Factors influencing entrepreneurial 

intentions of women in the South 

African digital ecosystem. 

Nombulelo Danisa  

 

A research report submitted to the Faculty of Commerce, Law and 

Management, University of the Witwatersrand, in partial fulfilment of the 

requirements for the degree of Master of Management in the field of 

Digital Business. 

Johannesburg, 2021 

 

  



 
ii 

ABSTRACT 

In line with Sustainable Development Goals (SDGs), there have been increasing 

calls in support of female entrepreneurship in Sub Saharan Africa. The 

entrepreneurial gender disparity against women in South Africa is a cause for 

concern considering the important role of female entrepreneurs in the economy. 

Potential female entrepreneurs face challenges and hostile environments that 

discourage entrepreneurial spirit. However, with the advent of the digital 

ecosystem, it is hoped that these institutional barriers would be overcome. Thus, 

an investigation on the influence of institutional factors and the digital 

environment on entrepreneurial intentions of women is imperative.  

Utilising the Theory of Planned Behaviour within an institutional embedded 

perspective, a model for the digital economy was inferred to analyse factors 

affecting female entrepreneurial intention in the South African digital ecosystem. 

This quantitative study utilised cross sectional data collected for a final sample of 

302 females across South Africa.  

Results reflected that entrepreneurial intentions of women are positively 

influenced by favourable perceptions of the cognitive and normative institutional 

dimensions. However, the regulatory dimension had a positive but insignificant 

influence with no evidence that the digital environment interacts with the 

institutional environment to influence entrepreneurial intentions. Therefore, it is 

recommended that government and policy makers play a leading role in 

promoting a culture that values female entrepreneurship. This would assist in 

creating favourable perceptions of the institutional environment and in turn 

encourage aspiring female entrepreneurs to start their own business.   

Keywords: female entrepreneurship, entrepreneurial intention, institutional 

factors, digital ecosystem, South Africa 



 
iii 

DECLARATION 

I, NOMBULELO DANISA, declare that this research report is my own work except 

as indicated in the references and acknowledgements. It is submitted in partial 

fulfilment of the requirements for the degree of Master of Management in the field 

of Digital Business at the University of the Witwatersrand, Johannesburg. It has 

not been submitted before for any degree or examination in this or any other 

university. 

 

Name:  Nombulelo Danisa  Signature: 

 

Signed at Johannesburg. 

 

On the 5th day of February 2021 

  



 
iv 

DEDICATION 

This thesis is dedicated to my father, the late Justin Danisa, who encouraged 
me to never stop learning.  I hope I continue to make you proud.   



 
v 

ACKNOWLEDGEMENTS 

I wish to express my gratitude to my Supervisor Dr Jabulile Msimango-Galawe 

who, with great patience, helped me through the completion of this report. I 

remain in awe and I am inspired by your knowledge, wisdom, and ability to impart 

what seemed at first to be impossible, in such a simple manner.  

I am grateful to everyone I have had the pleasure to exchange ideas with and 

learn from,  my colleagues in the 2019 MMDB Part-time Cohort who were more 

than eager to help me clarify my idea, especially my colleague Hasheel Govind. 

He encouraged me when I doubted myself and wanted to give up; his constant 

motivation helped me complete my thesis.  

I wish to thank my bosses Doug T Munatsi and Beki Moyo who encouraged me 

to keep learning and had to suffer some balls dropping in the office because I 

was not entirely present after sleepless nights of research. 

Most importantly I wish to thank my family, my loving husband Ishmael who was 

my sounding board for every idea I had, from choosing my topic and supporting 

me right to the end, while patiently listening and offering a new perspective when 

I was at crossroads.  My son Darren, whose energy and encouragement kept me 

going when I did not feel like writing. Thank you both for your love and support 

through my studies. To the rest of my family who were denied my physical 

presence at important family gatherings, or whose calls I returned after a month 

or still have not returned, thank you for your patience.    

