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UNIVERSITY OF GHANA
COLLEGE OF BASIC AND APPLIED SCIENCES
RISK ATTITUDES, RISK MANAGEMENT AND BUSINESS
SUCCESS OF MICRO AND SMALL INFORMAL AGRIBUSINESS
ENTREPRENEURS IN GHANA: THE CASE OF AGRI-FOOD PROCESSORS
BY
ALFRED ASUMING BOAKYE
(10192623)
A THESIS SUBMITTED TO THE SCHOOL OF GRADUATE
STUDIES, UNIVERSITY OF GHANA, LEGON IN PARTIAL FULFILMENT OF
THE REQUIREMENTS FOR THE AWARD OF DOCTOR OF PHILOSOPHY
DEGREE IN AGRIBUSINESS
DEPARTMENT OF AGRICULTURAL ECONOMICS AND AGRIBUSINESS
JULY, 2017
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DECLARATION
This thesis titled ‘Risk Attitudes, Risk Management And Business Success Of Micro
And Small Informal Agribusiness Entrepreneurs In Ghana: The Case Of Agri-
Food Processors’ is the result of research work undertaken by Alfred Asuming Boakye
in the Department of Agricultural Economics and Agribusiness, University of Ghana,
under the supervision of Prof. Daniel B. Sarpong, Dr. Edward E. Onumah and Dr. Y. B.
Osei-Asare. It has never been submitted in whole or in part for any degree in this
University or elsewhere. References to other people’s work have been duly
acknowledged.
………………………………..................... ……..……………………..
Alfred Asuming Boakye (PhD Candidate) Date
This thesis has been submitted for publication with our approval as supervisors
……………………………….............. .....……………………………
Prof. Daniel B. Sarpong (Principal Supervisor) Date
……………………………….............. …………………………………
Dr. Edward E. Onumah (Co-Supervisor) Date
……………………………… .......... …………………………………
Dr. Y. B. Osei-Asare (Co-Supervisor) Date
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ABSTRACT
Entrepreneurs operating micro and small informal firms including agribusinesses in
Ghana face many risks which adversely affect the growth of their firms and subsequent
business success. Although risk management is very critical to agriculture related firms,
agricultural policies in Ghana have been designed without much consideration to this
important aspect of firm management. The impending ‘one-district-one-factory’ policy
of the current government for example would need to consider risk management of the
firms that would be involved since risk management would influence managerial
decisions. The success of the policy would partly be hinged on the ability of
entrepreneurs involved to manage risks in a more informed position. The aim of this
study was three-pronged. First, it elicited the risk attitudes of entrepreneurs, assessed the
factors that influenced the attitudes and examined how entrepreneurs perceived certain
risk sources as important to the agribusiness environment in Ghana. Second, the study
sought to estimate the effect of entrepreneurs’ risk management practices on agribusiness
firm growth. Linear regression models were utilised in achieving these two objectives.
Third, the study sought to understand the personality traits of entrepreneurs and what
accounted for their business success applying the personality trait theory as attribution to
business success of the entrepreneur using an ordered logit model. To elicit
entrepreneurs’ risk attitudes, the psychometric theory was utilised. The Domain-Specific
Risk-Taking (DOSPERT) instrument which lists hypothetical questions that elicit risk
perception and risk propensity was used to elicit the risk attitudes of entrepreneurs. Risk
attitude was measured as a combination of the scores from risk perception and
propensity. The sample analysis utilised data from 159 entrepreneurs (owners) of micro
and small firms in agri-food processing in the Greater Accra and Ashanti regions of
Ghana. The results showed that about 61% of entrepreneurs were risk seeking
individuals. Using a linear regression model, aged and married entrepreneurs showed
more risk aversion behaviour. Education exerted no effect on risk attitudes. Male
entrepreneurs were more risk seeking compared to females. Results showed that aged
entrepreneurs do not give much credence to general economic/political risk as an
important risk source to affect them but considered human risk (sickness and death) as
important.  Female entrepreneurs did not consider market risk as important enough to
affect the business environment probably because they had found innovative ways in
marketing and these innovations reduced the effect of market risks on their businesses.
Subscription to formal insurance showed a positive and highly significant impact on firm
growth. To further understand the underlying factors responsible for levels of business
success achieved by entrepreneurs, their personality traits using three dimensions (locus
of control, self-efficacy and motivation) were estimated. The general conclusion was that
the entrepreneur’s psychological disposition has significant effects on business success.
Recommendations for this study include initiation and facilitation for the development of
marketing cooperatives (as a risk management tool for entrepreneurs) to negotiate fair
prices to help mitigate marketing risks. Again, targeted education   on the importance of
subscription to insurance packages (a risk management tool which significantly and
positively affected firm growth) to alert entrepreneurs of the need to insure their
businesses and enjoy the inherent benefits is suggested.
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DEDICATION
This work is dedicated to my wife Naomi and children Nana Akwasi, Ama Pokua and
Afia Asantewaa.
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ACKNOWLEDGEMENT
I express my heartfelt gratitude to God Almighty for his guidance and protection in all
spheres of my life. I owe my very existence to him. I would like to articulate my sincere
and heartfelt appreciation to Prof. Daniel B. Sarpong, my major advisor for giving me
the opportunity to achieve this goal. His invaluable and constructive advice, comments
and suggestions as well as his support and contributions throughout this work and
towards the successful completion of my programme is most appreciated. I would also
like to tender my deepest gratitude to Dr. Edward E. Onumah and Dr. Y. B. Osei-Asare,
my co- advisors, for their interest and willingness to supervise me in this study.  Their
suggestions and comments have been invaluable and have greatly influenced the shape
of this thesis.
I wish to also thank Prof. Felix A. Asante (Director of ISSER), Dr. Akwasi Mensah-
Bonsu, Dr. (Mrs) Irene S. Egyir and Dr. John B. D. Jatoe (all of the Department of
Agricultural Economics and Agribusiness, University of Ghana) for their kind pieces of
advice towards the completion of this work.
I would like to express my sincere thanks to other faculty in the Department of
Agricultural Economics and Agribusiness: Prof. Ramatu Alhassan, Prof. Wayo Seini, Dr.
George T-M. Kwadzo, Mr. D. P. K Amegashie, and Dr. Henry Anim-Somuah. I make
special mention of colleagues in my department: Prof. K. Afreh-Nuamah, Prof. K. G.
Ofosu-Budu, Prof. S. Adjei-Nsiah, Prof. G. O. Nkansah, Dr.  F. C. Brentu, Dr. S.
Torkpo, Dr. L. Mintah, Dr. C. Akotsen-Mensah, Mr. & Mrs. Afrifa, Mr. Alex Williams
Ayarna and Mr. Richard Kumi-Kyere.
I would also like to thank the University of Ghana for graciously granting me a study
leave, ORID for assisting me with financial support from the Faculty Development
Scheme, and the Leventis Foundation for Famers Training Programme for their financial
support towards my thesis research. I am very grateful.
This work would not have been possible without the support of my wife Naomi and my
children Nana Akwasi, Awura Ama and Ohemaa Afia. Your trustful and unlimited love
encouraged me in every step of this PhD journey.
My sincere gratitude to my parents Collins and Victoria Asabi Boakye, my uncle Prof.
Sam Asuming-Brempong  and his wife Dr (Mrs) Asuming-Brempong, siblings Vera,
Godfred,  Prince, Frank and Sammy. My cousins Elias, David, Sam, Hannah, Kof Sam,
Esther, and especially Eunice for her great help in this study.
I am also very grateful to my good friend Alex Apau for coordinating data collection and
supervising my enumerators: Nixon, Augustine, Kingsley, Selassie, Quayson, David and
Susan. Special thanks to Dayta Solutions (a data management firm) for managing data
for this study both on-field and thereafter. Thank you Nat and your staff.
Finally, my thanks also go to my colleagues Isaac Manu, Akua Agyeiwaa, Nana Kofi,
Alhassan Andani, Mavis Boimah, Adabe Kokou, Charles, Robert, Charlotte, Mercy,
Gabriel, Edna, Michael, Gyimah and all loved ones who helped me in one way or the
other.
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TABLE OF CONTENTS
DECLARATION............................................................................................................................. i
ABSTRACT ................................................................................................................................... ii
DEDICATION .............................................................................................................................. iii
ACKNOWLEDGEMENT............................................................................................................. iv
TABLE OF CONTENTS ................................................................................................................v
LIST OF TABLES ........................................................................................................................ ix
LIST OF FIGURES....................................................................................................................... xi
LIST OF ACRONYMS................................................................................................................ xii
CHAPTER ONE..............................................................................................................................1
INTRODUCTION...........................................................................................................................1
1.0 Background......................................................................................................................1
1.0.1 Risk management, firm growth and Business Success in the global context ..........1
1.0.2 Risk management, firm growth and Business Success in the Ghanaian context.....4
1.0.3 Characteristics and Definition of Micro and Small Enterprises in Ghana...............6
1.0.4 Context of business success in this study ................................................................7
1.1 Problem Statement...........................................................................................................8
1.2 Research questions ........................................................................................................12
1.3 Research Objectives ......................................................................................................12
1.4 Relevance of the study...................................................................................................13
1.5 Scope of the study .........................................................................................................14
1.6 Organisation of the thesis ..............................................................................................15
CHAPTER TWO...........................................................................................................................16
LITERATURE REVIEW..............................................................................................................16
2.0 Introduction ...................................................................................................................16
2.1 Overview of the Micro and Small Enterprise (MSE) sector in Ghana ..........................16
2.2 Risk theory and risk analysis ........................................................................................18
2.2.1 Analytical framework for risk analysis..................................................................19
2.3 Risk attitude, risk perception and risk propensity .......................................................20
2.3.1 Risk elicitation based on economic theory ............................................................21
2.3.2 Psychometric method of risk attitude elicitation ...................................................22
2.4 Theory of risk management in small firms....................................................................23
2.4.1 Identification.........................................................................................................24
2.4.2 Risk analysis and evaluation.................................................................................24
2.4.3 Risk assessment ....................................................................................................24
2.4.4 Risk monitoring ....................................................................................................25
2.5 Theory of the Firm and firm Growth............................................................................26
2.5.1 The Entrepreneur in the theory of firm  growth ...................................................26
2.5.2 Firm growth, and the heterogeneity of firm growth .............................................27
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2.5.3 Measuring growth.................................................................................................28
2.5.4 Risk management and firm growth.......................................................................30
2.5.5 Empirical evidence – Effect of risk management practices on firm growth.........31
2.6 Risks Facing MSEs in Ghana ........................................................................................32
2.7 Business success............................................................................................................33
2.7.1 Dimensions of entrepreneur’s personality trait as causal attribution to business
success.............. .....................................................................................................................35
2.7.1.1 Locus of Control...................................................................................................36
2.7.1.2 Motivation ............................................................................................................37
2.7.1.3 Self-efficacy..........................................................................................................37
CHAPTER THREE.......................................................................................................................39
METHODOLOGY........................................................................................................................39
3.0 Introduction ...................................................................................................................39
3.1 Conceptual framework: Risk Management and Business Success................................39
3.2 Risk attitudes of entrepreneurs ......................................................................................42
3.2.1 Elicitation of entrepreneur’s risk preferences - Analytical Framework.................42
3.2.2 Measuring Risk attitude.........................................................................................44
Risk perception......................................................................................................................44
Risk propensity......................................................................................................................44
3.2.3 Derivation of entrepreneur’s risk attitude ............................................................45
3.3 Perceived risk sources affecting the business environment of entrepreneurs................46
3.3.1 Factors affecting risk attitudes and Perceived risk sources to business.................47
3.3.2 Consistency of scale items used in eliciting risk attitude ......................................49
3.3.3 Perceived sources of risk and entrepreneurial demographic characteristics..........49
3.4 Factor analysis ...............................................................................................................56
3.4.1 Sample adequacy tests for factor analysis .............................................................57
3.4.2 Factor rotation.......................................................................................................59
3.4.3 Communality ........................................................................................................60
3.5 Effect of Risk management practices on firm growth ...................................................60
3.5.1 Measuring Firm growth .........................................................................................60
3.5.1.1 Increase in employee size .....................................................................................60
3.5.1.2 Increase in Sales volume ......................................................................................61
3.5.2 Risk management and firm growth........................................................................62
3.5.3 Description of variables..............................................................................................63
3.6 Business success and attribution based on personality trait of the entrepreneur .........64
3.6.1 Subjective business success...................................................................................64
3.6.2 Objective Business success ...................................................................................65
3.6.3 Categories of business success ..............................................................................67
3.6.4 Attribution of entrepreneur’s personality to business success ...............................68
3.6.5 Determinants of business success applying the dimensions of the entrepreneur’s
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personality trait as attribution to business success ..................................................69
3.6.5.1 Ordered logistic regression........................................................................................69
3.7 Research design .............................................................................................................72
3.7.1 Study Area .............................................................................................................72
3.7.2 Sampling Procedure...............................................................................................75
3.7.3 Sample size determination.....................................................................................76
3.7.4 Data........................................................................................................................77
CHAPTER 4..................................................................................................................................78
RESULTS AND DISCUSSIONS .................................................................................................78
4.0 Introduction .........................................................................................................................78
4.1 Sample characteristics .........................................................................................................78
4.1.1 Demographic profile by proportion.......................................................................78
4.1.2 Demographic and enterprise characteristics (mean statistics) ...............................80
4.1.3 Type of agri-food processors .................................................................................82
4.1.4 Firm age.................................................................................................................82
4.2 Risk attitudes of entrepreneurs ..................................................................................84
4.2.1 Entrepreneur’s risk attitude ...................................................................................85
4.3 Factors affecting risk attitudes and perceived risk sources .................................................86
4.3.1 Factors affecting risk attitudes of entrepreneurs....................................................86
4.3.2 Factors affecting sources of risk as perceived by entrepreneurs ...........................88
4.3.2.1 Factor analysis for perceived sources of risk.........................................................90
4.3.2.2 Data quality tests for perceived sources of risk .....................................................90
4.3.2.3 Factors influencing entrepreneurs’ perceived risk sources ....................................92
4.4 Risk management practices and firm growth ..................................................................96
4.4.1 Importance of risk management practices to entrepreneurs ..................................96
4.4.2 Determinants of firm growth .................................................................................97
4.4.2.1 Effect of risk attitudes and enterprise characteristics on firm growth ...................97
4.4.2.2 Effects of risk management practices on firm growth...........................................99
4.4.2.3 Risk reduction practices.......................................................................................100
4.4.2.4 Loss Management practices.................................................................................101
4.5 Business success................................................................................................................105
4.5.1 Categories of business success ...................................................................................105
4.5.2 Entrepreneur’s personality traits as attribution to business success ....................107
4.5.2.1 Dimensions of causal attribution to business success..........................................107
4.5.2.2 Factor analysis of dimension of causal attribution to business success...............110
4.5.2.3 Dimensions of personality traits of the entrepreneurs as attribution to business
success................. ................................................................................................................114
4.5.2.4 Marginal effects of significant constructs of personality trait as attribution to
business success...................................................................................................................116
CHAPTER FIVE.........................................................................................................................118
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SUMMARY, CONCLUSION AND POLICY RECOMMENDATIONS ..................................118
5.0 Introduction .................................................................................................................118
5.1 Summary......................................................................................................................118
5.2 Conclusions .................................................................................................................122
5.3 Policy recommendations.............................................................................................123
5.4 Future research ...........................................................................................................125
References ...................................................................................................................................127
LIST OF APPENDICES .............................................................................................................141
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LIST OF TABLES
Table 3.1:  Description of variables ........................................................................... 48
Table 3.2:  Sampling adequacy test............................................................................ 59
Table 3.3: Levels of business success ........................................................................ 68
Table 3.4: Description of variables - Dimension of personality traits attributed to
business success ......................................................................................................... 71
Table 3.5: Location of firm by region and district ..................................................... 76
Table 4.1: Demographic characteristics (%) .............................................................. 79
Table 4.2: Descriptive statistics of demographic and firm characteristics (mean) .... 81
Table 4.3: Type of agri-food processors (%) ............................................................. 82
Table 4.4: Mean score of scale items for risk attitude elicitation............................... 84
Table 4.5: Risk aversion of entrepreneur (%) ............................................................ 85
Table 4.6: Results - factors affecting the risk attitude of entrepreneurs..................... 87
Table 4.7: Perceived sources of risk in the business environment in Ghana – Likert
scores (%) ................................................................................................................... 89
Table 4.8: Rotated Component Matrix (factor scores) for risk constructs ................. 91
Table 4.9: Regression results of factors influencing entrepreneurs’ perceived risk
sources ........................................................................................................................ 93
Table 4.10: Average score and ranking of risk management practices (level of
importance)................................................................................................................. 96
Table 4.11: Results -effects of risk management practices on firm growth............... 98
Table 4.12: Mean score of indicators of business success ....................................... 106
Table 4.13: Levels of business success (%) ............................................................. 106
Table 4.14: Statements depicting locus of control (%) ............................................ 108
Table 4.15: Statements depicting self-efficacy of the entrepreneur (%).................. 109
Table 4.16: Statements for motivation to business success of the entrepreneur (%)110
Table 4.17: Rotated factor matrix for dimensions of locus of control to business
success ...................................................................................................................... 111
Table 4.18: Rotated factor matrix for Self efficacy to business success .................. 112
Table 4.19: Rotated factor matrix for motivational dimension of causal attribution to
business success ....................................................................................................... 113
Table 4.20: Correlations between factor scores of constructs of causal attribution and
business success ....................................................................................................... 114
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Table 4.21: Ordered logit results for effects of dimensions of causal attribution to
business success ....................................................................................................... 116
Table 4.22: Marginal effects of significant dimensional constructs on levels of
business success ....................................................................................................... 117
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LIST OF FIGURES
Figure 2.1: Illustration for analysing risk................................................................... 20
Figure 2.2: Risk Management Process ....................................................................... 26
Figure 3.1: Conceptual framework depicting the relationship between risk attitude,
risk management practices and business success ....................................................... 40
Figure 3.2: Map of study area .................................................................................... 74
Figure 4.1: Age of firms sampled (categorised)......................................................... 83
Figure 4.2: Location of firms (urban/rural) ................................................................ 83
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LIST OF ACRONYMS
CSF Critical Success Factor(s)
DOSPERT Domain-Specific Risk-Taking
ET Employee Turnover
EUF Expected Utility Framework
EUT Expected Utility Theory
FASDEP Food and Agriculture Development Policy
GDP Gross Domestic Product
GLSS Ghana Living Standards Survey
GPRS Ghana Poverty Reduction Strategy
GSS Ghana Statistical Service
KMO Kaiser–Meyer–Olkin
MOFA Ministry of Food and Agriculture
MSE Micro and Small Enterprises
NBSSI National Board for Small Scale Industries
OLS Ordinary Least Square
PCA Principal component analysis
PPP Public-Private Partnership
RPED Regional Project on Enterprise Development Ghana
SG Sales growth
SSA Sub-Saharan Africa
TE Turnover per Employee
VIF Variance Inflation Factor
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CHAPTER ONE
INTRODUCTION
1.0 Background
1.0.1 Risk management, firm growth and Business Success in the global context
Entrepreneurs whether they establish large or small businesses are not immune to the
issues of risk1 in decision making especially those that directly affect the management
of their businesses and how they perform. As elaborated by some authors (Stokes,
2000; Watson & Everett, 1996; Jennings & Beaver, 1997), the risk behaviour of the
entrepreneur and management of small firms are highly correlated. Economic decision
making under uncertainty means that the attitude of the individual with regards to risk
aversion plays a role in a variety of contexts that are critical to understanding their
behaviour. This highlights the importance of risk management especially in the
management of the firm.
There are two reasons highlighted by Cross (2000) which suggest why emphasis on risk
management continues to be paramount in the management of the firm. First, the
increased pace of change in the business environment has rendered decisions based on
past experiences  more and more unreliable mainly because new issues keep cropping
up in business and entrepreneurs now need to consider a whole range of futures in order
to minimize losses in the uncertain and changing environment. Second, surge in the
number of disasters have made public liability insurance more expensive and changes
to liability laws implies that business managers and  entrepreneurs (firm owners) may
now be held legally and personally responsible for any losses arising from poor  risk
1 Risk is defined as the occurrence of an event or a situation in which a business entity (represented
by the owner/entrepreneur) takes a decision which has the probability of more than one outcome
and the probability of occurrence of any of the outcomes can be estimated and can also have
influence on business objectives
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management decisions by the firm.  To counter these, risk management becomes
apparent.
There seem wide variations in definition of risk management by various authors
although they all aim at tools that are employed by entrepreneurs (business owners) in
reducing the exposure of their firms to risk while targeting maximum benefits from
their economic activities. For example Manab et al., (2012) define risk management as
an organizational tool employed by the business towards achieving set goals and
objectives, and strengthening corporate governance, while fulfilling its obligation
toward shareholders. Conversely, Chance & Brooks (2010) define risk management as
a firm’s decision with regards to the practice of setting a level the firm desires by
identifying its current risk level and using various methods and instruments to adjust
the actual level of risk it faces so the risks can be minimised.  The definition by Manab
et al., (2012) fits better in the direction of this study and will therefore be adopted. This
study does not delve into the concept of entrepreneurship but looks at risk attitudes of
entrepreneurs, their risk management practices that impact on the growth of their firms
(micro and small firms) and the subsequent business success achieved.
The aim of risk management is geared towards firm growth and ultimately the firm’s
business success.  The focal point in firm growth and success is the personality
characteristics of the entrepreneur in terms of their risk attitudes and this demonstrates
the inherent strength with which they manage the risks that face their businesses
(Ciavarella et al., 2004). Environmental factors and firm characteristics are also very
significant contributors to small firm growth but the entrepreneur’s personality
characteristics hold (Chittithaworn, Islam, Keawchana, & Yusuf, 2011)sway in the
hierarchy of factors that influence firm growth. For instance, the entrepreneur’s resolve
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to take certain decisions in the face of risks and turn inherent threats to opportunities
determines their risk attitudes and invariably affect their firm growth and their business
success.
There are divergent views as concerns how important the entrepreneur’s risk attitudes
influence firm growth and success. Some authors (Carland & Carland, 2015; Kim-Yin
et al., 2015; Zhao & Seibert, 2006) elevate the individual (entrepreneur) above all
other factors when studying firm growth and conclude that  there is a positive
correlation between risk attitudes and firm growth (Caliendo et al., 2010) and therefore
there is the need for further studies to understand the personality characteristics of
entrepreneurs that contribute to firm growth. The crust of entrepreneurship is the
ability to quickly identify business opportunities and the willingness at seizing such
opportunities by way of investing in them. The failure or success of Micro and Small
Enterprises (MSEs) the world over is greatly influenced by the decisions and
capabilities of entrepreneurs and is manifested by their different management strategies
and skills. This implies that successful businesses are managed by entrepreneurs who
have strategies to take risks linked to the investment opportunities available to them
and ultimately impact on the success of their businesses. However, as Drichoutis &
Lusk (2012) point out, firms do not operate in isolation but are closely linked with the
macroeconomic, political, and social environment. This implies that as much as the
socio-demographic characteristics of entrepreneurs are important, their environment is
equally important to ensure the success of their ventures.
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1.0.2 Risk management, firm growth and Business Success in the Ghanaian context
Although the FAO (2005) describes the nature of the agro industrial sector (which
encompasses the agribusiness sector) in Ghana as rudimentary and artisanal, there are
prospects of becoming robust if policies  especially those related to  trade and  aimed at
promoting the private sector are promptly pursued. The development of the
agribusiness   sector is critical to sustaining the overall development of the economy.
Within the agribusiness sector are Micro and Small Enterprises (MSEs) which make up
a significant portion of private investments in the agro industrial sector of the Ghanaian
economy( Setsoafia et al., 2015). MSEs are very critical for economic growth (Abor &
Quartey, 2010). In Ghana, they represent a significant portion of privately owned firms
(Abotsi et al., 2014) and are extremely important economic players in employment
provision, enhancing food security and sustaining the overall growth of the Ghanaian
economy. Despite their importance, there is evidence (Bannock, 2002; Berry et al.,
2002; Setsoafia et al., 2013) to show that most of them especially those in the agro
industrial sector fail to survive the first year of their existence.  This is corroborated by
Stokes (2000) who concludes that survival rates for small businesses across the globe
are low.
Numerous constraints including limited access to finance, poor public infrastructure,
higher input costs, increased regulatory burden, high bureaucracy, increasing
competition in the output market, and unfavourable prices received by primary
producers have been attributed to the failure rate of MSEs in the agribusiness sector
(Setsoafia et al., 2015).  Apart from these constraints, entrepreneurs operating these
MSEs face risks which threaten the growth and success of their businesses.  The ability
of the entrepreneur to address the dynamics of the Ghanaian market is largely
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influenced by their ability to carefully identify and analyse the type of risks their
business faces as well as examine the strategies that are necessary to manage them
(Abotsi et al.,2014). Severe losses are incurred when businesses are exposed to risks
with unfavourable outcomes. The risk associated with market price of commodities is
one of the obvious areas in risk management that has received much attention in
research but is still important especially in the Ghanaian context (Dellor, 2009). In
addition, other risks entrepreneurs face (financial, institutional, and personal/human
risks), form the basis of the business environment in which informal small businesses
in Ghana operate. The risks enumerated influence informed judgments made by
entrepreneurs (owners) in terms of strategies to manage the risks they face. It also
positions their businesses to capture the benefits that will accrue in future due to the
management decision taken to mitigate the effect of risks.
The risk attitude of entrepreneurs has received considerable attention in research the
world over but is scant in the Ghanaian context.  Dellor (2009) suggests that Ghanaian
entrepreneurs are generally risk averse in their investment decisions but are over-
confident with their abilities to manage risks and this pre-disposes them to economic
risks.  The extent to which individual risk attitudes of entrepreneurs and their risk
management practices might impact on firm growth and consequent success is largely
unexplored although empirical research has paid more attention  to differences in risk
attitudes between entrepreneurial and non-entrepreneurial  groups (Caliendo et al.,
2010)
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1.0.3 Characteristics and Definition of Micro and Small Enterprises in Ghana
The role played by Micro and Small Enterprises (MSEs) in economic development in
many countries across the world is significant (Fuseini, 2015) . The existence of a
strong small business sector is necessary for rapid economic development. They have
been ascribed the role of efficient and prolific job creators, the foundation of big
businesses and the fuel of national economic engines (Abor & Quartey, 2010). Their
importance to economic development is even more paramount since they form a large
portion of the informal sector in developing countries (Nichter & Goldmark, 2005).
The Ghanaian economy is dominated by the informal sector. It is estimated that about
70% of Ghanaian enterprises are micro to small sized (GSS, 2015).
There is no consensus on the definition of MSEs in Ghana. The Ghana Statistical
Service (GSS) defines these enterprises based on the number of employees and value of
fixed assets. Micro firms are those that employ up to five employees and have fixed
assets (excluding realty) not exceeding the value of $10,000 while Small firms employ
between six and twenty nine employees with fixed assets up to $100,000 (excluding
realty).  The National Board for Small Scale Industries (N.B.S.S.I) defines a micro
enterprise (firm) as one with less than five employees and a small firm  as one with a
total value fixed assets (excluding land, buildings and vehicles) not exceeding GH¢ 10
million and up to nine employees. The working definition of this study as used in the
Ghanaian context is hinged on a definition by the Regional Project on Enterprise
Development Ghana (RPED), which classified firms into: micro enterprise as firms
with less than five employees; small enterprise as firms with employees between five
and twenty nine employees; medium enterprises as those with employee number
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between thirty and ninety-nine; and large enterprises which have hundred and more
employees (Teal, 2002).
1.0.4 Context of business success in this study
In business management, success is a key term even though it is not always explicitly
stated (Chittithaworn et al., 2011). The classical concept of business success has been
associated with a firm’s financial performance but this is not universally accepted.
Studies in relation to the issue of success in business consider that it is much more than
just financial fulfilment. For example, Foley & Green (1989) and (Oyeku et al,. 2014)
suggest personal satisfaction such as being their own boss, independence, and realizing
creativity and innovative potential as measures of success. Storey (1994) concludes that
employment growth could be used as a standard to measure business success. Again,
Dunkelberg et al., (1987) define business success as growth in both the sales of the
firm’s product and number of employees as more reliable criteria in assessing business
success of small firms.
Trondsen (2002) argues that business success is measured by net profit but with
stringent accounting principles that consider regular depreciation in the value of the
currency. Chittithaworn et al., (2011) consider business success from a different angle
by reviewing a number of variables which include socio-demographic characteristics of
entrepreneurs, management and know-how, products and services, customer and
market, the way of doing business and cooperation, resources and finance, Strategy,
and external environment. Boit et al., (2014) in Kenya also measured business success
in terms of growth in volume of sales, profits, firm reputation, and increase in number
of employees on annual basis.
