Covariates ................................................................... 141 
Table 4. 4: Means (M), Standard Deviations (S.D.) and Correlations ............................ 147 
Table 4. 5: Cross Tabulations of Isomorphism, Investment Climate Constraints and 
Survival ............................................................................................................................ 148 
Table 4. 6: Isomorphism and SME Survival ................................................................... 150 
Table 5. 1: Collaborative Network Characteristics ......................................................... 172 
Table 5. 2: Failure Rate ................................................................................................... 174 
Table 5. 3: Operationalization of Control Variables ....................................................... 177 
Table 5. 4: Descriptive Results ........................................................................................ 182 
Table 5. 5: Alter Attributes and SME Survival ............................................................... 184 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
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LIST OF APPENDICES 
Appendix 1: Ghana Poultry Sector Statistics .................................................................. 230 
Appendix 2: Systematic Review Codebook and Keywords ............................................ 231 
Appendix 3: Definitions of Network Characteristics ...................................................... 232 
Appendix 4: Network Graphs .......................................................................................... 233 
Appendix 5: Geographical Map of Study Area ............................................................... 240 
Appendix 6: Research Instrument/Questionnaire ............................................................ 241 
Appendix 7: Ethical Clearance ........................................................................................ 245 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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CHAPTER ONE 
INTRODUCTION 
1.1 Background 
The global financial crisis that started in 2007 and the threat of collapse of many firms 
across several industries have brought to the fore again the concept of organizational 
failure. One form of such failure that continues to baffle practitioners, policy makers and 
academics is enterprise failure. Approximately ninety percent of SMEs are reported to fail 
by their second year of operations (Okpara, 2011). In an attempt to explain organizational 
failure there have been many approaches. These approaches have included the ecological 
view (Hannan & Freeman, 1986), the institutional view (Bianchi & Mena, 2004; Yildirim, 
2010), the organizational view (Hage, 1999: Analoui & Karami, 2002;) and the 
integrativists view (Mellahi & Wilkinson, 2004). The ecological and institutional views 
that form the deterministic approach asserts that organizations can do little to affect their 
survival emphasizing the role of the environment in their failure or survival (Hannan & 
Freeman, 1986; Barnett & Carroll, 1987; Baum & Oliver, 1991; Geroski, Mata, & 
Portugal, 2010). This approach also views firm survival purely from a population and 
market perspective. The organizational view sees firm survival from the managerial 
perspective and with a focus on internal factors under the control of managers (Hage, 
1999; Teece, 2007; Tsai, 2010; Vakola & Rezgui, 2011). The integrativists propose that all 
approaches be used in tandem to understand organizational survival since one approach 
cannot do a good job (Mellahi & Wilkinson, 2004). 
 
However, the aftermath of the global financial crisis has shown that survival or failure may 
not be organizational or purely environmental or integration but rather a social decision 
involving many actors both within and outside the firm, industry and/or country. Failure 
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can be a result of social structure and its underlying relational systems and the social 
capital that they confer on individuals and organizations.  Scott and Carrington (2011) 
have mentioned that relational systems of firms connecting and interacting has a huge 
influence on their performance and survival (Allen, Dawson, Madsen, & Chang, 2008). 
Since organizations operate in social systems, it will therefore be critical to consider 
enterprise failure or survival from a network theory perspective. This is in no means to 
supersede the current variable approach but to serve as a complementary explanation to 
enterprise failure especially in the developing world where social relations and trust have a 
stronger influence on business operations than weak formal institutional structures that 
exist in the developing world (Biggs & Shah, 2006).  
 
The aim of this study is to understand why SME organisational forms fail from a social 
network theory perspective. This theory, according to Borgatti and Foster (2003), offers an 
opportunity for the integration of many other theories in the explanation of organisational 
outcomes. The study will more specifically investigate how social capital, power relations, 
mimetic behaviour and diffusion in networks all influence the survival of SMEs in Ghana. 
In the following sections, the problem statement and research questions are formulated as 
well as a discussion of the literature review along the three broad approaches that have 
been discussed in the literature focusing on the theories utilized while presenting a touch 
of the emerging issues in the explanation of SME failure. I also develop a conceptual 
framework that binds all the empirical chapters together by briefly introducing social 
network theory and how other theoretical concepts can be used to explain SME failure. I 
also present issues relating to the context of the study, overview of methods used, the 
significance of the study and the structure that the thesis will take.  
 
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1.2 Problem Formulation and Research Questions 
Small and medium-scale enterprises (SMEs) are significant contributors to the 
development efforts of many Africa countries. Abor and Quartey (2010) have argued that 
SMEs are a major source of employment in many developing countries due to the fact that 
they are labour intensive and hence employ a lot of people. They specifically note that 
SMEs represent about 92% of Ghanaian businesses and contribute about 70% to Ghana’s 
GDP and over 80% to employment. This assertion had been corroborated earlier in the 
decade (Kayanula & Quartey, 2000b). However, one major problem that continues to 
plague SMEs is the high mortality rate with many SMEs failing before their second 
birthday (Okpara, 2011). Academics have studied and attempted to explain this problem of 
SME survival and mortality with three main approaches - deterministic, voluntaristic and 
integrativist (Mellahi & Wilkinson, 2004). The deterministic approach comes from the 
organisational ecology theory of sociology and industrial organisation in economics. 
Organisational ecology theory is motivated by the fact that under similar environmental 
conditions organisations, including SMEs, must have certain characteristics and it is the 
differences in these characteristics that explain failure of some in the population of 
organisations (Hannan & Freeman, 1993). Scholars of industrial organisation believe that 
failure of firms is due to changes in market preferences leading to exit ( Jovanovic, 1982; 
Hopenhayn, 1992). Although the deterministic approach comes from different disciplines 
they both suggest that firms fail due mainly to market and environmental reasons rather 
than managerial reasons. The voluntaristic approach suggests that managerial factors 
explain why some firms fail while others survive under similar environmental conditions 
(Hambrick & Mason, 1984). Some of the voluntaristic factors that have been found to  
influence organisational outcomes are top management characteristics (Patzelt, zu 
Knyphausen-Aufseß, & Nikol, 2008); absorptive capacity (Lund-Vinding, 2006); dynamic 
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capabilities (Wu, 2006); and strategic orientation (Acquaah, 2007). The integrativists 
perspective brings the two perspectives together to explain the survival or failure of 
organisations (Mellahi & Wilkinson, 2004).  
 
Despite the plethora of theories that have been proffered to explain why organisations (for 
the purposes of this thesis, SMEs) fail, social network theory has remained largely ignored 
even with the explosion of its usage (Zaheer, Gozubuyuk, & Milanov, 2010). This may be 
largely because of the assumption that firm performance will lead to its survival (Kang, 
Heshmati, & Choi, 2008). However, recent empirical evidence is beginning to prove that 
this relationship is complicated (Coad, Frankish, Roberts, & Storey, 2013; Delmar, 
McKelvie, & Wennberg, 2013; Gudmundsson & Lechner, 2013). Their argument 
stemming from the fact that growth and performance does not always lead to survival, 
especially when a firm’s survival is conceptualised to mean exit, which can include 
mergers and acquisitions. In such a conceptualisation, performing firms can fail because 
they are acquired. Also, small business studies have established for instance that poor 
succession leads to failure of some SMEs and hence performance may not always be the 
reason. Again, studies have shown that firms do not necessary survive due to their 
technical competence but because of structural forms that are legitimated by the relevant 
stakeholders (DiMaggio & Walter, 1991). Consequently, it is important for the academic 
literature to have space for the effects of social networks on enterprise survival especially 
in the developing world where social relations and connections are critical for business 
success (Buame, 1996; Akuetteh, 2009). This study therefore contributes to both the 
network theory and small business management literature in this direction.  
 
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Also, current studies on enterprise performance and survival using network theory has a 
narrow focus on the social capital emanating from the competition networks only (see 
Uzzi, 1996; Pennings et al., 1998; Hite & Hesterly, 2001; Zaheer & Bell, 2005; Rooks, 
Szirmai, & Serwanga, 2012). These studies focus on the ties between competitors in a 
sector (network). However, from stakeholder and resource dependency theory, we glean 
that there are other stakeholders other than competitors that own resources that firms need 
for survival (Pfeffer & Salancik, 2003; Pajunen, 2006). For example, employees own 
labour resources; investors have financial resources; promoters have entrepreneurial 
resources; while local communities own natural resources. Firms interact with all these 
stakeholders to gain resources that are critical in that case. The ties among these 
stakeholders (competitors, unions, investors, regulators and local communities) can 
represent some kind of stakeholder networks. Therefore, for firms to survive they require 
capital from these networks, and for the purposes of this study, social capital. In order to 
contribute to bridging the gap in literature in this direction; two of the empirical papers 
focus on financial stakeholders that own financial resources and distributors that own 
market resources. The study focuses on how relational capital in distribution networks 
influences the survival of SMEs; and the role of structural capital in financial networks 
influence on SME survival. 
 
Furthermore, social network and capital studies are conducted based on relationships 
between actors in a given network (Wasserman & Faust, 1994). These relationships and 
the pattern of the relationships can directly have an effect on enterprise outcomes or the 
social capital embedded in them can have an effect on these outcomes. Social capital is the 
‘resources embedded in a social structure which are accessed and/or mobilized in 
purposive action’ (Lin, 1999), or as ‘a collective resource that arises from (and is shaped 
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by) social relations between actors within a network’ (Tomlinson, 2011). However, many 
studies in enterprise research do not use the relationship between actors as the basis for 
social capital and network effects, but instead use the attributes of the actors (see examples 
in Biggs & Shah, 2006; Boso, Story, & Cadogan, 2013; Kinghan & Newman, 2015). 
These studies therefore do not effectively capture the underlying social structure that 
confers social capital to particular actors or components of the network in which the 
enterprises are embedded. In these studies, members of a particular social group can be 
thought of as having social capital, or members can be asked to report how they consider 
their use of social capital from stakeholders relative to the competition. These approaches 
do not capture the effect of another member of the group on how much social capital can 
be appropriate by the actor they interview. Consequently, they assume the social group has 
unlimited resources to support all ties but this is most often not the case (Borgatti & 
Halgin, 2011). In order to contribute to the literature in this direction the study attempts to 
capture the whole network in which SMEs in the study are embedded, and compute the 
network effect for all actors that are of interest and the effect on survival. 
 
Finally, the study makes a contribution by bridging a few context-based gaps in the 
literature. Studies in enterprise survival have had less emphasis on the Africa region with 
less than six studies emerging1 (Mambula & Sawyer, 2004; Sam, 2007; Akinbogun, 2008; 
Bekele & Worku, 2008; Okpara, 2011; Ali & Peerlings, 2012). These studies employ data 
from Nigeria and Ethiopia only. There is only one study that focuses on the agricultural 
sector (Ali & Peerlings, 2012), while the other three do not specify the sector of study 
while there is a lack of focus on rural contexts. The study by Ali & Peerlings (2012) 
explored the effect of clustering on the exit and entry decisions of household farm and 
                                                     
1
 This is based on systematic literature search and review. See the keywords for search and codebook in the 
appendix. 
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non-farm enterprise activities in Ethiopia. The authors find that household enterprises 
located in clusters are less likely to exit than those not operating in clusters. In this study, I 
attempt to contribute to the literature by providing evidence from rural agricultural 
enterprises. The departure from the Ali and Peerlings (2012) study is that while it focuses 
on just clustering effect this study focuses on the specific clustering effects like alter and 
isomorphic effects and moves beyond just clustering in competition networks to 
distribution and financial network effects. This study also helps to understand if the issues 
that are raised in urban studies are likely to vary in a rural context and provide a critical 
test of falsification of the underlying theories in these studies ( Popper, 2005; Flyvbjerg, 
2006). 
 
1.2.1 Research Objectives 
Based on the research problem the study objectives are as follows: 
1. To determine the impact of structural capital of bipartite2 finance networks on 
enterprise survival in Ghana; 
2. To assess the role of resource access mechanisms of bipartite distribution networks 
on enterprise survival in Ghana; 
3. To explore the role of industry collaboration network isomorphism on the survival 
chances of enterprises in Ghana; 
4. To evaluate the usefulness of alter attributes  in collaborative network matters for 
the survival chances of enterprises in Ghana; 
5. To explore the moderating mechanism of enterprise characteristics on the 
relationship between network effects and enterprise survival; and 
                                                     
2
 Bipartite refers to a two-party network. In a finance network it makes reference to financial institutions 
and their clients and in the distribution network it refers to manufacturers and their distributors. 
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6. To explore the moderating role of investment climate constraints on the 
relationship between network isomorphism and SME survival. 
 
1.2.2 Research Questions 
The main question this thesis seeks to answer is: how do the social networks (industry, 
finance and distribution) of small businesses (poultry farms) influence their survival? This 
question, however, is framed into much more specific questions for empirical testing. 
These specific questions are as follows: 
1. What is the impact of the structural capital of bipartite finance networks on 
enterprise survival in Ghana? 
2. What is the role of the resource access mechanism of bipartite distribution 
networks on enterprise survival in Ghana? 
3. Does industry collaboration network isomorphism influence the survival chances of 
enterprises in Ghana? 
4. Does alter attributes in a collaborative network matter for the survival chances of 
enterprises in Ghana? 
5. Do enterprise characteristics moderate the relationship between network effects and 
enterprise survival? 
6. Do network effects help SMEs overcome investment climate constraints? 
 
1.3 Literature Review  
This section reviews literature on SME survival. The review is conducted on the basis of 
three broad categories suggested by Mellahi and Wilkinson (2004) on the failure of 
organisations. These are the deterministic, voluntaristic and integrativists approaches. The 
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section will focus its efforts on exploring and discussing the key issues and evidences that 
have been found under these different approaches. I, however, start by defining briefly 
what an SME is and its survival or failure.  
 
1.3.1 Defining an SME and Its Survival or Failure 
The definition of the word SME (herein referred to as enterprise) is as complicated as the 
sector itself (Senderovitz, 2009). He notes that in many articles it is difficult to see whether 
the researcher has considered what a small firm actually is, whether the particular 
definition is appropriate, or what the consequences for the conclusion might be if other 
definitions were used. The National Board for Small Scale Industries defines SMEs as 
“any business employing 29 or fewer workers. Micro enterprises are those that employ 
between 1-5 people with fixed assets not exceeding 10,000 USD excluding land and 
building.  Small enterprises employ between 6 and 29 or have fixed assets not exceeding 
100,000 USD, excluding land and building”. The Brookings Institute defines an enterprise 
as “an organisation with annual turnover, in U.S. dollar terms, of between 10 and 1000 
times the mean per capita gross national income, at purchasing power parity, of the 
country in which it operates” (Gibson & van der Vaart, 2008). However, this study will not 
use this definition because it requires information on enterprises in the developing world 
that may not be readily available due to the high level of informality in the enterprise 
sector in Ghana (Yusuf & Saffu, 2005). In order to avoid the definitional debates and 
complexities in defining an enterprise, the study focuses on a context-based definition of 
enterprises that has been used by many Ghanaian authors which uses employee numbers as 
a basis (Kayanula & Quartey, 2000a;  Yusuf & Saffu, 2005; Hinson & Sorensen, 2006; 
Abor, 2007; Kyereboah-Coleman & Amidu, 2008; Saffu, Walker, & Hinson, 2008; Abor 
& Quartey, 2010; Abor & Biekpe, 2012). An enterprise is defined as any business 
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employing less than 30 people (Kayanula & Quartey, 2000a). This definition notes that 
enterprises can be divided into three broad categories: micro, small and medium with 
categorization based on micro enterprises having less than 6 employees; small enterprises 
having less than 30 employees while medium enterprises have less than 99 employees. 
This definition no doubt has its inefficiencies (does not account for turnover, assets, capital 
and balance sheet) but so far seems accepted by Ghanaian scholars and practitioners as a 
fair reflection of the Ghanaian enterprise sector.  
 
Enterprise survival and failure are two different sides of the same coin. An enterprise that 
has failed has not survived and an enterprise that has survived has not failed. For the 
purposes of this paper, the focus will be on defining failure with the reverse being true for 
survival. Enterprise failure has been called different names in the management literature to 
include bankruptcy, decline, retrenchment, enterprise death, downsizing and exit (Mellahi 
& Wilkinson, 2004). They, however, propose that I exclude retrenchment and downsizing 
as failures since they are management tools used by managers in recent times to 
turnaround the fortunes of ailing organizations. Their view of enterprise failure is that of 
the reduction in the adaptive ability and resources of an organization. Another definition 
that has been offered of a failed enterprise is “when an enterprise involuntarily becomes 
unable to attract new debt or equity funding to reverse decline; consequently, it cannot 
continue to operate under the current ownership and management. Failure is the endpoint 
at discontinuance (bankruptcy) and when it is reached, operations cease and judicial 
proceedings take effect” (Pretorius, 2009; pp 8). Hannan and Freeman (1986) also defined 
failure as “when an enterprise ceases to carry out routine actions that sustain its structure, 
maintains flows of resources and secure allegiance of its members”. They, as part of their 
definition, note four kinds of mortality - disbanding, acquisition, merger and radical 
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change. This paper also operationalizes organizational failure in the manner proposed by 
Hannan and Freeman (1986) but do not see acquisitions, mergers and radical change in this 
form as a failure. Enterprises do not lose the allegiance of their members when they are 
acquired, merge or changes form (Jones, 2005). Those are managerial manoeuvres to 
enable the enterprise to adapt to its environment and maintain functional utility for its 
members (Young, 1988; Greenwood & Suddaby, 2006). This study therefore defines 
enterprise failure as when an enterprise can no longer meet the utility of its stakeholders. 
This operationalization therefore is in line with the traditional closure of firms approach.  
1.3.2 SME Survival: Key Approaches, Issues and Evidence  
1.3.2.1 The Deterministic Approach 
This view considers the ecological view of survival and failure. The major underlying 
theory has been the organizational ecology theory. This approach suggests that firms can 
do little in the face of environmental factors affecting its survival. The approach notes 
some of the factors affecting survival as density, size, age, imprinting, linkages with 
environmental institutions and resource partitioning. 
 
Density Dependence 
There is a strong belief among organizational ecologists that the survival of a firm is 
highly dependent on the number of firms in a particular industry (Hannan & Freeman, 
1986; Barnett & Carroll, 1987; Amburgey, Kelly, & Barnett, 1993;). Density dependence 
is the term used to describe this phenomenon. Barnett and Carroll, (1987) suggests that the 
survival of firms in an industry and the number of firms in that industry follows a U-shape. 
This is because initially firms in an industry are few and gain a taken-for-granted view 
from society thereby gaining legitimacy. This legitimacy however draws more firms into 
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the industry and soon a keen competition emerges for resources for survival and ultimately 
some firms are starved of resources and hence put out of business. This approach is, 
however, an imperfect way of measuring survival because different organizations have 
different resource needs due to organizational niching in the same industry; competition 
from same type of industry but in a different location; and firms can migrate into other 
niches due to competition or firms that are part of the industry but also belong to another 
industry due to having more than one business unit (Delacroix, Swaminathan, & Solt, 
1989; Dobrev & Carroll, 2003; Nickel & Fuentes, 2004). This view has, however, 
provided the inspiration for other density based models such as cross-effects of density 
model; delay density model; direct competition model; and density relational model 
(Nickel & Fuentes, 2004). These new models provide a much more improved approach to 
understanding the effects of density on survival.   
 
Age Dependence 
Organizational failure has been seen to fall with organizational aging (Barnett & Carroll, 
1987). The explanation that has been offered for this is that it may be a spurious effect of 
population heterogeneity. If the mortality rate is stable over a period, soon the weak firms 
will be selected out and only the fit firms are left (Levinthal, 1991). This suggests that age 
dependence is not a population level issue, but is a firm level issue as the competences are 
built over time within a particular organization. Some authors have also suggested that 
organizations learn with time and hence can perfect their business and are less likely to fail 
(Sleuwaegen & Goedhuys, 1998; Hannan, 2005; Ruiz-Mercader, Meroño-Cerdan, & 
Sabater-Sánchez, 2006; Zdunczyk & Blenkinsopp, 2007). Learning reduces the likelihood 
of exit because the firm becomes dependable in the eyes of its stakeholders who continue 
to give the firm the required legitimacy to exist. The idea that young firms are more likely 
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to fail is the liability of newness (Geroski et al., 2010). Some other authors also maintain 
that there is the liability of adolescence (Michael & Kim, 2005; Kasimoglu, 2006; Bradley, 
Aldrich, Shepherd, & Wiklund, 2011; McKinley, 2011; Oertel & Walgenbach, 2012b). 
These authors suggest that some firms initially survive the liability of newness but die 
soon after that initial start-up phase. Some of the reasons that have been offered for this 
phenomenon is that initially firms start with a stock of resources (financial, human and 
natural) and after those have been exhausted they are not able to acquire more and cannot 
continue as a going-concern (Michael & Kim, 2005). There is also the thought that firms 
go through a period of unconditional trust, and based on their performance, the 
environment will provide or withdraw its legitimacy (Oertel & Walgenbach, 2012a). Some 
other authors have also presented the idea of obsolescence (Baum & Mezias, 1992; Perez, 
Llopis, & Llopis, 2004; Carroll & Khessina, 2005; Hannan, 2005). The idea of 
obsolescence suggests that, over time, firms become misaligned and have difficulty 
adapting to the environment (Carroll & Khessina, 2005; Hannan, 2005); there is 
sometimes challenges with succession among owner-managed firms (Perez et al., 2004) 
and fixed costs invariability (Baum & Mezias, 1992). 
 
Liability of Smallness 
The idea suggests that organizational failure is linked to the size of the organization 
(Azoulay & Shane, 2001; Sarkar, Echambadi, Agarwal, & Sen, 2006; Box, 2007; 
Gassmann, Enkel, & Chesbrough, 2010; Burger & Owens, 2013). The bigger the size of 
the firm the less likely it is that the firm will fail. The reasons are that small firms lack 
economies of scale, do not have inertia abilities to withstand environmental shocks, lack 
external legitimization and find it difficult accessing resources (Sarkar et al., 2006; 
Gassmann et al., 2010). Another way SMEs can fail is the growth path from a small 
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company to a large company; this is known as growth-paths. This refers to the trajectory 
that a small firm takes in its developmental process. Some of the evidence in the literature 
suggests that every possible growth-path seems to occur with roughly equal probability. 
However, growth-paths influence subsequent survival (Coad et al., 2013). Holmes, Hunt, 
and Stone (2010) also found that increases in initial plant size impact negatively on micro-
enterprise survival and positively on SME survival. This finding is closely related to the 
idea of liability of size where firms begin to face problems of inertia as a result of growth. 
The evidence also confirms the liability of smallness-hypothesis for a German 
manufacturing firm while with respect to firm age the results favour the liability of 
adolescence-hypothesis instead of a pure liability of newness (Strotmann, 2006). Another 
interesting finding has been the support for the notion that profitability enhances both 
survival and growth, and growth helps profitability but has a negative effect on survival 
(Delmar et al., 2013). Business cycles influence survival. Entrepreneurial firms are 
sensitive to and follow a pro-cyclical pattern of survival likelihood over the business cycle 
(Ejermo & Xiao, 2014). 
 
Imprinting 
Another deterministic explanation for firm failure is the idea of imprinting. Imprinting 
states that the environmental conditions at the time of founding has implications for the 
survival or failure of firms (Sorensen & Stuart, 2000; Geroski et al., 2010; Lamberg, 
Tikkanen, Nokelainen, & Suur-inkeroinen, 2009; Luo & Han, 2009; Zaring & Eriksson, 
2009). Geroski et al. (2010) note that firms formed in highly dense environments may face 
the liability of scarcity and tight niche-packing and may not be able to reposition 
themselves after that phase and hence fail. In effect, imprinting may prevent firms from 
taking advantage of opportunities for strategic change. Some authors have also suggested 
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that the impact of imprinting on failure may be due to the firms’ institutional structure, 
market orientation and capability conditions (Luo & Han, 2009). Overall, firms that do not 
take advantage of opportunities for strategic change may find they are unfit for the 
environment and selected out. SMEs that are born in a boom seem to have almost 
permanently high survival rates as they are able to gain the advantages of economies of 
scale (Geroski et al., 2010). This point is supported by Chung, Lo and Chen (2011) who 
found that the founding scale of large, corporate-sponsored new ventures have higher 
survival rates than independent ventures. However, when the founding scale is smaller, the 
reverse is true. Resource munificence related to sponsorship can potentially decrease or 
increase survival rates among new organizations and that these effects are contingent on fit 
of resource type with its respective geographic-based founding density (Amezcua, Grimes, 
Bradley, & Wiklund, 2013). Another founding effect that has a positive effect on firm 
survival is spin-offs. Spin-offs from a surviving parent and, to a lesser degree, industry-
specific experience positively affects the likelihood of survival (Dahl & Reichstein, 2007). 
Related to the spin-off idea is prior experience in the industry. Market experience is 
positively related to emerging market firms’ entry and survival (Thomas, Eden, Hitt, & 
Miller, 2007). Significant differences also exist between social and business-oriented 
entrepreneurship in the form and intensity of the independent variables related to survival 
(Simón-Moya, Revuelto-Taboada, & Ribeiro-Soriano, 2012). Founder’s educational 
background has been found to influence survival chances of SMEs (Saridakis, Mole, & 
Storey, 2008). This point has been corroborated by Millán, Congregado, and Román 
(2010) who found a positive impact on survival of formal education and previous 
experience within the labour market. The expenditure on start-up subsidies decreases the 
risk of exiting self-employment specifically for the group of individuals entering self-
employment from unemployment.    
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Resource Partitioning  
This theory argues that firms survive based on the segments of a market that an 
organization occupies (Carroll & William, 2004). The theory notes that firms occupy two 
broad segments that also determine their resource usage. There are specialists that occupy 
homogeneous segments of the market and survive within limited range of resources. There 
are also generalist that occupies broad segments of the market and require large resources 
to survive. Generalists have been found to have a higher survival rate than specialists as 
they have resources to diversify into other niches in times of crises ( Henderson & 
Mitchell, 1997; Hannan, 2005).  
 
Linkages with Institutions 
The pioneering work of Baum and Oliver (1991) suggested that firms with linkages with 
external institutions receive external legitimacy and are less likely to fail. In the face of 
intense competition, firms draw on the goodwill that they have with the institutions to 
avoid failure. Firms are able to gain this goodwill because their ties with the institution 
mean environmental values are passed to the firm as to accepted behavioural patterns that 
lead to selection (Alexander, Kaluzny, & Middleton, 1986). This may include preferential 
treatment from regulators, suppliers and customers (Lyles, 2004). Interactions with 
government also influence SME survival especially in the developing world where the 
government has the ‘largest’ purse. Hansen, Rand, and Tarp (2009) note that enterprises 
which have the state sector as their main customer perform better at survival than their 
counterparts. An increase in government payments has a small but statistically significant 
negative effect on the rate of business failure, and the magnitude of this effect increases 
with farm size (Key & Roberts, 2006). Businesses that receive funds from the government 
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at start-up are seen to have close to a minimum of three years survival threshold (Caliendo 
& Kritikos, 2009). Some authors have also found that generally governmental 
inefficiencies are to blame for the diminishing survival chances of SMEs (Akinbogun, 
2008). General institutional (environmental) factors both industrial and macro have also 
been seen to affect survival chances of SMEs (Mambula & Sawyer, 2004). Korunka, 
Kessler, Frank, and Lueger (2010) have found that a larger part of the survival variance is 
explained by environment/resource interactions. The attractiveness of the country of 
operations to foreign direct investment (FDI) also influences survival chances of SMEs. 
Burke, Görg, and Hanley (2007) found a net positive effect of FDI on survival. Limited 
access to finance, poor market conditions, inadequate staff, and lack of institutional 
support, as well as co-operation have all been found to limit the survival chances of SMEs 
(Franco & Haase, 2009). SMEs operating in high-crime niches in urban America appear 
not to be disadvantaged by crime. Crime’s impact may certainly be harmful, with other 
factors being constant, but the crux of the findings is that other factors are not constant 
(Bates & Robb, 2008). Ma and Lin (2010) have also found that it is not the ‘credit crunch’ 
and the restriction of credit itself that has an impact on SME survival, but rather the 
consequences arising from the recession.  
 
Industry Life Cycle Theory 
This notion states that industries follow a life cycle from fragmentation, through shakeout, 
maturity to decline. The fate of firms in an industry will also follow the same pattern 
(Klepper 2005). Agarwal et al. (2002) have also found that industries go through phases in 
their structures, competition and configurations that have a strong relationship with firms 
in that industry. There is also empirical evidence to suggest that the timing of entry into an 
industry has a strong influence on survival and this is linked with the idea of imprinting.  
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Location Theory 
Location is another important factor in the survivability of SMEs. Location theories 
generally, claim that firms can access resources from customers and suppliers while 
avoiding competition by virtue of their location and hence enhance survival chances of 
firms (Fritsch, Brixy, & Falck, 2006). Fertala (2007) for instance, has found that the tax 
rate leads to a significant increase in the hazard rate, while the population density enhances 
the survival chances. Support has also been found for the fact that regions vary in human 
capital competences and those regions with high human capital competencies have a 
higher survival chance than those who do not (Zhang & Yang, 2006). Industrial 
localization has a positive influence on new businesses survival while (Renski, 2011) 
aspects of the local liveability of neighbourhoods and of economic agglomeration are 
significantly related to individual firm survival and firm growth (Sleutjes, Oort, & 
Schutjens, 2012). Also in terms of the rural-urban divide, firms located in urban areas are 
at a higher risk of failure. Firms situated in counties that have achieved a high level of 
economic development stand a better chance of surviving (Christie & Sjoquist, 2012). 
Evidence has also been found for non-farm enterprises having higher survival chances 
when they operate in clusters (Ali & Peerlings, 2012).  
 
1.3.2.2 The Voluntaristic Approach 
This view sees organizational survival or failure as a function of internal firm mechanisms 
and pays little attention to the external environment. Some of the internal factors that have 
been discussed are leadership, absorptive capacity, innovation culture, firm orientations, 
resources and capabilities. Some of these are discussed in following sections. 
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Upper Echelon Theory 
Upper echelon theory refers to the ability of top management of organizations to motivate, 
manage and provide required structures for employees to achieve the goals of the 
organization and ultimately survive (Crossan & Apaydin, 2010). Two factors noted to 
influence survival are management heterogeneity and succession (Mellahi & Wilkinson, 
2004). On management heterogeneity, firms that have management with the same 
background values are more likely to lead firms to failure than their heterogeneous 
counterparts. This, Tsai (2010) notes, is because homogeneous groups are more likely to 
engage in groupthink, and are less likely to think outside the box for alternative solutions 
and repeat unsuccessful methods that achieve no results. Again, management teams that 
have been at post for long periods will more likely fail than new ones. Duncan et al. (2011) 
notes that this is because such management teams most often have exhausted their ideas 
and may bring nothing new in the face of crisis or a fight for survival. Another means by 
which top management, and in the case of SMEs owner managers, influence failure is 
through partner exit. This is a corporate governance issue. This is usually as a result of 
disagreement among partners about how the SME should be managed. Oertel and 
Walgenbach (2012c) have found that partner exit increases the mortality risk of 
organizations. Community characteristics, family achievements, family processes during 
change, business and owner characteristics, and business processes during stability 
affected long-term survival in family-owned businesses (Stafford, Bhargava, Danes, 
Haynes, & Brewton, 2010). Arribas and Vila (2007) found that specific aspects of human 
capital that are determinants of a company’s survival time are gender, previous work 
experience in the same activity or as the owner of a firm, and the number of partners. 
Evidence from Germany also suggests that partners’ educational background influences 
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survival rates. Certificates increase the survival chances of organizations. However, the 
positive influence of certificates decreases with the age of an organization. Moreover, 
enterprises without certificates show even better survival chances in the long run (Oertel & 
Walgenbach, 2011). Coad and Guenther (2012) have also found a positive impact on 
survival of formal education and previous experience within the labour market. Their 
results also show that the expenditure on start-up subsidies decreases the risk of exiting 
self-employment specifically for the group of individuals entering self-employment from 
unemployment. Bird and Sapp (2004) have also found a gender gap in small business 
success. They have found that men-owned businesses are more successful in both urban 
and rural settings. This, the authors attribute to socio-cultural constructions of what women 
must do and otherwise.  
 
Absorptive Capacity 
Absorptive capacity considers the internal ability of an organization to adopt useful 
information from its external environment (Levinthal, 1991). This information can come 
from the firm’s own research and development activities, mimic other firms, purchase 
from external sources or learn from its own internal operations (Nobelius, 2004; Vakola & 
Rezgui, 2000). Absorptive capacity is critical for firm growth and survival as it enables the 
firm to plan to adapt to its environment by purchasing competitive equipment to boost its 
production abilities. An orientation towards the market will also enable the firm to produce 
to meet market requirements ( Mavondo & Farrell, 2003; Hou, 2008). The survival of the 
firm is therefore linked to its ability to assimilate and use environmental information 
(Tanewski, Prajogo, & Sohal, 2003). However, employees’ knowledge abilities are critical 
for the absorption and use of new knowledge from the environment. Firms with highly 
skilled employees are more likely to absorb more information from its environment and 
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help the firm achieve greater growth than their other counterparts. Skilled employees can 
carefully assess pros and cons of information and how they fit strategically with firm goals 
(Tanewski et al., 2003). 
 
 
Innovativeness 
There is no ‘general theory’ of innovation. Many researchers believe a general theory is 
impossible due to the many complexities of innovation (Read, 2000). However, there is the 
general belief that it is the adoption of new ideas and methods that enables organisational 
survival and success (Drucker, 1985). Innovation comes from research and development 
(R&D) that is mainly organisation knowledge accumulation and imitating the innovations 
of other firms. There are contrasting empirical findings regarding the role of innovation for 
the survivability of small businesses. Tsvetkova, Thill, and Strumsky (2014) found that 
innovation, usually found to be a beneficial factor for the survival of SMEs, appears here, 
on the contrary, as unfavourable. However, Cefis (2005) found innovation extends firm 
life especially process innovation. This suggests that the type of innovation employed 
matters for survival. This is because process innovation reduces the probability of exit by 
radical restructuring, while product innovation, when not supported by process innovation, 
especially increases the probability of exit by mergers and acquisitions (M&As), inferring 
that exit strategies are intimately bound to the nature and synergies of innovative efforts. 
 
Managerial Orientations 
Managerial orientations refer to how enterprises are set up culturally. These orientations 
include market orientation, entrepreneurial orientation and strategic orientation. The study 
focuses on market and entrepreneurial orientation as small businesses are usually not 
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complex enough to engage in strategic orientation3. Entrepreneurial orientation (EO) refers 
to an enterprise’s strategic orientation, capturing specific entrepreneurial aspects of 
decision-making styles, methods, and practices (Covin & Miller, 2014). This, therefore, 
reflects how the firm operates rather than what is does, hence requiring it to be 
fundamentally proactive, innovative and take risks (Lumpkin & Dess, 1996; Quaye & 
Acheampong, 2013). Market orientation is derived from the application of a marketing 
concept suggesting that the key to organisational success is through the determination and 
satisfaction of the needs, wants, and aspirations of target markets (Mahmoud & Yusif, 
2012). MO is therefore a cultural orientation with behavioural implications since it 
channels organizational efforts towards learning about markets and developing strategies 
in response to market threats and/or opportunities (Cambra-Fierro, Florin, Perez, & 
Whitelock, 2011). Parry, Jones, Rowley, and Kupiec-Teahan (2012) found that, for 
marketing orientation to be useful, small businesses need to balance that with innovation. 
Criaco, Minola, Migliorini, and Serarols-Tarrés (2013) have found that entrepreneurial 
orientation improves the survivability of the small business. Their findings, however, are 
situated within the broader human capital theory within the business. Kim, Knotts, and 
Jones (2008) used artificial neural networks to establish that firms that use viable 
organizations to create and market products appealing to the customer are those that are 
also more likely to survive over a longer period of time. While long-term survival is not 
the only measure of the success of a small manufacturing firm, survived firms encompass 
other well-known success measures such as sales performance and profitability.  
 
                                                     
3
 This was observed during the field studies. When respondents were asked questions related to strategic 
orientation it was realised that there were few strategic level issues they could identify with. Hence this 
construct was dropped. Acquaah (2007) conducted a study of strategic orientation in Ghana but used large 
industrial firms.  
 
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Resources and Capabilities 
The resources and capabilities available to a company are critical to its growth and 
survival. Firms that have valuable, rare, inimitable and non-substitutable resources are 
more likely to be competitive and survive that of their other counterparts (Barney, 1991). 
Internal firm capabilities are also critical to growth and survival. Core competences have 
been noted to be more important than resources because when applied, competences do not 
get depleted like resources (Prahalad, 1993). Dynamic capability theorists have also noted 
that firms survive longer if management can build the capabilities of employees through 
learning from operational routines (Teece, 2007). In all, firms whose management is able 
to develop key resources build core competences and capabilities have a greater 
survivability than those firms that do not (Montresor, 2004). McKay and Chung (2005) 
linked dynamic capability to upper echelon theory and found that cooperation, sharing 
founder’s vision, time management, and developing organizational competencies have a 
significant effect on the survival chances of firms. Empirical studies suggest that business 
planning competencies do not confer any survival advantages to small businesses 
(Fernández-Guerrero, Revuelto-Taboada, & Simón-Moya, 2012). Competence-strategies 
enhance survival but flexibility-based strategies risk their very survival (Hiebl, 2013). 
Barbera and Hasso (2013) have found firms that have external accountants perform better 
at surviving than those that do not. This is because of the competencies that these 
professionals bring to the business. 
 
Other Voluntaristic Factors 
In this subsection, I include other managerial factors that cannot be included in the broad 
sections mentioned above. Organisational learning has been found to have a positive effect 
on firms survival chances (Esteve-Pérez, Máñez-Castillejo, & Sanchis-Llopis, 2007). The 
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evidence supports the existence of a sizeable ‘‘surviving-by-exporting’’ effect. Lee, 
Kelley, Lee, Lee, and Al (2012) confirm this assertion as they also found that 
internationalization is associated with better survival prospects, suggesting that failure risk 
does not increase with cross-border sales. The ethical values of the firm have also been 
found to support firms that pursue it (Vallejo, 2007). This is because firms that behave in 
unethical ways lose the social legitimation required to sustain the business. Gudmundsson 
and Lechner (2013) use cognitive theory to explain why some firms fail and found that 
overconfidence is the chief negative influence on survival. Optimism bias and distrust are 
conflicting cognitive biases influencing overconfidence, but showing a directly opposite 
influence on firm survival respectively.  
 
1.3.2.3 Integrativist Approach 
This view seeks to integrate the determinism of the ecological view with the voluntarism 
of the organizational view. Proposed by Mellahi and Wilkinson (2004, pp. 32), they note 
that “a fundamental axiom of the integrative framework is the different theoretical 
assumptions and linkages underlying each perspective are not only reconcilable but that, 
together, they provide a more comprehensive understanding of organizational failure than 
any single perspective by itself”. The authors propose that, by integrating the two 
perspectives, they can provide a much more pragmatic understanding of organizational 
failure. Since proposing this mechanism for understanding failure it has received 
widespread attention4. The challenge, however, is that there has been little application of 
the framework proposed with most scholars citing it for literature purposes.  
                                                     
4
 The publication that proposed the mechanism has received 229 citations according to Google Scholar as at 
April 15, 2015. 
 
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1.3.2.4 Emerging Trends 
While most of the views espoused above have focused on the reasons for failure or 
survival, new issues have begun to emerge on the subject matter. These include post-
failure behaviour (Cope, 2011); types of failure (Wennberg, Wiklund, DeTienne, & 
Cardon, 2010); and patterns of failure (Sam, 2007). The evidence from post-failure 
suggests that the social construction of entrepreneurial failure is driven by the need to 
maintain positive self-esteem and recover from the loss of the venture (Mantere, Aula, 
Schildt, & Vaara, 2013). Again, failure’s powerful learning outcomes are future-oriented, 
increasing the entrepreneur's level of entrepreneurial preparedness for further enterprising 
activities (Cope, 2011). Also, not all entrepreneurs experienced loss of self-esteem and felt 
grief, thus lending empirical support to the premise that failure of the firm does not always 
imply failure of the entrepreneur (Jenkins, Wiklund, & Brundin, 2014). The patterns in 
mortality also indicate that failure is fairly distributed along the life of an SME; with only 
a small fraction of the closures occurring in the infancy of the firms (Sam, 2007). This may 
be due to the liability of age effect described under the organizational ecology theory. The 
argument in the types of failure literature states that; entrepreneurs exit from both firms in 
distress and firms performing well (Wennberg et al., 2010); hence it will be unscrupulous 
to describe entrepreneurs as having failed. The literature suggests that the broad name be 
exit while sub-themes such as closure, failure and mortality are discussed (Wennberg & 
DeTienne, 2014). 
 
1.4 Conceptual Framework  
This section focuses on bringing together the theoretical perspectives required to create a 
conceptual framework for empirical testing of SME survival in Ghana. The main 
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theoretical mechanism employed to explain failure of SMEs in this is social network 
theory. Social network theory postulates that actors (individuals, firms, industries, 
countries and units) are connected by some ties (relationships). These ties confer 
opportunities and constraints that inform their social behaviour. Brass, Galaskiewicz, 
Greve, and Tsai (2004) describe the concept as ‘a network as a set of nodes and the set of 
ties representing some relationship, or lack of relationship, between the nodes. The 
particular content of the relationships represented by the ties is limited only by a 
researcher’s imagination. Typically studied under networks are strategic alliances and 
collaborations; flows of information (communication); affect (friendship); goods and 
services (workflow); influence (advice); and overlapping group memberships such as 
boards of directors. There are usually several reasons why networking occurs as 
enumerated above. However, there is a basic organising principle underlying all the 
networks that an individual can have called homophily. McPherson, Smith-Lovin, and 
Cook (2001) discuss that actors have an affinity to relate more to those who behave like 
them noting that geography, organisational structures, industries and isomorphism in social 
systems give rise to homophilious relational forms in humans and organisations. 
Homophily is just one of the concepts in social networks; but there are several others that I 
discuss and relate to enterprise survival. Homophily is related to the idea of mimetic 
isomorphism in institutional theory, which states that firms mimic successful firms by 
acting like them. There is also the idea of structural holes that states that firms that bridge 
gaps between other firms that have no connections among them in any social network have 
a stronger bargaining power for resources in that network (Burt, 2004). Some other 
concepts in social network theory include: embeddedness - the notion that all economic 
activities are found within some broader social context (Granovetter, 1985); board 
interlocks - the notion that organisations relate as a result of having board memberships 
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with implications for organisational dependencies and power and control for some elites 
(Borgatti & Foster, 2003; Pfeffer & Salancik, 2003); reciprocity - rewarding an actor’s 
connection with a connection (for example i to j and j to i friendships) (Leider, Möbius, 
Rosenblat, & Do, 2009); clustering and closure - actors being connected to actors 
connected to the connections (example people becoming friends with friends of their 
friends) (Levine & Kurzban, 2006); and centrality - this refers to the stars of organisational 
networks (those central to the network) (Rivera, Soderstrom, & Uzzi, 2010). 
 