 

  



 
vi 

 

TABLE OF CONTENTS 

ABSTRACT ..................................................................................... ii 

DECLARATION .............................................................................. iii 

DEDICATION ................................................................................. iv 

ACKNOWLEDGEMENTS ................................................................ v 

LIST OF TABLES ........................................................................... ix 

LIST OF FIGURES ......................................................................... xi 

LIST OF ACRONYMS ................................................................... xii 

CHAPTER 1. INTRODUCTION ...................................................... 1 

1.1 PURPOSE OF THE STUDY .................................................................... 1 

1.2 CONTEXT OF THE STUDY ..................................................................... 1 

1.3 RESEARCH PROBLEM ......................................................................... 4 

1.4 RESEARCH OBJECTIVES ...................................................................... 5 

1.5 SIGNIFICANCE OF THE STUDY .............................................................. 5 

1.6 DELIMITATIONS OF THE STUDY............................................................. 7 

1.7 DEFINITION OF TERMS ........................................................................ 7 

1.8 ASSUMPTIONS ................................................................................... 8 

1.9 REPORT STRUCTURE ......................................................................... 8 

CHAPTER 2. LITERATURE REVIEW ......................................... 10 

2.1 INTRODUCTION ................................................................................ 10 

2.2 BACKGROUND DISCUSSION ............................................................... 10 

2.3 THEORY OF PLANNED BEHAVIOUR (TPB) – ENTREPRENEURIAL 

INTENTIONS (EI) .............................................................................. 11 
 ATTITUDE TOWARDS BEHAVIOUR ........................................................................... 13 
 SUBJECTIVE NORM ............................................................................................... 13 
 PERCEIVED BEHAVIOURAL CONTROL ..................................................................... 14 

2.4 INSTITUTIONAL THEORY OF ENTREPRENEURSHIP – INSTITUTIONAL 

FACTORS ......................................................................................... 14 
 REGULATIVE DIMENSION ....................................................................................... 15 



 
vii 

 HYPOTHESIS 1 (H1) ............................................................................................. 17 
 COGNITIVE DIMENSION ......................................................................................... 18 
 HYPOTHESIS 2 (H2) ............................................................................................. 19 
 NORMATIVE DIMENSION ........................................................................................ 19 
 HYPOTHESIS 3 (H3) ............................................................................................. 21 

2.5 THE DIGITAL ECOSYSTEM ................................................................. 21 
2.5.1 THE DIGITAL ECOSYSTEM, INSTITUTIONS AND ENTREPRENEURSHIP ........................... 23 
2.5.2 HYPOTHESIS 4A (H4A) ............................................................................................. 25 
2.5.3 HYPOTHESIS 4B (H4B) ............................................................................................. 25 
2.5.4 HYPOTHESIS 4C (H4C) ............................................................................................. 25 

2.6 CONCLUSION OF LITERATURE REVIEW ............................................... 25 

CHAPTER 3. RESEARCH METHODOLOGY .............................. 27 

3.1 RESEARCH APPROACH ..................................................................... 27 

3.2 RESEARCH DESIGN .......................................................................... 28 

3.3 POPULATION AND SAMPLING FRAME ................................................... 28 
 POPULATION ........................................................................................................ 28 
 SAMPLE AND SAMPLING METHOD ........................................................................... 29 

3.4 THE RESEARCH INSTRUMENT ............................................................ 30 
 MEASUREMENT OF FOCAL VARIABLES .................................................................... 31 

3.5 PROCEDURE FOR DATA COLLECTION .................................................. 33 

3.6 VALIDITY AND RELIABILITY ................................................................. 33 

3.7 DATA ANALYSIS ................................................................................ 36 
 MISSING VALUES ANALYSIS ................................................................................... 36 
 DESCRIPTIVE STATISTICS ...................................................................................... 36 
 CORRELATION ANALYSIS ...................................................................................... 36 
 REGRESSION ASSUMPTIONS ................................................................................. 37 
 HIERARCHICAL MULTIPLE LINEAR REGRESSION ....................................................... 39 
 MODERATION ANALYSIS ........................................................................................ 40 

3.8 LIMITATIONS OF THE STUDY ............................................................... 42 

3.9 ETHICAL CONSIDERATIONS ................................................................ 42 

3.10 CONCLUSION ................................................................................... 42 

CHAPTER 4. PRESENTATION OF RESULTS ............................ 44 

4.1 SAMPLE CHARACTERISTICS AND DEMOGRAPHIC PROFILES OF 

RESPONDENTS ................................................................................. 44 

4.2 RELIABILITY OF MEASUREMENT SCALE ............................................... 46 
 ENTREPRENEURIAL INTENTION .............................................................................. 47 
 REGULATIVE DIMENSION ....................................................................................... 48 
 COGNITIVE DIMENSION.......................................................................................... 48 
 NORMATIVE DIMENSION ........................................................................................ 49 
 DIGITAL ENVIRONMENT ......................................................................................... 50 
 SUMMARY OF RELIABILITY ANALYSIS ...................................................................... 51 

