Graduate School of Management University of Zimbabwe...

An Evaluation of the Factors Affecting Growth of Small and

Medium Enterprises (SMEs) in Zimbabwe: A Case Study of

SMEs in Harare (2009 – 2015)

Oswald Musavengana Chiwara (R943770Q)

A dissertation research submitted in partial fulfillment of the

requirements for the degree of Master of Business

Administration

Graduate School of Management

University of Zimbabwe

2015

Supervisor: Dr P.G. Kadenge

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Dedications

This study is dedicated to my late father, Mr Onesimo Meyer Chiwara who was my source of

inspiration and encouragement. May His Soul Rest in Eternal Peace.

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Declarations

I, Oswald Musavengana Chiwara, do hereby declare that this dissertation is the result of my own

investigation and research, except to the extend indicated in the Acknowledgements, References

and my comments included in the body of the report, and that it has not been submitted in part or

in full for any other degree to any other University.

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Student’s Signature Date

This dissertation has been submitted for examination with my approval as the University

Supervisor.

____________________________________ ______________________

Supervisor’s Signature (Dr P.G Kadenge) Date

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Acknowledgements

I am exceedingly grateful to the All Mighty for taking me this far. It has surely been a long and

tiresome journey. I would also like to thank my wife, Melody and our three children Takudzwa,

Daviroyashe and Mukudzeyi for their unwavering love and support during the three years of my

MBA studies. You have been patient and the good Lord will surely bless you.

I would also like to thank my friends and relatives who have also been very supportive and

inspired me to achieve greater things.

Finally I would like to thank Dr P.G Kadenge, my supervisor for his guidance in this research

project. Your efforts are greatly appreciated.

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ABSTRACT

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Abbreviations

“SME” Small and Medium Enterprises

“WB” World Bank

“ADB” Africa Development Bank

“RBZ” Reserve Bank of Zimbabwe

“EU” European Union

“GoZ” Government of Zimbabwe

“GDP” Gross Domestic Product

“SPSS” Statistical Package for Social Science

“MoSMED” Ministry of Small and Medium Enterprise Development

“GoZ” Government of Zimbabwe

“SEDCO” Small Enterprise Development Cooperation

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Table of Contents

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2.3.1� Business Growth: Definitions and Measures��;�

2.3.2� Theories of Firm Growth��=�

2.3.3� Firm Growth: Empirical Evidence��44�

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2.4.1� Definitions��44�

2.4.2� Characteristics of SMEs��4��

2.4.3� Role of SMEs in Sustainable Development��4;�

2.4.4� Contribution of SMEs to the Zimbabwean Economy��'4�

2.4.5� Barriers to SME Growth��'8�

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List of Tables

Table Page

Table 1: Annual GDP Growth Rates .......................................................................................... 3

Table 2: SME Definitions Used By Multilateral Institutions ................................................... 13

Table 3: Definitions of SMEs South African National Small Business Act ............................ 13

Table 4: Zimbabwean Government definition of SME by number of employees .................... 14

Table 5: Study Sample .............................................................................................................. 42

Table 6: Reliability Tests of the Questionnaire ........................................................................ 46

Table 7: Position of Respondents ............................................................................................. 48

Table 8: Gender of Respondents ............................................................................................... 48

Table 9: Age Group of Respondents......................................................................................... 49

Table 10: Level of Education ...................................................................................................... 49

Table 11: Category of Business .................................................................................................. 50

Table 12: Number of Employees ................................................................................................ 51

Table 13: Legal Status of SME ................................................................................................... 51

Table 14: Business Location ....................................................................................................... 52

Table 15: KMO and Bartlett’s Test ............................................................................................ 53

Table 16: Factor Analysis of SME Growth FactorEEEEEEEEEEEEEEEEEEEEEEEEEEE��:8

Table 17: Growth Factor Loading............................................................................................... 55

Table 18: Factor Analysis of Growth Indicators ........................................................................ 56

Table 19: Loading of Growth Factors ......................................................................................... 56

Table 20: Shapiro-Wilk Normality Test ..................................................................................... 57

Table 21: Correlation Matrix ...................................................................................................... 59

Table 22: Summary of the Regression Model ............................................................................ 61

Table 23: Prediction Model for Factor Components .................................................................. 62

Table 24: Association between Size of Firm and Growth .......................................................... 63

Table 25: Association between Level of Education and Growth ............................................... 65

Table 26: Gender and Growth .................................................................................................... 66

Table 27: Management and Growth ........................................................................................... 67

Table 28: Legal Status and Growth ............................................................................................ 67

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Table 29: Age of SME and Growth ............................................................................................ 68

Table 30: Category of Business and Growth .............................................................................. 69

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Figure Page

Figure 1: GDP Contribution per Sector ..................................................................................... 19

Figure 2: Employment Contribution of SME............................................................................. 20

Figure 3: Access to Finance as a major constraint to SME Operations ..................................... 28

Figure 4: Barriers to Adoption of Technology ........................................................................... 33

Figure 5: Q-Q Normality Test for Growth Factors .................................................................... 58

Figure 6: Q-Q Normality Tests for Growth Indicators .............................................................. 58

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CHAPTER ONE

INTRODUCTION

1.1 Introduction

This research study is an investigation of factors affecting growth and development of Small and

Medium Enterprises (SMEs) operating in Harare. The role of SMEs in economic development is

being increasingly recognized by economic planners and development practitioners the world

over. SMEs play an important role in the economic growth and development of most low income

countries. Phillip (2005) points out that the vibrancy of the SME sector is critical for the

development of any economy. SMEs are an effective instrument for the alleviation of poverty

through the creation of jobs for both the entrepreneurs and their workers. They contribute to long

term industrial growth by producing future large business enterprises that spring out of the SME

sector. Abor and Quartey (2010) describe SMEs as efficient and prolific employment creators,

the seeds for future larger business corporations and engines for economic prosperity. SMEs also

promote free market enterprise, advance entrepreneurial skills and contribute significantly to

exports and trade. In Zimbabwe, the SME sector provides employment to a large proportion of

the working population. The FinScope SME Survey conducted in 2012 revealed that the SME

sector has 2,8 million entrepreneurs of various sizes and employs 2,9 million people (Reserve

Bank of Zimbabwe, 2014). The sector also contributes 60% of Gross Domestic Product (GDP).

Research on SMEs in both developed and developing countries has gained momentum over the

years on the backdrop of failure by economic policies to stimulate private sector driven growth,

especially in low income countries. There is now broad consensus that a vibrant SME sector is

one of the principal components of a thriving market economy and should not be allowed to fail.

Worku (2013) argues that the failure of SMEs represents the failure of the national economy.

The development of SMEs also promotes the decentralization of economic activity to previously

disadvantaged regions and accelerates the attainment of wider national economic and social-

economic wishes (Cook and Nixson, 2000). Rostow (1960) cites the promotion of small

companies as one of the key drivers of the industrialization process in Europe. According to

Mullineux (1997), the SME sector remains the biggest employer in the developed countries,

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accounting for more workers than those employed by multinational companies. In Asia, the

enhancement of SMEs in terms of size and productivity in countries such as China and Japan

was critical in increasing export capacity that resulted in economic growth (Singh, 1999).

In Zimbabwe, the economic meltdown between 2000 and 2008 and the resultant increase in the

level of unemployment has reinforced the importance of SME sector as an engine for economic

recovery and expansion. Given the importance of SMEs in the recovery process, it is imperative

for policy makers and the public to be aware of the factors affecting the growth of SMEs in the

current economic environment characterized by high unemployment, depressed demand for

products and services, low production output and liquidity challenges. It is hoped that this study

will inform policy makers, development practitioners and more importantly, players in the SME

sector about the factors inhibiting their growth prospects.

The objective of this study is to determine the factors affecting the growth of SMEs operating in

Zimbabwe.

1.2 Background to the Study

The Zimbabwean economy developed a strong industrial backbone during the pre-independence

period. After independence in 1980, GDP grew at an average rate of 5.5 percent between 1980

and 1990, a rate that surpassed the average for Sub-Saharan Africa (SSA) countries during this

period (Africa Development Bank, 2009). In the 1990s, economic growth slowed down and the

economy weakened on account of low investment, adverse macroeconomic environment and

cutback in industrial production. From 2000 to 2008, the Zimbabwean economy experienced its

highest decline in economic activity resulting in a cumulative decline in real GDP growth by

about 50% on the backdrop of high inflation levels, capital flight and foreign currency shortages.

The formal sector shrunk dramatically due to company closures with many workers losing their

jobs through retrenchments. The massive loss of jobs gave way to the growth and dominance of

the informal sector in Zimbabwe. The sector became the safety net where the majority of

Zimbabweans found their means of survival (Gangata, 2013).

Globally, during the 1990s, business development focus shifted from the formation of new

businesses to growing the already existing small enterprises. The Government of Zimbabwe

(GoZ) joined this bandwagon and by the end of the 1990s, the Department of the Informal Sector

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was established in the President’s Office in recognition of the emergence of the informal sector.

In 2002 the Department of Informal Sector was upgraded to the Ministry of Small and Medium

Enterprises Development (MoSMED), with the mandate to oversee the development of the SME

sector (MoSMED, 2002). Since then, the government has prioritized the SME sector growth as a

vehicle for addressing the national problems of employment, economic growth and equitable

distribution of wealth among Zimbabweans (Goriwondo, 2009).

After experiencing one of the historic levels of inflation, in 2009, the country adopted the

multicurrency regime which immediately stabilized the economy. The Zimbabwean economy

bounced back to positive GDP growth during this period of economic recovery as shown in the

table below:

Table 1: Annual GDP Growth Rates

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The contribution of SMEs during this period of economic recovery was significant. According to

the FinScope 2012 SME Survey, 60% of the country’s GDP came from the SME sector (RBZ,

2014).

Despite the reported growth in GDP, the post dollarization period has some “side effects” on the

health of the economy. The SME sector has not experienced the anticipated growth as they are

constrained by a number of factors, chief among them access to credit. After dollarization,

capital flows were expected to come into the economy and recapitalize all sectors. Unfortunately

this has not been the case. The economy has remained relatively sluggish with external investors

shunning the economy. Although the problem started at the onset of dollarization, it has

deteriorated recently and worsened the plight of all sectors of the economy. The debate has

therefore been whether the SMEs are struggling because of the external factors such as the

economic environment and government regulations or because of internal factors such as access

to finance, management skills, technical knowhow and experience, investment in technology,

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entrepreneurial skills or the product characteristics. This research study will investigate the

factors affecting the growth of SMEs in Harare, Zimbabwe.

1.3 Problem Statement

SMEs are increasingly playing an important role in the recovery of the Zimbabwean economy.

There is consensus among economists and academics that the development of SMEs in

Zimbabwe will catapult economic growth and poverty alleviation (Olawale and Garwe, 2010).

Widespread efforts are being made by government and its development partners to encourage the

development of SMEs on the backdrop of a shrinking industrial sector. The SME is increasingly

viewed as the backbone of the country’s economic recovery efforts and a solution to the national

problems of employment creation and poverty reduction.

However, there is a gap in information regarding the dynamics of SMEs growth nt in Zimbabwe.

An understanding of the factors constraining the growth of SMEs in Zimbabwe therefore

warrants investigation. A better understanding of the factors affecting the growth of SMEs will

result in the development of strategies and policies to accelerate the growth of SMEs in

Zimbabwe. This study critically evaluates the factors affecting the growth of SMEs operating in

Harare and examines how these SMEs can best develop in the current sluggish economic

environment.

Literature on the factors affecting the growth of SMEs in the post-dollarization period in

Zimbabwe is limited. Very few studies have been conducted to establish the characteristics of

SMEs operating in Harare and the factors affecting their growth. Most of the studies conducted

on the challenges facing SMEs, relate to the pre-dollarization period. McPherson (1992)

conducted a study to determine factors influencing growth of micro and small enterprises which

covered several countries in Southern Africa and was not specific to the study group and area.

Another study by Muponda and Chaneta (2014) looked at the reasons behind small-firm clusters

in the small furniture manufacturing firms in Harare. A study by Zindiye, Chiliya and Masocha

(2012) focused on the impact of government support on the performance of SMEs in the

manufacturing sector. Gandata (2013) conducted a survey in 2012 which only focused on the

challenges facing SMEs in accessing finance from financial institutions in Bulawayo.

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This study takes a different approach to the studies cited above. It is extremely important for the

factors affecting SMEs in the post dollarization period to be known with certainty in order to

proffer solutions to accelerate growth in this important sector. It is against this backdrop that the

research aims at investigating factors affecting the growth of SMEs in Harare, Zimbabwe.

1.4 Research Objectives

The objectives of this study are:

1. To establish the association between the characteristics of SME entrepreneurs and

business growth.

2. To ascertain the relationship between SMEs characteristics and business growth.

3. To determine the determinants of SME growth in Zimbabwe.

3. To make recommendations that can enhance the growth of SME in Zimbabwe.

1.5 Research Questions

This research study aims at answering the following questions:

1. What is the association between characteristics of SME entrepreneurs and business

growth?

2. What is the relationship between the SME characteristics and business growth?

3. What are the determinants of SME growth in Zimbabwe?

1.6 Significance of the Study

This study seeks to investigate the factors affecting the growth SMEs in Zimbabwe. SMEs

contribute significantly to economic growth and employment creation and are credited for

economic expansion in most developing countries. The Zimbabwean economy is currently facing

a lot of challenges, chief among them high unemployment, depressed local output, low levels of

investment, liquidity challenges and high importation costs. The SME sector provides a solution

to some of these challenges. The promotion of SMEs as a vehicle for economic growth and

employment creation is paramount and hence policy makers, financiers and the entrepreneurs

themselves must understand the factors that affect the growth of this important sector. The

promotion of the SME sector is justifiable as it generates economic growth tied with equity,

employment, income to the business operators and their workers, products for local consumption

and exports, and is the seedbed for large scale enterprises.

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Recommendations made in this study are targeted at assisting SME owners and managers to

achieve high growth rates. The Zimbabwean government, financiers and developmental agencies

that have invested in this sector for many years will get insights on ways to further improve this

sector. The information provided from this effort will assist them with generating appropriate

policies to address the challenges faced by SMEs. The research will also contribute to the small

but growing body of knowledge on SMEs, especially in developing countries and act as a

platform for further research in this area. It should also further enrich the debate on the

contribution of SMEs to development in low income countries.

1.7 Scope of the Study

This study evaluates the characteristics of the entrepreneur, the SME and the factors affecting the

growth of SMEs in Harare, Zimbabwe. The research project will be carried out in Harare where a

large concentration of SMEs operates from.

1.8 Dissertation Outline

This dissertation is arranged into five chapters.

Chapter 1 (Introduction) provides the foundation for the study. It includes the background of

the study, the problem statement, research objectives, significance of study, scope of study and

the dissertation outline.

Chapter 2 (Literature Review) is the chapter where the theoretical and empirical background

on SME performance and growth is laid out. This chapter presents the basis on which the

research findings are discussed.

Chapter 3 (Research Methodology) is the chapter covering the methodology of the research

study. It outlines the data collection methods and a justification of each of the methods used, how

the data is processed and analyzed.

Chapter 4 (Research Findings and Analysis) discusses the research findings and results while

Chapter 5 presents the conclusions, recommendations and suggestions about areas of further

studies.