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Considering the vastness in the different facets in assessing and defining business
success, this study uses a two-pronged approach in defining business success. First,
subjective business success defined as the assessment of the owner’s objectives for
setting up the business and his current level of satisfaction in meeting those objectives
(Foley & Green, 1989; Oyeku et al., 2014). This is subjective because it is influenced
by the owner’s personal assessment. The second part, objective business success is
defined as mean increases in objective indicators of small firm growth (growth in sales
volume, productivity per employee based on volume of sales and employee size) over a
period. The term objective business success is used because the indicators for
measuring business success of the firm are not subject to the views of the owner but are
based on figures which measure small firm growth
1.1 Problem Statement
MSEs in Ghana face many risks in their ventures and this is especially aggravated with
those directly related to agriculture (agri-food processing firms) because the progress of
their production activities is contingent on primary agricultural production which is
prone to many natural disasters.  Again, price volatility of farm output (as raw
materials) is of major concern to entrepreneurs in agri-food processing since the former
have direct effects on the cost of production. Between 2012 and 2016, Ghana faced
major economic challenges and this was manifest in the rapid depreciation of the
currency and precipitated by the energy crisis. The cost of the energy crisis to the
Ghanaian economy was estimated to be between US$ 320 and US$ 924 million
(excluding indirect cost) and translated to be 2% and 6% of GDP (Ackah, 2014). The
adverse impact of the crisis escalated the operating costs of businesses and limited
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production and hence output growth. Indeed, the informal sector which is dominated by
MSEs was hard hit by this situation which is debilitating to their progress in general.
These bring to fore, the issue of risk and uncertainly that many agribusiness MSE
entrepreneurs face. The risk situation is complicated by the fact that they operate in an
environment characteristic with weak markets (Dellor, 2009). They do not have access
to sufficient support institutions that can help them cope with risks. Among the
remedies prescribed for business operators for managing risk is to  subscribe to risk
sharing institutions like national insurance and credit schemes  or private insurance
products that help reduce the burden of risk to society (Zawoyski et al., 2015).
However, insurance penetration in Ghana is generally low. Insurance coverage in
Ghana is about 1.5 percent  owing to low levels of insurance literacy among majority of
the Ghanaian population (Nduna, 2013). Again, private sector insurance products in
agriculture and its related sectors are not well developed and rolled out in Ghana,
compelling MSE entrepreneurs to choose self insurance strategies that include social
mechanisms and diversification for coping with risk. The major goal for any MSE
entrepreneur in risk management is the ability to identify those risks and evaluate them
so that informed strategies can be adopted to minimise their effect. Thieke (2000)
indicates that such actions of the entrepreneur are both offensive and defensive because
good risk management enhances a business person’s risk capacity and shields the
business from unwanted risk.  Consequently, it is crucial that individual risk attitudes
and their influencing factors are assessed and better understood in order to design
effective policy instruments to support the agribusiness sector so as to promote
economic growth (Harrison et al., 2010; Nielsen et al., 2013) in Ghana.
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The one-district-one-factory policy is an initiative of the current Ghanaian government
and aims at establishing at least one factory in every district in Ghana under a Public-
Private Partnership (PPP) model with entrepreneurs (as private partners) and was
launched in mid 2017. The processing factories are agriculture-related and will target
the widely cultivated agricultural produce in their catchment areas. This implies that the
management of such factories will require a plethora of competence including the
ability to effectively manage risk in order to succeed. Almost all government
programmes (especially those in agriculture) within the current Fourth Republican
dispensation of Ghana have been designed without much credence to risk management
of the actors in the whole continuum – from primary to post primary production
(examples include  GPRS I & II; FASDEP I & II; and the recent ‘Planting for Food and
Jobs policy’ launched in April 2017). It is a known fact that activities related to
agricultural production are prone to risk and uncertainty (Musser & Patrick , 2002;
Ayinde et al., 2008). They have devastating effects if the main risk – natural disasters
strike. The risk attitude of those responsible for managing primary production firms
(i.e. farms) and secondary production firms (processing plants) in the one-district-one-
factory policy is crucial to the success of the one-district one- factory policy.  The
reason is to enable them understand how these firms can grow and become successful
with regards to managerial decisions mainly because such decisions would be
predicated on risky choices. The risk attitude of the management (entrepreneurs) will
be a telling effect on the kind of decisions taken and the business success of the firms
(Binici et al., 2003) that will participate in the policy initiative. It stands to reason that
one of the pillars of the policy will be risk management.
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Ghana’s agribusiness sector is not well developed and its contribution to the country’s
agricultural GDP is widely undocumented (although the whole agricultural sector
contributed about 19% to Ghana’s GDP in 2015)  but new markets and business
opportunities exist for the sector to become a major income earner for actors in the
sector, while creating thousands of jobs and these require investments (GSS, 2015;
Setsoafia et al., 2015). However, there seems to be a dearth of knowledge on what
drives agribusiness entrepreneurs especially those operating micro and small
enterprises in their managerial decisions and factors that influence the growth of their
firms and consequently their success. Studies on small enterprise success are largely
disjoint across the world. With the presupposition that there tend to be common
underlying factors that are associated with success (Hills & Narayana, 1990), many
small business studies have been undertaken to identify these success factors in
different countries. However most of these previous studies were based on the
experience of small firms operating in the western world (Luk,1996).
The style of MSE management especially for those in the informal sector in the non-
western world differs significantly from the conventional methods used by the western
world (Tung & Aycan, 2008). Again, Amaeshi et al., (2008) concludes that local
managers tend to combine different contextual approaches, as opposed to adopting
purely western managerial practices in small business management in Kenya. This
study therefore focuses on indigenous risk management practices of small firms in the
African context. It further applies dimensions of the personality trait (also known as
psychological disposition) from psychology literature as causal attribution to ascribe
the causes of business success from the perspective of the entrepreneur in order to
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obtain a clear picture of the underlying reasons for their current levels of business
success.
1.2 Research questions
From the on-going, the following research questions become necessary.
1. What factors determine the risk attitudes of entrepreneurs and perceived sources
of risk to their businesses?
2. What risk management practices do entrepreneurs employ and do these
practices have any effect on their firm growth ?
3. What factors do entrepreneurs attribute to their business success?
1.3 Research Objectives
The primary objective of this study is to elicit and assess risk attitudes of entrepreneurs
(owners of MSEs), estimate the effect of identified risk management practices on firm
growth and determine which factors are attributed to business success of micro and
small informal agri-food processors in the Greater Accra and Ashanti Regions of
Ghana.
Specifically, the study seeks to:
1. Elicit the risk attitudes of entrepreneurs and assess the factors that influence the
risk sources  they perceive as important in the context of their business
environment
i. Elicit the risk attitude of entrepreneurs using their risk perception and
risk propensity
ii. Estimate the effect of  factors that influence entrepreneurs’ risk attitudes
iii. Estimate determinants of risk sources entrepreneurs perceive as having
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significant effect on their businesses.
2. Estimate the impact of risk management practices used by entrepreneurs on firm
growth
3. Estimate the determinants of business success applying the dimensions of the
entrepreneur’s personality trait as attribution to business success
i. Apply indicators of business success to categorise levels of business
success for entrepreneurs
ii. Estimate the effects of the dimensions of the entrepreneur’s
personality trait as attribution to the levels of business success they have
attained.
1.4 Relevance of the study
Based on nascent literature on indigenous firm management especially as regards risks
and the management practices to be adopted, the contribution of this thesis is three-
pronged.  First, risk management practices identified to have significant influences on
firm growth would have important implications for owners of micro and small
enterprises as regards their risk management practices in the Ghanaian economy.
Eventually, this could positively affect the levels of output of their firms and contribute
to general economic growth in Ghana through creation of employment. The creation of
employment would be triggered by the proper risk management practices which
engender firm growth. Second, the results from this study will be important
information that can be incorporated in policy to assist entrepreneurs cope with
potential risks in the business environment of the whole Ghanaian economy.  Finally,
the application of entrepreneur’s personality trait (from psychology literature) to
business success makes a contribution to the existing literature on business success.
Therefore, this thesis becomes relevant and innovative in that it extends the on-going
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development of literature in understanding the current shift towards indigenous
management theories and practices. The main beneficiaries of this research which will
serve as reference material will be the many entrepreneurs who currently dominate
Ghana’s informal sector and who currently are not able to manage risk.
1.5 Scope of the study
Throughout this study, the term ‘entrepreneur’ is used interchangeably with the ‘owner’
of the firm. The concept of entrepreneurship (which is a whole study on its own) is not
the focus of this thesis and therefore the subject is not discussed. Only owners of micro
and small firms (MSEs) form the sample used for analysis. Again, micro and small
enterprises are often referred to as ‘small firms’ for ease of communication. This study
defines risk as the occurrence of an event or a situation in which a business entity
(represented by the owner/entrepreneur)   takes a decision which has the probability of
more than one outcome and the probability of occurrence of any of the outcomes can be
estimated and can also have influence on business objectives (Terzi, 2010; Essinger &
Rosen, 1991). Risk propensity is defined as the tendency to take risky actions, where
entrepreneurs with high risk propensity are more likely to engage in risky behaviour.
Risk perception refers to how risky a decision is perceived by the entrepreneur, where a
higher risk perception leads to less risky behaviour. Risk management involves
anticipating outcomes and planning a strategy in advance given the likelihood and
consequences of events. Targeted agri-food processors in this study are grain, fruit,
beverage, tuber, nuts and pulses, oil palm, meat and dairy processors operating
informal micro and small firms in Ghana.
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1.6 Organisation of the thesis
The thesis is divided into five chapters. While Chapter one introduces the background,
the research problem and objectives, Chapter two provides a review of relevant
literature on methods of risk attitude elicitation and sources of risk in business
especially those that affect micro and small firms, and the effect of risk management
practices on firm growth. The literature review also considers the link between business
success and application of theory of personality traits from the psychology literature.
Chapter three presents the theoretical framework and the methods of analysis of each
specific objective. The data and description of the process of data acquisition as well as
hypothesis for this study are also presented. In Chapter four, the results of the analysis
and hypotheses tested are presented. Chapter five summarises the main findings of the
thesis, draws conclusions and provides policy recommendations.
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CHAPTER TWO
LITERATURE REVIEW
2.0 Introduction
This chapter reviews relevant literature on risk management as pertains to small firms
and business success. It specifically spans risk theory and risk analysis, elicitation of
risk attitudes, the risk management process, the theory of the firm and firm growth, the
measurement of firm growth, risk management and firm growth, business success of
micro and small firms, and finally, causal attribution to levels of business success
achieved by entrepreneurs in micro and small firms.
2.1 Overview of the Micro and Small Enterprise (MSE) sector in Ghana
It is an established fact that the largest employer in the Ghanaian economy is the
private sector and this sector is made up of formal and informal enterprises(GSS, 2015;
Setsoafia et al., 2013; e Setsoafia et al., 2015). Formal sector enterprises use
professionals in their accounting records and the opposite is true for informal firms
(GSS, 2015). The informal sector mainly consists of micro and small enterprises
(MSEs) and account for about  92% of all registered firms in the country employing
about 85% of the total workforce in the country. They contribute about 90% of the
private sector’s contribution to the GDP of Ghana (Hayford, 2012). This affirms their
importance to Ghana’s economy in terms of job creation and poverty reduction.  They
are very crucial as engines to the growth objective of Ghana (as a middle income
economy).
An issue of paramount importance in global economic policies, particularly in Africa
is how the development of MSEs can be used to accelerate economic growth (Robson
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& Bennett, 2000). Lessons can be learnt from the developed world on the use of MSEs
in achieving accelerated economic growth and rapid industrialization (Ansobo, 2015;
Harris & Gibson, 2006; Sauser, 2005). Evidence indicate that the economic prosperity
gap between the developed and developing economies of the world  could be addressed
by a renewed focus on  MSE development  as a sure way of job creation and poverty
reduction  (Harris & Gibson, 2006; (Acs et al., 2008; Kang and Heshmati 2008;
Phillips and Bhatia-Panthaki 2007; Harson and Shaw, 2001).
According to Lee & Mokoyama (2015) and Silver (2015) most MSEs have ‘stunted’
growth or fail to make any meaningful progress in the first few years of their start-up
leading to collapse.  To address this major challenge, government of Ghana’s Industrial
Policy (2009) which included entrepreneurship development comes in the limelight.
The major areas under the policy included Entrepreneurial and Management Skills;
Marketing and Distribution of Industrial Products; and Adoption and Use of ICT in the
Manufacturing Sector. The policy ultimately aimed at enhancing the competitive and
employment levels of Ghanaian Micro, Small and Medium Scale Enterprises (MSMEs)
to accelerate economic growth. The entrepreneurship development objective of the
policy targeted the manufacturing sector characterised by low levels of entrepreneurial
and managerial skills. Within the managerial skills of the entrepreneur is their risk
management decision which has great impact on each firm’s performance which
cumulatively contributes to economic growth. Owing to this, the current thesis focuses
on micro and small agri-food processing firms.
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2.2 Risk theory and risk analysis
There are wide variations in the definition of risks in the literature (Isam, 2014;
Nguyen, 2007) but the underlying issue is that most risk definitions concur that any
framework for decision-making are based on two items: risk and uncertainty. Hardaker
et al., (1997) pointed out that uncertainty with no consequence has no risk involved
implying that while risk has uncertain consequences, uncertainty is imperfect
knowledge about a situation. Therefore risk is the probability of uncertain outcome
(whether negative or positive) while uncertainty is the probability of the occurrence of
the unknown (Lepori, 2010; Tseng, 2013). Risk is attached to our daily lives and affects
decisions taken which include health, pensions, insurance and investments. There is
always a distinction between risk attitude which is the overarching concept measured
by risk perception and risk propensity in risk analysis. Risk attitudes are viewed as the
willingness of the individual to take risky decisions or avoid the risky situation
altogether. Risk propensity is associated with the probability to take or avoid risk and
risk perception often relates to a specific technique, time and location.
In analysing risks, two approaches are prevalent – positive and normative approaches.
The normative approach hypothesises that individual decision-making is economically
rational so that this approach seeks to provide advice to individual entrepreneurs on the
kind of decisions to take(Hillbur, 2014). The positive approach predicts choices based
on empirical results such that entrepreneurial behaviour towards risks are explained
(Senkondo, 1999). This study will adopt the positive approach of risk analysis with a
focus on risk management by entrepreneurs in micro and small enterprises. Although
the normative approach can clearly point out the direction of the research in situations
where the research lacks a theoretical framework on the basis of which hypothesis can
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be formulated and tested, wrong causal relations may be identified (Huijsman, 1986).
In such cases, the alternative is to use the positive approach which involves close
observation of entrepreneur’s  risk attitude (Senkondo, 1999; Hillbur, 2014). The
positive analysis has the advantage of obtaining better descriptive theory of choice to
risks faced by entrepreneurs (Day, 1979; Barlett, 1980; Huijsman, 1986).  Apart from
Senkondo (1999), other researchers (Huijsman, 1986; Smidts, 1990) have used similar
applications of positive approach although their subjects were farmers while the
subjects in this study are entrepreneurs (owners) of agri-food processing firms in
Ghana.
2.2.1 Analytical framework for risk analysis
Figure 2.1 depicts an analytical framework of risk analysis which assumes that
entrepreneurs of micro and small informal agri-food processing enterprises would
normally behave rationally but they can also exhibit non-rational behaviour which
could be interpreted as 'bounded rationality. Rational behaviour of the entrepreneur
means that they make the best choice of a set of alternatives presented to them. The risk
analysis framework also distinguishes between risk attitudes, risk perceptions and
propensity to take risk as regards the behaviour of entrepreneurs.
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Figure 2.1: Illustration for analysing risk
RISK ANALYSIS
(Subjective)
POSITIVE ANALYSIS NORMATIVE ANALYSIS
(How entrepreneurs behave) ((How entrepreneurs should behave in
order to achieve specific goals)
Rational behaviour Irrational behaviour
Description of entrepreneurs' attitude and perception towards risk
Source: Author’s illuAstttraaintmioennt of research goals
2.3 Risk attitude, risk perception and risk propensity
Risk attitude is behavioural tendencies that emanate from long periods of exposure to
collection of feelings, and beliefs directed to risk (Walker, 1981). The assumption is
that risk attitude has overarching influence on the economic construct of risk preference
of the entrepreneur.  Walker (1981) concludes that risk attitude is therefore useful in
analysis geared towards policy perspectives. The decision maker’s attitude to the extent
of avoiding risk (risk aversion) or willingness to engage in risky behaviour (risk
seeking) using quantitative methods to derive their coefficient of relative risk aversion
or coefficient of absolute risk aversion is another definition by Dillon & Hardaker
(1993).  Risk perceptions on the other hand are not permanent and may not necessarily
be a culmination of events over a period to define the behaviour of the entrepreneur and
therefore are mental interpretations of the physical sensations produced by an external
stimulus. They are amenable to change as new information is received. They are linked
to household characteristics, information availability on a certain processing
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technology and the current decision environment of the entrepreneur. The emergence of
risk attitude and choice criteria are therefore linked to the entrepreneur’s risk perception
and risk propensity and these culminate in the risk management (Walker, 1981).
2.3.1 Risk elicitation based on economic theory
The subject of risk attitude has received a great deal of attention since it affects every
sphere of life and agricultural decision making has had its fair share (Hillbur, 2014;
Nmadu et al., 2016; Senkondo, 1999). Despite this attention, there exists a large gap in
understanding the risk attitudes among a surfeit of decision makers including
entrepreneurs (owners of firms). The gaps in fully understanding risk attitudes is
reflected in difficulties in separating risk-related reactions from other forms of
behaviour (Bond & Wonder, 1980).   This has resulted in different approaches to risk
attitude elicitation using economic theory.  The expected utility theory has widely been
used to elicit risk attitudes although other risk elicitation approaches have used other
theories like the prospect theory, and rank-dependent utility theory. Literature on
developing empirical measures of individual attitudes has resulted in two risk
preference measurement methods that utilise the above mentioned theories. The
methods are the direct elicitation of risk attitudes using interviews or experiments, and
indirect estimation of risk attitudes analysing observed investment decisions. These
methods are based on economic theory.
Direct method of risk elicitation
The direct elicitation method uses an experimental approach by asking hypothetical
questions regarding choices of risky lotteries accompanied by objectively-defined
probabilities and payoffs. A substantial number of Studies (Binswanger, 1980;
Binswanger, 1981; de Brauw & Eozenou, 2014; Eckel & Grossman, 2008; Harrison et
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al., 2007; Harrison et al., 2011; Harrison, et al., 2013; Holt and Laury, 2002) that have
used this approach have largely concentrated on smallholder farmers. The main
conclusion drawn indicate that farmers who have high risk aversion and variations in
risk altitudes do not change much with changing wealth of the subjects in the
experiment. This highlights the weakness of this method because theoretically, increase
in wealth tends to lessen the level of risk aversion (Guiso & Paiella, 2008). This
method has been criticised because it is expensive and time consuming ( Pennings &
Garcia, 2001).
Indirect method of risk elicitation
The indirect method applies econometric approach to estimate risk aversion by
comparing observed real economic decisions to expected behaviour predicted by
theoretical models incorporating risk and risk preferences (Antle, 1987; Charness,
Gneezy, & Imas, 2013; Chavas & Holt, 1996). A major criticism of using econometric
estimation to predict risk aversion stems from the fact that the difference between the
observed and predicted theoretical behaviour is entirely attributed to risk aversion. This
is not wholly accurate because the difference can also be reasonably linked to other
factors apart from risk aversion (Young, 1979). Again it is very difficult if not
impossible, to isolate the risk preference effect from other factors that can also
contribute to risk aversion.
2.3.2 Psychometric method of risk attitude elicitation
Due to the difficulties encountered in the risk elicitation methods above, this study uses
a method of risk elicitation developed by psychologists based the psychometric theory.
The underlying literature on this method of risk elicitation posits that it is appropriate to
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consider risk attitudes as a personality trait that is crucially contextual and not assume
that the individual will act the same way in different contexts. People act or react to
different situations and contexts. The method elicits risk attitude from domains that
depict the context in which the risk attitude is elicited. These domains include social,
recreational, health, safety, gambling, ethical, and investment/financial contexts
(Weber et al.,2002). The instrument used is the Domain-Specific Risk-Taking
(DOSPERT) scale designed by Weber et al., (2002).
2.4 Theory of risk management in small firms
Risk management is a chronological sequence structured with different tasks. There are
different definitions of the tasks by different researchers based on the way they are
ordered but the fundamentals in the different perspectives in the process are the same.
The risk management process is not a straight-jacketed or a linear one but more cyclical
so that the results of monitoring will inform the firm of how well the risks are managed
and if there loopholes, they could be corrected. The firm’s initial responsibility is to
understand the sources of risks it faces in order to manage them (Triantis, 2000). This
involves identification of the risk sources followed by analysis and evaluation.
Assessment of the identified risk source is fused in the daily running of the firm and
finally the monitoring phase which compares with the firm’s goals (which informs the
type of risks the firm faces) and experiences (based on how the risk management
process progressed). This informs the firm whether satisfactory progress has been made
in the risk management process and new strategies are proposed if the process is
deficient (Napp, 2011).
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2.4.1 Identification
This phase identifies all risks which could threaten the objectives of the firm in its
overall business development drive (Ping & Muthuveloo, 2015).  The critical factors
that pose risks can be identified by two approaches – Progressive and regressive. The
progressive approach tries to identify the possible deviations and losses of the firm
from typical risk factors (Napp, 2011) especially from external sources like changes in
market, and the legal framework of the country. Deviations could also come from
internal factors like those linked to financial management of the firm (Cox & Sadiraj,
1979). The regressive approach on the other hand tries to find possible reasons among
risk factors that could lead to deviations to initially set goals of the firm (Yegon, 2015).
2.4.2 Risk analysis and evaluation
This phase aims at determining the extent to which the identified risks would impact
on the firm  looking at how much it will cost the firm in managing such risks (Yegon,
2015; Dang, 2015).The first steps in this risk management phase requires that the
identified risk are categorised based on the nature of risk they pose whether they are
legal in nature, or financial or political. The categorisation allows for the firm to later
analyse whether some of the risks are related and whether some of them offset each
other (Ntallas, 2014). The costs of the management of the risks identified are also
estimated in this phase to aid in calculating the expected damages of the risk positions
if they occur.
2.4.3 Risk assessment
This phase in the risk management process involves creation of measures to handle the
risks identified. The measures go through a process that range from risk prevention,
risk reduction, risk transfer and risk acceptance (Napp, 2011). The firm can decide to
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avoid identified risks but the consequence of such a decision is borne by the firm. The
alternative is for the firm to embrace the risks identified and  work to reduce the impact
of the risks by either decreasing the probability of occurrence of the risk or limiting its’
financial impact on the firm. The risks can also be transferred to third parties  such as
insurance companies or go into relationships like outsourcing raw material acquisition
to third party markets players to avoid liabilities with raw material acquisition (Dang,
2015). Kagwathi et al., (2014) indicate that not all risks can be insured or transferred to
third parties because such risks are closely connected to the core business of the firm.
In such cases, measures are put in place to accept such risks. The importance of the risk
position for the firm and the urgency to mange it needs to be considered in order to
choose the appropriate measure for each risk position (Napp, 2011). Most firms employ
a mix of all the four risk measures enumerated above in their risk management
practices (Henschel, 2010).
2.4.4 Risk monitoring
This is the last phase of the risk management process and it entails setting up measures
to verify whether the risk identification, analysis and evaluation, and assessment phases
have been successful (Napp, 2011; Dang, 2015).  It is very critical so that the overall
risk situation of the firm is compared to the planned strategies so that deviations will be
detected and appropriate remedies triggered. The risk management process could be
started all over again if there are serious failures that prevented expected goals from
being met (Figure 2.2).
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Figure 2.2: Risk Management Process
Source: author’s illustration
2.5 Theory of the Firm and firm Growth
2.5.1 The Entrepreneur in the theory of firm growth
Firm size is contingent on the efficient allocation for given resources, including the
human resource (entrepreneurial skills) under available technology –according to the
static theory of competitive equilibrium (Yegon, 2015). According to the theory, the
size of the firm at a given time is its efficient size owing to minimization of cost of
production in the long run and therefore the firm will grow until its optimum size where
long run marginal costs and price are at par.  There are different schools of thought
(theories) on firm growth as postulated by different researchers. For example, Lucas
(1978) indicates that the firm and the entrepreneur or manager are assumed to be same
and therefore the output of the firm is a function of the entrepreneur’s managerial
ability, capital and labour. In this sense, entrepreneurs (and managers) of firms with
superior managerial abilities will have lower marginal costs and produce higher output
(i.e. higher efficiency levels)  with the same quantity of inputs compared to firms with
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entrepreneurs with lower managerial skills. This implies that the entrepreneurial skill of
the business owner is very important for growth of the firm.
On the other hand, Kihlstrom & Laffont  (1979) tie firm growth to the risk attitude of
the owner  (entrepreneur) by assuming  that any production technology adopted  has its
own levels of risk and therefore entrepreneurs who have  higher propensity to take risks
will take the riskier technologies which produce higher outputs. The difference between
the two schools on theory of the firm is the divergence on which characteristics of the
entrepreneur affects firm growth.  This study adapts the school of thought on the theory
of the firm aligned with the fact that firm growth ties in with the risk attitude of the
owner (entrepreneur).
2.5.2 Firm growth, and the heterogeneity of firm growth
Growth signals a change (positive) in size from a particular period of time under
consideration to another (Ronninko & Autio, 2012). Within the concept of small firm
growth, it represents an increase in sales, profit, employees or a change in the size of
assets (Chen et al., 2013). Firm growth is a multi-dimensional phenomenon.. In this
multi-dimensionality, Gibrat’s law seems to be probably the most popular way to test
the theory of firm growth Chen et al., 2013; Ronninko & Autio, 2012). Gibrat’s law
postulates that the size of the firm at any given point in time is the product of a series of
random growth rates in the history of the firm. This implies that the period within
which the growth of the firm is measured is independent of its size when the firm was
formed. Several factors have been considered as responsible for firm growth and these
include size of the firm expressed as growth in the number of employees, age of the
firm, financial resources, technology level adopted for operations, and positive change
in the volume sales of the firm’s product.
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the foregone highlights the presence of heterogeneity in considering firm growth
(Penrose, 1959; Hermelo & Vassolo, 2007). For example, Delmar et al., (2003) showed
that heterogeneity in firm growth exists. This was illustrated by applying different
growth indicators to a sample of firms classified as high growth.  Using cluster
examination, seven different types of ‘high growth firms’ were filtered, which
exhibited distinctive diverse growth patterns and background characteristics. The
conclusion was that firm growth is a multifaceted phenomenon and that diverse growth
forms may have different contributing factors and effects.
2.5.3 Measuring growth
Growth is a phenomenon that inevitably ensues over time for firms that survive.
Ideally, firm growth has to be analysed longitudinally at any rate in the sense that
evaluation of the predictors lead to evaluation of the outcome, i.e., the variation in size.
Regardless of this fact, a great number of research on growth were in reality cross-
sectional. This denotes that scholars have been engaged in ‘prediction of the past’ or
have made persuasive postulations in relation to contributory or pivotal order and/or
non-changeability of the predictors over time. Theoretical and empirical evidence leans
in favour of this important point. For example, Chandler et al., (2005) successfully
used transaction cost theory to explain the direction of the relationships between
growth in sales and employment. Their results indicated that the relationship between
the two indicators at certain periods in the firm’s life are mixed – mono directional at
one point and bidirectional in other times. This consolidates the view that the two
indicators of firm growth may not always have a positive correlation.
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An alternative would be to consider one growth indicator that best suits the theory of
the firm.  There is consensus that sales growth should be the preferred choice in cross-
industry studies (Ardishvili et al., 1998; Weinzimmer et al.,1998; Hoy et al., 1992).
This is because it is the most general of all the alternative indicators for firm growth
since all commercial firms need to have sales as a fundamental element for survival.
Indeed, sales growth is most popular among entrepreneurs of small firms in developing
economies (Barkham et al., 1996).  In addition, it may be argued that sales often
precede other firm growth indicators - it is the increase in sales that translates into asset
acquisition, larger employee size, and possibly growth in profits or market share
(Flamholtz, 1986). Using google scholar as the main search engine and "sales growth"
as search words, produced articles from peer-reviewed articles from 1959 to 2016
(from indexed journals). Upon reviewing each of these for content relevance, it was
discovered that sales growth constituted 30% of the reviewed literature. .Employment
growth is the next popular indicator of firm growth (29.1 percent of the reviewed
studies). Employment growth is very pertinent to public policy makers because of its
importance to economic growth.  Employment growth is also often applied in firm
growth analysis because of the ease of data availability.
Other indicators of firm growth are less commonly usable and therefore not as
frequently applied. The ‘market’ in market share computation may be immaterial for
small firms, and equating shares for firms functioning in diverse markets may be
invalid. The worth of assets is different with the capital intensity of industries and is
challenging to assess where the crucial asset being assessed as an indicator for firm
growth is knowledge which is intangible. While sales (in terms of volume) may be the
most universally applicable growth indicator, it does not always give best results
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(Davidsson, Achtenhagen, & Naldi, 2010) . As Penrose (1959; pp. 199) stated, “there is
no way of measuring an amount of expansion, or even the size of a firm, that is not
open to serious conceptual objections.” For example, high technology firms (which are
capital intensive rather than labour intensive) with relatively long expansion times, (for
example, biotech organisation), are not able to exhibit any growth in sales or returns for
elongated periods of time. Yet, all through this period they might still increase with
regards to assets— including knowledge assets such as patents”. The preceding brings
to fore the many ways firm growth is measured. Owing to the plethora of factors that
are considered in firm growth, this thesis uses the most popular indicators – growth in
employee size and growth in volume of sales.  A combination of indictors of firm
growth is considered better than the use of only one indicator to measure firm growth
(Delmar, 1997)
2.5.4 Risk management and firm growth
Small firms employ less formal procedures for risk identification and evaluation
compared to large firms. The entrepreneur is responsible for all managerial decisions
and this depends much more on their own experience and also learn more about risk
management from colleagues (Helliar et al., 2001). Literature detailing the
relationship between risk management of the firm and its performance (growth) appear
to be mixed. Nocco and Stulz (2006) suggest that firm growth is linked to risk
management. An entrepreneur who understands the risk the firm faces is able to
command resources required for engaging in productive ventures that underpin the
growth of the firm. Improvement in the risk awareness within the firm helps in making
better operational and strategic decisions thereby enabling management to meet
strategic goals, reduce earnings volatility, and increase profitability which culminate in
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firm growth (Gates et al., 2012; COSO, 2004). Risk management has the ability to
minimize the volatility of reported income accrued to the firm.  Other studies by
(Waweru & Kisaka, 2012; Hoyt & Liebenberg, 2008; Lai, Azizan & Samad, 2011)
conclude that risk management of the firm enhances the firm’s value and improves the
firm’s price to earnings ratio indicating firm growth. Managerial decisions taken by
entrepreneurs of micro and small firms on risk management practices that have effect
on firm growth cannot be delinked from their household decisions. This is especially
conspicuous for resource-constrained households since such decisions can contribute to
income smoothing strategies of the household.