4.3 EXPLORATORY FACTOR ANALYSIS (EFA) .......................................... 51 
 SUMMARY OF VALIDITY AND RELIABILITY ANALYSIS ................................................. 55 

4.4 DESCRIPTIVE STATISTICS .................................................................. 55 

4.5 CORRELATION ANALYSIS ................................................................... 56 



 
viii 

4.6 REGRESSION ANALYSIS .................................................................... 57 
 REGRESSION ASSUMPTIONS ................................................................................. 57 
 HIERARCHICAL MULTIPLE LINEAR REGRESSION RESULTS ........................................ 60 
 MODERATION ANALYSIS ........................................................................................ 64 

4.7 RESULTS AND HYPOTHESES .............................................................. 66 

4.8 CHAPTER SUMMARY ......................................................................... 67 

CHAPTER 5. DISCUSSION OF RESULTS ................................. 70 

5.1 INTRODUCTION ................................................................................ 70 

5.2 DISCUSSION PERTAINING TO THE HYPOTHESES ................................... 70 
 REGULATIVE DIMENSION AND EI ............................................................................ 70 
 COGNITIVE DIMENSION AND EI .............................................................................. 71 
 NORMATIVE DIMENSION AND EI ............................................................................. 72 
 DIGITAL ENVIRONMENT AND INSTITUTIONAL DIMENSIONS ON EI ............................... 73 

5.3 CONCLUSION ................................................................................... 74 

CHAPTER 6. CONCLUSIONS AND RECOMMENDATONS ....... 75 

6.1 INTRODUCTION ................................................................................ 75 

6.2 CONCLUSIONS OF THE STUDY ........................................................... 75 

6.3 RECOMMENDATIONS ........................................................................ 76 

6.4 SUGGESTIONS FOR FURTHER RESEARCH ........................................... 78 

References ................................................................................... 80 

APPENDIX A: Research Instrument............................................ 88 

APPENDIX B: Consistency Matrix ............................................ 111 

APPENDIX C: Ethics Certificate ................................................ 112 

APPENDIX D: Additional Results .............................................. 113 

 



 
ix 

LIST OF TABLES 

 

Table 1: Research Techniques ........................................................................ 30 

Table 2: Research Instrument .......................................................................... 31 

Table 3: Age ..................................................................................................... 44 

Table 4: Ethnic group ....................................................................................... 45 

Table 5: Education level ................................................................................... 45 

Table 6: Province ............................................................................................. 45 

Table 7: Summary of reliability analysis ........................................................... 46 

Table 8: Inter-item correlations (Entrepreneurial Intention) .............................. 47 

Table 9: Inter-item correlations (Regulative dimension) ................................... 48 

Table 10: Inter-item correlations (Cognitive dimension) ................................... 49 

Table 11: Inter-item correlations (Normative Dimension) ................................. 49 

Table 12: Item total statistics (Digital environment) .......................................... 50 

Table 13: Inter-item correlations (Digital environment) ..................................... 50 

Table 14: KMO test .......................................................................................... 52 

Table 15: Total variance explained .................................................................. 52 

Table 16: Pattern matrix ................................................................................... 53 

Table 17: Factor correlation matrix ................................................................... 55 

Table 18: Descriptive statistics ......................................................................... 56 

Table 19: Pearson correlation matrix ............................................................... 57 



 
x 

Table 20: Correlation matrix ............................................................................. 58 

Table 21: Model summary ................................................................................ 61 

Table 22: ANOVA ............................................................................................. 62 

Table 23: Regression coefficients .................................................................... 63 

Table 24: Regression results of regulative dimension with the digital environment 

as moderator. ................................................................................................... 64 

Table 25: Cognitive dimension and EI with digital environment as moderator . 65 

Table 26: Normative dimension and EI with moderation .................................. 65 

Table 27: Summary of hypotheses ................................................................... 68 

 



 
xi 

LIST OF FIGURES 

 

Figure 1: Illustration of TBP .............................................................................. 13 

Figure 2: Conceptual Model of the Digital Ecosystem ...................................... 22 

Figure 3: Research Model ................................................................................ 26 

Figure 4: Scree Plot ......................................................................................... 53 

Figure 5: Plot of Residuals ............................................................................... 60 