Granovetter (2005, pp. 42) has also discussed social networks and their impacts on 
economic outcomes. He notes that social networks affect labour markets, prices, 
productivity and innovation. He, however, concludes that the three main reasons why 
social networks affect economic outcomes in organisations are because: ‘social networks 
affect the flow and the quality of information. Much information is subtle, nuanced and 
difficult to verify, so actors do not believe impersonal sources and instead rely on people 
they know. Second, social networks are an important source of reward and punishment, 
since these are often magnified in their impact when coming from others personally 
known. Third, trust, by which refers to the confidence that others will do the "right" thing 
despite a clear balance of incentives to the contrary, emerges, if it does, in the context of a 
social network’. These offer mechanisms through which the consequences of social 
networks can be felt by organisations. However, in a detailed review and typology of 
social network theory in organisational paradigms, Borgatti and Foster (2003) note that the 
theory offers the opportunity for the inclusion of other theoretical mechanisms to explain 
network consequences. The study therefore develops a framework by adapting the 
typology of network consequences that these authors offer and moderate with existing 
theoretical elements. These elements are structural capital from social capital theory (Lin, 
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1999); resources access from resource dependence theory (Pfeffer & Salancik, 2003); 
isomorphism from institutional theory (DiMaggio & Powell, 1991); and contagion from 
diffusion of innovation theory (Rogers, 2010). In this section of the thesis, these issues are 
discussed in brief but expanded open in the theory and hypothesis sections of the four 
empirical papers that test different parts of this framework. A diagrammatic presentation of 
the framework is presented below. 
 
Figure1.1: Basic Conceptual Framework 
Network Effects 
 Outcome 
 
 
 
 
 
                                   Moderator Variables 
 
 
 
 
 
 
Source: Authors Conceptualization 
 
1.4.1 Structural Social Capital 
Social capital refers to ‘resources embedded in a social structure which are accessed and/or 
mobilized in purposive action’ (Lin, 1999, pp. 31) or as ‘a collective resource that arises 
from (and is shaped by) social relations between actors within a network’ (Tomlinson, 
2011, pp. 5). These descriptions of social capital have structural embeddedness, 
opportunities and action oriented uses. Thus, the basic understanding is that social capital 
comes from social relations. Oh, Labianca, and Chung (2006) have also proposed the idea 
of group social capital. They argue that this is the set of resources made available to a 
1. Structural Social 
Capital 
2. Resource Access 
3. Isomorphism 
4. Diffusion 
SME Survival 
1. Investment Constraints 
2. SME Characteristics 
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group through members’ social relationships within the social structure of the group and in 
the broader formal and informal structure of the organization and that greater group social 
capital resources lead to greater group effectiveness. There are three dimensions of social 
capital. These are structural, relational and cognitive dimensions (Liao & Welsch, 2005). 
The structural dimension focuses on the locations of actors within a network and the 
benefits they confer on such an actor. Issues investigated under network structure include 
centrality and structural holes. This study focuses on the structural dimension of social 
capital and deals with the centrality of individual actors in a network as these positions 
confer certain privileges on the actor. Some of these privileges comprise conferring certain 
competitive advantages as has been described as the final arbiter of competition among 
firms as no matter the amount of productive resources available to a firm, its ability to sell 
through its network is what ultimately matters (Burt, 1992; 2000). Empirical studies in 
social capital has focused on explaining variances in organisational outcomes (Moody & 
Paxton, 2009). Pennings, Lee, and Witteloostuijn (1998) also found that social capital 
predicted firm dissolution based on uniqueness and appropriation of social capital 
resources but also proposed future studies considering stimulating effects of organisational 
ecology and resource-based view perspectives. Despite these benefits of social capital, 
some authors have mentioned the ‘dark-side’ of social capital in which over-embeddedness 
can constrain firm innovations as there are few ties to outside actors (weak ties) ( Gulati, 
Nohria, & Zaheer, 2000; Gulati & Higgins, 2003). Liao and Welsch (2005) have also 
reported that social capital has no effect on entrepreneurial behaviour in organisations. 
Consequently, this variable will be used as an antecedent to SME survival while 
controlling for SME characteristics, owner characteristics, investment climate constraints, 
and managerial and technical competencies. 
 
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1.4.2 Resource Access 
Enterprises do not have resource sufficiency and hence depend on other organisations in 
the external environment for resources required for their functioning and survival. This 
dependence on the external organisation creates uncertainty as that organisation can exert 
power over the enterprise as a result of this dependence. This power can be exerted 
positively in favour of the enterprises or against it. In order for enterprises to limit the 
negative effect of the organisation on its operations it must develop positive relational 
linkages with the organisation. This phenomenon has been described as the resource 
dependence theory or the external control of organisations (Pfeffer & Salancik, 2003). 
Drees and Heugens (2013) have noted that the resource dependence theory (RDT) has long 
been a premier framework for understanding organization-environmental relations by 
explaining how enterprises form interorganizational arrangements like interlocks, 
alliances, joint ventures, in-sourcing arrangements, and mergers and acquisitions. It also 
theorizes how these arrangements make enterprises more autonomous and more legitimate. 
Again, RDT assumes exchange is affected by more complex social factors and focuses on 
the social context of the organization’s relationships with the environment. RDT states that 
firms manage their dependencies in the face of uncertainty and that, as the environment 
becomes more uncertain and dependencies increase, firms will seek closer relationships to 
improve information exchanges, commitment, legitimacy, and exchange stability (Fink, 
2006). The theory also assumes that organisations make active choices about their external 
relations in order to gain the required legitimacy to gain access to resources for their 
functioning (Hessels & Terjesen, 2008). Within organisational networks, this theory 
reflects the connectionist perspective (Zaheer et al., 2010). This approach suggests that the 
amount of resources that an actor can draw from its alter is dependent on the number and 
quality of ties it has with the alter (Borgatti & Foster, 2003). The ties act as conduits 
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through which the actor can extract the resources. This argument assumes that actors are 
rational agents that will exploit their relationship with alters rich in resources to benefit 
from those resources (Borgatti & Cross, 2003). Evidence from a sample of young 
biotechnology firms show that ties to prominent venture capital firms are particularly 
beneficial to IPO success during cold markets, while ties to prominent investment banks 
are particularly beneficial to IPO success during hot markets (Gulati & Higgins, 2003). 
Consequently, this variable will be used as an antecedent to SME survival while 
moderating SME characteristics and controlling for owner characteristics, investment 
climate constraints and managerial competencies. 
 
1.4.3 Isomorphism 
Isomorphism is the constraining effect in business environments or industry that forces 
firms to become similar with each other (DiMaggio & Powell, 1991). This occurs because 
firms seek to adjust their organisational forms to withstand these constraints. These effects 
work through formal and informal pressures (coercive), responses to uncertainty and 
ambiguity in the environment (mimetic) and industry norms (normative). Hambrick, 
Finkelstein, Cho, and Jackson (2004, pp. 320) observe from the theory that “organizations 
must compete for social success (power and legitimacy) as much as for economic success, 
and thus are subjected to isomorphic mechanisms, which – if present – produce an 
increasing degree of similarity among peer organizations over time”. Isomorphism in 
social structures is also the result of complex relational networks that foster effective 
practice; occupational specialization, principles of coordination as well as best practices 
developed by successful firms diffuse through the relational networks (Meyer & Rowan, 
1977). Within network studies, Borgatti and Foster (2003) has described it as the 
convergence in actor attitudes and practices as a result of similar networking 
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environments. Isomorphism can have a positive impact on organisational outcomes as a 
result of legitimation of structural form rather than technical efficiency; the logics of good 
faith and reduction in environmental turbulence (Scott, 1991). These can serve as currency 
through which other more tangible resources can be acquired for productive activities 
(Deephouse, 1996). In network environments, the impacts of isomorphism works through 
oligopolistic coordination and trust that reduces transaction costs as a result of having 
similar third parties (Gulati et al., 2000; Burt, 2005; Williamson, 2005). Beyond certain 
thresholds, isomorphism returns negative effects as similar firms begin to compete for 
similar resources to maintain their organisational forms leading to competitive crowding 
(Podolny, Stuart, & Hannan, 1996). Again, similar firms with connections to similar third 
parties can lead to redundant information flows that will make them obsolete and fail 
(Burt, 2005; Symeonidis, Tiakas, & Manolopoulos, 2010). Consequently, this variable will 
be used as an antecedent to SME survival while moderating for investment climate 
constraints and controlling for SME characteristics, owner characteristics and managerial 
competencies. 
 
1.4.4 Diffusion 
Ideas, attitudes and resources can spread from one actor to the other in a network. These 
shared attributes of actors in a network can be explained by the diffusion of innovations 
theory. The theory suggests that such resources need to diffuse through a medium over 
time in a social system to be made available to the alters that require them. This means 
that, for the resource to be diffused, the medium of diffusion, the timeline and the social 
system within which the diffusion takes place, are relevant (Rogers, 2010). Four main 
issues emerge from theory and these are: (1) the alter resource to be diffused; (2) the 
medium of diffusion; (3) time; and (4) the network when it is considered from a network 
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point of view. Borgatti and Foster (2003, pp. 1001) note that “ties are conceived of as 
conduits or roads along which information or influence flow and seen from the point of 
view of a single actor, her adoption of a practice is determined by the proportion of nodes 
surrounding her that have adopted”. Diffusion of resources within networks is very useful 
for SME outcomes because, according to Lichtenthaler (2008), SMEs can reap greater 
benefits from external collaborations as it can compensate for the scarcity of internal 
resources and competences. Mcdade and Malecki (1997) have also suggested that in 
industrial estates resource sharing is very common among individual entrepreneurs in 
Ghana as a mechanism for overcoming the resource constraints that they face. 
Consequently, this variable will be used as an antecedent to SME survival while 
controlling for SME characteristics, owner characteristics, investment climate constraints, 
and managerial and technical competencies. 
 
1.4.5 Moderators 
The strength of association between whether an SME survives and the network effects are 
moderated by some variables such as managerial competence, technical competence, SME 
characteristics, owner characteristics and investment climate constraints that have also 
been shown to have an effect on survival as individual constructs. In this study I focus on 
two of these moderators - SME characteristics and investment climate constraints. The 
effects of SME characteristics on SME survival has been discussed through the 
organisational ecology theory (Hannan & Freeman, 1993). More specifically, SME size 
has been seen to affect survival through the liability of smallness (Box, 2007); SME age 
has been observed to affect survival through the age dependence hypothesis (Thornhill & 
Amit, 2003); while the resource requirements of the SME concerning whether it 
specializes or not works through the resource partitioning effect (Carroll & William, 
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2004). In this study, the researcher specifically focuses on how these SME characteristics 
moderate the relationship between SME distributional ties as a market resource access 
mechanism and SME survival. The second moderator that is considered is investment 
climate constraints. These constraints include access to credit, access to raw materials, 
labour quality, land and electricity. Difficulty in accessing or overcoming these factors or 
issues in the operating environment imposes indirect costs on the functioning of the SMEs 
(Eifert, Gelb, & Ramachandran, 2008). The study considers how isomorphism of SME in 
the network environment can be useful in overcoming the general investment climate 
constraints. This is because isomorphic firms are judged on their structural forms rather 
than their technical efficiency in overcoming institutional constraints (Scott, 1991).  
 
1.5 Context of the Study 
1.5.1 Issues in the Ghanaian SME Sector 
Abor and Quartey (2010) have noted that SMEs in Ghana constitute 85% of the 
manufacturing sector’s employment. They are also believed to contribute about 70% to 
Ghana’s GDP and account for about 92% of businesses in Ghana. Notwithstanding the 
recognition of the important roles SMEs play, their development is largely constrained by 
a number of factors, such as lack of access to appropriate technology; limited access to 
international markets; the existence of laws; regulations and rules that impede the 
development of the sector; weak institutional capacity; lack of management skills and 
training; and most importantly, finance. Benzing and Chu (2009) researching motivations 
of small business owners in Ghana, Kenya and Nigeria found that the strongest motivator 
across countries was the opportunity to increase income. Their factor analysis found three 
motivation factors: a family factor; an external validation factor,; and a self-betterment 
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factor. The three countries showed significant differences with Ghanaian entrepreneurs by 
rating the family factor as more important.  
 
Mcdade and Malecki (1997) studied entrepreneurial clustering in Ghana and observed a 
paradox. On the one hand, there are many examples of ingenious innovation in the 
adaptation of material resources such as recycling worn-out rubber tires into ‘new’ hand-
tooled for precision-calibrated wheel bushings of expensive cars, such as Mercedes Benz. 
However, much of the energy of innovation is consumed in finding ways to accommodate 
the scarcity of basic economic and material resources. Economic difficulties do not also 
encourage SMEs to plan seriously. Studies show that firms that plan do not necessarily 
experience increased performance, with the exception of the manufacturing sector (Yusuf 
& Saffu, 2005). Mensah et al. (2007) note that most enterprise activities are for subsistence 
purposes in Ghana. The majority of the firms are sole proprietorships, with most of the 
hired labour being apprentices. A high proportion of rural-based proprietors and many 
urban-based proprietors have secondary occupations. The majority are unprofitable when 
the legal minimum wage was used as a proxy for the value of proprietors’ time. The small 
enterprises represent part of a ‘sustainable livelihoods’ strategy of lowering economic risk 
by diversifying income sources. 
 
Agyapong (2010) studying internationalization tendencies revealed that lack of 
international presence by Ghanaian SMEs is due mainly to lack of internal strategic 
capabilities. However, organization’s successful strategies are dependent partly on having 
internal strategic capability required for survival and success. The research further reveals 
that, due to lack of both tangible and intangible resources such as finance, information, 
reputation, knowledge and technology, the SMEs in Ghana are unable to compete in the 
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international markets on the product features, quality and price.  Buame et al. (2013) 
studying the gendered part of enterprise activities in Ghana noted that the indigenous 
Ghanaian woman entrepreneur exhibits many similarities with their male counterparts in 
terms of their personality traits. However, they differed in other aspects such as their 
educational backgrounds and modes of entrepreneurial skills acquisition. Through their 
entrepreneurial activities the women have made substantial contributions to the economic 
growth of Ghana in terms of innovation, job creation, and reduction in poverty and 
unemployment.  
 
In sum, the Ghanaian SME sector is a very complex one. The sector contributes 
significantly to national development in terms of employment and GDP. This is despite the 
scarcity and constraints they face. The enterprises exhibit a lot of ingenuity to overcome 
these structural challenges. Family networks play a huge role in small businesses. It is a 
popular belief in Ghana that family members help you build a business and also help to 
destroy it. There is also a huge lack of planning due to economic difficulties and 
sometimes simply lack of the ability to do so. This may account for their high mortality. 
Most Ghanaian SMEs use cheap labour and yet are unprofitable to even achieve their 
subsistence goal. There is a lack of international presence mainly due to the lack of 
internal strategic capabilities. The role of women is very high as most husbands provide 
capital for their housewives to trade or engage in some business activity.  
 
1.5.2 Ghanaian Poultry Sector: A Focus on the Dormaa Cluster 
The poultry sector in Ghana is one of the few sectors that have faced the harsh realities of 
trade liberalization. This has led to the collapse and below-capacity operation of many of 
the poultry clusters in the country before the mid-1980s to early 1990s (FAO, 2014). 
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Compared to cocoa, maize or rice, the poultry sector does not feature prominently in 
economic planning or agricultural policy and programme documents. Where it is referred 
to, the proposed measures appear to lack either purpose, focus or consistency, and it is 
difficult to discern a specific policy approach or strategy towards the poultry sector 
(Sumberg, Awo, Fiankor, Kwadzo & Thompson, 2013). The study researches the poultry 
sector in three districts of the Brong Ahafo Region - Dormaa Central (Dormaa Ahenkro), 
Dormaa West (Nkrankwanta) and Dormaa East (Wamfie). This cluster is popularly known 
in the agriculture literature as the Sunyani/Dormaa cluster (Mensah-Bonsu & Rich, 2010). 
At the moment the poultry industry, specifically table egg production, is operating at large 
scale levels. Poultry production in this cluster is one of the largest in the region and 
nationwide. Some of the major challenges encountered by the poultry farmers included; 
financing, diseases and absence of electricity for operations in most farms while the 
presence of feed processing mills, poultry input shops and availability of organised 
markets served as prospects that could be harnessed to boost the growth of the poultry 
industry in the district (Adei & Asante, 2012). The area seems to have formed strong 
collaborative partnerships with Foani Farms in La Cote D’Ivoire to avoid competition that 
was leading to collapse of some farms. Anecdotal evidence suggests (the researcher 
observed this) some farms that collapse are helped back to their feet by Foani Farms so 
long as there is a social or familial linkage. The owner of Foani farms is a family relation 
of the broader Unity Farms in Ghana, one of the big players in the Ghana poultry industry. 
The cluster in recent times is one of the few in the sector receiving support from the Ghana 
government as part of the broader National Poultry Support Project (NPSP) announced by 
the Ministry of Trade and Industry5 and the Ghana Broiler Revitalization Project 
(GHABROP) through the Ministry of Food and Agriculture (MOFA) in a ten year 
                                                     
5
 http://graphic.com.gh/news/general-news/22412-government-support-to-poultry-industry-
increasing.html 
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collaboration with the Ghana National Association of Poultry Farmers6. There are many 
farms in Dormaa owned by returnees from Europe or remittances from migrants to Europe 
who are seeking to return back to Ghana. Please see detailed statistics about sector in 
Appendix 1. 
 
1.6 Overview of Methodology 
This section seeks to present how the problem statement, research objectives and research 
questions were addressed. By definition, methodology can be seen as a way of thinking 
about and studying reality, whilst methods can be defined as “a set of procedures and 
techniques for gathering and analysing data” (Strauss & Corbin, 1998). This section 
presents a general overview of the various methodological issues utilized in the various 
empirical papers. The detailed research methods are explicated in the various papers. 
Specifically, this section first illustrates the methodology employed in this thesis by first 
describing higher level considerations such as philosophical views, research approach, 
research design, data sources, and research strategy,; and thereafter moving to more 
practical considerations and methods including data collection, questionnaire design, 
sampling, survey administration, and data analysis. Finally, the quality criteria applied to 
this study are then described and discussed. 
 
1.6.1 Philosophical World Views 
Although philosophical ideas remain largely hidden they still influence research and need 
to be identified (Creswell, 2009). Philosophical world views form the paradigms and 
frameworks by which researchers see the world (Saunders, Lewis, & Thornhill, 2007). 
                                                     
6
 http://www.thepoultrysite.com/poultrynews/32740/ghanas-broiler-sector-to-get-legup-from-
revitalization-project 
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This involves the way knowledge is sought and utilized, and the positions held by various 
researchers. There are two main philosophical issues that underpin research efforts: these 
are epistemological and ontological considerations (Bryman, 2008). Epistemology 
positions discuss what constitutes an acceptable level of knowledge in a particular field of 
study (Saunders et al., 2007). According to Bryman (2008, pp. 45), “the central issue in 
this debate is whether the social world can and should be studied according to the same 
principles, procedures and ethos as the natural sciences”. There are three major categories 
of epistemological positions of positivism, realism and interpretivism (Saunders et al., 
2007).  
 
Researchers that belong to the positivists’ tradition prefer to work with observable data and 
make generalizable conclusions about observations; those that belong to the realism 
tradition believe that both social science and natural science researchers should apply the 
same methods in data collection and explanation of reality; while interpretivists hold that 
researchers study humans that are constantly changing and hence law-like generalisations 
of positivists cannot explain social behaviour (Saunders et al., 2007; Bryman, 2008; 
Creswell, 2009). This study is underpinned by critical realism (belonging to the realist 
philosophical position), one of the key epistemological positions in management research. 
Critical realism holds that there are two steps in experiencing the world; first, the thing 
itself and the sensations it conveys; and second, the mental processing that goes on when it 
is experienced (Saunders et al., 2007). The critical realism proposition that the world is 
constantly changing and what can be captured in a study are the sensations that these 
changes convey (Dobson, 2002; Bhaskar, 2010), and is much more in line with 
management and business research which is often to understand the reason for a certain 
phenomenon (Saunders et al., 2007). 
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Ontological issues relating to research philosophy is concerned with the nature of reality 
(Rosenberg, 2002). According to Bryman (2008, pp. 48), “the central point of orientation 
here is the question of whether social entities can and should be considered objective 
entities that have a reality external to social actors, or whether they can and should be 
considered constructions built up from the perceptions and actions of social actors”. The 
three main ontological issues usually debated are objectivism, subjectivism and 
pragmatism (Saunders et al., 2007). Objectivism suggests that social phenomena confronts 
us as external facts that are beyond the study control while subjectivism implies that the 
social world is constructed by social actors and hence cannot be objective (Bryman, 2008). 
Guba and Lincoln (1994) suggest that the objective and subjective positions have a 
competitive outlook and are unrealistic positions and consequently propose pragmatism as 
another ontological position. The basic orientation of this philosophy is that the 
philosophical position chosen by a researcher should be based on the research question 
under investigation. This study is underpinned by the pragmatic ontological position. This 
is because, while I seek objective answers regarding the influence of network effects on 
the survival chances of small businesses, these effects have a lot of endogenous loops with 
actor characteristics and hence, in a sense, also subjective. Consequently, the research 
question is ambiguous and does not align clearly with either objective or subjective 
positions and hence pragmatism becomes the ontological position.      
 
1.6.2 Research Approach 
This section explores the dichotomies of inductive and qualitative research as against 
deductive and quantitative research and provides explanations for why this particular study 
has pursued a largely deductive and quantitative approach. The research in this thesis is of 
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the deductive type, as the empirical study is guided by models and hypotheses that have 
been derived from pre-existing theories and previous research in the area of organizational 
and SME survival. The deductive research approach begins with an abstract logical 
relationship among concepts and then moves towards concrete empirical evidence 
(Blaikie, 2009). This study develops a framework in this chapter which formed the basis 
for data collection to test the relationships between network effects and SME survival 
established in the framework. Rigid positions are often taken in favour of either 
quantitative or qualitative research by academicians. The positions are based on the 
approaches which provide the most accurate understanding of a particular phenomenon. 
Many quantitative researchers dismiss qualitative research as journalistic accounts while 
qualitative researchers think of quantitative research as yielding misleading information 
(Malhotra & Birks, 2007). The decision as to the research approach to adopt should be 
contingent on the research purpose (Potter, 1996). It is, however, impossible to create an 
absolute distinction between the two approaches, as many studies have been known to 
incorporate both qualitative and quantitative elements (Saunders et al., 2007). This study is 
to be considered quantitative and deductive in nature as it seeks to establish network 
effects as the basis for the survival or otherwise of SMEs. The results derived from 
quantitative research are assumed to be measurable and presentable in the form of numbers 
and statistics (Blaikie, 2009). The aim is to make generalizations based on the processed 
results of the investigation. The quantitative method is formalized, structured, and 
approaches the research problem from a broad perspective. Consequently, this explains 
why the study adopts a quantitative approach. 
 
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1.6.3 Research Method 
Research method is the road map for undertaking a systemic research of a phenomenon of 
interest (Creswell, 2009). Saunders et al. (2007) identify seven research methods, namely: 
survey; case study; action research; grounded theory; ethnography; and archival research. 
They describe action research as involving a close collaboration between researchers and 
practitioners; case study involves the study of a phenomenon in its real context; and 
archival research involves the analysis of administrative records. They further describe 
ethnography as research to explain the social world first hand; experiment as research 
where samples can be controlled; grounded theory as research by observation and survey 
as research that involves the structured collection of data from a population. Each strategy 
can be used for exploratory, descriptive and explanatory research (Yin, 2003). These 
strategies either belong to the inductive or deductive schools of thought: this is, however, 
not to say that one is better than the other. Rather, the emphasis should be on the one that 
enables the researcher to meet his/her objectives and answer the research questions. These 
strategies should not be thought of as being mutually exclusive since they can sometimes 
be used together. By first looking at the objectives of this study and applying them to the 
above reasoning, it is apparent that most of the strategies could be more or less applicable. 
However, due to the already established quantitative nature of this study and the need for 
primary information, the research method for this thesis became the network survey 
research with corresponding respondent attributes that focuses on the case of the poultry 
cluster in Dormaa Ahenkro. This enables the study appropriate the benefits of both survey 
and case study methods as described earlier.  
 
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1.6.4 Data Sources and Collection 
Having selected the survey method, the next decision involved choosing the sources of 
data and actual means of obtaining information from the informants. Sources used for data 
collection can take two forms: primary and secondary. Primary sources of data are 
collected and accumulated specifically for the research problem at hand, whereas 
secondary sources contain data that have been gathered and assembled at a previous time 
for purposes other than the current research problem (Hawkins & Tull, 1994; Yin, 2003; 
Hair, Black, Babin, Anderson, & Tatham, 2006). The advantage of secondary data is that it 
can usually be collected at a lower cost and more rapidly than primary data. On the other 
hand, since it was usually collected for a different purpose, its content might correlate 
poorly with the researchers’ current needs (Yin, 2003; Hair et al., 2006). This is one of the 
reasons why primary data sources were utilized in this study. Moreover, considering the 
relative complexity and depth of the information required by the current study, no 
presently available secondary sources were located that were sufficient. 
 
On the actual means of data collection, the options available were personal interviews, 
telephone interviews, or self-administered questionnaires (Malhotra & Birks, 2007). The 
study adopts the personally administered questionnaires with personal interviews as the 
information seeking strategy. In this strategy, respondents are interviewed qualitatively 
because of the qualitative nature of network relationships but the responses are reduced to 
quantitative responses on the questionnaire. This strategy also provides a sense of 
anonymity for the respondents and there is much less time constraint associated with self-
administered questionnaires than for other primary data collection methods. This might 
lead to answers that are more honest and information that is more correct, since the 
respondents have been given sufficient time to search for information on complicated 
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questions. The standardized nature of written questionnaires also facilitates comparisons of 
different respondents’ answers in subsequent data analysis (Saunders et al., 2007). 
 
From the above discussion, a composite list of 200 farmers was developed after 
discussions with industry associations, the Ghana Revenue Authority (GRA), the 
Municipal Assemblies, Assemblymen and women in each unit area of the district and the 
office of the paramount traditional chief (Omanhene). When the whole list was compiled 
and recurring farms taken care of, there were a total of 163 farms. All the farms were 
contacted to be interviewed of which 155 farmers participated in the study representing a 
95.05% response rate. It is important to note that not all the farmers in the cluster area 
were included in the compiled list because some of the farms were household farms that 
are not operating as an enterprise and hence were excluded from the study. The average 
interview using a research questionnaire (see in appendix) took on average between 45 and 
60 minutes. The farmers were asked for information regarding enterprise characteristics, 
owner characteristics and general organizational competences. In relation to the network 
data farmers were asked to name other farmers in the study area whom they collaborated 
with, and their distributors and financiers for the purposes of their business in line with the 
multiple name generator approach (Rooks, Szirmai, & Sserwanga, 2012). I then checked if 
the names provided were on my list; if not I further checked the location of the named 
farm. In most cases I found that farms that were not on my list were outside the study area 
and consequently were discounted for network boundary validity purposes Boutilier, 2007; 
Carpenter, Li, & Jiang, 2012). Distribution and financier networks were not subject to 
these locational requirements since this is impractical as these stakeholders operate 
nationwide. After every interview the face validity of the responses was assessed by 
ensuring that respondents’ had documentation to support the existence of a link. If none 
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were produced, the link was discounted. Two rounds of data were collected for the 
purposes of this study. The first round of data was collected in January 2014 and the 
second round of data was collected in March 2015. 
 
1.6.5 Data Analysis Techniques 
In this sub-section, the analytical strategy adopted in the study is discussed. Two broad 
analytical processes are undertaken: the network analysis leading to the development of 
network variables; and the empirical estimation of survival probability. First, I discuss the 
network analysis conducted using the UCINet (Borgatti, Everett, & Freeman, 2002). 
Respondents were asked to name financiers, distributors and other farmers in the poultry 
cluster they collaborate with for business purposes. For the finance network, a bi-partite 
finance network was developed by mapping each other to its named financiers for both 
2014 and 2015 (see network in appendix). The UCINet software was then employed to 
generate the closeness and eigenvector centralities as structural social capital within the 
network. The financial institutions were also classified into five broad categories7 
(universal banks, savings and loans companies, credit unions, micro-finance and rural 
banks). The UCINet is again used to generate the degree centralities of the poultry SMEs 
to each of these classes of financial institutions. For the distribution network (see network 
in appendix), the network software is used to generate the degree centrality of SMEs and 
the distributors. Finally, for the local industry network (see network in appendix), was 
used for two purposes. First, I use it to develop the structural equivalence of actors which 
is operationalised as the level of isomorphism of actors in the network. Secondly, it’s used 
to generate diffusion in the network by generating the direct and indirect ties as diffusion 
                                                     
7
 In line with Bank of Ghana Classification of Bank and Non-Bank Financial Institutions (see 
www.bog.gov.gh) 
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mechanisms while the alter attributes relating to market and technical competencies was 
also computed using UCINet as the resources that are diffused. The next stage of the 
research involved the empirical estimation strategy adopted. Survival is operationalized as 
SME persistence from 2014 to 2015. The study adopts the lagged independent variables 
approach by suggesting that SME characteristics in 2014 are what enable it to persist into 
2015. Since the dependent variable is a binary outcome, the study used the probit model8 
which employs the cumulative distribution function (cdf) of the standard normal 
distribution (snd) as its link function and the maximum likelihood as the estimator. The 
estimations were done in a hierarchical approach where covariates are estimated first and 
variables of interests and the interactions follow. The estimations were all robust to 
heteroscedasticity. The covariates include SME characteristics, owner characteristics, 
managerial and technical competencies as well as investment climate constraints. The 
hierarchical and lagged approach is useful in partialling-out some endogeneity that may be 
present in the variables of interest. These estimations are implemented using STATA 14. 
 
1.7 Significance of the Study 
The dynamic role of small and medium enterprises (SMEs) in developing countries has 
long been recognized (Kayanula & Quartey, 2000a). There is, however, reports of high 
mortality among these SMEs, with the majority dying before their second birthday 
(Okpara, 2011). However, these SMEs are associated with significant benefits. SMEs in 
Ghana are very labour intensive (Kayanula & Quartey, 2000b) and hence are a major 
source of employment especially to the youth. The poultry SMEs in Dormaa are 
anecdotally believed to employ approximately 1500 persons in the district. The study data 
suggests that approximately 1000 persons are employed by the SMEs that I studied. The 
                                                     
8
 A type of binary classification model 
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International Organisation for Migration (IOM) estimates that the majority of the youth 
that trek the desert in search of economic opportunities in Europe are from the study region 
and most of these youth engage in these perilous journeys because of the lack of 
employment opportunities and family poverty.9 In this sense the survival of these SMEs to 
provide employment to the youth is very critical. Abor and Quartey (2010) notes that 
SMEs in Ghana constitute 85% of manufacturing sector employment. They are also 
believed to contribute about 70% to Ghana’s GDP and account for about 92% of 
businesses in Ghana. An improvement therefore in the survival chances of these SMEs 
will have a very high impact of the development of the nations in which they are found. In 
respect of the rural agricultural cluster sample that the researcher used, there was a cross-
cutting effect in the form of stemming rural poverty, reducing rural-urban migration as 
well as improving the gender balance as women are some of the major actors in this sector. 
 
1.8 Structure of the Thesis 
In chapter one, the focus is on reviewing literature on organisational failure to understand 
the issues and formulate a research gap and questions. Subsequent to that, a conceptual 
framework was developed for testing in line with social network theory based on resource 
access, social capital, isomorphism and diffusion in networks. Chapter two focuses on 
market resource access through distribution ties and how they associate with SME 
survival. Chapter three focuses on structural social capital in finance network and how 
useful they are in SME survival by overcoming credit constraints. Chapter four focuses on 
network isomorphism using structural equivalence and how they can aid SME survival by 
serving as the mechanism through which investment climate constraints can be overcome. 
                                                     
9
 http://www.iom.int/files/live/sites/iom/files/Country/docs/IOM-Ghana-raises-concern-about-Ghanaian-
youth-embarking-on-perilous-journeys.html 
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The last empirical chapter focuses on how an SME can benefit from the resources owned 
by its alters and the mechanism through which these can be made available to it. This is 
useful in overcoming SME resource scarcities. The last chapter of the thesis summarizes 
and concludes the study and provides recommendations for practitioners and suggestions 
for future studies. The tabular structure of the thesis is as follows. 
 
Table 1.1: Structure of Thesis 
Chapter Issue Addressed 
Chapter 1 Introduction to the Thesis 
Chapter 2 Empirical Paper 1: Distribution Ties, SME 
Characteristics and Survival 
Chapter 3 Empirical Paper 2: Finance Network Capital, Credit 
Constraints and SME Survival 
Chapter 4 Empirical Paper 3: Isomorphism, Investment Climate 
Constraints and SME Survival 
Chapter 5 Empirical Paper 4: Benefitting from Alter Resources: 
Network Diffusion and SME Survival 
Chapter 6 Conclusion 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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CHAPTER TWO 
DISTRIBUTION TIES, SME CHARACTERISTICS AND SURVIVAL IN GHANA 
 
Abstract 
The studyThis study seeks to understand the effects of increased market access in the form 
of distribution network ties on enterprise survival in Ghana using data from a two year 
network survey of poultry enterprises. The study also explored the moderating effects of 
organisational ecology factors in the form of enterprise age, size and resource utilization. 
Data was collected from a cluster of poultry SMEs in Ghana using a network survey 
approach with corresponding SME and owner attributes. First, the study found that there 
was a high failure rate of approximately forty percent. The study also found that, contrary 
to popular belief, increases in market access through distribution ties does not necessarily 
lead to positive outcomes as the study observed a negative association between distribution 
ties and SME survival. The interaction between age of SME and the resource usage of the 
SMEs are found to also have a significant negative association with SME survival. 
 
Keywords: Distribution Ties, Networks, SME Survival, Organisational Ecology, Ghana 
 
2.1 Introduction 
Market access has been consistently reported as one of the major constraints to the 
development of small and medium scale enterprises (SMEs)10 in Africa (Kayanula & 
Quartey, 2000b; Abor & Quartey, 2010). Some academics have therefore suggested that 
SMEs increase their marketing activity (Harrigan, Ramsey, & Ibbotson, 2012; Parry et al., 
                                                     
10
 Businesses employing less than 99 employees in Ghana 
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2012) to aid their survival by increasing their access to both local and international 
markets. Implicit in this idea of increased market participation is the assumption that such 
participation will automatically lead to positive outcomes for SMEs because firms are 
viewed as rational agents that will exploit their market participation for their benefit 
(Borgatti & Foster, 2003). However, empirical evidence from the embeddedness theory 
(Granovetter, 2005) is showing that this assertion may not always hold as over-
embeddedness can also have negative consequences on the economic performance of firms 
(Uzzi, 1996).  
 
Specifically, SMEs are relationally embedded in a distribution network involving 
producers and sellers. The producers (herein SMEs) engage in the production of the 
products while the distributors serve as agents (sellers) in the market place. Both actors 
seek to maximize their gain in this network by increasing their connections to other actors 
in the other group. That is, the distributor seeks to gain more suppliers (SMEs) to ensure 
constant supplies and greater negotiation power while the SME seeks to gain more 
distributors in order to gain a greater access to the market for his products and increase in 
his prices. Focusing on the SMEs as producers; the study explored the effect of any 
additional increase in the number of distributor relations on the survival of the SME. While 
increased distribution ties can offer SMEs some benefits such as access to the market, 
market information and sometimes working capital (Benton & Maloni, 2005; Ireland, 
2008), there can also be significant negative effects as well. These negative effects can 
include cost of maintaining multiple relationships (learning, monetary and social costs), 
lost trust with other distributors that would expect a more exclusive relationship and the 
fact that there is an additional competitor for the share of consumers ‘wallet’ (Mizruchi & 
Stearns, 2001; Uzzi & Lancaster, 2003; Ropega, 2011). Consequently, the study theorized 
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that increased relational embeddedness in distribution networks can only be detrimental to 
the survival chances of an SME because the costs of new relationships are likely to 
outweigh the benefits associated. Also organisational ecology suggests that firm 
characteristics such as age, size and resource utilization have an effect on the survival 
chances. The study therefore explored the interaction effect of the distribution ties and 
these variables on the survival chances of SMEs in Ghana. The study used the number of 
distributors to represent market participation because most SMEs are usually small and 
have small capital outlays; hence cannot avoid the intermediation of distributors who link 
them to the market (Ropega, 2011).  
 
Pursuant to this, the study utilized network data and corresponding actor attributes from 
the Ghanaian poultry sector focusing on the Dormaa Ahenkro Cluster. The study used this 
sector because it better represents the agrarian nature of the Ghanaian economy. The study 
also limited its analyses to this cluster for two main reasons. First, to control the boundary 
of the network the study sought to analyse; lacking which will lead to sprawl and render 
network sparse, meaningless and lacking context (Carpenter et al., 2012). Secondly, the 
cluster is one of the best performing poultry clusters in Ghana (FAO, 2014) and can serve 
as a critical case-study and findings used to inform other clusters in the country (Flyvbjerg, 
2006). Using multiple name generators (Rooks et al., 2012), 155 poultry SME 
owners/managers were asked to provide a list of all their distributors. The data were 
collected in two waves: the first in January 2014 and second in March 2015. The data were 
then used to generate a two-mode affiliation network of SMEs and distributors for the two 
rounds of data. The study then modelled the failure (exit) of an SME from the second 
round using characteristics from round one. The study found that about 60% of the SMEs 
surveyed in 2014 persisted into 2015 giving credence to the anecdote that most SMEs do 
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not live to their second ‘birthday’. The study also found that, contrary to popular belief, 
increases in market access through distribution ties does not necessarily lead to positive 
outcomes as the study observed a negative association between distribution ties and SME 
survival. The interaction between age of SME and the resource usage were also found to 
have a significant negative association with SME survival. 
 
This paper is structured as follows. Section 2 presents the theory and hypotheses. Then in 
section 3 of the study presents the research methods focusing on study setting, network 
survey, measures and model specification. Section 4 presents the results of the analysis 
while sections 5 and 6 present the discussion of the results and conclusions respectively. 
 
2.2 Literature Review and Hypotheses Development 
This section relates to the literature review and hypotheses development to guide the study. 
The review covers distribution ties, SME characteristics and how they associate with SME 
survival. 
 
2.2.1 Distribution Ties and SME Survival 
Distributors are an important part of any business value chain (Tuominen, 2004). 
Distributors participate in the business activities of an SME by linking the SME to the 
market, providing market information and in some cases provide working capital. 
Distributors usually act as a link between the producer and the final consumer. This is 
because there are significant costs in transporting, warehousing and marketing to the final 
consumer. Since, most SMEs are usually small and have limited capital outlay; taking up 
distribution functions can be daunting or nearly impossible. This leaves the distributors as 
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an SMEs main access to the market (Ireland, 2008). Secondly, distributors serve as 
conduits for SMEs to receive market information since these distributors have a closer 
relationship with the final consumer (Benton & Maloni, 2005). This information can be 
used to tailor, improve and make significant adjustments that meet the final consumer’s 
needs. Finally, in markets or seasons where the products of the SME or the industry in 
general are difficult to get; distributors can pay in advance for the products. This advance 
payment can serve as working capital for SMEs who normally have difficulty in accessing 
financial resources from the financial sector in most developing countries (Kayanula & 
Quartey, 2000b). SMEs normally develop ties with distributors to access these benefits in 
line with the resource dependence theory that suggests that firms need to develop ties to 
organisations from those they need resources from to gain their legitimacy (Pfeffer & 
Salancik, 2003). In line with stakeholder theory that will see distributors as resource hubs 
that SMEs need to draw from (Mitchell, Agle, & Wood, 2013). However, are these 
linkages and benefits cost free? 
 
First, the study considered the problem of relational embeddedness. The central theme of 
embeddedness is that repetitive business relations lead to embedded logics of exchange 
(Granovetter, 1985). Such repetitive business relations may include increased relational 
ties to a particular group of actors in the marketplace. Uzzi (1996, pp. 682) found that, 
although embedding in market relations has positive effects, it reaches a threshold and 
returns negative effects. He argues that “a crucial implication is that embedded networks 
offer a competitive form of organizing but possess their own pitfalls because an actor's 
adaptive capacity is determined by a web of ties, some of which lie beyond his or her 
direct influence. Thus a firm's structural location, although not fully constraining, can 
significantly blind it to the important effects of the larger network structure, namely its 
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contacts' contacts”. Uzzi and Lancaster (2003) have also found that, when firms are linked 
by embedded ties, they are more likely to engage in exploratory learning behaviour. This 
behaviour can have dire consequences for small businesses that have limited resources and 
legitimation; and hence if such explorations do not yield positive outcomes it can lead to 
their failure.  
 