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CHAPTER TWO

Literature Review

2.1 Introduction

This chapter discusses the theoretical concepts and interrogates empirical literature on the subject

in question. The theme of this research study is the growth of SMEs and the factors influencing

this growth in the SME sector. The chapter also discusses other related issues that include

contribution of SMEs to economic growth and challenges faced by businesses in Zimbabwe.

2.2 Concept of Entrepreneurship

The term entrepreneurship is defined by Nieman and Bennett (2006) as the development and

growth of new businesses enterprises. Cronje, Du Toit and Motlatla (2000) describe

entrepreneurship as the establishment and operation of a business, taking greater risk than normal

by mobilizing resources to satisfy the needs of society, create jobs and realize profits for the

owner of the business. Entrepreneurship involves the continuous ownership and direction of the

business by the business operator. Entrepreneurship has also been described by Kuratiko and

Hodgetts (2004) as a dynamic process of vision change and creation. Rwigema and Venter

(2004) describe entrepreneurship as the process of conceptualizing, organizing and nurturing a

business idea and opportunity into a growing venture.

An entrepreneur is the individual who starts the business with the motive of making profit and

assumes financial and business risk in the process. There are of number of attributes that are

discussed in literature that good entrepreneurs have. These include risk taking, innovation and

creativity, vision, passion, and identification of business opportunities. Risk is the likelihood of

suffering harm, experiencing loss or danger. It can also be described as the variability of

expected returns from an investment. The ability and willingness to take calculated risk is one of

the characteristics of an entrepreneur. Hellriegel et al. (2004) argue that the risks taken by

entrepreneurs are calculated carefully to reduce the occurrence of losses. In an effort to share

financial and business risk, entrepreneurs invite shareholders, financiers and suppliers to

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contribute resources into the business. In addition to risk taking, an entrepreneur has to be

innovative by creating new ideas and implementing changes that enhance business performance.

Innovation involves incremental or radical changes to processes, products and services.

Wickham (2004) argues that the main purpose for innovation is to meet the needs of the

customer. Nieman et al. (2006) adds that innovation enhances value to customers and improves

the financial performance of the business and promotes sustained growth. Nieman et al. (2006)

concludes that an organization attains competitive advantage over its rivals through innovation

and creation of unique products, development of new markets and lowering production costs.

Entrepreneurship is key to the startup and development of small businesses. According to

English and Henault (2005), private initiative, imagination and innovation are the key to the

development of businesses in African countries. Worku (2013) discovered in his study of SMEs

in Pretoria, South Africa that business enterprises that were operated by individuals with

adequate entrepreneurial skills performed better than those run by individuals who had no

adequate entrepreneurial skills. A study of SMEs in Ghana, also confirmed that the performance

of SMEs is linked to the entrepreneurial skills of the proprietor (Inkoun, 2003). English et al.

(2005) claims that there was a general misconception that Africa lacked entrepreneurship and

cites studies under the Growth and Equity through Microenterprise Investment and Institutions

(GEMINI) program and those under the Regional Program on Enterprise Development (REED)

that have shown that many Africans are willing to start businesses. Some of the challenges facing

aspiring business people are that of mobilizing enough capital and acquiring sufficient skills to

start businesses.

2.3 Business Growth

2.3.1 Business Growth: Definitions and Measures

McPherson (1996) defines firm growth in employment terms, as the annual change in

employment from the start of the enterprise to the time of the survey, inclusive of the proprietor.

Growth of SMEs can be measured using several indicators that include sales, employment,

assets, profits and output, both in absolute and relative terms. According to Govori (2013) the

commonly used indicators of firm growth are sales and employment measures. McPherson

(1996) corroborates Govori (2013) and state that sales and employment figures are more

preferable because they are usually readily available for most SMEs. Although SMEs usually

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have poor record keeping skills, they attach high importance to sales data as an indicator of

business performance. Olawale et al. (2010) supports the use of sales data as more appropriate

measure of business growth. This is also supported by McPherson who argues that in the absence

of measurement errors, defining growth in terms of turnover and profits is more accurate than a

labour based measurement. Barringer et al. (2005) concurs with the use of sales figures to

measure growth and stresses that they are a precise barometer of how a firm is faring relative to

its competitors. Other researchers prefer to measure firm growth in employment terms. A

business is assumed to grow if it increases its employment capacity over time. Davidsson,

Delmar and Wiklund (2006) recommend the use of employment data over sales data as a

measure of the growth of SMEs because the data on sales may be affected by inflation. While

other authors advocate for the measurement of business growth using increases in market share

and production levels, the problem with these two measures is that they vary widely across

industries and therefore difficult to use for comparisons. Govori (2013) further argues that profit

as an indicator of growth and development is not relevant unless measured over a period of time.

This study measures growth predominantly in terms of employment growth but sales turnover,

profitability and net asset values are also used to complement the main measurement indicator.

Evans (1997) discovered that estimating business growth using employment figures will yield

similar results to those using sales figures. Parker and Dondo (1991) registered similar results in

a study of two manufacturing sectors in Kibera, Kenya.

2.3.2 Theories of Firm Growth

There are no theories specific to SME growth in developing countries. Theories of growth that

are discussed in literature relate to firms and are used to guide the analysis of SME growth in this

discussion. Much of the early literature about growth of firms concentrated on the relationship

between growth and firm size. Traditional neoclassical economic theory posits that the size of

the firm is determined by factors that affect the long term average costs of firms such as the

available technology and market size. This theory assumes a perfect competitive market structure

and dictates that labour is added to the production process until marginal product of the

additional labour equal the wage rate of the last worker. McPherson (1992) supports the

neoclassical theory and postulates that growth in firms occurs in response to changes in the wage

rate, technology and price of the product. Gebreeyesus (2007) criticizes the neoclassical theory

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by pointing out that the theory does not give a definite prediction of the size distribution of firms

that influences supply and demand for the product. The Law of Proportionate Effect, better

known as the Gibrat’s Law was advanced in response to the shortcomings of the neoclassical

theory. According to Gibrat’s Law, yearly growth of firms is independent of current size, but

follows a random distribution of growth rates. Gibrat’s Law predicts identical growth chances for

both large and small firms. The Gibrat’s Law has however been criticized and contrary to its

predictions many empirical studies have shown that, in fact an inverse relationship exists

between firm size and growth (McPherson, 2002).

The Learning Model by Jovanovic (1982) theorized that managerial efficiency and learning were

the key determinants of firm growth. The Model postulates that firms become aware of their

efficiency level after the firm starts operating and they have to update their prior predictions

through experience (McPherson 1992). Growth is therefore said to be a function of the accuracy

of the managers in predicting their ability and estimation of this efficiency improves as the firm

ages. The model attributes slower rate of growth on older firms to the low variance between

actual efficiency and the predicted efficiency. The growth of firms attributed to management

effort is therefore the difference between the true efficiency of management and their predictions

of their managerial efficiencies. The implication of the Learning Model is that firms that are

inefficient exit the market while those that are efficient survive the competition and grow. The

model also suggests that bigger firms have slower growth rates compared to smaller firms due to

their efficiency and confidence in predicting their costs, resulting in a decrease in the mean and

variance of its growth rate. The model is criticized as being “passive” because it depends heavily

on the efficiency of managers. The Learning Model was modified by Pakes and Erickson (1987)

with the efficiency parameter changing through human capital development. The “active model”

by Pakes and Erickson implies that firms that invest in human capital and research and

development are assumed to be more efficient and grow faster. Gebreeyesus (2007) concludes

that as a result of the model by Parkes and Erickson, investment in human capital through

education and experience is critical for the growth of SMEs in developing countries.

Churchill and Lewis (1983) came up with a theory to explain firm growth in terms of growth

stages. They suggest that as firms start operating, they pass through a number of growth stages

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and propose five stages of growth, namely existence stage, survival stage, success stage, take-off

stage and resource maturity stage. As a small firm develops it passes through some of these

stages with each stage having its own characteristics, problems and requirements. Olawale et al.

(2010), states that the Churchill Lewis Model provides insights into the dynamics of SME

growth and business processes involved.

2.3.3 Firm Growth: Empirical Evidence

Empirical studies have weighed in with evidence of the determinants of the growth of business

enterprises. Some of the factors that are cited in literature as affecting growth of firms include

lack of access to financial resources, shortage of entrepreneurial skills, poor managerial skills,

poor infrastructure, access to markets and a hostile regulatory environment. These factors are

discussed in detail under Barriers to Growth of SME in Section 2.2.5.

2.4 Small and Medium Enterprises

2.4.1 Definitions

In development economics, the role of SMEs is a subject of intense debate. Gibson and Van der

Vaart (2008) attribute the debate on the role of SMEs in economic growth and development to

the varying and broad spectrum of definitions of SMEs. There is no universal and single

generally accepted definition of SMEs in the literature. Several indicators are used in defining

the SME sector. As early as 1971, the Bolton Committee (London) made an “economic and

statistical” definition of a small firm based on its relative size in the market, its contribution to

GDP, employment and exports, and its management structure (Bolton, 1971). One notable result

of the Bolton Committee in England was the different definitions of small firms across sectors.

Other authors have presented their own definitions of SMEs. Abor and Quartey (2010) observe

that authors have different definitions of SMEs depending on the target group (operational

definition). Helmsing (1993) agrees with the view that there is no global agreed definition for an

SME and noted that even in a single jurisdiction, SME’s can have several definitions depending

on the industry they operate in. Weston and Copeland (1998) argued that definitions of the size

of enterprises suffer from global application because they are conceived in different contexts.

Some of the criteria extended to define SMEs in literature include use of capital assets criteria,

number of employees, type of industry, ownership of enterprise and turnover levels. SME are

also defined in terms of their legal status and method of production (Abor et al., 2010). Kim and

4'��

Gallent (2000) prefers to classify business enterprises according to the number of employees,

annual turnover and total assets.

The definition of SMEs also varies from one country to another and between reporting entities.

Different countries and institutions set their own guidelines in defining SMEs based on sales,

assets and headcount (Nyanga et al., 2013). In Egypt SMEs are defined as having more than 5

and fewer than 50 employees, Vietnam considers SMEs to have between 10 and 300 employees

(Nyanga et al., 2013). The World Bank define SMEs as those enterprises with a maximum of

300 employees, US$15 million in annual revenue, and US$15 million in assets. The European

Union (EC) defines SMEs largely based on the number of employees and categorizes SMEs as

business enterprises (outside agriculture, hunting, forestry and fishing) which employ less than

500 persons. The EC distinguishes between micro (less than 9 workers), small (between 10 and

99 workers) and medium (between 100 and 499 workers). Storey (1994) argues that the

European Union definition is more convincing as it assumes that the SMEs are heterogeneous

and that their definition is based solely on employee numbers rather than a multiplicity of

criteria. The United Nations Industrial Development Organisation (UNIDO) defines SMEs in

employment terms but they provide different classifications for developed and developing

countries (Elaian, 1996). For developed countries, small firms are those with 99 or fewer

employees, medium firms are those with 100 to 499 employees while large firms have 500 or

more workers. For developing countries micro enterprises are firms with less than 5 employees,

small firms have 5 to 19 workers, medium firms are those with 20 to 99 workers and large firms

have 100 or more workers. Table 2 below, shows some of the definitions used by multilateral

institutions such the World Bank (WB) and Africa Development Bank (ADB).

�

Table 2: SME Definitions Used By Multilateral Institutions

Source: Brookings Global Economy and Development

From Table 2, it becomes evident how varied the def

institutions perspective can be

definition includes enterprises that are three time

turnover or assets than the SME under the Multilate

National Small Business Act of 1996 uses the number

combined with the annual turnover and the gross ass

of the categorization of businesse

Table 3: Definitions of SMEs according to the South African

Enterprise Size Number of Employees

Medium Fewer than

depending on industry

Small Fewer than 50

Very Small (Micro) Fewer than 10 to 20


Micro Fewer tha

Source: Abor and Quartey (2010)

47�

SME Definitions Used By Multilateral Institutions

Source: Brookings Global Economy and Development (2008)

From Table 2, it becomes evident how varied the definitions of SMEs

can be. It can be observed from above table that the Worl

definition includes enterprises that are three times larger by employees and five times larger by

turnover or assets than the SME under the Multilateral Investment Fund. In South Africa, the

National Small Business Act of 1996 uses the number of employees per company’s criterion,

combined with the annual turnover and the gross assets (excluding fixed property). The summary

of the categorization of businesses in South Africa is given in the Table below;

Definitions of SMEs according to the South African National Small Business Act

Number of Employees Annual Turnover (In SA

Rands)

Fewer than 100 to 200,


Less than R4 million to

R50 million, depending

upon industry

Fewer than 50 Less than R2 million to

R25 million, depending on

industry

Fewer than 10 to 20,


Less than R200 000 to

R500 000, depending on

industry

Fewer than 5 Less than R150 000

�

initions of SMEs from a multilateral

. It can be observed from above table that the World Bank

s larger by employees and five times larger by

nvestment Fund. In South Africa, the

of employees per company’s criterion,

ets (excluding fixed property). The summary

s in South Africa is given in the Table below;

Definitions of SMEs according to the South African National Small Business Act

Gross Assets (Excluding

Fixed Property)


R18 million, depending on

industry


R4,5 million, depending

on industry

Less than R150 000 to

R500 000, depending on

industry

Less than R100 000

48��

In Zimbabwe, there are three commonly used definitions of SMEs, all of which are based on the

number of employees, the asset base and the legal status. SEDCO (2010) defines a small and

medium enterprise as a firm that has no more than one hundred (100) employees and maximum

annual sales turnover of US$830 000. They further distinguish between informal enterprises and

SMEs based on their legal status. Informal enterprises are defined as those operations that are not

registered in terms of the Company Act (Chapter 190) and the Factory and Works Act (Chapter

283) while SMEs are businesses that are registered in terms of the Companies Act (Chapter 190)

and employing less than one hundred employees (Ruzivo Trust, 2013). The Ministry of Small

and Medium Enterprises and Cooperative Development (MoSME & CD) defines a small and

medium enterprise as a legal business entity with the following attributes:

(a) Turnover of less than US$800 000,

(b) It is not a subsidiary or branch or associate of a large business organization and,

(c) Maximum number of full-time permanent employees as given in the following table:

Table 4: Zimbabwean Government definition of SME by number of employees

Sector/Sub-Sector Size Max Number of Full Time

Employees

Agriculture, Manufacturing and

Mining

Micro

Small

Medium

5

50

100

Construction, transport, retail,

tourism, catering, arts and crafts,

wholesale and fisheries

Micro

Small

Medium

5

30

50

Source: MoSMED Policy Document (2009)

From the above definitions, the commonly used variable to define SMEs is employment. The

employment variable was used by Van der Wijst (1989) and Jordan et al. (1998) in their studies

4:��

of South African SMEs. Other jurisdictions prefer to use sales threshold to distinguish SMEs

from large scale operations. Lopez and Aybar (2000) considered SMEs as companies with a

turnover of less than 15 million euros. However Storey (1994) criticized the use of sales figures

to determine the size of business because it results in firms in the same sectors all being

classified as small whilst some in other sectors can all be classified as large scale enterprises.

Abor et al. (2010) concludes that there are varying definitions of SMEs that vary across

industries and also jurisdictions. However in spite of the varying definitions of SMEs, there are

three important generic elements of SMEs and these are the number of employees, level of

capital and legal status.