Figure 6: Homoscedasticity Test……………………………………………………61 

 



 
xii 

LIST OF ACRONYMS 

CD  – Cognitive Dimension  

DEE  - Digital Entrepreneurship Ecosystem 

DTI -              Department of Trade and Industry  

EFA -            Exploratory Factor Analysis  

EI  –  Entrepreneurial Intent  

GEM -           Global Entrepreneurship Monitor 

ITE  -  Institutional Theory of Entrepreneurship 

ND  – Normative Dimension  

RD  – Regulative Dimension  

SDGs-           Sustainable Development Goals 

SPSS -          Statistical Package for Social Sciences 

TPB  – Theory of Planned Behaviour  

 

 



 
1 

CHAPTER 1. INTRODUCTION 

1.1 Purpose of the study 

The purpose of this quantitative method of study is to investigate factors that 

ignite female entrepreneurial intentions in the South African digital ecosystem. 

This report focuses on institutional factors, and how these influence the formation 

of entrepreneurial intentions among females in the digital environmental context.  

1.2 Context of the study 

Globally, there has been an increase in the number of women venturing into 

entrepreneurship (Meyer & Mostert, 2016). There has been a general acceptance 

among policy makers that entrepreneurship can potentially help women achieve 

their dreams and overcome barriers to career development (Hytti, 2010). This 

does not end with the individual, but extends through to country level, where 

employment levels and the economy are positively impacted (Okeke-Uzodike, 

Okeke-Uzodike & Ndinda, 2018). 

Despite the benefits of female empowerment, South Africa still lags in female 

entrepreneurship development (Gore & Fal, 2019). The Global Entrepreneurship 

Monitor (GEM) 2017/2018 report revealed that in South Africa, men account for 

a greater percentage in early entrepreneurial activity (Herrington & Kew, 2018). 

The GEM statistics show that for every 10 male entrepreneurs, 7 female 

entrepreneurs engage in early-stage entrepreneurship.  

The report further shows a decrease in female opportunity driven 

entrepreneurship, from 71.6 percent in 2016 to 65.7 percent in 2017, as well as 

an increase in necessity driven entrepreneurship. This, according to Herrington 

and  Kew (2018) is due to lower education levels, poor business networking, 

capital constraints, family responsibilities, lack of confidence and cultural and 

social influences.  The Entrepreneurial Dialogues highlights family responsibility 

as a major factor that contributes to the gender gap in entrepreneurship, as 



 
2 

females usually devote more time to family than men, which leads to men 

obtaining more time to explore business opportunities (Gore & Fal, 2019). 

It is hoped that with the advent of the digital economy, female entrepreneurs now 

have an opportunity to overcome entrepreneurial barriers through leveraging 

digital enablers that enhance ease of doing business (Malik, 2017). South Africa 

has a diverse range of participants in the digital ecosystem. The environment is 

characterised by dynamic venture development and improved inclusive 

entrepreneurial support initiatives, which is a stark contrast from the segregated 

Apartheid era. However, despite these changes, the South African economy 

continues to experience low growth rates, with unemployment and inequalities 

prevalent among the youths (National Treasury, 2020).  

The Global Entrepreneurship and Development Institute (GEDI) 2017 report 

ranks South Africa at 65th globally, pointing out that it still lags in technology 

adoption and digital inclusion. This report recommends that South Africa ramps 

up its digital inclusion efforts by making digital technologies in the form of 

broadband, spectrum, and information technology (IT) connectivity accessible 

and easy to use for the country’s greater population. According to the 2014 United 

Kingdom (UK) Digital Inclusion Strategy, “helping more people to go online can 

also help tackle wider social issues, support economic growth and close equality 

gaps” (GEDI, 2017). 

The role of female entrepreneurs in economic development should not be 

overlooked, as research revealed that support for female entrepreneurs is lacking 

(Mandipaka, 2014).  Women in South Africa continue to face credit access 

barriers, skills development, and educational opportunity exclusions (Gore & Fal, 

2019). The lack of digital entrepreneurship training and knowledge sharing, and 

access to capital and financial skills continues to be a barrier to entrepreneurship 

for female entrepreneurs. Improvements in awareness and perception of 

entrepreneurship is important for economic transformation (Swartz, Marks, & 

Scheepers, 2020). 