Secondly, the study discussed the lost transaction cost benefits as a result of increased 
embeddedness in distribution networks. Transaction cost economics (TCE) suggests that 
different agents engage in economic exchanges for mutual benefit (Williamson, 2005); and 
some of these benefits include lower costs of doing business as a result of trust. Hence in 
that sense, TCE is a relational theory (Borgatti & Foster, 2003). Thus, for example, 
companies that are embedded in high-closure networks may have better functioning 
alliances with lower costs (Zaheer et al., 2010). However, this view would suggest that all 
the parties, herein, the distributors and SMEs are all honest but this is not likely; leading to 
the ‘dishonest’ participant problem. More intuitively, this problem is likely to occur 
because both sets of actors are competing in a sense for a greater share of the consumers 
‘wallet’. This is because both sets want to make a bigger margin from every transaction on 
a particular product. Consider that the study have a fixed price for the SMEs products. The 
SME will want to sell that product to the distributor as close to that fixed price as possible 
while the distributor attempts to pay as low as possible. Once such self-interest arises the 
potential for greediness becomes higher and one party loses out. This, the study argue, will 
be against the SME in line with Marxist capital formation theory (Marx, 1986). 
Consequently, the policing and enforcement costs that TCE argues will vanish if they have 
a good relationship does not accrue to the SME (Williamson, 1981). This is especially true 
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if the study consider that it takes 710 days, 23% of claim and 38 procedures to enforce 
contracts in Ghana (World Bank, 2014).  
 
Also, there is the manner in which the whole relationship with distributors is managed. 
SME failure is not just a function of managerial or firm actions alone but knock-down 
effects from the actions of other firms (Ropega, 2011) and these include the SMEs 
distributors. Ropega (2011, pp. 479) further notes that: ‘distribution is an area that shapes 
the company’s position in the market. The fundamental mistake in this area is 
inappropriate deals to market needs. This reason is often combined with a lack of or 
inappropriate price policy’. This view suggests SMEs are quick to create market ties 
without reflecting the market needs of the business and how to extract those needs from 
the distribution network. Consequently, setting-up deals that will prove detrimental to the 
existence of the SME.  
 
These factors discussed above can greatly determine the growth and survival chances of an 
SME or otherwise. For an SME to access these benefits associated with distributors, the 
SME will needs to have ties with the distributors in the value chain. These benefits are not 
costs free as they are an indication of the SMEs dependence on the distribution network 
and its distributors. The level of dependence of an actor on the others will determine 
whether it’s controlled by the other or vice versa. The study suggests from the above 
discussion that SMEs are more dependent on distributors. This is because these firms are 
usually small, lack resources and cannot control the relationship. Hence increases in these 
ties will only compound the problems for the SME. Consequently, the study hypothesizes 
that: 
 
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H1: An increase in SME distribution ties is associated with a decrease in probability of 
survival  
 
2.2.2 Distribution Ties, SME Characteristics and Survival 
The question the study sought to answer here is whether variations in the number of 
distribution ties of an SME with regard to its characteristics matters for its survival. The 
study used the organisational ecology theory to explain the impact of SME level 
characteristics and focused on three ecological variables of age of SME (age dependence 
hypothesis), size of the SME (liability of size), and specialisation (resource partition) 
(Hannan & Freeman, 1993). The age of the SME influences the survival of the SME either 
through the liability of newness or the liability of obsolescence. The newness argument 
suggests that competences for survival are built over time and hence in the period where a 
new SME lacks these competences then its survival chances are limited (Thornhill & 
Amit, 2003). Similarly, learning reduces the likelihood of exit because the SME becomes 
dependable in the eyes of its stakeholders who continue to give it the required legitimacy 
to exist. The liability of obsolescence idea also indicates that, over time, SMEs become 
misaligned and have difficulty adapting to the environment (Carroll & Khessina, 2005; 
Hannan, 2005); there is sometimes challenges with succession among owner-managed 
SMEs (Perez et al., 2004); and fixed costs invariability (Baum & Mezias, 1992) lead to 
failure. The study therefore posit that the older an enterprise gets the more distributors it’s 
likely to have as a result of assumed growth of the enterprise with age. Hence, the 
problems associated with increased distribution ties will be adverse for older firms than for 
younger SMEs. Consequently, the study hypothesizes that: 
 
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H2a: An increase in the age of the SME is associated with a decrease in survival 
probability 
H2b: The interaction between SME age and distribution ties is associated with decreased 
survival probability for older SMEs 
 
The study now considers whether the SME is a specialist or generalist. This relates to the 
resources required for the SME to function and is known as resource partition (Carroll, 
Dobrev, & Swaminathan, 2002). This concept argues that SMEs survive based on the 
segments of a market that an SME occupies (Carroll & William, 2004). The concept notes 
that SMEs occupy two broad segments that also determine their resource usage. There are 
specialists that occupy homogeneous segments of the market and survive within limited 
range of resources. There is also generalist that occupies broad segments of the market and 
requires large resources to survive. Since generalists require a lot more resources from the 
environment including distributional stakeholders; the study suggests that the negative 
effects of increased distribution ties will be more intense for generalists as compared to 
specialists. The study therefore hypothesizes that: 
 
H3a: SMEs that specialize in a specific segment of the industry have a higher survival 
probability compared to generalists 
H3b: High distribution ties for generalist SMEs are associated with lesser survival 
probability compared to specialist SMEs 
 
Size of the SME is an important ecological variable with relevant implications for its 
survival. The idea suggests that organizational failure is linked to the size of the 
organization (Azoulay & Shane, 2001; Sarkar et al., 2006; Box, 2007; Gassmann et al., 
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2010; Burger & Owens, 2013). The bigger the size of the firm the less likely it is that the 
firm will fail. The reasons are that small firms lack economies of scale, do not have inertia 
abilities to withstand environmental shocks, lack external legitimization and find it 
difficult accessing resources ( Sarkar et al., 2006; Gassmann et al., 2010) . The study 
therefore offer that the bigger an SME gets the better it becomes at managing its 
relationships with distributors and can extract value from the relationship as a result of its 
inertia withstanding capabilities. The study therefore hypothesizes as follows: 
 
H4a: The larger an SME, the more likely it is to survive 
H4b: The interaction between SME size and distribution ties is associated with decreased 
survival probability for smaller SMEs 
 
2.3 Research Methods 
2.3.1 The Research Setting 
The poultry sector in Ghana is one of the few sectors that have faced the harsh realities of 
trade liberalization. This has led to the collapse and below-capacity operation of many of 
the poultry clusters in the country before the mid-1980s to early 1990s (FAO, 2014). 
Compared to cocoa, maize or rice, the poultry sector does not feature prominently in 
economic planning or agricultural policy and programme documents. Where it is referred 
to, the proposed measures appear to lack either purpose, focus or consistency, and it is 
difficult to discern a specific policy approach or strategy towards the poultry sector 
(Sumberg, Awo, Fiankor, Kwadzo & Thompson, 2013). The study researches the poultry 
sector in three districts of the Brong Ahafo region: Dormaa Central (Dormaa Ahenkro); 
Dormaa West (Nkrankwanta); and Dormaa East (Wamfie). This cluster is popularly 
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known in the agriculture literature as the Sunyani/Dormaa cluster (Mensah-Bonsu & Rich, 
2010). At the moment the poultry industry, specifically table egg production, is operating 
at a large-scale level. Poultry production in this cluster is one of the largest in the region 
and nationwide. Some of the major challenges encountered by the poultry farmers 
included: financing; diseases; and absence of electricity for operations in most farms. 
Meanwhile the presence of feed processing mills, poultry input shops and availability of 
organised markets served as prospects that could be harnessed to boost the growth of the 
poultry industry in the district (Adei & Asante, 2012). The area seems to have formed 
strong collaborative partnerships with Foani Farms in La Cote D’Ivoire to avoid 
competition that was leading to collapse of some farms. Anecdotal evidence suggests (the 
researcher observed this) some farms that collapse are helped back into business by Foani 
Farms. The cluster in recent times is one of the sectors receiving support from the Ghana 
government as part of the broader National Poultry Support Project (NPSP) announced by 
the Ministry of Trade and Industry11 and the Ghana Broiler Revitalization Project 
(GHABROP) through the Ministry of Food and Agriculture (MOFA) in a ten year 
collaboration with the Ghana National Association of Poultry Farmers12. 
  
2.3.2 Network Survey  
The study used the network survey approach to sample and collect data (Boutilier, 2009) in 
two rounds: the first in January 2014 and second in March 2015. Network analysis requires 
high rates of responsiveness (Sparrowe, Wayne, & Kraimer, 2001); therefore, a census 
approach was used to survey the poultry farmers in the three districts. To do this, a 
                                                     
11
 http://graphic.com.gh/news/general-news/22412-government-support-to-poultry-industry-
increasing.html 
12
 http://www.thepoultrysite.com/poultrynews/32740/ghanas-broiler-sector-to-get-legup-from-
revitalization-project 
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composite list of all poultry farms operating in the three districts as at January 2014 were 
compiled from the industry association (DPFA), the Ghana Revenue Authority (GRA), the 
Municipal Assemblies, Assemblymen and women in each unit area of the district and 
office of the paramount traditional chief (Omanhene). When the whole list was compiled 
and recurring farms taken care of the study had a total of 163 farms. 155 farmers agreed 
and participated in the study representing a 95.09% response rate. It is important to note 
that not all the farmers in the cluster area were included in the compiled list because some 
of the farms were not for commercial purposes; a basic requisite for participation. Such 
farms were excluded from the dataset. The average interview took between 35-45 minutes. 
Multiple name generators was used to generate the name of distributors (Rooks et al., 
2012). To ensure data integrity respondents were asked to provide evidence of a 
relationship between itself and a named distributor by providing evidence (line of credit, 
receipt copies, etc) to support the existence of a link. If none were produced, the link was 
discounted. The cohesive characteristics of the distribution networks generated for each 
year is presented in Table 2.1 below. 
 
 Table 2.1: Distribution Network Characteristics 
Measure 2014 2015 (∆2015-2014) 
Diameter 9 9 0 
Average Distance 3.934 4.409 0.475 
Fragmentation 0.009 0.015 0.006 
Density 0.044 0.030 -0.014 
Transitivity 0.380 0.392 0.012 
 
The study discusses the network characteristics in Table 2.1 above and their implications 
for the actors embedded in them below. The definitions (see in appendix) of the 
computations are based on analyses of two-mode13 data used in the UCINET (Borgatti & 
Everett, 1997; Borgatti, Everett, & Freeman, 2002). The study found that, within the 
                                                     
13
 This represents network graphs with two different classes of actors like buyers and sellers 
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period under study, there was no change in the diameter indicating that the longest number 
of walks within the network did not change. However, the size of the network is large as it 
requires nine steps to move from one end of the network to the other. There is a slight 
increase in the average distance between the various actors in the network of about 0.475. 
When the study consider the level of dyadic connection (density) between the actors the 
study also find that there was a slight decrease although the density figures across the two 
years is still low, indicative of the fact that resource and information diffusion in the 
network may be slow. The level of transitivity in the network did not change much 
hovering between 0.38 and 0.392, allowing for divide-and-rule, mediational and clustering 
behaviour in the network. The level of fragmentation remained fairly around 1% of all ties 
in both waves. 
 
2.3.3 Measures and Operationalization 
Dependent Variable: SME Survival 
The definition of the word SME is as complicated as the sector itself (Senderovitz, 2009). 
He notes that in many articles it is difficult to see whether the researcher has considered 
what a small SME actually is; whether the particular definition is appropriate, or what the 
consequences for the conclusion might be if other definitions were used. Consequently, the 
study focus on a context-based definition of SMEs that has been used by many Ghanaian 
authors using employee numbers as a basis (Kayanula & Quartey, 2000b; Yusuf & Saffu, 
2005; Hinson & Sorensen, 2006; Abor, 2007; Kyereboah-coleman & Amidu, 2008; Saffu 
et al., 2008; Abor & Quartey, 2010; Abor & Biekpe, 2012). An SME is defined as any 
business employing less than 99 people (Kayanula & Quartey, 2000b). This definition 
notes that SMEs can be divided into three broad categories: micro, small and medium with 
categorization based on micro SMEs having less than 6 employees; small SMEs having 
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less than 30 employees while medium SMEs have less than 99 employees. This definition 
no doubt has its inefficiencies (does not account for turnover, assets, capital and balance 
sheet) but so far seems accepted by Ghanaian scholars and practitioners as a fair reflection 
of the Ghanaian SME context.  
 
SME survival and failure represent two sides of the same issue. SME failure has been 
called different names in the management literature to include bankruptcy, decline, 
retrenchment, SME death, decline, downsizing and exit (Mellahi & Wilkinson, 2004). A 
failed SME is “when an SME involuntarily becomes unable to attract new debt or equity 
funding to reverse decline; consequently, it cannot continue to operate under the current 
ownership and management. Failure is the endpoint at discontinuance (bankruptcy) and 
when it is reached, operations cease and judicial proceedings take effect” (Pretorius, 2009). 
Hannan and Freeman (1986) also defined failure as “when an SME ceases to carry out 
routine actions that sustain its structure, maintains flows of resources and secure allegiance 
of its members”. This study therefore defines SME failure as when an SME can no longer 
meet the utility of its stakeholders and be operationalized using the traditional closure 
approach. In order to do this, the study compiled a list of all poultry farms operating in the 
three districts as at January 2014 for the first wave of data collection. A follow-up was 
made on the same list in March 2015. Those SMEs that had gone out of business by this 
time were deemed to have failed in this respect. SMEs that survived were coded as 1 and 
those that failed were coded as 0. At the end of the period 63 SMEs had failed while 92 
survived out of the 155 SMEs that participated in the study. See details in Table 2.2 below. 
The high mortality rate may be symptomatic of the many years of neglect as mentioned 
earlier under research setting. This is why it is imperative to understand the networking 
mechanisms that enable survival in the absence of government support (Allen et al., 2008).  
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Table 2.2: Survival Rate 
    2014 2015 
2014 
  
Survived 155 92 (59.35%) 
Failed  63 (40.65%) 
 
Variable of Interest: Distribution Ties 
The study operationalised the number of distribution ties of a given SME using the degree 
centrality of the SME in the distribution network generated from the network survey. 
Degree refers to the local connectedness to the actor in the network at distance one 
(Borgatti, 2005). The degree centrality of a given farm is the number of ties it has in a one-
mode network14 (Freeman, 1979). However, the distribution network is a two-mode 
network consisting of two sets of actors: SMEs and their distributors. Consequently, the 
study adopt the approach of Borgatti and Halgin (2011) in analysing affiliation networks 
and deriving the degree centrality (DC) of a given SME by specifying degree centrality as: 
 
DC = ∑DN: i -> j,  i ∈ V1 and j ∈ V2 
    
where DN is the distribution network; i is a given SME; j is a given distributor; V1 is the 
vector containing the list of SMEs and V2 is the vector containing the list of distributors.  
  
 Interaction Variables 
The study also moderated for the interaction effects between distribution ties and 
organisational ecology variables such as size of SME, age of SME and resource partition. 
Size of SME was operationalized as the number of employees the SME has; age of SME 
was operationalised as the number of years the SME has been operational while resource 
partition was operationalised as whether an SME was a specialist (producing broilers or 
                                                     
14
 A network involving one-set of actors; either sellers or buyers alone 
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layers only) and coded as 0 and if it did both generalist and coded as 1. To create a given 
interaction effect the study followed the approach suggested by Balli and Sørensen (2012) 
and specify it as follows:  
 
I = (dti – dtm)(mi – mm) 
 
where I is the demeaned interaction effect; dti is the distribution ties of a given SME; dtm is 
the mean of distribution ties;  mi is a given moderator variable and mm is the mean of that 
moderator. 
 
Covariates 
The study controlled for owner characteristics such as education of owner, experience of 
owner and gender of the owner. Education provides formal training and experience 
provides practical knowledge of the industry (Patzelt et al., 2008) while the different sexes 
have been associated with different managerial styles (Park, 1996; Rand & Tarp, 2011); 
and these variables influence enterprise outcomes. Education was operationalized as the 
highest educational attainment of the owner. Owners with no education were coded as 1, 
those with informal education were coded as 2, those with primary education as 3, 
secondary education as 4 and tertiary education as 5. Experience was operationalised as 
the number of years of industry experience of the owner. Gender was operationalised as a 
male-dummy with male-owned businesses coded as 1 and female ones 0. The study also 
controlled for the effect of firm level competencies such as market orientation, 
entrepreneurial orientation, absorptive capacity and dynamic capability. Market orientation 
involves issues relating to intelligence generation, dissemination and market response in 
the light of the intelligence (Kohli, Jaworski, & Kumar, 1993) and was operationalised as 
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having customer meetings, having informal discussions to resolve customer issues and 
review of marketing plans. Entrepreneurial orientation was operationalised as the risk-
taking, proactive and innovation tendencies of the organisational culture (Altinay & Wang, 
2011). Absorptive capacity concerns issues relating to the acquisition, assimilation, 
transformation and exploitation of resources external to the firm (Flatten, Engelen, Zahra, 
& Brettel, 2011) and was operationalised as search for industry information, application in 
problem solving and the adoption of new methods. Dynamic capability also relates to 
issues such as coordination of enterprise activities, learning and competitive strategic 
response (Protogerou, Caloghirou, & Lioukas, 2011); and was operationalised as business 
planning, on-the-job training and industry benchmarking. The study also controlled for the 
perceived effects of the investment climate constraints such as cost and access to finance, 
land access, access to electricity, inflation and quality of labour. These managerial and 
investment climate operationalisations were measured on a Likert scale from 1 (the least) 
and 7 (the highest) as perceptions of owners on the performance of their enterprises in 
relation to these variables. The operationalisations of each construct was added and 
standardized for the construct. 
 
2.3.4 Model Specification and Estimation  
The study model SME survival by specifying a probit model of a form: 
 
P(ESt+1=1) = Φ (β0 + β1SMEt + β2OCt + β3MCOMPt + β4ICCt + β5Mt + β6It + εt)  
 
where ESt+1 indicates that an SME survives from period t (2014) to t+1 (2015); SMEt is 
the SME characteristics; OCt is represents the owner characteristics; MCOMPt is a vector 
containing the managerial capabilities and orientations; ICCt represents the perceived 
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impact of investment climate constraints on SME operations; Mt is the effect of the 
variable of interest distribution ties; It is a vector carrying the effects of the interaction 
terms of distribution ties and SME characteristics; εt is the statistical noise and Φ is the 
cumulative distribution function of the standard normal distribution. The magnitude of the 
coefficients of the probit model cannot be interpreted and hence the study also estimated 
for the average marginal effects (AME) of the model which can be interpreted. The study 
model the average marginal effects of the study probit using the following function: 
 
∂ES/∂Vi = βi Φ (β0 + β1Vt + εt) 
 
where ∂ES/∂Vi is a partial derivative with respect to V (a given vector ) and the index i 
refers to the ith independent variable in V.  
 
After estimating the model, the study conducted some post hoc analysis to ensure that the 
model was fairly robust. First, the study employed the hierarchical regression approach to 
specify three models: a control model containing only the covariates; a second model 
where the study added the main effect of distribution ties; and a third that the study added 
the interaction terms. This enabled us to check if the study main effect is robust to different 
specifications. Secondly, the study specifies models that are robust to heteroskedasticity. 
Thirdly, the study also uses two different estimators (the logit and the ordinary least 
squares (OLS)) to model the effects of distribution ties on survival of SMEs (see results in 
appendix). Also, the study tried to deal with possible endogeneity in the study variable of 
interest by including variables in line with existing theory relating to organisational failure 
(Mellahi & Wilkinson, 2004). Finally, on possible selection problems the Food and 
Agriculture Organisation has a 2014 database on farms in the study area in which they 
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reported that 202 farms operate in the area (FAO, 2014). The study also had 200 farms but 
after accounting for recurring farms and same ownership the study ended up with 163 
farms of which 155 farms participated. Consequently, issues of selection may not arise. 
 
2.4 Results 
In this section the study present the results of the analysis conducted on the data collected.  
 
2.4.1 Descriptive Analysis 
Table 2.3 presents the means, standard deviations and correlation matrix of the variables 
used in the study. The mean age of any given SME is approximately 8 years while the 
mean size is also around 6 employees. The mean education is around secondary education 
for the owners’ highest educational attainment while in terms of experience the study 
found that it is around 9 years. The difference between SME age and owner experience 
may suggest a one year apprenticeship before an owner ventured out to set up his/her own 
business. The average number of distribution ties is approximately 3. The majority of the 
SMEs were specialist while most owners were men. Market orientation, entrepreneurial 
orientation, dynamic capability and absorptive capacity are standardized variables while 
interaction variables are demeaned. In terms of the correlation between the variables, the 
study observed that the least correlation of -0.71 is between SME survival and distribution 
ties while the highest correlation of 0.64 is between entrepreneurial orientation and 
absorptive capacity of the SME; yet none of these exceeds the absolute threshold of 0.8 
that is suggested to lead to multicollinearity problems (Hair, Black, Babin, & Anderson, 
2010).  
 
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In Table 2.4, the study present a cross tabulation of distribution ties, moderator variables 
and survival. The study found that 74% of the firms were specialists that focus on solely 
producing broilers or layers. 93.48% of the SMEs that survived were specialists while the 
majority of those that failed were generalists that produced both broilers and layers. The 
study also observed that about 57% of the SMEs were young firms below 8 years of age 
while, of the failed firms, approximately 65% were some of these young firms. In terms of 
size, 77% were micro firms with less than 5 employees while 23% were small and large 
firms. Considering the failed SMEs, 94% were micro-SMEs. There was a fair balance 
between those SMEs that had ties above and below the average of 3 – 51% and 49% 
respectively. However, of those that survived 78% had ties below the average while of 
those that failed 94% had ties that were above the average. 
 
 
 
 
 
  
 
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     Table 2.3: Means (M), Standard Deviations (S.D.) and Correlations 
 
 
 M S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 
Survival 0.59 0.40 1                
Age (A) 7.50 6.08 0.15 1               
Size (S) 6.35 11.20 0.23 0.42 1              
Generalist (=1) 
(G) 
0.26 0.44 -0.53 -0.20 -0.17 1             
Education 3.65 0.86 -0.23 0.17 0.12 0.03 1            
Experience 8.96 6.70 0.34 0.61 0.32 -0.30 -0.12 1           
Gender 
(Male=1) 
0.75 0.44 0.07 0.07 0.02 -0.13 -0.15 0.30 1          
Market 
Orientation 
-0.10 0.99 0.43 -0.01 0.06 -0.25 -0.23 0.22 0.22 1         
Entrepreneurial 
Orientation 
-0.10 1.00 0.51 0.13 0.38 -0.28 -0.19 0.27 0.07 0.54 1        
Absorptive 
Capacity 
-0.18 0.95 0.43 0.16 0.35 -0.22 -0.06 0.24 0.07 0.52 0.64 1       
Dynamic 
capability 
-0.12 1.02 0.55 0.11 0.29 -0.33 -0.17 0.26 0.13 0.52 0.60 0.58 1      
Investment 
Constraints 
-0.20 0.96 -0.49 0.05 0.15 0.23 0.22 -0.17 -0.20 -0.30 -0.18 -0.07 -0.31 1     
Distribution 
Ties (DT) 
3.39 1.64 -0.71 -0.12 -0.12 0.46 0.06 -0.18 0.02 -0.23 -0.29 -0.27 -0.32 0.33 1    
DT*A -1.01 11.47 -0.02 -0.46 -0.20 -0.01 -0.22 -0.21 0.09 0.08 -0.10 -0.08 0.00 0.01 0.07 1   
DT*G 0.33 0.58 0.02 0.01 0.00 0.14 -0.06 -0.03 0.00 0.02 0.00 0.13 -0.03 -0.09 -0.11 -0.18 1  
DT*S -2.02 15.85 0.03 -0.27 -0.71 -0.01 -0.16 -0.13 0.03 -0.04 -0.19 -0.18 -0.15 -0.14 -0.09 0.36 -0.11 1 
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Table 2.4: Cross Tabulation of Ties, Moderators and Survival 
 Failed (%) Survived (%) Total (%) 
Specialist 29 (43.03%) 86 (93.48 ) 115 (74.19 ) 
Generalist 34 (53.97 ) 6 (6.52 ) 40 (25.81 ) 
Younga 41 (65.08 ) 48 (52.17 ) 89 (57.42 ) 
Olda 22 (34.92 ) 44 (47.83 ) 66 (42.58 ) 
Microa 59 (93.65 ) 60 (65.22 ) 119 (76.77 ) 
Small & Largea 4 (6.35 ) 32 (34.78 ) 36 (23.23 ) 
Few Tiesa 4 (6.35 ) 72 (78.26 ) 76 (49.03 ) 
Many Tiesa 59 (93.65 ) 20 (21.74 ) 79 (50.97 ) 
aThe mean of these variables are used as thresholds for binarization 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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2.4.2 Regression Analysis 
Table 2. 5: Distribution Ties and SME Survival 
 AME (1) AME (2) AME (3) 
Age (A) 0.001 
(0.006 ) 
0.001 
(0.003 ) 
-0.004 
(0.004 ) 
Size (S) 0.017 
(0.011 ) 
0.006* 
(0.004 ) 
0.015*** 
(0.005 ) 
Generalist (=1) (G) -0.230*** 
(0.041 ) 
-0.138*** 
(0.030 ) 
-0.182*** 
(0.033 ) 
Education -0.019 
(0.027 ) 
-0.043*** 
(0.014 ) 
-0.048*** 
(0.013 ) 
Experience 0.012** 
(0.005 ) 
0.004 
(0.003 ) 
0.004** 
(0.002 ) 
Gender (Male=1) -0.164*** 
(0.048 ) 
-0.059** 
(0.026 ) 
-0.070*** 
(0.018 ) 
Market Orientation 0.007 
(0.029 ) 
0.016 
(0.011 ) 
0.028*** 
(0.010 ) 
Entrepreneurial 
Orientation 
0.075** 
(0.029 ) 
0.047** 
(0.020 ) 
0.033 
(0.021 ) 
Absorptive Capacity 0.108*** 
(0.032 ) 
0.082*** 
(0.021 ) 
0.067*** 
(0.018 ) 
Dynamic capability 0.041 
(0.029 ) 
0.034** 
(0.017 ) 
0.018 
(0.015 ) 
Investment 
Constraints 
-0.243*** 
(0.032 ) 
-0.127*** 
(0.029 ) 
-0.107*** 
(0.025 ) 
Distribution Ties 
(DT) 
 -0.059*** 
(0.006 ) 
-0.060*** 
(0.011 ) 
DT*A   -0.004** 
(0.002 ) 
DT*G   -0.082*** 
(0.028 ) 
DT*S   0.004 
(0.005 ) 
    
Wald 56.98*** 56.98*** 57.77*** 
Pseudo R2 0.696  0.880 0.902 
Observations 155 155 155 
+Standard errors in parentheses are robust to heteroskedasticity 
++Significance Levels: 1%***; 5%**; 10%* 
 
In Table 5, the study presents the econometric analysis. The study report the probit 
estimates and their corresponding average marginal effects (AMEs) and since the 
interpretation of the betas of the probit models (see in Appendix) present some challenges 
the study focussed on the AMEs that are reported. The study starts by reporting the fitness 
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of the study model to the data. All the study three probit models show a significant Wald 
statistic of 56.98*** for column 1; 56.98*** for column 2 and 57.77*** for column 3. The 
Pseudo R2 also range between 0.696 for the control model in column 1; 0.880 for the main 
model in column 2 and 0.902 for the interacted model in column 3. The study finds that 
SME size has a positive and significant effect (0.006 and 0.015) on survival in columns 2 
and 3. Generalists have a negative and significant possibility of survival across all the 
models (-0.230, -0.138 and -0.182) while education has a negative and significant effect (-
0.043 and -0.048) in columns 2 and 3 and experience of the owner is positive and 
significant only in column 1. Male-owned have a negative survival probability (-0.164, -
0.059 and -0.070) and this is well determined in all the models. The effect of market 
orientation is positive and significant only in column 3 with an estimate of 0.028 while 
entrepreneurial orientation is positive and significant (0.075 and 0.047) in columns 1 and 
2. The study also observe that absorptive capacity has a positive effect (0.108, 0.082 and 
0.067) on survival of SMEs and is well determined while dynamic capability is positive 
and significant only in column 2 with an estimate of 0.034. Investment climate constraints 
are also seen to have a negative and significant effect (-0.243, -0.127 and -0.107) on 
survival probability. Turning to the study hypothesized variables the study find that 
distribution ties have a negative and significant effect on survival and the effect (-0.059 
and -0.060) is robust to the introduction of interaction terms. The study also observes that 
the interaction effect between distribution ties and age of SME is negative and significant 
with an estimate of -0.004; the interaction effect between distribution ties and resource 
usage is negative and significant with an estimate of -0.082. The interaction effect of SME 
size and distribution ties is positive but does not rise to significance. 
 
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2.5 Discussion of Results 
The assertion in small enterprise development is that increases in market access are 
associated with positive outcomes as they present opportunities for SMEs to sell their 
products. The study tested this assertion by constructing SME access to the market as a 
function of the number of relational ties it has with distributors in the market place and its 
impact on the survival of these enterprises. The study adopted this approach mainly 
because there are significant costs in transporting, warehousing and marketing to the final 
consumer and since SMEs are small and have limited resources using distributors proved 
intuitive. The study used primary data from two rounds of data collection from the poultry 
industry in three districts of rural mid-western Ghana. The purpose is to control the 
boundaries of the network while the cluster is one of the best performing in the country 
(FAO, 2014). The study also tested for the interaction effects of organisational ecology 
effects (age dependence, resource partition and liability of size) on this relationship 
between distribution ties and survival outcomes while controlling for investment climate 
constraints, owner characteristics and managerial competencies. The study estimated the 
study specified models using a cross-sectional probit model. In this section the study 
present a discussion of findings based on column six in table five in the previous section 
since it has a higher pseudo r-square compared to the other models and presents some 
implications for enterprise development. 
 
The study first discussed the effects of the control variables on the study outcome variable. 
The study included owner characteristic variables such as owner education, experience and 
gender in line with upper echelon theory (Hambrick & Mason, 1984; Hambrick, 2007). 
The study found that education and gender operationalised as a male dummy had a 
negative and significant effect on survival probability while experience of owner is 
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positive but does not rise to significance. Existing literature has tended to imply that 
female owners are discriminated against in business environments due to socio-cultural 
reasons (Brush, Bruin, & Welter, 2009); however, the study found the opposite in that 
male-business are failing. The study finding is in-line with the other findings that are 
beginning to show that female entrepreneurs are either favoured or only capable females 
are selected into entrepreneurship and hence are able to compete (Hansen & Rand, 2014a, 
2014b). The study also find that education has a negative effect on survival and is contrary 
to earlier findings (Jo & Lee, 1996). However, contextually this may be the case as the 
Ghanaian grammar educational system trains students to look for jobs in the formal sector 
(Buame, 1996) consequently it is those without such opportunities that engage in 
entrepreneurship (Acheampong & Esposito, 2014) for survival. Hence, most people re-
select into formal employment once their educational attainment offers them such 
opportunities and with it the failure of their enterprises. The study also controlled for 
managerial competencies in line with management theory (Mellahi & Wilkinson, 2004; 
Miles, 2012). The study controlled for competencies such as market orientation, 
entrepreneurial orientation, dynamic capabilities and absorptive capacity. The study find 
that market orientation and absorptive capacity have a positive and significant effect on 
survival. This is intuitive as managerial competencies are to help manage the enterprise in 
ways that helps mitigate the negative effects of environmental conditions on the firm 
(Lane, Salk, & Lyles, 2001; Ropega, 2011; Mahmoud & Hinson, 2012). The study also 
controlled for the investment climate constraints and found a negative effect on survival of 
the SME. This finding is in line with existing empirical findings that suggests that the 
investment climate in Africa imposes indirect costs on businesses (Eifert et al., 2008).  
 
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The study turn to the study hypothesized effects. The study hypothesized that a unit 
increase in the distribution ties of an SME is associated with a reduced survival probability 
and confirm the study hypothesis. This is likely because increasing network relations with 
distributors can also significantly lead to loss of other significant resources from other 
networks (Uzzi, 1996) like finance and supply networks. Another reason could be that 
increased embeddedness leads to exploratory learning behaviour (Uzzi & Lancaster, 
2003). Such behaviours exposes the SME to the flexibility problem and makes it 
susceptible to failure (Hiebl, 2013). Also, the reduced transaction costs associated with 
embedding in networks may not have been realised as a result of the fact that both SMEs 
as producers and distributors as agents in the marketplace compete indirectly for a share of 
the consumers’ ‘wallet’. Since SMEs are small and contextually have perishable products 
the distributors have the upper hand (Marx, 1986). Consequently, such competition will 
leave the SME still policing its transactions with distributors in line with TCE theory 
(Williamson, 1979). Also, SMEs sometimes simply engage in bad deals in attempt to 
market and distribute their products leading to the failure of the businesses (Ropega, 
2011). The study also explored the interaction effect of organisational ecology variables of 
age, size and resource partition on the above relationship. The age of the SME has a 
negative association with the survival of SME but did not rise to significance. However, 
the moderation of age and distribution ties had a negative and significant effect on 
survival. The study suspects that this may be due to the fact that, as SMEs get older, they 
develop many competencies such as building distribution ties. Age in this sense facilitates 
the acquisition of distribution ties and attendant with its negative effects on survival.  
Generalists in resource partition theory are seen to have a higher probability of failure as a 
result of their broad range of resource requirements from the environment (Carroll & 
William, 2004). This high resource requirement will in turn lead to the acquisition of many 
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distribution ties that leads to intense negative effects of distribution ties. The study also 
found that size has a positive effect on survival. This is in line with existing literature that 
suggests that large SMEs are able to withstand external shocks, benefit from economies of 
scale and gain legitimacy ( Sarkar et al., 2006; Enkel, Gassmann, & Chesbrough, 2009). 
However, although the interaction effect with distribution ties is positive it does not rise to 
significance.    
 
The study now discusses some enterprise management and research implications of the 
study findings. The study starts by suggesting two points for enterprise management. First 
the study suggest, based on the negative effects of distribution ties on survival and 
negative effects of the moderation of age of SME and distribution ties on survival, that 
SMEs engage in a process of backward integration in the value chain as they age. The 
process helps cut-off distributors thereby still having access to the market but through the 
enterprises’ own market infrastructure. The benefit is that the margins of the distributors 
will then accrue to the enterprise in the long run. The costs of accessing the market directly 
can be daunting for small businesses; in that case, collaborations among small clusters of 
firms to share such costs can be useful. Currently, the Unity Group15 has provided such a 
model that may be worth emulating. Secondly, the study suggest, based on the findings of 
the generalist and its interaction with distribution ties, that SMEs specialise in a particular 
niche of the market, limit their resource requirement, and hence improve their survival 
probability. There are a few cautions in interpreting the study findings. First, the study 
moderated effects are robust to the logit estimator but not to the ordinary least squares 
(OLS) estimator (see in Appendix) although the study variable of interest is robust to them 
                                                     
15
 Unity Brothers constitute 7 farms owned by individuals but have familial relations that collaborate in 
transporting their produce from their farms to major trading centres in Accra directly without middlemen. 
The researcher observed that they had stable and bigger farms relative to the other farms. 
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all. Secondly, the study use cross-sectional data. The study encourage other studies to use 
panel data to validate the results from this cross-sectional data. Also, the study study 
focuses on agricultural SMEs. New studies can also focus on new sectors as well as still 
study the agricultural sector but in different geographical jurisdictions. 
 
2.6 Conclusions 
The overall aim of this article is to come to grips with the effect of SME distributional ties 
on its survival. The study find that increases in distributional ties have a negative effect on 
the survival chances of SMEs. The study also sought to understand the moderation effects 
of organisational ecology variables such as age, size and resource utilization on the 
distribution ties and survival relationship. The study found that age and resource utilization 
have a negative moderation effect on the survival of SMEs while size is positive but does 
not rise to significance. Consequently, the study conclude that increasing SMEs’ access 
through distribution ties is associated with a higher risk of failure and older firms and 
generalists who use broad resources from the environment face a much higher risk.  
 
 
 
 
 
 
 
 
 
 
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APPENDIX 
Table 2A1: Ghana Poultry Sector Statistics 
National  Brong Ahafo Region 
Region No. of Birds 
(%) 
Egg 
Production (in 
millions) 
No. of 
Farms+ 
District No. of 
Farms+ 
Greater 
Accra 
2,547,219 (7.02%) 542 475 Dormaa 202 
Central  903,702 (2.49%) 437 312 Jaman South 9 
Western 1,406,642 (3.88%) 247 102 Berekum 34 
Eastern 3,886,914 
(10.72%) 
358 213 Sunyani 65 
Volta 1,071,622 (2.95%) 68 98 Techiman 25 
Ashanti 10,180,760 
(28.07%) 
5,321 697 Tano South 16 
Brong-
Ahafo 
10,743,897 
(29.62%) 
3,989 510 Nkoranza 32 
Northern 3,625,149 (9.99%) - 30 Kintampo 
South 
1 
Upper East 1,325,835 (3.66%) - 33 Asunafo 
North 
17 
Upper West 579,474 (1.60%) - 34 Asunafo 
South 
0 
    Asutifi 10 
    Wenchi 9 
    Sunyani West 90 
+Based on FAO Sector 1/2/3 Classification 
Sources: Veterinary Services Directorate (2010, 2011, 2013) in FAO, 2014 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table 2A2: Probit Estimates 
 Probit (1) Probit (2) Probit (3) 
Age (A) 0.011 
(0.050 ) 
0.027 
(0.063 ) 
-0.096 
(0.116 ) 
Size (S) 0.150 
(0.099 ) 
0.142 
(0.092 ) 
0.404** 
(0.164 ) 
Generalist (=1) (G) -2.038*** 
(0.463 ) 
-3.100*** 
(0.933 ) 
-4.869*** 
(1.332 ) 
Education -0.170 
(0.238 ) 
-0.975** 
(0.434 ) 
-1.284** 
(0.584 ) 
Experience 0.104** 
(0.045 ) 
0.095 
(0.074 ) 
0.119* 
(0.069 ) 
Gender (Male=1) -1.450*** 
(0.440 ) 
-1.338* 
(0.718 ) 
-1.872*** 
(0.647 ) 
Market Orientation 0.064 
(0.253 ) 
0.368 
(0.273 ) 
0.758** 
(0.323 ) 
Entrepreneurial 
Orientation 
0.666** 
(0.272 ) 
1.060** 
(0.534 ) 
0.885 
(0.664 ) 
Absorptive Capacity 0.959*** 
(0.293 ) 
1.842*** 
(0.707 ) 
1.800** 
(0.722 ) 
Dynamic capability 0.365 
(0.253 ) 
0.766** 
(0.336 ) 
0.476 
(0.372 ) 
Investment 
Constraints 
-2.155*** 
(0.399 ) 
-2.863*** 
(0.935 ) 
-2.865*** 
(0.783 ) 
Distribution Ties 
(DT) 
 -1.334*** 
(0.351 ) 
-1.615*** 
(0.383 ) 
DT*A    -0.105* 
(0.055 ) 
DT*G    -2.207** 
(0.804 ) 
DT*S    0.096 
(0.136 ) 
     
Wald 56.98*** 56.98*** 57.77*** 
Pseudo R2 0.696  0.880 0.902 
Observations 155 155 155 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table 2A3: Logit and Average Marginal Effects  
 Logit AME Logit AME Logit AME 
Age (A) 0.014 
(0.090 ) 
0.001 
(0.006) 
0.045 
(0.116 ) 
0.001 
(0.003 ) 
-0.229 
(0.262 ) 
-0.004 
(0.003) 
Size (S) 0.264 
(0.210 ) 
0.018 
(0.013 ) 
0.304 
(0.257 ) 
0.007 
(0.005 ) 
0.864 
(0.556 ) 
0.016*** 
(0.004) 
Generalist (=1) 
(G) 
-3.521*** 
(0.963 ) 
-0.235*** 
(0.050 ) 
-5.834** 
(2.792 ) 
-0.141*** 
(0.043) 
-9.797** 
(4.980 ) 
-0.187*** 
(0.031 ) 
Education -0.279 
(0.468 ) 
-0.019 
(0.031 ) 
-1.894* 
(1.126 ) 
-0.046*** 
(0.017 ) 
-2.746 
(1.970 ) 
-0.052*** 
(0.014 ) 
Experience 0.172** 
(0.077 ) 
0.011** 
(0.005 ) 
0.159 
(0.131 ) 
0.004 
(0.003 ) 
0.196* 
(0.110 ) 
0.004 
(0.003 ) 
Gender 
(Male=1) 
-2.378*** 
(0.765 ) 
-0.159*** 
(0.050 ) 
-2.666 
(1.775 ) 
-0.064** 
(0.031 ) 
-3.707** 
(1.868 ) 
-0.071*** 
(0.016 ) 
Market 
Orientation 
0.133 
(0.530 ) 
0.009 
(0.035 ) 
0.719 
(0.601 ) 
0.017 
(0.012 ) 
1.477* 
(0.801 ) 
0.028*** 
(0.010) 
Entrepreneurial 
Orientation 
1.102** 
(0.526 ) 
0.074** 
(0.035 ) 
2.189 
(1.714 ) 
0.053 
(0.033 ) 
2.140 
(2.310 ) 
0.041 
(0.027 ) 
Absorptive 
Capacity 
1.589*** 
(0.571 ) 
0.106*** 
(0.039) 
3.561* 
(2.044 ) 
0.086*** 
(0.032 ) 
3.727 
(2.362 ) 
0.071*** 
(0.016 ) 
Dynamic 
capability 
0.613 
(0.454 ) 
0.041 
(0.031 ) 
1.220** 
(0.607 ) 
0.029* 
(0.018 ) 
0.823 
(0.616) 
0.016 
(0.014 ) 
Investment 
Constraints 
-3.658*** 
(0.737 ) 
-0.245*** 
(0.035 ) 
-5.409** 
(2.725 ) 
-0.130*** 
(0.040 ) 
-5.529** 
(2.600 ) 
-0.105*** 
(0.022 ) 
Distribution 
Ties (DT) 
  -2.514** 
(1.180 ) 
-0.061*** 
(0.016) 
-3.039** 
(1.253 ) 
-0.058*** 
(0.011 ) 
DT*A     -0.225 
(0.149 ) 
-0.004*** 
(0.002 ) 
DT*G     -4.525* 
(2.476 ) 
-0.086*** 
(0.024 ) 
DT*S     0.287 
(0.389 ) 
0.005 
(0.005 ) 
       
Wald 52.06***  22.59***  33.94***  
Pseudo-R2 0.691  0.881  0.906  
Observations 155 155 155 155 155 155 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table 2A4: OLS Estimates 
 Control Model +Main Effect +Interaction Effects 
Age (A) -0.001 
(0.006 ) 
-0.002 
(0.005 ) 
-0.001 
(0.005 ) 
Size (S) 0.004 
(0.003 ) 
0.004* 
(0.002 ) 
0.008*** 
(0.002 ) 
Generalist (=1) (G) -0.356*** 
(0.074 ) 
-0.182*** 
(0.063 ) 
-0.171** 
(0.066 ) 
Education -0.059* 
(0.031 ) 
-0.059** 
(0.029 ) 
-0.055* 
(0.030 ) 
Experience 0.006 
(0.005 ) 
0.006 
(0.005 ) 
0.005 
(0.005 ) 
Gender (Male=1) -0.146** 
(0.065 ) 
-0.081 
(0.056 ) 
-0.086 
(0.056 ) 
Market Orientation 0.018 
(0.044 ) 
0.024 
(0.032 ) 
0.031 
(0.033) 
Entrepreneurial 
Orientation 
0.060 
(0.043 ) 
0.050 
(0.034 ) 
0.044 
(0.035 ) 
Absorptive Capacity 0.055 
(0.049 ) 
0.025 
(0.034 ) 
0.024 
(0.036 ) 
Dynamic capability 0.071** 
(0.034 ) 
0.062** 
(0.029 ) 
0.062** 
(0.030 ) 
Investment Constraints -0.171*** 
(0.029 ) 
-0.113*** 
(0.028 ) 
-0.117*** 
(0.028 ) 
Distribution Ties (DT)  -0.130*** 
(0.016 ) 
-0.125*** 
(0.016 ) 
DT*A   0.000 
(0.002 ) 
DT*G   -0.011 
(0.044 ) 
DT*S   0.004*** 
(0.001 ) 
    
F-Stat 51.93*** 61.28*** 53.55*** 
R2 0.599 .0728 0.736 
Observations 155 155 155 
 
 
 
 
 
 
 
 
 
 
 
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CHAPTER THREE 
FINANCE NETWORK CAPITAL, CREDIT CONSTRAINT AND SME SURVIVAL 
 
Abstract 
The aim of this study is to understand the usefulness of financial network capital to the 
survival of SMEs in Ghana. The study also explored the association between financial 
institution class ties and SME survival. The study used data from a network survey with 
associated attribute data on poultry SMEs in Ghana. The study found that the survival rate 
of SMEs is approximately sixty percent. The study also found that the prestige of 
financiers that an SME has is positively associated with the survival chances of that SME. 
Furthermore, SMEs with ties to universal banks and cooperative credit unions have a 
better survival chance while those with ties to savings and loans companies have a reduced 
survival probability. The value of the study is in its use of relational data in predicting the 
survival chances of SMEs in a developing economy. 
 