2.4.1.1 Number of Employees

Human capital is one of the critical factors of production. The number of employees in an

enterprise can be used as a measure of the size of the organization. An increase in personnel

signals the growth of an organization (Zindiye, 2008). In both local and international definitions,

the maximum number of employees for small enterprises is 50. For medium enterprises, the

minimum number of employees is 50 and the maximum number is 500 depending on industry or

sector. In the Zimbabwean definitions, the maximum number of employees in an SME is 100.

2.4.1.2 Capital Base

Capital is another essential factor of production without which production cannot take place. In

both definitions, the capital requirements differ mainly due to currency differences but in all

cases sufficient capital must be provided to ensure that production takes place. The amount of

capital required differs between industries and sectors and across borders due to the different

currencies used in different countries. Assets in the form of current and fixed assets are also

essential in the operation of a business enterprise. The maximum threshold for capital base is

$830 000.

2.4.1.3 Registration

The legal status of the enterprise differentiates formal and informal businesses. SMEs must be

registered for purposes of government taxation and calculation of Gross Domestic Product

(GDP). Formalizing a business venture may entail costs in the form of government taxes and

transactional costs but the benefits accrued through business registration include access to

financial resources from lending institutions and assistance from government and donor

4��

programs. The SEDCO definition requires SMEs to be registered in terms of Chapter 190 and

this differentiates them from businesses in the informal sector. However the Ministry of Small

and Medium Enterprise Development definition of SME is not specific about the registration

requirements of SMEs in Zimbabwe.

2.4.2 Characteristics of SMEs

The SME sector is the cornerstone of many developed countries’ economies and constitutes

about 95% of the business enterprises (OECD, 2005). The sector is less developed in low income

countries and includes a wide range of businesses, which differ in their technical advancement

dynamism, and risk attitude (Bouri et al., 2011). Ayaggari, Beck and Kunt (2005) claim that

most SMEs are relatively stable in their technology, market and scale, while a few are more

technically advanced, providing crucial product and services to niche markets. Others are

dynamic and high risk taking while others require high-tech start-ups. Despite all these

differences, there are some distinct characteristics of SMEs. Cronje, Du Toit and Motlatla (2001)

provide the following characteristics of SMEs in developing countries:

• SME are more labor intensive and technically inferior to large scale businesses.

• They are vehicles for using talent and entrepreneurship skills for people who cannot

achieve these objectives in large businesses.

• SMEs generate more employment opportunities per unit of capital invested.

• SMEs flourish by providing services to small and restricted markets which are not

attractive of large enterprises.

• SMEs create social stability by stimulating personal savings, less damage to the

environment and improve the general participation of citizens in a country.

• SMEs are the seed bed of new industries and development of entrepreneurial skills.

Gibson et al. (2008) proposes the following attributes for SMEs:

• Less negotiating power with government and the financial sector for fiscal incentives or

government benefits.

• Managed by their owners or more centralized management with weaker delegation and

departmentalization.

• Focused on short term need and medium term survival than on long term profitability and

growth.

4(��

• Less technologically sophisticated and slower at adapting to available technology.

• More flexible and easy adapting to changing regulatory and economic environment.

• More reliant on employing unskilled workers who receive on the job training.

• More dependent upon personal relationship between management are workers and

between management and customers. ��

Kayangula and Quartey (2000) categorized SMEs into organized and unorganized enterprises

with the former having a registered offices and employing salaried workers. The unorganized

enterprises are made up of artisans who employ a few unsalaried workers who are mostly family

members or apprentices. The unorganized enterprises operate from open spaces or at home. They

further argue that most SMEs are one-person businesses with the families of the proprietor

usually participating in the business without receiving a salary. They estimate that hired workers

and trainees constitute a quarter of the SME workforce. Cronje et al. (2001) corroborates the

above findings and agrees to the point that SMEs in Africa are labour intensive and have lower

capital costs associated with employment creation.

In terms of their business activities, SMEs in developing countries are mostly involved in

manufacturing, trading and retailing (Kayangula et al., 2000). The proportion of SME

involvement in manufacturing has been observed to depend on the availability of raw materials,

tastes of domestic consumers and accessibility to export markets. According to Phillip (2005),

SMEs are highly diverse and heterogeneous, and are traditionally dominant in the industrial sub-

sectors of food and allied products, light engineering, textiles, transport and wood products. Abor

(2010) is of the view that whilst the majority of SMEs fall under the retail category, their

proportion varies from one country to another and between rural and urban centers. In terms of

labour productivity, it varies within and across industries and is related to the level of adaption of

technology and management skills (Fisher et al., 2000). Although, SMEs are more flexible to

changing economic environment and bring innovations to the market place, their innovative

contributions take time to increase productivity as the innovations are usually implemented faster

by large firms. A study by Porter and Turner (2004) concluded that most successful and vibrant

SMEs are characterized by innovation, attention to quality, excellence in service provision and

dedication to satisfying customer needs. They argue that service excellence results in a loyal and

4;��

solid customer base and that the dedication to providing quality service is a prerequisite for

sustained growth of small businesses.

2.4.3 Role of SMEs in Sustainable Development

The importance of SMEs in economic and social development is undisputed. According to Nyanga

et al. (2013) economic growth is the process of economic transition involving structural

transformation of the economy through industrialization, rising GDP and per capita income.

SMEs and entrepreneurs play a significant role in all economies and are the key generators of

employment and income, and drivers of innovation and growth (OECD, 2009). A World Bank

Report (2008) argues that SMEs are the backbone of all economies and a key source of economic

growth, job creation and innovation in both developed and emerging markets. In the past, the

notion was that SMEs were not linked to the formal sector and would disappear once industrial

development is achieved (Gebreeyesus, 2007). There is now growing consensus among development

practitioners globally that SMEs are crucial contributors to sustainable development in both high

and low-income countries (World Bank, 2008). The World Bank Report (2008) credits the

presence of SMEs as one of the strongest contributors to the growth of GDP in many countries.

SMEs are the drivers of sustainable economic growth in developing countries through which

growth objectives of low income countries can be realized.

Worku (2013) argues that it is very difficult to grow the economy of developing countries on a

sustainable basis without the sustained growth and development of SMEs. Swanson (2007)

supports this view and states that the realization of SME growth is critical for sustained growth

and development at the national level. SMEs contribute to the nations GDP by producing

products and provision of services to consumer markets and also provide inputs to other

businesses. They also contribute significantly to exports and to the promotion of international trade.

The manufactured goods that are exported to foreign countries increases the country’s export

earnings and reduces dependency on the export of primary commodities in most developing

countries. Figure 1 shows the contribution of the SME sector to GDP in both low and high

income countries. It shows that SMEs contribute more than half (51%) of GDP in high income

countries and about 15% of GDP in low income countries.

�

Figure 1: GDP Contribution per Sector

SMEs are a major contributo

contribute significantly to employment and poverty

Abor (2010) also recognized the importance of SMEs

SMEs are important sources of employment and income in many developing

viewed as the panacea to the problem of high rate o

countries which has been exacerbated by increasing population

most developing countries are

International Labour Organization

informal enterprises and wage employment in

agricultural employment in all regions of the devel

dominance of SMEs in employment

developing and developed countries. Ahmed (1999) ci

employment contribution of SMEs is a common phenome

figures ranging from 70% to 90% in Africa to 40% to

estimates that 80% of new jobs worldwide are being created b

striking about SMEs in terms of employment

poor members of society and women who have lower ch

businesses. They are more labour intensive than lar

4=�

GDP Contribution per Sector

SMEs are a major contributor to employment creation. Olawale (2011)

contribute significantly to employment and poverty alleviation in most developing countries.

Abor (2010) also recognized the importance of SMEs in employment creation by stating that

sources of employment and income in many developing countries. They are

viewed as the panacea to the problem of high rate of unemployment in most developing

exacerbated by increasing population. The public and formal sectors in

t developing countries are failing to cope with the ever increasing demand for jobs.

International Labour Organization (ILO) 2002 report estimates that self

informal enterprises and wage employment in SMEs represents more than half of

agricultural employment in all regions of the developing world. Abor (2010

MEs in employment generation and in the number of business enterprises

developing and developed countries. Ahmed (1999) cited in Phillip (2005) adds that the

employment contribution of SMEs is a common phenomenon in low income countries with

figures ranging from 70% to 90% in Africa to 40% to 70% in other regions. Worku (2013)

at 80% of new jobs worldwide are being created by the SME sector.

striking about SMEs in terms of employment creation is the sector’s absorption

poor members of society and women who have lower chances of employment in large

businesses. They are more labour intensive than large businesses and have a lower capital costs

�

e (2011) asserts that SMEs

alleviation in most developing countries.

in employment creation by stating that

sources of employment and income in many developing countries. They are

f unemployment in most developing

The public and formal sectors in

the ever increasing demand for jobs. The

ILO) 2002 report estimates that self-employment in the

represents more than half of total non-

oping world. Abor (2010) observes the

and in the number of business enterprises in both

ip (2005) adds that the

non in low income countries with

70% in other regions. Worku (2013),

y the SME sector. What is more

absorption of marginalized

ances of employment in large

ge businesses and have a lower capital costs

�

associated with job creation. Since SMEs are more l

better suited to succeed in rural areas and smaller

in remote areas and slow down rural

equitable distribution of incomes among the citizen

efficacy in resource allocation through the adoption of labour

developing countries where labour is in abundance. The Figure below sho

employment contribution to employmen

middle income countries, upper middle countries and

Figure 2: Employment Contribution of SME

Source: Dalberg Report on SME IN Developing Countri

In addition to promoting the diversification of economic activities,

entrepreneurial skills and promote private ownershi

SMEs, besides introducing new products and new technolo

innovation and they bring competitive pressure on established firms.

important role in the optimal utilization of local

and local technologies and thereby

local and indigenous technologies. Kayanula

scarce resources and improve the efficiency of the

economic growth. Abor et al

' �

associated with job creation. Since SMEs are more labour intensive than large fi

better suited to succeed in rural areas and smaller towns where they promote economic activity

in remote areas and slow down rural to urban migration. This dispersion facilitates a more

equitable distribution of incomes among the citizens of any country. Furthermore, SMEs creat

resource allocation through the adoption of labour intensive production techniques in

where labour is in abundance. The Figure below sho

to employment (in percentage terms) in low income countries, lowe

middle income countries, upper middle countries and in high income countries.

�

Employment Contribution of SME

Source: Dalberg Report on SME IN Developing Countries, November 2011

the diversification of economic activities,

entrepreneurial skills and promote private ownership (Phillip, 2005). Olawale (2010) argue that

new products and new technologies, they are also

bring competitive pressure on established firms.

important role in the optimal utilization of local inputs. They use mainly locally produce

thereby promote demand for local inputs and stimulate the

local and indigenous technologies. Kayanula et al. (2000), states that SME

scarce resources and improve the efficiency of the domestic market which results in long term

al. (2010), argue that SMEs have an advantage over the large scale

abour intensive than large firms, they are

towns where they promote economic activity

urban migration. This dispersion facilitates a more

country. Furthermore, SMEs create

nsive production techniques in

where labour is in abundance. The Figure below shows share of

(in percentage terms) in low income countries, lower-

in high income countries.

the diversification of economic activities, SMEs also stimulate

Olawale (2010) argue that

they are also a key source of

bring competitive pressure on established firms. SMEs also play an

use mainly locally produced inputs

stimulate the growth of

states that SME utilize domestic

domestic market which results in long term

SMEs have an advantage over the large scale

'4��

enterprises due their easy adaptation to local market conditions, resilience to adverse economic

conditions and adoption of new technologies. SMEs also absorb productive resources from all

sectors of the economy thereby helping the country to establish resilient economic systems

where all firms, large or small are interlinked. Kayanula and Quartey (2000) attribute their

resilience to adverse economic conditions to the flexible nature of the SME sector.

Worku (2013) describes the importance of SMEs with respect to competiveness, innovation and

economic development. He argues that the contribution of SMEs to the national economy is

rooted on their ability to generate employment opportunities, utilize available resources, expand

local output, develop local technologies, produce intermediate goods, promote sustainable and

balanced development, lower income disparities and increase the revenue base of the

government. In terms of their geographical distribution, SMEs tend to be widely dispersed.

Nyanga et al. (2013) argue that their geographical distribution help in the development and

diffusion of entrepreneurial skills, technical skills and technology. SMEs initiate technological

innovations and play a critical function as creators of new products and services. They also

support large firms by supplying them with inputs and often act as subcontractors of these large

businesses and also consume output from the large industrial firms. According to Worku (2013),

the capital and output ratio in the SME sector is lower as compared to large companies and the

sector has a higher multiplier effect per unit of investment. The development of information

technology and global integration provides more opportunities for SMEs to enhance their

competitiveness and contribute to national development (Nyanga et al., 2013).

2.4.4 Contribution of SMEs to the Zimbabwean Economy

In Zimbabwe, SMEs have taken a central role in social and economic development of the

country. According to Mudavanhu et al.(2011) SMEs are the engines of economic empowerment

and growth in Zimbabwe in both the formal and informal sectors. They are viewed as the engine

for economic growth because of their contribution to employment, wealth creation, income

generation and strategic linkages with large companies across the economic clusters (RBZ,

2014). The Reserve Bank of Zimbabwe describes SMEs as the key drivers of the Zimbabwe

Agenda for Sustainable Socio-Economic Transformation (RBZ, 2014). The development of the

SME sector is also part of the government strategy of achieving broader developmental goals

which include employment creation, poverty alleviation, indigenization and women

''��

empowerment (Karedza et al., 2014). Chidoko et al., (2011) agrees with the view that that the

SME sector is crucial for the country’s broader developmental goals which include spreading of

employment opportunities, poverty alleviation and increasing indigenous ownership of national

resources. Below we discuss the specific contributions of SMEs to the Zimbabwean economy;

2.4.4.1 Employment Creation

The Zimbabwean economy has over the years experienced long periods of economic decline

characterized by company closures and retrenchments. This resulted in the shrinking of the

formal employment opportunities. Nyoni (2002) claims that investment levels in the economy

have not been sufficient to create employment for over 300 000 annual graduates from the

country’s training institutions. The SME sector has become the primary source of employment

and a source of livelihood for millions of people (Zindiye et al., 2012). The 2012 FinScope SME

survey estimates the SME sector to employ 2.9 million people in the country (RBZ, 2014).

Perhaps the most striking revelation is the absolute size of the sector which has around 2,8

million owners. The existence of employment opportunities results in disposable income for the

citizens of Zimbabwe and increases local demand for goods and services.

2.4.4.2 Contribution to Economic Growth

SMEs have been identified as key contributors to the economic recovery process in Zimbabwe.