2.5.5 Empirical evidence – Effect of risk management practices on firm growth
A wide range of risk management practices have been used to study their impact on
firm growth. Haka (1987) assessed the impact of specific risks on capital budgeting
practices between firms using NPV-methods and those that did not. His conclusion was
that firms using NPV-methods in their capital budgeting outperform matching firms not
using NPV-methods. This was contingent on the fact that there must be predictability
on the firms’ financial markets and competitors. Ho & Pike (1998) indicated that risk
management is crucial to general management of the firm and its subsequent growth.
They showed that the risk analysis approach which impact on proper risk management
of the firm provides useful insights that improve decision-making and increases
decision confidence of the manager and this eventually offers many qualitative benefits
to the manager and the firm as a whole. There was positive relationship between socio-
economic uncertainty (governmental regulations, actions of trade unions and behaviour
of financial/capital markets) and the application of risk management techniques on
capital budgeting practices of firms (Ho & Pike, 1998). They highlighted the
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importance of risk management to the overall performance of firms. Kiriro (2013)
hypothesised that risk management becomes an underlying factor in the whole
continuum of the management of the firm and that risk management practices adopted
are influenced by the specific circumstances in which the firm finds itself. The adopted
management practices including those targeting risk eventually lead to firm growth.
Ping and Muthuveloo (2015) concluded that firm size (a measure of firm growth) is
significantly influenced by the extent of implementation of risk management practices
of the enterprise of publicly listed firms in Malaysia. Ndung’u, (2013) concluded that
risk management practices have a positive effect on the performance (growth) of firms
in Kenya.  The need to find the impact of risk management practices on volume of sales
and employee size as measures of small firm growth becomes glaring in this thesis..
2.6 Risks Facing MSEs in Ghana
Due to their sizes and mode of operations, micro and small enterprise (MSEs) are
subject to risks across the globe. Risks to micro and small enterprises in developing
countries including Ghana are high and they border on political, economic, and natural
environmental risks (Deloitte 2006). The peculiar characteristics of MSEs that expose
them to risk include poor or non-existent record keeping, lack of management skills as
a result of high illiteracy among entrepreneurs (owners), limited access to financial
services, lack of capital and stability and inability to grow substantially (Deloitte, 2006;
Mwaniki, 2006; Ng’ang’a et al., 2015). They also lack the robustness to manage and
control risk (Raghavan, 2005) thereby finding it difficult to mitigate the effects of risk.
Other risks which impede the business success of micro and small firms include low
productivity, poorly trained employees, difficulties in recruitment of highly skilled
labour and high employee turnover rate (Rogerson, 2004; Watt, 2007). Factors related
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to low demand, poor understanding of competition, increased competition leading to
dwindling market share, and poor locations of firms (in relation to markets) have also
been identified as having influential negative effects on business success of micro and
small firms (Huang & Brown, 1999; Naicker, 2006). Abotsi et al (2014) also enumerate
risks faced by MSEs in Ghana. They include risk of escalating operating costs, risk of
reduced demand for products and services,  risks posed by competitors, fire risk , risk
associated with lack of  credit, occupational health and safety risks.
2.7 Business success
Generally, success portrays the achievement of goals and objectives. Business success
has traditionally been measured by financial performance (Getz & Carlsen, 2000;
Howard 2006; Simpson et al., 2004) and other indicators like growth in revenue
(Walker & Brown, 2004). There is no convergence of the definition of business success
especially as regards the factors that are used as indicators for business success. A
plethora  of factors that have been used to measure business success in the literature
include Customer/client satisfaction; Sense of achievement (self-fulfilment); Pride;
Growth of revenue; Personal satisfaction; Earn enough to live on; Staff satisfaction;
Make profit; Teamwork Allowing lifestyle valued; Contribution to welfare of
community; Recognition by others; Children wanting to be part of business; Being
one’s own boss; and running a successful business (Vilkinas et al., 2011; Foley &
Green, 1989). These indicate the multidimensionality underpinning business success
and have resulted in their classification into two main aspects. These are 1) financial vs.
other success indicators; and 2) short- vs. long-term success (Chittithaworn et al.,
2011). In the literature of small firm dynamics, business success is popularly  measured
by  survival, profit; return on investment, growth in volume of sales, sales turnover per
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employee,  increase in number of employees, happiness, reputation, and so on (Foss,
2014; Luke, 2005).
Business success is underpinned by certain factors described as critical success factors
(CSFs). The CSFs generally mean the number of areas or factors associated with
certain objectives of running an organisation which eventually will lead to high
performance of that organisation when the results in those critical areas are satisfactory.
These factors are so important that they must produce the right results for the business
to flourish. If results in these areas are not adequate, the organisation’s efforts within
the framework of the period under consideration will be deemed undesirable (Boynton
& Zmud, 1984). The CSFs indeed are a few critical pointers that ensure success and are
the most important for overall organisational objectives, mission and strategies.
Findings from earlier research have used different indicators as measures for business
success. The indicators border on a combination of variables under the following
critical success factors:  the way of doing business and cooperation (Hitt & Ireland
2000; Jarillo, 1988); resources and finance (Swierczek & Ha, 2003; Kristiansen, et al.,
2003); external environment (Huggins, 2000; Nurul & Langenberg, 2005);
characteristics of the entrepreneur (Kristiansen et al.,2003); products and services  of
the firm (Wiklund & Shepherd, 2004; Hitt & Ireland, 2000); managerial skills and
know-how of the entrepreneur  (Swierczek & Ha, 2003); customers and markets
(William et al.,2005); and characteristic of SME (Kristiansen et al., 2003). All through
literature, small firm business success has been measured in generic terms with no
differentiation of the levels of business success. This study measures small firm
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business success and attempts to disaggregate this into three different levels for
subsequent analysis.
2.7.1 Dimensions of entrepreneur’s personality trait as causal attribution to business
success
Entrepreneurs’ personality traits which refer to their psychological disposition have
been applied in this study and have been linked to what they perceive as causal
attribution to their levels of business success. The personality characteristics of the
entrepreneur demonstrate a stable and inherent strength of how they have managed
their business and consequent business success achieved.  Although causal attribution
theory is applied, it is only to the extent of the linkage to the dimensions that describe
the entrepreneur’s psychological disposition and how they attribute these to their levels
of business success.   The psychological trait of the entrepreneur has been used as a
valid predictor of firm growth or performance as demonstrated by various studies
(Chamorro-Premuzic & Furnham, 2010; Ones et al., 2007).
Different studies have used a combination of various personality traits of the
entrepreneurs (as causal attribution) to predict firm growth or performance. For
example, Chu (2000) identified two dimensions (motivation and self efficacy) of the
psychological trait of the entrepreneur as predictors of firm performance. Rauch &
Frese (2000) stressed that the psychological traits of entrepreneurs that are more likely
to predict firm growth or performance are those that are related to their work
environment. They used dimensions of the psychological trait of the entrepreneurs
under the need for achievement, risk- taking, proactive personality, self-efficacy,
internal locus of control and innovativeness to predict attribution to firm growth. The
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specific findings concluded that the entrepreneur’s psychological traits (personality
traits) were significantly correlated to business creation and business success. Barrick
et al., (2001) also found a positive relationship between their personality traits like
conscientiousness and, Emotional Stability and higher performance. This study uses
three dimensions of the entrepreneur’s psychological disposition (locus of control, self-
efficacy and motivation) as basis of causal attribution considering that they best
represent the dimensions that can describe business success of the entrepreneur in the
context of this study.
2.7.1.1 Locus of Control
The theory of locus of control was initiated as a component of a wider social learning
theory of personality (Rotter, 1966).  The foundation of the concept is linked to the
perception of the individual concerning the control of events and their outcomes. The
underlying fact in this concept is that the perception of the individual concerning
outcomes of events are either within or beyond their own understanding and personal
control (Slate & Slate, 2014). This exposes two classifications of control – internal and
external locus of control.  Individuals who link outcomes of events to internal locus of
control attribute positive outcomes of events to the influence of their personal abilities,
their own destinies, skills and efforts (Snider, 2015). They sometimes also credit
themselves with luck as a basis for success (Henry et al., 2003). Individuals with
external locus of control attribute the primary cause of outcomes of events to the
influenced of external forces such as fate, luck, or other external circumstances they
believe is out of reach of their control (Mueller & Thomas, 2000; Kibia & Sikalieh,
2010).
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2.7.1.2 Motivation
Motivation to achieve excellence borders on the entrepreneur’s inherent drive to build
and grow a successful business (Collura & Applegate, 2000). The steadiness of the
entrepreneur’s attributions is the best predictor of motivation (Mantere, Aula, Schildt,
& Vaara, 2013; Ojiako, Chipulu, Marshall, & Baboolall, 2014; Weiner, 1995). People
are said to be inherently driven to engage in a particular task if it matches their
prevailing preferences and interests. Goal and result-oriented entrepreneurs have a
drive to achieve and grow; have a low need for status and power; are interpersonally
supporting; and are aware of their strengths and weaknesses (Bygrave & Zacharakis,
2011).
2.7.1.3 Self-efficacy
An individual’s anticipated performance on a certain task decides on the tendency to
carry out the task and the level of determination put in the task. As stated by Barbalet
(1998) in his study about action and confidence,  “confidence is characterized by
assured expectation, which is a positive encouragement to action”. This also means that
being self-confident regarding executing a specific duty is linked to an anticipated
result of being built with peculiar skills to carry out that duty (Barbalet, 1998).
Furthermore, the belief in one’s personal capability to undertake a particular task and
achieve a favourable result describes the person’s self-efficacy (Blackburn et al., 2013;
Khuong & An, 2016). Self-efficacy refers to the confidence level that someone has in
his or her ability to productively complete a task or attain a particular outcome that is
anticipated (Bandura, 1986). High levels of task specific self-efficacy have been
connected with better performances of firms (Bandura, 1997). In view of this, people
having confidence in their capabilities, assume success in their performances, focus
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their thoughts on how they can attain the desired target and have high probability to
overcome difficulties (Bandura 1997; Bandura and Cervone 1983; Tyszka, et al.,
2011) implying that people tend to avoid tasks, for which they have a low level of self-
efficacy. Largely, self-efficacy enables individuals to put greater effort into a task,
increase their expectations, concentrate on goals and overcome difficulties.
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CHAPTER THREE
METHODOLOGY
3.0 Introduction
This chapter presents the conceptual, theoretical and empirical framework for risk
management and factors that affect the risk attitude of entrepreneurs, and the method of
analysis for each objective. Following from these, the hypotheses to be tested are set.
The research design is also presented.
3.1 Conceptual framework: Risk Management and Business Success
The conceptual framework presented in Figure 3.1 is based on the perspective that
there is interaction and mutual influence between the personal characteristics of the
entrepreneur including risk attitudes, characteristics of the enterprise and the risks in
the macro environment all of which influence the risk management practices the
entrepreneur adopts as mitigation measures (Delmar, 1996). The assumption within this
framework is that there is efficient allocation of the firm’s resources.  Risk
management in small enterprises has been shown to be connected with entrepreneurial
skills because general management of these enterprises is directly under the control of
the entrepreneur (Tyszka et al., 2011). Risk-taking is very relevant in firm growth since
its absence diminishes the prospects thereof. The growth and subsequent success of the
firm therefore hinges on the ability of the entrepreneur to incorporate risk management
into the planning and operational processes of the enterprise (Chapman & Ward, 2003).
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Figure 3.1: Conceptual framework depicting the relationship between risk attitude, risk management practices and business success
Demographic Risk attitude Risk management Indicators of
characteristics practices Firm growth objective
- Age - Risk propensity business success
- Gender - Risk perception - Diversification
- Education - Insurance - Increase in -Turnover per
- Experience - Forward contracting sales employee (sales)
- Marital status - Cooperative -Sales growth
marketing -Employee
- Borrowing - Increase turnover
- Savings Employee
- Sale of assets size
Enterprise - Temporary wage
Causal
attribution
characteristics employment Business to business
success success
-Firm age (yrs) Indicators of
-No. of months firm subjective
operates in a year business success
-Firm size
-Months open within Entrepreneur’s
year satisfaction with
-Product type by Feed back start-up objectives
firm Risk Sources (Mitigation of risks and creation of
-Firm location opportunities)
(region) - Economic -Sales (value; volume)
/Political -Overall demand for products
- Market -Market share
- Financial - Quality of products
- Human - Training of employees
Source: Author’s illustration
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In analysing the prospective risks (general economic/political risk, financial risk,
market risk and human risk) the enterprise may face, the entrepreneur needs first to
analyse the risk, determine the extent to which the risk will affect the operations of the
enterprise, draw an elaborate plan to deal with the risk while taking cognisance of the
competitive and economic environment within which it operates. All of these culminate
in the deeper understanding of the entrepreneur’s business in terms of   the
interrelationships between his attitudes towards risks which affect the kinds of risk
management practices adopted which in turn affect the growth of the firm. The
decisions taken concerning the risk management practices the entrepreneur uses in
mitigating risks faced by the firm cannot be fully delinked from household decisions
and by extension household resources. The entrepreneur allocates resources to a set of
consumption, production, and investment activities which, in turn, act to gratify both
household and firm needs. If the risk management practices adopted by the
entrepreneur prove counter- productive or do not provide maximum utility to the
entrepreneur, there must be feedback mechanisms that can correct the anomalies
encountered so they are addressed to meet set targets.
The framework further demonstrates that business success is multidimensional (i.e.,
measured from different angles). First, objective business success which is a
combination of indicators (volume of sales turnover per employee, volume of sales
growth in the firm’s product, and low employee turnover) of firm growth. The
interaction of these indicators provides a basis to measuring business success
objectively. Second, subjective business success is based on the perception of the
entrepreneur in terms of satisfaction with the objectives of setting up the business. The
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firm’s business success can be explained further by the entrepreneur with regards to the
reasons  attributed (Delmar, 1996; Alasadi & Abdelrahim, 2007).
3.2 Risk attitudes of entrepreneurs
3.2.1 Elicitation of entrepreneur’s risk preferences - Analytical Framework
The study uses the psychometric theory (popular among psychology researchers) to
elicit the risk behaviour of entrepreneurs. Although other theories (expected utility
theory and prospect theory) exist, the Psychometric theory is simpler to use compared
to the expected utility theory EUT) which has weaknesses (Hurley, 2010). While EUT
is the predominant theory for characterizing risky decisions in literature, it is not
without limitations because there are numerous examples of its limitation to adequately
characterize observed behaviour as examples in developed countries (Tversky &
Kahneman, 1992; Davis & Holt, 1993; Camerer, 1998; Mosley & Verschoor, 2005).
However, Pennings and Garcia (2001) suggest that the use of Expected Utility
Framework (EUF) and the multi-item scale which originates from the psychometric
theory  produce similar results.
Among the two, the psychometric theory is easier to use. It (psychometric theory)
employs the description of risk itself to explain the individual’s perception of risk and
propensity to take risk and assigns numbers to the phenomenon (Starbird & Baker,
2004). Risk attitude describes the tendency to exhibit risk averse, risk seeking or risk
neutral behaviour (Versluis, 2015), which implies making a risky decision.
Entrepreneurs maximize their utility by making a choice in an ordinal scale (Likert
scale) which closely relates to their risk behaviour (Lusk & Coble, 2005). Risk
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perception and risk propensity are two key dimensions of the entrepreneur’s risk
attitude (Willebrands, 2010; Boermans & Willebrands, 2017).
A widely used instrument for  risk attitude elicitation in the psychometric theory is that
originally developed by Weber et al., (2002) and replicated by other authors (Lucas &
Pabuayon, 2011; Lammers et al., 2010).  The instrument - Domain-Specific Risk-
Taking (DOSPERT) Scale, allows for assessment of perceived-risk attitudes in six
commonly encountered content risk domains  and grouped in different subscales (i.e.
Ethical, Gambling, Health/Safety, Investing/Financial, Recreational, and Social).  Each
subscale measures risk attitude in the domain they are specified with. The DOSPERT
Scale allows for assessment of conventional risk attitudes – risk propensity (reported
level of risk-taking), and risk perception (reported willingness to engage in a risky
activity). The risk attitude of entrepreneurs was measured by using the financial
psychometric subscale to assess their risk propensity and risk perception. This subscale
measures the entrepreneur’s choices on financial outcomes of actions taken.
The difficulty in obtaining accurate data and time constraints dictated that the most
relevant content risk domain to this study (financial subscale) within the DOSPERT
was adapted to measure risk attitudes. The scale items were modified to make it easier
and more practical for respondents to relate to and understand. A 7–point Likert scale is
used in the original scale but to suit the Ghanaian context considering the sample of this
study, the Likert scale was modified to a 5-point scale to minimise respondent error.
The questions were also modified to suit the Ghanaian context but care was taken not to
lose the meaning of each question. The scale in the DOSPERT questionnaire allows for
a computation of scores used to measure each individual’s risk attitude. A number of
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studies (Boermans & Willebrands, 2017;  Lammers, et al., 2010; Willebrands, 2010)
have used the same domain in risk attitude elicitation within the African context (in
Nigeria and Tanzania). This method of risk elicitation has been described as one of the
most useful measures of risk propensity across a number of everyday situations.
3.2.2 Measuring Risk attitude
Risk perception
For risk perception, the scale elicits assessment of each risky activity on a 5-point
Likert scale ranging from 1 (Not at all risky) to 5 (Extremely risky). Higher mean
scores suggest perceptions of greater risk associated with the scale item assessed.  The
Likert scale was used as the format for measurement because of its appropriateness for
the purposes of this study (Spector, 1992; Devellis, 1991).  The relationship is given
by:
[(nr *1)  (sr * 2)  (rr *3)  (vr * 4)  (er *5)
  ---(3.1)
N
Where is  is the mean score of responses of each entrepreneur;  is the weighted
frequency of responses for each risk perception category; and N is the sum of the
highest possible score for each entrepreneur. The notations used for the weighted
frequencies were nr (not at all risky), sr (slightly risky), rr (risky), vr (very
risky), er(extremely risky), respectively
Risk propensity
For risk propensity, the scale asked entrepreneurs to indicate on a 5-point Likert scale
how likely they are to engage in six different kinds of risky behaviour using the same
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set of hypothetical questions. The response categories ranged from ‘1 (Extremely
unlikely) to 5 (Extremely likely)’.
[(eu*1)(mu*2)(l*3)(ml*4)(el*5)]-----(3.2)
 
N
Where is  is the mean score of responses of each entrepreneur;  is the weighted
frequency of responses for each risk perception category; and N is the sum of the
highest possible score for each entrepreneur. The notations used for the weighted
frequencies were eu (extremely unlikely), mu (moderately unlikely), l (likely),
ml (moderately likely), and el (extremely likely), respectively
3.2.3 Derivation of entrepreneur’s risk attitude
The scales (for risk perception and propensity) were reverse-coded to indicate the
inverse relationship between risk propensity and risk perception (Pennings & Garcia,
2001). For example, a risk averse entrepreneur may choose “extremely risky =5” on the
risk perception scale and choose “extremely unlikely = -1 on the risk propensity scale.
The sum of mean scores for this individual will be equal to 4 – proving that that
entrepreneur is risk averse. The argument here is that individuals with high risk
propensity scores perceive lower risks which inform their tendency to take part in
activities with higher risks. More aptly, an individual exhibiting a high risk-taking
propensity (risk-seeking person) may tend to underestimate the risks involved in a
situation and likely overestimate the probability of a gain relative to the probability of a
loss (Brockhaus, 1980; Vlek & Stallen, 1980).
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On the other hand, individuals with high risk perception are more likely to be cautions
in concurring to such situations since risk perception and risk propensity are inversely
related (Keil et al., 2000; Schneider and Lopes, 1986). Literature (eg. Stewart & Roth,
2001) also indicate that many entrepreneurs exhibit risk seeking behaviour. Summation
of the mean scores from the risk perception and risk propensity scales determines the
risk attitude of the entrepreneur.
Following Pennings & Garcia (2001) and Raskin & Cochran (1986), the risk attitude
R of the entrepreneur is given is by; (A negative score signals more risk seeking
behaviour)
R < 0 risk seeking;
R =0 risk neutral;
R >0  risk averse.
Hypothesis
Following from (3.1) and (3.2), hypothesis is tested based on literature that many
entrepreneurs exhibit risk seeking behaviour
H0: Majority of entrepreneurs will not exhibit risk seeking behaviour
Ha: Majority of entrepreneurs will exhibit risk seeking behaviour
3.3 Perceived risk sources affecting the business environment of entrepreneurs
Entrepreneurs were asked to rank risk sources they considered important and could
have significant effects on their businesses using a 5- point Likert scale (1= very
unimportant to 5 very important as risk sources to their businesses). The risk sources
most commonly faced by micro and small firms in the Ghanaian context and used in
this study are: 1. Losses associated with depreciation of local currency; 2. Poor access
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to transportation for input and output movement; 3. Change in trade policy; 4.
Government interference in business environment; 5. Labour shortage for firm’s
productive activities; 6. High cost of labour; 7. Input price volatility; 8. Poor market
information on price; 9. High interest rates; 10. Changes in taxation policy; 11. Poor
access to credit; 12. Output price fluctuations; 13. Fast-pace in technology
advancement for marketing; 14. War and civil commotion/disturbances; 15. Death or
sickness of entrepreneur or employee; and 16. Difficulties in sourcing raw materials.  A
factor analysis was performed on the listed sources of risk in order to form a
convergence of risk constructs. Four main risk constructs were identified after the
analysis. These were General economic/Political risk, Financial risk, Human risk and
Market risk.
3.3.1 Factors affecting risk attitudes and Perceived risk sources to business
The effect of the risk attitude of the entrepreneur, relevant firm and owner
characteristics on firm growth measured by growth in employee size and volume of
sales was analyzed using a multiple linear OLS regression with the maximum
likelihood estimator. The dependent variable was the score obtained from elicitation of
risk attitudes (risk propensity and risk perception score) using (3.1) and (3.2) of the
entrepreneur. The OLS was chosen because the nature of the dependent variables
(growth in employee size and sales volume) were linear in nature. The relevant data
diagnostic tests were performed to ascertain they do not violate the assumption for
efficient and sufficient estimates of the model.
Empirical framework
The focus here was to estimate the magnitudes in the factors that affect the risk
attitudes of entrepreneurs and the perceived risk sources to their business environment.
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Following Jirgi (2013) and Isam (2014) the linear regression model in this regard is
given as:
Y riskatt   0  X i ........  X n   i ---------------------------(3.3)
Where Y = risk attitude scores,  = coefficient to be estimated, X = demographic and
enterprise characteristics and i = the error term.
Table 3.1: Description of variables
Variable Description a priori
expectation
Risk attitude
Age of entrepreneur Continuous  variable (years) -/+
Sex Dummy variable: Male =1, female =0
Household size Number of individuals living under the -
same roof and eating from the same
pot with the entrepreneur
Age status Dummy variable: Youth =1, adults =0 +/-
Educational level
None No education at all =1, others =0 +
Primary educated up to primary level =1, others =0 +
JHS/Middle school educated up to JHS or middle school level =1, others =0 -
SHS/secondary school educated up to SHS/secondary level =1, others =0 -
Tertiary educated up to tertiary level =1, others =0 -
Business asset Total value of business assets as a proxy of wealth accumulation of
the business -
Firm size Continuous variable (Proxied by number of employees) -
Firm age The age of the firm (years) -
Experience Years of experience in business area -
Regional location of firm Dummy variable (Ashanti region =1, Greater Accra =0) +/-
Area firm is located Dummy variable (Urban=1, rural=0) +/-
Type of processed
product
Meat processors (1= involved, 0=others) +/-
Grain processors (1= involved, 0=others) +/-
Fruit juice  processors (1= involved, 0=others) +/-
Beverage processors (1= involved, 0=others) +/-
Nut and pulse processors (1= involved, 0=others) +/-
Tuber processors (1= involved, 0=others) +/-
Dairy processors (1= involved, 0=others) +/-
Palm oil processors (1= involved, 0=others) +/-
Source: author; Note: Positive sign implies that the variable has a direct influence on risk attitude of the
entrepreneur implying that increase in the variable leads to increase in entrepreneur exhibiting more
risk averse behaviour and the converse is true for negative sign; agri-food processors are disaggregated
to find their individual effects on risk attitude
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3.3.2 Consistency of scale items used in eliciting risk attitude
Analysis to test consistency was performed on the scale items used in measuring the
risk attitudes (perception and propensity) of entrepreneurs, perceived sources of risk as
well as variables that  were used in determining the causal attribution of entrepreneurs’
business success. The most common measurement in this regards is the Cronbach’s
coefficient alpha (Bard & Barry, 2000; Lagerkvist, 2005; Bardhan et al., 2006; Hair et
al., 2010; Isam, 2014).  Coefficient alpha measures the proportionality associated with
communal variation due to true differences in the data of interest.
It is given by:
-------------------------------------------------(3.4)
 2k  (1 i
k 1  2y
where is Cronbach’s coefficient alpha, is the number of statements in the scale,  2i
is the variance of the ith statement, and 
2
y is the variance of the -statement scale.
Coefficient alpha ranges between 0 and 1. The commonly acceptable range of
aggregated Cronbach’s coefficient alpha should be between 1 and 0.65 (Bard & Barry,
2000; Nunnally, 1994) while other literature (Cox & Flin, 1998; Harvey et al., 2002)
suggest a range of between 1 and of 0.7 as acceptable.
3.3.3 Perceived sources of risk and entrepreneurial demographic characteristics
The perceived risk sources that affect the business environment of entrepreneurs in
agri-food processing were determined after factor analysis which converged various
risk sources under the following risk constructs: general economic/political risk,
financial risk, human risk and market risk-these risk constructs served as dependent
variables using their factor scores from the principal component analysis. Socio-
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economic and enterprise characteristics are hypothesised to influence  entrepreneurs’
risk attitude and their perception of risk sources (Mbanasor & Nwankwo, 2001; Alimi
& Ayanwale, 2005; Mikhaylova, 2005; Nwaru et al., 2007;Nto et al., 2011; Bishu,
2014).
Empirical Framework
The linear OLS model in this regard is given as:
Y risksource   0   X i ........   X n   i ----------------------------(3.5)
Where Y risksource = sources of risk measured as factor scores,  = coefficient to be
estimated , X = demographic and enterprise characteristics and  = the error term.
Description of variables and Hypothesis tested
Based on reviewed relevant literature, the following hypothesis  are tested on variables
such as age, gender, marital status (whether married or not), level of education, regional
location of firm (Ashanti/ Greater Accra), status of firm location (rural /urban) firm
size, years of experience and type (sector) of processing activity.
Age
General economic/Political risk: Both young and old entrepreneurs are affected by the
political environment and the risk inherent. Age comes with experience and knowledge
about these events which lead to acquisition of skills for mitigation of the effects of
political risk.
H1a: older entrepreneurs perceive general economic/ political risk as less important
source of risk to the their businesses
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Market risk: Young (youth) entrepreneurs are expected to be physically strong and
technologically savvy to seek market information compared to older ones.
H1b: younger entrepreneurs perceive market risk as less important source of risk to
their businesses
Financial risk: older entrepreneurs with longer years in the business are expected to
have built networks within the financial service providers and therefore may not be
affected to higher degree as young and less experienced entrepreneurs.
H1c: older entrepreneurs perceive financial risk as less important source of risk to the
their business
Human risk: injuries, death and sickness as components of human risk are not expected
to be influenced by age since the occurrence of this risk is not age-bound- therefore this
is not determined apriori.
H1e: age could be negatively or positively related to perceived human risk as very
important source of risk to entrepreneur’s business
Gender
General economic/Political risk: Males in developing countries are better placed in
terms of access to financial resources (Bishu, 2014; FAO, 2011) compared to females.
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Thus, male entrepreneurs are better able to cope with economic and political risk so
will perceive them as having less influence on their business.
H2a : males perceive political risk as less important compared to females in  terms of
the  impact of the risk on their businesses
Market risk: males are believed to have more access to market information through
network than females.
H2b: male entrepreneurs perceive market risk as having less impact on their businesses
compared to female entrepreneurs
Financial risk: females are presumed to be less financially resourced compared to
males.
H2c: female entrepreneurs perceive financial risk as having great impact on their
businesses compared to males
Human risk: male entrepreneurs are assumed to be in a better position to arrange labour
for the firm’s activities than female counterparts.
H2d: human risk is perceived as more important source of risk to the business
environment by females
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Household size
General economic/Political risk: Entrepreneurs with larger household sizes are
expected to consider political risk as less important source of risk in the business
environment apriori. This is because they are wont to rely on family labour for their
processing activities in situations of labour shortage or high labour costs.