Keywords: Finance Networks, Social Capital, SMEs, Survival, Access to Finance, Ghana 
 
3.1 Introduction 
Access to finance remains one of the major reasons for the failure of small and medium 
scale enterprises (SMEs)16 in Ghana ( Kayanula & Quartey, 2000b; Abor & Quartey, 2010; 
Abor & Biekpe, 2012). The challenge with finance has to do with the lack of availability 
of finance and when available the cost of the finance. Some of the reasons that have been 
alluded to for banks not lending to the SME sector is poor financial management skills and 
                                                     
16
 Businesses employing less than 99 employees in Ghana 
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high risk of default thresholds (Okpara, 2011). If SMEs are to survive and grow then 
liquidity remains one of the issues that need to be tackled. Without finance, SMEs cannot 
acquire or absorb new technologies nor can they expand to compete on the global market 
or even strike business linkages with larger firms. However, the reasons for banks 
unwillingness to lend to the sector is very institutional and can be resolved in the long-term 
(Beck & Demirguc-Kunt, 2006). For instance, the high risk thresholds of SMEs are 
indicative of the poor identification and addressing systems in the country. Again, the poor 
financial management skills are symptomatic of the underlying educational systems 
(Buame, 1996). Most of these challenges can only be addressed in the long-term. In the 
short-term, however, the SMEs need to survive these binding constraints. Some authors 
have proposed that SMEs develop their social capital in financial networks by developing 
strong relationships with their bankers (Hernández-Cánovas & Martínez-Solano, 2007; 
Akuetteh, 2009).  
 
Social capital theory suggests that relations of actors in a network can lead to embedded 
resources that can be mobilized for purposive action (Lin, 1999). These resources can be 
structural or relational. Structural resources represent the opportunities that are made 
available to an actor by virtue of its location in a network or the nature of its surrounding 
environment in the network (Borgatti & Foster, 2003; Burt, 2005; Kim & Skvoretz, 2012). 
In this study, the study sought to explore if financial network capital is useful for SME 
survival in Ghana. The study focused on two structural social capital factors. These are 
closeness to financial institutions, prestige of financiers and the financial institution’s ties 
that the SMEs have. Closeness to financial institutions considers how close an SME is to 
all the stakeholders in the financial network. It enables the SME to engage in direct 
bargaining with financial institutions, gain access to information about credit opportunities 
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in the network as well as gain the trust of the financiers in a sector where the default rate is 
believed anecdotally to be high and a main reason why financial institutions do not want to 
lend to the sector (Lin, 1999; Pfeffer & Salancik, 2003; Hanneman & Riddle, 2005). The 
prestige of the financier refers to how popular a financial institution is to SMEs in the 
financial network. An SME with connections to these important financiers is also in a very 
prestigious position. This can be useful in achieving SME survival since the SME focuses 
on a few relevant financiers that can offer it the required resources needed for functioning. 
It can also enable it to send signals about their attributes to other firms that may not be 
embedded in the network who have no means of evaluating the quality of those attributes 
(Zaheer et al., 2010). Signals may also help to reinforce beliefs about those attributes and 
continued access to resources within and without the network (Lin, 1999). Consequently, 
the study explore if social capital embedded in financial constrained networks is useful for 
SME survival in Ghana. The study also asks if ties to certain classes of financial 
institutions can have an impact on an SME’s survival. Although this is not structural it’s 
very important within the context of constrained financial networks. This is because 
Berger and Udell (2002) found that small and local banks are more likely to lend to SMEs 
because they are better suited to engage in relationship lending as a result of loan officer’s 
‘soft’ information about the SME applicants. Beck, Demirgüç-Kunt, and Maksimovic 
(2008) also suggest that large banks are better suited at financing SMEs through arm’s 
length mechanisms (Uzzi & Lancaster, 2003) such as fixed-asset lending and credit 
scoring. In Ghana and specifically in the study area five broad classes of financial 
institutions exist, namely: universal banks; cooperative credit unions; rural and community 
banks,; savings and loans; as well as microfinance firms.  
 
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The study seek answer to these questions within the Ghanaian poultry sector focusing on 
the Dormaa Ahenkro cluster as a critical case. The study collected financial network data 
using multiple name generators (Rooks et al., 2012) and attribute data on 155 poultry 
SMEs in the cluster. The data was collected in two waves – the first in January 2014 and 
second in March 2015. The study then modelled survival from 2014 to 2015 with a probit 
model with lagged independent variables. The study found that approximately 40% of the 
SMEs failed within the period under study. The study also found that credit is a binding 
constraint to SME survival. Focusing on the study research questions the study found that 
prestige of financiers has a positive and significant association while closeness to financial 
institutions is not significant. The study also found that universal banks and credit unions 
have a positive and significant effect on survival while savings and loans company ties 
have a negative and significant effect on survival. Descriptively, the study also observes 
that SMEs with prestigious financiers and those with ties to universal banks were less 
likely to be credit constrained in the network. This chapter is structured as follows: section 
2 presents the theory and hypotheses; then in section 3 the study presents the research 
methods focusing on study setting, network survey, measures and model specification. 
Section 4 presents the results of the analysis while sections 5 and 6 present the discussion 
of the results and conclusions respectively. 
 
3.2 Literature Review 
In this section the study review literature on the financial constraints faced by small 
businesses, classes of financial institutions in Ghana, and financial network capital based 
on social network and social capital theories. 
 
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3.2.1 Small Business Credit Constraints 
SMEs remain significant contributors to the development efforts of developing countries 
(Abor & Quartey, 2010; Ardic, Mylenko, & Saltane, 2012) yet these businesses remain 
constrained by several factors in the business environment especially access and cost of 
finance. Access to finance has been identified as a key element for SMEs to succeed in 
their drive to build productive capacity, to compete, to create jobs and to contribute to 
poverty alleviation in developing countries (UNCTAD, 2001). Without finance, SMEs 
cannot acquire or absorb new technologies nor can they expand to compete in global 
markets or even strike business linkages with larger firms. It is in this regard that the G-20 
2010 Summit in Seoul, South Korea that the Global Partnership for Financial Inclusion 
(GPFI)17 was established to help reduce the SME financing gap as part of its aims. At the 
national level governments are undertaking broad level of policies aimed at improving 
SME access to finance. In Ghana, the government as engaged in several programmes and 
funds18 have been set-up to aid SME to access credit for various purposes. These include 
the Ghana Private Sector Development Fund (GSPDF), Skills Development Fund (SDF), 
Export Development and Agricultural Investment Fund (EDAIF), Microfinance and Small 
Loans Centre (MASLOC), Business Assistance Fund (BAF), and Funds for Small and 
Medium Scale Enterprise Development (FUSMED). Despite these programmes constraints 
continue to persist.  
 
Cetorelli (2014, pp. 1172) studied competition in credit markets using data from the United 
States and concludes that credit constraints have a significant influence on population 
dynamics of small businesses; and that these dynamics “imply significant effects on firms’ 
life expectancy profile, and these effects are heterogeneous across firms of different 
                                                     
17
 http://www.gpfi.org/ 
18
 Abor and Biekpe (2006) discuss these Ghanaian schemes in detail 
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vintage. Modifications in life expectancy are likely to affect firms’ incentives in 
undertaking future capital investment and likewise investments in technological 
innovation. Hence, these changes in overall firms’ population dynamics characterize an 
explicit mechanism through which finance can affect real economic activity”. Ma and Lin 
(2010, pp. 295) studied the aspects of SME credit during the credit crunch that affected 
their survival. They suggest that “it may not be the ‘credit crunch’ and the restriction of 
credit itself that has an impact on SME survival, but rather the consequences arising from 
the recession. The difficulties may lie with downturn in trade leading to reduction in cash 
flow and turnover, and this may be exacerbated by a slowdown in the rate of payments for 
all businesses. These aspects feed through to the higher risk premium that may be charged 
to SMEs”. Fajnzylber, Maloney, and Gabriel (2009) studied small businesses in Mexico 
and found that accessed credit from both formal and informal sources are more likely to 
survive than those who did not, although the firms that survived had lower income growth. 
This poor income growth may be attributable to the high interest rates charged on accessed 
loans. More specifically they observed that only 12% of enterprises actually sought credit 
and 90% of them received the credit; the high approval rate may underlie a self-selection 
mechanism of only those with the possibility of gaining access to credit applying. 4% 
borrowed from formal sources while 7% borrowed from informal sources. 86% of SMEs 
indicated that they did not need the credit or prefer to use their own resources while the 
rest were constrained. Ardic et al. (2012) have noted that, compared to large firms, SMEs 
are more likely to be constrained by the cost and access to finance. These constraints arise 
from a lack of collateral; difficulty in proving credit worthiness; small cash flows; 
inadequate credit history; high risk premium; under-developed bank borrower 
relationships; and high transaction costs. 
 
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Ghanaian SMEs face similar financial constraints to those in other countries discussed 
above. Tagoe, Nyarko, and Anuwa-amarh (2005) have opined that financial sector 
liberalisation has presented challenges for financing needs of SMEs in Ghana. They 
conclude that government borrowing; the general economic climate; availability of 
collateral; and SME investor relations can influence the access and cost of SME credit. In 
a follow-up study, they also showed that SMEs that have better record keeping skills had 
better access to credit than those that did not (Tagoe, Anuwa-Amarh, & Nyarko, 2008) 
since such information helped financial institutions better access their credit risk. 
However, most Ghanaian SMEs do not keep proper records and hence find it difficult to 
access credit from the Ghanaian financial system. The evidence also suggests that, for an 
SME to be able to access finance in the formal sector, it will need to provide landed 
property as collateral (Domeher, 2012) although microfinance firms can relax this rule and 
compensate for it with higher interest rates. Akuetteh (2009) studied 496 entrepreneurs in 
Ghana and reported that less than 5% of them used formal sources of credit in the line of 
their business. These, they suggest, may be due to the “discouraged borrower effects” 
resulting from an underlying self-selection mechanism. Agyei-Mensah (2010, pp. 12) has 
also concluded from a study of working capital management practices of Ghanaian 
enterprises that “possess limited formal education, weak managerial and financial 
management skills within the sector. They also lack qualified accounting staff and suitable 
accounting software which are motivators to effective working capital management 
practices. Owners/managers were found to act as barriers to efficient usage of working 
capital management practices”. The effect of this is that small business owners misapply 
funds borrowed from banks making them less attractive to banks. In the face of all these 
issues Quartey (2003) implies that firms that are able to access credit are more likely to 
perform better than those that do not. This may be due to the fact that such liquidity 
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enables the small business to acquire the requisite resources that maintains its 
organisational structure. Consequently, the study hypothesizes that: 
 
H1: SMEs that are credit constrained are more likely to fail 
 
3.2.2 Financial Network Capital 
Ardic et al. (2012) have noted that, compared to large firms, SMEs are more likely to be 
constrained by the cost and access to finance. These constraints arise from a lack of 
collateral, difficulty in proving credit worthiness, small cash flows, inadequate credit 
history, high risk premium, under-developed bank borrower relationships and high 
transaction costs. These are symptomatic of relationships where identification and trust 
may be a challenge. Consequently, some authors have proposed that SMEs develop their 
social capital in financial networks by developing strong relationships with their bankers ( 
(Hernández-Cánovas & Martínez-Solano, 2007; Akuetteh, 2009). Social network theory 
postulates that actors (financial institutions and SMEs) are connected by some ties 
(relationships). These ties confer opportunities and constraints that inform their social 
behaviour. Granovetter (2005) has noted that social networks affect economic outcomes in 
organisations because social networks affect the flow and the quality of information. Much 
information is subtle, nuanced and difficult to verify, so actors do not believe impersonal 
sources and instead rely on people they know. Second, social networks are an important 
source of reward and punishment, since these are often magnified in their impact when 
coming from others personally known. Third, trust, by which refers to the confidence that 
others will do the "right" thing despite a clear balance of incentives to the contrary, 
emerges, if it does, in the context of a social network. 
 
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The mechanism through which organisations can appropriate the benefits of social 
networks for organisational functional has been described as social network capital. More 
formally, it has been defined as ‘resources embedded in a social structure which are 
accessed and/or mobilized in purposive action’ (Lin, 1999, pp. 31) and as ‘a collective 
resource that arises from (and is shaped by) social relations between actors within a 
network’ (Tomlinson, 2011, pp. 5). Consequently, the study define financial network 
capital as the collective resources embedded in a bipartite network of financial institutions 
and SMEs that can be mobilized for purposive action such as SME business operations and 
financial institution lending capability. These descriptions of social capital have structural 
embeddedness, opportunities and action oriented uses. Thus the basic understanding is that 
social capital comes from social relations. There are three dimensions of social capital. 
These are structural, relational and cognitive dimensions (Liao & Welsch, 2005). In this 
study the study are interested in the structural dimension of social capital and how they 
particularly affect SME survival. The structural dimension focuses on the locations of 
actors within a network and the benefits they confer on such an actor. The implicit 
assumption is that SMEs that either have certain topological network environments or lie 
in certain structural locations can appropriate certain benefits for their functioning since 
they are viewed as rational agents that will exploit their structural locations for their 
benefit (Borgatti & Foster, 2003). The structural location of an SME is measured by its 
centrality; in this study the study consider two forms of centrality: closeness centrality that 
measures how close an actor is to all actors in the network; and eigenvector centrality that 
measures the connections to prestigious alters. Hence in this study the study measure how 
structurally close an SME is to financial institutions and how prestigious their financial 
alters are in the finance network and the consequences of these for its survival. 
 
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3.2.2.1 Closeness to Financial Institutions 
Closeness to financial institutions considers how close an SME is to all the stakeholders in 
the financial network. Hanneman and Riddle (2005) suggests that closeness enables the 
SME to engage in direct bargaining/exchange as well as be the reference point for 
discussions. SMEs that have a close relationship with financial institutions can directly 
bargain for resources and policy support without going through intermediaries who can 
‘water-down’ messages or extract service charges. In terms of being the reference point for 
discussions in networks; SMEs that have close relationships become the standard by which 
other SMEs judge themselves or are judged. This means that policy support from financial 
stakeholders will be framed with these SMEs in mind and ultimately specifically aid their 
survival. Another mechanism through which closeness to financial institutions can aid the 
survival is the access to fine-grained information about credit opportunities in the financial 
network. Social network theory suggests that actors located in vantage points in a network 
by virtue of their location in the network structure are able to access resources owned by 
other actors to enhance their performance and survival chances (Borgatti & Foster, 2003; 
Pfeffer & Salancik, 2003). These actors may have the ability to access these resources by 
bridging structural holes or being embedded in dense networks (Burt, 2005). Informational 
resources are useful for firm performance and survival because markets are usually 
imperfect and there are a lot of information asymmetries. Actors with strategic locations in 
networks can therefore have access to informational resources that may not be available to 
its competition and thus provide it with more opportunities and choices (Lin, 1999). Actors 
with strategic locations in networks can also have direct access to informational resources 
without having to go through intermediaries. This helps reduce the transaction costs of 
these actors and improve survival chances relative to its competition. In a study of advice 
and hindrance networks (Sparrowe et al., 2001) found that actors central to networks 
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perform better because of their access to information not available to those in the periphery 
because of their favoured network positions. Zaheer and Bell (2005) in a study of 
Canadian mutual fund companies also found that being located in vantage points in a 
network bestows informational resource advantages to firms that they can benefit from. 
Again, closeness to banks may enable the SME to gain the trust of the financiers in a 
sector where the default rate is believed anecdotally to be high and a main reason why 
financial institutions do not what to lend to the sector. Lin (1999) describes it aptly as the 
certification of the social credentials of an ego in a given network. This certification of 
social credentials has three implications for economic outcomes: (1) helps to reduce 
transactional uncertainty that is difficult to price or enforce contractually; (2) helps 
facilitate resources and information that is crucial for high performance by serving as 
governance mechanism; and (3) strengthens an actor’s adaptation abilities towards 
unforeseen problems (Uzzi, 1996). Trust has been noted to be associated with low 
transaction costs as a result of improved efficiency among actors in a network because of 
closure and clustering in trust networks (Zaheer et al., 2010). Consequently, the study 
hypothesizes that: 
 
H2: Closeness to financial institutions has a positive relationship with the probability of 
SME survival 
 
3.2.2.2 Prestige of Financiers 
Prestige of financier refers to how popular a financial institution is to SMEs in the 
financial network. SMEs with connections to these financiers are also in a very prestigious 
position. This can be useful in achieving SME survival since the SME focuses on few 
relevant financiers that can offer it the required resources needed for it functioning. This 
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means that the SME can avoid redundant network relations that take resources away from 
the SME and limits its possibility of survival (Rowley, Behrens, & Krackhardt, 2000). 
Since SMEs have limited resources having only relevant connections can bode well for its 
survival. Also, SMEs that are located in central locations have a structural position from 
which to develop ties with prestigious financiers. This enables them to send signals about 
their attributes to other firms that may not be embedded in the network who have no means 
of evaluating the quality of those attributes (Zaheer et al., 2010). Signals may also help to 
reinforce beliefs about those attributes and continued access to resources within and 
outside the network (Lin, 1999). For example, a small firm that has an alliance with a 
reputable large firm may send signals as a promising new venture and can attract new 
resources by virtue of the alliance. Attracting these new resources puts this small firm in a 
competitive position relative to its market and increases its survival chances. Gulati and 
Higgins's (2003) findings from a sample of young biotechnology firms show that ties to 
prominent venture capital firms are particularly beneficial to Initial Public Offering (IPO) 
success during cold markets, while ties to prominent investment banks are particularly 
beneficial to IPO success during hot markets. Consequently, the study hypothesizes that: 
 
H3: Prestige of financiers has a positive relationship with the probability of SME survival 
 
3.2.3 Financial Institution Type 
Financial institution characteristics have been found to have an effect on whether the bank 
lends to the SME sector or not (Ardic et al., 2012). Berger and Udell (2002) found that 
small and local banks are more likely to lend to SMEs because they are better suited to 
engage in relationship lending as a result of loan officer’s ‘soft’ information about the 
SME applicants. Beck et al. (2008) also suggest that large banks are better suited at 
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financing SMEs through arm’s length mechanisms (Uzzi & Lancaster, 2003) such as 
fixed-asset lending and credit scoring. In Ghana, the Bank of Ghana classifies financial 
institutions into different classes. These include universal banks; cooperatives and credit 
unions; rural and community banks; savings and loans; as well as microfinance firms19. 
These institutions have different foci for which they are set-up. Their influences and nature 
of services offered to SMEs are likely to differ and so will their influence on the survival 
of the SMEs. The study therefore explores these differences. 
 
First, the study considers universal banks. Traditionally, banking in Ghana used to be 
segmented into commercial, merchant and development banks until in 2003 when the 
Bank of Ghana introduced universal banking to level the sector (Hinson, Mohammed, & 
Mensah, 2006). These banks operate in the major cities with their main target customers as 
importers, exporters, mining and manufacturers. While some of these banks have an SME 
banking unit these do not represent their major business as SMEs are seen as high risk 
(Abor & Biekpe, 2012). According to Narteh (2013), SMEs in Ghana select to these banks 
mainly due to availability of credit, bank guarantees, cash transfers, financial training and 
advisory services. Accessing loans from these banks anecdotally can take about a month 
with a lot of paper work. These procedures are unfriendly to the SME owner-managers 
most of whom are uneducated and find such procedures intimidating (Kayanula & 
Quartey, 2000b). These banks however offer SMEs access to major cities where most of 
the trading of poultry products takes place. Again, they also have large capital outlays and 
are able to offer credit facilities that can help SMEs significantly expand their operations. 
Consequently, the study hypothesizes that:  
 
                                                     
19
 See Bank of Ghana website (http://www.bog.gov.gh/#) 
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H4a: SME ties to universal banks are associated with a higher probability of survival 
 
Another class of financial institutions found in Ghana is the savings and loans companies 
(SLCs). This is a statutory term used to describe some class of non-bank financial 
institutions in Ghana licensed under the Financial Institutions Non-Banking Law of 1993. 
These companies operate like universal banks but can only take deposits and give out 
credit. This, according to Haveman (1993), maybe due to “loosening of regulatory 
constraints that has provided substantial new opportunities for savings and loans to change 
their basis of activities and to expand into new domains”.  According to a Centre for Policy 
Analysis report20 SLCs remain a major means through which small businesses are able to 
access funds for their businesses. However, a recent banking survey report by Price 
Waterhouse Coopers21 notes that SMEs are largely neglected by the universal banks and 
hence they turn to savings and loan companies that they term ‘loan sharks’. This maybe 
because this class of financial institutions have very high interest rates that affect the 
profitability of SMEs. Mensah (2009) studied the savings and loans sector and reported 
that SLCs engage in certain practices such as credit rationing, short-term loan and high 
interest rates based on expected profit margins rather than Bank of Ghana prime rates. 
These practices are  not conducive to the operations of SMEs in Ghana (Akuetteh, 2009). 
Consequently, the study hypothesizes that:  
 
H4b: SME ties to savings and loans companies are associated with a lower probability of 
survival 
 
                                                     
20
 http://www.cepa.org.gh/publications/Issues%20Paper%20Series%20183.pdf 
21
 https://www.pwc.com/en_GH/gh/pdf/ghana-banking-survey-2013-pwc.pdf 
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Credit unions are member-owned local financial institutions established to encourage a 
savings culture in its members while offering credit to its members at competitive rates. 
Credit unions have been in existence in Ghana since 1952 with their umbrella body as 
Ghana Cooperative Credit Union Association22. While a credit union’s focus is to provide 
credit to its members at competitive rates large credit unions sometimes provide credit to 
non-members at a rate higher than those offered to members but yet lower than those of 
the universal banks (Ofei, 2001). Due to the local focus of credit unions they can better 
identify with the context within which rural SMEs operate and more likely to grant them 
loans especially if they are members. Also, the competitive interest rates that credit unions 
offer make them an alternative institution that SMEs can utilize to fund their operations. 
Consequently, the study hypothesizes that: 
 
H4c: SME ties to credit unions are associated with a higher probability of survival 
 
Historically, banking in Ghana was focussed in the major cities with the main targets being 
importers, exporters, mining and manufacturers. In 1976 the Bank of Ghana conceived the 
idea of a lower level of financial institutions to provide financial services to the rural areas 
with its main role as financial intermediation and capital formation in rural areas (Ajai & 
Fissha, 2010). Rural and Community Banks (RCBs) were created. These RCBs offer their 
services to cottage industries, farmers, fishermen and the general rural folk (Obeng, 2008). 
Due to their rural focus the study believes RCBs are more inclined to offer financial 
services that reflect the context within which the SMEs the study study operate. 
Consequently, the study hypothesizes that: 
 
                                                     
22
 http://www.cuagh.com/cua 
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H4d: SME ties to rural and community banks are associated with a higher probability of 
survival 
 
Microfinance refers to the provision of small loans and other facilities like savings, 
insurance, transfer services to poor low-income household and micro-enterprises 
(Gallardo, 2002). Micro-finance is not new to Ghanaians. Traditionally, it is called ‘susu’ 
and thought to have originated from northern Nigeria (Afrane, 2002). Some of these 
institutions are able to provide credit facilities in less than 24 hours when the credit 
application is filed. However, the interest rates of these institutions are very high as a result 
of the costs associated with daily visits to collect deposits and loan payments used to 
mitigate the high risk of lending to the SME sector. Micro-finance institutions operate at a 
local level and seek to remove the risk associated with lending to the micro-enterprise 
sector by engaging in daily visits. Due to this function of micro-finance, firms are able to 
lend more easily to SMEs than traditional banks. Consequently, the study hypothesizes 
that:  
 
H4e: SME ties to micro-finance institutions are associated with a higher probability of 
survival 
3.3 Research Methodology 
3.3.1 The Research Setting 
The poultry industry in Ghana is one of the few industries that have faced the harsh 
realities of trade liberalization. This has led to the collapse and below-capacity operation 
of many of the poultry clusters in the country before the mid-1980s to early 1990s (FAO, 
2014). Compared to cocoa, maize or rice, the poultry industry does not feature prominently 
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in economic planning or agricultural policy and programme documents. Where it is 
referred to, the proposed measures appear to lack either purpose, focus or consistency, and 
it is difficult to discern a specific policy approach or strategy towards the poultry industry 
(Sumberg, Awo, Fiankor, Kwadzo and Thompson, 2013). The study researches the poultry 
industry in three districts of the Brong Ahafo region: Dormaa Central (Dormaa Ahenkro); 
Dormaa West (Nkrankwanta); and Dormaa East (Wamfie). This cluster is popularly 
known in the agriculture literature as the Sunyani/Dormaa cluster (Mensah-Bonsu & Rich, 
2010). At the moment the poultry industry, specifically, table egg production, is operating 
at large-scale levels. Poultry production in this cluster is one of the largest in the region 
and nationwide (see national poultry statistics in Appendix). The study argue that the study 
area is a critical case for poultry in Ghana since its one of the best performing in the 
country and the findings can be generalized to the sector in the country in line with 
Flyvbjerg (2006). Flyvberg notes in line with the ‘black swan’ argument and Galileo’s 
falsification of Aristotelian gravity model that critical cases can form the bases of 
generalizations. Some of the major challenges encountered by the poultry farmers include 
financing; diseases; and absence of electricity for operations in most farms: while the 
presence of feed processing mills, poultry input shops and availability of organised 
markets served as prospects that could be harnessed to boost the growth of the poultry 
industry in the district (Adei & Asante, 2012). The area seems to have formed strong 
collaborative partnerships with Foani Farms in La Cote D’Ivoire to avoid competition that 
was leading to collapse of some farms. Anecdotal evidence suggests (the researcher 
observed this) some farms that collapse are helped back into business by Foani Farms. The 
cluster in recent times is one of the clusters receiving support from the Ghana government 
as part of the broader National Poultry Support Project (NPSP) announced by the Ministry 
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of Trade and Industry23 and the Ghana Broiler Revitalization Project (GHABROP) through 
the Ministry of Food and Agriculture (MOFA) in a ten year collaboration with the Ghana 
National Association of Poultry Farmers24. The study focus the study study in this area in 
order to provide a boundary for the study network effects without which the findings will 
be sparse, lacking variation and context (Hanneman & Riddle, 2005).  
      
3.3.2 Network Survey  
The study used the network survey approach to sample and collect data (Boutilier, 2009) in 
two rounds: the first in January 2014 and second in March 2015. Network analysis requires 
high rates of responsiveness (Sparrowe et al., 2001); therefore, a census approach was used 
to survey the poultry farmers in the three districts. To do this, a composite list of all 
poultry farms operating in the three districts was compiled from the industry association 
(DPFA), the Ghana Revenue Authority (GRA), the Municipal Assemblies, Assemblymen 
and women in each unit area of the district and office of the paramount traditional chief 
(Omanhene). When the whole list was compiled and recurring farms taken care of the 
study had a total of 163 farms. 155 farmers agreed and participated in the study 
representing a 95.09% response rate. It is important to note that not all the farmers in the 
cluster area were included in the compiled list because some of the farms were not for 
commercial purposes; a basic requisite for participation. Such farms were excluded from 
the dataset. Each farming enterprise was asked to name its financiers or bankers that were 
used to generate the financial network in line with the multiple name generators approach 
(Rooks et al., 2012). The average interview took between 40-45 minutes. To ensure data 
                                                     
23
 http://graphic.com.gh/news/general-news/22412-government-support-to-poultry-industry-
increasing.html 
24
 http://www.thepoultrysite.com/poultrynews/32740/ghanas-broiler-sector-to-get-legup-from-
revitalization-project 
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integrity respondents were asked to provide evidence of a relationship between itself and a 
named financier by providing some document (line of credit, deposit receipt copies, 
cheque book, ‘susu’ card and any verifiable evidence) to support the existence of a link. If 
none of these were produced, the link was discounted. The study then developed an 
affiliation matrix comprising SMEs and financiers where the study mapped SMEs and 
their corresponding financier. The cohesive characteristics of the financial network 
generated for each year is presented in Table 3.1 below. 
 
Table 3. 1: Finance Network Characteristics 
Year Density Avg. Distance Diameter Fragmentation 
2014 0.073 4.614 10 0.218 
2015 0.132 2.786 6 0 
∆ 0.059 -1.828 -4.000 -0.218 
 
The study discusses the network characteristics and their implications for the actors 
embedded in them below. The definitions of the computations are based on analyses of 
two-mode data used in the UCINET (Borgatti & Everett, 1997; Borgatti, Everett, & 
Freeman, 2002). The study finds that the density of the network increased significantly 
from 0.073 to 0.132. This has two implications for actors embedded in the network: the 
increase in density suggests a faster rate of resource diffusion in the network but can also 
be an indication of network constraint for the actors embedded in the network. The faster 
resource diffusion should favour SMEs that are close to financial institutions. The network 
constraint may not be a problem if the study consider the diameter (longest geodesic 
(walk) of the network) - which reduced from 10 to 6 suggesting that resources embedded 
in the peripherals can be accessed by other actors in the other peripheral. There was also a 
reduction in the average distance and fragmentation between actors in the survival period 
under study. This might be indicative of an industry that is bracing itself for difficult times 
by enhancing its overall resource utilisation. 
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3.3.3 Measures and Operationalization 
Dependent Variable: SME Survival 
The definition of the word SME is as complicated as the sector itself (Senderovitz, 2009). 
He notes that in many articles it is difficult to see whether the researcher has considered 
what a small SME actually is; whether the particular definition is appropriate, or what the 
consequences for the conclusion might be, if other definitions were used. Consequently, 
the study focus on a context-based definition of SMEs that has been used by many 
Ghanaian authors using employee numbers as a basis (Kayanula & Quartey, 2000b; Yusuf 
& Saffu, 2005; Abor, 2007; Hinson & Sorensen, 2006; Kyereboah-coleman & Amidu, 
2008; Saffu et al., 2008; Abor & Quartey, 2010; Abor & Biekpe, 2012). An SME is 
defined as any business employing less than 99 people (Kayanula & Quartey, 2000b). This 
definition notes that SMEs can be divided into three broad categories: micro, small and 
medium with categorization based on micro SMEs having less than 6 employees; small 
SMEs having less than 30 employees; while medium SMEs have less than 99 employees. 
SME survival and failure are two different sides of the same coin. SME failure has been 
called different names in the management literature to include bankruptcy, decline, 
retrenchment, SME death, mortality, downsizing and exit (Mellahi & Wilkinson, 2004). A 
failed SME is “when an SME involuntarily becomes unable to attract new debt or equity 
funding to reverse decline; consequently, it cannot continue to operate under the current 
ownership and management. Failure is the endpoint at discontinuance (bankruptcy) and 
when it is reached, operations cease and judicial proceedings take effect” (Pretorius, 2009, 
pp. 8). Hannan and Freeman (1986, pp. 62) also defined failure as “when an SME ceases to 
carry out routine actions that sustain its structure, maintains flows of resources and secure 
allegiance of its members”. This study therefore defines SME failure as when an SME can 
no longer meet the utility of its stakeholders and operationalized it using the traditional 
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closure approach. In order to do this, the study compiled a list of all poultry farms 
operating in the three districts as at January 2014 for the first wave of data collection. A 
follow-up was made on the same list in March 2015. Those SMEs that had gone out of 
business by this time were deemed to have failed in this respect. SMEs that survived were 
coded as 1 and those that failed were coded as 0. At the end of the period 63 SMEs had 
failed while 92 survived out of the 155 SMEs that participated in the study as seen in Table 
3.2. 
 
Table 3. 2: Survival Rate 
    2014 2015 
2014 
  
Survived 155 92 (59.35%) 
Failed  63 (40.65%) 
 
Variables of Interest: Financial Constraint  
The study operationalized financial constraint with the question: “on a scale of 1 to 7 to 
what extent does the access to finance (cost and availability) negatively affect your 
business operations?” 1 means the cost and access of finance was not a constraint at all; 2 
means it was rarely a constraint; 3 means to a limited extent; 4 means moderately; 5 means 
to some extent; 6 means to a large extent and 7 means to a very large extent. The study 
developed a binary financial constraint variable from these responses where those who 
said it was not a constraint at all was coded as zero and all others that said it was a 
constraint in some magnitude was coded as 1.     
 
Closeness to Financial Institutions 
This variable refers to how structurally close an SME is to actors in the financial network 
(Freeman, 1978). The study operationalises this variable using the closeness centrality of 
the SME in the network. Closeness centrality is the sum of geodesic distances of a given 
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SME i to all other nodes in the network n-1. However, since the financial network is a two-
mode network consisting of two sets of actors (SMEs and their financiers), the closest an 
SME can be to financiers is n2 while the closest it can be to other SMEs is 2(n1-1). 
Closeness to other SMEs is important because it offers a mechanism through which the 
SME can connect to other financiers in the network through other SMEs that are connected 
to its financiers. Consequently, the study adopted the approach of Borgatti and Halgin 
(2011) in analysing affiliation networks and deriving the closeness centrality (CCs
*) as: 
 
CCs
* = [n2 + 2(n1-1)] / CCs 
 
where n2 is the finance node set; n1 is the SME node set and CCs is the theoretical 
minimum closeness for an SME in the financial network. 
 
Prestige of Financiers 
The prestige of financiers refers to how popular a given SME’s financiers are within the 
financial network. The study operationalized this variable using the eigenvector centrality 
of a given SME in the financial network ( Bonacich, 1987; Bonacich & Lloyd, 2001). 
Consider two SMEs k and q who both have two ties to other actors in the network a, b and 
c, d respectively. The actors a and b also have two ties each to other actors in the network 
while c and d do not. In this case, actor k is in a more prestigious position than actor q 
because it has ties to alters who also have ties and is more likely to have influence in the 
network than q. Consequently, the study formalizes the eigenvector centrality (Ei) as: 
 
Ei = λ ∑ FNij ej, i ∈ V1 and j ∈ V2 
 
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where FN is the financial network; λ is a constant required so that the equations do not 
have a non-zero solution; i is a given SME; j is a given financier in the network; ej is the 
degree centrality of a given financier; V1 is the vector containing the list of SMEs and V2 is 
the vector containing the financiers in the network.   
 
Type of Financiers 
The study operationalized the type of financier based on the number of ties to a particular 
class of financiers (see classification in appendix) in the financial network. There were five 
broad classes in line with the Bank of Ghana’s sub-classification25 system. These are 
universal banks, savings and loans companies, rural and community banks, credit unions 
and microfinance companies. The study used the degree centrality of a given SME to a 
particular class as its ties to that class. Degree refers to the local connectedness to the actor 
in the network at distance one (Borgatti, 2005). The degree centrality of a given SME is 
the number of ties it has in a one-mode network (Freeman, 1979). However, the financial 
network is a two-mode network consisting of two sets of actors: SMEs and their financiers. 
Consequently, the study adopted the approach of Borgatti and Halgin (2011) in analysing 
affiliation networks and deriving the degree centrality to a particular class of financiers as 
(DCc) of a given SME by specifying degree centrality as: 
 
DCc = ∑FN: i -> j,  i ∈ V1 and j ∈ V2 
    
where FN is the financial network; i is a given SME; j is a given class of financier in the 
network; V1 is the vector containing the list of SMEs and V2 is the vector containing the 
class of financiers in the network.   
                                                     
25
 See www.bog.gov.gh 
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Control Variables 
The study controlled for owner characteristics such as education, years of experience in the 
poultry industry and gender. Education provides formal training and experience provides 
practical knowledge of the industry (Patzelt et al., 2008) while the different sexes have 
been associated with different managerial styles (Park, 1996; Rand & Tarp, 2011). 
Education was operationalized as whether the owner has had secondary education and 
above in which case it was coded 1 and otherwise 0. Experience was defined as the 
number of years of industry experience of the owner. Gender was operationalised as a 
male-dummy with male-owned businesses coded as 1 and female ones 0. Beyond owner 
characteristics the study also controlled for SME level characteristics such as number of 
paid employees and generalists. The number of paid employees variable seeks to control 
for the liability of smallness effect in the model (Tsvetkova et al., 2014) and measured as 
the number of paid labour the enterprise has. Generalists variable was also used to control 
for the resource partition effect in the model (Carroll et al., 2002) and was measured as 
whether an enterprise undertakes only broiler or layer farming (0) and whether the 
enterprise does both (1). The study also controlled for the effect of firm level managerial 
competencies, which included variables such as market orientation, entrepreneurial 
orientation, absorptive capacity and dynamic capability. Market orientation involves issues 
relating to intelligence generation, dissemination and market response in the light of the 
intelligence (Kohli et al., 1993) and was operationalised as having customer meetings, 
having informal discussions to resolve customer issues and review of marketing plans. 
Entrepreneurial orientation was operationalised as the risk-taking, proactive and 
innovation tendencies of the organisational culture (Altinay & Wang, 2011). Absorptive 
capacity concerns issues relating to the acquisition, assimilation, transformation and 
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exploitation of resources external to the firm (Flatten et al., 2011) and was operationalised 
as the search for industry information, application in problem solving and the adoption of 
new methods. Dynamic capability also relates to issues such as coordination of enterprise 
activities, learning and competitive strategic response (Protogerou et al., 2011) and was 
operationalised as business planning, on-the-job training and industry benchmarking. The 
study also controlled for the perceived effects of the investment climate constraints such as 
cost and access to finance, land access, access to electricity, inflation and quality of labour. 
These managerial and investment climate operationalisations were measured on a Likert 
scale from 1 (the least) and 7 (the highest) as perceptions of owners on the performance of 
their enterprises in relation to these variables. The operationalisations of each construct 
was added and standardized for the construct. Another competence that was controlled for 
is the technical competence of the SME in poultry production. The study proxy this 
variable with the SMEs ties to technical agencies and companies such as the veterinary 
services department and the Ministry of Food and Agriculture (MOFA).  
 
3.3.4 Model Specification and Estimation  
The study model SME survival by specifying a general probit model of a form: 
 
P(ESt+1=1) = Φ (β0 + β1SMEt + β2OCt + β3COMPt + β4ICCt + β5Mt + εt)  
 
where ESt+1 indicates that an SME survives from period t (2014) to t+1 (2015); SMEt is 
SME characteristics; OCt represents owner characteristics; COMPt is the technical and 
managerial capabilities of the SME; ICCt represents the perceived effect of investment 
climate constraints; Mt is the effect of the variables of interest: self-reported financial 
constraint, closeness to financial institutions and prestige of financiers; εt is the statistical 
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noise and Φ is the cumulative distribution function of the standard normal distribution. The 
magnitude of the coefficients of the probit model cannot be interpreted and hence the study 
also estimated for the average marginal effects (AME) of the model which can be 
interpreted. The study model the average marginal effects of the study probit using the 
following function: 
 
∂ES/∂Vi = βi Φ (β0 + β1Vt + εt) 
 
where ∂ES/∂Vi is a partial derivative with respect to V (a given vector) and the index i 
refers to the ith independent variable in V.  
 
In estimating the study models the study ensured that the model is fairly robust. First, the 
study employed the hierarchical regression approach to specify three models: a control 
model containing only the covariates; and a second model where the study add the main 
effects (of financial constraint, closeness to financial institutions and prestige of financiers) 
and a third model with only financier type as main effects. This enabled us to check if the 
study main effect is robust to controls. Secondly, the study specified models that are robust 
to heteroskedasticity. Thirdly, the study also attempted to partially reduce endogeneity by 
modelling survival by the lagged independent variables. Also, in relation to endogeneity 
the study tried to include variables in line with existing theory relating to organisational 
failure (Mellahi & Wilkinson, 2004). Finally, on possible selection problems the Food and 
Agriculture Organisation has a 2014 database on farms in the study area in which they 
report that 202 farms operate in the area (FAO, 2014). The study also had 200 farms but 
after accounting for recurring farms and same ownership the study ended up with 163 
farms of which 155 farms participated. Consequently, issues of selection may not arise. 
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3.4 Results 
This section reports the analysis of the data collected from the study site.  
 