The contribution of SMEs to economic growth is premised on their ability to create employment,

lowering income disparities, utilization of locally produced inputs, production of intermediate

goods, increasing national output, promotion of balanced development and increase in

government revenues (Worku, 2013). Zindiye et al. (2012) argue that SMEs offer opportunities

for the development of new products and services, innovation and promote competition in the

business landscape. This competition eliminates monopoly by large enterprises resulting in a free

market economy. They contribution of SMEs to the nation’s GDP is estimated at about 60%

(RBZ, 2014). According to the 2012 FinScope SME Survey, the increased growth in the SME

sector has had a direct bearing on the GDP growth due to increased output, value addition and

profits. GDP is also affected indirectly by SME growth through increased innovation and

productivity that enhances macroeconomic resilience. The sector also benefits the economy

through their propensity to innovate, low start-up capital requirements, flexibility and potential to

develop rapidly (Maseko, 2011). Bukaliya and Hama (2012), claim that a stronger SME sector in

Zimbabwe can improve the national economy’s resilience through diversification and broadening

'7��

economic activities, consequently reducing its vulnerability to periodic shocks. The flexibility

and innovativeness of SMEs allows them to easily adapt to the changes in the economic

environment, market needs and production techniques.

SMEs do not only produce goods and services for the consumers but also provide products that

are tailor made in size and quantity for the domestic market. Products from the SME sector are

relatively cheaper and competitive because of their proximity to markets which keeps the

transport and other costs lower. Moreover, the high reliance of small businesses on locally

produced raw materials, local skills and production methods allows the country to reduce its

foreign currency payments and promote exploitation of local resources (RBZ, 2014). SMEs

benefit the Zimbabwean economy through nurturing the entrepreneurial skills in the people of

this country. They mobilize and stimulate the vast potential for business entrepreneurship,

creation of wealth and widening the capacity of the local economy (Maseko, 2011). Nyoni

(2002) added that the utilization of the country’s human resources and local commodities creates

wealth and an economic structure that is self-sustaining with advanced linkages among its

different sectors. SMEs also contribute to long term industrial growth by producing an increasing

number of firms that grow up and out of this sector.

The World Bank (WB) and multilateral institutions have realized the potential of SMEs to the

acceleration of economic growth and poverty alleviation and have provided targeted assistance to

sector. According to Zindiye et al. (2012), the World Bank policy on SMEs is rooted on three

arguments. Firstly, SMEs promote competition and entrepreneurship that results in economic

efficiency, innovation and growth in productivity. Secondly, SMEs are generally more

productive than large firms because of their ability to specialize in niche products. SMEs also

have the advantage over large business in terms of greater flexibility and ability to launch new

products and services more easily. Lastly, SMEs can make decisions more easily and are thus

more efficient based on prompt action and solutions adjusted to market circumstances.

2.4.4.3 Poverty Alleviation

SMEs have become increasingly important as a source of livelihood for many Zimbabweans.

The FinScope SME survey report (2012) also pointed out that SMEs play a critical role in

poverty alleviation for the entrepreneurs and their employees (RBZ, 2014). Through provision of

'8��

employment and business opportunities, SMEs provide workers and entrepreneurs with

disposable income which improves their standards of living and alleviate poverty.

2.4.5 Barriers to SME Growth

Although SMEs play a critical role in accelerated economic growth and development in many

developing countries, they are often plagued by several factors that prevent realization of their

full potential (Abor et al., 2010). Literature on SMEs is pregnant with a host of common

challenges affecting their growth, irrespective of the country where they operate. Myles (2010)

cited in Nyanga et al. (2013) estimates that more than 50% of SMEs collapse within the first five

years of their operations as a result of numerous challenges. These challenges as advanced by

Muranda (2003) include the economic recession, poor management skills, access to credit

financing, technology, barriers to global sourcing, low productivity, and the regulatory

environment. Mboko and Hunter (2009) single out access to credit, lack of access to formal

business and social network as the major challenges to SME development in Zimbabwe. English

and Henault (2005) outline inadequate infrastructure, inappropriate institutional framework, lack

of adequate financing schemes and inefficient information systems as the major obstacles that

hamper the organized development of SMEs in Africa. Fatoki (2006) stresses the bureaucratic

government regulations, inadequate financing, quality of infrastructure, access to markets,

acquisition of skills and managerial expertise as the major constraints to SME development in

low income countries.

Nyoni (2002) weighed in with the lack of access to finance and the cost of available finance,

poor marketing skills and market knowledge, inadequate management and entrepreneurial skills,

poor access to infrastructure, lack of access to land, lack of information and a hostile regulatory

environment as the major constraints to the performance and growth of SME. Some of these

factors are discussed in detail below.

2.4.5.1 Entrepreneur’s Characteristics

Gebreeyesus (2007) argues that the characteristics of the entrepreneur which include age, sex,

and marital status affect firm growth. These characteristics he further argues, determine the

business operator’s ability and aggressiveness. Some studies have supported the notion that male

headed enterprises perform better and grow faster that those headed by their female counterparts

(Liedholm, 2002; McPherson, 1996,). This has however been disputed by Phillips (2005) who

':��

found no association between gender of business operator and its effect on the firm’s growth

prospects.

According to Hisrich and Drnovsek (2002), the entrepreneurial competencies measured by the

level of education, startup experience and the knowledge of the business have a positive

influence on the performance of the business enterprise. McPherson (1996) also found the level

of education and experience of the business operator affects the growth of SMEs. Educational

background and training of the business operator enhances their entrepreneurial skills. Education

and training programs also improve the capabilities of not only the entrepreneur but his manager

and workers. The attainment of vocational training further enhances the capabilities of the

entrepreneur and resultantly increases the chances of growth of the business enterprise. Evidence

from surveys carried out on entrepreneurship show consistent correlation between entrepreneur’s

education level and their success (English et al., 2005). Herrington and Wood (2003) in their

study of South African firms observed that the lack of education and training reduces the

capacity of managers and attributes the lack of education to low entrepreneurial creation and the

high failure of new businesses. Mudavanhu et al. (2011) agrees with the notion of education as a

predictor for the success of any business. Matern (2005) cited in Manyani et al. (2014)

conducted a study on the success of small businesses in Canada and also discovered that the

educational background of the business operator is positively related to the success of the

business.

Entrepreneurial skills are important for running viable SMEs and a proprietor holding any

business related qualification is expected to make better decisions that reduce the chances of

enterprise failure. Worku (2013) encourages those who do lack entrepreneurial skills to improve

themselves through training in business related courses. According to Abor (2010) the bulk of

small business that fail in their first three years of operating are characterized by poor

entrepreneurial skills. Manyani et al. (2014) supports the argument and claims that education

increases the knowledge of the business operator in such important areas as management,

leadership, finance and marketing thereby enhancing the chances of business success.

'��

In addition to the educational background and experience of the entrepreneur, good leadership

skills are important to the achievement of the goals of SMEs. Entrepreneurship and leadership

are intertwined. Judge and Piccolo (2004) argue that successful entrepreneurs have good

leadership skills. Worku (2013) claims that the leadership style of the business operator

contributes to the ability of the business to retain customers. In cases where customers complain

about poor service or product quality, able business leaders resolve the problems to the

satisfaction of the customer. Different leadership styles account for differences in performance

among SMEs.

2.4.5.2 Characteristics of the SME

The characteristics of the business enterprise have been found to affect its growth and

performance. According to Kiggundu (2002), the registration status of the business enhances its

growth prospects. Although registration of the business involves fiscal and transaction costs, it

also allows the business to access financial resources from banks and other providers of credit.

The initial size of the firm and age has been found to be negatively related to growth

(McPherson, 1996). However Chandler (2009) disagrees with this view and argues that the older

the firm and the bigger it is, the more likely it can withstand tough economic conditions and

hence the more chances for growth. This view is also supported by Bougheas, Mizen and Yalcin

(2005) cited in Olawale et al. (2011) who argue that young firms are more prone to failure as

compared to established firms.

The type of industry and location also has a bearing on its growth prospects. Liedholm (2002)

discovered that SMEs operating in the service and manufacturing industries tend to grow faster

than those in retail. Liedholm (2002) also discovered that SMEs operating from rural areas and

those that are home based have lower grown rates than those operating from urban centers.

2.4.5.2 Access to Finance

SMEs are a vital component of any country’s economic matrix (Bouri et al., 2011). They play a

critical role in advancing growth, innovation and economic prosperity. However, SMEs require

financial support to fund operating and expansion needs. Finance is also required for capital

structure adjustments such as mergers or acquisition transactions. Access to finance improves

firm performance through facilitating market entry, growth, risk reduction and promoting

entrepreneurial activity and innovation (Beck, Thorsen and Demirglic-Kunt 2008). Furthermore,

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firms with greater access to credit and finance are more capable to take advantage of existing

investment opportunities and growth opportunities. SMEs have problems in accessing finance

from formal financial institutions or face unfavorable lending requirements. As a result SMEs are

mainly financed from the entrepreneurs own funds or from external sources such as family and

friends (Olawale et al., 2010). Further growth in established small businesses is also financed

from retained profits. A study conducted on SMEs in Nigeria, concluded that the bulk of initial

financing for SMEs comes from personal savings and additional finance for business expansion

comes from the informal sources (Ekpenyong et al., 1992). Another study conducted in the

United Kingdom also revealed that the initial funding of SMEs comes from immediate family

members while after two years, there is high reliance on own savings and support from financial

institutions (Manyani, 2014). SMEs are however unable to finance their growth requirements

from internal resources because their turnover and profit levels are limited (Gangata, 2013).

Several studies highlight access to credit and finance as one of the key determinants of business

success. Beck (2007) claims that access to finance and the cost of finance are rated as major

constraint by around 30% and 35% of small and medium enterprises in developing countries,

respectively. According to Bouri et al. (2011), improved access to finance for SMEs improves

economic performance of low income countries by fostering innovation, GDP growth and

macro-economic resilience. The lack of access to credit and financial resources is also cited by

Cessar (2004) as a constraint to business growth. Masuko and Marufu (2003) supports the

importance of bank and non-bank credit as being central to the performance of business ventures

through the provision of startup and working capital. According to Ekpenyong (1992), there are

three main sources of financing that SME can access;

1. Formal Financial institutions such as commercial banks, micro-finance institutions,

development banks, merchant banks, insurance companies and savings banks.

2. Informal Financial institutions such as money lenders, credit and savings associations,

relatives and friends.

3. Personal savings.

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Leasing of production equipment or business assets

sources of SME financing. National governme

also provide schemes for financing SME

Bouri et al. (2011) observe that financial constraints are highe

SMEs in these environments are constrained by gaps in the financ

financial gap include high administrative costs, hi

SME entrepreneurs in dealing

function of size and therefore th

the SME sector. They often earn high returns from serving large p

and are unwilling to assume additional risk by lend

transaction costs per loan due to their small loan

access to finance as a constraint to the SME sector performan

World Bank.

Figure 3: Access to Finance

Source: Dalberg Report on SME in Developing Countries (

In Zimbabwe, Olawale et al.

to new business failure, after education and traini

to finance and the high cost of finance as the two

Zimbabwe. According, to RBZ (2006), access to finance

establishment, survival and growth of SMEs in Zimbabwe.

large corporations for financial resources from ban

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Leasing of production equipment or business assets and government subsidies are the other

sources of SME financing. National government, local and international developmental agencies

also provide schemes for financing SMEs.

(2011) observe that financial constraints are higher in low income countries and that

are constrained by gaps in the financial system. The reasons for the

financial gap include high administrative costs, high collateral requirements and inexperience

SME entrepreneurs in dealing with financial intermediaries. Typically, transacti

function of size and therefore the costs and risks of serving SMEs are too high for

. They often earn high returns from serving large private and public corporations

and are unwilling to assume additional risk by lending to the SME sector

transaction costs per loan due to their small loan sizes. Figure 3 show how businesses rated

finance as a constraint to the SME sector performance in a survey conducted by the

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Access to Finance as a major constraint to SME Operations

on SME in Developing Countries (2011)

(2010) rank the lack of financial support as the se

to new business failure, after education and training. Zindiye et al. (2012) also cite limited access

to finance and the high cost of finance as the two principal constraints facing SMEs i

According, to RBZ (2006), access to finance is a major

survival and growth of SMEs in Zimbabwe. SMEs in Zimbabwe compete with

large corporations for financial resources from banks. However financial institutions

and government subsidies are the other

nt, local and international developmental agencies

r in low income countries and that

ial system. The reasons for the

gh collateral requirements and inexperienced

with financial intermediaries. Typically, transaction costs are a

e costs and risks of serving SMEs are too high for banks serving

. They often earn high returns from serving large private and public corporations

ector where they incur high

show how businesses rated

ce in a survey conducted by the

(2010) rank the lack of financial support as the second contributor

(2012) also cite limited access

principal constraints facing SMEs in

major impediment to the

SMEs in Zimbabwe compete with

inancial institutions are more

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biased towards supporting large firms and they develop their financial products to suit the needs

of the large corporations. Karedza et al. (2014) explains that banks are unwilling to support

SMEs due to collateral issues. Collateral is an important consideration for SMEs to access credit

as it reduces the riskiness of the loan to the SME by availing a tangible asset to the financial

institution. Nyanga et al. (2013) attributes the lack of regulatory support and lender information

to support SMEs as the reasons for the unavailability of financing facilities for SMEs. Myles

(2010) cites the size and short term nature of the borrowing requirements for SMEs which are

not attractive to banks. Again the poor loan repayment reputation of SMEs discourages banks to

continue lending to this sector. Manyani (2014) attributes the failure by SMEs to access financial

resources on poor management and accounting practices. According to English et al. (2005), the

failure by financial institutions to support SMSs is attributed to the following factors;

1. Their historical bias towards large firms, import trade and government.

2. The restructuring that they perform to clean up portfolios.

3. Tight monetary policies that limits the volume of credit.

4. Government borrowing that crowds out the private sector.

5. Failure to introduce technology that reduces transaction costs and risks in small loans as

substitute for collateral.

Gebreeyesus (2007) recommends the formalization of SMEs to improve the credibility of the

sector to lenders that are more comfortable to deal with businesses that operate formally. He

therefore concludes that formalizing SMEs enhances their prospects for growth. While most

literature point to the importance of credit in the growth of small businesses, Phillip (2005) in his

study of SMEs in Bangladesh found the availability of finance to have an insignificant effect on

the performance of SMEs. Biggs and Srivastava (1996) found no evidence to support the

argument that access to credit is a major obstacle to growth of manufacturing businesses in Sub-

Saharan Africa.

2.4.5.3 Managerial Skills

Hellriegel et al. (2008) define management skills as a set of knowledge skills, behaviors and

attitudes that contribute to personal effectiveness. Managerial skills are crucial to the

7 ��

performance and growth of any new business including SMEs. The managerial competencies are

assessed in terms of the manager’s ability to generate good business plans, record keeping,

auditing, introducing appropriate technologies and expertise, acquiring innovative business skills

from competitors, motivating staff, investing in staff trainings and resolving disputes (Worku,

2013). The lack of proper management skills is a huge challenge cited in literature. This is

because SMEs rely on owner managers who are reluctant to engage knowledgeable and

experienced managers. Lack of managerial skills and expertise constrains the development of

SMEs. There remains a skills gap in the SME sector and Abor (2010) attributes this gap to

complacency on the part of the SME operators. Where SMEs attract motivated managers, they

hardly match the caliber of managers found in the large corporations. McPherson (1994) asserts

that most failures and poor performances in SMEs are tied to the lack of management attention to

strategic issues. Nyanga et al. (2013) argues that there is reluctance among entrepreneurs in the

SME sector to move away from the owner-manager arrangement which results in poor decisions.