H3a: household size is inversely related to the perception of political risk as important
source of risk in the business environment.
Market risk: entrepreneurs with larger household sizes are better able to search for
more market information both from input and output angles. Therefore they may
perceive market risk as having less impact compared to those with smaller household
sizes.
H3b: household size is inversely related to perceived market risk as important source of
risk in the business environment
Financial risk: compared to larger household sizes, entrepreneurs with smaller
household sizes are assumed unable to pool more financial resources in times of need.
On the other hand, entrepreneurs with larger household sizes are faced with tighter
budgetary constraints with respect to substantial household expenditure
H3c: Effect of household size on perceived financial risk in the business environment is
bidirectional
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Human risk: entrepreneurs with larger household sizes are assumed to able to rely on
family labour (on short notice) in times of injury, sickness or death of employee.
H3d: household size is inversely related perceived human risk in the business
environment
Level of Education
General economic/Political risk: Education is believed to improve entrepreneurs’
decision-making rendering them more likely to anticipate economic and political risk
so they can build anticipatory mechanisms to minimise risks and shocks associated
with their processing activities. The more educated the entrepreneur, the less the
concern about risk linked to the economic and political environment and its effect on
the business environment.
H4a: educational level is negatively associated with the influence of political risk in the
business environment
Regional location firm
Dynamics of the region the firm is located has an impact on the kind of risks perceived
as important to the business environment.
H5a: Regional location of the firm will determine the kind of risks perceived as
important
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Years of business experience
The number of years spent in the current or similar business provides a platform for the
entrepreneur to have acquired relevant knowledge about risks in the business
environment and would have developed strategies on how to mitigate the effects.
H6: more years of experience is negatively related to the importance of perceived risks
in the entrepreneur’s business
Rural/urban dichotomy
The peculiarity of risk faced by firms in rural and urban area is apparent. Entrepreneurs
whose firms are located in either the rural or urban areas are expected to perceive risks
differently. The influence of firm location on perceived risk sources is not determined
apriori
H7: the location of the firm (rural or urban) is bidirectionally related to the importance
of risks as perceived risks by entrepreneurs in the business environment.
Sector (type of agri-food processed)
The type of product (animal based or crop based) could influence the entrepreneur’s
perception and its importance on risk sources. For example, those engaged in
processing crop-based products may perceive some risk sources as more important
compared to their counterparts in animal based products. The impact of one risk source
on each group of processors may be different (Lammers et al., 2010).
H8; entrepreneurs processing different products will place more importance on the
perceived risk sources that have great impact on their businesses.
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3.4 Factor analysis
To examine the determinants of perceived risk sources (objective 1) and causal
attribution to business success (objective 3), factor analysis for data reduction and
summarisation was used. Factor analysis is very important in identification of
underlying dimensions, or factors, that explain the correlations among a set of variables
(Ahsan, 2011; Flaten et al., 2005). It makes a new smaller set of uncorrelated variables
to replace the original set which can subsequently be used in multivariate analysis
(discriminant or regression analysis).
Empirical framework
In obtaining a model for factor analysis, the following structure is observed (Jobson,
2012)
X 1  1  a11 F1  a12 pF2rp  ......  a1r Fr  U 1
 .-----------------------------------------(3.6)
X p   p  a p1F1  a p 2 F2  ......  a pr Fr  U p
Where Xi , i =1,2,3,....., p are observed variables, and Fj and Ui , i = 1,2,3,...... , p; j
=1,2,3...., r are unobserved.  Equivalently, the set of equations can be written as:
(x   )  Af  u ---------------------------------------------------------------------------(3.7)
Where A is the factor pattern matrix consisting of its elements; aij which are called
factor loadings, x is the p x 1 vector of elements Xi , i =1,2,3,....., p andis a vector of
their means.
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Note that f is the r x 1 vector of elements Fj , j =1,2,3...., r are called common factors
assumed to have mean 0 and a variance 1. Ui , i = 1,2,3,...... , p are unique factors and
are assumed to have mean 0, but variance  2i , i =1,2,3,....., p, they form the p x1
vector u. Additionally, it is assumed that the  unique and common factors are
uncorrelated.
By merging the covariance matrix of x with ∑, (3.7) turns into:
  E [( x   )( x   ) '  AA '   ---------------------------------------------(3.8)
where  is the vector of variances of Ui , the right side of (3.8) consists only of
unobserved data but this process is not unique because different factors can be
obtained.
The assumptions underlying Factor Analysis Model are:
1. Measurement error has constant variance and is, on average is equal to zero
Var (e j )  E(e j )  0 ---------------------------------------------------------------------------(3.9)
2. No association between factor and measurement error
Cov (F ,e j )  0 ---------------------------------------------------------------------------------(3.10)
3. No association between errors
Cov(e j ,ek )  0 ---------------------------------------------------------------------------------(3.11)
3.4.1 Sample adequacy tests for factor analysis
To determine whether it is appropriate to perform the factor analysis, the measure of
sampling adequacy is essential.  The first step in this process is to determine whether
the sample size used is adequate for a factor analysis and this is tested by the ratio of
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cases to variables to be used in factor analysis. A sample size is adequate for factor
analysis if the ratio of cases to variables in a principal component analysis is at least 4:1
i.e. there should be at least four times as many observations (sample size) as there are
variables (Jirgi, 2013; Cerny & Kaiser,1977; Kaiser, 1974 ). The result from this test
showed a ratio of 10:1 for perceived risk sources in the Ghanaian business environment
and 11:1, 10:1, 9:1, 23:1 for dimensions of personality trait of the entrepreneur  (locus
of control, motivation, and self efficacy) respectively (Table 3.2).
The second step performs the Kaiser-Meyer-Olkin (KMO) test which gives a measure
of sampling adequacy and determines the suitability of individual variables for use in
factor analysis.  The KMO test is a measure of the extent to which a variable “belongs
to the family” of the larger group of variables and the minimum acceptable KMO-value
should be greater than 0.50 (Cerny & Kaiser, 1977; Kaiser, 1974). As a rule of thumb,
KMO values between 0.7 and 1 indicate the sampling is adequate, while values
between 0.5 and 0.6 could be used but do not indicate that sampling is fully adequate.
Values less than 0.5 indicate that partial correlations between variables are large i.e.
correlations between pairs of variables cannot be explained by other variables making
factor analysis inefficient (Nmadu, Eze, & Jirgi, 2012).
The Kaiser-Meyer-Olkin (KMO) measure is given as:
 r 2
KMO  k j jkj  2 2 ---------------------------------------------(3.12)k j r jk  k j q jk
0  KMO  1
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Where, KMOj is the Measure of Sampling Adequacy for the jth variable, rjk represents
an element of the correlation matrix R, and qjk represents an element of the anti-image
correlation matrix Q,
Table 3.2: Sampling adequacy test
Ratio of cases to set of variables Set of variables Ratio - cases  to variables
Perceived sources of risk 15 10:1
Dimensions of personality trait
Locus of control 15 11:1
Motivation 16 10:1
Self efficacy 17 9:1
Source: Author’s estimation (survey data)
3.4.2 Factor rotation
The necessity of factor rotation lies in its ability to simplify the factor structure (or
constructs) and enhance its interpretability (Kleinbaum et al., 1988). The orthogonal
and oblique rotations are the two types of rotation in factor analysis. The difference
between these two rotations lies in the status of correlations between extracted factors
(Habing, 2003).  There is no correlation between the extracted factors in orthogonal
rotation and the opposite is true for oblique rotation. According to Habing (2003), it is
best to use an orthogonal rotation which can be varimax or quartimax. While the
Varimax maximises the sum of the squared factor loadings across the columns and
forces each variable to load highly on as few factors as possible the quartimax performs
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the same function but across rows. The Varimax rotation was used in the factor analysis
in this study.
3.4.3 Communality
Communality refers to the share of variance in a variable that is explained by factors
that are retained in the factor analysis (Pohlmann, 2004).  In essence, communality
represents the R-squared value if these variables were regressed on the retained factors
(NCSS, 2007). The values from communality are known as the factor loadings. Low
communality implies that the variables analysed have little in common with each
another. Communality values 0.5 and above show evidence that variables are closely
related and indicate converge in a common construct while values lower than 0.5 are
rejected because they are considered low.
3.5 Effect of Risk management practices on firm growth
3.5.1 Measuring Firm growth
Firm growth was defined from two indicators – growth in number of employees  and
growth in volume of sales (Anderson & Eshima, 2003).
3.5.1.1 Increase in employee size
The relative change in a firm’s number of employees over period 2012 to 2015
expressed as a percentage on annual basis was used to measure firm growth. The
change in the number of employees as a measure of firm growth is important because
it’s quite popular in extant literature and considering the context of this study (micro
and small firms in the Ghanaian context), the decision by the  entrepreneur to employ
more is a highly discretionary management decision (Wennberg et al., 2016).
Smoothing of the growth rate was done and this meant that a percentage change
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between the number of employees in a year and the following year was used to measure
firm growth (by employee size).  For example, the percentage growth in employee size
between 2012 (initial year of analysis) and the following year 2013 was estimated and
replicated for periods 2013 to 2014, and 2014 to 2015. The mean growth rate over the
period was calculated as the growth of employee size and used as the dependent
variable in the model to be estimated. Smoothing of the growth rate was done so as to
avoid bias in estimations for the growth in employee sizes because it would have been
misleading considering there could be fluctuations in the rate of growth over the period.
Smoothing simply involved estimating the growth rate on annual basis and finding the
mean growth rate over the period (2012 to 2015) rather than estimating the growth rate
between 2012 and 2015.
3.5.1.2 Increase in Sales volume
The second measure of firm growth was estimated as increase in volume of sales. This
in actual sense is often a response to market demand (Chandler et al., 2005). Consider
a firm with only one output (all firms in the study produced only one product), total
sales volume is given by:
M
S qi --------------------------------------------------------------------------------------(3.13)
i1
Where S is the total volume of sales over a certain time m, and q the quantity of output
sold
Then the firm growth rate g represented by change in S is given by:
M
 q
S  i
g   it -----------------------------------------------------------------------------(3.14)
t t
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Where t is the time t of measuring firm growth (from December 2012 and December
2015) .
This equation is a modified version  by Machek & Machek(2014) who used both price
and quantity of sales to measure firm growth and introduced the number of customers
and the frequency of their visits as a composite equation for measuring firm growth.
The estimation of the change in volume of sales followed the same procedure as that
for the change in employee size and was done over the period (2012 to 2015) to ensure
consistency in results.
3.5.2 Risk management and firm growth
Risk management is an integral part of firm management whether it is small firm or
large one. There are differences in terms of the factors that influence the risk
management practices between large firms and micro and small firms. While risk
management practices are not subject to the individual preference of the firm owner in
large firms but subject to some laid down policy, the individual preference of the owner
in the micro and small firm holds sway in terms of the risk management practices
adopted by the firm. The effects of the following risk management practices on firm
growth were estimated: Diversification of economic activities; Borrowing; Subscription
of formal insurance services; Forward contracting; Cooperative marketing; Savings for
business purposes; Sale of business assets; and Temporary wage employment outside
of the firm. These risk management practices were enumerated by entrepreneurs during
a pre-survey exercise. They fit as indigenous risk management practices especially as
the sample of this study were entrepreneurs of informal firms who do not use laid down
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enterprise risk management (ERM) practices but have adopted risk management
practices to suit their local risk situations (Boermans & Willebrands, 2017)..
Risk attitudes have underlying causes which include not only the nature of the
environment in which the firm operates but also by the owner’s (entrepreneur) style and
behaviour towards the risks faced by the firm (Harland et al., 2003). This is apparent
considering that the personal opinion and behaviour of the owner greatly influences any
decision on risk management. It is argued that risk management of the firm results in a
better overall management of the firm and this leads to firm growth (Cho, 1988). Again
Pagach and Warr (2010) conclude that risk management decisions lead to increase in
the value of the firm.  It is hypothesised that risk management practices have a positive
effect on firm growth.
3.5.3 Description of variables
Firm Characteristics
Under firm characteristics, variables that are traditionally encountered in empirical
studies of firm growth  have included  firm size and age, sector (proxied by the type of
product by the firm whether they are plant-based or animal-based), firm’s location
whether they are in urban or rural areas. To account for any period of inactivity which
will depress sales in the firm, the number of months that the firm operates within a year
was included.. There is evidence to confirm Jovanovic’s model that there is a negative
relationship between firm growth, age and size (Jovanovic, 1982). Brock & Evans
(1986) found a negative relationship between firm growth and firm age for small firms.
Given that the sample in this study consists of micro and small firms, it is expected that
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there is a negative relationship between firm growth and age of the firm (Nto, et al.,
2011).
Empirical Framework
The linear OLS model in this regard is given as:
Y g   0   X i ........   X n   i --------------------------------------(3.15)
Where Y g = firm growth,  = coefficient to be estimated , X = demographic, enterprise
characteristics and risk management practices  = the error term.
3.6 Business success and attribution based on personality trait of the
entrepreneur
There is scant literature on how to measure business success in the African and by
extension Ghanaian context. However, literature on determinants of business success
elsewhere abound (Chittithaworn et al., 2011; Pervan and Višić, 2012; Chowdhury et
al., 2013; Eriksson and Li, 2012; Nkonoki, 2010; Krejcí, et al., 2015). This study
measured business success from the subjective and objective angles.
3.6.1 Subjective business success
The subjective business success was based on a set of seventeen questions (see
Appendix 4 – questionnaire section 4 for details) that elicited the perception of how
successful entrepreneurs had been, comparing the initial objective to set up the business
and the current satisfaction with regards to how those objectives have been achieved. It
was measured by a 5-point Likert scale showing the levels of satisfaction with
objectives for setting up the business by the entrepreneur (ranging from 1 = very
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dissatisfied to 5 =very satisfied). All firms in the study were at least 4 years old and
were deemed to have operated for a number of years considered enough to provide a
fair judgement as to the trend entrepreneurs expected in terms of their satisfaction to
the objectives of setting up. The level of satisfaction of the entrepreneur on each
objective served as proxy to measure how they were successful in their business. The
argument here is that because there is subjectivity with the judgement of each
individual, their levels of satisfaction with the objective of setting up the business will
determine how successful they have been. Mean scores of all seventeen questions for
each entrepreneur were estimated and scaled down to between 0 and 1.
3.6.2 Objective Business success
Objective business success was measured by indicators that are not subject to the
entrepreneur’s notion about how successful the business has been but was rather based
on three quantitative measures not influenced by the perception of the entrepreneur.
The indicators used are Sales volume turnover per employee (TE); Employee turnover;
and Growth in firm’s volume of sales (Delmar, 1996; Kamau &Mccormick, 2016).
Sales volume turnover per employee
Sales volume turnover per employee (TE) measures the firm’s efficiency in utilizing
available labour force by using the volume of sales generated per employee (Loth,
2015). In effect, relatively high volume of sales per employee signals a positive sign
that suggests the firm’s labour force is productive. This measure is suitable for labour-
intensive firms and therefore a good measure for firms in this study which are labour-
intensive. To obtain this measure, an estimation of the firm’s volume of sales (i.e
annual total sales) was done over a three-year period (2013 to 2015).  Sales rather than
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revenue figures were used for estimation of TE because records of revenue generated
with respect to pricing within the period under consideration were not available. The
volume of sales for each year was divided by the number of employees in that
particular year. The average sales per employee for the three year period were then
estimated. Using this figure, the mean percentage contribution of each employee to
total firm sales over the three year period was estimated and scaled to a range of 0 to 1
and scored.  A higher score per employee meant higher efficiency in labour utilisation
for production and sales of the firm’s products and vice versa. This indicator is
influenced by industry and product-line characteristics therefore it is best used either to
track individual firms over time or to compare firms with comparable products (Kamau
& Mccormick, 2016).
Growth in volume of Sales
Sales growth (SG) refers to the increase in the quantum of the sales of the firm’s
products over a defined period of time. The reference period in this study was between
2012 and 2015. The percentage change in the volume of sales from 2012 to 2013 was
estimated and repeated for period 2013 to 2014 and 2014 to 2015. The mean percentage
change in sales over the period served as score of average sales growth for each firm.
Employee turnover
Employee turnover (ET) refers to the number or percentage of the firm’s employees
who left the firm within a period.  ET has been used as a measure of business success
by other authors (Kamau & Mccormick, 2016; Delmar,1997;  Delmar, 1996).  ET was
estimated over a three-year period – between 2012 to 2015 by finding the percentage
difference in the number of employees from end of 2012 to 2013, end of 2013 to 2014
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and end of 2014 to 2015 (to account for ET within a particular year). The lower the ET,
the more successful the firm is in retaining labour which is crucial for operations.  In
order to avoid estimating misleading scores for business success, a reverse scoring
approach was used to score ET values for each firm. The reverse score implied that
instead of higher scores for firms which high ET, they were rather scored lower since
high ET implies lower level of business success for the firm. For example, a firm with
average ET of 0.6 (60 percent) will have a score of 0.4 (40 percent) while a firm with
ET of 0.2 (20 percent) will have a score of 0.8.  Measuring employee turnover can be
helpful to employers that want to examine reasons for turnover or estimate the cost-to-
hire for budget purposes (Kamau and Mccormick, 2016).
3.6.3 Categories of business success
Business success was obtained by dividing the total score of all three indicators for the
ith firm by the sum of most desirable score of all the indicators, thereby reducing
business success to a scale of 0 ≤ BS ≤ 1. The most desirable score was unity (or 100%
percent). For example a firm that has full score for each of the indicators would have a
mean score of 1 (or 100%) for business success.
BSi (STEj ,SGj ,ETj )/T --------------------------------------------------------------(3.16)
where BSi represents the ith entrepreneur’s business success; STE is the sales turnover
per employee for the ith entrepreneur’s firm, SG is the sales growth of the ith
entrepreneur’s firm, ET represents the employee turnover for the ith entrepreneur’s firm
and T represents the number of indicators under consideration (T= 3). All indicators
were measured using percentage changes.
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For analysis and establishment of thresholds that will inform policy decisions about the
business success of entrepreneurs’ in the informal agri-food processing sector in
Ghana, levels of business success were categorized into three as firms or entrepreneurs
with low, moderate and high business success (Asante et al., 2012) under both
subjective and objective business success of entrepreneurs’ firms (Table 3.3).
Table 3.3: Levels of business success
Mean score Level of business success
Less than 0.33 (BSi <0.33) Low business success
0.33 to 0.66 (0.33 ≤ BSi <0.66) Moderate business success
0.67 to 1.00 (0.66 ≤ BSi <1.00) High business success
Source: author’s estimations
3.6.4 Attribution of entrepreneur’s personality to business success
Within the psychology literature, the concept of personality trait (also referred to as
psychological disposition) can be linked to how entrepreneurs’ firms are managed and
their current levels of business success based their inherent strengths (Ansobo, 2015;
Rauch & Frese, 2000; Weinman et al., 2000). Dimensions that measure the
psychological disposition of the entrepreneur with regards to business success include
(but not limited to) locus of control, self efficacy, and motivation (Silver, 2015;
Delmar, 1996).  These three measures were applied in this study. These dimensions
provided insight as to the underlying reasons ascribed by entrepreneurs to their
business success.
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Different sets of statements measuring the three dimensions were posed to
entrepreneurs. Using a 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 =
neutral, 4 = agree and 5= strongly agree), entrepreneurs graded the levels of
importance of each statement under each dimension. Fifteen statements measured locus
of control; seventeen measured self-efficacy; and sixteen statements measured issues
related to motivation (Appendix 4 section 4).  A factor analysis using the principal
component method with a varimax rotation was conducted to determine the
convergence of constructs within each dimension under causal attribution (Zheng,
2012).
3.6.5 Determinants of business success applying the dimensions of the entrepreneur’s
personality trait as attribution to business success
3.6.5.1 Ordered logistic regression
Considering the nature of the dependent variable (three different levels of business
success in an ordered scale), the most appropriate empirical model to estimate the
determinants of casual attribution to business success (subjective and objective) is the
ordered logistic regression (Hedeker, 2002).  The ordered logistic analysis is based on
the structural specification in (3.18) where Xi is the vector explanatory variables, β is a
k×1 vector of unknown regression parameters to be estimated with the first element
being the intercept and εi is the error effect.
y *   ' X i   i ----------------------------------------------------------------(3.17)
Considering a latent (unobserved) variable y* which maps to an ordered observed
variable y,
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y *  m if m1  y
*
i m for m = 1, 2.....J --------------------------(3.18)
with’s as thresholds or cut points. If the continuous latent variable y * is related to
the ordinal variable, then the extreme categories are 0   and  j   (Long, 1997).
For an ordinal dependent variable yi with j categories,
yi 0 if y  0
yi 1 if 0  y  1
yi 2 if 1  y  2 -----------------------------------------------------------------------(3.19)
.
.
.
.
yi  J if y   J 1
2
The error term εi is logistically distributed with mean of 0, variance of  3 and a
probability density function (pdf) as well as cumulative density function (cdf) as shown
in equations 3.24 and  3.25 respectively (ibid).
exp( )
( ) 
2 ---------------------------------------------------------------------------(3.20)[1 exp( )]
exp( )
 ( )  --------------------------------------------------------------------------(3.21)
1 exp( )
With a dependent variable Y with values 0, 1 and 2 for three ordinal responses (low,
moderate and high levels of business success), the probability of obtaining an outcome
are represented as P1 = Pr (y=0), P2 = Pr (y=1) and P3 = Pr (y =2) for outcomes of 0, 1
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and 2 respectively. The parameter estimates are obtained using maximum likelihood
estimation procedure.
The ordered logistic regression model which expresses the relationship between
categorised business success and dimensions of causal attribution is empirically
specified as follows:
BS  1OWNABLC 2PWFOTHLC 3LUCKLC 4RESILSE 5MKTINOVSE 6COMPSE ---(3.22)
7BIZMGTSE 8GWLFMTV 9SUCCMTV 10ENTSKMTV 11FMTRADMTV 
Table 3.4: Description of variables - Dimension of personality traits attributed to
business success
Dimension of Entrepreneur’s attribution to business success apriori
personality sign
traits
BS Level of entrepreneur’s business success (dependent variable ) at three
levels (in an ordinal scale)
Locus of OWNAB Own abilities responsible for business success +
control PWFOTH Attribution of business success to influence of  powerful +/-
others
LUCK Attribution of business success to pure luck +
Self efficacy RESIL Attribution of business success to personal -resilience +
MKTINOV Attribution of business success to ability to create innovative +
ideas about marketing of products
COMP Attribution of business success to willingness to work in a +
very competitive environment
BIZID Attribution of business success to ability to develop new +
business ideas frequently
Motivation GWLF Improvement in general welfare of family as a motivation to +
business success
SUCC Resolve to succeed in life as a motivation to business success +
ENTSK Put entrepreneurial skills to good use as a motivation factor +
FMTRAD Continuation of trend in family as business people as +
motivation a factor
Source: author
Note: factor scores from the above dimensions are used in the ordered logistic regression as
explanatory variables.
Hypotheses
Following from the above, the following hypotheses are tested:
H90: Entrepreneurs will not ascribe locus of control to their subjective and objective
business success
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H9a: Locus of control has significant influence on subjective business success but not
objective business success
H100: Self efficacy is not attributed to business success
H10a: Self efficacy is positively and significantly related to business success
H11a: Motivation has no significant effect on business success
H11a: Motivation is positively and significantly related to business success
3.7 Research design
3.7.1 Study Area
The data for this study took place in two regions of Ghana (Greater Accra and Ashanti
regions). The choice of these two regions was based on the fact that they are the two
largest economically important regions and control more than half of all economic
activities in the country. These two regions have the highest concentration of micro and
small firms involved on agri-food processing in Ghana (GSS, 2015). The Greater Accra
Region houses the political and commercial capital of Ghana - city of Accra. Among
the 10 administrative regions of Ghana, it is the smallest in terms of land size,
occupying a total land surface of 3,245 km2 equivalent to 1.4 per cent of the total land
area of Ghana and a population size of 4,010,054 according to Ghana’s 2010
population and housing census. Sampled firms in this study were located in the
following administrative districts: Accra metropolis, Ashaiman, Ga West, Ga East, Ga
South and Tema. The region’s economy is largely informal private sector-driven
although there are a significant number of formal private sector businesses. The
agricultural industry is very prominent in terms of provision of employment. Within
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this industry, agri-food processing firms involved in production of animal-based and
crop-based products are predominant (Setsoafia et al., 2015).
The Ashanti region is located in the centre of Ghana with the Kumasi Metropolis as its
capital. Kumasi is the second commercially important city in Ghana after the national
capital Accra. It is the most populous region with a population size of about 4,780,380
according to the 2010 housing and population census. The major economic activity in
all the districts in the region is agriculture (crop and animal production) save for the
Kumasi metropolis, where commerce is the major economic activity. Majority of the
economically active population are self-employed, mainly in the private informal sector
and engage in both agricultural and non-agricultural economic activities. Just like the
Greater Accra region, one of the major agriculture-related activities is agri-food
processing. The districts sampled in this region were four and includes Kumasi metro,
Ejisu-Juaben, Atwima Nwabiagya and Atwima Kwanyoma.
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Figure 3.2: Map of study area
Source : CERSGIS, University of Ghana
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3.7.2 Sampling Procedure
A combination of purposive, and snow balling sampling techniques were used to select
the sample for this study. This technique was deemed appropriate given the nature of
the study (targeting only owners of micro and small firms in agri-food processing).
The sampling procedure involved an initial purposive selection of the Greater Accra
and Ashanti Regions because these two regions house most of the agri-food processing
firms in the country. Second, a list of agri-food processors was obtained from the
Ministry of Food and Agriculture (MoFA) and the National Board for Small Scale
Industries (NBSSI). A purposive sampling technique selected only micro and small
agri-food firms from the lists obtained.  Appointment dates for interviews were then
booked with owners of targeted firms.
After repeated postponement of interview schedules, it became apparent that targeted
owners were not interested in interviews. A new approach was therefore adopted. This
approach involved acquisition of contact addresses of agri-food processing firms from
labels of products on the shelf and subsequent trace to the premises of firms to conduct
interviews. The snowballing sampling technique was then utilised based on the
availability of firm owners.  The networking between owners of similar firms was
exploited so that enumerators were directed to such firms. In all, less than 5% of firms
from the list obtained from MoFA and NBSSI were interviewed – the rest came from
firms not listed with any of these two state institutions.
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3.7.3 Sample size determination
The Slovin’s sample size formula was used to estimate the sample size . The formula is
given by:
N
n 
2 ----------------------------------------------------------------------------------(3.23)1 N (e)
Where, n is the sample size, N is the population size, and e is the percentage error
margin (0.05). With a population size of 300 firms (obtained from MoFA and NBSSI),
a sample size of 171 firms was targeted in the survey. Despite the challenges
encountered as enumerated above, a total of 159 firms, representing 93 percent of
targeted sample size were eventually interviewed and used in data analysis (Table 3.5).
Similar sample sizes have been used elsewhere – (Setsoafia et al., 2015 – 120 firms in
Ghana; Muburi, 2014 – 50 firms in Kenya; Ankomah, 2012– 102 firms in Ghana;
Eriksson & Li, 2012 – 20 firms in Sweden; Smit, 2012 – 158 firms in South Africa; and
Gordon et al., 2009 – 112 firms in USA).
Table 3.5: Location of firm by region and district
Region District Number Percent
Ashanti Atwima-Mponua 12 18
Ejisu-Juaben 12 18
Atwima Nwabiagya 9 14
KMA 33 50
Total 66 100
Greater Accra AMA 47 51
Ashaiman 9 10
Ga West 8 9
Ga East 13 14
Ga South 8 9
Tema 8 9
Total 93 100
Source: author
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3.7.4 Data
The analysis was based on both panel and cross-sectional data using a structured
questionnaire. Limited panel data covering a period of 4 years (end of 2012 to end of
2015) were collected on sales volume and the number of employees. These data were
used as basis for measuring firm growth and as indicators to score levels of business
success of the firms in this study. Cross-sectional data on demographic and firm
characteristics of entrepreneurs, their risk attitudes and attribution to their current levels
of business success were collected. The data collection process was preceded by a pre-
test of the survey instrument to ascertain suitability for the sample and to ensure all
salient data required for analysis were adequately captured. The results from that
exercise informed the data collection team of the kind of risk management practices
used by entrepreneurs of micro and small informal firms in Ghana and included in the
questionnaire. The data collection team comprised of seven enumerators and one
supervisor apart from the author of this study.
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CHAPTER 4
RESULTS AND DISCUSSIONS
4.0 Introduction
This chapter presents the results of the study. First, estimations of risk attitudes of
entrepreneurs and factors affecting their risk attitudes are presented. The next section
presents results on risk management practices that affect firm growth.  The last section
presents results on business success and levels attained by entrepreneurs.
4.1 Sample characteristics
4.1.1 Demographic profile by proportion
The study conducted an enterprise survey that involved only owners (entrepreneurs) of
the firms in the sample. The analyses on the demographic and enterprise characteristics
of the sample are presented in two parts (Table 4.1 and 4.2). The analyses in Table 4.1
details the proportions of demographic features of the sample by gender and age status
(whether they were youth or aged) and Table 4.2 reports the mean statistics of
demographic and enterprise characteristics disaggregated by the regional location of the
firms, gender and age status of the entrepreneur. Youth were defined as those
entrepreneurs with ages from 18 years to 35 years and those older than 35 years t as
aged. Ownership of firms was dominated by females across age status. About 56% of
firms were owned by female among youthful entrepreneurs and 62 % of firms were
owned by females among aged.  This identifies with GSS (2014) which reports that
90.9% of women and 81% of men work in the informal sector in Ghana.  This could be
due to the fact that majority of processed food (sobolo, gari, Burkina, fruit juice and
tom brown) produced by the sampled firms are typically associated with women in the
Ghanaian context.