3.4.1 Descriptive Analysis 
Table 3.3 presents the descriptive statistics of the variables used in the analysis, table 3.4 
presents a cross-tabulation of financial network variables, financial constraint and survival 
while table 3.5 presents the results of the probit model of survival. The study first reports 
the descriptive statistics in table 3. The study found that few of the SMEs reported that 
they were not financially constrained (45.2%). This may be due to some underlying self-
selection mechanism or discouraged-borrower effect (Akuetteh, 2009; Fajnzylber et al., 
2009). The means of prestige of financiers and closeness to financial institutions was 0.050 
and 0.761 respectively with corresponding standard deviations of 0.063 and 2.473 
indicating a high variability in closeness to financial institutions among SMEs in the 
financial network. The ties to bank types had means between a low of 0.052 for savings 
and loans ties and a high 0.432 for universal bank ties while the standard deviations varied 
between a low of 0.222 and 0.665 for the same variables. In terms of enterprise 
characteristics, the majority of the SMEs had approximately 6 paid employees and 
specialised in layer production (74.2%). When the study considered owner characteristics: 
most of the owners had at least attended secondary school (52.9%), were male (74.8%) and 
had an average of 9 years of industry experience. It is, however, important to note that the 
number of paid employees and owner experience showed a great deal of variability with 
standard deviations of 11.196 and 6.698 respectively. The mean score of technical 
competence was 0.955 with a standard deviation of 0.914.  
 
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The study now turns to Table 3.4, which shows the results of the cross-tabulations between 
financial constraints, financial network and survival variables. It is important to note that, 
for the purposes of the cross-tabulation analysis, the study have binarized financial 
network capital variables, which are continuous variables in all other analysis. Prestige of 
financiers and closeness to financial institutions were binarized along the mean while 
financial institution type variables were binarized as having no tie (0) and one or more tie/s 
(1). The study start by focusing on closeness to financial institutions. The study found that 
70% of SMEs had closeness to financial institution scores that were below the mean. Of 
the 85 SMEs that reported that they were not financially constrained, 56% had a low 
closeness to financial institutions; while of those 70 SMEs that were constrained, 72% had 
a low closeness to financial institutions in the network. When the study consider survival 
of the 92 SMEs that survived, 55% had a low closeness to financial institutions while, of 
the 63 that failed, 92% had a low closeness to financial institutions. Also, the study found 
that 30% of SMEs had prestigious financiers. Of the 85 SMEs that reported that they were 
not financially constrained, 67% had prestigious financier ties while, of those 70 SMEs 
that were constrained, 31% had prestigious financier ties in the financial network. When 
the study consider survival of the 92 SMEs that survived, 45% had prestigious financier 
ties while, of the 63 that failed, 8% had prestigious financier ties. When the study 
considers ties to types of financial institutions; the study find that 35% of SMEs had ties to 
universal banks. Of the 85 SMEs that reported that they were not financially constrained, 
53% had ties to universal banks while, of those 70 SMEs that were constrained, 28% had 
ties to universal banks in the financial network. When the study consider survival of the 92 
SMEs that survived, 52% had ties to universal banks while, of the 63 that failed, 10% had 
ties to universal banks. Secondly, when the study considers ties to savings and loans 
companies (SLCs) the study find that 95% of SMEs had no ties to SLCs. Of the 85 SMEs 
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that reported that they were not financially constrained 99% had no ties to SLCs while, of 
those 70 SMEs that were constrained, 74% had no ties to SLCs in the financial network. 
When the study consider survival of the 92 SMEs that survived, 99% had no ties to SLCs 
while, of the 63 that failed, 89% had no ties to SLCs. Thirdly, when the study consider ties 
to credit unions the study find that 32% of SMEs had ties to credit unions. Of the 85 SMEs 
that reported that they were not financially constrained 41% had ties to credit unions while, 
of those 70 SMEs that were constrained, 35% had ties to credit unions in the financial 
network. When the study consider survival of the 92 SMEs that survived 44% had ties to 
credit unions while, of the 63 that failed, 16% had ties to credit unions. Again, when the 
study considers microfinance institutions, the study find that 16% of SMEs had ties to 
them. Of the 85 SMEs that reported that they were not financially constrained 7% had ties 
to microfinance institutions while, of those 70 SMEs that were constrained, 40% had ties 
to microfinance institutions in the financial network. When the study consider survival of 
the 92 SMEs that survived 7% had ties to microfinance institutions while, of the 63 that 
failed, 30% had ties to microfinance institutions. Finally, the study finds that 23% of 
SMEs had ties to rural and community banks (RCBs). Of the 85 SMEs that reported that 
they were not financially constrained 21% had ties to RCBs while, of those 70 SMEs that 
were constrained, 39% had ties to RCBs in the financial network. When the study consider 
survival of the 92 SMEs that survived 21% had ties to RCBs while, of the 63 that failed, 
27% had ties to microfinance institutions. 
 
 
 
 
 
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Table 3.3: Descriptive Statistics 
Variable Obs. Mean Std. Dev. 
Financial Constraint 155 0.452 0.499 
Prestige of Financiers 155 0.050 0.063 
Closeness to Financial Institutions 138 0.761 2.473 
Universal Bank Ties 155 0.432 0.665 
Rural Bank Ties 155 0.239 0.443 
Credit Union Ties 155 0.348 0.530 
Savings & Loans Ties 155 0.052 0.222 
Microfinance Ties 155 0.174 0.413 
No. of Paid Employees 155 6.348 11.196 
Generalist 155 0.258 0.439 
Education (Secondary and above=1) 155 0.529 0.501 
Owner Experience 155 8.961 6.698 
Male 155 0.748 0.435 
Technical Competence 155 0.955 0.914 
Managerial Competence 155 -0.127 0.812 
Investment Climate Constraints 155 0.192 1.061 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table 3.4: Cross-Tabulation of Network Capital, Financial Constraint and Survival 
  No 
Constraint 
% Constraint % Failed % Survived % Total % 
Closeness to 
Financiers1 
Low  48 56.47 61 71.76 58 92.06 51 55.43 109 70.32 
High  37 43.53 9 28.24 5 7.94 41 44.57 46 29.68 
Prestige of 
Financiers1 
Low  28 32.94 59 69.41 58 92.06 51 55.43 109 70.32 
High  57 67.06 11 30.59 5 7.94 41 44.57 46 29.68 
Universal 
Bank2 
No 40 47.06 61 71.76 57 90.48 44 47.83 101 65.16 
Yes 45 52.94 9 28.24 6 9.52 48 52.17 54 34.84 
Savings & 
Loans2 
No 84 98.82 63 74.12 56 88.89 91 98.91 147 94.84 
Yes 1 1.18 7 25.88 7 11.11 1 1.09 8 5.16 
Cooperative 
Credit Union2 
No 50 58.82 55 64.71 53 84.13 52 56.52 105 67.74 
Yes 35 41.18 15 35.29 10 15.87 40 43.48 50 32.26 
Microfinance2 No 79 92.94 51 60.00 44 69.84 86 93.48 130 83.87 
Yes 6 7.06 19 40.00 19 30.16 6 6.52 25 16.13 
Rural Bank2 No 67 78.82 52 61.18 46 73.02 73 79.35 119 76.77 
Yes 18 21.18 18 38.82 17 26.98 19 20.65 36 23.23 
Total  85 54.84 70 45.16 63 40.65 92 59.35 155 100.00 
1These variables have been binarized along the mean 
2These variables binarized as having no tie (0) and one or more tie (1) 
 
 
 
 
 
 
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3.4.2 Regression Analysis 
Table 3.5: Finance Network and SME Survival 
 SME Survival 
 (1) (2) (3) 
No. of Paid Employees 0.019** 0.055* 0.014* 
 (0.008) (0.028) (0.007) 
Generalist -0.267*** -0.496** -0.196*** 
 (0.040) (0.239) (0.038) 
Education (Secondary and 
above=1) 
0.023 0.554** 0.025 
 (0.047) (0.274) (0.033) 
Owner Experience 0.009** 0.016* 0.001 
 (0.004) (0.008) (0.004) 
Male -0.131*** -0.099 -0.030 
 (0.043) (0.061) (0.034) 
Technical Competence 0.073*** 0.061 0.026 
 (0.024) (0.043) (0.026) 
Managerial Competence 0.217*** 0.009 0.134*** 
 (0.039) (0.034) (0.033) 
Investment Climate 
Constraints1 
-0.193*** -0.233** -0.115*** 
 (0.030) (0.101) (0.024) 
Financial Constraint  -0.644**  
  (0.309)  
Prestige of Financiers  5.512**  
  (2.685)  
Closeness to Financial 
Institutions 
 -0.002  
  (0.002)  
Universal Bank Ties   0.224*** 
   (0.053) 
Rural Bank Ties   0.063 
   (0.039) 
Credit Union Ties   0.162*** 
   (0.050) 
Savings and Loans Ties   -0.288*** 
   (0.103) 
Microfinance Ties   0.062 
   (0.041) 
    
Wald 35.91*** 30.40*** 499.20*** 
Pseudo R-Square 0.706 0.911 0.811 
Observations 155 138 155 
Robust standard errors in parentheses 
*** p<0.01, ** p<0.05, * p<0.1 
Average marginal effects reported 
1This has no financial constraint 
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The study now turns to the probit models in Table 3.5. In column 1 the study present the 
control model, column 2 presents the financial network capital model with financial 
constraints and the third model in column 3 is the type of financial institution model. The 
study find that financial constraints have a negative effect on survival probability (-
0.644**); prestige of financiers has a strong positive effect on survival probability 
(5.512**); and closeness to financial institutions has a negative effect on survival but is not 
significant (-0.002). Universal bank ties and credit union ties were seen to have a positive 
and significant effect on survival probability with 0.224*** and 0.162*** betas 
respectively while savings and loans company ties have a negative effect on survival (-
0.288***). Microfinance and rural bank ties had a positive effect on survival but were not 
seen to be significant. The study turn to the control variables. The number of paid 
employees was seen to be positive and significant in all the three models (0.019**, 0.055* 
and 0.014*); generalists were seen to have a negative and significant effect in all the 
models (-0.267***, -0.496*** and -0.196***); education was seen to have a positive 
effect on survival but was only significant in the model 2 (0.023, 0.554** and 0.025); 
owner experience was seen to have a positive association with survival and significant in 
all models except model three (0.009**, 0.016* and 0.001); male-owned businesses were 
seen to be less likely to survive compared to female-owned ones but only significant in the 
first model (-0.131***, -0.099 and -0.030); technical competence was seen to have a 
positive effect on survival but only significant in the first model (0.073***, 0.061 and 
0.026); managerial competence has a positive association with survival and significant in 
models one and three only (0.217***, 0.009 and 0.134***); and investment climate 
constraints was unsurprisingly seen to have a negative association with survival in the 
models (-0.193***, -0.233*** and -0.115***).     
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3.5 Discussion of Results 
The objective of the study was to answer questions relating to the usefulness of financial 
network capital for SME survival in a financial network. The study operationalized 
financial network capital as closeness to financial institutions and prestige of financiers 
with closeness and eigenvector centralities as the measures. The study also explored the 
effect of specific class of financier class ties on survival using the degree centralities of the 
SMEs. The study was implemented within the context of the Ghanaian poultry industry 
using the Dormaa cluster as a critical case (Flyvbjerg, 2006). In this section the study 
discuss the study findings in relation to the existing literature. The study modelled SMEs 
survival into 2015 using a probit model with lagged independent variables while 
controlling for SME characteristics, owner characteristics, technical competences, 
managerial competencies and investment climate constraints.     
 
The study first discusses the study hypothesized associations. The study found that 
financially constrained SMEs are more likely to fail than those that are less constrained. 
This is notwithstanding the fact that about 55% of the SMEs said that they were not credit 
constrained which the study suspect may be a ‘discouraged borrower effect’ (Akuetteh, 
2009) or having alternative sources of income.26 The reasons for financial constraint 
having a negative effect on survival maybe due to the SMEs inability to acquire or absorb 
new technologies nor can they expand to compete in global markets or even strike business 
linkages with larger firms due to a lack of funds. The study also found that prestige of 
SMEs financiers have a positive and significant effect on survival. The implication of this 
finding is that the SME has connections with very relevant financiers and hence is also in a 
very prestigious position in the network. This can be useful in achieving SME survival 
                                                     
26
 Anecdotally most SMEs poultry enterprises are started with income from family members in Europe and 
America 
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since the SME focuses on a few relevant financiers from which it extracts the required 
resources needed for it functioning. Also, ties to prestigious financiers enable SMEs to 
send signals about their attributes to other firms that may not be embedded in the network 
who have no means of evaluating the quality of those attributes (Zaheer et al., 2010). 
Signals may also help to reinforce beliefs about those attributes and continued access to 
resources within and outside the network (Lin, 1999). For example, a small firm that has 
an alliance with a reputable large firm may send signals as a promising new venture and 
can attract new resources by virtue of the alliance. Descriptively, the study also observed 
some interaction effects in table 3.4 where the cross-tabulations show that SMEs that have 
prestigious financiers are less likely to report being credit constrained while those that are 
not are more likely to report such constraints. The study also explored the financier class 
ties on survival and found that universal bank credit union ties have a positive effect on 
survival. The mechanism through which ties to universal banks can aid survival may be 
through their ability to offer large capital outlays to SMEs that are able to meet their 
requirements to expand their operations significantly and hence aid the survival of those 
SMEs. The means through which credit unions can improve survival chances may be due 
to the fact that credit unions are local and can better identify with the context within which 
rural SMEs operate and are more likely to grant them loans (especially if they are 
members) as well as offering competitive interest rates (Ofei, 2001). The study also found 
that savings and loan company ties have a negative association with survival. This may 
confirm the issues raised in the a recent banking survey report by Price Waterhouse 
Coopers27 which notes that SMEs are largely neglected by the universal banks and hence 
they turn to savings and loan companies that they term ‘loan sharks’. Some of the negative 
practices of savings and loans companies include small and short term loans, and high 
                                                     
27
 https://www.pwc.com/en_GH/gh/pdf/ghana-banking-survey-2013-pwc.pdf 
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interest rates that are detrimental to SME business development (Mensah, 2009). The 
study also controlled for some variables in line with existing theory and finds them 
significant. The positive effect and significance of the number of paid employees confirms 
the liability of size hypothesis that small firms are more likely to fail due to a lack of 
legitimacy and economies of scale (Hjalager, 2000). The negative effect of generalist also 
confirms the theory of niche packing that claims SMEs that require a broad range of 
resources are more likely to fail (Carroll et al., 2002). The findings in education and 
experience confirms the fact that training and relevant industry experience can be useful 
for SME performance (Jo & Lee, 1996). The positive effects of managerial and technical 
competence indicate how knowledge and organisational skills are useful in achieving 
enterprise goals (Mellahi & Wilkinson, 2004; Mensah-Bonsu & Rich, 2010). 
Unsurprisingly, investment climate constraints are seen to have a negative effect on 
survival and this may be due to the imposition of indirect costs on the operations of SMEs 
(Eifert et al., 2008).   
 
The study now discusses the implications of the results for enterprise management and 
research. In terms of financial network capital the study suggest that small business owners 
develop ties to financiers that are prestigious or respected in the network as they are likely 
to offer them the required resources or be a mechanism through which they can send 
positive signals about their operations. It may also help them reduce their credit constraint. 
Secondly, the study recommends that SMEs develop ties with universal banks, and credit 
unions while avoiding ties to savings and loans companies. Universal banks can provide 
them with large capital outlays when required for expansion and not feel credit 
constrained; credit unions can offer competitive interest rates when required for working 
capital management but unfortunately savings and loans companies can charge interest 
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rates that can be detrimental to their businesses. There are a few caveats that readers need 
to bear in mind when interpreting the study findings. First, the study measure of financial 
constraints is self-reported. While such measures have been extensively used in the 
literature (Beck & Uc, 2005; De Maeseneire & Claeys, 2012) they may sometimes not 
reflect the reality when the data is observed reflecting the usual difference between 
perceived and actual. The study focussed on the Dormaa cluster in Ghana but the study can 
be replicated in other jurisdictions within either Ghana or other countries. Such studies can 
aim to either confirm or extend this study. In this study a tie is either present or absent 
hence another way that new studies can improve upon this study is measuring the intensity 
of the relationship between financial institutions as this may have implications for the 
findings.  
 
3.6 Conclusions 
The aim of this study is to understand the usefulness of financial network capital to the 
survival of SMEs in Ghana. The study also explored the association between financial 
institution class ties on SME survival. The study found that the prestige of SMEs 
financiers, namely, universal bank and credit union ties have a positive and significant 
effect on survival; while savings and loans company ties have a negative effect on 
survival. The study therefore conclude that being located in network positions that give an 
SME access to prestigious financiers such as universal banks improves the survival 
probability of such enterprises. While firms that enjoy lower interest rates offered by credit 
unions improves survival while those tied to savings and loans companies are likely to fail 
due to high interest rates charged by such financiers. 
 
 
 
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APPENDIX 
Table 3A1: Ghana Poultry Sector Statistics 
National  Brong Ahafo Region 
Region No. of Birds 
(%) 
Egg 
Production (in 
millions) 
No. of 
Farms+ 
District No. of 
Farms+ 
Greater 
Accra 
2,547,219 
(7.02%) 
542 475 Dormaa 202 
Central  903,702 (2.49%) 437 312 Jaman South 9 
Western 1,406,642 
(3.88%) 
247 102 Berekum 34 
Eastern 3,886,914 
(10.72%) 
358 213 Sunyani 65 
Volta 1,071,622 
(2.95%) 
68 98 Techiman 25 
Ashanti 10,180,760 
(28.07%) 
5,321 697 Tano South 16 
Brong-
Ahafo 
10,743,897 
(29.62%) 
3,989 510 Nkoranza 32 
Northern 3,625,149 
(9.99%) 
- 30 Kintampo 
South 
1 
Upper 
East 
1,325,835 
(3.66%) 
- 33 Asunafo 
North 
17 
Upper 
West 
579,474 (1.60%) - 34 Asunafo 
South 
0 
    Asutifi 10 
    Wenchi 9 
    Sunyani 
West 
90 
+Based on FAO Sector 1/2/3 Classification 
Sources: Veterinary Services Directorate (2010, 2011, 2013) in FAO, 2014 
 
 
Table 3A2: Financial Institution Classification 
Class Frequency List 
Universal Bank 5 Societe-General, Barclays, Ecobank, Agricultural 
Development Bank, Ghana Commercial Bank 
Savings & Loans 2 Multi-Credit Financial Services, Procredit 
Cooperative Credit 
Union 
3 Brong Ahafo Catholic Cooperative for Social 
Development (BACCSOD), Dormaa Area 
Teachers Co-operative Credit Union (DATCCU), 
Agape Credit Union 
Micro-Finance 4 Satellite Financial Services, First Liberty Financial 
Services, African Financial Services, Noble Dream 
Financial Services 
Rural Bank 3 Kaaseman Rural Bank, Wamfie Rural Bank, 
Nkrankwanta Rural Bank 
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Table 3A3: Correlation Matrix 
 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 
Survival 1                 
Financial Constraint -0.8 1                
Prestige of 
Financiers 
0.5 -0.4 1               
Closeness to 
financial Institutions 
0.1 -0.1 0.0 1              
Universal bank 0.4 -0.4 0.8 0.1 1             
Rural Bank -0.1 0.0 -0.4 -0.1 -0.3 1            
Credit Union 0.3 -0.2 0.4 -0.1 0.0 -0.3 1           
Savings & Loans -0.2 0.2 -0.1 0.0 -0.1 -0.1 -0.1 1          
Microfinance -0.3 0.3 -0.3 0.0 -0.2 -0.1 -0.2 -0.1 1         
Size 0.2 -0.2 0.4 0.0 0.5 -0.1 -0.1 0.0 -0.1 1        
Generalist -0.6 0.5 -0.3 0.1 -0.2 0.0 -0.1 0.2 0.1 -0.2 1       
Education -0.2 0.3 0.0 -0.1 0.1 -0.1 -0.1 0.2 0.1 0.2 0.0 1      
Experience 0.3 -0.3 0.4 0.0 0.4 -0.1 0.2 -0.1 -0.2 0.4 -0.3 -0.1 1     
Male 0.0 -0.1 -0.1 0.0 -0.1 0.3 0.0 0.0 -0.1 0.0 -0.1 -0.1 0.3 1    
Technical 
Competence 
0.2 -0.2 0.3 0.0 0.3 0.0 0.2 0.1 0.0 0.2 0.0 0.0 0.1 -0.1 1   
Organizational 
Competence 
0.6 -0.7 0.3 0.0 0.3 0.0 0.1 -0.1 -0.3 0.3 -0.3 -0.2 0.3 0.1 0.1 1  
Investment Climate 
Constraints 
-0.4 0.4 -0.2 -0.1 -0.1 -0.1 -0.2 0.2 0.2 0.2 0.2 0.3 -0.1 -0.1 0.0 -0.2 1 
 
 
 
 
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CHAPTER FOUR 
ISOMORPHISM, INVESTMENT CLIMATE CONSTRAINTS AND SME 
SURVIVAL IN GHANA 
 
Abstract 
The aim of this study is to understand the importance of SME isomorphism to its survival 
using data from a two year network survey of poultry enterprises in rural Ghana. The study 
measured isomorphism as structural equivalence between actors in the collaborative 
network the study developed from the data. First, the study found that there is a high 
failure rate among the SMEs of about forty percent. The study found that isomorphic 
SMEs have a better survival probability and such isomorphism was very useful in 
overcoming investment climate constraints. However, the isomorphism reaches an 
optimum and returns negative effects. 
 
Keywords: Structural Equivalence, Isomorphism, Investment Climate, SME Networks, 
Ghana 
 
4.1 Introduction 
Small and medium scale enterprises (SMEs)28 have consistently reported investment 
climate constraints as one of the major factors hampering their development (Delios & 
Henisz, 2003; ; Bigsten & Soderbom, 2006; Frynas, Mellahi, & Pigman, 2006; Ackah, 
Aryeetey, Ayee, & Clottey, 2010). These investment climate constraints have led to the 
failure of most SMEs with it being anecdotally reported that approximately 90% fail 
                                                     
28
 Businesses employing less than 99 employees in Ghana 
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before their second birthday (Kayanula & Quartey, 2000b; Franco & Haase, 2009; Abor & 
Quartey, 2010; Okpara, 2011). These constraints include: difficulty in accessing and cost 
of finance; problems with land tenure systems; difficulty in accessing government permits 
and licenses; quality of labour force; access to electricity from the national grid; the effect 
of the tax system on productive activities; corruption in government relations; poor quality 
of road networks hampering mobility of finished products; as well as high inflation that 
does not aid business planning. Eifert, Gelb, and Ramachandran (2008) have noted that 
these investment climate constraints mitigate the development of small businesses by 
imposing indirect costs on the productive activities. According to the World Bank’s (2010) 
report on infrastructural development in Ghana, poor electricity supply led to a 0.5% loss 
in growth as a result of it hampering productive activity in Ghana. However, under the 
concept of isomorphism in a new institutional theory in organisational analysis scholars 
have argued that organisations, which are isomorphic to their environment, are more likely 
to survive because they receive legitimation for their conformance to the rules of 
engagement rather than their technical efficiency and endorsement of structural elements 
by organisations in the external environment that reduces turbulence and improves its 
stability (Meyer & Rowan, 1977; Scott, 1991; Archibald, 2004; Barman & MacIndoe, 
2012). DiMaggio and Walter (1991) argue that within an environment a small business 
will face coercive, mimetic and normative pressures to be isomorphic barring which it will 
not be legitimated leading to the loss of other resources critical for the survival of the 
business (Deephouse, 1996). Consequently, do isomorphic small enterprises have a better 
survival chance than those that are not in constrained investment climates?   
 
Also, consider a small business that is a new entrant facing decisions as to where to source 
for raw materials for its production, assuming that these materials are sourced within a 
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constrained climate. It will be faced with two options of choosing suppliers either from 
self-determined decisions (that may be random or not) or to copy from other producers in 
the industry. When the small business makes a self-determined decision and the product is 
cheaper compared to the competition, then it can have a better survival chance compared 
to the competition. However, when the self-determined decision is wrong then the small 
business risks going out of business because the competition will be more competitive than 
the firm. Will it be more profitable to mimic the decision patterns of other firms in the 
industry as a precaution against being wrong when you develop your own supply patterns? 
However, when a model bases its business structures on the competition they will 
increasing become alike and compete for the same resources. Ultimately, firms will crowd 
around these resources with the possibility of the resource prices going up or the industry 
reducing its prices (Burt, 1992). If the study consider distributors as a resource to access 
the market; then if two small businesses have ties to the same distributors without 
collusion then the distributors can pitch the two businesses against each other and drive 
prices downwards (Bresnahan, 1987). Therefore, will it be reasonable for the small 
business to copy the structural patterns of other businesses in the industry infinitely? And 
will such a business survive long term?  
 
In order to answer these questions, the study collected network data from SMEs in the 
poultry industry in the three Dormaa (East, West and Central) districts of Ghana. The 
study used this industry because it better allows us to represent the Ghanaian economy, 
which is largely agrarian. In terms of the location, it enables us to control for network 
sprawl; without which the network will be sparse and lacking variation in scores 
(Hanneman & Riddle, 2005). According to statistics from the national veterinary office 
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and the Food and Agriculture Organisation (FAO) this particular cluster is one of the best29 
in the country for poultry production (FAO, 2014). The poultry sector generally does not 
feature prominently in economic policy debates compared to cocoa, maize, gold and/or 
timber (Sumberg, Awo, Fiankor, Kwadzo & Thompson, 2013). The three Dormaa districts 
are in rural mid-western Ghana along the border with La Cote d’Ivoire where government 
policy effects may be limited at best (Adei & Asante, 2012). Within the three districts 
approximately 150030 people are employed by the sector. The study argue for the 
generalizability of the findings to other clusters in line with Flyvbjerg (2006) who note that 
critical case studies can be extrapolated for other cases in line with the black swan 
argument. The argument suggests that; once a single case disproves the theory then the 
theory needs modification. Using multiple name generators (Rooks et al., 2012), farm 
SME owners were asked to identify other farmers that they collaborate with in the course 
of their business operations. The data were collected in two rounds: the first in January 
2014 and second in March 2015. The study then constructed a one-mode network from 
which the study further constructed a similarity matrix for each year utilizing the Jaccard 
coefficient (Wasserman & Faust, 1994) and then computed the level of similarity score for 
all enterprises as the level of isomorphism. The study modelled survival into period two 
based on characteristics in period one. The main variables were level of isomorphism 
measured as a level of similarity or structural equivalence with other actors; investment 
climate constraints; and the interaction between isomorphism and investment climate 
constraints; while controlling for enterprise, owner characteristics as well as organisational 
competencies. 
 
                                                     
29
 Based on FAO 1-2-3 Classification 
30
 Based on data from 2014 survey 
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The study found that there was a high failure rate of approximately forty percent. This 
finding supports the anecdotal evidence in the SME scholarship that most SMEs do not see 
their second birthday. The study also found, unsurprisingly, that investment climate 
constraints have a negative effect on the survival chances of SMEs. The study results also 
indicate that the more isomorphic a small business is the more likely it is to survive. This 
is attributable to the legitimation they receive from their peers. The study also observe that 
covariates such as age of SME, speciality of the business, experience of the owner, gender 
of the owner, entrepreneurial orientation and absorptive capacity of the business are 
relevant.  
 
In the following pages the study discuss the theory and hypotheses, the research methods 
employed, the study findings and the conclusions derived from the study. 
 
4.2 Theory and Hypotheses 
A social network consists of nodes and edges (Wasserman & Faust, 1994). When applied 
to small scale industries and sectors, nodes usually represent particular SMEs and the 
edges represented the relationships between them. Networks tell us who is connected to 
whom in the population and by what relationship (Krause, Croft, & James, 2007). An 
SME’s network position also has important survival consequences. Another important 
property of network environments of organisations is common attitude formation that 
occurs by means of structural equivalence (Galaskiewicz & Burt, 1991). This property of 
networks represents the institutional theory tradition (Borgatti & Foster, 2003) if networks 
are noted as just another operating environment that organisations, including SMEs, must 
contend with. The importance of this mechanism for organisational variation was 
recognized by Jepperson (1991) who suggested that organisations in an industry represent 
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low constructions held together by interaction ties. This is indicative of the fact that SMEs 
are different functional units within a given sector and hence face common resource and 
selection constraints and hence are forced to adopt similar organisational forms that aid 
survival. In this section the study reviews literature regarding isomorphism in 
organisational networks using social network theory as the theoretical basis as well as 
investment climate constraints and how they relate to SME survival. 
 
4.2.1 Isomorphism and SME Survival 
Isomorphism is a constraining process that forces an organisation in an environment to 
resemble other organisations as a result of increasing compatibility with environmental 
constraints (DiMaggio & Powell, 1991). There is diversity in the initial phase of industrial 
development but certain organisational forms are selected out leaving other forms that are 
legitimated (winnowing phase) (Hannan & Freeman, 1986). Isomorphism emerges out of 
the general structuration (Giddens, 1979) of the operating environment during the 
winnowing phase. DiMaggio and Powell (1991) note three mechanisms by which firms 
become isomorphic as coercive, mimetic and normative isomorphism. Coercive 
isomorphism stems from political influence and legitimacy. This results from formal and 
informal pressures exerted on the organisation upon which they are dependent and cultural 
expectations of the network in which they operate. Mimetic isomorphism relates to 
standard responses to uncertainty and periods of ambiguity. Firms model their structures 
on those firms that are successful when there is uncertainty. Normative isomorphism is 
based on professionalization of an industry. This describes the collective struggle of 
industry participants to define the rules of engagement in the industry. The three 
components are for analytical purposes and may not always be empirically distinct. 
Isomorphism in social structures is also the result of complex relational networks that 
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foster effective practice; occupational specialization, and principles of coordination as well 
as collective organisations developed by successful firms diffuse through the relational 
networks (Meyer & Rowan, 1977).  
 
Isomorphism can occur within an enterprise (or organisational) network if the study 
operationalise a network as another environment within which firms have to operate. This 
is especially true if the study note Jepperson (1991) who points to the fact that firms in an 
industry are held together by relational ties. What happens is the emergence and 
structuration of a network as a result of the different firms networking and the 
homogenization of their networking behaviour that is accepted by industry participants. 
What the study mean by network environment or ‘field’ as institutionalism will call it, is 
the network graph within which the actors (enterprises) are embedded. For the purposes of 
this study, the graph is a one-mode network consisting of the collaborations between 
industry participants in the poultry industry. The homogeneity that DiMaggio and Powell 
(1991) theorize is the structural equivalence that occurs within a network graph. This 
refers to the level of similarity of network patterns between different actors in a network 
graph (Scott & Carrington, 2011). The similarity between enterprises in network analysis 
is measured by the level of structural equivalence between it and other enterprises. Two 
enterprises in an industry are therefore isomorphic if they have the collaborative ties to the 
same enterprises in the industry. Consequently, such firms can be considered to be 
structurally similar. Some reasons adduced for why network isomorphism may lead to 
positive outcomes are discussed below. 
 
Isomorphism can have impacts on organisational outcomes. These include external 
legitimation of the organisational form rather than its efficiency; employment of external 
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assessment criteria to endorse the value of structural elements; and the dependence on 
externally fixed organisations which reduces turbulence and maintains stability (Meyer & 
Rowan, 1977). Ultimately, isomorphism therefore promotes the success and survival of 
organisations. Technical environments represent those environments in which efficiency 
and effectiveness are rewarded. In institutional environments actors must conform to the 
rules of engagement to receive support and legitimacy from the environment (Scott, 1991). 
There is also the logic of good faith that suggests that institutionalized SMEs can appear 
useful despite their lack of technical validation because of the confidence and good faith of 
internal participants and external constituents (ibid). From the discussion above, it can be 
inferred that selection mechanisms (survival) is not only based on technical efficiency but 
how isomorphic an organisation is to its environment. Specifically, within a network 
environment Gulati et al. (2000) discussed the role of strategic networks on firm 
performance and note that similar firms can engage in oligopolistic coordination behaviour 
in either tacit form or otherwise. This is because these enterprises have ties to similar third 
parties that may offer the opportunity for this behaviour to occur. Secondly, similarity has 
been observed to breed connection among agents in a network (McPherson et al., 2001); 
such connection, has been noted to also lead to trust ( Coleman, 1988; Burt, 2005). Trust 
has also been argued to lead to lower transaction costs as firms spend less on enforcing and 
policing costs in line with transaction cost economics (Williamson, 1979; 2005). 
Consequently, the study hypothesizes that: 
 
H1: The more isomorphic an enterprise is to its environment the higher the probability of 
survival 
 
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It is important to note that when a firm becomes too isomorphic to other enterprises in the 
network it can also experience negative consequences on enterprise outcomes. Two main 
reasons have been adduced for why firms that are similar to others in their environment are 
likely to fail within the network literature. These are triadic closure and competitive 
intensity problems. First, let us consider the issue of triadic closure. This occurs because 
similar organisations tend to cluster together and that provides opportunities for transitive 
behaviours (Symeonidis et al., 2010). Transitivity generally refers to the presence of triadic 
relations (of this nature i->j, j->k and k->i) in a network (Hanneman & Riddle, 2005). 
Transitivity leads to redundancy in a network as critical information may be locked in a 
triad and not able to be moved out if not bridged by an outside actor (Buskens, 1998). This 
redundancy prevents enterprises from engaging in bridging behaviour (Burt, 1992) which 
is critical for the development of technical competences and exchange of resources. 
Another reason that has been mooted for why being too similar to other firms can lead to 
failure is the problem of competitive intensity (Burt, 1992). The competition arises 
because the firms become substitutable: a key condition for them to be structurally 
equivalent (Scott & Carrington, 2011). This is because structurally similar firms’ need the 
same resources to maintain their organisational forms and this is noticeable from the fact 
that they tie to the same alters. This need for the same resources leads to competitive 
crowding of some firms (Podolny et al., 1996). This leads to a situation where enterprises 
closely tied together through third parties engage in bickering (Burt, 2000) in order to gain 
the first right to resources embedded in the networks. The study deduce that, while being 
isomorphic to your environment can help legitimate an enterprise beyond a certain 
threshold, such isomorphism can lead to competitive intensity as a result of niche 
crowding and lead to failure. The study therefore hypothesizes that: 
 
H2: At higher levels of isomorphism an enterprise’s probability of survival reduces 
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4.2.2 Investment Climate Constraints  
Every firm operates within some institutional environment, whether it is the macro or 
industry environments. These environments are classified as the investment climate and 
plays a very significant role in the performance, growth and survival of firms (Delios & 
Henisz, 2003; Box, 2007; Yildirim, 2010). This environment can be divided into two 
broad groups: the climate and the institutions. The climate is the out-workings of the 
institutions that make them. The investment climate that has been seen to affect business 
activities are politico-legal, economic, socio-cultural, technological and infrastructural. 
The environment has been found to present businesses with a lot of uncertainty (Delios & 
Henisz, 2003). Inability to navigate the political, socio-cultural, technological and 
infrastructural landscapes in any business environment will lead to lack of performance 
and ultimately enterprise mortality (Henisz & Zelner, 2003; Henisz, 2004; Frynas, Mellahi, 
& Pigman, 2006). As mentioned earlier these environments are out-workings of some 
institutions. The institutions that firms have to deal with are formal (for licenses for 
operations); informal (for socio-cultural legitimacy); and/or supranational (for 
international certifications) ones (Lounsbury & Glynn, 2001; Bloom & Van Reenen, 2002; 
Edelman & Suchman, 2013). SMEs, like any other organizations, need to manage these 
environmental and institutional interfaces of their relationships with the broader society or 
risk going out of business.  
 
Eifert, Gelb, and Ramachandran (2008) studied the investment climate constraints in 
Africa using data from the World Bank enterprise surveys and the costs they impose on 
businesses. They reasoned that the investment climate imposes more indirect costs on 
businesses through poor infrastructure and public services compared to elsewhere in the 
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world. They suggest that this leads to African businesses having to pay high prices for 
inputs yet have to sell their products for lower prices and have lower technological levels. 
Consequently, businesses on the continent face a higher mortality level. Reviewing 
evidence from papers that used manufacturing data from the Regional Program on 
Enterprise Development (RPED) on Africa; Bigsten and Soderbom (2006) note that the 
business environment is the prime suspect for poor enterprise performance on the 
continent and therefore improving the investment climate should be a policy priority. The 
most frequently mentioned investment constraints are access and cost of finance; 
corruption; infrastructure; and inflation: researchers have also found taxes rates and tax 
administration to be significant. They also note that the implication of such a climate is 
high costs of manufacturing inputs. Okpara (2011) studied the investment constraints 
affecting the survival of small businesses in Nigeria. Results show that corruption is 
negatively associated with small business failure and this is hardly surprising as Nigeria is 
considered one of the most corrupt nations in the world. He notes situations where local 
administrators in a federal small business advocacy office consistently misapply funds 
meant to finance small businesses at the local level.  
 
More specifically on evidence from Ghana, Ackah, Aryeetey, Ayee, and Clottey (2010, pp. 
6) studied the state-business relations in Ghana and observe that “civilian governments 
have generally promoted and enjoyed good rapport with the business community while 
military governments especially in the 1980s have tended to have confrontations with the 
private sector”. They found that business managers with close contacts within the 
government or political elites have experience positive outcomes compared to other firms. 
These results indicate that political and governmental connections help such enterprises 
lobby to overcome some of the difficulties confronting enterprises, such as the number of 
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procedures it takes to obtain licenses and permits and the number of days it takes to clear 
imported goods from the port. Another major challenge is the quality of labour force in 
Ghana. Kayanula and Quartey (2000) note that, although most Ghanaian enterprises rely 
on simple technology, where skilled labour is required there is an insufficient supply of 
this labour. This then tends to limit specialization and raises the cost of production. This is 
linked to an earlier discussion by Buame (1996), who noted in a praxis and discourse on 
entrepreneurialism in Ghana that a grammar education system that emphasizes less 
technical skills hence limiting small business development. Another major constraint the 
investment climate faces is electricity supply, currently popularly called ‘dumsor’. Adom 
(2013) notes that Ghana’s electricity supply suffers power outages with very high 
uncertainty levels in the timing, frequency and duration. This makes it difficult for 
households and businesses to re-optimise their behaviour accordingly, as well as coming 
with its own attendant high costs of replacing lost power. The impact of uncertain power 
supply is huge on productivity and sales. A report on Ghana’s infrastructure by the World 
Bank (2010) notes a few constraints imposed by current infrastructural state on business 
activities. The report notes that Ghana has succeeded in increasing household access to 
telephone, power, and water services by developing its national infrastructure backbone 
and this development is not limited to urban areas alone. The report also notes that the 
major challenge however lies with the energy supply sector with unreliable power supplies 
holding growth back by 0.5 percentage points. Consequently, the study hypothesizes in 
line with this discussion that: 
  
H3: Investment climate constraints are associated with a negative effect on SME survival 
 
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4.2.3 The Interaction Effect 
The institutional environment within which the SME operates presents constraints such as 
lack of access to finance, land litigation, corruption, infrastructural constraints and many 
others (Bigsten & Soderbom, 2006). Can network isomorphism help SMEs overcome 
some of the constraints of the investment climate? The study suggests that SMEs that are 
isomorphic to their environment have a better survival chance than those that do not. This 
is because if the study consider the formulation suggested by DiMaggio & Walter (1991) 
as to why businesses become isomorphic under new institutional theory; it’s because they 
require institutional legitimacy. This they can achieve by being isomorphic to their 
environment (Deephouse, 1996). Under the organisational ecology theory young 
businesses suffer from the liability of newness while small firms suffer from the liability of 
smallness (Thornhill & Amit, 2003; Hannan, 2005; Sam, 2007; Geroski, Mata, & Portugal, 
2010; Wagner, 2012; Lam, Klein, Freisthler, & Weiss, 2013). These liabilities result from 
the fact that these firms are not reliable in the eyes of environmental actors and hence do 
not legitimate them. SMEs are usually small and young businesses with little resources 
available and require environmental legitimation to survive. Zimmerman and Zeitz (2002) 
have argued that it is important that small ventures move beyond surviving to building 
legitimacy that can help them access other resources from the environment. Small 
businesses can gain legitimacy by being isomorphic to its environment. This helps the 
business to model its structure on those of stable firms in its environment and become 
stable itself. More specifically, small business can avoid ambiguity and uncertainty 
resulting from investment climate constraints by modelling its actions on other successful 
businesses in its environment: mimetic isomorphism. Also, small enterprises can gain 
access to resources allocated to an industry by conforming to the rules of engagement that 
shape the industry standards accepted as proper: normative isomorphism. Finally, small 
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businesses need to conform to formal structures that guide the operation of businesses 
within a broader national and supra-national framework and risk the loss of legitimation by 
such bodies: coercive isomorphism. This loss of legitimation leads to loss of resources and 
consequent failure of these small businesses. The study therefore hypothesize that 
 
H4: SMEs that are isomorphic to their environment have a better survival chance than 
those that do not. 
4.3 Research Methods 
4.3.1 The Research Setting 
The study is set in the poultry cluster of three districts in the Brong Ahafo Region of 
Ghana. These districts are Dormaa Central (Dormaa Ahenkro), Dormaa West 
(Nkrankwanta) and Dormaa East (Wamfie). This cluster is popularly known in the 
agriculture literature as the Sunyani/Dormaa cluster (Mensah-Bonsu & Rich, 2010). 
Poultry production in this cluster is one of the largest in the region and nationwide (see 
national poultry statistics in appendix). Some of the major challenges encountered by the 
poultry farmers included financing; diseases; and absence of electricity for operations in 
most farms. The presence of feed processing mills, poultry input shops and availability of 
organised markets served as prospects that could be harnessed to boost the growth of the 
poultry industry in the district (Adei & Asante, 2012). Sectorally, the poultry sector in 
Ghana is one of the few sectors that have faced the harsh realities of trade liberalization. 
This has led to the collapse and below-capacity operation of many of the poultry clusters in 
the country before the mid-1980s to early 1990s (FAO, 2014). Compared to cocoa, maize 
or rice, the poultry sector does not feature prominently in economic planning or 
agricultural policy and programme documents. Where it is referred to, the proposed 
measures appear to lack either purpose, focus or consistency, and it is difficult to discern a 
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specific policy approach or strategy towards the poultry sector (Sumberg, Awo, Fiankor, 
Kwadzo & Thompson, 2013). Recently, the government of Ghana has initiated two 
programmes aimed at rescuing the sector from collapse. These projects are the National 
Poultry Support Project (NPSP) announced by the Ministry of Trade and Industry31 and 
the Ghana Broiler Revitalization Project (GHABROP) through the Ministry of Food and 
Agriculture (MOFA) in a ten year collaboration with the Ghana National Association of 
Poultry Farmers32. 
     