Cronje et al. (2003) echoes the same view and argues that the primary cause of failure in SMEs

is poor management. In a study of the management competencies in SMEs operating in South

Africa by Martin and Staines (2008), they discovered that the lack of managerial skills and

experience are the main reasons for the failure of new firms.

SMEs in Zimbabwe lack marketing skills, human resources skills, financial management and

general management skills to run their businesses (Zindiye et al., 2012). The managerial

weaknesses are attributed to the lack of financial resources to hire qualified and professional

staff. Again, the managerial inadequacies are due to low levels of education among entrepreneurs

and their managers. Myles (2010) argues that most SMEs are started by individuals who have

special technical skills or entrepreneurial skills but without good managerial skills. The situation

is compounded by the scarcity of management talent which is prevalent in most developing

countries. Even in countries where there are training institutions, there still remains a skills gap

because entrepreneurs need to appreciate the need for management training (Kayanula et al.

2000). The high cost of training also hampers management skills development in developing

countries. There is also evidence that entrepreneurs in the SME sector in Zimbabwe attach low

priority to management skills development and are unwilling to participate in such training

programmes.

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Efforts have been made to upgrade managerial skills of SME operators in Zimbabwe by the

government through its agencies such as SEDCO and multilateral institutions such as World

Bank and Africa Development Bank. The focus of such training is on business planning,

financial management, business ethics, strategic marketing and information technology.

However most of these initiatives have not been successful because the SMEs operators do not

participate in such training programmes (Zindiye et al., 2012).

2.4.5.4 Regulatory Environment

The performance of SMEs is influenced by both internal (firm specific) and external (systemic)

factors (Beck, 2007). Systemic factors comprise of macroeconomic factors, informational and

contractual factors, environment, technology, social factors and the regulatory environment. The

systemic factors are beyond the control of SMEs. In most developing countries, negative

attitudes towards entrepreneurship manifests through the restrictive regulations.

Entrepreneurship need to be rewarded and respected to achieve its intended goal of economic

development. Governments and other policy making bodies must incorporate entrepreneurship

into the national policy agenda (English et al., 1995). Zindiye (2012) admits that the regulatory

environment is so complex and that the bureaucratic requirements are a major constraint on

SMEs in Zimbabwe. The process of registering the business and getting the necessary approvals

and licensing is so cumbersome to the extent of discouraging entrepreneurship. These procedures

in some instances, take years to accomplish.

The cost of compliance with regulatory policies is usually very high and threatens the

performance and graduation of SMEs into large firms. There is wide agreement in developing

countries that government policy favours large business enterprises at the expense of small

businesses. While large firms enjoy direct benefits such as restriction on competition (through

tariffs and quotas), access to credit, trade licensing and access to foreign technology, small

businesses are often deprived of these benefits and at times harassed by the authorities

(Ekpenyong and Nyong, 1992). Most SMEs do not operate within the system of state regulations

and hence are unable to access assistance programmes.

Nyanga et al.(2013), claims that in the past, SMEs were exempt from several government

regulations but this has changed. SMEs are now exposed to the same regulations as large

corporations and at times the regulations are so complex for SME operators to understand and

7'��

comply with. The legal framework is also another aspect that deters the development of the SME

sector. Even for SMEs that are already operational, they are unable to produce statements of

accounts and produce tax returns - conditions necessary to access formal credit from financial

institutions. Ndhlovu (2002) claims that compliance with the taxation laws in Zimbabwe require

huge business resources in the form of personnel and time, most of which costly to SMEs. Such

conditions not only limit the performance of SMEs but also encourage them to remain small and

informal in an effort to avoid regulations.

2.4.5.5 Information Technology

Technology is increasingly becoming important in the operations of most SMEs. Investment and

adoption of new technology and keeping abreast with developments in information technology is

important to the growth of SMEs. Technology improvements results in the introduction of new

production techniques, unique products and services and new distribution methods. Technology

also improves the quality of existing products and introduces new ways of storing and

propagating information (Phillips, 2005). According to Zindiye (2012), SMEs find difficulty in

identifying appropriate technologies for their operations due to limited knowledge. Inappropriate

technology results in high cost of production which reduces the competitiveness of the SME

sector. Olawale et al.(2010), stresses the point that improvements in technology not only

maximize business opportunities but also reduce the cost of producing the goods and services.

Nyanga (2013) identified the lack of information technology support as a hurdle that prohibits

SMEs from competing domestically and globally. He recommends that SMEs should strive to

use the most efficient ways of producing its output and reduce wastages in order to remain

competitive and achieve growth. Figure 4, illustrates the barriers to technology adoption:

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Figure 4: Barriers to Adoption of Technology

Source: Storey and Westhand (1994)

2.4.5.6 Business Location

The location of the business enterprise relative to

the growth of the business operations. Proximity to

of distribution and ensures that the business monit

et al. (2010) argues that proximity to the market enables

earlier than competition and found the factor signi

SMEs. Access to markets bo

operating in Zimbabwe (Zindiye, 2012). This has bee

information and market intelligence to identify business opport

The export capacity of the SME sector has also not develop

disadvantages of the local SME sector and the compli

2.4.5.7 Infrastructure

The quality of basic infrastructure such as operati

telecommunication is key to the growth of businesse

77�

Barriers to Adoption of Technology

Storey and Westhand (1994)

The location of the business enterprise relative to its market and suppliers of inputs is critical for

the growth of the business operations. Proximity to suppliers and to the market reduces the costs

of distribution and ensures that the business monitors the ever changing market trends. Olawale

(2010) argues that proximity to the market enables SMEs to exploit existing opportunities

earlier than competition and found the factor significant in determining the performance of

SMEs. Access to markets both local and foreign remains a huge challenge for m

operating in Zimbabwe (Zindiye, 2012). This has been attributed to the lack of sufficient

and market intelligence to identify business opportunities and to follow market trends.

port capacity of the SME sector has also not developed sufficiently due to competitive

disadvantages of the local SME sector and the complicated export procedures.

The quality of basic infrastructure such as operating structures, electricity, transport and

telecommunication is key to the growth of businesses. The provision of workspace (buildings)

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its market and suppliers of inputs is critical for

suppliers and to the market reduces the costs

the ever changing market trends. Olawale

SMEs to exploit existing opportunities

ficant in determining the performance of

th local and foreign remains a huge challenge for many SMEs

n attributed to the lack of sufficient

unities and to follow market trends.

ed sufficiently due to competitive

cated export procedures.

ng structures, electricity, transport and

s. The provision of workspace (buildings)

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closer to the sources of raw materials and the market is key to the development of the SME

sector. The absence of business incubators which are temporary structures established to house

newly started businesses is also cited as a constraint to the development of SMEs in Zimbabwe.

The need for transport to carry raw materials to the production premises and finished products to

the market cannot be overemphasized. Zindiye (2012) cites the cost of transport as one of the

challenges facing SMEs in Zimbabwe. Olawale (2010) however found infrastructure as the least

important determinant of SME performance in South Africa.

Gono (2006) highlighted the problems facing the SME sector in Zimbabwe and recommended

the following measures to government for improving the performance of the SME sector;

1. Creation of an economic environment conducive to entrepreneurship and the development of

the SME sector.

2. The development of management skills and technical knowledge among SME operators and

managers through training programmes.

3. Provision of targeted financial resources to the SME sector.

4. Provision of cheap and affordable premises available for leasing or outright purchase by

SMEs.

5. Establishment of appropriate professional support programmes for players in the SME sector.

2.5. Chapter Summary

The chapter has interrogated literature on the theoretical and empirical orientations of the

research subject. The underlying principle of entrepreneurship was examined, followed by the

theoretical underpinning of the growth and development of business enterprises. The chapter

emphasized entrepreneurship as the root of business development and growth in developing

countries. The importance of private initiative, risk taking and innovation is also highlighted as

attributes of business entrepreneurs.

The varying definitions of small and medium enterprises were examined in detail. There exists

no universal definition of SMEs and definitions used in literature vary from country to country,

across sectors and between reporting entities. The description of SMEs centers on three

7:��

important criteria, namely, employment numbers, legal status and turnover. The characteristics

of SMEs in developing countries are discussed in this chapter and include a wide range of

businesses that differ in dynamism, risk attitude and technical advancement. They are mostly

labor intensive, managed by their owners and less technologically sophisticated. Their business

activities range from manufacturing, retailing and trading.

The importance of SMEs in the development of low income countries is undisputed. They play

an important role in employment creation, economic growth, poverty alleviation, production of

exports and provision of goods and services. In Zimbabwe, SME sector employs a large

proportion of workers and is at the center of the economic recovery process in the country. The

chapter also examines barriers to the growth of SMEs in developing countries and in Zimbabwe.

The challenges faced by SMEs include lack of entrepreneurial skills, poor managerial skills,

access to finance, unfavorable regulatory environment, access to modern technology and markets

and poor infrastructure. Access to financial assistance is cited as the most important ingredient in

the development and growth of the SME sector.

The next chapter outlines the methodology used in this research project.

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CHAPTER THREE

METHODOLOGY

3.1 Introduction

This chapter describes the way the research project has been carried out. It details the design and

methodologies used to collect, analyze and present the research data. The chapter begins by

discussing the various research designs and justification of the research design used in the study.

The study population, sampling techniques, research instruments, data collection methods, data

processing, analysis and presentation procedures are also discussed.

3.2 Research Philosophy

The research philosophy relates to the development of knowledge and the nature of that

knowledge (Saunders, Lewis and Thornhill, 2009). According to Burrell and Morgan (1979), the

debate about research philosophy is underpinned by the theory of society and the philosophy of

science. While some people may view the world around us as factual reality, others may see it as

a social construction. It is therefore fundamental to describe the viewpoint from which the

research is being conducted and determine the methodology to be used. Ontology refers to the

nature of reality and the ontological debate concerning the phenomena under investigation i.e.

the business world is whether it exists external to social actors (objectivism) or whether the

business world is created from perceptions and consequent actions of individuals (subjectivism).

The objectivist would therefore believe that the information about business world can be

observed and obtained through surveys while subjectivists prefer to use participatory research

and use of diaries.

According to Saunders et al. (2009), epistemology relates to the constitution of acceptable

knowledge in the field of study. Hughes and Sharrock (1990) define epistemology in the

business context as the nature of business knowledge and how it is communicated to

stakeholders. There are two sides to the question of epistemology, namely positivism and

interpretivism. Positivism identifies knowledge as being observable, hard and real phenomena

that is capable of being acquired. Visagie (2010) argues that positivism is applies the logic and

methods of physical sciences to unearth social phenomena. It seeks to formulate laws that are

7(��

universal and give reasons for objectively observable and measurable behavior. Anti-positivism

or interpretivism assumes that the application of laws adopted from the physical sciences is

insufficient to explain social phenomena and that reality is socially constructed through the

management of meaning. Interpretivists view knowledge as being subjective, softer and based on

personal insight and experience. The conscious participation of individuals in a research study

allows them to interpret the situation and construe meaning. While positivism explains the

business phenomena through causal relationships between variables, interpretivism advocates for

the participation of individuals in the activities of the study.

Due to the differing philosophical viewpoints, a researcher has choices to make and justify

regarding the research approach. If the researcher is more inclined towards positivism, they are

more likely to take a quantitative approach whilst those inclined to interpretivism will most

certainly prefer to take the qualitative approach to their research.

3.3 Research Design

Kerlinger (1986) defines a research design as a strategy and structure for investigation developed

to ensure that the researcher get answers to the research question at hand. It is the master plan

that outlines the techniques and procedures for collecting and analyzing data relevant to the

research study (Zikmund, 1997). The research design outlines the procedures for carrying out the

research study and serves to provide accurate answers to the research questions. It is therefore

the framework for inquiry that prevents the collection of irrelevant data.

There are basically three types of research designs, namely, explanatory research, exploratory

research and experimental (causal) research. These are examined in detail below.

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Exploratory studies are preferred in situations where the researcher lacks understanding and

knowledge of the research problem. It allows the researcher to gather information, get an insight

into the problem into the new or unclear problem. Exploratory studies also help in finding new

relationships and solve unstructured problems. According to Visagie (2010), exploratory studies

help in the development of concepts more clearly, generate operational definitions and improve

the final research design. It is performed through literature searches and in depth interviews. The

7;��

data collection methods used in exploratory research include focus group discussions, case

studies, search for secondary data and interviewing knowledgeable people.

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According to Ghauri, Gronhaug and Kristianslund (1995), descriptive studies are aimed at

describing a subject by profiling a group of people, problems or events by collecting data and

tabulating the research variables’ frequencies or their interaction. This type of research design

describes the event or phenomena being studied as they exist without manipulation. The research

problem is very clear and well-structured. The data collection methods associated with

descriptive studies are case studies, observations and surveys. Descriptive studies are

quantitative in nature and very conclusive.

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The experimental research, also known as causal research is used to establish causation, that is,

direct cause effect relations between variables. Experimentation is defined by Boyd (1981) as a

research process that is a carefully designed to manipulate the causal agent systematically while

holding others constant, in order to measure changes on the other variable. This type of research

design is used to solve structured problems and is quantitative in nature.

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From the different research designs discussed above, a quantitative descriptive approach has

been chosen predominantly, complemented by the gathering of contextual qualitative data. This

approach has been chosen because of the structured nature of the problem under study which has

existing claims. Again this approach matches methodologies used in similar studies carried out in

this area. The research is also not investigative which would have required the use of exploratory

or a qualitative approach. The data will therefore be collected using questionnaires which are a

relatively cheaper and easier method of surveying. Questionnaires also dovetail with descriptive

research designs.

3.4 Population and Sampling Techniques

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According to Tackman (1994), the study population is the aggregation elements that the

researcher is interested in making inferences. Taylor et al. (2006) define the target population as

7=��

the totality or collection of persons or objects that comprise the center of the study and on which

the conclusions will be drawn. It is the group that is the focus of a scientific query and the

researcher intends to generalize the findings of the study. Saunders et al. (2009), contends that

defining the population under study is useful in selecting a representative sample. In reality it is

difficult to access the target population because of logistical and economic reasons and as such

the researcher can draw results from a representative sample. According to Saunders et al.

(2009), the sampling framework is the list of all elements drawn from the population.

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Sampling is a procedure in which elements in a population are selected to represent the whole

population (Cooper and Schindler, 2003). The purpose for sampling is to assist the researcher to

draw inferences about some attitude, characteristic or behavior of the entire population.

According to Farber (1974), sampling is a small part of the whole, designed to represent its

nature and quality. It addresses the key research questions of whom to survey, how many to

survey and how to select sample elements (Schiffman and Kanuk, 2000).

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Wegner (1995) defines sampling techniques as methods for selecting a sample from the entire

population. Sampling techniques are classified into two groups, namely probability sampling

techniques and non-probability sampling techniques.

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Probability sampling, also known as representative sampling, involves the selection of sample

elements through a random process. The random process gives every element in the population

an equal and independent probability of selection. Saunders et al. (2009) describes probability

random sampling as the method that gives each element a mathematical chance of being selected

and is mostly associated with survey based research strategies. Wegner (1995) argue that the

strength of probability sampling is that it eliminates bias. It is however expensive and difficult to

obtain the sampling frame. The four probability sampling techniques are systematic random

sampling, simple random sampling, cluster random sampling and stratified random sampling.

The choice of the probability sampling techniques to use depends on the research objectives and

research questions of the study.