Several formal organisations including university-based initiatives were 

established to support entrepreneurs. These include the University of Cape 



 
3 

Town, University of Pretoria, and Stellenbosch University, where accelerators 

and incubators are emerging (Swartz et al., 2020). The LaunchLab has graduated 

several organisations in the areas of agri-tech and food, clean-tech, paid media, 

edu-tech, fin-tech, and big data.  

Government initiatives to support female entrepreneurs have also been set up 

through the Department of Trade and Industry (DTI), such as the Isivande 

Women’s Fund (IWF), to provide favourable financial solutions to women owned 

businesses (Mandipaka, 2014).  Technology for Women in business (TWIB) is 

another DTI programme which facilitates access to networks and support for 

female entrepreneurs in the ICT space.  Since TWIB’s introduction, many women 

have been given the opportunity to leverage technology to expand their business 

operations (DTI, 2012).  

Other governmental agencies include Khula Enterprise Finance Limited and 

Ntsika Enterprise Promotion Agency which are aimed at improving access to 

loans and equity capital to Small Medium and Micro Enterprises (SMMEs). Ntsika 

Enterprise Promotion Agency also assists entrepreneurs in entrepreneurial and 

business training, business networks and information search.  

Informal support for women entrepreneurs exists in the form of free credit and 

advice from friends, relatives, and business partners. Of great importance is the 

role of informal institutions which encompass norms and values of society, 

attitude towards entrepreneurship and social acceptance. According to the GEM 

2018 report, South African entrepreneurial culture is weak, characterised by  

welfare  grant dependency, a hand to mouth me-too business mentality with a 

high failure rate, thereby  entrenching negative perceptions about 

entrepreneurship (Herrington & Kew, 2018).  

We live in the digital age and countries, organisations and entrepreneurs who do 

not embrace the digital ecosystem are likely to lag from a technological 

standpoint. It is hoped that South African female entrepreneurs will be motivated 

to leverage the associated benefits that the digital ecosystem presents in order 

to reduce the gender divide in the entrepreneurial space.  



 
4 

The Entrepreneurial Dialogues report also notes that female entrepreneurs in 

South Africa receive less recognition and they lack both formal and informal 

entrepreneurial skills (Gore & Fal, 2019).  W. Li, Du, and Yin (2017) emphasised 

the importance of institutional support for females as they are increasingly turning 

to entrepreneurship as a necessity. Thus, the role of the institutional environment 

in facilitating the entrepreneurship process can not be overlooked. It is therefore 

against this backdrop that we should examine the institutional factors that shape 

female entrepreneurial intentions.  

1.3 Research problem 

The entrepreneurial gender disparity against women in South Africa is a cause 

for concern considering the important role of female entrepreneurs in the 

economy in terms of innovation, employment and wealth creation (Meyer & 

Mostert, 2016). According to the GEM 2017/2018 a significant gender gap exists 

in the entrepreneurship landscape where women only account for 31 percent of 

South Africa’s entrepreneurs (Herrington & Kew, 2018). The advent of the digital 

economy has improved the entrepreneurial ecosystem because of the ease of 

conducting business (Malik, 2017). However, generally, it is believed that females 

adopt technology at a lower rate than males (Kamberidou, 2020).  As such, 

women need strong institutional support systems if the gender gap is to be 

reduced and for the economy to benefit from women’s enhanced involvement. 

The entrepreneurial space has been male dominated for decades but the gap 

has been slowly shrinking as women are forced by necessity to be entrepreneurs.  

Women’s entrepreneurship ambitions have however been hampered by the need 

to have a husband and/ or a male family member along to sign off any initiatives 

(Mandipaka, 2014). Female entrepreneurs are subjected to barriers that create 

hostile environments when compared to their male counter-parts and these 

challenges include inter alia, discrimination, lack of education and training, lack 

of exposure to markets, and difficulty in acquiring capital (Matiwane, 2005). 

Understanding the formation of EIs is key in entrepreneurship development, more 

especially in emerging economies like South Africa (Urban, 2013a).  Literature 



 
5 

recognises that the digital environment can ease the burden on females and 

increase their chances of becoming entrepreneurs (Kamberidou, 2020; Malik, 

2017). Shaw and Urban (2011) note that the institutional profile of a country, that 

is, the regulative, normative, and cognitive dimensions directly and indirectly 

affect entrepreneurial intentions. However, literature on how the institutional 

environment of a country interacts with the digital ecosystem in shaping EIs of 

females remains scarce. As such, it becomes imperative to investigate the 

influence of the institutional environment on female EIs in the digital ecosystem 

as this will help in building an enabling environment for aspiring female 

entrepreneurs to partake in the entrepreneurial ecosystem.  