4.3.2 Data Collection 
To collect network and attribute data from poultry farmers, a composite list of 200 farmers 
was developed after discussions with industry association, the Ghana Revenue Authority 
(GRA), the Municipal Assemblies, Assemblymen and women in each unit area of the 
district and office of the paramount traditional chief (Omanhene). When the whole list was 
compiled and recurring farms taken care of, the study had a total of 163 farms. The study 
contacted all the farmers to interview them, of which 155 farmers participated in the study 
representing a 95.05% response rate. It is important to note that not all the farmers in the 
cluster area was included in the compiled list because some of the farms were household 
farms that are not operating as an enterprise and hence were excluded from the study. The 
average interview took between 45 and 60 minutes. The farmers were asked for 
information regarding enterprise characteristics, owner characteristics and general 
organisational competences. In relation to the network data, farmers were asked to name 
other farmers in the study area that they collaborated with for the purposes of their 
business in line with the multiple name generator approach (Rooks et al., 2012). The study 
                                                     
31
 http://graphic.com.gh/news/general-news/22412-government-support-to-poultry-industry-
increasing.html 
32
 http://www.thepoultrysite.com/poultrynews/32740/ghanas-broiler-sector-to-get-legup-from-
revitalization-project 
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then checked if the names provided were on the study list; if not, the study further checked 
the location of the named farm. In most cases the study found that farms that were not on 
the study list were outside the study area and consequently were discounted for network 
boundary validity purposes (Boutilier, 2007; Carpenter, Li, & Jiang, 2012). After every 
interview the face validity of the responses was assessed by ensuring that respondents’ had 
evidence to support the existence of a link. If none was produced, the link was discounted. 
Two rounds of data were collected for the purposes of this study. The first round of data 
was collected in January 2014 and the second round of data was collected in March 2015. 
The cohesive characteristics of the network are presented below. 
 
Table 4. 1: Collaborative Network Characteristics 
Measure 2014 2015 (∆2015-2014) 
Density 0.013 0.023 0.01 
Centralization 0.073 0.028 -0.045 
Closure 0.183 0.063 -0.12 
Diameter 19 10 -9 
Dyadic Reciprocity 0.074 0.042 -0.032 
 
The collaborative network characteristics and their implications for the actors embedded in 
them are discussed in line with the definitions and computations of one-mode data 
implemented in the UCINET (Borgatti, Everett, & Freeman, 2002). In this network the 
study observes very low levels of network density although the study observe a marginal 
increase in density between 2014 and 2015. The low levels of density may be indicative of 
a network with a slow diffusion of resources, and also has low levels of network 
constraints as well. The overall network centralisation was reduced by approximately 
4.5%. The study also observed a negative change in network closure indicative of the fact 
that there has been a reduction in possible trust as a result of loss in the local 
connectedness of actors in the network (Coleman, 1988). There was also a reduction in the 
diameter which may signal the fact that enterprises in the network can more easily reach 
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all parts of the network to access resources owned by specific local neighbourhoods 
(Hanneman & Riddle, 2005). The level of dyadic reciprocity was also reduced. This is 
indicative that actors reduced the number of times that they returned collaborative 
initiatives and favours. 
 
4.3.3 Measures and Operationalization 
Dependent Variable: SME Survival (SURV) 
In this section the study operationalizes the class of businesses within which the study 
measure survival. The study measure the survival of small and medium scale enterprises 
(SMEs). The definition of the word SME is as complicated as the sector itself 
(Senderovitz, 2009). Senderovitz (2009) notes that in many articles it is difficult to see 
whether the researcher has considered what a small SME actually is, whether the particular 
definition is appropriate, or what the consequences for the conclusion might be if other 
definitions were used. Many different definitions have been offered by several scholars 
and institutions considering the turnover, assets, number of employees, working capital 
and balance sheet of the business ( Kayanula & Quartey, 2000b; Gibson & van der Vaart, 
2008). However, developing an operationalization that considers all these issues raises 
some of the complexities discussed by Senderovitz (2009). Also, the operationalisation of 
such definitions even if feasible may be practically impossible considering that most 
Ghanaian businesses do not keep accurate information and records (Yusuf & Saffu, 2005) 
that will aid effective classification. Consequently, the study fall on the number of 
employees based definition that has been utilized by several Ghanaian scholars ( Kayanula 
& Quartey, 2000b; Yusuf & Saffu, 2005; Hinson & Sorensen, 2006; Abor, 2007; 
Kyereboah-coleman & Amidu, 2008; Saffu et al., 2008; Abor & Quartey, 2010; Abor & 
Biekpe, 2012). The employee based definition notes that an SME cannot employ more 
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than 99 employees. This definition notes that SMEs can be divided into three broad 
categories: micro, small and medium with categorization based on micro SMEs having less 
than 6 employees; small SMEs having less than 30 employees while medium SMEs have 
less than 99 employees. This definition, no doubt, has its inefficiencies (does not account 
for turnover, assets, capital and balance sheet) but so far seems accepted by Ghanaian 
scholars and practitioners as a fair reflection of the Ghanaian SME context as well as 
easily ‘operationalizable’ within the context of this study.   
 
Next, the study focus on what survival means in the context of the study. SME survival 
and failure are two different sides of the same coin. SME failure has been called different 
names in the management literature to include bankruptcy, decline, retrenchment, SME 
death, mortality, downsizing and exit (Mellahi & Wilkinson, 2004). The study 
operationalize failure in line with Hannan and Freeman (1986) who defined it as “when an 
SME ceases to carry out routine actions that sustain its structure, maintains flows of 
resources and secure allegiance of its members”. They, as part of their definition, note four 
kinds of mortality: disbanding, acquisition, merger and radical change. This paper also 
operationalizes organizational failure in the manner proposed by Hannan and Freeman 
(1986) but do not see mergers and radical change in form as a failure. SMEs do not lose 
the allegiance of their members when it merges or changes form (Jones, 2005). Those are 
managerial manoeuvres for the SME to adapt to its environment and maintain functional 
utility for its members ( Young, 1988; Greenwood & Suddaby, 2006). This study therefore 
defines SME failure as when an SME can no longer meet the utility of its stakeholders and 
are operationalized using the traditional closure approach. The study conducted two rounds 
of data collection and those SMEs that had gone out of business by the second round were 
deemed to have failed in this respect. SMEs that survived were coded as 1 and those that 
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failed were coded as 0. At the end of the period 63 SMEs had failed while 92 survived out 
of the 155 SMEs that participated in the study. See details in table below. The high 
mortality rate may be symptomatic of the many years of neglect as mentioned earlier under 
the research setting but also confirms what small business scholars have opined - that 
approximately 90% of SMEs do not see their second birthday. The study also observed 
that 28 new entrants entered the sector in the area under study representing 23.33% of the 
number of businesses operating in the sector as at 2015. 
 
 
Table 4. 2: Exit and Entry Rate 
    2014 2015 
2014 Survived 155 92 (59.35%) 
  Failed  63 (40.65%) 
2015 New Entrants  28 (23.33%)33 
 
 
Main Effect: Isomorphism (ISO) 
The study operationalize isomorphism as how structurally equivalent an enterprise is in the 
network (Galaskiewicz & Burt, 1991). This measures the number of times that two given 
enterprises i and j are tied to the same alter q divided by the total number of alters n that 
are available to them. However, applying such a measure will make the network sparse 
and provide little variation in the structural equivalence of the enterprises and hence the 
study utilize the Jaccard coefficient approach (Hanneman & Riddle, 2005). Consider that 
enterprise i has a total of q+p ties and enterprise j has a total of q+r ties and q represents 
the number of ties that they are tied together: then their structural equivalence sq can be 
given as the following formula (Borgatti & Everett, 1997):  
 
𝑠𝑞 = 𝑞/ (𝑝 + 𝑞 + 𝑟) 
                                                     
33
 As percentage of firms existing as at March 2015 
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This formula yields a correlation matrix of the percentage (between 0 and 1) of 
equivalence between all enterprises in the network. In order to get the total similarity score 
for each actor the study sum its equivalence values across its column for its total structural 
equivalence score.   
Investment Climate Constraints (ICC) 
The study operationalizes investment climate constraints with sixteen (16) self-reported 
constraints in the business operating environment. These questions were adopted from the 
World Bank Enterprise Surveys Questionnaire for Ghana in 2007 and are related to the 
ease and cost of accessing finance; difficulty in accessing land for poultry farming; 
difficulty in acquiring permits from local government authorities; corruption in relating to 
government agencies; how court related procedures impede business; problems related to 
theft of farm equipment and produce; ease of clearing imported goods from the ports; 
access and reliability of electricity supply; quality of labour force; issues relating to 
regulations; political stability; competition from the informal sector; constraining effects of 
tax rates and administration; challenges in transporting products across regions; and the 
challenges posed by inflation. The questions were implemented with a Likert scale from 
one (1) to seven (7). The respondents were asked to indicate 1 when the variable was not a 
constraint and 7 when it was highly constraining. The study then summed all the responses 
and standardized the variable for each respondent for a composite investment climate 
score. 
 
Interaction Effects 
To create a given interaction effect the study followed the approach suggested by Balli and 
Sørensen (2012) and specify it as follows:  
 
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INT = (ISOi – ISOm)(Mi – Mm) 
 
where INT is the demeaned interaction effect; ISOi is the isomorphism score of a given 
SME; ISOm is the mean of isomorphism;  Mi is a given moderator variable and Mm is the 
mean of that moderator. This approach is used to create the interactive variable between 
isomorphism and investment climate constraints and the squared isomorphism term. 
Covariates and Moderators 
In this section the study present the covariates that the study used as controls in the study 
models, how they are operationalised, and their sources in the academic literature. 
 Table 4.3: Operationalization of Covariates 
Covariate Operationalization Source 
Age (AGE) The number of years the enterprise has been 
operating. 
(Mens & Hannan, 
2014) 
Generalist (GEN) Whether an enterprise undertakes only 
broiler or layer farming (0) and whether and 
enterprise does both (1)  
(Carroll et al., 2002) 
Size The number of employees the enterprise has  (Tsvetkova et al., 
2014) 
Education (EDUC) This is rank data with no education as 1 and 
tertiary education as 5 
(Jo & Lee, 1996) 
Experience (EXP) The number of years of industry experience 
of owner including prior to establishing the 
enterprise 
Gender (M) The gender of enterprise owner if male is 1 
and 0 if female. 
(Park, 1996) 
Entrepreneurial 
Orientation (MO) 
This is operationalized as the risk taking, 
proactive and innovative tendencies of the 
enterprise. This is measured using a Likert 
scale with 1 as strongly disagree and 7 as 
strongly agree. The study then standardizes 
the variable.  
(Jantunen, 
Puumalainen, 
Saarenketo, & 
Kyläheiko, 2005) 
Market Orientation 
(MO) 
This is operationalized as customer 
meetings, informal customer discussions 
and review of marketing approaches. This is 
measured using a Likert scale with 1 as 
strongly disagree and 7 as strongly agree. 
The study then standardizes the variable. 
(Kohli et al., 1993) 
Absorptive 
Capacity (AC) 
This is operationalized as new information 
search, cross-enterprise problem solving, 
knowledge application and adoption of new 
technology. This is measured using a Likert 
(Flatten et al., 2011) 
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scale with 1 as strongly disagree and 7 as 
strongly agree. The study then standardizse 
the variable. 
Dynamic Capability 
(DC) 
This is operationalized as business 
planning, on-the-job training and effective 
industry benchmarking. This is measured 
using a Likert scale with 1 as strongly 
disagree and 7 as strongly agree. The study 
then standardize the variable 
(Protogerou et al., 
2011) 
 
4.3.4 Model Specification 
To examine the relationship between network isomorphism and enterprise survival the 
study generated three probit models. The first model investigates the relationship between 
covariates and survival; in the second model the study add the investment climate and 
isomorphism variables and in the third model the study add the interaction term between 
investment climate constraints and the squared isomorphism term. The study specify the 
general probit model as: 
 
P(ESt+1=1) = Φ (β0 + β1SMEt + β1OCt + β1MCOMPt + β1ICCt + β2Mt + β3INTt + εt) 
 
where ESt+1 indicates that an SME survives from period t (2014) to t+1 (2015); Ct is SME 
characteristics; OCt is a vector containing owner attributes; MCOMPt represents SME 
capabilities and orientations; ICCt is the perceived effects of investment climate on 
business activities; Mt is the vector containing the main effects of investment climate 
constraints and isomorphism; INTt is a vector carrying the effects of the interaction and 
squared terms; εt is the statistical noise and Φ is the cumulative distribution function of the 
standard normal distribution. The magnitude of the coefficients of the probit model cannot 
be interpreted and hence the study also estimated for the average marginal effects (AME) 
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of the model which can be interpreted. The study model the average marginal effects of the 
study probit using the following function: 
 
∂ES/∂Vi = βi Φ (β0 + β1Vt + εt) 
 
where ∂ES/∂Vi is a partial derivative with respect to V (a given vector) and the index i 
refers to the ith independent variable in V.  
 
The study performed three robustness checks in the estimation of the study models. First, 
all the study models are estimated with robust standard errors to guard against 
heteroskedasticity. Secondly, the study specifies the study models in line with the 
hierarchical regression approach to ensure that the study models are fairly robust to 
different specifications and control variables. Thirdly, the study also uses two different 
estimators (the logit and the ordinary least squares (OLS)) to model the effects of 
distribution ties on survival of SMEs (see results in appendix). On possible endogeneity, 
the study tried to include as many control variables as possible in line with existing 
literature on organisational failure (Mellahi & Wilkinson, 2004). On possible selection 
problems, the study attempted to conduct a district census of all poultry farms in the study 
area34. The Food and Agriculture Organisation has a 2014 database on farms in the study 
area in which they report that 202 farms operate in the area (FAO, 2014). The study also 
had identified 200 farms but after accounting for recurring farms and same ownership, the 
study ended up with 163 farms of which 155 farms participated. Consequently, issues of 
selection did not arise. 
 
                                                     
34
 See appendix on poultry statistics in Ghana 
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4.4 Results  
In this section the study present the results of the study data analysis.  
 
4.4.1 Descriptive Results 
The study presents the results for means, standard deviations and correlations of variables. 
The study also present results for cross tabulations of isomorphism, investment climate 
constraints and survival as well as those of the probit and average marginal effects. The 
mean age of an SME is 7 years and 6 months with 6 years standard deviation among them. 
The average enterprise had approximately 6 employees but there was high variability 
among the firms of about 11 employees. The average manager has approximately 9 years 
of experience with standard deviation of 6 years and 7 months. The difference between the 
SME age and experience of owner may suggest a one and a half year apprenticeship or 
understudy. The study also observed that the majority of the farmers specialised either in 
layers or broiler production with only a few generalist (26%). The majority of the owners 
are male (75%) while the average educational level was secondary education. The mean 
isomorphism score for the SMEs is 1.82 with standard deviation of 1.17. The highest 
correlation among variables was 0.64 between absorptive capacity and entrepreneurial 
orientation while the least was between survival and the generalist dummy: these were all 
below the 0.8 absolute figure suggested to lead to multicollinearity problems in estimations 
(Hair et al., 2010). The study also conducted a cross-tabulation between investment 
climate constraints, isomorphism and SME survival. The first investment climate 
constraint the study considered was access to finance. The study found that 64.52% of 
SMEs had low constraints when it came to cost and access to finance while 35.48% 
indicated that they were constrained. This may be because anecdotally most of the 
businesses are started with funds from family relations living outside Ghana and savings 
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from formal employment. The majority of the SMEs (76.39%) who had low constraints 
had a high isomorphism. 45.78% of the firms with low constraints also had low 
isomorphism. All the firms that had low constraints survived; 87.30% of the firms that had 
high constraints failed; while 12.70% that had low constraints failed. Secondly, the study 
considered access to land and 97.42% of farmers mentioned that they face low constraints 
in this area. This is because the traditional authority in Dormaa Ahenkro offers land to 
those who want to go to poultry farming in the traditional area35. All those with low 
isomorphism also had low constraints while 94.44% of those with high isomorphism had 
low constraints. Surprisingly, all those that failed had low constraints while 95.65% of 
those that survived also had low constraints. Thirdly, the study considered challenges with 
customs and port services especially considering the fact that poultry inputs such as drugs, 
supplements, soya beans and wheat are imported (FAO, 2014). 85.16% reported that they 
face low constraints in this area. This may be because most of the importation is done by 
input dealers; and hence SMEs do not interface directly with customs and port authorities. 
However, this has a feed through mechanism to affect their operations through increased 
prices. 86.75% of those with low isomorphism had low constraints while 83.33% those 
with high isomorphism also had low constraints. 14.29% of those that failed had high 
constraints while 15.22% of those that survived had high constraints. The study also 
investigates access to electricity and found that 58.06% reported that it was not a constraint 
while 41.94% reported it as a constraint. 51.81% of those with low isomorphism had low 
constraints while 65.28% of those with high isomorphism also had low constraints. 
65.08% of those that failed had high constraints while 26.09% of those that survived had 
high constraints. The study also assessed the quality of the labour force that was employed 
by the poultry SMEs considering the fact that poultry is a science that requires specific 
                                                     
35
 This was known during an interview with the Sumankwahene of the traditional area who is also a poultry 
farmer 
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activities (Mensah-Bonsu & Rich, 2010). 76.13% reported it as a low constraint while 
23.87% reported it as a high constraint. 74.70% of those with low isomorphism had low 
constraints while 77.78% those with high isomorphism also had low constraints. 30.16% 
of those that failed had high constraints while 19.57% of those that survived had high 
constraints. The findings with electricity and quality of labour force may point to the idea 
suggested by Kayanula and Quartey (2000) that SMEs in Ghana use basic technology and 
hence skilled labour may not be required and it is when they are required that quality of 
skilled labour is found lacking. The study further analysed the constraining effect of tax 
rates in the municipalities on the small businesses. 65.81% reported it as a low constraint 
while 34.19% reported it as a high constraint. 67.47% of those with low isomorphism had 
it as a low constraint while 63.89% of those with high isomorphism also had it as a low 
constraint. 31.75% of those that failed had it as a high constraint while 35.87% of those 
that survived had it as a high constraint. The study also considered the constraining effect 
of the road transport network on their distribution activities. 59.35% reported it as a low 
constraint while 40.65% reported it as a high constraint. 62.65% of those with low 
isomorphism had it as a low constraint while 55.56% those with high isomorphism also 
had it as a low constraint. 38.10% of those that failed had it as a high constraint while 
42.39% of those that survived had it as a high constraint. Finally, the study assessed the 
constraining effects of currency inflation. 45.81% reported it as a low constraint while 
54.19% reported it as a high constraint. 39.76% of those with low isomorphism had it as a 
low constraint while 52.78% those with high isomorphism also had it as a low constraint. 
77.78% of those that failed had it as a high constraint while 38.04% of those that survived 
had it as a high constraint.    
 
 
 
 
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Table 4. 4: Means (M), Standard Deviations (S.D.) and Correlations 
 M S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 
Survival 0.59 0.40 1               
Age 7.50 6.08 0.15 1              
Size 6.35 11.20 0.23 0.42 1             
Generalist 0.26 0.44 -0.53 -0.20 -0.17 1            
Education 3.65 0.86 -0.23 0.17 0.12 0.03 1           
Experience 8.96 6.70 0.34 0.61 0.32 -0.30 -0.12 1          
Male 0.75 0.44 0.07 0.07 0.02 -0.13 -0.15 0.30 1         
Market 
Orientation 
-0.10 0.99 0.43 -0.01 0.06 -0.25 -0.23 0.22 0.22 1        
Absorptive 
Capacity 
-0.18 0.95 0.51 0.13 0.38 -0.28 -0.19 0.27 0.07 0.54 1       
Entrepreneurial 
Orientation 
-0.10 1.00 0.43 0.16 0.35 -0.22 -0.06 0.24 0.07 0.52 0.64 1      
Dynamic 
Capability 
-0.12 1.02 0.55 0.11 0.29 -0.33 -0.17 0.26 0.13 0.52 0.60 0.58 1     
Investment 
Constraints (ICC) 
-0.20 0.96 -0.49 0.05 0.15 0.23 0.22 -0.17 -0.20 -0.30 -0.18 -0.07 -0.31 1    
Isomorphism 
(ISO) 
1.82 1.17 0.42 0.14 0.34 -0.26 0.05 0.18 0.07 0.26 0.38 0.34 0.35 -0.14 1   
Isomorphism 
Squared 
1.35 1.90 -0.01 0.03 0.20 -0.01 -0.03 0.05 0.01 0.19 0.29 0.22 0.16 0.02 0.36 1  
ISO*ICC -0.14 1.19 0.04 0.05 0.22 0.00 0.00 0.00 0.00 -0.05 -0.07 -0.09 -0.02 0.06 -0.18 -0.29 1 
 
 
 
 
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     Table 4. 5: Cross Tabulations of Isomorphism, Investment Climate Constraints and Survival 
    Low 
Isomorphism 
(%) 
High 
Isomorphism 
(%) 
Failed (%) Survived 
(%) 
Total (%) 
Finance  
  
Low Constraint 45 (54.22 ) 55 (76.39 ) 8 (12.70 ) 92 (100.00 ) 100 (64.52 ) 
High Constraint 38 (45.78 )  17 (23.61 ) 55 (87.30) 0 (0.00 ) 55 (35.48 ) 
Land Access 
  
Low Constraint 83 (100.00 ) 68 (94.44 ) 63 
(100.00) 
88 (95.65 ) 151 (97.42 ) 
High Constraint 0 (0.00 ) 4 (5.56 ) 0 (0.00 ) 4 (4.35 ) 4 (2.58) 
Permits 
  
Low Constraint 14 (16.87 ) 18 (25.00 ) 10 (15.87) 22 (23.91 ) 32 (20.65 ) 
High Constraint 69 (83.13 ) 54 (75.00 ) 53 (84.13) 70 (76.09 ) 123 (79.35 ) 
Corruption 
  
Low Constraint 55 (66.27 ) 52 (72.22 ) 44 (69.84) 63 (68.48 ) 107 (69.03 ) 
High Constraint 28 (33.73 ) 20 (27.78 ) 19 (30.16) 29 (31.52 ) 48 (30.97 ) 
Courts 
  
Low Constraint 50 (60.24 ) 32 (44.44 ) 36 (57.14) 46 (50.00 ) 82 (52.90 ) 
High Constraint 33 (39.76 ) 40 (55.56 ) 27 (42.86) 46 (50.00 ) 73 (47.10 ) 
Crime 
  
Low Constraint 64 (77.11 ) 54 (75.00 ) 48 (76.19) 70 (76.09 ) 118 (76.13 ) 
High Constraint 19 (22.89 ) 18 (25.00 ) 15 (23.81) 22 (23.91 ) 37 (23.87 ) 
Customs 
  
Low Constraint 72 (86.75 ) 60 (83.33 ) 54 (85.71) 78 (84.78 ) 132 (85.16 ) 
High Constraint 11 (13.25 ) 12 (16.67 ) 9 (14.29 ) 14 (15.22 ) 23 (14.84 ) 
Electricity 
  
Low Constraint 43 (51.81 ) 47 (65.28 ) 22 (34.92) 68 (73.91 ) 90 (58.06 ) 
High Constraint 40 (48.19 ) 25 (34.72 ) 41 (65.08) 24 (26.09 ) 65 (41.94 ) 
Workforce 
Quality 
Low Constraint 62 (74.70 ) 56 (77.78 ) 44 (69.84) 74 (80.43 ) 118 (76.13 ) 
High Constraint 21 (25.30 ) 16 (22.22 ) 19 (30.16) 18 (19.57 ) 37 (23.87 ) 
Labour 
Regulations 
Low Constraint 18 (21.69 ) 18 (25.00 ) 13 (20.63) 23 (25.00 ) 36 (23.23 ) 
High Constraint 65 (78.31 ) 54 (75.00 ) 50 (79.37) 69 (75.00 ) 119 (76.77 ) 
Political 
Stability 
Low Constraint 51 (61.45 ) 46 (63.89 ) 36 (57.14) 61 (66.30 ) 97 (62.58 ) 
High Constraint 32 (38.55 ) 26 (36.11 ) 27 (42.86) 31 (33.70 ) 58 (37.42 ) 
Informal Low Constraint 54 (65.06 ) 43 (59.72 ) 42 (66.67) 55 (59.78 ) 97 (62.58 ) 
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Sector High Constraint 29 (34.94 ) 29 (40.28 ) 21 (33.33) 37 (40.22 ) 58 (37.42 ) 
Tax 
Administration 
Low Constraint 60 (72.29 ) 42 (58.33 ) 40 (63.49) 62 (67.39 ) 102 (65.81 ) 
High Constraint 23 (27.71 ) 30 (41.67 ) 23 (36.51) 30 (32.61 ) 53 (34.19 ) 
Tax Rates 
  
Low Constraint 56 (67.47 ) 46 (63.89 ) 43 (68.25) 59 (64.13 ) 102 (65.81 ) 
High Constraint 27 (32.53 ) 26 (36.11 ) 20 (31.75) 33 (35.87 ) 53 (34.19 ) 
Transportation Low Constraint 52 (62.65 ) 40 (55.56 ) 39 (61.90) 53 (57.61 ) 92 (59.35 ) 
High Constraint 31 (37.35 ) 32 (44.44 ) 24 (38.10) 39 (42.39 ) 63 (40.65 ) 
Inflation Low Constraint 33 (39.76 ) 38 (52.78 ) 14 (22.22) 57 (61.96 ) 71 (45.81 ) 
High Constraint 50 (60.24 ) 34 (47.22 ) 49 (77.78) 35 (38.04 ) 84 (54.19 ) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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4.4.2 Regression Results 
Table 4. 6: Isomorphism and SME Survival 
 AME (1) AME (2) AME (3) 
Age (A) -0.006 
(0.007) 
0.008 
(0.005) 
0.007*  
(0.004) 
Size (S) 0.012 
(0.010) 
0.011 
(0.009) 
0.005  
(0.008) 
Generalist (=1) (G) -0.285*** 
(0.046) 
-0.212*** 
(0.043) 
-0.203*** 
(0.046) 
Education -0.059** 
(0.029) 
-0.014 
(0.023) 
-0.024 
(0.025) 
Experience 0.009 
(0.006) 
0.010** 
(0.004) 
0.010***  
(0.004) 
Gender (Male=1) -0.085 
(0.059) 
-0.193*** 
(0.042) 
-0.164*** 
(0.050) 
Market Orientation 0.030 
(0.033) 
0.033 
(0.025) 
0.034  
(0.024) 
Entrepreneurial 
Orientation 
0.049 
(0.039) 
0.084*** 
(0.026) 
0.089***  
(0.024) 
Absorptive Capacity 0.034 
(0.037) 
0.124*** 
(0.030) 
0.130***  
(0.029) 
Dynamic Capability 0.111*** 
(0.031) 
0.039 
(0.028) 
0.035  
(0.025) 
Investment Climate 
Constraints (ICC) 
 -0.267*** 
(0.039) 
-0.247*** 
(0.035) 
Isomorphism (ISO)  0.083*** 
(0.018) 
0.058***  
(0.015) 
ISO Squared   -0.053*** 
(0.014) 
ISO*ICC   0.050*  
(0.027) 
    
Wald 71.07*** 37.67*** 34.29*** 
Pseudo R2 0.506 0.755 0.801 
Observations 155 155 155 
+Standard errors in parentheses are robust to heteroskedasticity 
++Significance Levels: 1%***; 5%**; 10%* 
 
The study now turns to the study econometric analysis. The study reports the average 
marginal effects (AME) of the probit estimates (see in appendix). In column 1, the study 
presented the covariate model, in column 2, the study added the main effects (isomorphism 
and investment climate constraints) and in column 3, the study added the isomorphism 
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squared and interaction between isomorphism and investment climate constraints. The 
study began by reporting the fitness of the study models to the data. All the models 
showed a significant Wald statistic of 71.07*** for column 1; 37.67*** for column 3 and 
34.29*** for column 5. The Pseudo R2 for the models range from 0.506 to 0.801. The 
study found that age of SME has a positive and significant relationship with survival 
(0.095 and 0.007) in column 3. The generalist dummy is seen to be negatively associated 
and well determined in all the columns (-0.285, -0.212, and -0.203). Education of owner is 
seen to be significant in only the covariate model (-0.059). Experience of the owner is 
positive and significant in the main effect and interaction models. Gender of owner which 
is a male dummy has a negative and significant effect in the main effect and interacted 
models (-0.193, and -0.164). Entrepreneurial orientation (0.084 and 0.089) and absorptive 
capacity (0.124, and 0.130) are positive and well determined in the main effects and 
interacted models. Dynamic capability is seen to be significant in the covariate model only 
(0.111). Investment climate constraints are seen to be negative and well determined in the 
main effects and interaction model (-0.267 and -0.247). Isomorphism is also seen to have a 
positive and significant association with SME survival (0.083 and 0.058) in columns 2-3. 
Isomorphism squared is seen to be negatively significant (-0.053) while the interaction 
between isomorphism and investment climate constraints is seen also to be positively 
significant (0.050) in the interacted model.  
 
4.5 Discussions of Results 
In this section the study discuss the findings of the study in line with existing literature. 
The study sought to explain the association between level of SME isomorphism and 
survival; and whether this isomorphism was useful in overcoming the burdens of 
investment climate constraints. The study operationalizes isomorphism as the level of 
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structural equivalence of an SME in a collaborative network survey in 2014 and survival 
as the persistence of the SME into 2015. The data was collected from a poultry cluster in 
rural mid-western Ghana that is one of the best performing in the country as a critical case 
(Flyvbjerg, 2006; FAO, 2014); and also to avoid network sprawl and sparseness 
(Hanneman & Riddle, 2005). The study modelled the study data with a cross sectional 
probit model with lagged independent variables while controlling for the SME 
characteristics, owner characteristics and organisational competencies. In this section the 
study discuss the findings based on the AMEs results in column six of table 4.6 presented 
in the previous section. 
 
The study first discusses the effects of the covariates on survival. The study controlled for 
SME characteristic such as age, size and resource utilization relating to organisational 
ecology theory. The study found that age of the SME was positively related to survival 
while generalist as a dummy for resource utilization is negatively related. The findings on 
age of SME conforms to the age dependence hypothesis that suggests that as a firm ages it 
is less likely to suffer the liability of newness as it acquires the competencies required to 
survive in the industry and also becomes reliable in the estimations of relevant 
stakeholders (Mens & Hannan, 2014). The findings on the generalist dummy confirms the 
resource partition hypothesis that indicates that firms that require broad resources from the 
environment and hence, when not legitimated by stakeholders, can fail (Carroll et al., 
2002). The study also controlled for owner characteristics of education, gender and 
experience. The study found that businesses of experienced owners are more likely to 
survive while businesses owned by men are more likely to fail compared to those owned 
by women. The findings on gender conforms to an emerging stream of literature on gender 
in small business that suggests that women may be favoured or only capable women are 
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selected into entrepreneurship (Hansen & Rand, 2014b). The findings on experience is in 
line with top management literature that suggests that experienced managers are more 
likely to gain positive outcomes (Jo & Lee, 1996). Furthermore, the study also controlled 
for the effects of organisational competencies like market orientation, entrepreneurial 
orientation, absorptive capacity and dynamic capability in line with management theory. 
The study found that all the variables have a positive effect on survival but only 
entrepreneurial orientation and absorptive capacity rise to significance. The general 
positive effect of managerial and organisational competencies confirms management 
literature that managers must manage their firms to reduce the negative effects of 
environmental pressures (Miles, 2012). The significance of absorptive capacity suggest 
that small businesses that develop systems that enable them to absorb resources from the 
environment have a better survival likelihood (Zahra & George, 2002), while that of 
entrepreneurial orientation suggest that those firms that can take risks, be proactive and 
innovative will survive (Jantunen et al., 2005). 
 
The study now turns to the study hypothesized effects. The study hypothesized that as a 
small business becomes increasingly isomorphic to its environment the more likely it will 
survive and confirm the study hypothesis. This is likely because, from an institutional 
theory perspective, isomorphic businesses conform to the coercive, mimetic and normative 
pressures and hence are legitimated (DiMaggio & Walter, 1991). This legitimation is then 
useful in acquiring other resources that are needed for the fight for survival (Deephouse, 
1996). Also, by being isomorphic the small businesses are validated by their structural 
forms and not their technical efficiency and hence are able to survive in line with the logic 
of good faith (Scott, 1991). Also, isomorphism enables the small business to copy more 
stable businesses in its environment to reduce turbulence and maintain stability that aids 
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survival (Meyer & Rowan, 1977). From a network theory perspective, this may be possible 
because isomorphic businesses are similar in structure and links, therefore can engage in 
oligopolistic coordination (Gulati et al., 2000). Again, the similarity breeds cohesion which 
is a major ingredient in network trust and such trust is useful in reducing transaction costs 
with each other; hence, improving survival chances ( Williamson, 1979; Coleman, 1988; 
McPherson et al., 2001). The study also hypothesized that when the firms get too similar it 
can have negative consequences on survival and confirm this hypothesis. This, the study 
argue, may be due to the fact that when businesses become structurally similar they 
compete for the same resources and hence lead to competitive crowding and failure of 
some of the businesses (Podolny et al., 1996). Also from network theory, similar firms 
engage in triadic closure and hence redundant information is locked in it and there are 
limited opportunities to engage in bridging behaviour for new information (Burt, 2004; 
Symeonidis et al., 2010). Again, the study hypothesized that investment climate 
constraints reduce the survival chances of small businesses and unsurprisingly confirm the 
hypothesis. This may be possible because they impose indirect costs on productive 
activities of these businesses (Eifert et al., 2008). Furthermore, the study hypothesized that 
isomorphism can be useful in overcoming investment climate constraints. The study found 
that the interaction between investment climate constraints and isomorphism is positive 
which confirms the hypothesis. A reason for this may be because small businesses are able 
to reduce their liabilities by being isomorphic and gaining the good faith of relevant 
stakeholders which becomes a currency to acquire other critical resources in that 
environment (Deephouse, 1996). 
 
The study now discusses the implications of the results for enterprise management and 
research. The study suggested to managers of small business to copy the activities of other 
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SMEs in their industry as this will offer them the needed stability through the legitimation 
of their activities in line with the findings on isomorphism and survival. However, the 
study also cautioned that there is an optimum point beyond which such ‘copying’ will 
return negative returns. In the study data the optimum point is the second order derivative 
of the squared term but the experience of the manager in leading his business to this 
optimum point may be critical. Another incentive for small businesses to base their actions 
on those of other businesses is that it can be useful in overcoming investment climate 
constraints. This mechanism may work through the logics of good faith where small 
businesses can avoid certain indirect costs imposed by the investment climate. On research 
implications, the study focussed the study study on a particular poultry cluster as a critical 
case. The study will encourage other studies to focus on other poultry clusters in the 
country to replicate this study. The study also used structural equivalence to measure 
similarity but other studies can use measures of cohesion as a measure of similarity to 
replicate the study.   
 
4.6 Conclusion 
The main objective of this study was to understand if an SME becomes isomorphic to 
other SMEs in its environment, it survival probability increases and if such isomorphism 
returns negative results at some point. The study also considered whether this isomorphism 
can also be useful in overcoming investment climate constraints. The study found that 
isomorphic SMEs have a better survival probability, and such isomorphism was very 
useful in overcoming investment climate constraints. However, the isomorphism reaches 
an optimum and thereafter returns a negative effect. Consequently, the study conclude that 
it is useful for firms to be isomorphic to their environments however there is need for 
caution as this can return some negative effects after some point. 
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APPENDIX: 
 
Table 4A1: Ghana Poultry Sector Statistics 
National  Brong Ahafo Region 
Region No. of Birds 
(%) 
Egg 
Production (in 
millions) 
No. of 
Farms+ 
District No. of 
Farms+ 
Greater 
Accra 
2,547,219 (7.02%) 542 475 Dormaa 202 
Central  903,702 (2.49%) 437 312 Jaman South 9 
Western 1,406,642 (3.88%) 247 102 Berekum 34 
Eastern 3,886,914 
(10.72%) 
358 213 Sunyani 65 
Volta 1,071,622 (2.95%) 68 98 Techiman 25 
Ashanti 10,180,760 
(28.07%) 
5,321 697 Tano South 16 
Brong-
Ahafo 
10,743,897 
(29.62%) 
3,989 510 Nkoranza 32 
Northern 3,625,149 (9.99%) - 30 Kintampo 
South 
1 
Upper 
East 
1,325,835 (3.66%) - 33 Asunafo 
North 
17 
Upper 
West 
579,474 (1.60%) - 34 Asunafo 
South 
0 
    Asutifi 10 
    Wenchi 9 
    Sunyani West 90 
+Based on FAO Sector 1/2/3 Classification 
Sources: Veterinary Services Directorate (2010, 2011, 2013) in FAO, 2014 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table 4A2: Probit Estimates 
 Probit (1) Probit (2) Probit (3) 
Age (A) -0.033 
(0.041) 
0.085 
(0.056) 
0.095* 
(0.057) 
Size (S) 0.068 
(0.056) 
0.118 
(0.101) 
0.070 
(0.105) 
Generalist (=1) (G) -1.548*** 
(0.300) 
-2.296*** 
(0.619) 
-2.687*** 
(0.845) 
Education -0.322** 
(0.159) 
-0.149 
(0.258) 
-0.314 
(0.353) 
Experience 0.050 
(0.035) 
0.113** 
(0.048) 
0.134*** 
(0.051) 
Gender (Male=1) -0.464 
(0.331) 
-2.093*** 
(0.508) 
-2.179*** 
(0.674) 
Market Orientation 0.163 
(0.184) 
0.362 
(0.272) 
0.448 
(0.310) 
Entrepreneurial 
Orientation 
0.265 
(0.213) 
0.907*** 
(0.323) 
1.186*** 
(0.409) 
Absorptive Capacity 0.186 
(0.203) 
1.340*** 
(0.370) 
1.721*** 
(0.497) 
Dynamic capability 0.601*** 
(0.182) 
0.418 
(0.318) 
0.461 
(0.352) 
Investment Climate 
Constraints (ICC) 
 -2.899*** 
(0.641) 
-3.275*** 
(0.788) 
Isomorphism (ISO)  0.906*** 
(0.256) 
0.768*** 
(0.258) 
ISO Squared   -0.710*** 
(0.242) 
ISO*ICC   0.666* 
(0.368) 
    
Wald 71.07*** 37.67*** 34.29*** 
Pseudo R2 0.506 0.755 0.801 
Observations 155 155 155 
+Standard errors in parentheses are robust to heteroskedasticity 
++Significance Levels: 1%***; 5%**; 10%* 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table 4A3: Logit and Average Marginal Effect Estimates 
 Logit AME Logit AME Logit AME 
Age  -0.058 
(0.081) 
-0.006 
(0.009) 
0.151 
(0.111) 
0.008 
(0.006) 
0.183 
(0.113) 
0.008** 
(0.004) 
Size  0.112 
(0.092) 
0.012 
(0.010) 
0.201 
(0.200) 
0.011 
(0.011) 
0.110 
(0.185) 
0.005 
(0.008) 
Generalist (=1)  -2.596*** 
(0.535) 
-0.279*** 
(0.049) 
-4.140*** 
(1.322) 
-0.223*** 
(0.052) 
-5.211** 
(2.232) 
-0.221*** 
(0.060) 
Education -0.536* 
(0.287) 
-0.058* 
(0.031) 
-0.308 
(0.512) 
-0.017 
(0.027) 
-0.669 
(0.723) 
-0.028 
(0.028) 
Experience 0.084 
(0.070) 
0.009 
(0.007) 
0.185** 
(0.081) 
0.010** 
(0.004) 
0.229** 
(0.096) 
0.010** 
(0.004) 
Gender (Male=1) -0.803 
(0.601) 
-0.086 
(0.062) 
-3.484*** 
(0.900) 
-0.188*** 
(0.045) 
-3.643*** 
(1.366) 
-0.154** 
(0.064) 
Market Orientation 0.289 
(0.324) 
0.031 
(0.034) 
0.563 
(0.554) 
0.030 
(0.030) 
0.735 
(0.634) 
0.031 
(0.028) 
Entrepreneurial 
Orientation 
0.473 
(0.393) 
0.051 
(0.042) 
1.620** 
(0.653) 
0.087*** 
(0.030) 
2.270** 
(0.967) 
0.096*** 
(0.026) 
Absorptive 
Capacity 
0.334 
(0.383) 
0.036 
(0.041) 
2.383*** 
(0.788) 
0.128*** 
(0.037) 
3.274*** 
(1.240) 
0.139*** 
(0.034) 
Dynamic 
Capability 
1.062*** 
(0.329) 
0.114*** 
(0.032) 
0.778 
(0.644) 
0.042 
(0.033) 
0.868 
(0.768) 
0.037 
(0.029) 
Investment Climate 
Constraints (ICC) 
  -5.157*** 
(1.371) 
-0.278*** 
(0.047) 
-6.116*** 
(2.004) 
-0.259*** 
(0.041) 
Isomorphism (ISO)   1.620*** 
(0.549) 
0.087*** 
(0.022) 
1.420** 
(0.590) 
0.060*** 
(0.017) 
ISO Squared     -1.326** 
(0.556) 
-0.056*** 
(0.015) 
ISO*ICC     1.109* 
(0.660) 
0.047* 
(0.027) 
       
Wald 58.78***  29.58***  23.69**  
Pseudo R2 0.500  0.753  0.802  
Observations 155 155 155 155 155 155 
+Standard errors in parentheses are robust to heteroskedasticity 
++Significance Levels: 1%***; 5%**; 10%* 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table 4A4: Ordinary Least Squares (OLS) Estimates 
 Control 
Model 
+Main 
Effects 
+Interaction 
Effects 
Age  -0.003 
(0.006) 
0.000 
(0.006) 
0.000 
(0.005) 
Size  0.001 
(0.002) 
0.003 
(0.002) 
0.002 
(0.002) 
Generalist (=1)  -0.401*** 
(0.079) 
-0.336*** 
(0.072) 
-0.312*** 
(0.070) 
Education -0.073** 
(0.033) 
-0.068** 
(0.030) 
-0.069** 
(0.029) 
Experience 0.010* 
(0.006) 
0.006 
(0.005) 
0.006 
(0.005) 
Gender (Male=1) -0.115* 
(0.069) 
-0.149** 
(0.064) 
-0.150** 
(0.060) 
Market Orientation 0.041 
(0.044) 
0.014 
(0.043) 
0.019 
(0.041) 
Entrepreneurial 
Orientation 
0.068 
(0.044) 
0.049 
(0.042) 
0.070* 
(0.042) 
Absorptive Capacity 0.023 
(0.051) 
0.049 
(0.049) 
0.054 
(0.048) 
Dynamic Capability 0.117*** 
(0.036) 
0.064** 
(0.032) 
0.056* 
(0.031) 
Investment Climate 
Constraints (ICC) 
 -0.163*** 
(0.029) 
-0.157*** 
(0.029) 
Isomorphism (ISO)  0.065*** 
(0.022) 
0.096*** 
(0.023) 
ISO Squared   -0.046*** 
(0.012) 
ISO*ICC   0.028 
(0.021) 
    
F-Stat 48.55*** 44*** 42.80*** 
R2 0.516 0.618 0.653 
Observations 155 155 155 
+Standard errors in parentheses are robust to heteroskedasticity 
++Significance Levels: 1%***; 5%**; 10%* 
 
 
 
 
 
 
 
 
 
 
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CHAPTER FIVE 
BENEFITTING FROM ALTER RESOURCES: NETWORK DIFFUSION AND 
SME SURVIVAL 
Abstract 
The objective of this study is to understand whether alter resources can be useful for the 
survival of SMEs in Ghana. The study utilize data from two rounds of poultry SME 
network survey and accompanying SME attributes in rural Ghana in 2014 and 2015. The 
study specifically focused on the resources that diffuse to SMEs from their alters and the 
mechanism through which these resources diffuse to the SME. The study observed that 
alter market and technological resources are significant for the survival of the SMEs. The 
study also explored the effect of variations in alter competences along the lines of the 
resource and found that market resources have a positive effect and technological resource 
variations have a negative effect but these do not rise to significance. Finally, the study 
assessed the impact of the mechanism through which the resources are diffused and found 
that both direct and indirect tie mechanisms have a positive effect on survival of the SMEs 
but the effect of direct ties was higher than for indirect ties.   
 