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Non probability sampling is a sampling method where the elements in a sample are gathered in a

way that does not give each element an equal chance of being selected. These techniques select

samples based on the researcher’s subjective judgment. Saunders et al. (2009) recommends the

use of such methods in the exploratory stages of a research study such as in a pilot survey. The

four types of non-probability sampling are quota, convenience, judgmental and snowball

sampling.

3.5 Sources of Data

This section outlines the data sources used in this research study. According to Wegner (1995)

data can be classified into primary and secondary data or internal and external data. All these

categories of data are used to complement each other and meet the objectives of the study.

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Primary data is data collected for the first time and for a specific purpose. According to

Denscombe (1998), primary data is captured at point. This gives it the advantage that it is more

relevant to the study and has a high degree of accuracy. The disadvantage with primary data is

that it is more expensive to gather and time consuming (Patzer, 1996).

The study used primary data collected from SMEs that were selected into the sample.

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Secondary data is data that already exists which would have been collected and assembled for

some other purposes. It is primarily gathered and processed by others not for the problem at

hand. The advantage of secondary data is that it already exists and can be used in the shortest

period and without much cost. The problem with secondary data is that it is not project specific

and may not address the research questions (Schiffman and Kanuk, 2000).

3.6 Population and Sampling Method Used

The target population for this study consisted of eight hundred and fifty five SMEs operating in

Harare, Zimbabwe. The sampling frame was a database of membership to the Small and Medium

Enterprises Association of Zimbabwe (SMEA). Since it was not possible to measure the whole

target population, a sample of two hundred and seventy two SMEs was drawn from the

population using the stratified random sampling technique. The researcher opted for this

84��

sampling technique to give each of the SME in each category a proportional chance of being

selected into the sample and thereby ensure representativeness. The sample size was calculated

using the formula shown below:

n = N / [1+ N (e)2]

where n = Sample Size

N = Total population

e = sampling error or precision level at 5% (95% confidence level)

Applying the formula to the study population of 855 SMEs:

n = 855 / [1+1555(0.05)2]

n = 855 / (3.1375)

n = 272

The sample size was statistically significant since it met the minimum recommended sample size

of thirty for quantitative surveys (Martins, Loubser and Wyk De, 1999). The formula used in the

calculation of the sample size was also used in similar studies where probability sampling

methods were used.

3.7 Sample

The target population was divided into the nine significant strata, in line with the business sectors

identified. Simple random sampling sample was then used to proportionately draw the sample

from each of the discrete strata. The table below shows the proportionate sample calculation

from each of the identified strata:

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Table 5: Study Sample

Business Category Number of SME

in Target

Proportion Proportionate

Number in Sample

Agriculture 24 2.8% 8

Manufacturing & Engineering 136 15.51% 42

Services 176 20.6% 55

Transport and Logistics 195 22.8% 62

Retail & Wholesale 120 14,0% 38

Mining 52 6,07% 17

ICT 48 5.6% 15

Food & Beverages 74 9.12% 25

Education 30 3.5% 10

TOTAL 855 100% 272

Source: SME Association of Zimbabwe

3.8 Data Collection Instrument

The primary method of collecting data for this study was the questionnaire. According to Kervin

(1999), a questionnaire is an instrument for collecting data in quantitative survey research from

respondents who record their own answers. Questionnaires consist of structured questions that

are designed to bring out opinions and facts from the respondents. The advantage of using

questionnaires in survey research is that it can be administered to a large number of research

subjects at the same time and is cost effective (Fraenkel and Wallen, 1996). Questionnaires also

allow respondents to be anonymous and this result in more honest responses. They are also a

convenient way of collecting research data.

The major drawback of using questionnaires is that of a low response rate. Kervin (199) argue

that respondents usually fail to fill and return the questionnaires to the researcher. It is also

difficult to ascertain the individual who would have completed the questionnaire as in some

cases the questionnaires are completed away from the researcher. In cases where the

questionnaire is completed in the absence of the researcher, respondents may have difficulties in

answering certain questions.

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The structure of the questionnaire used in this research project was kept simple and clear to

encourage full participation of the selected respondents. Care and precision was taken in the

phrasing and wording of the questions to ensure completion of the questions by respondents.

This is in line with the recommendations by Fowler (1993) who stated that the research

instrument should clear, simple and have reasonable length. The questionnaire consisted of

predominantly closed ended questions, with a few open ended questions. The closed ended

questions provided respondents with choice of answers and made it easier and faster for the

respondents to complete the questions. They also reduce non-response errors and are easy to

code and analyze. Open ended questions were included in the questionnaire to capture the

diverse views and attitudes of the respondents and in their own words. It was also used to gather

data on complex and variable issues.

The closed ended questions in the questionnaire comprised of dichotomous, multiple choice and

a five point Likert-Scale type questions. Dichotomous questions offer only two alternative

answers while multiple choice questions provide more than two alternative answers. The Likert-

Scale requires the respondent to indicate the level of agreement or disagreement to the question.

Dichotomous questions were used because some questions such as the gender of the entrepreneur

have only two possible answers while the use of multiple choice questions provided respondents

with ease of choosing between alternative answers. The Likert-Scale was used for some of the

questions to assess the perceptions, beliefs and attitudes of respondents to some of the questions.

The use of the Likert-Scale also standardizes responses and makes it easy for the researcher to

code and analyze the data.

3.8.2 Administering the Research Instrument

The population of research instrument (questionnaire) will be administered to entrepreneurs and

managers of the 272 SMEs selected for the sample. The questionnaires were administered

through emails of the respondents. This method of administering questionnaires was selected

because all the respondents had email addresses and to minimize the cost. The questionnaires

were accompanied by a covering letter, explaining the purpose of the research.

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3.9 Pilot Study

The questionnaire was pre-tested on a small proportion of the sample. Pre-testing refers to the

testing of the research instrument on a small portion of the respondents with a view of

identifying and correcting the flaws and limitations in the questionnaire. The questionnaire was

tested for layout of questions, the wording sequence and time required to complete. Problems

with the content of questions include misinterpretation of concepts, terms and meaning of the

whole question. Fowler (1993) argue that the self-administered questionnaire should be checked

for formatting problems as they may lead to loss of important information. A pilot study was

carried out to improve the validity of the research instrument. The pilot study resulted in some

amendments to the questionnaire

3.10 Validity and Reliability

Saunders et al. (2009) define validity as the ability of the research instrument (questionnaire) to

measure what the researcher intends to measure. According to Cooper and Schindler (2003),

content validity is a judgemental process that can be carried out through the researcher’s

judgement or through consulting a panel of experts to assess whether the instrument meet the

standards. The researcher used much of personal judgement and also sought assistance from

statisticians to determine the validity of the research instrument. The researcher conducted a pre-

test and a pilot study to identify flaws, weaknesses and limitations of the questionnaire. The

necessary improvements were made to the questions.

Reliability refers to the ability of questionnaire to produce consistent results under different

circumstances and at different times. According to Saunders et al. (2009) reliability is concerned

with the robustness of the research instrument. According to Kumar (2005), findings from a large

sample have more certainty than for small samples. The researcher used a large sample of 272

SMEs as calculated by the sample size formula. Tests for internal consistency were done through

the calculation of Cronbach’s Alpha as recommended by Saunders et al. (2009).

3.11 Data Presentation and Analysis

Data analysis involves reducing gathered data into summaries, and deducing patterns through the

application of statistical analysis techniques. Cooper and Schindler (2003), states that data

8:��

analysis involves the interpretation of the research findings in relation to the research questions

and also checks if the findings are consistent with the research hypotheses.

The results of this study are presented in tables, charts and graphs to outline concisely the

findings of the research study. Data were collected, entered and analyzed using Statistical

Package for Social Sciences (SPSS) Version 21. Descriptive statistics in the form of percentages,

frequencies and mean scores were calculated for the different variables and tabulated into

frequency tables to make understanding easy. The presentation of data in graphical and pictorial

formats promotes effective communication and understanding of the information by

stakeholders.

Analysis of Variance (ANOVA) and Independent T-tests were carried out to compare the means

of the different factors and test for statistical significance at 5% level of significance. ANOVA is

a statistical tool that tests differences in the means across different population groups. Factor

analysis was also conducted to extract the important factors for further analysis. Correlation tests

were conducted to understand the relationships between the extracted factors (predictors) and

growth of SMEs (outcome). Linear regression analysis was then done to establish the

explanatory power of the identified factors to the growth of SMEs in Zimbabwe. In this study the

extracted independent variables, namely entrepreneurial skills, access to capital, management

skills and the regulatory environment were tested for their effect on SME growth.

3.12 Chapter Summary

This chapter has discussed the research philosophy and the research design used in the study and

the justification for choosing the latter. The literature and underlying concepts for the selection

of study population, sampling techniques, research methods and research instruments are also

discussed. The chapter also outlines the measures used to ensure validity and reliability of the

results. The chapter concludes by examining the way the research data was analyzed and

presented.

The next chapter discusses the results of the study. Tables and pictorial diagrams are used to aid

the analysis and understanding of the research results.

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CHAPTER FOUR

RESEARCH FINDINGS AND DISCUSSIONS

4.1 Introduction

This chapter discusses the analysis and interpretation of the research findings and linking them to

the relevant literature discussed in Chapter Two. The analysis of results was conducted using a

statistical package known as SPSS (Version 21). The chapter begins with the demographic

characteristics of the respondents such as age, gender, educational level and experience and that

of the business enterprise such as nature of business, size of business and registration status.

Correlation analysis is then employed to determine the relationship between SME growth

determinants and growth while regression analysis is used to determine the explanatory power of

the determinant factors on SME growth. An Analysis of Variance (ANOVA is also used to

analyze differences in the means of demographic factors and SME growth.

4.2 Response Rate

A total of 272 questionnaires were distributed to owners and managers of SMEs selected from

the membership of the Small and Medium Enterprises Association of Zimbabwe. One hundred

and ninety questionnaires were completed and collected and returned to the researcher. This

represented a response rate of 69.9 % which is high enough to ensure validity and reliability of

the research findings. According to Saunders et al. (2009), this response rate is adequate for data

analysis. They argue that a response rate of between 50% and 92% was an acceptable response

rate for quantitative research studies.

4.3 Reliability Tests

The internal consistency of the questionnaire was assessed quantitatively for each of the

variables under study. It is extremely important to assess the content validity and reliability of

the research instrument and these are measured quantitatively by the Cronbach’s Alpha. An

alpha value more than 0.7 is considered the acceptable minimum score (Wixom and Watson,

2001).

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Table 6: Reliability Tests of the Questionnaire

Growth Factors Number of Items Cronbach’s Alpha

Entrepreneurial Skills 3 0,691

Technical Skills 3 0.719

Managerial Skills 4 0.705

Access to Finance 6 0.732

Government Regulations 4 0.675

Infrastructure 3 0.715

ICT 3 0.795

Total 26 0.701

Growth Indicators Number of Items Cronbach’s Alpha

Total 4 0.751

Overall for Questionnaire 30 0.705

Table 6 above reveals that the research instrument had a reliability constant of 0.705. This

demonstrates good reliability and consistency and that the listed items are well correlated with

each other. The research instrument therefore passed the internal consistency test for reliability.

Since the questionnaire passed the reliability test, it implies that the instrument is capable of

gathering valid data and thereby ensuring its validity.

4.4 Demographic Characteristics of Entrepreneur/Manager

Section A of the research instrument had questions about the characteristics of the respondents.

These range from the position of respondents, their gender, age group and level of education.

The results of the demographic characteristics of the entrepreneur are presented in this section.

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The researcher asked respondents about their position in the SME to determine the distribution of

owners and managers involved in the study. The results are shown in Table 7 below:

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Table 7: Position of Respondents

Item Frequency Percentage (%) Cumulative Percentage (%)

Owner 102 54.0 54.0

Manager 88 46.0 100

Total 190 100.0

Source: Primary Data

Table 7 above shows that 54% of the respondents were owners of SMEs while 46% were

managers. The results indicate that there were slightly more owner operated SMEs in the study.

This implies that most SMEs in Zimbabwe are owner managed.

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The research was keen to determine the gender distribution of the respondents. The frequency

tabulation in Table 8 below was used by the researcher to present results of the gender

distribution of the respondents:

Table 8: Gender of Respondents


Male 103 54.2 54.2

Female 87 45.8 100

Total 190 100.0


Table 8 above demonstrates that 54.2 % of the respondents were males while 45.8% were

females. The distribution was slightly dominated by males but the implication of the results is

that females are also participating in business enterprises. This is testimony of gender

empowerment policies instituted by the Zimbabwean government.

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The researcher wanted to find out the age group distribution of the respondents that participated

in the study. Table 9 shows the age group distribution of the respondents.

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Table 9: Age Group of Respondents


18 -28 30 15.8 15.8

29 – 38 64 33.7 49.5

39 – 48 51 26.8 76.3

49 – 58 37 19.5 95.8

59 and Above 8 4.2 100

Total 190 100.0


The age group distribution results indicate that the SME owners/managers were predominately

within the 28-38 (33.7%) and 39-48 (26.8) age brackets. The 59 and above age group had the

lowest number of participants (4.2%). Nearly half of the respondents (49.5%) are in the youth

category, implying that young people in Zimbabwe are venturing into small and medium

businesses unlike in the past when they would seek employment in large scale businesses. �

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The researcher asked respondents to indicate their level of education. The results are shown in

Table 10 below:

Table 10: Level of Education


Primary 15 7.9 7.9

Secondary 63 33.2 41.1

Diploma 37 19.5 60.5

Degree 35 18.4 78.9

Postgraduate 40 21.1 100

Total 190 100.0


The results also indicate that 33.2% of the respondents attained secondary education, 7.9% had

primary education, 19.5% had diplomas, 18.4% had degrees and 21.1% have postgraduate

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qualifications. About 59% of the respondents had tertiary qualifications (diploma and above) and

this indicates high education levels among SME operators in the country,

4.5 Demographic Characteristics of the SMEs

Section B of the research instrument asked respondents questions relating to the characteristics

of the business enterprise. The questions range from category of the business, number of

employees, registration status of the business and location. An analysis of results is presented in

this section.

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The researcher asked respondents to indicate the nature of the business that they managed. Table

11 below shows the category of SMEs that were surveyed:

Table 11: Category of Business


Manufacturing 36 18.9 18.9

Retail and Wholesale 33 17.4 36.3

Services 28 14.7 51.1

Transport and Logistics 53 27.9 78.9

Mining 10 5.3 84.2

ICT 5 2.6 86.8

Food and Beverages 20 10.5 97.4

Education and Arts 5 2.6 100

Total 190 100.0


According to Table 11, out of the SMEs that were surveyed 27.9% were in the transport and logistics

sector, (18.9%) were in the manufacturing sector, 17.4% were in the retail and wholesale sector and the

food and beverages sector had 10.5%. The results indicate that most the respondents were in the transport

and logistics business and the least in ICT and Education and Arts.