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Higher proportions of between 95% and 98% among males were heads of their
households but barely half of aged females and no female youth were household heads.
This reflects the typical household structure in the Ghanaian society and compares with
results from the latest Ghana Living Standards Survey (GLSS V) which estimates male
headed households within a range of 62% and 84% within Ghana (GSS, 2014).
Household sizes in the sample ranged from 1 to more than 10 with relatively larger
proportion of aged female entrepreneurs (about 22%) having household membership
exceeding 10 people.  A substantial proportion (up to 97%) of respondents were
married. In all, most entrepreneurs were educated with no illiterate among youthful
male entrepreneurs and only 7% among aged entrepreneurs in this group. Among
females, a substantial proportion (36%) of aged entrepreneurs was illiterate. Indeed this
result compares favourably with results from the GLSS VI (2014) which suggest about
53% of females across Ghana are illiterate (Table 4.1).
Table 4.1: Demographic characteristics (%)
Male(N=63) Female (N=96)
Youth Aged Youth Aged
Age status 33.3 66.7 28.1 71.9
Firm ownership 43.8 37.8 56.3 62.2
Household head? No 4.8 2.4 100.0 55.6
Yes 95.2 97.6 44.4
Total 100.0 100.0 100.0 100.0
Household size categorised 1-3 members 57.1 11.9 74.1 21.7
4-6 members 33.3 59.5 22.2 56.5
6-10  members 4.8 23.8
> 10 members 4.8 4.8 3.7 21.7
Total 100.0 100.0 100.0 100.0
Married or not? No 38.1 7.1 63.0 29.0
Yes 61.9 92.9 37.0 71.0
Total 100.0 100.0 100.0 100.0
Highest level of education attained Primary 14.3 11.9 18.5 11.6
JHS/Middle school 33.3 42.9 37.0 36.2
SHS/Secondary 19.0 19.0 33.3 11.6
school
Tertiary 33.3 19.0 3.7 4.3
None 7.1 7.4 36.2
Total 100.0 100.0 100.0 100.0
Source: survey results
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4.1.2 Demographic and enterprise characteristics (mean statistics)
Mean values of variables are presented in Table 4.2.  The mean age of youthful
entrepreneurs was 29.3 years and 48.8 years for aged entrepreneurs with maximum
ages of  35 years and 78 respectively (Appendix 1).  A mean household size of 3
persons was observed among youthful entrepreneurs and 5 persons among aged
entrepreneurs. This may be an indication of higher monthly expenditure among aged
entrepreneurs due to the relatively higher household sizes (ceteris paribus).
On average, youthful entrepreneurs were more educated compared to aged – the former
had mean number of years in school estimated at 9.8 years and 7.8 years for the latter.
There was statistically significant difference between equity in firms owned by aged
and those owned by youth. Firms owned by aged showed a mean of  GH¢ 24,700 as
compared to GH¢ 4,600 among the youthful entrepreneurs. This may be explained by
the relatively longer years of experience of the aged entrepreneurs with an average of
20 years compared to about 6 years of experience by youthful entrepreneurs.  On
average, firms opened for operations throughout the year (11.7 months). The average
number of months the firm works throughout the year is important to determine the
months of non-activity as this could explain possible inconsistencies in indicators that
measure firm growth. Youthful female entrepreneurs in the Ashanti region committed
more work hours (64.5 hours) per week to their businesses as compared to their
counterparts in the Greater Accra region (12.5 hours per week. This reflected in the
percentage firm growth of 50% and 30% for same groups respectively.
The years of experience was estimated as the difference between the year the
entrepreneur started or acquired the business and the year the survey was conducted.
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Aged entrepreneurs paid higher monthly salaries (on average) to their employees
compared to youthful entrepreneurs although they were not statistically different. On
average,  the salary paid by youthful entrepreneurs was GH¢ 190 while aged
entrepreneurs paid GH¢ 220. This result is consistent with the current minimum wage
of GH¢ 8.8 (per day) approved by government of Ghana for 2017. Percentage growth
of employee size was measured by the percentage change in the number of employees
from end of year 2012 to end of year 2015. Average percentage change in employee
size was 30% across firms.   The age of firms corresponded with the age status of the
entrepreneurs - 7 years as mean age of firms owned by youthful entrepreneurs
compared to about 14 years for aged entrepreneurs.
Table 4.2: Descriptive statistics of demographic and firm characteristics (mean)
Ashanti Greater Accra overall
Male Female Male Female
Age status Youth Aged Youth Aged Yout Aged Yout Aged Yout Aged t value
h h h
Age (years) 28.8 49.7 30.8 51.6 29.9 46.7 28.5 46.4 29.3 48.8 -6.42***
(5.5) (10.1) (3.3) (10.3) (3.9) (11.3) (3.9) (6.9) (4.0) (10.0)
Household Size 3.0 6.0 3.0 6.0 3.0 6.0 2.0 404 3.0 5.0 -0.64
(1.3) (2.4) (1.0) (1.9) (1.6) (2.1) (1.4) (1.7) (1.4) (2.0)
No. of Yrs in Education 7.8 9.6 6.7 5.6 12.7 10.2 9.1 7.7 9.8 7.8 -1.20
(2.1) (6.6) (6.1) (6.7) (3.4) (3.1) (3.2) (4.8) (4.1) (5.7)
Owner's equity in the business 16.9 37.1 1.4 37.8 5.9 21.2 1.1 2.5 4.6 24.7 2.50**
(GH¢ ‘000) (14.8) (67.2) (1.8) (159.6) (6.7) (59.0) (0.9) (2.9) (8.0) (105.
2)
No. of years of experience in the 5.7 23.2 6.7 22.4 5.9 18.4 5.8 16.6 5.9 20.0 -0.01
business (entrepreneur) (3.9) (15.0) (3.9) (12.6) (3.3) (12.2) (2.7) (10.2) (3.1) (12.3)
No. of months business normally 12.0 12.0 11.5 11.4 11.6 11.8 12.0 11.8 11.8 11.7 0.76
operates in year (0.0) (0.0) (1.2) (1.3) (1.1) (1.3) (0.0) (1.0) (0.8) (1.1)
Average No. of work hours/week 32.8 35.8 64.5 33.7 34.1 43.6 12.5 12.9 28.3 31.5 -0.87
(entrepreneur) (27.1) (31.5) (49.9) (26.7) (27. (37.9) (12. (17.3) (30.5 (30.7)
6) 9) )
Average No. of work hours/week 51.0 55.3 56.0 49.1 50.8 58.0 49.1 48.7 0.3 0.3 -0.61
(employee) (10.6) (10.9) (12.4) (4.8) (9.6) (11.8) (4.3) (4.8) (0.3) (0.5)
Average monthly salary of 2.7 2.9 2.2 2.7 2.9 2.8 2.1 2.1 1.9 2.2 1.46
employee (GH¢) (1.6) (2.0) (0.4) (1.5) (2.1) (1.9) (0.4) (0.5) (1.5) (2.0)
No. of permanent workers by 2.9 2.3 1.3 1.8 2.1 2.2 1.5 2.4 1.8 2.1 -0.57
end 2015 (2.3) (1.7) (0.5) (2.6) (1.7) (1.8) (0.9) (1.4) (8.3) (8.9)
Average Percentage growth in 0.2 0.3 0.5 0.4 0.3 0.4 0.3 0.3 0.3 0.3 0.16
employee size (2013 to 2015) (0.3) (0.2) (0.1) (0.3) (0.3) (0.8) (0.3) (0.3) (1.4) (1.5)
Age of the firm (years) 12.5 24.0 6.8 16.5 6.7 9.6 5.6 9.2 7.0 13.7 1.50
(10.4) (14.1) (2.9) (11.2) (3.5) (7.3) (1.2) (6.7) (4.6) (10.7)
Source: survey results; standard deviations are in parentheses
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4.1.3 Type of agri-food processors
Most of the entrepreneurs (50%) used grain as raw material for processing tom brown.
Other food commodities which served as raw material for processing include fruits for
fruit juice processing, tubers (especially cassava for gari processing), meat (mainly for
sausage production), bissap (for beverage – sobolo), and dairy (for production of
yoghurt and burkina). Only a fraction (about 10%) of aged females were engaged in
palm oil processing mainly because traditionally, males are not attracted to production
of this food because of the notion that it is meant for females. Youthful females were
also not involved because they plausibly did not find it attractive due to the drudgery
involved (Table 4.3).
Table 4.3: Type of agri-food processors (%)
Male(N=63) Female (N=96)
Youth Aged Youth Aged Total
Meat (Beef) 7.7 4.3 3.6 - 3.1
Grain (corn, millet) 50 65.2 28.6 41.9 45.3
Fruit (pineapple, orange) 3.8 4.3 3.6 4.1 4.4
Beverage (bissap ) 11.5 2.2 25 13.5 12.6
Tuber (cassava) 11.5 13 25 14.9 15.7
Dairy (from cattle) 7.7 2.2 14.3 5.4 5.7
Nut and Pulses (peanuts and 7.7 8.7 - 10.8 8.8
cowpeas)
Palm oil - - - 9.5 4.4
Total 100.0 100.0 100.0 100.0 100.0
Source: survey results
4.1.4 Firm age
The categorised ages of firms in the sample is presented in Figure 4.1. Firm ages reflect
age status of the entrepreneur (whether youthful or aged). Most firms (between 80%
and 96%) belonging to youthful entrepreneurs were no more than 10 years as compared
to those belonging to the aged – older than 10 years. In all, more than 70% of the entire
firms in the study were not older than 10 years implying that they were still in their
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active growth years in concurrence with Boermans & Willebrands (2017) who indicate
that young firms tend to grow at a faster rate than long established firms.
Figure 4.1: Age of firms sampled (categorised)
100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
Youth Aged Youth Aged
Male Female
Up to 10 years 81.0 59.5 96.3 60.9
11 - 20 years 14.3 21.4 3.7 18.8
21 - 30 years 4.8 4.8 13.0
> 30 years 14.3 7.2
Source: Author
The location of the firm in terms of urban or rural area has important implications for
firm dynamics on a wide range of issues including access to relevant services, raw
materials, labour, market prices (especially for output) and tax obligations. More than
70% of all firms were located in urban areas (Figure 4.2).
Figure 4.2: Location of firms (urban/rural)
100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0 Rural
20.0 Urban
10.0
0.0
Youth Aged Youth Aged
Male Female
Rural 19.0 23.8 29.6 15.9
Urban 81.0 76.2 70.4 84.1
Source: survey results
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4.2 Risk attitudes of entrepreneurs
A measure of the internal reliability of each of the scales used in measuring risk
perception and risk propensity is shown by the Cronbach’s alpha coefficient displayed
in Table 4.4.
Table 4.4: Mean score of scale items for risk attitude elicitation
Cronbach’s alpha coeff 0.62 0.63
  R
Ashanti Male Youth 2.78 -3.08 -0.31
Aged 2.74 -3.08 -0.35
Female Youth 2.31 -2.83 -0.53
Aged 2.71 -3.19 -0.48
Greater Accra Male Youth 2.89 -3.28 -0.39
Aged 2.76 -3.37 -0.62
Female Youth 3.48 -3.56 -0.08
Aged 3.55 -3.61 -0.06
Source: author’s computation , - Mean score for risk perception,  - Mean score for risk propensity
R - Mean score for risk attitude
There were six items (questions) in each scale (Appendix 4 section 2). The Cronbach’s
alpha was 0.62 for the risk perception scale and 0.63 for the risk propensity scale. The
results are inconsistent with those found by Blais and Weber (2006), which were 0.83
for risk perception and 0.68 for risk propensity. They better compare with the results by
Willebrands (2010) in Nigeria - 0.54  and 0.58 for the  risk perception and risk
propensity scales respectively. However, the coefficients for the two measures of risk
attitude can still be used for analysis. A mean score of 2.78 (risk perception) for
youthful male entrepreneurs in the Ashanti region indicate that generally, they
perceived activities related to the scale items as ‘slightly risky’  to  ‘risky’. This
translates into slightly higher likelihood for majority of them to make risky decisions
and engage in risky activities. Youthful female entrepreneurs had lower perceptions of
risk (2.74) and but the same mean score (3.08) for their male counterparts with respect
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to their risk propensity indicating they are likely  to engage in risky activities compared
to their male counterparts.
Within the Greater Accra region, female entrepreneurs (both youth and aged) had
higher mean perceptions of risk and therefore their risk propensity was lower implying
they would be cautious to engage in risk activities. Similar results by Lammers et al.(
2012) in Nigeria and Boermans & Willebrands (2017) in Tanzania show consistency in
this regard.  The mean scores for risk attitude indicate that generally, entrepreneurs are
risk seeking.  (For a description of the scale items used for measuring risk perception
and risk propensity and mean scores for each item, see Appendix 2.
4.2.1 Entrepreneur’s risk attitude
Entrepreneurs exhibit risk seeking behaviour across gender and age status. In all, more
than half of respondents (about 61%) exhibited risk seeking behaviour (Table 4.5). This
result is not surprising considering that previous research (e.g. Stewart & Roth, 2001)
suggests entrepreneurs are mostly risk seeking individuals.
Table 4.5: Risk aversion of entrepreneur (%)
Risk seeking Risk Neutral Risk averse
entrepreneur entrepreneur entrepreneur
Ashanti Male Youth 33.3 16.7 50.0
Aged 66.7 8.3 25.0
Female Youth 66.7 16.7 16.7
Aged 71.4 7.1 21.4
Greater Accra Male Youth 60.0 6.7 33.3
Aged 73.3 3.3 23.3
Female Youth 42.9 14.3 42.9
Aged 48.1 18.5 33.3
Overall 61.0 10.1 28.9
Source: author’s computation
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4.3 Factors affecting risk attitudes and perceived risk sources
4.3.1 Factors affecting risk attitudes of entrepreneurs
The maximum likelihood estimator was used in estimating the factors that affect the
risk attitude of the entrepreneur. The model measures the effect of demographic and
some enterprise characteristics on the risk attitudes of entrepreneurs (Table 4.6).  Data
diagnostic checks conducted indicated the absence of multicollinearity – none of the
explanatory variables had a variance inflation factor (VIF) value above 3.7. The mean
VIF value for all explanatory variables used in the model was 2.06 (see Appendix 3a
for details). To forestall any incidence of heteroskedasticity, a robust estimation of the
standard errors was specified. Age is positively related to risk attitude of entrepreneur
(at 10% significance level) implying that an additional year in the age  of the
entrepreneur will lead to an increase in the tendency of being more risk averse by 1.4%.
This result  runs contrary to Kisaka - Lwayo et al., (2005) who suggest  that older
people are less risk averse compared to younger people.
Although the relationship between risk perception and age is positive (as expected) it is
not significant. Owing to the inverse relationship between risk perception and risk
propensity, the relationship between the latter and age was expected to be negative but
the result shows the opposite and it is also insignificant. The effect of entrepreneur’s
gender on risk attitude shows a significance (at 5%) that males are  more risk seeking
compared to females and have lower perceptions of risk which would lead to higher
risk propensity (although there is no significant relationship between gender and risk
propensity). This is not surprising considering that literature affirms that between the
two sexes, males are more risk loving. The household size of the entrepreneur has no
significant effect on risk attitude.
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Table 4.6: Results - factors affecting the risk attitude of entrepreneurs
(1) (2) (3)
VARIABLES Risk attitude Risk perception Risk propensity
Age 0.0137* 0.00289 0.0108
(0.00743) (0.00777) (0.00719)
Sex(1=.male, 0=female) -0.281** -0.349** 0.0680
(0.137) (0.143) (0.133)
Age status(1=youth, 0=aged) -0.294 -0.123 -0.172
(0.193) (0.202) (0.187)
Household size -0.0199 -0.0362 0.0164
(0.0318) (0.0333) (0.0308)
Marital status(1 Married, 0=others) -0.0446 0.231** -0.275***
(0.101) (0.106) (0.0981)
Level of education
None -0.480 -0.0574 -0.130
(0.275) (0.265) (0.172)
Primary 0.239 0.0351 0.204
(0.222) (0.232) (0.215)
JHS/Mid. School 0.179 0.0170 0.162
(0.178) (0.186) (0.172)
SHS/Sec. School 0.102 0.105 -0.00311
(0.224) (0.234) (0.217)
Tertiary 0.383 0.134 0.249
(0.239) (0.250) (0.231)
Regional location (1=Ashanti ,0=Accra) -0.102 -0.674*** 0.573***
(0.143) (0.149) (0.138)
Meat processors (1= involved, 0=others) -1.271*** -1.564*** 0.292
(0.409) (0.428) (0.396)
Grain processors(1= involved, 0=others) -0.144 -0.217 0.0736
(0.209) (0.218) (0.202)
Fruit juice  processors(1= involved, 0=others) -0.450 -0.292 -0.158
(0.345) (0.361) (0.334)
Beverage processors(1= involved, 0=others) -0.205 -0.388 0.183
(0.249) (0.260) (0.241)
Nut and pulse processors(1= involved, 0=others) -0.116 -0.493* 0.378
(0.247) (0.258) (0.239)
Tuber processors(1= involved, 0=others) -0.178 0.00794 -0.186
(0.217) (0.227) (0.210)
Dairy processors(1= involved, 0=others) -0.197 -0.497* 0.300
(0.272) (0.284) (0.263)
Palm oil processors(1= involved, 0=others) -0.719** -0.575 -0.143
(0.347) (0.362) (0.336)
Area (1=urban, 0=rural) -0.296* 0.283* -0.579***
(0.160) (0.167) (0.155)
Constant -0.0879 3.102*** -3.190***
(0.425) (0.444) (0.412)
Observations 159 159 159
R-squared 0.2836 0.3273 0.3622
Adj. R-squared 0.1857 0.2353 0.2670
Prob > F 0.0002 0.0000 0.0077
Source: Author’s computation
Robust Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1
The relationship between being married (or not)  and the two dimensions of measuring
risk attitude – risk perception and propensity meets expectations since a higher risk
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perception (positively related) will lead to a lower risk propensity. This result concurs
with the finding of Willebrands (2010) that entrepreneurs who are married do not show
a higher willingness to take risk. This could probably be due to their fear of loss.
Considering family obligations, they tend to engage in less risky activities in order not
to lose much in case of poor outcomes from risky business decisions made. Education
across all levels are not significantly related to the risk attitude of the entrepreneur. In
terms of enterprise characteristics, the regional  location of the firm, entrepreneurs
engaged in meat processing, nut and pulse processing, dairy processors and the area the
firm is located (whether urban or rural area) are significantly related to the risk attitude
of the entrepreneur.
Entrepreneurs whose firms are in the Ashanti region have lower perceptions of risk and
therefore have higher tendency to take risk compared to those in the Greater Accra
region. This could be due to the fact that the Ashanti region is closer to sources of raw
materials (agricultural produce) compared to the Greater Accra region. Meat processors
exhibit risk seeking behaviour (significance at 1% level) and this is reflected in their
lower perception of risk. It is not surprising that meat processors show less risk averse
behaviour because the risk associated with livestock production is generally relatively
lower compared to crop production and this implies a relatively reliable source of raw
material (Table 4.6).
4.3.2 Factors affecting sources of risk as perceived by entrepreneurs
Fifteen sources of risk were considered and entrepreneurs were asked to rate them
according to their perceived severity in terms of their effects on the business
environment. A five point Likert scale (1 as ‘very important’ and 5 as ‘very
unimportant’) was introduced to entrepreneurs for rating (Appendix 4).  The ratings
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showed the level of importance entrepreneurs considered for each source of risk to the
business environment. A major risk source to many businesses in Ghana is high cost of
borrowing (i.e. high interest rates) which was expected to show high ratings by
entrepreneurs. Results rather showed lower than expected percentage (about 35%
consider this as very important or important) of entrepreneurs who considered this as
having substantial significance on their businesses as shown in Table 4.7. The reason is
that most of entrepreneurs relied on equity or borrowed from family and friends with
no or lower interest charges. War and civil commotion/disturbances received a lot of
attention (about 67% consider this as very important or important) as a risk source to
the business environment because of the devastating effects of such occurrences in the
past. High cost of labour and seasonal labour shortages was considered as very
important sources of risk by entrepreneurs (between 58% and 65% of entrepreneurs
consider these risks very important or important)
Table 4.7: Perceived sources of risk in the business environment in Ghana – Likert
scores (%)
Very Important Neither Unimportant Very Total
Important important unimportant
nor
unimportant
Change in trade policy 20.8 44.0 6.9 8.8 19.5 100.0
Government interference in 10.7 38.4 6.9 15.1 28.9 100.0
business environment
War and civil 42.1 25.8 6.3 9.4 16.4 100.0
commotion/disturbances
Seasonal Labour shortage 8.8 49.1 8.8 20.1 13.2 100.0
High cost of labour 17.6 47.2 10.7 11.9 12.6 100.0
Input price volatility 23.9 41.5 9.4 7.5 17.6 100.0
Poor market information on 6.3 40.9 17.0 21.4 14.5 100.0
price
High interest rates 6.9 29.6 10.1 23.9 29.6 100.0
Poor transportation for 18.2 47.2 7.5 15.1 11.9 100.0
input and output movement
Death or sickness of 7.5 48.4 12.6 17.6 13.8 100.0
entrepreneur or employee
Output price fluctuations 18.9 37.7 10.1 15.1 18.2 100.0
Changes in taxation policy 13.8 45.9 10.7 14.5 15.1 100.0
Poor access to credit 11.9 42.1 10.1 19.5 16.4 100.0
Technology for Marketing 6.9 21.4 13.2 22.0 36.5 100.0
Source: Author
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4.3.2.1 Factor analysis for perceived sources of risk
A total of 15 risk sources and their importance (in terms of how they would affect the
entrepreneur’s business) were scored on a 5 –point Likert scale (1 = very important and
5 very unimportant). Factor analysis reduced the large number of  risk sources into
constructs that converged (Bishu, 2014; Habing, 2003). The Principal component
analysis (PCA) using varimax rotation was used to aggregate common factors into
convergent constructs. Factor loadings represent the weights and correlations between
each source of risk and the components (constructs) produced as results from the factor
analysis. Factor loadings are reported for each variable used in the analysis. Higher
loadings indicate convergence in the corresponding construct being measured by the
variables. In all, four risk constructs grouped under the following: general
economic/political risk; financial risk; human risk; and market risk were retained as the
converged sources of risk faced by entrepreneurs. The Varimax rotation technique was
used and the standardised factor scores for each variable rated by each entrepreneur
was subsequently used as  the  dependent variable for a linear OLS regression for
estimating which factors affected the  perceived risk construct (Table 4.8).
4.3.2.2 Data quality tests for perceived sources of risk
Data consistency and reliability
The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy which checks the
factorability of the correlation matrices of the variables under consideration was found
to be 75% with Bartlett’s Test of Spherecity significant at 1% implying the constructs
in patterns of correlations in the risk sources were relatively compact and therefore
factor analysis could be used as an appropriate tool for analysis. Factors with latent root
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criterion (eigen values) greater than 1(one) were retained for analysis and this conforms
to literature (Bishu, 2014; Ahsan, 2011 ). Factor loadings which reported absolute
values less than 0.5 were excluded from analysis. These were excluded because they
indicated weak convergence with other variables to form compact constructs to be used
for further analysis. The Cronbach’s alpha for the risk sources as perceived by
entrepreneurs was 0.88 and this was considered acceptable (Table 4.8).
Table 4.8: Rotated Component Matrix (factor scores) for risk constructs
General Financial Human Market
economic/ risk risk risk
Political risk
Depreciation of local currency 0.169 0.136 0.016 0.837
Poor Transportation for input and output -0.088 0.027 -0.006 0.858
movement
Change in trade policy 0.698 0.398 -0.075 -0.087
Government interference in business 0.799 0.236 -0.188 0.218
environment
Labour shortage 0.111 -0.009 0.875 0.029
High cost of labour 0.134 0.263 0.827 -0.070
Input price volatility 0.011 -0.301 0.332 0.611
Poor market information on price 0.087 0.112 0.368 0.548
High interest rates 0.076 0.602 0.351 0.343
Changes in taxation policy 0.740 0.133 0.241 -0.108
Poor access to credit 0.395 0.510 0.281 0.181
Output price fluctuations -0.073 0.009 0.105 0.747
Technology for Marketing 0.165 0.272 -0.091 0.716
War and civil commotion/disturbances 0.627 0.268 0.045 -0.320
Death or sickness of entrepreneur or 0.271 0.143 0.674 0.115
employee
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.750
Bartlett's Test of Sphericity Approx. Chi- 972.003
Square
df 105.000
Sig. 0.000
Cronbach’s alpha coefficient 0.88
Cumulative variance explained by all constructs 69.3%
Source: Author’s computation
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4.3.2.3 Factors influencing entrepreneurs’ perceived risk sources
Testing heteroskedasticity
A major assumption in OLS estimations emphasises the need for  homoskedasticity in
the error terms of the estimated model so that efficiency  is met and most importantly,
the biases in the estimated standard errors would not lead to invalid inferences since
the opposite, heteroskedasticity causes standard errors to be biased. The test of
heteroskedasticity was also performed to test the null hypothesis that there are equal
error variances among the variables in the model versus the alternate hypothesis that
the error variances are a multiplicative function of one or more variables. The Breusch-
Pagan / Cook-Weisberg test was used to test for heteroskedasticity.  This test is
appropriate for linear forms of heteroskedasticity. The results from the testing for
heteroskedasticity showed low chi square values (0.32 for general economic and
political risk; 0.00 for financial risk; 1.79 for human risk; and 2.15 for market risk)
indicating heteroskedasticity was not a setback in the model..
Results from the regression model (Table 4.9) indicates the influence of demographic
and enterprise characteristics on the entrepreneurs’ perception of the importance of the
identified risks to their business environment. Age of the entrepreneur is negatively
and significantly related to the importance of general economic and political risk to the
business environment (at 10 % level of significance). That is, older entrepreneurs
perceived this risk source as less important to affect their businesses compared to their
younger colleagues.  General economic and political risks in this study are associated
with war and civil commotion/disturbances; change in trade policy; and government
interference in the business environment.
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Table 4.9: Regression results of factors influencing entrepreneurs’ perceived risk
sources
(1) (2) (3) (4)
variables General human Market Financial
economic/ risk risk risk
political risk
Age -0.0242* 0.0201* -0.00806 0.0158
(0.0142) (0.0112) (0.0125) (0.0137)
Sex(1=.male, 0=female) -0.290* -0.684*** 0.504*** -0.263
(0.171) (0.135) (0.152) (0.165)
Age status(1=youth, 0=aged) 0.327 -0.332 0.146 -0.387
(0.258) (0.204) (0.229) (0.249)
Household size -0.0500 -0.0233 0.0172 -0.0163
(0.0448) (0.0355) (0.0397) (0.0433)
Marital status(1 Married, 0=others) -0.0664 -0.00500 -0.0789 -0.000852
(0.0479) (0.0380) (0.0425) (0.0463)
Level of education
None -0.0673 -0.124 -0.133 0.629
(0.285) (0.182) (0.178) (0.192)
Primary 0.231 -0.221 -0.334 -0.223
(0.302) (0.239) (0.267) (0.292)
JHS/Mid. School -0.0720 -0.0930 -0.211 -0.0479
(0.227) (0.180) (0.201) (0.219)
SHS/Sec. School 0.0466 0.286 -0.539** -0.233
(0.257) (0.204) (0.228) (0.249)
Tertiary 0.00942 -0.374 -0.271 -0.00849
(0.301) (0.238) (0.266) (0.290)
Regional location (1=Ashanti ,0=Accra) 0.348* -1.190*** -0.762*** 0.688***
(0.195) (0.154) (0.173) (0.188)
Years of experience 0.0228* -0.0143 0.0112 -0.0176
(0.0115) (0.00914) (0.0102) (0.0111)
Meat processors (1= involved, 0=others) -0.000451 0.0246 0.0191 0.0461
(0.170) (0.135) (0.151) (0.164)
Grain processors(1= involved, 0=others) -0.209 -0.148 -0.270* -0.419**
(0.169) (0.134) (0.150) (0.164)
Tuber processors(1= involved, 0=others) -0.102 -0.0567 -0.0262 -0.0193
(0.168) (0.133) (0.149) (0.162)
Dairy processors(1= involved, 0=others) -0.373 0.0683 0.0571 -0.388
(0.319) (0.253) (0.283) (0.308)
Palm oil processors(1= involved, 0=others) -0.574 0.259 -0.0834 0.0444
(0.401) (0.318) (0.356) (0.388)
Area (1=urban, 0=rural) 0.0338 0.280 0.545*** 0.387*
(0.213) (0.169) (0.189) (0.206)
constant 1.038* 0.443 0.158 -0.192
(0.534) (0.423) (0.473) (0.516)
observations 159 159 159 159
R-squared 0.2793 0.1549 0.3912 0.2493
Adj. R-sqaured 0.1749 0.1039 0.3029 0.1405
Prob. F 0.0004 0.0023 0.0000 0.0027
Source: author
The effect of age on the perceived importance of this risk source supports that older
entrepreneurs perceive general economic/ political risk as less important source of risk
to the their businesses largely because older entrepreneurs would have built a wealth of
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experience and mechanisms to deal with occurrence of this risk as compared to younger
entrepreneurs. The effect of age on human risk indicates that older entrepreneurs
perceive this risk as more important to affect their business compared to younger
entrepreneurs. This is rightly so considering that older entrepreneurs are more likely at
risk of injury and sickness as a natural occurrence associated with age. Age does not
exert any influence on market and financial risk (Table 4.9). Male entrepreneurs
perceive general economic /political risk as less important to their business
environment as against the perception of female entrepreneurs in that regard. This
finding is consistent with literature (FAO, 2011; Bishu, 2014) that males in developing
countries are better able to access financial resources compared to females. The
influence of sex of the entrepreneur on human risk and market risk is highly significant
(at 1% level). Female entrepreneurs perceive human risk as more important source of
risk to their business compared to male entrepreneurs. This finding is in consonance
with hypothesis H2d which indicates that human risk is perceived as more important
source of risk to the business environment by females.