Keywords: Diffusion of Innovations, Networks, Alter Resources, SMEs, Survival, Ghana 
  
5.1 Introduction 
Small and medium scale enterprises (SMEs)36 in developing parts of the world face several 
resource constraints. These constraints include raw material supplies, market, 
technological, quality labour, and transport resources (Kayanula & Quartey, 2000b; Abor 
                                                     
36
 Businesses employing less than 99 employees in Ghana 
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& Quartey, 2010). In order to address this challenge some authors have called for 
collaborations among SMEs for better resource sharing. Lichtenthaler (2008) for instance, 
asserts that SMEs can reap greater benefits from external collaborations as it can 
compensate for the scarcity of internal resources and competences. Mcdade and Malecki 
(1997) have suggested that in industrial estates, resource sharing is very common among 
individual entrepreneurs as a mechanism for overcoming the resource constraints that they 
face. Narteh (2008) also studied knowledge as a resource and its transfer within the context 
of collaborations between developed and developing countries and reports that the 
resource and the mechanism of transfer are critical to the usefulness of the resource. 
Hinson and Sorensen (2006) have noted that the internet is a mechanism through which 
resources can diffuse to small business exporters in Ghana. SMEs are, however, embedded 
in set enterprise networks that provide opportunities or constraints on their successful 
operations (Buame, 1996; Naudé, Zaefarian, Najafi-Tavani, Neghabi, & Zaefarian, 2014). 
That is, what resources can be made available from their connections for their benefit.  
 
These suggestions imply a diffusion of resources from an actor in an enterprise network to 
its alters that may require such resources. The diffusion of innovation theory of Rogers 
(2010) has been employed to explain this phenomenon. The theory suggests that such 
resources need to diffuse through a medium over time in a social system to be made 
available to the alters that require them. This means that, for the resource to be diffused, 
the medium of diffusion, the timeline and the social system within which the diffusion 
takes place are relevant. If the study hold the time and social system constant for all the 
actors in the SME network then the resource and medium are variable. In this study the 
study ask if two types of resources embedded with a SMEs alters (market and 
technological) are relevant for the survival probabilities of the SME. This is considering 
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the fact that marketing and technological capabilities vary among SMEs, and whether such 
diffusion will aid the survival of an SME. Also, will such variability matter for the survival 
of SMEs if the study consider that SMEs must make a decision as to which of the alters 
resources it needs to adopt for its usage and choosing the wrong technology can have 
negative influences on the survival of the business. Again, the mechanism through which 
resources can diffuse among alters can be both direct and indirect. The study ask if these 
mechanisms are also relevant for the survival of the SMEs.   
 
Pursuant to these research questions, the study collected network data from SMEs in the 
poultry industry in the three Dormaa (East, West and Central) districts of Ghana. The 
study used and limited the study to this industry and study site because it enabled us to 
control for network sprawl; without which the network would have been sparse and 
lacking variation in scores (Hanneman & Riddle, 2005). According to statistics from the 
national veterinary office and the Food and Agriculture Organisation (FAO), the cluster is 
one of the best37 in the country in poultry production (FAO, 2014). The sector is also a 
critical case for the industry considering its one of the best performing poultry clusters in 
the country. The study argue for the generalizability of the findings to other clusters in line 
with Flyvbjerg (2006) who note that critical case studies can be extrapolated for other 
cases in line with the black swan argument that critical cases can be major test of theory 
falsification. Using multiple name generators (Rooks et al., 2012), farm SME owners were 
asked to identify other farmers that they have collaborated with in the course of their 
business operations in the last six (6) months. The data was collected in two rounds: the 
first in January 2014 and second in March 2015. The study then constructed a one-mode 
network from which the study derived the direct and indirect ties, actor alter attributes and 
                                                     
37
 Based on FAO 1-2-3 Classification 
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the variations in those attributes. The study then modelled survival into 2015 as a function 
of lagged independent variables from 2014. The study found that there was a high failure 
rate of approximately forty percent. This finding supports the anecdote in the SME 
scholarship that most SMEs do not see their second birthday. The study also found that the 
mechanisms through which the resources diffuse and the resources themselves have a 
positive association with survival; the variations have no significant effect; market 
resource variations have a positive effect; while technological variations have a negative 
effect. In the following pages the study discuss the literature and hypotheses, the research 
methods employed, the study findings and the conclusions derived from the study. 
 
5.2 Literature Review and Hypotheses 
Borgatti and Foster (2003) in a review of network studies argue that diffusion occurs in 
networks in the form of shared attitudes, culture and practices. This occurs through the 
interaction between an actor and its alters linked by their ties. Therefore a given actor’s 
adoption of a particular culture, attitude or practice is dependent on the number of its alters 
that have that attitude, culture or practice and its intensity. The theoretical mechanism that 
has been used to explain this phenomenon is the diffusion of innovation (DoI) theory 
(Rogers, 2010). The theory defines diffusion as the process through which an innovation is 
communicated through certain medium over time among members of a social system. 
Consequently, the study defines diffusion within the context of networks in this study as 
the process by which the resources of an actor is made available to others through its ties 
over time among SMEs in an industry network. Four main issues emerge from this: (1) the 
alter resource; (2) the medium of transfer; (3) time; and (4) the network. In this study the 
study focus on the alter resource and the medium of transfer of the resource since the time 
and network are fixed for all the SMEs and offers no variation. The study, however, 
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include and test the effects of variation in alter resource outcomes on SME survival. In the 
following paragraphs the study discusses and hypothesizes these issues.      
 
5.2.1 The Alter Resources, Variations and SME Survival 
In this section the study focus on two alter resources and the variations in them that an 
SME can appropriate for its survival. These are market and technological resources and 
their consequent variations as a result of different alters having different levels of the 
resource. The first alter resource the study discuss is market resources. An actor’s 
marketing competence in the network is based on their level of market orientation (Kohli 
& Jaworski, 1990). Market orientation (MO) is derived from the application of marketing 
concepts suggesting that the key to organisational success is through the determination and 
satisfaction of the needs, wants, and aspirations of target markets (Mahmoud & Yusif, 
2012). MO is therefore a cultural orientation with behavioural implications since it 
channels organizational efforts towards learning about markets and developing strategies 
in response to market threats and/or opportunities (Cambra-Fierro et al., 2011). Parry, 
Jones, Rowley, and Kupiec-Teahan (2012) found that marketing orientation is useful for 
small business performance. SMEs with higher levels of MO will have greater access to 
the market and its resources than those that do not, but can offer some of these to other 
SMEs it is connected to. Consider an SME that, due to its market competences, receives a 
large order it is not able to meet on its own. It will likely contact other SMEs that it has ties 
with to meet part of that order to enable it to meet the customer needs. This other SME that 
is contacted will therefore be benefitting from its alters market resources. Consequently, 
the study hypothesizes that:    
 
H1a: SMEs with ties to alters rich in market resources are more likely to survive 
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However, alters are likely to have differing levels of market competences and therefore 
resources it can offer an SME. Can these variations have a negative effect on SME 
survival? In terms of market resources, this is less likely to be the case as an SME will 
only be selling to diverse markets and is more likely to give it bargaining power (Cook & 
Yamagishi, 1992). Consider the situation where an SME receives ‘help orders’ to meet its 
customer’s requests from its alters whether small or large this should not affect the 
functioning of the SME since a sale is made in each case. The only time this can be 
possible, is when such orders tend to be large and the alter exerts some power over the 
SME that it is not able to meet its own customer needs. However, this situation is less 
likely to occur in the study area and many agricultural enterprises where supply usually 
outstrips demand (Adei & Asante, 2012; FAO, 2014; Sumberg, Awo, Fiankor, Kwadzo 
and Thompson, 2013) and demand of all kinds are likely to be welcome. Consequently, the 
study hypothesizes that: 
 
H1b: SMEs with high variations in alter market resources are more likely to survive 
 
The next resource the study considers is technological resources. Technological resources 
are very important within the context of SMEs as it allows businesses to expand quickly 
and efficiently by serving as an enabler of production and service functions of the business 
(Aa & Elfring, 2002). Segarra and Callejon (2002) noted that technology is very important 
to the survival of small businesses focusing on Spanish data and evidence. They mentioned 
that technology is a key variable in a set of factors in market structure and dynamics in line 
with the Schumpeterian market theory (ibid). They also concluded that the SMEs that 
faced the lowest exit probabilities were those that had better technologies. Despite the 
importance of technology to the operations of SMEs there still persists low usage. This has 
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been attributed to mainly financial and organisational reasons (Consoli, 2012). Finance is a 
major challenge as the initial capital outlay required to buy a new technology is usually 
very high and most of these SMEs in the end are not able to acquire it. It is important to 
also note that SMEs are usually credit constrained (UNCTAD, 2001) and therefore this 
problem can only be compounded. Again, even if the technology is available there is the 
lack of skilled labour and a coherent strategy to utilize the technology (Kayanula & 
Quartey, 2000b). However, according to Rogers (2010), the biggest reason why SMEs 
may not be adopting new technologies is the lack of information and uncertainty. This is 
because SMEs can look for the funds to finance the technology and train employees to use 
the technology but if they are unsure about its possible benefits then adoption becomes 
problematic. However, having ties to alters that have these resources and competences can 
help reduce the uncertainty associated with technology and access to the new technology 
especially when the technology has led to increases in production. Consequently, the study 
hypothesizes that: 
 
H2a: SMEs that have alters with high technological resources are more likely to survive  
 
However, in reality an SME can have more than one alter and these may vary in their 
technological competences. This makes the evaluation of the technology and its associated 
uncertainty even more uncertain. This is because the reason why SMEs look to their alters 
is for information to erase the uncertainty associated with new technology (Rogers, 2010). 
Consequently, high variation in the technological resource competences and resources can 
have a negative effect on SME outcomes. This is because the SME may not be able to 
verify the technology and hence may not adopt the technology and suffer from using 
possible obsolete technology. The study therefore hypothesizes that: 
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H2b: SMEs with high variations in alter technological resources are less likely to survive  
 
5.2.2 Direct Ties, Indirect Ties and SME Survival 
Resources that flows from one alter to the other require a medium. The DoI theory 
suggests that these are communication channels (Rogers, 2010) but within this study the 
study consider this as the ties that exists between the alters. These ties can be direct ties or 
indirect ties (the study focuses on distance two only). The study starts the study discussion 
with the direct ties. The number of direct ties SME maintains has been known to positively 
influence organisational outcomes (George, Zahra, Wheatley, & Khan, 2001). Their 
influence is in the form of knowledge sharing, complementarity and scale (Ahuja, 2000). 
SMEs in clusters can share knowledge on what is best practice and this can lead to higher 
levels of performance for their sustained growth. They also achieve complementarity by 
bringing different skills sets to perform a task; especially, when it comes to meeting the 
orders of larger corporate clients where one SMEs capability may not be enough to 
achieve the required output. This is related to the scale factor. Increasingly, SMEs 
(especially, agricultural ones) must meet the raw material requirements of institutional 
buyers, and collaboration enables them to access support from their immediate 
environment to meet these large-scale project demands. Direct ties can help SMEs achieve 
these through strong ties (Granovetter, 1973); exploitative behaviour (Rowley et al., 2000) 
and embedding (Uzzi & Lancaster, 2003). Direct ties can be seen as strong ties with which 
an SME has a relationship. SMEs with direct, strong or intense relationships can easily 
access resources from each other whether it is knowledge, complementary skills and 
scaling opportunities. The same can be said for exploitation and embedding where SMEs 
can exploit or utilize their immediate contacts to gain access to knowledge, complementary 
skills and scaling opportunities. Taken together resource access from direct ties represents 
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local (an actor’s immediate neighbourhood) resource utilization (Zaheer et al., 2010). 
However, since the study are discussing diffusion from a given SMEs alters then such 
direct ties will have to be of incoming direct tie form. Consequently, the study 
hypothesizes that: 
 
H3: An increase in a given SME’s incoming direct ties is associated with a higher survival 
probability 
 
Beyond direct incoming ties SMEs can also benefit from diffusion of resources from 
indirect ties. Indirect ties refer to the ties an actor has outside his local neighbourhood as a 
result of ties of connections held by its direct ties to other ties it has no direct tie to. This is 
popularly referred to as the ‘friend-of-friend’ phenomenon (Goodreau & Kitts, 2009). 
Indirect ties can be a resource gathering and/or processing/screening mechanism (Ahuja, 
2000). In SME networks, resources can diffuse from SMEs to other SMEs beyond their 
immediate catchment area to access information and resources that can be critical for their 
functioning and existence. Indirect ties can also be used to screen information received 
from direct ties as well. For example, if information received from a direct tie is different 
from what the tie sent to its other direct ties then the trustworthiness of such information 
should be doubted. Closer indirect ties can also be used to screen information flowing to 
the SME from much more distant indirect ties. Indirect ties can also be thought of as weak 
ties (Granovetter, 1973), an explorative mechanism (Rowley et al., 2000) and/or arm’s 
length relations (Uzzi & Lancaster, 2003). The weak ties explanation suggests that SMEs 
with many indirect ties will be able to access resources from other SMEs that are not in 
their immediate environment. This lends itself to the flow of resources between groups 
rather than within groups. Linked to this idea is the explorative mechanism of indirect ties. 
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If SMEs move from their immediate groups in search of resources, which can be thought 
of as explorative, as compared to exploiting the local resources direct ties offer. These 
resources can then diffuse through the tie mechanism to the SME that sought. This is 
because the distant the indirect tie is, the less reliable the resource is likely to be. 
Exploration offers the SME the chance to gain resources that are not in its immediate 
catchment area. The arm’s length part of the argument suggests that indirect relations are 
cool, impersonal and atomistic and therefore require less investment. Like ‘books-on-a-
shelf’ SMEs can activate them as and when resources are needed. This helps them to 
largely avoid the constraining effects of over-embeddedness. Taken together, indirect ties 
represent a global access to resources by an SME embedded in a network. This is because 
these resources are beyond the SMEs immediate ties. Consequently, the study 
hypothesizes that: 
 
H4: An increase in a given SME’s indirect ties is associated with a higher survival 
probability 
 
5.3 Research Methods 
5.3.1 The Research Setting 
The study tests the above hypotheses within the Dormaa poultry cluster in three districts of 
the Brong Ahafo Region of Ghana. These districts are Dormaa Central (Dormaa Ahenkro), 
Dormaa West (Nkrankwanta) and Dormaa East (Wamfie). This cluster is popularly known 
in the agriculture literature as the Sunyani/Dormaa cluster (Mensah-Bonsu & Rich, 2010). 
Poultry production in this cluster is one of the largest in the region and nationwide (see 
national poultry statistics in appendix). Some of the major challenges encountered by the 
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poultry farmers include; financing, diseases, and absence of electricity for operations in 
most farms. The presence of feed processing mills, poultry input shops and availability of 
organised markets served as prospects that could be harnessed to boost the growth of the 
poultry industry in the district (Adei & Asante, 2012). Sectorally, the poultry sector in 
Ghana is one of the few sectors that have faced the harsh realities of trade liberalization. 
This has led to the collapse and below-capacity operation of many of the poultry clusters in 
the country before the mid-1980s to early 1990s (FAO, 2014). Compared to cocoa, maize 
or rice, the poultry sector does not feature prominently in economic planning or 
agricultural policy and programme documents. Where it is referred to, the proposed 
measures appear to lack either purpose, focus or consistency, and it is difficult to discern a 
specific policy approach or strategy towards the poultry sector (Sumberg, Awo, Fiankor, 
Kwadzo & Thompson, 2013). Recently, the Government of Ghana has initiated two 
programmes aimed at rescuing the sector from collapse, namely: the National Poultry 
Support Project (NPSP) announced by the Ministry of Trade and Industry;38 and the Ghana 
Broiler Revitalization Project (GHABROP) through the Ministry of Food and Agriculture 
(MOFA) in ten year collaboration with the Ghana National Association of Poultry 
Farmers39. The cluster the study used is a critical case for poultry production in Ghana as it 
is one of the best performing clusters in Ghana (FAO, 2014) and hence the findings from 
the study can be generalized for the poultry sector in Ghana (Flyvbjerg, 2006).  
     
                                                     
38
 http://graphic.com.gh/news/general-news/22412-government-support-to-poultry-industry-
increasing.html 
39
 http://www.thepoultrysite.com/poultrynews/32740/ghanas-broiler-sector-to-get-legup-from-
revitalization-project 
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5.3.2 Data Collection and Network Survey 
To collect network and attribute data from poultry farmers a composite list of 200 farmers 
was developed after discussions with the industry association, the Ghana Revenue 
Authority (GRA), the Municipal Assemblies, Assemblymen and women in each unit area 
of the district and the office of the paramount traditional chief (Omanhene). When the 
whole list was compiled and recurring farms taken care of, the study had a total of 163 
farms. The study contacted all the farmers to interview them, of which 155 farmers 
participated in the study representing a 95.05% response rate. It is important to note that 
not all the farmers in the cluster area were included in the compiled list because some of 
the farms were household farms that are not operating as a business; hence were excluded 
from the study since this was a primary requisite for participation. The average interview 
took approximately 45 minutes. The farmers were asked for information regarding 
enterprise characteristics, owner characteristics and general organisational and technical 
competences. In relation to the network data, farmers were asked to name other farmers in 
the study area that they collaborated with for the purposes of their business in line with the 
multiple name generator approach (Rooks et al., 2012). The study then checked if the 
names provided were on the study list; if not, the study further checked the location of the 
named farm. In most cases the study found that farms that were not on the study list were 
outside the study area and consequently was discounted for network boundary validity 
purposes (Boutilier, 2007; Carpenter, Li, & Jiang, 2012). After every interview the face 
validity of the responses was assessed by ensuring that respondents had evidence to 
support the existence of a link. If none was produced, the link was discounted. Two rounds 
of data were collected for the purposes of this study. The first round of data was collected 
in January 2014 and the second round of data was collected in March 2015. The cohesive 
characteristics of the networks in both rounds are presented below. 
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Table 5. 1: Collaborative Network Characteristics 
Measure 2014 2015 (∆2015-2014) 
Density 0.013 0.023 0.01 
Centralization 0.073 0.028 -0.045 
Closure 0.183 0.063 -0.12 
Diameter 19 10 -9 
Dyadic Reciprocity 0.074 0.042 -0.032 
 
 
The collaborative network characteristics and their implications for the actors embedded in 
them are discussed in line with the definitions and computations of one-mode data 
implemented in the UCINET (Borgatti, Everett, & Freeman, 2002). In this network the 
study observe very low levels of network density although the study observed a marginal 
increase in density between 2014 and 2015. The low levels of density may be indicative of 
a network with a slow diffusion of resources but also has very low levels of network 
constraints as well. The overall network centralisation also reduced by approximately 
4.5%. The study also observed a negative change in network closure indicative of the fact 
that there has been a reduction in possible trust as a result of loss in the local 
connectedness of actors in the network (Coleman, 1988). There was also a reduction in the 
diameter which may signal the fact that enterprises in the network can more easily reach 
all parts of the network to access resources owned by specific local neighbourhoods 
(Hanneman & Riddle, 2005). The level of dyadic reciprocity also reduced. This is 
indicative that actors reduced the number of times that they returned collaborative 
initiatives and favours. 
 
5.3.3 Measures and Operationalization 
Dependent Variable: SME Survival (SURV) 
In this section the study operationalizes the class of businesses within which the study 
measure survival. The study measure the survival of small and medium scale enterprises 
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(SMEs). Many definitions have been offered based on assets, employees, turnover and 
balance sheet (Kayanula & Quartey, 2000b; Gibson & van der Vaart, 2008) and all these 
present their complexities when they have to be operationalized. In Ghana, 
operationalizing an SME in line with assets, turnover and balance sheet presents peculiar 
complexities and difficulties (Senderovitz, 2009) as most Ghanaian businesses do not keep 
accurate information and records (Yusuf & Saffu, 2005) that will aid effective 
classification. Consequently, the study fall on the number of employees based definition 
that has been utilized by several Ghanaian scholars (Kayanula & Quartey, 2000b; Yusuf & 
Saffu, 2005; Hinson & Sorensen, 2006; Abor, 2007; Kyereboah-coleman & Amidu, 2008; 
Saffu et al., 2008; Abor & Quartey, 2010; Abor & Biekpe, 2012). The employee based 
definition notes that an SME cannot employ more than 99 employees. This definition notes 
that SMEs can be divided into three broad categories: micro, small and medium with 
categorization based on micro SMEs having less than 6 employees; small SMEs having 
less than 30 employees while medium SMEs have less than 99 employees. This definition, 
no doubt, has its inefficiencies (does not account for turnover, assets, capital and balance 
sheet) but so far seems accepted by Ghanaian scholars and practitioners as a fair reflection 
of the Ghanaian SME context as well as easily ‘operationalizable’ within the context of 
study. Next, the study focus on what survival means in the context of the study. SME 
survival and failure are two different sides of the same coin. SME failure has been called 
different names in the management literature to include bankruptcy, decline, retrenchment, 
SME death, mortality, downsizing and exit (Mellahi & Wilkinson, 2004). The study 
operationalize failure in line with Hannan and Freeman (1986, pp. 62) who defined failure 
as “when an SME ceases to carry out routine actions that sustain its structure, maintains 
flows of resources and secure allegiance of its members”. They, as part of their definition, 
note four kinds of mortality, namely: disbanding, acquisition, merger and radical change. 
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This paper also operationalizes organizational failure in the manner proposed by Hannan 
and Freeman (1986) but do not see acquisitions, mergers and radical change in form as a 
failure. SMEs do not lose the allegiance of their members when it is acquired, merges or 
changes form (Jones, 2005). Those are managerial manoeuvres for the SME to adapt to its 
environment and maintain functional utility for its members (Young, 1988; Greenwood & 
Suddaby, 2006). This study therefore defines SME failure as when an SME can no longer 
meet the utility of its stakeholders and operationalized using the traditional closure 
approach. The study conducted two rounds of data collection and those SMEs that had 
gone out of business by the second round were deemed to have failed in this respect. SMEs 
that survived were coded as 1 and those that failed were coded as 0. At the end of the 
period 63 SMEs had failed while 92 survived out of the 155 SMEs that participated in the 
study as detailed in table 5.2 below. The high mortality rate may be symptomatic of the 
many years of neglect as mentioned earlier under research setting but also confirms what 
small business scholars have opined that approximately 90% of SMEs do not see their 
second birthday. The study also observed that 28 new entrants entered the sector in the 
area under study representing 23.33% of the number of businesses operating in the sector 
as at 2015. 
 
 
Table 5. 2: Failure Rate 
    2014 2015 
2014 Survived 155 92 (59.35%) 
  Failed  63 (40.65%) 
 
 
Variables of Interest 
Alter Resources 
This refers to the attributes of a given SMEs alters that can diffuse for its use. For this, let 
us consider that an SME has 3 incoming alters with 4, 6 and 2 levels of competences for a 
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particular resource. For the amount of resources available to the SME the study sum the 
alter competences and divide by the number of alters and in this example the study arrive 
at 4. Formally, the study can represent this as: 
 
ARi = (∑EN: i <- jc)/ni 
 
Where ARi is the average amount of resources available to the SME from its alters; EN is 
the enterprise network within which the SME is embedded; i is a given SME; jc is the total 
incoming degree of the SME with associated resources from a given alter and ni is the 
number of alters the SME has. This approach is used to compute the market and 
technological resources made available to the SME from its alters. This approach requires 
that the study know the competence levels of all alters in the network. For market resource 
the study used the market orientation of alters in the network. This is operationalized as 
customer meetings, informal customer discussions and review of marketing approaches. 
This is measured using a Likert scale with 1 as strongly disagree and 7 as strongly agree. 
The study then summed the responses and averages it for the average market orientation. 
For technological competences the study operationalized it with the statement “the study 
adopt new technology to improve work” in the questionnaire. This is measured using a 
Likert scale with 1 as strongly disagree and 7 as strongly agree. 
 
Variations in Alter Resources 
In the considered example under alter resources the study find that alters have varying 
competences. Do these variations have implications for SME survival? To measure this 
variable the study compute the standard deviation in alter competences. This the study 
represent as: 
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σ = [∑(xj – xm)/ni]
1/2 
 
where σ is the standard variation in alter scores for a given SME; xj is the score of a 
particular alter; xm is the mean of alter scores for a given SME and ni is the number of 
alters the SME has. This approach is used to compute the variations in technological and 
market resources.  
 
Direct Resources 
The study operationalized the direct resources that diffuse to a given SME with the 
incoming degree centrality of the SME. This is because the resources are assumed to flow 
from alters to the SME. The study compute this with the formula (Borgatti, 2005): 
 
Di = ∑EN: i <- j 
 
where Di is the incoming degree of a given SME; EN is the enterprise network; i is a given 
SME and j is a given alter. 
 
Indirect Resources 
Resources can also flow from the friends-of-their friends and beyond. However, for the 
purposes of this study the study limit it to distance two. That is the ties of SMEs ties. The 
study use the eigenvector centrality that measures the popularity of a given SMEs ties to 
operationalize indirect resources but using this measure alone will fail to account for the 
SMEs own tie to the SME. The study therefore subtract the degree centrality of the SME 
from the eigenvector centrality to arrive at only the popularity of the tie without the SME 
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in question. What this effectively does is to sum all ties of distance two to the study given 
SME but yet discount that SMEs own tie. The study can represent this mathematically as: 
 
IDi = {[λ ∑ ENij ej] – [∑EN: i <- j]} 
 
where IDi is the indirect resources diffusing to a given SME i from a given alter j, EN is 
the enterprise network and λ is a constant required so that the equations do not have a non-
zero solution.  
 
Covariates and Moderators 
In this section the study present the covariates that the study used as controls in the study 
models, how they are operationalised and their sources in the academic literature. 
 
Table 5. 3: Operationalization of Control Variables 
Covariate Operationalization Source 
Age (AGE) The number of years the enterprise has been 
operating. 
(Mens & Hannan, 
2014) 
Generalist (GEN) Whether an enterprise undertakes only 
broiler or layer farming (0) and whether and 
enterprise does both (1)  
(Carroll et al., 2002) 
Size The number of employees the enterprise has  (Tsvetkova et al., 
2014) 
Education (EDUC) Whether owner has attained secondary 
education or higher is 1 and otherwise 0 
(Jo & Lee, 1996) 
Experience (EXP) The number of years of industry experience 
of owner including prior to establishing the 
enterprise. 
Gender (M) The gender of enterprise owner if male is 1 
and 0 if female. 
(Park, 1996) 
Entrepreneurial 
Orientation (MO) 
This is operationalized as the risk taking, 
proactive and innovative tendencies of the 
enterprise. This is measured using a Likert 
scale with 1 as strongly disagree and 7 as 
strongly agree. The study then standardizes 
the variable.  
(Jantunen et al., 
2005) 
Market Orientation 
(MO) 
This is operationalized as customer 
meetings, informal customer discussions 
and review of marketing approaches. This is 
(Kohli et al., 1993) 
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measured using a Likert scale with 1 as 
strongly disagree and 7 as strongly agree. 
The study then standardizes the variable. 
Absorptive 
Capacity (AC) 
This is operationalized as new information 
search, cross-enterprise problem solving, 
knowledge application and adoption of new 
technology. This is measured using a Likert 
scale with 1 as strongly disagree and 7 as 
strongly agree. The study then standardizes 
the variable. 
(Flatten et al., 2011) 
Dynamic Capability 
(DC) 
This is operationalized as business 
planning, on-the-job training and effective 
industry benchmarking. This is measured 
using a Likert scale with 1 as strongly 
disagree and 7 as strongly agree. The study 
then standardize the variable 
(Protogerou et al., 
2011) 
Technical 
Competence 
Another competence that was controlled for 
is the technical competence of the SME in 
poultry production. The study proxy this 
variable with the SMEs ties to technical 
agencies and companies such as veterinary 
services department and the ministry of 
agriculture (MOFA). The number of ties 
was used as level of competence. 
(Mensah-Bonsu & 
Rich, 2010) 
Investment Climate 
Constraints 
Operationalize investment climate 
constraints with sixteen (16) self-reported 
constraints in the business operating 
environment. The constraints include access 
to finance, labour quality, tax rates, inflation 
and transport infrastructure. The questions 
were implemented with a Likert scale from 
one (1) to seven (7). The respondents were 
to indicate 1 when the variable was not a 
constraint and 7 when it was highly 
constraining. The study then summed all the 
responses and standardized the variable for 
each respondent for a composite investment 
climate score. 
World Bank 
Enterprise Survey 
Ghana 2013 
 
5.3.4 Model Specification 
To examine the relationship between network diffusion and enterprise survival the study 
generated four probit models. The first model investigates the relationship between 
covariates and survival; in the second model the study added the direct and indirect 
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resource variables; in the third model the study removed the direct and indirect resources 
and replaced that with alter market resources and its variations and in the final model the 
study replaced market resources with technological resources. The study specify the 
general probit model as: 
 
P(ESt+1=1) = Φ (β0 + β1SMEt + β2OCt + β3MCOMPt + β4ICCt + β5Mt + εt) 
 
where ESt+1 indicates that a SME survives from period t (2014) to t+1 (2015); SMEt is a 
vector containing SME characteristics; OCt is represents owner characteristics; MCOMPt 
is a vector containing SME competencies and capabilities; ICCt represents perceived 
effects of investment climate on business operations; Mt is the vector containing the main 
effects. These are the direct and indirect resources diffusing to a given SME from its alters, 
alter resource competences and variations in alter resource competences; εt is the statistical 
noise and Φ is the cumulative distribution function of the standard normal distribution. The 
magnitude of the coefficients of the probit model cannot be interpreted and hence the study 
also estimated for the average marginal effects (AME) of the model which can be 
interpreted. The study modelled the average marginal effects of the study probit using the 
following function: 
 
∂ES/∂Vi = βi Φ (β0 + β1Vt + εt) 
 
where ∂ES/∂Vi is a partial derivative with respect to V (a given vector ) and the index i 
refers to the ith independent variable in V.  
 
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The study performed three robustness checks in the estimation of the study models. First, 
all the study models are estimated with robust standard errors to ensure its robustness 
against heteroskedasticity. Secondly, the study specified the study models in line with the 
hierarchical regression approach to ensure that the study models were fairly robust to the 
control variables. Thirdly, the study also adopted a lagged dependent variable to minimize 
as well as partial out some endogeneity in the model. Still on possible endogeneity, the 
study tried to include as many control variables as possible in line with existing literature 
on organisational failure (Mellahi & Wilkinson, 2004). Also, on possible selection 
problems the study attempted to conduct a district census of all poultry farms in the study 
area40. The Food and Agriculture Organisation has a 2014 database on farms in the study 
area in which they report that 202 farms operate in the area (FAO, 2014). The study also 
had 200 farms but after accounting for recurring farms and same ownership the study 
ended up with 163 farms of which 155 farms participated. Consequently, issues of 
selection may not arise. 
 
5.4 Results 
In this section the study present the findings of data analysis.  
5.4.1 Descriptive Statistics 
The study first presents the study descriptive results. The mean direct resources available 
from a given SME’s alters is 1.974 while that of indirect ties is 2.080 indicating SMEs 
benefited slightly more from indirect alters than they did from direct alters. However, the 
standard deviation was much higher for indirect alters also indicating a high variation in 
scores. When the study compare the means of surviving and failed SMEs the study find 
                                                     
40
 See appendix for poultry statistics in Ghana 
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that surviving SMEs have higher mean scores relative to the failed ones. The mean for 
alter market resources were seen to be higher than that of alter technological resources. 
This is consistent in the total, including surviving and failed samples. This may indicate 
that actors in the enterprise network have more marketing capabilities available to them 
than technological. In terms of variation in these alter resources, the study find that there 
was a greater variation in the technological resources and even when the sample is 
decomposed into failed and survived SMEs the same pattern emerges. In terms of 
enterprise characteristics, the majority of the SMEs had approximately 6 paid employees 
and specialised in layer production (74.2%). When the study considered owner 
characteristics most of the owners had at least attended secondary school (52.9%); were 
male (74.8%); and had an average of 9 years industry experience. It is, however, important 
to note that the number of paid employees and owner experience showed a great deal of 
variability with standard deviations of 11.196 and 6.698 respectively. The mean score of 
technical competence was 0.955 with a standard deviation of 0.914. Managerial 
competence and investment climate constraints were standardized and hence the study 
expected their means to approach 0 and the standard deviations to approach 1.  
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table 5. 4: Descriptive Results 
 Total Sample Survived 
Sample 
Failed Sample 
Mean Std. Dev. Mean Std. 
Dev. 
Mean Std. 
Dev. 
Direct Alter Resources 1.974 3.355 2.717 4.157 0.889 0.764 
Indirect Alter Resources 2.080 5.558 3.830 6.505 -0.475 1.858 
Alter Market Resources 3.230 1.899 3.833 1.503 2.350 2.077 
Variation in Alter MRs 0.409 0.735 0.611 0.843 0.113 0.387 
Alter Technological Resources 2.697 1.866 3.226 1.718 1.923 1.814 
Variation in Alter TRs 0.473 0.827 0.678 0.925 0.174 0.537 
Age of SME 7.497 6.077 8.261 6.551 6.381 5.157 
Size of SME 6.348 11.196 8.489 14.050 3.222 2.218 
Generalist 0.258 0.439 0.065 0.248 0.540 0.502 
Education (>Primary=1) 0.529 0.501 0.435 0.498 0.667 0.475 
Owner Experience 8.961 6.698 10.83 6.780 6.222 5.581 
Male 0.748 0.435 0.772 0.422 0.714 0.455 
Technical Competence 0.955 0.914 1.109 0.977 0.730 0.766 
Entrepreneurial Orientation -0.104 1.002 0.319 0.936 -0.721 0.747 
Market Orientation -0.099 0.990 0.252 0.968 -0.612 0.779 
Dynamic Capability -0.121 1.016 0.344 1.040 -0.799 0.436 
Absorptive Capacity -0.185 0.949 0.149 0.935 -0.672 0.740 
Investment Climate Constraints -0.205 0.964 -0.59 0.901 0.362 0.752 
 
5.4.2 Regression Analysis 
The study now turns to the results of the study econometric model. The study report 
average marginal estimates of the study probit models. The study first discuss the fit the 
study models to the data. All the study models had significant Wald statistics with the 
exception of model three that has the covariates and alter market resources and its 
variations. This shows that the model is not significantly better than the null likelihood 
model but the study still interpret the significant variables in the model since they are still 
useful individually in predicting survival of SMEs. In terms of the variance explained by 
the study models the study found that none of the study models had a pseudo r-square less 
than 0.733 implying that the study models explain at least more than 70% of the variance 
in the survival outcome. When the study consider the variables of interest, the study found 
that direct and indirect alter resources have a significant and positive effect on survival 
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(0.081*** and 0.011***) however, the coefficient for direct resources is higher than for 
indirect. The study also found that alter market resources have a positive effect (0.032**) 
on probability of survival and the variations in market resources is also positive (0.036) but 
it does not rise to significance. The study also observed that technological resources from 
alters have a positive effect (0.020*) on survival of SMEs while variations in technological 
resources from alters have a negative effect (-0.012) on survival but it does not rise to 
significance. The study found that age and size of SME have a positive effect on survival 
but only the size of the SME rises to significance. The study also found that SMEs that are 
generalists are less likely to survive compared to specialists. In terms of owner 
characteristics, education and owner experience have a positive effect of survival but male-
owned are more likely to fail than female-owned enterprises. In terms of organisational 
competences, the study found that entrepreneurial orientation, market orientation, 
absorptive capacity and dynamic capabilities have a positive effect on survival. Technical 
competence in poultry production is also seen to have a positive effect on survival. 
Investment climate constraints unsurprisingly have a negative effect on survival of SMEs.   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table 5. 5: Alter Attributes and SME Survival 
 (1) (2) (3) (4) 
Age of SME 0.003 0.004 0.006 0.003 
 (0.005) (0.006) (0.004) (0.005) 
Size of SME 0.018** 0.013* 0.013* 0.015* 
 (0.008) (0.007) (0.007) (0.009) 
Generalist -0.286*** -0.218*** -0.290*** -0.279*** 
 (0.043) (0.036) (0.042) (0.038) 
Education (>Primary=1) 0.015 0.017 0.015 0.005 
 (0.048) (0.037) (0.043) (0.049) 
Owner Experience 0.010** 0.009** 0.014** 0.010** 
 (0.005) (0.004) (0.006) (0.005) 
Male -0.152*** -0.116*** -0.192*** -0.170*** 
 (0.048) (0.043) (0.071) (0.057) 
Technical Competence 0.074*** 0.040** 0.070*** 0.073*** 
 (0.021) (0.017) (0.023) (0.019) 
Entrepreneurial Orientation 0.094*** 0.102*** 0.122*** 0.103*** 
 (0.030) (0.024) (0.027) (0.027) 
Market Orientation 0.024 0.039** 0.051 0.025 
 (0.025) (0.019) (0.032) (0.027) 
Dynamic Capability 0.025 0.011 0.003 0.015 
 (0.029) (0.021) (0.029) (0.028) 
Absorptive Capacity 0.101*** 0.114*** 0.147*** 0.111*** 
 (0.029) (0.026) (0.045) (0.031) 
Investment Climate Constraints -0.268*** -0.262*** -0.340*** -0.261*** 
 (0.040) (0.043) (0.072) (0.042) 
Direct Resources  0.081***   
  (0.022)   
Indirect Resources  0.011**   
  (0.005)   
Market Resources (MR)   0.032**  
   (0.015)  
Variation in Alter MRs   0.036  
   (0.035)  
Technological Resources (TR)    0.020* 
    (0.012) 
Variation in Alter TRs    -0.012 
    (0.030) 
     
Wald 36.07*** 74.46*** 18.06 30.18*** 
Pseudo R2 0.733 0.821 0.777 0.745 
Observations 155 155 155 155 
Robust standard errors in parentheses 
*** p<0.01, ** p<0.05, * p<0.1 
 
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5.5 Discussion of Results 
In this section the study discuss the findings of the study in line with existing literature. 
The study sought to explain whether network diffusion was useful for the survival of 
SMEs by utilizing alter resources. More specifically, the study focused on the mechanism 
through which the resources are made available both direct and indirect; the particular 
resource both market and technological as well as variations in alter resources. The study 
operationalizes survival as the persistence of the SME into 2015 from 2014. The data was 
collected from a poultry cluster in rural mid-western Ghana that is one of the best 
performing in the country as a critical case (Flyvbjerg, 2006; FAO, 2014) and also to avoid 
network sprawl and sparseness (Hanneman & Riddle, 2005). The study model the study 
data with a cross sectional probit model with lagged independent variables while 
controlling for the SME characteristics, owner characteristics and organisational 
competencies. The reported results are the average marginal estimates from the probit 
models.  
 