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The researcher asked respondents to indicate the number of employees engaged in their

businesses. This question was aimed at establishing the size of the business. The results of the

question are presented in Table 12:

Table 12: Number of Employees�


0 – 25 158 83.2 83.2

26 – 50 19 10 93.2

51 – 75 13 6.8 100

75 – 100 0 0

Total 190 100.0


Table 12 demonstrates that 83.2% of the SMEs had less than 25 employees, 10% had 26 to 50

employees and 6.8% had 51 to 75 employees. None of the SMEs were in the 76 to 100 employee

category. According to the Ministry of Small and Medium Enterprise Development classification

of businesses criteria, 93.2% of those surveyed operated small businesses while only 6.8% were

medium enterprises. This confirms the notion that most SMEs in Zimbabwe are in the small

business category.

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The question on the legal status of the SMEs was asked to establish whether the SME is

registered with government agencies. Table 13 below:

Table 13: Legal Status of SME


Registered 122 64.2 64.2

Not Registered 68 35.8 100

Total 190 100.0


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According to Table 13 shows that the majority (64.2%) of SMEs surveyed were registered while 35.8%

indicated that they were not registered. Registration of businesses is important because it facilitates access

to resources from financial institutions and assistance from government and donor agencies.

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The researcher wanted to find out where the SMEs operated from. Table 14 shows the results:

Table 14: Business Location


Commercial District 44 23.2 23.2

Downtown Area 28 14.7 37.9

Industrial Area 42 22.1 60

Residential Area 41 21.6 81.6

Home Based 35 18.4 100

Total 190 100.0


The results above (Table 14) show that 23.4% of the surveyed SMEs operate from the Commercial

Districts, 22.1% from the Industrial Area, 21.6 % from the Residential Area and 18.4% are home based.

The results show a balanced distribution in the location of businesses. It implies that SMEs operators are

locating their businesses in residential areas and at their homes in an effort to be close to the market and

reduce transport and other operating costs.

4.6 Factor Analysis

This section seeks to establish the principal factors affecting SME growth in Zimbabwe and

address Objective 3 of the research study. Factor analysis was employed to extract the principal

determinants of SME growth. Data reduction is important as it improves that reliability of the

data. The Principal Factor Analysis was used as the method of extracting the key factors for

SME growth.

Before factor analysis could be carried out, the Kaiser-Meyer-Olkin (KMO) Measure of

Sampling Adequacy (MSA) and the Bartlett’s test of Sphericity were carried out first to ascertain

whether the data was appropriate for factor analysis. The Bartlett’s test of Sphericity matches the

observed correlation matrix to the identity matrix and checks whether there is a certain

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redundancy between the variables that can be summarized with few factors. The KMO uses the

same concept of factorizing the original variables by comparing correlations but it uses partial

correlations to measure the relationship between two variables and eliminates the effect of the other

factors.

The results of the KMO Measure of Sampling Adequacy (0.621) and the Bartlett’s Test for

Sphericity (�2 (153) = 791 144, p-value = 0.000) are shown in Table 15 below. According to

Didacticiel (2013), the data qualifies for factor analysis.

Table 15: KMO and Bartlett’s Test

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Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .621

Bartlett's Test of

Sphericity

Approx. Chi-Square 791.144

Df 153

Sig. .000


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Table 16 below shows results of the factor analysis. Only seven out of twenty one factors were

extracted based on their eigenvalue (eigenvalue > 1) and they explained 67,455% of the total

variance. Factor 1 was the most important factor with 18.452% of total variance and consisted of

financial issues, entrepreneurial skills while Factor 2 had 11.412% of the total variance.

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Table 16: Factor Analysis of Growth Factors

Component

Initial Eigenvalues

Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance Cumulative % Total

% of

Variance

Cumulative

%

1 3.321 18.452 18.452 3.321 18.452 18.452 2.153 11.959 11.959

2 2.054 11.412 29.864 2.054 11.412 29.864 2.085 11.583 23.542

3 1.694 9.410 39.274 1.694 9.410 39.274 1.915 10.642 34.184

4 1.489 8.274 47.549 1.489 8.274 47.549 1.648 9.156 43.339

5 1.312 7.290 54.839 1.312 7.290 54.839 1.570 8.720 52.059

6 1.251 6.948 61.787 1.251 6.948 61.787 1.487 8.261 60.321

7 1.020 5.668 67.455 1.020 5.668 67.455 1.284 7.134 67.455

8 .900 5.002 72.457

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The Scree Plot showing graphically, the number of factors that have an eigenvalue > 1 is shown

in Appendix 1.

Table 17 below shows the Component Matrix with variables (marked in red) that were

considered to be significant and included in the seven extracted factors. Only variables with a

factor loading equal to or greater than 0.05 were selected, in line with the recommendations by

Anderson (1998) who prescribed 0.4 as the minimum threshold. The results shows that Factor 1

(the most important Factor) was loaded with issues to do with finance and management skills,

while Factor 2 consisted of entrepreneurial skills, technical skills and management skills. Factor

3 and 5 were loaded with regulatory issues. The implication of factor analysis is that the

extracted facets are well correlated with the variables. Issues of finance and availability of skills

are dominant in the extracted facets. The evident importance of managerial, entrepreneurial skills

and technical skills is supported by the Learning Model of growth by Erickson (1987) that

advocates for investment in human capital development in order to foster business growth.

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Table 17: Growth Factor Component Matrix

Component

1 2 3 4 5 6 7

Level of Entrepreneurship

Skills .041 .708 .317 .036 .159 .060 .090

Importance of

Entrepreneurship Skills.239 .250 .080 .611 .337 -.339 .097

Level of Technical Skills -.112 .589 .233 .521 .010 -.038 .150

Importance of Technical

Skills.455 .381 -.127 -.016 -.345 .326 -.100

Level of Managerial Skills .291 .629 -.063 -.374 .234 .123 -.086

Importance of Management

Skills.739 .229 .006 .228 -.033 -.200 -.151

Ease of Financial Resources .641 .023 .108 .159 -.108 .040 .111

Importance of Financial

Resources.530 -.225 .045 -.068 .342 .016 .382

High Cost of Finance -.580 .110 .387 .136 -.236 -.068 .567

Collateral Requirements .378 -.290 .359 .005 -.205 .638 .302

Ease of Business License -.447 -.142 .645 -.076 .105 -.136 .152

Taxation .329 .065 .271 .408 -.576 -.133 -.271

Government Support .476 -.399 .124 -.430 .252 -.280 -.087

Government Policies -.062 .205 -.363 .412 .591 .205 .119

Adequacy of Premises -.354 -.119 .522 .255 .300 .349 -.138

High Cost of Rent, Transport .482 -.232 .219 -.103 .224 -.332 -.261

Access to Foreign

Technology .439 -.306 -.402 .006 .028 .294 .259

Importance of Modern

Technology .390 -.118 .321 -.004 .240 .543 -.448


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Factor analysis was also carried out on the four indicators of growth, namely number of

employees, level of sales turnover, level of asset value and annual profit. Table 18 reveals results

of the factor analysis of growth factors.

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Table 18: Factor Analysis of Growth Indicators

Compo

nent

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 2.395 59.885 59.885 2.395 59.885 59.885

2 .932 23.291 83.176

3 .408 10.209 93.385

4 .265 6.615 100.000

Extraction Method: Principal Component Analysis.

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Only one factor (Factor 1) out of the four was extracted because it had an eigenvalue greater than

1 and it accounts for 59.885% of the total variance. The loading of this one facet is shown in

Table 19 below (red markings):

Table 19: Loading of Growth Factors

Growth indicators Component

1

Number of Employees .432

Annual Sales Turnover .850

Net Asset Value .848

Annual Profit .876

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The above component matrix show that only three variables with a loading factor greater than

0.5 were selected. These are annual sales turnover (0.850), net asset value (0.848) and annual

profits (0.876). The extracted facet is highly correlated with the other variables.

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4.7 Normality Tests

Before the Pearson’s Correlation Test could be carried out, the researcher had to carry out the Q-

Q test and the Shapiro-Wilk (s-w) tests for normality to determine whether the data was normally

distributed. Figure 20 below shows the results of the normality test on the factors affecting

growth of SMEs in Zimbabwe:

Table 20: Shapiro-Wilk Normality Test

Number of Employees

Kolmogorov-Smirnova Shapiro-Wilk

Statistic Df Sig. Statistic Df Sig.

Growth Strongly Agree .175 47 .175 .949 47 .837

Agree .162 15 .162 .988 15 .872

Not Sure Disagree Strongly Disagree

.155

.179

.161

32 42 24

.155

.179

.161

.826

.812

.906

32 42 24

.856

.829

.864


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Table 20 above shows results of the Shapiro-Wilk test for normality. Since the significance value

for the Shapiro-Wilk test are greater than 0.05 (0.949, p >0.05) it implies that data are normally

distributed. The Q-Q normality test was also conducted to test the data for normality. Results of

the tests are shown in Figure 5 for growth factors and 6 for growth indicators. Both diagrams

show that the data points are closer to the diagonal line and therefore both data are evenly

distributed. The data therefore qualified for parametric testing using the Pearson’s Correlation

Tests.

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Figure 5: Q-Q Normality Test for Growth Factors

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Figure 6: Q-Q Normality Tests for Growth Indicators

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4.8 Pearson’s Correlation Coefficient Test

The study sought to measure the degree of association between that identified factors and this

was in line with Objective 3 of the study. The Pearson Correlation Test was conducted using

SPSS V21 to determine the relationship between these variables. The Correlation Test is an

analytical technique used to establish the relationships between two continuous quantitative

variables. The Pearson’s correlation is a parametric statistical test for measuring the linear

relationship between two variables The Pearson’s correlation coefficient (r) ranges from -1 to 1,

with r = -1 indicating a perfect negative relationship between the two variables and r = 1

indicating a perfect positive relationship between variables. An r = 0 indicates no linear

relationship between the two variables. The Pearson’s Correlation test was conducted to establish

the level of association between the SME growth factors and SME growth. The analysis yielded

the results shown in the Table 21 below:

Table 21: Correlation Matrix

1 2 3 4 5 6

Growth (1) Pearson Correlation 1

Sig. (2-tailed)

Entrepreneurial Skills (2) Pearson Correlation .365 1 .

Sig. (2-tailed) .006 .

Managerial Skills (3) Pearson Correlation .473 .358 1 .

Sig. (2-tailed) .023 .000 .

Access to Finance (4) Pearson Correlation .563 .009 .011 1

Sig. (2-tailed) .005 .906 .876

Regulatory Environment (5) Pearson Correlation .690 .130 -.229 .055 1

Sig. (2-tailed) .000 .075 .001 .448

Technical Skills (6) Pearson Correlation .203 .160 .233 .018 .053 1

Sig. (2-tailed) .156 .028 .001 .806 .465

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The above table shows the correlation coefficients between the extracted growth factors and

SME growth. The results indicate that the four factors, namely entrepreneurial skills, managerial

skills, access to finance and the regulatory environment were positively and statistically related

to growth. The regulatory environment (r=0.690, p=0.00) had a strong and positive association

with SME growth. Access to finance (r=0.563, p=0.005) was also strongly and positively related

to SME growth while managerial skills (r=0.473, p=0.23) and entrepreneurial skills (r=0.365,

p=0.006) were moderately and positively related to small business growth. Although technical

skills had a weak positive association with SME growth, the result was not statistically

significant since the p-value >0.05 (0.156). The implication of the results is that when SME

operators and managers have high entrepreneurial and management skills, this would have a

weak positive influence on SME growth. Access to financial resources had a strong positive

influence of SME growth. This implies that when SME are able to access better financial

resources, this leads to improved growth. The regulatory environment had the highest positive

effect on SME growth. This implies that an improvement in government regulations and policies

enhances the growth prospects for SME.

The above results are consistent with previous studies. The results corroborate findings by

Muranda (2003), who found out that access to finance, management skills and regulatory

environment are main determinants of growth of SMEs in Zimbabwe. The results are also in line

with findings by Nyoni (2002) who identified access to capital, management and entrepreneurial

skills and a hostile regulatory environment as major challenges to the growth of SMEs in

Zimbabwe. The results are also supported by Mboko and Hunter (2009); Fatoki (2006); Olawale

(2010); McPherson (1994) and Zindiye (2012) all of whom identified access to credit,

management and entrepreneurship skills and government regulations as key to the growth of

SMEs.

The correlation results for the level of technical skills (r = 0.203, p = 0.156) to SME growth

indicate a weak positive relationship. However the result is not statistically significant since p >

0.05. The weak associations between SME growth and technical skills identified in the analysis,

suggests that the effect of the two factors on growth can only be explained by other factors. The

findings differ from discoveries by McPherson (1996) that the attainment of vocational and

technical skills enhances the entrepreneur’s capabilities and in turn enhances SME growth.

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4.9 Regression Analysis

Besides testing for correlation between the study variables, the study also sought to assess the

predictive power of the variables on SME growth. Correlation does not assure causality and

therefore it was necessary to conduct regression analysis. Linear regression analysis tests the

extent to which the dependent variable (SME growth) is predicted by the independent variables

(entrepreneurial skills, management skills, access to finance, regulatory environment and level of

technical skills).

Table 22: Summary of the Regression Model�

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .704a

.496 .473 3.05006

a. Predictors: (Constant), Technical Skills, Finance, Regulatory

Environment, Entrepreneurial Skills, Managerial Skills

The summary of the Regression Model is given in Table 22 above and it gives the overall

predictive power of the model. The overall results show how much of the variation in SME

growth is explained by variations in the factors under investigation. The R value is 0.704 and it

shows a strong level of association between SME growth and the extracted predictors. The

predictive fir is measured by R2 = 0.496 and it shows the percentage of variance in SME growth

that is explained by the variation in the predictors. The model shows that the level of

entrepreneurial skills, management skills, technical skills, the regulatory environment and access

to finance explain 47.3% of SME growth (Adjusted R Square = 0.473). The Adjusted R squared

value of 0.473 shows that the model is a good predictor of SME growth. Since the model is

statistically significant (p<0.05) at 95% confidence level, it can be concluded that SME growth is

indeed influenced by extracted factors. However the overall model implies that the growth of

SMEs is also explained by other factors outside this model.

The regression model was statistically significant (F(5) = 7.313, p = 0.00 ).The results of the

regression analysis are shown Table 23 below:

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Table 23: Prediction Model for Factor Components�

Model

Unstandardized Coefficients

Standardized

Coefficients

T Sig.B Std. Error Beta

1 (Constant) 4.280 1.967 2.176 .031

Entrepreneurial Skills .418 .192 .147 .2.172 .031

Managerial Skills .322 .216 .115 1.487 .039

Finance .591 .188 .234 3.146 .002

Regulatory Environment .726 .151 .348 4.815 .000

Technical Skills -.442 .271 -.114 -1.631 .105

a. Dependable Variable: Growth

F(5) = 7.313

p-value = 0.000

In the above model (Table 23), the regression slope coefficient represented by the standardized

coefficients (Beta) shows the average change in the dependent variable due to a unit increase in

the independent variable. It can be either positive or negative, depending on the nature of the

relationship between the variables. The most significant predictors for SME growth were found

to be the regulatory environment (Beta = 0.348, p = 0.000), access to finance (Beta = 0.234, p =

0.02), entrepreneurial skills (Beta = 0.147, .p = 0.031) and management skills (Beta = 0.115, p =

0.039). Technical skills (Beta = -0.114, p = 0.105) were found to be affecting SME growth in a

negative way.