Males perceive market risk as more important to their business environment compared
to females. This finding runs contrary to the Hypothesis H2b that male entrepreneurs
perceive market risk as having less impact on their businesses compared to female
entrepreneurs. The contrary expectation was that market risk could be more important
for women to decipher as a major source of risk probably due to their little exposure to
information as compared to men. The education variable was decomposed into the
different levels in order to identify which level of education has an effect on perceived
risk sources. The different levels of education of the entrepreneur show no significant
effect on the importance of perceived risk  on the business environment save for those
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educated up to senior high school/secondary school levels. Findings indicate that they
perceive market risk as less important compared to those with no educational
background.
Regional location of firms displays very important perceptions of risks to the business
environment. Owners of firms located in the Ashanti region perceive general economic
and political risk as more important to the business environment while entrepreneurs
whose firms are located in the Greater Accra region may perceive this risk source as
less important. This may be influenced by the fact that the Ashanti region is a political
hot bed and of major interest to the two major political parties in Ghana. Again, the
economy of this region is largely driven by investment from indigenous entrepreneurs
with very low financial buffer. Therefore any negative economic occurrence is seen as
significant risk to the businesses in that area. Owners of firms located in the Greater
Accra region perceive human and market risks as very important risk sources to the
business environment and the relationship is very significant (at 1%).
The effect of the human risk to the business environment is linked to labour issues in
terms of shortage and high cost (Table 4.8). Comparatively, the cost of labour is higher
in the Greater Accra than in the Ashanti region. This may explain why entrepreneurs in
the Greater Accra region attach more importance to human risk. Entrepreneurs consider
financial risk as critical to the business environment and the underlying cause is high
interest rates charged by financial institutions.  Years of experience in the business
wields a positive influence on the perception of entrepreneurs on the general economic
and political risk in the business environment and this is justifiably so because the more
experienced the entrepreneur is in the business, the more they have encountered
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economic and political risks and would have developed coping mechanisms to counter
its effect.
4.4 Risk management practices and firm growth
4.4.1 Importance of risk management practices to entrepreneurs
The results show that savings for business purposes is largely the most important risk
management practice ranked by entrepreneurs across age status and gender with least
mean scores between 1.37 and 1.81 (Table 4.10). The deduction here is that
entrepreneurs are mitigating the effects of financial risk. Temporary wage employment
elsewhere is the second most important risk management practice ranked by
entrepreneurs. Other risk management practices include diversification of the
entrepreneur’s own economic activities, subscription to formal insurance packages,
forward contracting, cooperative marketing, borrowing and sale of assets.
.
Table 4.10: Average score and ranking of risk management practices (level of
importance)
Male Female Mean comparison
Male- Male-
Female Female
youth Aged
Youth Rank Aged Rank Youth Rank Aged Rank t value t value
Savings for 1.81 1 1.43 1 1.37 1 1.59 1 1.510 -1.11
business purposes
Temporary wage 3.10 2 3.81 2 3.22 2 3.83 2 -1.131 -0.63
employment
Diversification to 5.33 7 5.50 7 6.04 8 5.39 7 -1.103 0.11
other activities
Subscription to 5.95 8 5.24 5 5.41 6 5.77 8 0.920 -1.37
formal insurance
Forward 5.00 5 5.48 6 5.67 7 5.16 6 -0.921 0.60
contracting
Cooperative 5.34 6 5.98 8 5.19 5 5.04 4 0.129 2.87***
marketing
Borrowing 4.71 3 4.43 4 3.96 3 4.09 3 1.325 0.97
Sale of Assets 4.76 4 4.14 3 5.15 4 5.13 5 -0.596 -2.21**
Source: author’s computation
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A test of the differences in the mean scores indicate that across board, there were no
significant differences in the rankings of the risk management practices except the
rankings by male and female aged people on cooperative marketing and sale of assets.
This is reflected in the importance any of these groups place on the risk management
practices. Male aged entrepreneurs rank cooperative marketing as least important as
against aged female entrepreneurs who rank this risk management practice as number
four. This means that while aged male entrepreneurs are able to negotiate better prices
for their products, their female counterparts prefer to sell their products through the
cooperative marketing system in order to increase their bargaining power. The
difference in ranking of sale of assets to offset firm debt or to meet some household
obligations could be attributed to divergent behavioural tendencies between males and
females as regards retention of household assets – males are likely to sell off assets
compared to females in the Ghanaian society. This reflects the ranks attached by males
(3) as more important to that of females (5).
4.4.2 Determinants of firm growth
4.4.2.1 Effect of risk attitudes and enterprise characteristics on firm growth
Risk perception and risk propensity of the entrepreneur are both included in the models
to see their effect on firm growth. Contrary to other studies (Lammers et al., 2010;
Willebrands, 2010) who found a strong correlation between risk perception and risk
propensity, these two measures of risk attitude were not highly correlated ( 0.103,
p=0.080) and  VIF values of 1.67 and 1.54 for risk perception and risk propensity
respectively confirms the absence of multicolinearity between these two variables. The
expected directions of the influence of risk attitudes (risk perception and risk
propensity) are observed on both measures of firm growth. The risk perception of the
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entrepreneur is expected to lower firm growth (in terms of growth in employee size) by
about 10%. The low risk perception is will to lead to an increase in the risk propensity
of the entrepreneur. The increase in the risk propensity is will influence employee
growth by 0.1 percent (although not statistically significant) implying that the
entrepreneur’s should be cautious in risky decision making since risk propensity will
not positively affect the firm’s growth.
Table 4.11: Results -effects of risk management practices on firm growth
(1) (2)
Variables Growth in employee Growth in sales
size
Risk perception -0.0917*** -0.00449***
Risk propensity 0.00103 0.00293*
Demographic Age -0.000537 -8.12e-06
characteristics Sex(1=.male, 0=female) -0.00447 -0.0161***
Level of education
None -0.127 -0.00705
Primary -0.205* -0.00800
JHS/Mid. School -0.0446 -0.00480
SHS/Sec. School -0.0995 -0.00630
Tertiary -0.0234 0.00256
Marital status(1 Married, 0=others) -0.0899 -0.00213
Age status(1=youth, 0=aged) 0.0511 5.52e-05
Regional location (1=Ashanti ,0=Accra) -0.0867 0.00712*
Enterprise characteristics Area (1=urban, 0=rural) -0.121 -0.000537
Years of experience -0.00629** 3.31e-05
Firm age 0.0288 0.000534
Food processors -0.0438 -0.00307
Meat processors (1= involved, 0=others)
Grain processors(1= involved, 0=others) -0.121* 0.00193
Fruit juice  processors(1= involved, 0=others) -0.0327 0.000520
Beverage processors(1= involved, 0=others) -0.0595 0.00178
Tuber processors(1= involved, 0=others) 0.00564 -0.000779
Dairy processors(1= involved, 0=others) 0.0333 -0.000498
Palm oil processors(1= involved, 0=others) 0.0118 0.00914
Diversification of economic activities 0.0239 0.00170*
Borrowing 0.0402 0.00266**
Subscription to formal insurance 0.0437* 0.00244**
Risk management Forward contracting 0.0374 0.00336***
practices Cooperative marketing 0.0368 0.00309**
Savings 0.0459 0.00125
Sale of assets 0.0386 0.00333***
Temporary wage employment 0.0564* 0.00314**
Constant -0.947 0.144***
Observations 159 159
R-sqaured 0.3187 0.3378
Adj. R-sqaured 0.1845 0.1879
Prob. F 0.0007 0.0004
Source: author’s computation
*** p<0.01, ** p<0.05, * p<0.1
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The effect of risk perception of the entrepreneur again leads to a decrease in the sales
growth of the firm and a corresponding effect of risk propensity is observed - an
increase in sales volume of the firm. The effect of education (at different levels) is
generally statistically insignificant on firm growth save for those educated up to
primary school level which exerts a negative effect on firm growth. Firm location is
observed to significantly affect growth in volume of sales of the firm – firms located in
the Ashanti region seem to have location advantage over those in the Greater Accra
region. Years of experience negatively affects firm growth (growth in employee size)
and is inconsistent with extant literature (e.g. Boermans & Roelfsema, 2016; Kraus et
al., 2012) which suggest that years of experience positively affects firm growth.
4.4.2.2 Effects of risk management practices on firm growth
Risk management practices have significant positive influence on firm growth (Table
4.11) and it is therefore expected that risk management practices will positively affect
firm growth. The following risk management practices are most commonly used by
entrepreneurs: Diversification of economic activities; Subscription to formal insurance;
Forward contracting; Cooperative marketing; Borrowing; Savings for business
purposes; Sale of assets; and Temporary wage employment outside the business.  In the
context of this study, the risk management practices are those taken by the entrepreneur
which are not the typical enterprise risk management decisions found in most literature
(e.g Aziz and Yazid, 2015; Ng’ang’a, et al., 2015; Wanjohi and Ombui, 2013) about
enterprise risk management but those considered indigenous to entrepreneurs. The risk
management practices in this study have been named to suit the Ghanaian context with
respect to entrepreneurs in the micro and small firms.  They have been grouped under
risk reduction and loss management practices.
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4.4.2.3 Risk reduction practices
Diversification of economic activities
There are basically two distinct forms of diversification - diversification of product
composition (product diversification) and the diversification to other markets (Yoshino,
2008). However, these forms of diversification were not utilised by entrepreneurs.  In
the context of this study, diversification refers to the entrepreneur’s practice of
engaging in other productive enterprises which are not necessarily part of their business
(i.e. agri-food processing). This involves investment in more than one portfolio which
included engaging in primary agricultural production of food crops, sale of fresh
vegetables and  fruits (especially during peak harvest). Other entrepreneurs also
engaged in non-agricultural enterprises which helped in lowering the variants of
incomes from only agriculture- related activities. Diversification is a crucial risk
management tool to firm growth and is positively related to risk management. The
argument is that diversification can be provoked as a means of spreading perceived risk
such that the impact of unfavourable outcomes that can lead to total or partial failure of
business firm is minimised
Entrepreneurs engaged in income-smoothing strategies in order to reduce ex ante risk
exposure of the firm and secure a smooth income flow to the firm and by extension to
their households. The firms were more or less operated as family businesses so it was
difficult to treat the entrepreneur’s household and business as two fully distinct entities
since business decisions also affected the household. Considering that most of the
entrepreneurs in this study operated low-income firms, they would be harder hit by
severe consequences in the event of a loss in a decision concerning the firm.  To
mitigate these effects, diversification was used by entrepreneurs as a risk reduction
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practice. The economic portfolio of the entrepreneur included activities of economic
value and these activities were either agricultural or non-agricultural. Diversification of
the entrepreneur’s economic portfolio did not have any significant effect on employee
growth (as one of the measures of firm growth) but significantly affected sales growth
(0.0017, at 10% significance level) implying that an entrepreneur’s decision to
diversify the economic portfolio will enhance the sales of the firm.. The effect of
diversification on firm growth although not substantial is consistent with the argument
that resource-constrained entrepreneurs are not able to invest income originally meant
for investment in the firm to shore up growth (by increasing employee size) because
these resources end up in other economic activities. This is typical of risk averse
entrepreneurs since they prefer activities with low risk.
Risk loving entrepreneurs engage in high risk activities which translate into higher
returns. The overall effect of diversification on firm growth is very low. The major
diversified economic activities included primary agricultural production like cultivation
of tree crops (cocoa, oil palm), and annual crops (maize, cassava and vegetables).
These economic activities were mainly practiced by entrepreneurs in the Ashanti
region. On the other hand, entrepreneurs in the Greater Accra region specifically
engaged in retailing of food (small grocery shops for finished goods) and non-food
items (e.g plastic products).
4.4.2.4 Loss Management practices
Borrowing
Access to credit (borrowing) is very critical insurance mechanism that can be relied on
for consumption smoothing. Results from the empirical model indicates that borrowing
(as an insurance mechanism for production smoothing) is a loss management practice
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that influences firm growth (growth in volume of sales) but does not significantly
influence firm growth through increases in employee size. While borrowing (to invest
in production) increases sales (as a measure of firm growth) by 0.3 percent, growth in
employee size is not affected.  The logic underpinning this result emanates from the
fact that the firm needs to grow in sales significantly enough to accommodate increases
in employee size. Any firm growth maximisation objective which hinges on borrowing
for payment of salaries has a higher tendency of failure because the foundation of such
growth would not be based on growth in production. The main source of borrowing in
this study was informal (from family and friends)
Subscription to formal insurance services
Subscription to formal insurance products is also a very important part of risk
management.  Two insurance products were subscribed to by entrepreneurs: Personal
accident and illness; and Commercial property insurance. The personal accident and
illness insurance covered the entrepreneur against illness and accidents in their business
operations while the commercial property insurance covered the loss of or damage to
the business’ property. None of the firms in this study subscribed to any insurance
cover for employees. The effect of subscription to formal insurance products (as a risk
management practice) has a positive effect on both measures of firm growth.
Subscription to insurance product increases firm growth (in terms of increase in
employee size) by about 4 percent and increases volume of sales (also a measure of
firm growth) by about 0.2 percent. The positive effect of subscription to insurance on
firm growth could plausibly be attributed to the assurance entrepreneurs have
concerning their personal security and that of their firms. Minimised exposure to
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personal and especially property risk means the entrepreneur is more likely to engage in
risky decision making with higher expected outcome.
Forward contracting
This risk management practice is basically an agreement made between the
entrepreneur and some specific buyer on the delivery of a specific quantity of the firm’s
product (to the buyer) at some pre-arranged price.  Among the types of forward
contracts, the one practiced by entrepreneurs was the fixed price contract which
required the entrepreneur to commit to deliver a certain quantity of products with a
specified quality. Payment was made at the time of delivery.  This is a risk management
practice in that price volatility (especially downwards movement) as concerns the
firm’s product is avoided and the assurance of anticipated price is secured although the
entrepreneur carries the opportunity risk of losing potential gains when market prices
rise. Results indicate that forward contracting had no significant effect on employee
size (as measure of firm growth) while it significantly affected sales growth) positively
– engaging in a forward contract with a trading partner is expected to increase sales by
approximately 0.34 percent. Backward contracting which involves contractual
agreement with suppliers of raw material as a risk management practices was not
important to entrepreneurs. The main reason was that there were no difficulties in
accessing raw materials for processing.
Cooperative marketing
This risk management practice was popular among entrepreneurs with firm locations in
rural areas (and especially among processors of tuber – gari, and palm oil processors).
The practice reduces market risk and risk associated with unfavourable pricing since
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the firm’s products are sold through the cooperative which has stronger bargaining
power compared to the individual entrepreneur’s. Although cooperative marketing is
positively related to employee growth as a measure of firm growth, it is not statistically
significant. On the other hand, the effect of marketing the firm’s products through a
cooperative positively affects the sales growth of the firm and is significant (implying
that engaging in cooperative marketing increases the firm’s sales by about 0.3 percent)
Savings for business purposes
Savings is treated as building a self insurance mechanism by entrepreneurs in the event
of a loss or dire need for investment in the business. This self-insurance mechanism is
built by entrepreneurs when faced with conditions of imperfect markets for labour,
credit, and insurance. Saving purposely for the business was expected to significantly
affect firm growth (because it is treated as a store of resources meant for reinvestment
in the business). Although it positively relates the two measures of firm growth, it is not
statistically significant. The irony associated with this risk management practice is the
fact that it was ranked the most important risk management practice by all
entrepreneurs. Further research is required in this case given that it is a risk
management practice which could involve a plethora of decision making choices by the
entrepreneur.
Sale of business assets
This risk management practice was also considered a self insurance mechanism since
entrepreneurs liquidate their assets as a coping strategy in times of exposure to financial
risk. The sale of assets which are held primarily as stores of value serve a purpose of
providing avenues for reinvestment in the firm when there is difficulty in accessing
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funding. Sale of assets is positively related to both measures of firm growth although it
has no significant effect on increases in employee size but significantly affects volume
of sales probably as a result of investment in activities that promote sales.
Temporary wage employment outside the firm
This risk management practice is referred to as a reversible mechanism ( in this study)
and it involves the entrepreneur seeking wage employment albeit temporarily as a
coping strategy to deal with financial risk especially income loss as a result of poor
sales or non-fulfilment of forward contract agreement with a partner. This risk
management practice has a two-pronged effect – as a consumption-smoothing
mechanism for the entrepreneur’s household and for investment in the firm. The result
from the empirical model indicates that seeking temporary wage employment is
positively related to firm growth (both increase in employee size and increase in
volume of sales). This is inconsistent with the theory of firm growth which posits that
exposure to financial risk (in such devastating manner as to cause the entrepreneur to
seek employment elsewhere) reduces the potential for firm growth.
4.5 Business success
4.5.1 Categories of business success
Using indicators of business success (sales turnover per employee, employee turnover
and mean percentage change in growth of volume of sales), firms were categorised into
three different levels of business success under objective business success. Subjective
business success was measured by scores on a 5-point Likert scale by entrepreneurs as
concerns their current levels of satisfaction with the main objectives of setting up the
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firm. This measure is subjective because it is based on the view of individual
entrepreneurs. The mean scores used in measuring both objective and subjective
business success are summarised in Table 4.12. The mean scores were within a scale of
0 and 1. Three levels of business success were defined from this scale – mean scores up
to 0.33 were categorised as low business success, scores between 0.33 and 0.66 as
moderate business success and scores greater than 0.66 (up to 1) were classified as high
business success. On the whole, an average score of 0.47 for objective business success
indicates that most firms achieved moderate business success and same is observed of
subjective business success (0.56). These results reflect in Table 4.13 where about 57%
of firms achieved moderate objective success and 73% had subjective success.
Table 4.12: Mean score of indicators of business success
(N=159) Minimum Maximum Mean Std. Dev.
Average percentage growth in volume of sales 0.02 0.88 0.32 0.17
2013 to 2015
Percentage of average volume of sales per 0.02 0.85 0.27 0.19
employee  (2013 to 2015)
Average employee turnover (2013 and 2015) 0.14 1.00 0.82 0.29
Mean score for objective measurement of business
0.17 0.87 0.47 .16165
success.
Mean score for subjective measurement of 0.20 1.00 0.56 .15084
business success.
Source: Author’s computation
Table 4.13: Levels of business success (%)
objective business subjective business  success
success
Low business success 27.0 8.2
Moderate  business success 56.6 73.0
High  business success 16.4 18.9
Total 100.0 100.0
Source: Author’s computation
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4.5.2 Entrepreneur’s personality traits as attribution to business success
This section presents results on three main dimensions of the personality trait of the
entrepreneur as causal attribution to the entrepreneur’s current business success
(whether objective or subjective). The dimensions are locus of control, self-efficacy
and motivation.
4.5.2.1 Dimensions of causal attribution to business success
Locus of control
Locus of control shows how individuals believe their current situations (in this case
their current levels of business success) are contingent on their own actions or from
external sources.  Those who believe they have control over the events that happen in
their lives and are responsible for the outcomes have internal locus of control and the
reverse is true for those with external locus of control. The results from Table 4.14
show that most entrepreneurs are inclined towards internal locus of control implying
that they attribute their business success to their own abilities. This is reflected in their
strong agreement with constructs which attributes their success to their own abilities.
For example, a large chunk of entrepreneurs strongly agree or agree with the statements
that attribute their business success to their hard work (about 89%) or their own
abilities (about 84%). Other entrepreneurs also believe their current levels of business
success is based on pure luck or chance extent to which people believe they have power
over events in their lives (Table 4.14)
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Table 4.14: Statements depicting locus of control (%)
5 4 3 2 1 Total
My success depends on  luck 16.4 42.8 9.4 15.7 15.7 100
My success is controlled by accidental 1.9 8.8 14.5 55.3 19.5 100
happenings
My success is purely by chance 4.4 28.9 18.2 28.9 19.5 100
My success is determined by my own 30.8 53.5 12.6 2.5 0.6 100
actions/abilities
My success is based on my hard work 44.7 44.7 8.2 2.5 - 100
I can pretty much determine what will happen 9.4 28.3 25.2 30.2 6.9 100
in my life.
My success depends on pleasing people above 0.6 10.1 21.4 50.3 17.6 100
me
I feel that my success is mostly determined by - 8.8 12.6 59.1 19.5 100
people in powerful positions
Source: Author’s computation.
Numbers 1-5 represent scores under categories (1= Strongly Disagree; 2  = Disagree; 3 = Somewhat
Agree; 4 = Agree; 5= Strongly Agree)
Self efficacy
This causal attribution dimension is related to how the individuals assign their abilities
to produce their designated level of business success. The statements in Table 4.15
were posed to entrepreneurs and they scored their abilities to perform such tasks with a
5-point Likert scale (1 completely unsure to 5 completely sure). These statements were
posed to respondents to find out if they believed their capabilities in business were
responsible for their current business success or not. Apart from ability to develop new
products and business plans, majority of entrepreneurs (between 50% and 85%) were
sure or very sure of their abilities to perform tasks which were deemed to have
contributed to their business success.
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Table 4.15: Statements depicting self-efficacy of the entrepreneur (%)
5 4 3 2 1 Total
Work under pressure to achieve business 5.7 29.6 30.2 31.4 3.1 100
objectives
Not afraid of competition 0.6 6.9 13.2 60.4 18.9 100
Enter new markets locally 3.1 13.8 29.6 46.5 6.9 100
Develop new ideas for business 1.9 10.7 32.1 50.9 4.4 100
Expand share of firm's current market 3.1 11.9 64.8 20.1 100
Introduce new ways of marketing 1.3 13.2 25.2 54.1 6.3 100
Develop a business plan 3.8 27.7 32.7 30.8 5 100
Do financial analysis of business 1.3 7.5 18.9 54.7 17.6 100
Take calculated risks 0.6 25.8 28.9 35.2 9.4 100
Make decisions under uncertainty - 11.3 29.6 49.1 10.1 100
Cope with unexpected events - 6.3 22 59.7 11.9 100
Set business goals - 8.2 26.4 50.3 15.1 100
Source: Author’s computation. Numbers 1-5 represent scores under categories.
(1= Completely Unsure; 2 = Unsure; 3 = Slightly Unsure/Sure; 4 = Sure; 5= Very Sure)
Motivation
This dimension is hinged on the fact that the entrepreneur’s achievement (in terms of
business success) will affect motivation for the achievement and they will likely
attribute this to positive reasons (Russell, 1982). In consonance with the assumption
that the individual interprets the environment around the level of achievement to
maintain positive self-image, the results indicate entrepreneurs agreed with statements
connoting positive reasons to business success. Entrepreneurs did not consider
statements that did not portray positive self-image compared to those that conveyed
reasons deemed positive to self-image (Table 4.16).
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Table 4.16: Statements for motivation to business success of the entrepreneur (%)
5 4 3 2 1 Total
To increase level and security of 29.6 56 7.5 4.4 2.5 100
household income
To improve status in community 8.8 52.8 22.6 7.5 8.2 100
To gather freedom over your life 10.7 65.4 11.9 4.4 7.5 100
Limited other  ways to earn income 4.4 49.1 21.4 17.6 7.5 100
To achieve a flow of money quickly 22.6 55.3 10.1 8.2 3.8 100
Had little or no choice 1.9 30.2 26.4 27.7 13.8 100
To make full use of your skills 6.9 49.1 10.7 28.9 4.4 100
Had lost my previous job 0.6 12.6 13.2 46.5 27 100
Parents were business people 8.2 44 20.8 20.1 6.9 100
Risks were small 8.2 46.5 19.5 17.6 8.2 100
Wanted To be an entrepreneur 27.7 49.1 9.4 8.8 5 100
To continue a long standing family 5.7 30.8 13.2 30.8 19.5 100
tradition/business
Greater satisfaction from running own 28.3 49.1 9.4 8.8 4.4 100
business
Source: Author’s computation; numbers 1-5 represent scores under Likert scale.
(1= Very unimportant; 2  = Unimportant; 3 = Neither important nor unimportant; 4 = Important; 5=
Very Important)
4.5.2.2 Factor analysis of dimension of causal attribution to business success
Factor analysis was conducted on the dimensions of causal attribution to establish
convergence among the factors that determine attribution to business success. Within
this framework, three constructs under locus of control, four constructs under self
efficacy and four  constructs under motivation were obtained and used to estimate their
effect on the two types of  business success – subjective and objective business success
using an ordered logit model. Under locus of control, three constructs observed as
major contribution to business success of the entrepreneur (influence of powerful
others; Own abilities of the entrepreneur and pure luck) were attributed to business
success. Among these, own abilities of the entrepreneur falls under internal locus of
control and the other two constructs are external locus of control. This suggests that
both internal and external locus of control are responsible for entrepreneurs’ business
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success. The Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy of 0.88
indicates the results from the factor analysis were efficient (Table 4.17)
Table 4.17: Rotated factor matrix for dimensions of locus of control to business success
Components
Influence of Own Pure luck
powerful others abilities
My success depends on  luck 0.262 0.051 0.777
My success is controlled by accidental happenings 0.390 -0.363 0.724
My success is purely by chance 0.371 -0.040 0.770
My success is determined by my own actions/abilities -0.312 0.720 -0.043
My success is based on my hard work -0.201 0.860 -0.073
My success depends on pleasing people above me 0.756 -0.320 0.176
I feel that my success is mostly determined by people in 0.790 -0.236 0.207
powerful positions
I can pretty much determine what will happen in my life. 0.055 0.798 -0.806
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.88
Bartlett's Test of Sphericity Approx. Chi-Square 1307.02
df 105.00
Sig. 0.00
Cronbach’s alpha coefficient 0.81
Cumulative variance explained by all constructs 65.9%
Source: Author’s computation; Factor loadings in bold converge in a construct
The dimension of self efficacy produced four constructs. These constructs include
personal resilience – measuring how entrepreneurs assess their own abilities with
regards to certain tasks which significantly contributed to their business success. Other
constructs include high marketing skills, high affinity to competition and business
management skills. A KMO value of 0.70 indicates acceptable sampling adequacy for
the factor analysis (Table 4.18).
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Table 4.18: Rotated factor matrix for Self efficacy to business success
Components
Personal High High Business
resilience marketing affinity to management
skills competition skills
Work under pressure to achieve business 0.986 0.053 -0.023 0.046
objectives
Not afraid of competition -0.058 0.048 0.902 0.064
Enter new markets locally 0.070 0.962 0.117 -0.019
Expand share of firm's current market 0.032 0.910 0.064 0.050
Develop new ideas for business 0.038 0.133 0.099 0.862
Not afraid of competition 0.100 -0.120 0.779 0.133
Introduce new ways of marketing 0.029 0.942 0.085 -0.019
Do financial analysis of business 0.074 -0.006 0.048 0.756
Take calculated risks 0.770 0.206 -0.132 -0.136
Make decisions under uncertainty 0.985 0.054 -0.016 0.017
Set business goals 0.385 -0.115 0.062 0.680
Cope with unexpected events 0.984 0.038 -0.013 0.061
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.70
Bartlett's Test of Sphericity Approx. Chi-Square 2310.72
df 136.00
Sig. 0.00
Cronbach’s alpha coefficient 0.73
Cumulative variance explained by all 78%
constructs
Source: Author’s computation; Factor loadings in bold converge in a construct
Four main constructs were observed under the dimension of motivation (Table 4.19).
These include the necessity to succeed, Improvement in general welfare of family, Put
entrepreneurial skills to good use and continuation of trend in family as business
people. In all, attribution statements with factor scores above 0.5 (across all
dimensions) were used in the regression analysis to estimate their effects on business
success.
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Table 4.19: Rotated factor matrix for motivational dimension of causal attribution to
business success
Components
Necessity to Improvemen Put Continuatio
succeed t in general entrepreneuria n of trend in
welfare of l skills to family as
family good use business
people
To increase level and security of -0.008 0.700 0.077 0.097
household income
To improve status in community -0.057 0.634 0.350 -0.157
To gather freedom over your life 0.185 0.599 0.395 0.095
Limited other  ways to earn income 0.065 0.230 0.765 0.247
To achieve a flow of money quickly 0.320 0.584 0.267 -0.115
Had little or no choice 0.323 0.138 0.617 0.083
To make full use of your skills 0.391 0.177 -0.004 0.596
Had lost my previous job 0.580 0.194 0.082 0.230
Parents were business people 0.022 -0.024 0.059 0.557
Risks were small 0.760 0.459 0.233 -0.005
Wanted To be an entrepreneur 0.367 0.458 0.778 0.441
To continue a long standing family 0.462 -0.180 0.482 0.754
tradition/business
Greater satisfaction from running own 0.026 0.043 0.863 0.122
business
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.78
Bartlett's Test of Sphericity Approx. Chi-Square 835.75
df 120.00
Sig. 0.00
Cronbach’s alpha coefficient 0.57
Cumulative variance explained by all 86%
constructs
Source: Author’s computation; Factor loadings in bold converge in construct indicated
Significant correlations were found between the factor scores of the constructs of the
dimensions underlying casual attribution and business success (objective and
subjective). The results show positive and strong correlations between constructs of
motivation (necessity to succeed and improvement in general welfare of family) and
subjective business success at 1% significance (Table 4.20). Generally, the pattern of
correlations across the three dimensions of causal attribution show weak association
between them and business success.  Correlations between influence of powerful others
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as a construct under locus of control shows a highly significant but negative and weak
association with subjective business success.  The weak association between objective
business success and attribution constructs under motivation (continuation of trend in
family as business people), locus of control (Own abilities and pure lack) are obvious.