The study found that alter resources generally are useful for the survival probability of 
SMEs. This is because SMEs can reap greater benefits from external collaborations as it 
can compensate for the scarcity of internal resources and competences. This means SMEs 
can appropriate the market and technological resources of its alters for its survival. These 
market resources will enable the SME to better determine and satisfy the needs and wants 
of target markets as well as deal with inherent threats while maximizing the benefits that 
may arise in such markets ( Cambra-Fierro, Florin, Perez, & Whitelock, 2011; Mahmoud 
& Yusif, 2012). Technological resources also offer the methods for SME to expand 
quickly and efficiently by serving as an enabler of production and service functions of the 
business (Aa & Elfring, 2002). However, market resources have a stronger effect on 
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survival than technological resources. This may be a purely contextual matter since the 
operations of most SMEs in Ghana are labour intensive and require little sophisticated 
technology (Kayanula & Quartey, 2000b). Again, this may be due to the fact that 
agricultural produce in many rural parts of Ghana do not reach many market centres due to 
poor road infrastructure (Buame, 1996) and a general lack of access to markets (FAO, 
2014). The study also observed that the variations in the resources received from alters had 
no significant effect on survival. However, the directions of their effects are informative. 
High variations in alter market resources have a positive effect while those of 
technological resources have a negative effect. This goes to support the point that high 
variations in alter technological resources can have a negative effect as uncertainty creeps 
in to create doubt and hence may not be able to benefit from technological resources while 
the risk of choosing the wrong technology can be high (Rogers, 2010). The study also 
found that the mechanism through which the resources diffuse also matters for the 
survival. Both the direct and indirect tie mechanisms have a positive effect on the survival 
of the SME. The effect of direct ties may be working through complementarities, 
knowledge sharing and scaling of activities of SMEs that directly collaborate with each 
other in the network (Ahuja, 2000). Indirect ties may also work because they present arm’s 
length relations that require little investment (Uzzi & Lancaster, 2003) and it also helps 
SMEs gain access to resources beyond its local neighbourhood and avoid informational 
and resource redundancy related problems (Burt, 2005). The direct tie mechanism has a 
bigger coefficient compared to the indirect ties.  
 
The study now turns to the study covariates. First, regarding the study SME characteristics 
the study found that SME age and size affect survival positively while generalist SMEs are 
more likely to fail. The findings on age and size of SME support the liability of smallness 
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while the generalist findings support the resource partitioning theory. The bigger the size 
of the firm the less likely it is that the firm will fail (Burger & Owens, 2013) while young 
firms are more likely to fail in line with the liability of newness in the age dependence 
hypothesis (Geroski et al., 2010). The resource partition theory suggests that SMEs with 
broad requirements from the environment are more likely to fail in difficult times due to 
difficult in attaining systematic legitimacy (Carroll & William, 2004).  Secondly, when the 
study consider the owner characteristics the study find that education and owner 
experience has a positive effect on survival while male-owned businesses are more likely 
to fail. Experienced and educated owners are expected to possess a broad range of skills 
and knowledge to steer their businesses through varying environmental constraints to 
achieve positive outcomes (Jo & Lee, 1996). The surprising finding here is that male-
owned businesses are more likely to fail against conventional wisdom. However, a new 
strand of literature is emerging that seems to suggest that either only capable women are 
selected into entrepreneurship or there is a general favouritism of females in business 
environments (Hansen & Rand, 2014a, 2014b). The study also found that technical 
competence in the area of operation was significant. This means that firms that have strong 
capability in poultry production are more likely to survive than those that are not. Also, 
organisational competences such as market orientation, entrepreneurial orientation, 
absorptive capability and dynamic capability all have a positive effect on SME survival. 
This is in line with management theory that suggest that managers of businesses must 
motivate, manage and provide required structures for employees to achieve the goals of the 
organization and ultimately survival (Mellahi & Wilkinson, 2004; Crossan & Apaydin, 
2010; Miles, 2012). Unsurprisingly, investment climate constraints are seen to have a 
negative effect on survival as these have been seen to impose indirect costs on the 
activities of SMEs (Eifert et al., 2008).     
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The study now discusses the implications of the results for enterprise management and 
research. The study suggest to managers of small businesses to explore collaborations with 
their alters as it can provide a mechanism through which they can access resources to 
mitigate their own scarcity in resources and competences. These can come in the form of 
knowledge sharing, complementarities and scaling opportunities. Secondly, they need to 
be wary of significant variations in alter technological resources as these can have a 
negative effect on their survival chances. This is because wrong technological choices can 
prove costly in production down times especially when high variations in alter 
technological approaches defeats the assumption that alter ties should help SMEs reduce 
their uncertainty in such situations (Rogers, 2010). Lastly, they need to look beyond their 
immediate neighbourhoods in the network as resources embedded with indirect ties 
represent resources with little constraints and provides the vison advantage required to be 
innovative. On research implications, the study use degree and eigenvector centrality-
adapted measures as the study tools in measuring diffusion; however, new approaches are 
emerging that can handle specifically diffusion based issues in networks albeit with some 
data constraints (Jackson, 2010). Also, as the study focus on a critical case in Ghana, 
replications of this study can take place in other jurisdictions to attempt a falsification of 
the study findings. This will provide the needed stress test of the study approach and 
findings. 
 
5.6 Conclusion 
The aim of this study was to come to grips with the usefulness of alter resources for SME 
survival in Ghana. The study found that alters market resources and technological 
resources are significant for the survival of the SMEs. The study also explored the effect of 
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variations in alter competences along the lines of the resource and found that market 
resources have a positive effect and technological resource variations have a negative 
effect, but these do not rise to significance. Finally, the study assessed the impact of the 
mechanism through which the resources are diffused and found that both direct and 
indirect tie mechanisms have a positive effect on survival, but the effect of direct ties was 
higher than that for indirect ties. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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APPENDIX 
Table 5A1: Ghana Poultry Sector Statistics 
National  Brong Ahafo Region 
Region No. of Birds 
(%) 
Egg 
Production (in 
millions) 
No. of 
Farms+ 
District No. of 
Farms+ 
Greater 
Accra 
2,547,219 (7.02%) 542 475 Dormaa 202 
Central  903,702 (2.49%) 437 312 Jaman South 9 
Western 1,406,642 (3.88%) 247 102 Berekum 34 
Eastern 3,886,914 
(10.72%) 
358 213 Sunyani 65 
Volta 1,071,622 (2.95%) 68 98 Techiman 25 
Ashanti 10,180,760 
(28.07%) 
5,321 697 Tano South 16 
Brong-
Ahafo 
10,743,897 
(29.62%) 
3,989 510 Nkoranza 32 
Northern 3,625,149 (9.99%) - 30 Kintampo 
South 
1 
Upper East 1,325,835 (3.66%) - 33 Asunafo 
North 
17 
Upper West 579,474 (1.60%) - 34 Asunafo 
South 
0 
    Asutifi 10 
    Wenchi 9 
    Sunyani West 90 
+Based on FAO Sector 1/2/3 Classification 
Sources: Veterinary Services Directorate (2010, 2011, 2013) in FAO, 2014 
 
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Table 5A2: Correlation Matrix 
 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 
SME Survival 1                   
Direct Alter Resources 0.3 1                  
Indirect Alter Resources 0.4 0.7 1                 
Alter Market Resources 0.4 0.2 0.2 1                
Variation in Alter MRs 0.3 0.6 0.5 0.3 1               
Alter Technical Resources 0.3 0.2 0.3 0.7 0.2 1              
Variation in Alter TRs 0.3 0.6 0.6 0.3 0.8 0.3 1             
Age of SME 0.2 0.3 0.3 0.0 0.3 0.2 0.2 1            
Size of SME 0.2 0.8 0.6 0.1 0.5 0.2 0.4 0.4 1           
Generalist -0.5 -0.2 -0.2 -0.3 -0.2 -0.3 -0.2 -0.2 -0.2 1          
Education (>Primary=1) -0.2 0.2 0.0 0.0 0.1 0.0 0.0 0.1 0.2 0.1 1         
Owner Experience 0.3 0.2 0.3 0.1 0.3 0.3 0.3 0.6 0.3 -0.3 -0.1 1        
Male 0.1 -0.1 -0.1 0.1 -0.1 0.1 0.0 0.1 0.0 -0.1 -0.1 0.3 1       
Technical Competence 0.2 0.2 0.2 0.0 0.0 0.1 0.0 0.2 0.2 0.0 0.0 0.2 0.0 1      
Entrepreneurial Orientation 0.5 0.3 0.5 0.2 0.3 0.1 0.3 0.1 0.4 -0.3 -0.2 0.3 0.1 0.0 1     
Market Orientation 0.4 0.0 0.2 0.1 0.1 0.2 0.1 0.0 0.1 -0.2 -0.2 0.2 0.2 -0.1 0.5 1    
Dynamic Capability 0.6 0.2 0.4 0.4 0.2 0.3 0.2 0.1 0.3 -0.3 -0.2 0.3 0.1 0.2 0.6 0.5 1   
Absorptive Capacity 0.4 0.3 0.4 0.2 0.3 0.2 0.2 0.2 0.3 -0.2 -0.1 0.2 0.1 0.1 0.6 0.5 0.6 1  
Investment Climate Constraints -0.5 0.1 -0.1 -0.3 0.0 -0.3 -0.1 0.1 0.2 0.2 0.3 -0.2 -0.2 -0.1 -0.2 -0.3 -0.3 -0.1 1 
 
 
 
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CHAPTER SIX 
SUMMARY, CONCLUSION AND RECOMMENDATIONS 
 
6.1 Introduction 
Small and medium scale enterprise (SME) failure is one that continues to intrigue many 
academics and policy makers especially when coupled with the fact that most of them fail 
before their second year of operations (Okpara, 2011). Three broad approaches of 
determinism, voluntarism and integrativism have been employed to explain why SMEs fail 
(Mellahi & Wilkinson, 2004). The deterministic approach posits that SMEs fail due to 
external reasons emanating from changes in market behaviour and preferences (Jovanovic, 
1982; Hopenhayn, 1992) and changes in the ecology of SME populations (Hannan & 
Freeman, 1986). The voluntaristic approach suggests that failure is mainly due to lack of 
managerial competence in managing the negative externalities to achieve positive 
outcomes for the SME. These have come from many theoretical and conceptual thoughts 
such as dynamic capabilities (Teece, 2007); absorptive capacity (Cohen & Levinthal, 
1990); managerial orientations (Covin & Miller, 2014); and top management 
characteristics (Hambrick, 2007). However, one theory that has received little attention in 
relation to the issue of survival is social network theory despite its wide usage in 
understanding several other organisational outcomes such as performance (Naudé et al., 
2014), innovation (Gronum, Verreynne, & Kastelle, 2012) and learning (Golub & Jackson, 
2010). This is maybe due to the fact that most scholars assume that a performing SME is a 
surviving SME but in recent empirical studies this relationship has been shown to be 
complicated (Wennberg et al., 2010; Coad et al., 2013). Hence, it is important to consider 
how networks of firms relating can influence directly their survival especially in Africa 
where relational systems of firms connecting and interacting has a significant influence on 
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SME outcomes (Biggs & Shah, 2006). Also, most of the studies that attempt to even 
explore association between networks and SME outcomes mostly focus on competition 
networks (Zaheer & Bell, 2005; Rooks et al., 2012) to the neglect of other relevant 
networks that are important for SME outcomes such as finance, distribution and supplier 
networks from which resources flow ( Pfeffer & Salancik, 2003; Pajunen, 2006). Again, 
studies (Biggs & Shah, 2006; Boso, Story, & Cadogan, 2013) have mainly measured 
network effects using attribute approaches rather than the relational approaches that have 
been suggested as the more appropriate approach (Scott & Carrington, 2011). 
Consequently, the study focused on relational network effects at the ego-level of analysis 
as suggested by earlier social network scholars (Wasserman & Faust, 1994; Scott & 
Carrington, 2011). The study also contributes to literature by filling some contextual gaps 
with network evidence from Ghana’s rural poultry industry.  
 
Four empirical papers are presented that explore the association between four network 
issues (relational ties, structural capital, isomorphism and diffusion) and SME survival 
using data from the Dormaa poultry cluster. This cluster is one of the best performing 
clusters in the nation and is used as a critical case study (Flyvbjerg, 2006). The study 
collected distribution, financial and collaboration network and attribute data of the SMEs 
that participated in the study. The attribute data related to SME characteristics, owner 
characteristics, organisational competencies, technical competencies and investment 
climate constraints. Survival is operationalized as persistence from 2014 to 2015 and 
modelled using lagged independent variables from 2015. The specific issues discussed and 
analysed are addressed in detail in the empirical papers in chapters’ two to five. In this 
chapter the summary findings are presented, conclusions drawn, recommendations are 
made and limitations and the opportunities they present for future research are discussed. 
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6.2 Summary of Findings 
The thesis sought to explore the role of network effects on enterprise survival in Ghana. In 
this section I present a summary of the findings of this thesis specifically referring to the 
research question each finding answers in the study. 
 
6.2.1 Empirical Paper 1 
This empirical paper is used to answer research questions two (2) and five (5). In this 
paper, the study explored the effects of distribution ties on the survival probability of 
SMEs and the moderating effects of SME characteristics on this relationship. It was 
hypothesized that increases in the number of distribution ties is associated with lower 
probability of survival as SMEs are not able to draw required resources for their survival. 
It was reasoned that an increased number of distribution ties takes attention from other 
networks like supply and finance networks that are also critical for the survival of the 
SME. Another reason is that embeddedness can lead to exploratory learning behaviour that 
leads to over-flexibility which makes the SME susceptible to fail (Brian Uzzi & Lancaster, 
2003; Hiebl, 2013). Again, distributors also have better negotiation power in the market 
relations and as a result have a bigger share of the customers’ ‘wallet’. This is because the 
produce of the farmer SMEs is perishable and hence not selling it will mean the produce 
goes bad; hence distributors dictate the price and an additional distributor may just take 
more margin from the SME. Also, SMEs that sell themselves are more likely to keep all 
the value made in the value chain and hence be more profitable and more likely to survive. 
On this point, I also discuss the lost transaction costs. The transaction cost idea is that high 
ties creates relations that relieve policing and enforcing costs associated with transactions 
(Williamson, 1979) but where there is competition in these ties then the benefits do not 
arise. Also, SMEs sometimes simply engage in bad deals in an attempt to market and 
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distribute their products leading to the failure of the businesses (Ropega, 2011). I 
confirmed the hypothesis that many ties are associated with a low survival probability. The 
theoretical ideas suggested are then offered as possible reasons for this effect and 
direction. To be sure that the result was not being driven by SME characteristics, SME 
age, size and production specialisation were interacted with the number of distribution ties 
the SME had. I found that SMEs that are generalists and older SMEs still experienced 
negative and significant association. However, larger SMEs experienced a positive 
association but this did not rise to significance. The suspicion is that older SMEs are either 
more likely to distribute themselves or use fewer and more reliable distributors. The key 
contribution of this paper is to show that increased ties to distributors as a mechanism to 
the market does not always lead to positive outcomes because there is competition among 
these two actors for customers’ resources and SMEs are more likely to be on the losing 
end as they have less bargaining power.  
 
6.2.2 Empirical Paper 2 
This empirical paper is used to answer research question one (1) in chapter (one) 1. Access 
to capital remains one of the major reasons why SMEs fail. In this study I sought to 
establish if social capital in a financial network was useful for SME survival in Ghana. It is 
found that SMEs that reported being financially constrained were less likely to survive. 
This is notwithstanding the fact that about 55% of the SMEs said that they were not credit 
constrained which I suspect may be from the ‘discouraged borrower effect’ (Akuetteh, 
2009) or having alternative sources of income41. Also, SMEs that had prestigious or 
popular financiers were associated with better probability of survival. This may be due to 
the fact that SMEs with ties to these financiers focus on a few relevant SMEs, are able to 
                                                     
41
 Anecdotally most SMEs poultry enterprises are started with income from family members in Europe and 
America 
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send signals about their positive attributes that is not easily verifiable, and enforce their 
access to resources in and out of the network (Lin, 1999; Zaheer et al., 2010). 
Descriptively, I also observe some interaction effects in table 3.4 where the cross-
tabulations show that SMEs that have prestigious financiers are less likely to report being 
credit constrained while those that are not are more likely to report such constraints. The 
study also explored the effects of the type of financial institution ties on the survival of 
SMEs. It is found that universal banks and credit unions are associated with positive 
survival chances while savings and loans companies are associated with negative survival 
probability. The mechanism of these associations may be that universal banks are able to 
offer large capital outlays to SMEs that are able to meet their requirements to expand their 
operations significantly (Narteh, 2013); credit unions offer competitively low interest rates 
compared to the other types of financial institutions (Ofei, 2001) while savings and loans 
companies charge high interest rates and have been described as loan sharks42 that engage 
in practices detrimental to the business operations of SMEs (Mensah, 2009). 
 
6.2.3 Empirical Paper 3 
In this paper the study sought to establish if SME network isomorphism with other SMEs 
in its operating environment was more likely to survive and whether such isomorphism 
effects were infinite. This paper helps to answer the research questions three (3) and six 
(6). It is observed that SMEs that are isomorphic to other SMEs are more likely to survive. 
This may be because such SMEs are able to acquire institutional legitimacy that enables 
them to acquire resources for their functioning; they are validated based on their structural 
forms not technical efficiency; they benefit from the logics of good faith; and also copy 
stable firms in the environment and hence are stable and experience less turbulence (Meyer 
                                                     
42
 https://www.pwc.com/en_GH/gh/pdf/ghana-banking-survey-2013-pwc.pdf 
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& Rowan, 1977; Scott, 1991; Deephouse, 1996). From a network theory perspective this 
may be possible becomes isomorphic businesses are similar in structure and links; 
therefore can engage in oligopolistic coordination (Gulati et al., 2000). Again, the 
similarity breeds cohesion which is a major ingredient in network trust and such trust is 
useful in reducing transaction costs with each other and hence improving survival chances 
( Williamson, 1979; Coleman, 1988; McPherson et al., 2001). I also asked if the benefits 
of network isomorphism were infinite. This is because when SMEs get too similar there is 
competition for resources and hence the benefits of network isomorphism may have a 
limit. I found that at higher levels isomorphism or similarity SMEs survival was associated 
with negative probabilities. The study opines that this may  be the result of competitive 
crowding resulting from structural similarity of SMEs (Podolny et al., 1996) due to high 
levels of isomorphism. Also, within the network this may be the result of triadic closure 
and the redundant information that is locked in the triads that may not be bridged for new 
sources of resources and information (Burt, 2004; Symeonidis et al., 2010). It also 
explored whether such isomorphism was useful in overcoming investment climate 
constraints in the operating environment. I found that the interaction between investment 
climate constraints and isomorphism is positive and confirm this hypothesis. A reason for 
this may be because small businesses are able to reduce their liabilities by being 
isomorphic and gaining the good faith of relevant stakeholders which becomes a currency 
to acquire other critical resources in that environment (Deephouse, 1996). 
 
6.2.4 Empirical Paper 4 
In this paper the study sought to establish whether SMEs can benefit from the attributes 
(competencies and resources) of its alters by assuming a diffusion of resources from these 
alters to the SMEs. This paper helps understand and answer research question four (4) in 
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chapter one (1). I focused on two types of resources: market and technological. It is found 
that alter resources are useful for SME survival. This might be because alter resources may 
provide a means by which the SME is able to compensate for the scarcity of internal 
resources and competences. Market resources enable the SME to benefit from the 
marketing skills of its alters while technological resources enable the SME to reduce the 
uncertainty associated with new technologies while skills training may be provided by its 
alters. Alter market resources has a greater association to survival compared to 
technological resources. This is possibly due to the high need for markets as the SMEs’ 
products (eggs) are perishable and need to get to be marketed as quickly as possible. The 
study also tested if variations in the attributes affected survival and found that variations in 
market resources had a positive association with survival while variations in technological 
competences had a negative association. This goes to support the point that high variations 
in alter technological resources can have a negative effect as uncertainty creeps in to create 
doubt and hence they may not be able to benefit from technological resources while the 
risk of choosing the wrong technology can be high (Rogers, 2010). Furthermore, the study 
sought to ascertain if the mechanisms through which resources are diffused (direct and 
indirect ties) from alters to SMEs matters for the survival of SMEs. I found that the 
medium for diffusion was important. Direct tie mechanisms provide complementarities, 
knowledge sharing and scaling of activities of SMEs that directly collaborate with each 
other in the network (Ahuja, 2000). Indirect ties may also work because they present arm’s 
length relations that require little investment (Uzzi & Lancaster, 2003) and it also helps 
SMEs gain access to resources beyond their local neighbourhood and avoid informational 
and resource redundancy related problems (Burt, 2005). 
 
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6.3 Reflections 
In this section, the study seeks to reflect of the whole research process to identify the 
implications of alternative approaches on the outcomes achieved. Reflections are made 
relative to the theory, the conceptual framework and research methodology. 
 
6.3.1 Theory 
The core theory used in this study is the social network theory and is complemented by 
social capital, resource dependence, isomorphism and diffusion of innovation theories: 
these complementary theories are operationalized within the network theory. Social 
network theory has been criticized for lacking consistency in operationalisations and 
definitions; the confusions in the categorical versus anti-categorical approaches and what 
constitutes a network tie (for review see Miles, 2012). It is therefore possible to use these 
complementary theories on their own as has been done by earlier scholars (Deephouse, 
1996; Sanidas, 2004; Acquaah, 2007; Hessels & Terjesen, 2008). However, adopting such 
an approach loses sight of the interdependencies between SMEs and other actors in their 
operating environment that social network theory helps capture despite its challenges as 
mentioned above. Capturing such interdependencies is critical because SMEs do not 
operate in a vacuum and in this sense social network theory is relevant as it helps in 
understanding the relational dimensions of power, organisational convergence, spread of 
ideas in industries and development of collective resources. These become nearly 
impossible to understand without the network theory despite its inadequacies.   
 
6.3.2 Framework 
A conceptual framework was developed to guide the study. The framework focused on the 
relationship between network effects (of structural social capital, resource access, 
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isomorphism and diffusion) and SME survival and the moderating effects of investment 
climate constraints and SME characteristics. Beyond these variables that the study 
included in the model, there are others such as owner characteristics, managerial 
competencies and technical competencies in production and selling activities that also 
matter for survival which was not included in the current framework. The framework was 
limited to these variables mainly due to the research questions that the study sought to 
answer. Consequently, it is suggested that the study framework is representative of the 
objectives the research project sought to achieve and not exhaustive. These variables that 
are not included in the framework are however controlled for, in order to partial out the 
parts of the relationship among the variables of interest and SME survival that may be 
endogenous to these variables not included in the study framework. 
 
6.3.3 Methodology 
In this section, reflections are made regarding methodological issues such as research 
approach, data collection, operationalisations and estimation strategies. In terms of 
research approach, the study adopted a quantitative and deductive approach within the 
critical realist tradition. This approach helped to generalize findings from the study by 
developing findings from respondent averages. In this process, peculiar attributes of 
respondents that can have an impact on results may be lost and hence mixed methods may 
have offered a route out of this challenge. The mixed methods approach helps develop 
hypothesis based on qualitative insights and analyses the data quantitatively. However, 
since most of the hypotheses that this study sought to confirm have been established within 
the complementary theories the mixed methods approach was deemed superfluous 
although it would be interesting to consider the impact of such an approach on the study 
findings. Also, data was collected using the name generator approach in the Dormaa 
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poultry cluster. While the name generator approach is easy to implement since respondents 
have to name their alters there is the challenge that alters may not remember some 
significant alters that they do not interact with regularly. The study design was 
implemented in the Dormaa poultry cluster although other clusters are operating in Ghana. 
This presents some problems with generalising the findings of the study. This is because 
other clusters may have their own peculiarities and organising mechanisms that may 
influence their survival or otherwise. However, this cluster is one of the best performing in 
the country and can serve as a critical case (Popper, 2005; Flyvbjerg, 2006). Finally, on the 
estimation strategy, the study is a cross-sectional one with lagged independent variables 
predicting survival. While such an approach is straightforward and easy to implement it is 
not able to control for the fixed effects that may influence survival outcomes. A panel 
study approach would therefore have been appropriate: however, this was not possible due 
to data and time constraints within which the study design was to be implemented. 
 
6.4 Contributions of the Study 
In this section the contributions made by the study are presented. The major contributions 
relating to literature, framework, measurement and new evidence are discussed. 
 
6.4.1 Synthesis of Literature 
The study makes a contribution to the survival literature especially with respect to SME 
survival. The last significant review of organisational survival was conducted by Mellahi 
and Wilkinson (2004) over a decade ago. Hence a recent review to update the state of the 
literature is relevant and this study provides this review to which other scholars can turn. 
More specifically, a systematic synthesis of SME survival literature is made. Two broad 
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issues of determinants and strategies for survival emerge from the review emanating from 
socio-economic and managerial perspectives. 
 
6.4.2 Framework  
The synthesis of the literature revealed that the explanation of SME survival with network 
effects remains largely nascent. Consequently, the study developed a conceptual 
framework that addresses this relationship. The framework serves as a guide to explaining 
how network effects such as structural social capital, resource access, isomorphism and 
diffusion can influence SME survival in the face of investment climate constraints and 
SME characteristics. Beyond the explained relationship, the framework can serve as the 
starting point for other studies seeking to understand SME outcomes from a network 
perspective. 
 
6.4.3 Measurement 
Social networks focus on relations between social and organisational actors and hence 
require a relational data approach to truly capture network effects (Wasserman & Faust, 
1994). However, most studies in management and economics (Biggs & Shah, 2006; Boso, 
Story, Micevski, & Kadic-Maglajlic, 2013; Kinghan & Newman, 2015) have rather used 
an attribute-data-based approach which does not capture the dependencies in network 
mechanisms. The study makes a contribution to the measurement of network effects by 
suggesting the use of relational data extracted and analysed at the nodal or ego level.  
 
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6.4.4 New Evidence  
The study contributes to both SME survival and social network theory literature by 
contributing new evidence through its empirical findings (papers). Also, prior to this study 
the synthesis of literature revealed that most studies on SME survival were undertaken 
outside of Africa and those with an African focus were conducted either in Nigeria or in 
Ethiopia with an industrial emphasis. New evidence is therefore presented from Ghana and 
from a non-traditional agricultural sector (poultry). The findings suggest that network 
isomorphism, diffusion and structural social capital have a positive link with SME survival 
while increased market access through distribution ties is linked with reduced survival 
probability. These findings can either form the basis of a hypothesis to be tested in 
replication studies or form an explanatory mechanism through which other findings may 
be placed. 
 
6.5 Conclusion 
The aim of this study was to come to terms with why SMEs fail from a social network 
theory perspective. The study focuses on four main network issues: resource access, 
structural capital, isomorphism and diffusion ( Borgatti & Foster, 2003). The key takeaway 
from the study is that network effects matter for SME survival. More specifically, market 
resource access through distribution ties can be detrimental to the survival chances of 
SMEs; structural capital in the form of prestige of financiers, has a positive association 
with survival; network convergence as isomorphism also has a positive association with 
survival but reaches a threshold after which it diminishes and alter resources are important 
for the survival of SMEs both from direct and indirect sources. Network effects are useful 
in overcoming some of the constraints that SMEs experience in the operating environment. 
Isomorphism can be useful in overcoming investment climate constraints, financial 
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network capital is useful in overcoming financial constraints and diffusion of resources 
from alters can also be useful in overcoming SME resource scarcities.  
 
6.6 Practical Implications 
Based on the study findings I offer the following practical recommendations to small 
business owners and managers. These are presented below. 
 
First, the study suggests based on the negative effects of distribution ties on survival and 
negative effects of the moderation of age of SME and distribution ties on survival that 
SMEs engage in a process of backward integration in the value chain as they age. The 
process helps cut-off distributors thereby still having access to the market but through the 
enterprise’s own market infrastructure. The benefit is that the margins of the distributors 
will then accrue to the enterprise in the long run. The costs of accessing the market directly 
can be daunting for small businesses in that case collaborations among small clusters of 
firms to share such costs can be useful. Currently, the Unity Group43 has provided such a 
model that may be worth emulating.  
 
Second, it is suggested, based on the findings of the generalist and its interaction with 
distribution ties, that SMEs specialise in a particular niche of the market and limit their 
resource requirements and hence improve their survival probability. 
 
In terms of financial network capital it is offered that small business owners develop ties to 
financiers that are prestigious or respected in the network as they are likely to offer them 
                                                     
43
 Unity brothers constitute 7 farms owned by individuals but have familial relations that collaborate in 
transport of their produce from their farms to major trading centres in Accra directly without middlemen. 
The researcher observed that they had stable and bigger farms relative to the other farms. 
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the required resources or be a mechanism through which they can send positive signals 
about their operations. It may also help them reduce their credit constraints. 
 
It is recommended that SMEs develop ties with universal banks, credit unions while 
avoiding ties to savings and loans companies. Universal banks can provide them with large 
capital outlays when required for expansion and not feel credit constrained, credit unions 
can offer competitive interest rates when required for working capital management but 
unfortunately savings and loans companies can charge interest rates that can be detrimental 
to their businesses. 
 
Managers of small business can copy the activities of other SMEs in their industry as this 
will offer them the needed stability through the legitimation of their activities in line with 
the findings on isomorphism and survival. However, I also caution that there is an 
optimum point beyond which such ‘copying’ will result in negative returns. 
 
Another incentive for small businesses to base their actions on those of other businesses is 
that it can be useful in overcoming investment climate constraints. This mechanism may 
work through the logics of good faith where small businesses can avoid certain indirect 
costs imposed by the investment climate. 
 
Managers of small business need to explore collaborations with their alters as it can 
provide a mechanism through which they can access resources to mitigate their own 
scarcity in resources and competences. These can come in the form of knowledge sharing, 
complementarities and scaling opportunities.  
 
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Managers need to be wary of significant variations in alter technological resources as these 
can have a negative effect on their survival chances. This is because wrong technological 
choices can prove costly in production down times especially when high variations in alter 
technological approaches defeats the assumption that alter ties should help SMEs reduce 
their uncertainty in such situations (Rogers, 2010).  
 
Lastly, managers need to look beyond their immediate neighbourhoods in the network as 
resources embedded with indirect ties represent resources with little constraints and 
provides the vision advantage required to be innovative. 
 
6.7 Future Research 
While the study is very useful in predicting survival of SMEs by highlighting the various 
network effects, it has limitations as well. These limitations provide opportunities for other 
researchers to pursue future studies that attempt to address these gaps. These future 
research opportunities are highlighted below. 
 
The study utilizes an agricultural (poultry sector) data set to test the study hypotheses. I 
encourage other studies to test these hypotheses in other industrial sectors in Ghana. 
Again, I utilized the Dormaa poultry cluster in Ghana whereas other studies could be 
replicated in the poultry industry but in other clusters in Ghana or other African countries. 
These could form the basis of strong validation and replication studies to establish the 
robustness of the study findings especially if they are context driven. 
 
The measure of financial constraints in the second empirical paper is self-reported. While 
such measures are indicative and have been extensively used in the literature (Beck & Uc, 
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2005; De Maeseneire & Claeys, 2012) they may sometimes not reflect the reality when the 
data is observed reflecting the usual difference between perceived and actual.  
 
In this study a tie is either present or absent: hence another way that new studies can 
improve upon this study is measuring the intensity of the relationship between SMEs and 
their alters as this may have implications for the findings. 
 
In the third empirical paper isomorphism was measured as structural equivalence in 
network of other SMEs but other studies can use measures of cohesion as a measure of 
similarity to replicate the study. Structural equivalence is also a very strict measure of 
similarity. Further studies can used other equivalence measures such as regular and 
automorphic equivalence.  
 
In empirical paper four, the study uses degree and eigenvector centrality-adapted measures 
as the study tools used in measuring diffusion; however, new approaches are emerging that 
can handle specifically diffusion-based issues in networks albeit with some data 
constraints (Jackson, 2010). 
 
Studies of enterprise survival usually rely on data on entry decisions of firms from longer 
range of years; usually spanning from five years and beyond especially in organizational 
ecological analysis (see Hannan and Freeman, 1993). However, data constraints have 
forced many studies in Africa to rely on shorter ranges (see Ali and Peerlings, 2012 for 
example). This study is also no different in this respect. However, future studies can build 
on this study and collect panel data based on this two year panel that is utilized in this 
study.  
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APPENDICES: 
 
Appendix 1: Ghana Poultry Sector Statistics 
National  Brong Ahafo Region 
Region No. of Birds 
(%) 
Egg 
Production (in 
millions) 
No. of 
Farms+ 
District No. of 
Farms+ 
Greater 
Accra 
2,547,219 (7.02%) 542 475 Dormaa 202 
Central  903,702 (2.49%) 437 312 Jaman South 9 
Western 1,406,642 (3.88%) 247 102 Berekum 34 
Eastern 3,886,914 
(10.72%) 
358 213 Sunyani 65 
Volta 1,071,622 (2.95%) 68 98 Techiman 25 
Ashanti 10,180,760 
(28.07%) 
5,321 697 Tano South 16 
Brong-
Ahafo 
10,743,897 
(29.62%) 
3,989 510 Nkoranza 32 
Northern 3,625,149 (9.99%) - 30 Kintampo 
South 
1 
Upper East 1,325,835 (3.66%) - 33 Asunafo 
North 
17 
Upper West 579,474 (1.60%) - 34 Asunafo 
South 
0 
    Asutifi 10 
    Wenchi 9 
    Sunyani 
West 
90 
+Based on FAO Sector 1/2/3 Classification 
Sources: Veterinary Services Directorate (2010, 2011, 2013) in FAO, 2014 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Appendix 2: Systematic Review: Codebook and Keywords 
Codebook 
Code  Definition 
Quantitative Variables Coded   
 Authors  List of authors 
 Year  Year of publication 
 Title  Title of the article 
 Journal  Publication in which journal was published 
 Type  Theoretical, empirical or conceptual 
 Theory  The main theory used in study 
 Approach  Research approach adopted 
 Strategy  Main research strategy used in data 
collection 
 Data Source  Primary, secondary or mixed 
 Type of Data  Attribute or Relational 
 Analytical Method  Method of data analysis 
 Location  Rural or urban 
 Sector  Industry from which data was collected 
 Continent  Continent from which data was collected 
Qualitative Variables Coded   
 Issue  Research objective stated in article 
 Key Findings  Key findings stated in article 
 
 
 
Keywords Used for Search 
Enterprise survival, Enterprise failure, Enterprise closure, Enterprise mortality and 
Enterprise exit 
Organizational survival, Organizational failure, Organizational closure, Organizational 
mortality and Organizational exit 
SME survival, SME failure, SME closure, SME mortality and SME exit 
Business survival, Business failure, Business closure, Business mortality and Business exit 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Appendix 3: Definitions of Network Characteristics 
1. Density (D) = t/ n(m)  
 
where t is the number of ties in the network; n is the number of rows and m is the number 
of columns for  a bi-partite network. However, in a one-mode network the formula is 
modified to be: 
 
D = t/T 
 
where t is the number of ties in the network and T is the maximum number that is possible 
with diagonals ignored.  
 
2. Transitivity (T) = Q4/L3 
 
where Q4 is the number of quadruples with 4 legs and L3 is the number of quadruples with 
3 legs or more (For two-mode networks only) 
 
3. Diameter 
It is the longest geodesic in the network in a given component. Geodesic is the walk from 
one actor to the other. 
 
4. Average Distance 
This is the average geodesic path length in the network, within components 
 
5. Fragmentation (F) = (1 – C) 
 
where C is the proportion of nodes that are reachable in the network 
 
6. Centralization (Ct) = (Vn/Vs)*100 
 
where Vn is the degree of variability in the network and Vs is the degree of variability in a 
‘star network’ of similar size as network. 
 
7. Dyadic Reciprocity (DR) = Dr/Da 
 
Where Dr is the total number of reciprocated ties and Da is the total number of adjacent 
ties in the network 
 
8. Closure is defined using transitive closure. Hence, 
 
Closure (Cl) = T3/P2 
 
where T3 is the number of triples that are that are transitive and P2 is number of paths of 
length two. A triple xik,xij,xjk is transitive if xik is 1 whenever xij and xjk are both 1. 
 
 
 
 
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Appendix 4: Network Graphs 
Graph 1: Industry Collaborative Network 2014 
 Survived into 2015  Failed 
 
 
 
 
 
 
 
 
 
 
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Graph 2: Industry Collaborative Network as at 2015  
 
 
 
 
 
 
 
 
 
 
 
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Graph 3: Structural Equivalence of Industry Collaborative Network 2014 
 Survived into 2015  Failed 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Graph 4: Bipartite Distribution Network as at 2014 
SME = Circle |  Distributor = Square |   Survived into 2015 |    Failed  
 
 
 
+Size of the square and circle is degree centrality-based 
++Survival is tracked only for SMEs 
 
 
 
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Graph 5: Bipartite Distribution Network as at 2015 
SME = Circle |  Distributor = Square 
 
 
 
 
 
 
 
 
 
 
 
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Graph 6: Bipartite Finance Network as at 2014 
SME = Circle | Financial Institution = Square |   Survived into 2015 |    Failed  
 
 
 
 
 
 
 
 
 
 
 
 
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Graph 7: Bipartite Finance Network as at 2015 
SME = Circle | Financial Institution = Square 
 
 
 
 
 
 
 
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Appendix 5: Geographical Map of Study Area 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Appendix 6: Research Instrument/Questionnaire 
UNIVERSITY OF GHANA BUSINESS SCHOOL 
DEPARTMENT OF MARKETING 
SURVEY INSTRUMENT 
 
Dear Sir/Madam, 
This is a survey instrument by a PhD student of the University of Ghana Business School 
seeking to examine “Enterprise Survival in Ghana”. Information provided for the 
purposes of this research will be treated confidentially and used for research purposes 
only. The study is interested in your opinions as a representative of your organization. 
Kindly take a few minutes to fill out this questionnaire based on your objective opinion. 
Your participation is completely voluntary. Thank You! 
 
Section A: Enterprise Ecology 
1. How old is this business?   ………………….. 
2. How many workers do you have?  ………………….. 
3. What was the starting capital of the business?………………….. 
4. Is the business registered   Yes [ ] No [ ] if Yes where 
……………. 
5. What is the speciality of the business? Broilers [ ] Layers [ ] Both [ ] 
 
Section B: Enterprise Location 
 
1. Where is the farm located…………………………………. (Please choose from list 
below) 
2. Do they have farms in more than one location Yes [  ] No [  ]; if Yes 
then main farm location should be indicated in (1) 
 
Kofiasua Sromani Antwirifo Badukrom Gonnokrom Koraso 
 Atesikrom 
 
Ahenkro Maasu Road Kuren  Diabaa  Besease Diabaa 
 Wamfie 
 
Kyeremasu Road Wamanafo Road Asuhyia Aboabo 
 
 
Section C: Enterprise Leadership 
1. What is the highest educational attainment of owner/manager? 
No Education [   ]   Non formal [   ]   Primary Education [   ] Secondary Education [   ] 
University [   ] 
2. How many years industry experience does the owner/manager 
have?……………….. 
3. How old is the owner/manager?     ……………….. 
4. What is the gender of the owner/manager?    Male [ ] Female 
[ ] 
5. Do you consider the owner/manager as charismatic?  Yes [ ]  
 No [ ] 
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Section D: Enterprise Managerial Factors 
On a scale of 1-7, please indicate the extent to which the following statements reflect your 
firm’s managerial competences. [1= Not at all; 2= Rarely; 3= To a limited extent; 4= 
Moderately; 5= To some extent; 6= To a large extent; 7= To a very large extent] 
No.  Statement  1 2 3 4 5 6 7 
Entrepreneurial Orientation        
1. We take bold decisions necessary to achieve the firms goals        
2. We are motivated to be creative in methods of operation        
3. We take the lead for competitors to follow        
         
Absorptive Capacity        
1. We actively search for information about our industry        
2. We engage in cross-unit problem solving        
3. We apply knowledge gained to practical work        
4. We adopt new technology to improve work        
         
Market Orientation        
1. We meet with customers to discuss our products        
2. We hold internal discussions about customer needs        
3. We review our operations to meet customer needs        
         
Dynamic Capabilities        
1. We engage in systematic business planning        
2. We provide on-the-job training for workers        
3. We benchmark against industry standards        
 
 
Section E: Performance/Survival Measures 
 
Financial Performance 
1. Average weekly sales Eggs  
Broilers  
2. Average weekly productivity Eggs  
Broilers  
3. Value of current fixed assets   
    
Social Performance 
1. Employment Direct  
Indirect  
2. Community Donations 
(Philanthropy) 
Do you make donations? Yes [ ] No [ ]  
Estimated amount (Yearly)  
3. Pollution Do people complain about 
location of the farm? 
Yes [ ] No [ ] 
How many complaints have 
you received since 
incorporation? 
 
 
 
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Section F: Stakeholder Relations 
 
The study wants to understand the relationships you have in the stipulated stakeholder 
categories below. Please provide us with a maximum of 7 stakeholders in each category 
that you have both a business and personal relationship with. These can be organisations 
or individuals. 
 
Customers 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
Suppliers and Input Dealers 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
Poultry Farmers 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
Government/Municipal Agencies 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
Community Relations/Land Owners 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
Local Farmers (Non-Poultry) 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
Transporters 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
Egg Distributors/Specialised Agents 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
Financiers/Banks 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
Associations 
1. 
2. 
3. 
4. 
5. 
6. 
7. 
 
 
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Section G: Investment Climate Constraints 
On a scale of 1-7, please indicate the extent to which the following statements affect your 
firm’s operations. [1= Not at all; 2= Rarely; 3= To a Limited extent; 4= Moderately; 
5= To some extent; 6= To a large extent; 7= To a very large extent] 
 
No.  Statement  1 2 3 4 5 6 7 
1. Access to finance (availability and cost)        
2. Access to land        
3. Business licensing and permits        
4. Corruption        
5. Courts        
6. Crime, theft and disorder        
7. Customs and trade regulations        
8. Electricity        
9. Inadequately educated workforce        
10. Labour regulations        
11. Political instability        
12. Practices of competitors in the informal sector        
13. Tax administration        
14. Tax rates        
15. Transportation of goods, supplies, and inputs        
16. Inflation        
 
 
Section H: Respondent Profile (If Not Owner/Manager) 
 
1. What is your position:………………………………………………………... 
2. How long have you been in your position?.................................................... 
3. Gender:                Male [  ]   Female [   ] 
4. Age:                     18-25 [  ]   26-35 [   ]   36-45 [   ] 46-55 [   ] 55-above [   ] 
5. Marital Status:     Married [   ]   Single [   ]    Divorced [   ]   Widowed [   ] 
6. Educational Attainment: No Education [   ]   Primary Education [   ]           
Secondary Education [   ] University [   ]    
 
 
Section I: Validation Questions (To be answered by interviewer) 
1. Do you think the respondent understood the questions?  Yes [ ] No [ ] 
2. Do you think the respondent answered the questions truthfully? Yes [ ] No [ ] 
3. How many minutes did it take to go through the questions?  ……………. 
4. Did the respondents show you some documents to back some answers? Yes [ ] 
No [ ] 
 
 
 
  
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Appendix 7: Ethical Clearance 
 
 
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