The implication of the positive Beta for the regulatory environment is that a unit change in the

regulatory environment would result in a 0.348 change in SME growth. Similarly a unit change

(increase) in access to finance leads to a 0.234 change (increase) in SME growth. Managerial

skills (Beta = 0.115), and entrepreneurial skills (Beta = 0.147), and were found to be weak

predictors for SME growth. A unit increase in managerial skills and entrepreneurial skills results

in 0.115 and 0.147 increase in SME growth, respectively. For technical skills a unit increase in

technical skills results in a -0.114 unit variation (decrease) in SME growth. However the result is

not statistically significant since the p- value > 0.05 (p-value = 0.105).

�7��

The levels of significance for entrepreneurial skills, management skills, access to finance and

regulatory environment are all less than 0.05 and therefore we can conclude that their

relationship with SME growth is true and not by chance. However they do not account for all the

growth. There are other factors outside this model that explain SME growth in Zimbabwe. These

results point to the importance of the regulatory environment and access to credit as major

determinants of SME growth. This supports finding by Beck (2007); English and Henault

(1995), Nyanga et al. (2013) and Zindiye (2012), all of whom identified the two factors as

paramount to the growth of SMEs in developing countries.

4.10 Analysis of Variance (ANOVA) and Independent T-Test

This section presents results of the Analysis of Variance (ANOVA) and Independent t-test

carried out to address Objective 1 and 2 of the research study. These objectives as stated in

Chapter One are:

• Objective 1: To determine the association between the characteristics of the SME

entrepreneurs and business growth.

• Objective 2: To ascertain the relationship between the SME characteristics and business

growth.

The one way Analysis of Variance (ANOVA) test was used to investigate whether there were

relationships between the characteristics of the SME entrepreneur, characteristics of the SME

and business growth. The ANOVA technique is an inferential statistical test that examines the

variations in the means of any group of variables. The assumption of ANOVA is that no

differences in the mean would exist.

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Table 24 below gives a summary of descriptive statistics on whether growth of SMEs differs

according to the size of the business enterprise.

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Table 24: Association between Size of Firm and Growth

Growth

N Mean Std. Deviation Std. Error No. of Employees

0-25 158 11.2658 3.36981 .26809

26-50 19 9.1053 1.41007 .32349

51-75 13 7.9231 1.75412 .48650

Total 190 10.8211 3.29495 .23904

F(4) = 9.895

df = 0.189

Sig (p-value) = 0.000

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The results above indicate the existence of variances (F (2) = 9.895, p = 0.00) measured at 95%

confidence. The results shows that smaller SME employing less than 25 people had a higher

growth rate (mean = 11.2658) followed by the 26 – 50 category (mean = 9.1053).The 51-75

employee category had the least growth rate (mean = 7.9231). The implication of the results is

smaller SMEs measured in employment terms, experience higher growth rates.

The results are supported by McPherson (1996) who found a negative relationship between the

initial size of the firm and growth. However, the findings are contrary to the views of Chandler

(2009) who argued that bigger were capable of withstanding external shocks and they grow

faster that smaller businesses.

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Analysis of Variance (ANOVA) was used to determine difference in the means of the different

categories of the level of education. The results are shown in Table 25.

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Table 25: Association between Level of Education and Growth

Growth

N Mean Std. Deviation Std. Error

Primary 15 9.0571 .50709 .13093

Secondary 63 9.1351 2.59030 .32635

Diploma 37 10.2000 3.07465 .50547

Degree 35 12.3333 2.72184 .46007

Postgraduate 40 14.4000 3.48771 .55145

Total 190 10.8211 3.29495 .23904

F(4) = 17.694

df = 0.189


Table 25 shows the results of the ANOVA test on the relationship between the level of education

and SME growth. The results show the existence of variances (F (4) = 17,694, P=0.000)

measured at 95% confidence level. The descriptive statistics show that SMEs managed by

owners/managers with different levels of education, experience varying growth rates. SMEs

operated by owners/managers with postgraduate qualifications experienced the highest growth

(mean = 14.4000) followed by those managed by those with degrees (mean = 12.3333) and those

with diplomas (mean = 10.2000). SMEs managed by owners/managers with primary education

had the lowest growth rates.

The findings support earlier work by McPherson (1996); English and Henault (2005); Herrington

and Wood (2003) and Mudavanhu et al (2011) who all observed a strong positive relationship

between level of education and business growth. The high growth rates in SMEs managed by

tertiary graduates could be a result of better decision making by the operators as they understand

business processes better that their counterparts.

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The Independent t-test was carried out to determine differences in the mean of the males and

females. The t-test was employed because there were only two categories of gender. The results

are shown in Table 26 below:

Table 26: Gender and Growth

Growth

Gender


Male 103 10.4854 3.59156 .35389

Female 87 11.2184 2.87501 .30823

Total 190 10.8211 3.29495 .23904

F(1) = 2.350

df = 0.189


Table 26 shows the means of the two gender groups. Variances were recorded (F(1) = 2.360, p

=0.127), measured at 95% confidence level. However since the level of significance (p – value)

is greater than 0.05, it implies that there is no difference in the means of the two gender group.

Therefore SME growth Zimbabwe is not influenced by whether the operator/manager was male

or female.

The results above are confirmed by Phillips (2005) who stated that there was no association

between gender of business operators and growth. They however differ from those obtained by

Liedholm (2002) and McPherson (1996) that male headed enterprises grow faster than female

headed businesses.

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The Independent t-test was again employed to determine the variations in the means of SMEs

operated by owners or those by employed managers. The results are shown in Table 27 below:

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Table 27: Management and Growth

Growth

Management


Owner Manager 125 10.4560 3.42077 .30596

Employed Manager 65 11.5231 2.93741 .36434

Total 190 10.8211 3.29495 .23904

F(2) = 3.476

df = 0.189


Table 27 shows results of the Independent t-test on the management structure of surveyed SMEs.

Difference in means were found to exist between the two management groups (F(2) = 3.476, p =

0.000). According to the descriptive statistics SMEs who were managed by the operators

experienced lower growth (10.4560) than those run by employed managers (11.5231). This result

confirms findings by Ekpenyong et al (1992) that SMEs run by employed managers were more

likely to grow faster because the employed managers tend to have appropriate skills and

expertise to compete in the market place.

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The Independent t-test was also used to determine differences in means of the two registration

categories. The results are shown in Table 28

Table 28: Legal Status and Growth �

Growth


Registered 122 12.1176 3.25350 .29456

Not Registered 68 10.0984 2.97517 .36079

Total 190 10.8211 3.29495 .23904

F(1) = 17.862

df = 0.189


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The table above is a summary of the descriptive statistics of the registration status of the SMEs.

The results indicate that variances existed at 95% confidence level (F (1) = 17.862, p = 0.000).

Results reveal that registered SMEs experienced a high growth rate (mean = 12.1176) as

compared to unregistered ones (mean = 10.0984). This could be explained by their ability to

access credit from financial institutions, government assistance programs and tenders.

The results are supported by Kiggundu (2002) who found out that registered businesses grew

faster than unregistered businesses because the can access credit finance from banks and other

lenders. Registered businesses also access government and donor agency support programs.

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The study sought to establish the number of years the SME has been operating use the ANOVA

to relate that to its growth. The descriptive statistics relating to the age of the SME are presented

in Table 29 below:

Table 29: Age of SME and Growth

Growth


Less than 1 Year 27 11.9412 2.83949 .54646

1-5 Years 112 12,7143 3.18710 .30115

6-10 Years 20 12.7037 2.39022 .53447

11-15 Years 14 10.2889 3.49568 .93426

Above 15 Years 17 9.1500 3.43640 .83345

Total 190 10.8211 3.29495 .23904

F(4) = 6.665

Df = 0.189

Sig (p-value = 0.000

The results in Table 29 indicate that variances existed at 95% confidence level (F (4) = 6.665, p

= 0.000). SMEs that have been operating for between 1 and 5 years had the highest growth rates

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(12.7143), followed by the 6 to 10 year age category (12.7037). The lowest growth was

experienced by SME that are above 15 years (9.1500). The implication of the results is that

younger SMEs grow at a faster rate that older SMES. The results are contrary to the Gibrat’s

Law which predicts identical growth rates between small and large businesses. This result is

supported by the Learning Hypothesis advanced by Jovanovic (1982), Gebreeyesus (2007) and

McPherson (1996).

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The researcher asked respondents to indicate the type of industry that they were operating in. The

ANOVA results are presented in Table 30 below:

Table 30: Category of Business and Growth

Growth


Manufacturing and

Engineering 36 9.3396 3.50815 .58469

Retail and Wholesale 33 11.5000 2.51021 .43697

Services 28 12.2000 2.29417 .43356

Transport and

Logistics53 13.6500 3.39084 .46577

Mining 10 11.6364 2.63523 .83333

Information and

Communication

Technology

5 12.0833 .00000 .00000

Food and Beverages 20 8.6786 1.72520 .38577

Education and Arts 5 10.0000 3.83406 1.71464

Total 190 10.8211 3.29495 .23904

F(7) = 8.414

Df – 0.189


The results in the table above indicates the existence of variance (F(7) = 8.414, p-value = 0.000).

The highest growth was experienced in the Transport and Logistics sector (13.6500), followed

( ��

by the Services (12.2000) and then Information and Technology sector (12.0833). The lowest

growth was experienced in Food and Beverages sector (8.6786).

The above results corroborates earlier studies that pointed at differences in growth rates between

indusctries (Liedholm, 2002; Gebreeyesus ,2007; Liedholm and Mead, 1993).

4.11 Chapter Summary

This chapter presented findings of the determinants of SME growth in Zimbabwe. The Chapter

begins by testing the research instrument before analyzing the demographic characteristics of the

respondents. Factor Analysis is then employed to extract the principal factors affecting growth of

SMEs in Zimbabwe and these came out as entrepreneurial skills, management skills, access to

finance, the regulatory environment and level of technical skills. Correlation and regression

analyses were then employed to determine the associations and explanatory power of the

determinant factors and SME growth, respectively. The results from both tests reveal the

importance of skills development, finance and a condusive regulatory environment.

The next chapter focuses on the conclusions and recommendations of this research study and

provides suggestions for further studies.

(4��

CHAPTER FIVE

CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

This chapter presents the major conclusions and recommendations of the study. These are

derived from the findings of the research project. The chapter also presents suggestions for

further research.

5.2 Conclusions of the Research

This section elaborates and concludes the study. The conclusions and recommendations of the

research study were made with reference to the study objectives. The primary objective of the

study was to identify the determinants of SME growth in Zimbabwe. The study also intended to

establish the nature of the relationship between the identified factors and SME growth. The other

objectives of the study were to establish the relationships between the characteristics of the

business operator, characteristics of the SME and growth. The problem that prompted the study

was the observation that SME were not growing and given their importance in employment

creation and to economic growth.

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With the first objective of identifying the key determinant of SME growth, it can be concluded

that the study has produced significant evidence in line with empirical and theoretical evidence.

The study identified four key determinants of SME growth in Zimbabwe. These are

entrepreneurial skills, access to capital, management skills and regulatory environment. A further

objective was to find if there exist a strong positive relationship between these factors and SME

growth. Further analysis of the factors using correlation tests and regression analysis revealed

that the regulatory environment was the most important determinant of SME growth. Therefore

improving government policies on issues such as taxation and provision of support programs for

SMEs, increases the growth prospects of SMEs. This is also supported by empirical literature

('��

discussed in Chapter 2. The government of Zimbabwe should therefore craft legislation and

implement support programs such as providing space and tax incentives to stimulate SME

growth.

Access to finance was the second major determinant of SME growth. The study revealed that

finance is a major challenge for the most the SMEs surveyed but it is positively related to SME

growth. Financial institutions and government agencies such as SEDCO are not providing

adequate credit to support to the SME sector. SMEs require credit facilities to finance working

capital requirement and new investments to ensure competitive advantage and growth. There is

therefore need to promote finance clubs for SME operators to pull resources together for onward

lending to members. Microfinance institutions must also be capacitated to meet the demands of

this important sector.

The importance of human capital development is also depicted in the study through the

prominence of entrepreneurial skills and management skills as some of the major determinants

for SMEs growth. Promoting human capital development is important for SME growth in

Zimbabwe. There is therefore need to craft policies and programs that impact skills to managers

and owners of SMEs.

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The study also sought to establish the relationship between the characteristics of the

entrepreneurs and SME growth. The entrepreneur’s characteristics such as gender, level of

education and experience were tested for their relationship with firm growth and the results

showed that SME operated or managed by tertiary graduates (diploma, degree and

postgraduates) had higher growth rates than those operated by those with primary and secondary

education as their highest qualifications. The level of education therefore correlates positively

with SME growth. This highlights again the importance of training for SME operators and

managers.

(7��

The research study found no relationship between the gender of the business operator/manager

and SME growth. The conclusion was that gender does not influence the growth of SMEs. It

does not matter whether the SME is headed by a male or female to guarantee growth.

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The research study was also aimed at finding the relationship between SME characteristics such

as size, age, management, formality and type of industry and growth. It was found that the size

and age of the business has a bearing on growth prospects. Small and younger businesses that

employed less than 25 workers grew faster than medium enterprises that employed 51 to 75

employees. Also SMEs that operated for 1 to 5 years experienced higher growth than those that

were in operation for more than 15 years. The conclusion therefore is that smaller and younger

firms grow at a faster rate than their large counterparts. The promotion of small businesses is

therefore paramount due to their ability to grow faster than large enterprises.

The formality of the SME was found to have a positive relationship with growth. SMEs that

were registered experienced higher growth rates as compared to those they were not registered.

This is explained by the ability of registered SMEs to access loans from financial institutions and

government and donor support programs. Registered SMEs also participate in government

tenders and private sector contracts. SME s should therefore be encouraged to formalize their

operations in order to expand their business portfolios. The type of industry that the SME

operates was also found to have an influence on growth. Higher growth was experienced in the

Transport and Logistics sector and the Service industry while the lowest growth was in the Food

and Beverages sector. This is explained by the lower entry barriers in those sectors as compared

to manufacturing and mining that require huge capital outlays.

From the discussion in the above section, it can therefore be concluded that the research

objectives have been achieved and the research questions have been addressed.

(8��

5.3 Recommendations

5.3.1 The research study has highlighted the importance of human capital development. SME

operators need to be capacitated with entrepreneurial skills, management capabilities,

vocational and technical competencies. As such Government and donor agencies need

to develop tailor made skills based training programs to equip SME operators and

managers with appropriate business skills.

5.3.2 The importance of a conducive and enabling business environment was prominent in

the study. National and local governments should develop policies and programs that

nurture and promote growth of the SME sector in developing countries.

5.3.3 Access to credit finance was highlighted in the study. It is therefore important that credit

finance be made available to SME. The capitalization of SEDCO and resuscitation of

government credit support programs is recommended. The Financial institutions should

also be encouraged to lend to SME and relax their requirements high collateral

requirements. The government and other independent guarantee funds could provide

credit guarantees for SMEs when they borrow from banks.

5.3.4 The SMEs should form associations and clubs that represent their interests in national

policy dialogues and pull financial resources for onward lending to members. Such

important associations and clubs could partner governments and donors in developing

policies and assistance programs to the SME sector.

5.3.5 The research study also recommends the implementation of a longitudinal study on the

determinants of SME growth where some of the assistance programs proposed in this

study are offered to the SMEs. This would allow some of these interventions to be

tested for their effectiveness.

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