In all, the relationship between business success and factors of causal attribution shows
skewness towards negative and weak association rather than the reverse.
Table 4.20: Correlations between factor scores of constructs of causal attribution and
business success
objective business subjective business
success success
Pearson Sig. (2- Pearson Sig. (2-
Correlation tailed) Correlation tailed)
Necessity to succeed 0.129 0.106 0.524 0.000***
Improvement in general welfare of 0.127 0.112 0.515 0.000***
family
Put entrepreneurial skills to good use -0.053 0.506 0.376 0.000***
Continuation of trend in family as -0.214 0.007** 0.229 0.004**
business people
Personal resilience 0.127 0.114 0.001 0.989
High marketing skills 0.099 0.217 0.120 0.135
High affinity to competition 0.136 0.089* 0.059 0.460
Business management skills 0.062 0.441 0.055 0.493
Influence of powerful others -0.022 0.785 -0.309 0.000***
Own abilities -0.207 0.009** 0.084 0.294
Pure luck 0.132 0.099* 0.122 0.127
Source: Author’s computation; *** 1%; ** 5%; *10% significance levels
4.5.2.3 Dimensions of personality traits of the entrepreneurs as attribution to business
success
An ordered logit regression model was used to estimate the effects of constructs related
to the dimensions of personality traits of entrepreneurs as causal attribution observed
(under factor analysis) on business success – subjective and objective. The estimated
coefficients in ordered logit models cannot be used as the basis for interpreting the
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effects of changes in the explanatory variables on the predicted probabilities of falling
under one of the categories of the dependent variable (Greene, 2003). Such
information is rather provided by the marginal effects of the explanatory variables,
evaluated at the sample mean of the other variables. Attribution of the effect of
powerful people to business success has a negative but significant (5%) relationship
with subjective business success and positive relationship with objective business
success (but insignificant). There is no effect among the constructs of self efficacy on
subjective business success. Factor constructs linked to motivation to improve the
general welfare of family showed highly significant (1%) and positive relationship with
subjective business success.
.
For objective business success, entrepreneurs attributed their business success to their
own abilities under locus of control. This is consistent with literature which suggest
that individuals always attribute positive outcomes to their abilities to improve their
self-image but attribute negativity to external sources (Russell, 1982).  Personal
resilience and high marketing skills as constructs under self efficacy were significant
(1% and 5% respectively) and positively related objective business success. The effect
of motivation related to the necessity to succeed was positively related to objective
business success and was highly significant (1%). The effect of continuation of trend in
family as business people (as a motivation) construct exerted a significant (10%) but
negative effect on objective business success (Table 4.21).
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Table 4.21: Ordered logit results for effects of dimensions of causal attribution to
business success
(1) (2)
Dimensions of causal Variables subjective objective
attribution business success business
success
Own abilities 0.0879 0.749***
(0.232) (0.219)
Influence of powerful others 0.0477 -0.0396
Locus of control (0.249) (0.220)
Pure luck -0.563* -0.150
(0.300) (0.257)
Personal resilience 0.121 0.329***
(0.203) (0.181)
Very innovative with Marketing 0.398* 0.0694**
Self efficacy (0.206) (0.173)
Willingness to work in a very 0.0590 -0.00467
competitive environment (0.252) (0.222)
Business management skills -0.203 -0.121
(0.196) (0.180)
Improvement in general welfare of 0.792*** -0.437*
family (0.258) (0.229)
Resolve to succeed in life 0.129 0.769***
motivation (0.220) (0.217)
Put entrepreneurial skills to good use -0.207 -0.0873
(0.207) (0.186)
Continuation of trend in family as 0.147 -0.557**
business people (0.246) (0.226)
cut1
Constant -2.758*** -1.280***
(0.323) (0.211)
cut2
Constant 1.711*** 2.023***
(0.238) (0.258)
Observations 156 156
Source: author’s computation
Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1
4.5.2.4 Marginal effects of significant constructs of personality trait as attribution to
business success
Each of the attribution constructs used in the model (i.e explanatory variable) is
assumed to have a linear relationship with the levels of business success (the factor
scores from PCA are linear in nature). This means that a marginal increase in the
explanatory variable is expected to result in increases in the probability of achieving a
high, moderate or low level business success. Results from estimation of the marginal
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effects of constructs of attribution suggest that t the tendency to attribute subjective
business success to pure luck increases the probability to experience subjective
business by 4% among those with low business success but reduces the likelihood to
business success by 3.9% and 3.3% among entrepreneurs with moderate and high
levels of business success (Table 4.22).
An increase in motivation attribution of the entrepreneur will decrease the probability
of subjective business success by 2.8% among entrepreneurs with low business success
but increase the subjective business of those with high subjective business success by
about 10%.  The entrepreneur’s own abilities and personal resilience as attribution to
objective business success follow similar patterns. An increase in the measure of
personal resilience of the entrepreneur is expected to decrease the probability of
business success by 6% among entrepreneurs with low levels of objective business
success (Table 4.22).
Table 4.22: Marginal effects of significant dimensional constructs on levels of business
success
Subjective business success
low moderate high
Pure luck 0.040 -0.039 -0.033
High marketing skills 0.003 -0.090 0.085
Improvement in general welfare of family -0.028 -0.088 0.096
Objective business success
low moderate high
Own abilities -0.142 0.048 0.065
Personal resilience -0.060 0.071 0.005
High marketing skills 0.131 0.075 0.032
Improvement in general welfare of family 0.040 0.060 -0.102
Put entrepreneurial skills to good use -0.108 -0.030 0.049
Continuation of trend in family as business 0.040 0.076 -0.078
people
Source: author’s computation
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CHAPTER FIVE
SUMMARY, CONCLUSION AND POLICY RECOMMENDATIONS
5.0   Introduction
This is the last chapter of this study and it provides a summary of the research
undertaken, conclusion, policy recommendations, and suggests future research in the
research area for better results and further development of literature in the research
area.
5.1 Summary
Micro and small enterprises (MSEs) are very crucial to Ghana‘s economic development
due to the pertinent roles they play which includes provision of employment.   Besides
their positive contribution to the economy, there are setbacks which hamper their
growth leading to high failure rates especially in the initial years of set up.  The
entrepreneurs (owners) of firms which survive face a lot of challenges which mainly
border on risks they face in a developing economy like Ghana’s.  The risks are apparent
and manifest along institutional, market, and financial angles. The panacea is to design
and implement risk management practices that suit their context. The major challenge
in this effort manifests in scanty reference material to help affected entrepreneurs make
informed choices to maximise the benefits from the choices they make in risk
management options for their firms.
To find a solution to the challenge of scant research in this area, this study became
necessary.  The study had three main objectives. First, it elicited the risk attitudes of
entrepreneurs and assessed the factors that influence the attitudes and examined how
they perceive certain risk sources as important to the business environment in Ghana.
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Second, the study sought to estimate the impact of entrepreneurs’ risk management
practices on firm growth.  Third, the study sought to understand the personality traits of
entrepreneurs and what accounted for their business success based on the dimension of
their personality traits.  The sample size was 159 comprising only entrepreneurs
(owners) of micro and small firms in agri-food processing in the Greater Accra and
Ashanti regions of Ghana. Majority of sampled entrepreneurs processed grains (tom
brown). Other food commodities processed by the sampled firms include tuber- cassava
(gari), meat - beef (sausage), beverage- Hibiscus flower (bissap sobolo), dairy
(yoghurt, burkina), nuts and pulses (bottled peanuts and peanut butter) and palm oil.
The socio-economic characteristics of the sample show a dominance of female owned
firms (60.4%) in the sample. Results were disaggregate by age status (whether youthful
or aged owners of firms). Among youthful entrepreneurs, about 56 % of firms were
owned by female and 62 % of firms were owned by females among aged.
In all, most entrepreneurs were educated with no illiterate among youthful male
entrepreneurs and only 7% among aged entrepreneurs within this gender group. The
mean age of youthful entrepreneurs was 29.3 years and 48.8 years for aged
entrepreneurs with maximum ages of   35 years and 78 respectively. There was
statistically significant difference between equity in firms owned by aged and those
owned by youth. Firms owned by aged showed a mean of about GH¢ 24,700 as
compared to GH¢ 4,600 among the youthful entrepreneurs. Youthful female
entrepreneurs in the Ashanti region committed more work hours (64.5 hours) per week
to their businesses compared to their counterparts in the Greater Accra region (12.5
hours per week. This reflected in the percentage firm growth of 50% and 30% for same
groups. For risk attitude elicitation, results showed that 61% of entrepreneurs were risk
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seeking confirming literature that most entrepreneurs are risk seeking. Age, gender,
marital status, firm location among other variables impacted on entrepreneurs’ risk
attitudes. Aged entrepreneurs were more risk averse compared to younger ones, among
males and females, males showed higher risk seeking tendencies. Perceived sources of
risk culminated in four main risk constructs – general economic/political risks, human
risks, financial risks and market risks. Indications from analysis show that risks
associated with labour shortage (although not the most important risk) is a tethering
issue that has impact on operations of micro and small informal agri-food processing
firms in Ghana.
Impact of risk management practices on firm growth
Entrepreneurs ranked risk management practices in terms of importance to the growth
of their firms. In all, savings for business purposes was ranked as the most important
risk management practice. The plausible explanation is that entrepreneurs found ways
of mitigating the effects of financial risk which involves difficulty in accessing funding
in Ghana especially from formal financial institutions. Temporary wage employment
was the next ranked most important risk management practice. Other risk management
practices include diversification to other economic activities; subscription to formal
insurance, forward contracting, cooperative marketing, borrowing and sale of asset.
The effect of the dimensions of risk attitude (risk perception and risk propensity) on
firm growth was tested. Firm growth was measured by average annual growth in the
firm’s volume of sales and employee size. The expected directions of the influence of
risk attitudes (risk perception and risk propensity) were observed on both measures of
firm growth. The risk perception of the entrepreneur was expected to lower firm growth
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(in terms of growth in employee size) by about 10%. The low risk perception was
expected to lead to an increase in the risk propensity of the entrepreneur. The increase
in the risk propensity was expected to influence employee growth by 0.1 percent
(although not statistically significant) implying that it is prudent for entrepreneurs to be
cautions in taking risky decisions.  Almost all risk management practices used by
entrepreneurs had positive impact on sales growth of the firm.
Interestingly, the most important risk management practice ranked by entrepreneurs –
savings for business purposes did not have any significant effect on firm growth.
Among the risk management practices that positively and significantly affected firm
growth were borrowing, subscription to formal insurance, forward contracting,
diversification of entrepreneurs’ economic activities and cooperative marketing. In all,
the risk management practices served the purposes for which they were used by
entrepreneurs although their effects on firm growth were not escalatory. The results
indicate that generally, indigenous risk management practices used by entrepreneurs
have a positive effect on firm growth.
Business success and causal attribution to business success
Using indicators of business success (sales turnover per employee, employee turnover
and mean percentage change in sales growth), business success of firms was
determined under two types - objective and subjective business success. The obtained
scores for business success were differentiated into three levels to observe their
distribution on a scale of 0 to 1 and to also ascertain the factors that contributed to such
success. Firms were categorised into three different levels under objective and
subjective business success. Results revealed that firms classified as having moderate
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business success made a chunk (56.6% for objective success and 73% for subjective
business success) of all businesses. To further understand the underlying factors
responsible for the levels of business success chalked by entrepreneurs, their
personality traits (also called psychological disposition) using three dimensions - locus
of control, self-efficacy and motivation were estimated
5.2 Conclusions
Risk attitudes of entrepreneurs and factors influencing perceived risk sources
The results from this study indicate that majority of entrepreneurs were risk seeking
individuals confirming the hypothesis that more than 50% of the sample (who are all
entrepreneurs) would exhibit risk seeking behaviour. Results from factor analysis
revealed four main risk constructs (general economic/political risk, financial risk,
human risk and market risk) as very important to the business environment of
entrepreneurs. It has been revealed that labour issues (high cost of labour, and seasonal
labour shortages) as a risk source are prevalent. The effects of the perceived risk
sources on the business environment of entrepreneurs are mixed. While aged
entrepreneurs do not give much credence to general economic/political risk as an
important risk source to affect them but considered human risk (sickness and death) as
important, the opposite is true for younger entrepreneurs. Female entrepreneurs did not
consider market risk as important enough to affect the business environment probably
because they had found innovative ways (cooperative marketing) in marketing their
products and these innovations reduced the impact of market risks on their businesses.
Generally, the perceived risk sources have both positive and negative effects on
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entrepreneurs’ business environment in Ghana. Findings showed important
implications of risk sources to the business environment in Ghana as depicted by
entrepreneurs.    It has already been established that risks faced by entrepreneurs
hamper the growth of their firms. Findings bordering on firm location revealed that
human risk (as a larger construct of risk source that include labour risk) is a major
challenge to firms especially as regards labour supply and cost. Labour issues have
important cost of production and revenue implications for micro and small firms since
they are labour intensive and require more labour to increase production.
Risk management practices (mostly indigenous – not the typical risk management
practices used by formal firms and most common in western countries) impact
positively on firm growth. On business success and effects of entrepreneurs’
personality traits as attribution to their business success, the general conclusion is that
across board, some factors within all three dimensions of entrepreneurs’ personality
were attributed to business success. The foregone implies that the entrepreneur’s
psychological disposition has effect on business success.
5.3 Policy recommendations
The knowledge concerning the risk attitudes of entrepreneurs, important sources of risk
and management strategies should serve as a guide to formulating and implementing
development policies that will improve the SME sector in Ghana. To mitigate high
labour costs and seasonal shortage of labour, entrepreneurs are encouraged to cross-
train employees to perform multiple tasks and compensated adequately as firm policy.
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Among the most important risk sources that were perceived to have significant negative
influence on the business environment were market and human risks. To mitigate the
effects of market risk on micro and small firms, efforts to improve access to market
information  especially on pricing and availability of inputs (for agri-food processors)
through the use of telecom  services is recommended. Currently, a few private entities
(e.g Esoko) provide such services for primary agricultural producers (farmers) and
market women.   This service could be extended to cover agri-food processors.
However, explicit policy guidelines on the provision of telecom services in marketing
are lacking. The ministry of Trade and Industry in collaboration with the private sector
players could develop policy in this regard.
To reduce financial risk, a review on interest rate policies by financial institutions could
be encouraged. Recently, the Bank of Ghana reduced the policy rate and this was
expected to reflect in downward reviews of interest rates in the Ghanaian financial
market. Similar reduction in policy rates by the central bank in the past had no
commensurate reaction from financial institutions in terms of downward review in
interest rates. Investigation to find underlying reasons for this phenomenon will inform
future actions by the central bank. It is recommended that the major reasons responsible
for this trend would proffer solutions to reduce the high cost of credit and this
eventually will reduce financial risks faced by entrepreneurs in the Ghanaian economy.
The importance of cooperative marketing as a risk management tool by female
entrepreneurs impacted positively on firm growth. Initiating and facilitating the
development of marketing cooperatives to negotiate fair prices are important tools that
can help mitigate marketing risks to the entrepreneur.  Subscription to formal insurance
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packages as risk management tool by entrepreneurs showed positive results on firm
growth and this is attributed to the feeling of security by entrepreneurs in terms of
insurance of business premises. This allowed them the peace of mind to develop new
business ideas that eventually impacted firm growth. Targeted education   on the
importance of subscription to insurance packages should be undertaken by the National
Insurance Commission of Ghana to alert entrepreneurs of the need to insure their
businesses and the inherent benefits.
Business success was influenced by personality traits. Education policy especially at
the tertiary level should consider convergence of academic programmes (eg. Business
management and psychology) to guarantee understanding underlying certain concepts
in academia.
5.4 Future research
There are multiples of ways to improve this study. First the sample was selected form
only two regions of Ghana due to budgetary constraints and attitude of targeted
entrepreneurs in the initial stages of the study. Although the sample size is
representative of MSEs in Ghana, there is the likelihood of producing biased results
since there could be significant differences in results if samples from other regions of
Ghana were included. Future research could consider this. Second, the analysis was
based on primary data gathered through a cross sectional design (except data on sales
and employee size which were panel over a 3-year period). Parameter estimates of the
empirical model used cross sectional data which were static rather than dynamic.
However, the determinant variables on risk could also be dynamic factors that change
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over time and the static model did not capture the change in risk sources over time. A
longitudinal study in future is proposed to improve the results in this regard.
Third, different methods of risk attitude elicitation abound in literature but one method
was used in this study to elicit the risk attitudes of entrepreneurs. Ideally, a comparison
of results from at least two methods could help validate results. Future research could
consider using two methods for comparison and validation of results. Finally, savings
for business purposes was ranked by entrepreneurs as the most important risk
management practice (in this study). However, its effect on firm growth was not
significant. Considering that the decision to save purposely for the firm comes with a
plethora of decisions which will affect not only the firm but the entrepreneur’s
household, further study needs to find the reason for this result and also assess the
dynamics surrounding this decision and implications for household income.
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LIST OF APPENDICES
APPENDIX 1: Descriptive statistics : Demographic And Enterprise Characteristics (overall statistics)
Regional gender Age Age Household No. of Yrs Owner's No. of Average Average Average No. of Average Age of No. of years
location status Size in equity in months No. of No. of monthly permanent Percentage the firm of experience
Education the business work work hours salary of workers growth in (years) in the
business normally hours (employee) employee by end employee business
(‘000 operate (owner) (’00 2015 size (2013 (entrepreneur)
GH¢) in year GH¢) to 2015)
Ashanti Male Youth Min 22 1 6 6 12 8 42 2 1 0 5 2
Max 35 4 11 45 12 72 72 6 7 0.8 30 11
Mean 28.8 3.2 7.8 16.9 12 32.8 51 2.7 2.9 0.3 12.5 5.7
Std. 5.5 1.3 2.1 14.8 0 27.1 10.6 1.6 2.3 0.3 10.4 3.9
Dev
Adults Min 40 1 0 8 12 6 48 2 1 -0.2 5 3
Max 70 11 17 250 12 84 72 9 7 0.5 42 46
Mean 49.7 5.8 9.6 37.1 12 35.8 55.3 2.9 2.3 0.2 24 23.2
Std. 10.1 2.4 6.6 67.2 0 31.5 10.9 2 1.7 0.2 14.1 15
Dev
Female Youth Min 28 2 0 0.5 9 8 48 2 1 0.3 5 2
Max 35 4 16 5 12 157 72 3 2.3 0.7 11 11
Mean 30.8 3.2 6.7 1.4 11.5 64.5 56 2.2 1.3 0.5 6.8 6.7
Std. 3.3 1 6.1 1.8 1.2 49.9 12.4 0.4 0.5 0.1 2.9 3.9
Dev
Adults Min 36 2 0 0.4 8 6 42 2 1 -0.1 5 1
Max 75 10 30 1,000.00 12 84 72 7 16 1.7 40 44
Mean 51.6 5.5 5.6 37.8 11.4 33.7 49.1 2.7 1.8 0.4 16.5 22.4
Std. 10.3 1.9 6.7 159.6 1.3 26.7 4.8 1.5 2.6 0.3 11.2 12.6
Dev
Greater Male Youth Min 21 1 7 1 8 8 42 1.5 1 0 5 2
Accra
Max 35 7 16 20 12 84 72 10 7 0.7 17 11
Mean 29.9 2.6 12.7 5.9 11.6 34.1 50.8 2.9 2.1 0.3 6.7 5.9
Std. 3.9 1.6 3.4 6.7 1.1 27.6 9.6 2.1 1.7 0.3 3.5 3.3
Dev
Adults Min 36 2 5 0.8 5 6 48 1.5 1 -0.2 5 2
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Regional gender Age Age Household No. of Yrs Owner's No. of Average Average Average No. of Average Age of No. of years
location status Size in equity in months No. of No. of monthly permanent Percentage the firm of experience
Education the business work work hours salary of workers growth in (years) in the
business normally hours (employee) employee by end employee business
(‘000 operate (owner) (’00 2015 size (2013 (entrepreneur)
GH¢) in year GH¢) to 2015)
Max 78 12 16 300 12 105 72 10 7 3.7 35 54
Mean 46.7 5.8 10.2 21.2 11.8 43.6 58 2.8 2.2 0.4 9.6 18.4
Std. 11.3 2.1 3.1 59 1.3 37.9 11.8 1.9 1.8 0.8 7.3 12.2
Dev
Female Youth Min 20 1 2 0.2 12 6 45 1.5 1 0 5 1
Max 35 6 12 4 12 56 66 3 5 1 8.3 11
Mean 28.5 2.4 9.1 1.1 12 12.5 49.1 2.1 1.5 0.3 5.6 5.8
Std. 3.9 1.4 3.2 0.9 0 12.9 4.3 0.4 0.9 0.3 1.2 2.7
Dev
Adults Min 36 2 0 0.2 7 2 42 1.5 1 -0.3 5 3
Max 60 8 18 10 12 91 72 3.5 6 1.3 30 36
Mean 46.4 4.4 7.7 2.5 11.8 12.9 48.7 2.1 2.4 0.3 9.2 16.6
Std. 6.9 1.7 4.8 2.9 1 17.3 4.8 0.5 1.4 0.3 6.7 10.2
Dev
Overall Min 20 1 0 0.2 5 2 42 1.5 1 -0.3 5 1
Max 78 12 30 1,000.00 12 157 72 10 16 3.7 42 54
Mean 42.9 4.5 8.4 18.6 11.7 30.5 51.7 2.5 2 0.3 11.7 15.7
Std. 12.5 2.2 5.4 88.4 1 30.6 8.7 1.5 1.9 0.4 9.8 12.3
Dev
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APPENDIX 2: scores for scale items measuring risk perception and risk propensity of
the entrepreneur
Mini Max Mean Std. Dev
Betting a day's income of  GH¢ 500 at a high-stake -5 -1 -4.01887 0.889259
card game, such as poker (as risk propensity)
Investing 10% of your annual income (which is equal -5 -2 -4.16981 0.608043
to GH¢ 20,000) in a new business venture (as risk
propensity)
Betting a day's income of GH¢ 500 on the outcome of -5 -1 -2.83019 1.43292
a sporting event, such as soccer (as risk propensity)
Investing 10% of your annual income (which is equal -5 -1 -3.21384 1.304155
to GH¢ 20,000) in stocks (as risk propensity)
Investing 10% of your annual income (which is equal -5 -1 -2.77358 1.594788
to GH¢ 20,000) in a ponzi financial scheme (like
DKM) that promises high returns on savings
Investing 10% of your annual income (which is equal -5 -1 -2.96855 1.299568
to GH¢ 20,000) in a new processing technology
Betting a day's income of  GH¢ 500 at a high-stake 1 5 2.798742 1.4266
card game, such as poker (as risk perception)
Investing 10% of your annual income (which is equal 1 5 2.691824 1.405008
to GH¢ 20,000) in a new business venture (as risk
perception)
Betting a day's income of GH¢ 500 on the outcome of 1 5 3.201258 1.301313
a sporting event, such as soccer (as risk perception)
Investing 10% of your annual income (which is equal 1 5 3.509434 1.301741
to GH¢ 20,000) in stocks (as risk perception)
Investing 10% of your annual income (which is equal 1 5 2.943396 1.441893
to GH¢ 20,000) in a ponzi financial scheme (like
DKM) that promises high returns on savings)
Investing 10% of your annual income (which is equal 1 5 2.679245 1.402172
to GH¢ 20,000) in a new processing technology (as
risk perception)
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APPENDIX 3a: VIF values of variables used in estimating factors affecting risk
attitude and perceived risk sources of entrepreneurs
VIF 1/VIF
Age 2.89 0.346587
Sex(1=.male, 0=female) 1.52 0.659217
Age status(1=youth, 0=aged) 2.65 0.377544
Household size 1.69 0.592021
Marital status(1 Married, 0=others) 1.55 0.646032
Level of education
Primary 1.92 0.521608
JHS/Mid Sch 2.51 0.397912
SHS/Sec Sch 2.53 0.395986
Tertiary 2.03 0.491501
Regional location (1=Ashanti ,0=Accra) 1.67 0.59813
Meat processors (1= involved, 0=others) 1.73 0.579071
Grain processors(1= involved, 0=others) 3.68 0.271807
Fruit juice  processors(1= involved, 0=others) 1.7 0.589963
Beverage processors(1= involved, 0=others) 2.4 0.416912
Nut and pulse processors(1= involved, 0=others) 1.65 0.605919
Tuber processors(1= involved, 0=others) 2.24 0.445512
Dairy processors(1= involved, 0=others) 1.61 0.622346
Palm oil processors(1= involved, 0=others) 1.71 0.584747
Area (1=urban, 0=rural) 1.42 0.701982
Mean VIF 2.06
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APPENDIX 3b: VIF values of variables used in estimating factors affecting firm
growth
Variable VIF 1/VIF
Risk perception score 1.91 0.523403
Risk propensity score 1.67 0.599983
Age 2.89 0.144034
Sex(1=.male, 0=female) 1.53 0.654941
Level of education
Primary 1.87 0.535371
JHS/Mid Sch 2.57 0.388421
SHS/Sec Sch 2.12 0.470965
Tertiary 1.74 0.573898
Marital status(1 Married, 0=others) 1.49 0.671878
Age status(1=youth, 0=aged) 3.01 0.331825
Regional location (1=Ashanti ,0=Accra) 2.95 0.339237
Area (1=urban, 0=rural) 1.74 0.575492
Firm size 1.39 0.718166
Years of experience 4.79 0.208842
Meat processors (1= involved, 0=others) 1.98 0.504511
Grain processors(1= involved, 0=others) 2.66 0.375416
Fruit juice  processors(1= involved, 0=others) 1.61 0.619709
Beverage processors(1= involved, 0=others) 1.98 0.504211
Tuber processors(1= involved, 0=others) 1.95 0.512545
Dairy processors(1= involved, 0=others) 1.52 0.6582
Palm oil processors(1= involved, 0=others) 1.68 0.594769
Diversification of economic activities 3.32 0.301154
Borrowing 2.46 0.407244
Subscription to formal insurance 3.18 0.314724
Forward contracting 4.25 0.23505
Cooperative marketing 3.15 0.317343
Savings 1.76 0.567481
Sale of assets 5.22 0.191443
Temporary wage employment 3.22 0.310386
Mean VIF 2.61
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APPENDIX 4: Questionnaire On Risk Management, Investment Decisions And
Business Success Of Micro And Small Entrepreneurs In Ghana’s Agribusiness
Sector: The Case Of Agro-Processors
Introduction to the respondent:
Throughout this survey, you will be asked questions about your perceptions about risk sources in your business, and risk
management strategies, your investment decisions and your business success. The interview will take about an hour and the
answers will be completely confidential and strictly for academic purpose only.
SECTION 1
DEMOGRAPHIC CHARACTERISTICS AND ENTERPRISE PROFILE
Name of respondent/contact (optional)
Gender 1. Male 2. Female
Age ……………………….
Household size…………………
Regional location of enterprise (use code)
District location of enterprise (use code)
Are you the head of your household? 1. Yes 0. No
What is your marital status?
Single 2. Married  3. Widowed  4. Separated   5. Divorced
What is your highest level of education?
1. Did not complete Primary school  2. Completed Primary School  3. Did not Complete JHS or MS  4. Completed JHS or MS  5.
Did not Complete SHS (SEC/TECH)  6. Completed SHS  7. Did not Complete Tertiary Education (Polytechnic, Training college or
University) 8. Completed Tertiary Education (Polytechnic, Training college or University)  9. Non-formal education  10.  None
(skip to question 11)
Number of years in education
How much is your worth in terms of assets? (GH¢)
Are you the owner of this business? 1. Yes 2. No     (if yes, skip to Q14)
If No, who takes major decisions?
Have you changed business in the past year?  1. Yes 2. No
If yes, what was the nature of the business? ….…………………………………………………
Is the business registered? (Circle one)
Not registered: 1
Registered with district assembly: 2
Registered with Registrar General: 3
What is the main reason the business is not registered?
_________________________________________________________________________________________________________
_________________________________________________________________________________________________________
___________________________________________________________________________
Nature of business:
In which of the following is your agro-processing business involved? (Circle one per line):
Yes No
Meat processing (sausage): 1 0
Grain processing: 1 0
Fruit processing: 1 0
Beverage: 1 0
Nut and Pulses Processing: 1 0
Tuber (cassava) processing: 1 0
Diary processing: 1 0
Oil palm processing: 1 0
What are the 3 most important raw materials (in descending order of importance) to the business? (Use commodity code)
…………………………..………………………………
Which of the following activities do you undertake (Circle one per line)?
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Yes No
Drying: 1 0
Canning: 1 0
Frying: 1 0
Baking: 1 0
Roasting: 1 0
Cutting/Grating: 1 0
Cooking: 1 0
Bottling: 1 0
Grinding/Milling: 1 0
Bagging: 1 0
Brewing: 1 0
Distilling: 1 0
Packaging 1 0
Other (Specify):________________
In what type of area is the business located? (Circle one)
Urban town/city: 1
Rural town: 2
Rural village: 3
Other (Specify): ___________________ 4
What is the physical location of the business? (Circle one)
Inside house/homestead: 1
Outside house/homestead: 2
Traditional market: 3
Industrial site: 4
Commercial district: 5
Roadside, not in commercial district: 6
Mobile: 7
Other (Specify): ___________________
How many months in the year does the business normally operate? __________ months
On average, how many hours do you work in the business per week? What about five years ago?
Now: ___________
Five years ago/2011: ___________
Please provide number of permanent and casual workers  as of January for the respective years below
Year 2011 2012 2013 2014 2015 2016
No. of permanent workers
No. of casual workers
No. of women (permanent workers)
No. of women (casual workers)
Excluding yourself, on average how many hours does a typical permanent employee work in the business per week? _________
hours
How much is the highest pay for a permanent worker?
Amount GH¢: ___________ A. Monthly B. Day
If days, how many hours are normally worked per day? ___________
How much is the lowest pay for a permanent worker?
Amount GH¢: ___________ A. Monthly B. Days
If days, how many hours are normally worked per day? ___________
How much is the highest pay for a casual worker?
Amount GH¢: ___________ A. Monthly B. Day
If days, how many hours are normally worked per day? ___________
How much is the lowest pay for a casual worker?
Amount GH¢: ___________ A. Monthly B. Days
If days, how many hours are normally worked per day? ___________
What proportion of your household’s income is provided by the business
Now: ___________ %
Five years ago: ___________%
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Business establishment:
I would now like to ask you some questions about how and why the business was started.
For how long has this business been in operation?
Months: __________
Years: __________
Did you operate your own business before this one? (Circle one) Yes: 1  No: 0
At what age did you operate your own business?   ________ Years
Did you start the business yourself? (Circle one)    Yes: 1 No: 0
If no to Q 37, Who started the business? (Circle one)
Father: 1
Mother: 2
Other male relative: 3
Other female relative: 4
Trainer/master: 5
Other non-relative: 6
Other (Specify): _________
How much of your own financial resources have you invested in your business ? GH¢_________
Of the businesses in this area that started up or that were acquired around the same time as your own business, approximately what
proportion is still operating today? ____________%
Considering the number of buyers of similar products like yours, what proportion do you think are your customers? ............%
How much does your business owe currently? GH¢........................
SECTION 2
'Risk elicitation, Perception of sources of risk and risk management strategies
Imagine that you have wealth valued at GH¢ 1 million. A reputable bank gives you some options concerning investment portfolios.
The portfolios come with 2 options from which you choose one. The first option is such that there is a certainty to receive an
amount on your investment while there is a 50% probability to DOUBLE your investment on the second option. However, there is
an equal probability that you can lose the amount you invested in the second option. What percentage of your wealth would you be
willing to invest in each option? Choose from the following scenarios as depicted in the table
Risk attitude elicitation (Using the MPL method)
Amount to invest Scenario Preferred
option (tick
only  one
option)
1 0
GH¢ 900,000 or 90% of your 0. Receive GH¢ 900,000 in a risky investment But there is a possibility
wealth of total loss of your investment
OR
GH¢ 100,000 or 10%  of your 1. Receive a sure amount of GH¢ 1000 on a less risky investment
wealth
GH¢ 800,000 or 80% of your 0. Receive GH¢ 800,000 in a risky investment But there is a possibility
wealth of total loss of your investment   OR
GH¢ 200,000 or 20%  of your 1. Receive a sure amount of GH¢ 2000 on a less risky investment
wealth
GH¢ 700,000 or 70% of your 0. Receive GH¢ 700,000 in a risky investment But there is a possibility
wealth of total loss of your investment
GH¢ 300,000 or 30%  of your 1. Receive a sure amount of GH¢ 3000 on a less risky investment
wealth OR
GH¢ 600,000 or 60% of your 0. Receive GH¢ 600,000 in a risky investment But there is a possibility
wealth of total loss of your investment      OR
GH¢ 400,000 or 40%  of your 1. Receive a sure amount of GH¢ 4000 on a less risky investment
wealth
GH¢ 500,000 or 50% of your 0. Receive GH¢ 500,000 in a risky investment But there is a possibility
wealth of total loss of your investment      OR
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GH¢ 500,000 or 50%  of your 1. Receive a sure amount of GH¢ 5000 on a less risky investment
wealth
Which of the following choices would you prefer:
0. Investment of GH¢ 1 million with a probability of doubling it to GH¢ 2 million or totally losing your
investment OR
1. Lose GH¢ 15000 with certainty on an investment of GH¢ 1 million.
0. Investment of GH¢ 1 million with a probability of doubling it to GH¢ 2 million or totally losing your
investment OR
1. Lose GH¢ 20,000 with certainty on an investment of GH¢ 1 million.
0. Receive GH¢ 17 000 on an investment in 3 months
OR
1. Receive GH¢ 15000 on the same investment in 1 month
0. Receive GH¢ 20 000 on an investment in 3 months
OR
1. Receive GH¢ 15000 on the same investment in 1 month
Receive GH¢ 17 000 on an investment in 1 year 3 months  OR
Receive GH¢ 15000 on the same investment in 1 year
Receive GH¢ 20 000 on an investment in 1 year 3 months  OR
Receive GH¢ 15000 on the same investment in 1 year
Please indicate your likelihood to take any of the under listed actions
Risk Perception
Not at all Slightly Risky Very risky Extremely
risky(1) risky (2) (3) (4) risky (5)
Betting a day's income of  GH¢ 500 at a high-stake
card game, such as poker
Investing 10% of your annual income (which is
equal to GH¢ 20,000) in a new business venture
Betting a day's income of GH¢ 500 on the outcome
of a sporting event, such as soccer
Investing 10% of your annual income (which is
equal to GH¢ 20,000) in stocks
Investing 10% of your annual income (which is
equal to GH¢ 20,000) in a ponzi financial scheme
(like DKM) that promises high returns on savings
Investing 10% of your annual income (which is
equal to GH¢ 20,000) in a new processing
technology
Risk Propensity
Extremely moderately Not Moderately Extremely
likely (1) likely (2) sure (3) unlikely (4) unlikely (5)
Betting a day's income of  GH¢ 500 at a high-stake
card game, such as poker
Investing 10% of your annual income (which is
equal to GH¢ 20,000) in a new business venture
Betting a day's income of GH¢ 500 on the outcome
of a sporting event, such as soccer
Investing 10% of your annual income (which is
equal to GH¢ 20,000) in stocks
Investing 10% of your annual income (which is
equal to GH¢ 20,000) in a ponzi financial scheme
(like DKM) that promises high returns on savings
Investing 10% of your annual income (which is
equal to GH¢ 20,000) in a new processing
technology
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Please indicate your perception concerning the levels of importance of these sources of risk in your business
Sources of risk
Risk sources Very Important Neither Unimportant Very
important important nor unimporta
unimportant nt
Depreciation of local currency
Change in trade policy
Government interference in business environment
War and civil commotion/disturbances
Labour shortage
High cost of labour
Input price volatility
Poor market information on price
High interest rates
Transportation for input and output movement
Death or sickness of entrepreneur or employee
Output price fluctuations
Changes in taxation policy
Poor access to credit
Technology for Marketing
Please rank the following risk-mitigating strategies as pertains to your business (1- 8 with 1 as most important)
Risk management strategy Rank
Diversification to other enterprises
Insurance
Forward contracting
Cooperation marketing
Borrowing
Savings
Sale of Assets
Temporary wage employment outside your business
Please indicate how you utilise the risk strategies ranked above to mitigate risk you face in your business
Diversification to other enterprises......................................................................................................................................................
Insurance .................................................................................................................................................................................................
Forward contracting ..............................................................................................................................................................................
Cooperative marketing ..........................................................................................................................................................................
Borrowing ...............................................................................................................................................................................................
Savings .....................................................................................................................................................................................................
Sale of assets.............................................................................................................................................................................................
Temporary wage employment outside your business..........................................................................................................................
Perception on Risk Management Strategies
Please indicate the level of importance of each risk management strategy as pertains to your business.  For each strategy, tick in the
last column if "Not Applicable" (NA) or ''Not in Place" (NP). Then tick one across of each line according to your perception.
Very Important Neither Unimportant Very
Important important Unimportant
nor
Unimportant
Financial management
Loan allocation in productive activity
Put cash in cooperative or bank in saving account
Minimize debt (efficient loan repayment)
Borrowing from formal financial institutions
Borrowing from informal source (relative, friends,
money lender)
Reduce consumption expenditure
Insurance
Forward contracting
Investment  in other enterprises eg, farming
Sale/transfer asset
Savings
Selling productive assets (like processing
equipment)
Selling personal asset (like gold, jewellery)
Lease in or lease out own land
Join cooperative marketing
Use of telephone or mobile phone to access
market information
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Use of telephone or mobile phone to disseminate
market information
Use of broker .
Seeking temporary employment outside busienss
Invest in efficient production technology
Do you currently have paid employment elsewhere? (Circle one) Yes: 1 No: 0
If yes, where are you employed? (Circle one)
Formal sector company: 1
Informal sector enterprise: 2
Public sector: 3
NGO: 4
Other (Specify): _______________
For how many years have you been engaged in this employment? _____________ years
Marketing:
I would now like to ask you about your marketing activities (input and output).
At what stage do you buy your most important input/raw material? (Circle one) What about five years ago? (Circle one)
Now Five Years Ago
Make or grow own inputs: 1 1
Buy unprocessed inputs: 2 2
Buy semi-processed inputs: 3 3
Buy finished products for sale: 4 4
Other (Specify): __________ 5 5
Approximately, what proportion of your main input do you obtain from others? ___________%
Overall, how easy is it for you to gain access to a reliable supply of your most important input/raw material? (Circle one)
Very Difficult Neither Easy Very
Difficult Difficult nor Easy
Easy
5 4 3 2 1
Do you obtain credit from your input/raw material suppliers? (Circle one)Yes:1 No: 0
Approximately, for what proportion of your inputs/raw materials do you get credit from suppliers? _________%
What is the most important market for your business? (Circle one)  What about five years ago? (Circle one)
Now Five Years Ago
Consumers: 1 1
Urban retail businesses: 2 2
Urban wholesale businesses: 3 3
Urban manufacturing businesses: 4 4
Rural retail businesses: 5 5
Rural wholesale businesses: 6 6
Rural manufacturing businesses: 7 7
Export: 8 8
Other (Specify): __________ __________
How far is it to the main market for your products? (If main market is in immediate vicinity mark zero) __________ km
Overall, how easy is it for you to gain access to markets for your products? (Circle one)
Very Difficult Neither Easy Very
Difficult Difficult nor Easy
Easy
5 4 3 2 1
Do you give credit to your customers for goods or services supplied? (Circle one)
Yes: 1 No: 0
Approximately, for what proportion of your sales do you give credit to customers? ______%
Over the last year, by what percentage did your business grow? ________%
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Who would you consider to be your most important business competitor today? (Circle one)  What about five years ago? (Circle
one)
Now Five Years ago
No competitors: 1 1
Informal businesses nearby: 2 2
Formal businesses nearby: 3 3
Informal businesses elsewhere: 4 4
Formal businesses elsewhere: 5 5
Public enterprises: 6 6
Other (Specify): __________ __________
How has each of the following changed over the last five years? (Circle one per line)
Large Small No Small Large
Increase Increase Change Decrease Decrease
Competition faced by your business: 5 4 3 2 1
Number of businesses like yours locally: 5 4 3 2 1
Overall demand for products like yours: 5 4 3 2 1
Value of your own businesses sales: 5 4 3 2 1
Volume of your own businesses sales: 5 4 3 2 1
Profitability of your business: 5 4 3 2 1
Market share of your business: 5 4 3 2 1
Number of markets you supply: 5 4 3 2 1
Quality of your products/services: 5 4 3 2 1
Are you a member of a trade, producer and/or business association? (Circle one)
Yes: 1 No: 0
Do you have linkages or arrangements with any other businesses? (Circle one)
Yes: 1 No: 0
What linkages do you have with other businesses? (Circle one per line)
Yes No
Joint production of inputs: 1 0
Joint procurement of inputs: 1 0
Joint marketing of products: 1 0
Savings: 1 0
Sharing equipment/tools: 1 0
Sharing transportation: 1 0
Sharing storage: 1 0
Obtaining credit: 1 0
Contract supply: 1 0
Other (Specify): _____________
What form do these linkages take? (Circle one per line)
Yes No
Informal agreement: 1 0
Cooperative: 1 0
Association: 1 0
Contract: 1 0
Partnership: 1 0
Other (Specify): _____________
What types of business do you have linkages with? (Circle one per line)
Yes No
Informal micro and small enterprises: 1 0
Formal micro and small companies: 1 0
Large companies: 1 0
Public corporations: 1 0
Other (Specify): _____________
Sales and costs:
I would now like to ask you some questions about your sales and costs
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In a typical year, indicate whether your sales are high, average, low or none in each of the following months? (Circle one per line)
High Average Low None
January: 4 3 2 1
February: 4 3 2 1
March: 4 3 2 1
April: 4 3 2 1
May: 4 3 2 1
June: 4 3 2 1
July: 4 3 2 1
August: 4 3 2 1
September: 4 3 2 1
October: 4 3 2 1
November: 4 3 2 1
December: 4 3 2 1
In a ‘high’ sales month what was the volume and value of sales in your business in the following years?
Year Value of sales (GH¢) Volume sold(Kg)
2013
2014
2015
In an ‘average’ sales month what is the volume and value of sales in your business in the following years?
Year Value of sales (GH¢) Volume sold(Kg)
2013
2014
2015
In a ‘low’ sales month what is the volume and value of sales in your business in the following years?
Year Value of sales (GH¢) Volume sold(Kg)
2013
2014
2015
In a month with a typical month how much money did you spend on the following inputs for the following years?
Input Amount spent (GH¢) Amount spent (GH¢) Amount spent (GH¢)
2013 2014 2015
Labour
Raw materials
Items for resale
Transport,
Electricity
Water
Fuel
Rent
Maintenance
Taxes
Registration fee
Insurance
How would you judge each of the following compared to your competitors? (Circle one)
Much Better About the Worse Much
Better Same Worse
Quality of products 5 4 3 2 1
Costs of production: 5 4 3 2 1
Quality of labour: 5 4 3 2 1
Access to inputs: 5 4 3 2 1
Access to markets: 5 4 3 2 1
Prices received for products: 5 4 3 2 1
Reputation among your customers: 5 4 3 2 1
Did you make any of the following changes to your businesses over the last five years? (Circle one per line)
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Yes No
Expanded size of business facility: 1 0
Reduced size of business facility: 1 0
Purchased business facility: 1 0
Relocated business: 1 0
Upgraded tools/equipment: 1 0
Purchased new transportation: 1 0
Added new products/significantly changed existing products: 1 0
Produced fewer products: 1 0
Hired more workers: 1 0
Reduced number of workers: 1 0
Improved quality of products: 1 0
Reduced costs with cheaper source of inputs: 1 0
Reduced costs by bulk-buying inputs: 1 0
Reduced costs by using cheaper source of credit: 1 0
Reduced costs by improving processing efficiency: 1 0
Entered new markets: 1 0
Left markets: 1 0
Researched consumer demand: 1 0
Promoted products more and/or better: 1 0
If you encounter a problem with your business, how do you generally solve it? (Circle one per line)
Yes No
Trial and error/muddle through: 1 0
Problem gets unsolved: 1 0
Consult family/friends: 1 0
Consult other business operators: 1 0
Get training: 1 0
Consult BAC: 1 0
Consult extension officer: 1 0
Consult NGO: 1 0
Other (Specify): _______________ 1 0
Did you complete any program of training or training course before starting the business? (Circle one) Yes: 1 No:
0
Were you offered or became aware of any training opportunities, but did not take up these opportunities? (Circle one) Yes:
1 No: 0
Why did you not take up the offer? (Circle one per line)
Yes No
Time needed: 1 0
Cost prohibitive: 1 0
Did not meet needs: 1 0
Bad previous experiences: 1 0
Doubted value: 1 0
Other (Specify): _______________
Did you have to pay for this training? Yes: 1 No: 0
Did you attend any training course related to your line of business last year?   Yes: 1  No:  0
If yes who provided the training? .............................................
What did the training entail (choose as many that apply): 1. Financial management 2. Investment 3. Processing technology 4. HR
management  5.  business management
SECTION 3: Investment Decisions
Note to Academic advisor: we follow (Nwibo, and Alimba 2013) – psychological factors (including riskiness attitude), family
history in entrepreneurship, can be used explicitly to describe investment behaviour.
Please answer the following items by choosing one alternative you feel most comfortable with.
0) Receiving GH¢ 22,000 for sure.
1) An 80% chance of getting GH¢ 30,000.
0) Receiving GH¢ 50,000 for sure.
1) A 20% chance of getting GH¢ 150,000.
0) Receiving GH¢ 180,000 for sure.
1) A 90% chance of winning GH¢ 200,000
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0) Receiving GH¢ 6,000 for sure.
1) A 10% chance of getting GH¢ 60,000
0) Receiving GH¢ 2,500 for sure.
1) A 50% chance of getting GH¢ 5,000.
Does/did your father engage in any entrepreneurial activity? 1. Yes 2. No
If yes, what was the nature of the business? ………………………………………………….
Does/did your mother engage in any entrepreneurial activity? 1. Yes 2. No
If yes, what was the nature of the business?.............................................................................
How many different investments products (e.g. shares, funds, bonds, certificates) did you hold within the last
year?_________________________
What is the value of your current investment (from Q88)  (GH¢) ................
Source of investment capital (formal = 0, informal = 1)
Start-off capital (GH¢)_____________________
Nature of enterprise ownership (family = 0, sole (personal) = 1, partnership = 2, joint stock = 3, cooperative society = 4)
What proportion of the business do you own?   ____________%
What is your share of the market where your business operates? …………….
Rate the following attributes as regards investments relative to your abilities
Very High Moderate Low Very Don’t
high low know
Your Knowledge about stock markets and financial markets
Ability to identify stocks that will beat the market in the future
your stock forecasts are always correct
Losses and gains in stock markets are just a matter of chance
you can accurately forecast the total demand for your business
You can accurately forecast when larger competitors will enter
the market
You can make your business a success, even though others may
fail
Investment decisions based on savings behaviour
Do you save for business purposes? Yes: 1 No: 0
If yes, where do you save? 1. Commercial bank 2. Micro finance institution 3. Micro finance 4. Informal arrangements like susu 5.
Self savings within household 6. Save with household member
What is the distance to the institution where you save (km)........
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SECTION 4: Business success
Application of attribution (using personality trait) to success of entrepreneurs
To what extent do you agree or disagree with each of the following? (Circle one per line) – locus of control
Strongly Agree Somewhat Disagree Strongly
agree agree disagree
5 4 3 2 1
My success depends on whether I am lucky enough to be in
the right place at the right time
To a great extent my life is controlled by accidental
happenings
It is not always wise for me to plan too far ahead because
things turn out to be a matter of good or bad fortune
When I get what I want it is usually because I am lucky
I am usually able to protect my personal interests
My life is determined by my own actions
When I get what I want it is usually because I worked hard for
it
Getting what I want means I have to please people above me
Whether or not I am successful depends mostly on my
abilities
I feel that what happens in my life is mostly determined by
people in powerful positions
My life is chiefly controlled by powerful others
In order to have my plans work I must make sure they fit with
the desires of those who have power over me
I can pretty much determine what will happen in my life
I feel in total control of my life
Whatever my abilities, I will not be successful if my actions
conflict with the powerful around me
How sure are you about your ability to do each of the following? (reverse coded) – self-efficacy
Very sure Slightly unsure Completely
Sure Unsure/sure Unsure
5 4 3 2 1
Set and meet sales objectives
Set and meet profit targets
Analyze market for products
Understand consumer demand
Develop new products
Enhance product quality
Enter new markets locally
Expand share of current markets
Develop new ideas for business
Compete successfully against competition
Introduce new production methods
Introduce new ways of marketing
Enter new markets in other locations
Develop a business plan
Do financial analysis of business
Take calculated risks
Make decisions under uncertainty
Take responsibility for business decisions
Cope with unexpected events
Work under pressure
Manage time effectively
Set business goals
To what extent do you agree or disagree with each of the following? (Circle one per line) -Environment
Growth Goal Orientation Strongly Agree Somewhat Disagree Strongly
agree agree disagree
5 4 3 2 1
If I earn enough money for my family that is good
enough
I want my business to grow as much as possible
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Risk – Taking behaviour
I prefer to remain in a business that has problems that I
know about rather than take the risk of starting a new
one that has unknown problems even if the new one
offers greater rewards.
I view risk in business as a situation to be avoided at all
cost
Macro environment
Very risky – businesses collapse easily
Rich in investment and marketing opportunities
Hostile (there is a lot of pressure from competitors)
High taxes limit profit levels
High taxes limit opportunities for expansion
Political
Very safe, little threat to the survival of my firm
Little political interference in the business environment
Thinking back, how important was each of the following to you as personal objectives or reasons to start/acquire the business?
(Circle one per line)
Very Important Neither Unimportant Very
important important unimportant
nor
unimportant
To increase level and security of household 5 4 3 2 1
income:
To improve status in community: 5 4 3 2 1
To get greater freedom over your life: 5 4 3 2 1
Limited other ways to earn income: 5 4 3 2 1
To achieve a flow of money quickly: 5 4 3 2 1
Had little or no choice: 5 4 3 2 1
Wanted to be self-employed: 5 4 3 2 1
To make full use of your skills: 5 4 3 2 1
Had lost my previous job: 5 4 3 2 1
Costs of entering the business were low: 5 4 3 2 1
To make full use of the assets you had: 5 4 3 2 1
Risks were small: 5 4 3 2 1
Limited income from other opportunities: 5 4 3 2 1
To be an entrepreneur: 5 4 3 2 1
To continue a family tradition/business: 5 4 3 2 1
Better fit with other business activities: 5 4 3 2 1
Greater satisfaction from running own business: 5 4 3 2 1
Overall, how easy is it for a new business such as your own to start? (Circle one)
Very easy: 1
Easy: 2
Neither easy nor difficult: 3
Difficult: 4
Very difficult: 5
Overall, how easy is it for a business such as your own to move into a new line of business? (Circle one)
Very easy: 1
Easy: 2
Neither easy nor difficult: 3
Difficult: 4
Very difficult: 5
To what extent are you satisfied with your business’ success with respect to each of these objectives? (Circle one per line)
Very Important Neither Unimportant Very
important important unimportant
nor
unimportant
To increase level and security of household 5 4 3 2 1
income:
To improve status in community: 5 4 3 2 1
To get greater freedom over your life: 5 4 3 2 1
Limited other ways to earn income: 5 4 3 2 1
To achieve a flow of money quickly: 5 4 3 2 1
Had little or no choice: 5 4 3 2 1
Wanted to be self-employed: 5 4 3 2 1
To make full use of your skills: 5 4 3 2 1
Had lost my previous job: 5 4 3 2 1
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Costs of entering the business were low: 5 4 3 2 1
To make full use of the assets you had: 5 4 3 2 1
Risks were small: 5 4 3 2 1
Limited income from other opportunities: 5 4 3 2 1
To be an entrepreneur: 5 4 3 2 1
To continue a family tradition/business: 5 4 3 2 1
Better fit with other business activities: 5 4 3 2 1
Greater satisfaction from running own business: 5 4 3 2 1
Please indicate your level of agreement with the following statements
Strongly Somewh Strongly
Agree Disagree
agree at agree disagree
5 4 3 2 1
Relative to other businesses in my field, I am successful
I am more profitable than other business in my field
I am more successful than my competitors.
Sympathize with others’ feelings
Get chores done right away
Subjective profitability: please rate the following whether they declined = 1, did not change = 2 or increased = 3
indicator 1 2 3
Sales (2010 to 2012)
Sales (2013 to 2015)
Profits  (2010 to 2012)
Profits (2013 to 2015)
On the whole, is your business currently profitable? Yes: 1 No: 0
How much of a problem or constraint is each of the following to you in becoming successful in your business? (Circle one per line)
Very Major Major Moderate Minor Not a Problem
Problem Problem Problem Problem at all
Access to credit: 5 4 3 2 1
Cost of credit: 5 4 3 2 1
Financial resources within business: 5 4 3 2 1
Managerial skills: 5 4 3 2 1
Technical skills: 5 4 3 2 1
Quality of transportation facilities: 5 4 3 2 1
Reputation among customers: 5 4 3 2 1
Quality of storage facilities: 5 4 3 2 1
Access to appropriate business location: 5 4 3 2 1
Access to required labour: 5 4 3 2 1
Access to electricity: 5 4 3 2 1
Access to water: 5 4 3 2 1
Crime/corruption: 5 4 3 2 1
Access to markets: 5 4 3 2 1
Access to training courses: 5 4 3 2 1
Access to business support services: 5 4 3 2 1
Access to ICT 5 4 3 2 1
Access to raw materials: 5 4 3 2 1
Access to packaging materials: 5 4 3 2 1
Overall economic conditions: 5 4 3 2 1
Quality of roads: 5 4 3 2 1
Access to equipment, tools, etc: 5 4 3 2 1
Costs of production: 5 4 3 2 1
Competition from other businesses: 5 4 3 2 1
Financial obligations in community: 5 4 3 2 1
Financial obligations to family: 5 4 3 2 1
Political interference 5 4 3 2 1
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APPENDIX 5: Correlation matrix for measuring sampling adequacy for risk sources
Governme
Poor nt Civil War
Losses Transportatio interferenc Input Poor High Output commotion/
associated n for input e in Labour High price market intere Poor price Technolog disturbances as
with and output Chang business shortag cost of volatilit informatio st Changes in access fluctuatio y for risk source Death or
depreciatio movement e in environme e as labour y as n on price rates taxation to credit ns Marketing sickness of
n of local trade nt as risk risk as risk risk as risk as risk policy as as risk entrepreneur
Risk sources currency policy source source source source source source risk source source or employee
Losses associated with
depreciation of local
currency 0.636 a -0.501 0.046 -0.240 0.036 -0.050 -0.215 0.079 -0.080 -0.059 -0.020 0.137 -0.161 0.105 -0.079
Transportation for input
and output movement -0.501 0.545 a -0.089 -0.162 -0.172 0.089 0.054 0.065 -0.152 -0.006 0.054 0.057 -0.020 0.242 0.135
Change in trade policy 0.046 -0.089 0.805 a -0.278 0.100 -0.096 -0.130 -0.008 0.165 -0.096 0.101 0.102 -0.157 -0.401 -0.254
Government
interference in business
environment as risk
source -0.240 -0.162 -0.278 0.785 a 0.097 0.088 0.110 -0.141 0.058 0.185 -0.100 -0.364 -0.044 -0.300 -0.134
Labour shortage as risk
source 0.036 -0.172 0.100 0.097 0.568 a -0.716 0.000 -0.162 0.069 0.000 0.148 -0.114 0.097 0.061 -0.276
High cost of labour as
risk source -0.050 0.089 -0.096 0.088 -0.716 0.653 a -0.056 0.032 -0.128 -0.059 -0.187 0.074 -0.039 -0.192 0.153
Input price volatility as
risk source -0.215 0.054 -0.130 0.110 0.000 -0.056 0.716 a -0.274 0.034 0.038 -0.021 -0.246 0.309 -0.067 -0.037
Poor market
information on price as
risk source 0.079 0.065 -0.008 -0.141 -0.162 0.032 -0.274 0.841 a -0.150 -0.049 0.071 -0.049 -0.182 0.087 -0.201
High interest rates as
risk source -0.080 -0.152 0.165 0.058 0.069 -0.128 0.034 -0.150 0.777 -0.253 -0.296 0.048 -0.216 -0.066 -0.098
Changes in taxation
policy as risk source -0.059 -0.006 -0.096 0.185 0.000 -0.059 0.038 -0.049 -0.253 0.803 -0.074 -0.003 -0.099 -0.333 0.090
Poor access to credit as
risk source -0.020 0.054 0.101 -0.100 0.148 -0.187 -0.021 0.071 -0.296 -0.074 0.768 -0.263 0.002 0.109 -0.265
Output price
fluctuations 0.137 0.057 0.102 -0.364 -0.114 0.074 -0.246 -0.049 0.048 -0.003 -0.263 0.805 -0.019 -0.114 -0.003
Technology for
Marketing -0.161 -0.020 -0.157 -0.044 0.097 -0.039 0.309 -0.182 -0.216 -0.099 0.002 -0.019 0.787 -0.034 0.001
War and civil
commotion/disturbances
as risk source 0.105 0.242 -0.401 -0.300 0.061 -0.192 -0.067 0.087 -0.066 -0.333 0.109 -0.114 -0.034 0.787 0.008
Death or sickness of
entrepreneur or -0.079 0.135 -0.254 -0.134 -0.276 0.153 -0.037 -0.201 -0.098 0.090 -0.265 -0.003 0.001 0.008 0.834
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Governme
Poor nt Civil War
Losses Transportatio interferenc Input Poor High Output commotion/
associated n for input e in Labour High price market intere Poor price Technolog disturbances as
with and output Chang business shortag cost of volatilit informatio st Changes in access fluctuatio y for risk source Death or
depreciatio movement e in environme e as labour y as n on price rates taxation to credit ns Marketing sickness of
n of local trade nt as risk risk as risk risk as risk as risk policy as as risk entrepreneur
Risk sources currency policy source source source source source source risk source source or employee
employee
Measures of Sampling Adequacy(MSA)
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