1.4 Research objectives  

The main objective of this research is to investigate the influence of institutional 

factors on female entrepreneurial intentions in South Africa as well as the 

moderating effect of the digital environment on this relationship. 

The sub-objectives are:  

1. To investigate the influence of the regulatory dimension on female 

entrepreneurial intention.   

2.  To investigate the influence of the cognitive dimension on female 

entrepreneurial intention.  

3.  To investigate the influence of the normative dimension on female 

entrepreneurial intention.  

4. To investigate the moderating effect of the digital environment on the 

influence of institutional dimensions on female entrepreneurial intention. 

1.5 Significance of the study 

The need to promote and develop entrepreneurship has taken centre stage in 

many countries especially in South Africa, as governments aim to achieve 

economic prosperity. This study acknowledges various studies on how 

entrepreneurial intentions are affected by the institutional factors in different 

countries’ contexts (Díaz-Casero, Ferreira, Mogollón, & Raposo, 2012; Fatoki, 



 
6 

2010). Other studies also compared intentions across the gender divide 

(Malebana,2015). The contribution of female entrepreneurs to the South African 

economy was investigated by Mandipaka (2014) whereas Meyer and Mostert 

(2016) focused on the barriers and success factors of female entrepreneurs 

enrolled in an entrepreneurial program.  

Despite the presence of studies on the impact of the institutional environment on 

EI (Shaw & Urban, 2011; Urban, 2013a), little has been done on analysing female 

entrepreneurial intentions, which this study intends to focus on, with an added 

angle of the moderating effect of the digital environment. Therefore, this study 

makes an important contribution by extending literature on under-researched 

factors that affect entrepreneurial intentions.  

The digital age and technological developments are changing business models 

from traditional to platforms that leverage exponential networks, reducing 

transaction costs supported by connectivity and mobility.  Women stand to benefit 

from the convenience of working and doing business anytime and anywhere. This 

aids in work life balance and higher productivity with a resultant contribution to 

employment and economic development (Kamberidou, 2020). However, for 

female entrepreneurial intentions to be ignited and developed into successful 

entrepreneurial sustained activity, strong institutional support will be instrumental.  

As such, this study hopes to guide and assist future researchers in assessing the 

combined influence of the digital environment and institutional factors in shaping 

female entrepreneurial intentions in South Africa.  

If female entrepreneurship development is regarded as a key driver of inclusive 

economic growth and development, a better understanding of the role of the 

institutional environment in influencing women’s entrepreneurial intentions is 

required. The findings will assist government and policy makers to accurately 

develop targeted policies, cultivate a socially inclusive environment and 

knowledge sharing culture for female entrepreneurs to thrive as well as reduce 

the gender gap. It will also assist the business community to engage better with 

its female stakeholders from a financial and network support perspective, while 

the aspiring female entrepreneur will be educated on existing possibilities in the 

entrepreneurial ecosystem. 



 
7 

1.6 Delimitations of the study 

The scope of this study is limited to:  

i. The South African context  

ii. Institutional factors which are: 

a. Regulative dimension (South Africa’s laws, policies and business 

support initiatives among others) 

b. Cognitive dimension (entrepreneurial knowledge, skills, training and 

education)  

c. Normative dimension (social norms, values and beliefs regarding 

female entrepreneurs in South Africa) 

iii. Female entrepreneurial intentions from an institutional theoretical point of 

view that is limited to the above dimensions. 

iv. Females across South Africa with a tertiary education.  

1.7 Definition of terms 

i. Digital ecosystem- is an environment where there is wide use of digital 

facilities such as software, applications, mobile phones, digital banking 

facilities (J. Li, Westbrook, Callen & Georgiou, 2012). 

ii. Entrepreneurship- is the ability to realise and act on opportunities in one’s 

environment to produce and convert dreams into projects that facilitate 

living (Bozkurt, 2000). 

iii. Entrepreneurial Intention (EI)- is the willingness to start a business or 

become self- employed (Nguyen, 2018). 

iv. Institutions-  “Rules of the game” of a society (North, 1990). Also referred 

to as institutional environment and used interchangeably in this report.  

v. Institutional dimensions- Kostova (1997) defines an institutional dimension 

or profile as “a set of all relevant institutions that have been established 

overtime, operate in that country and get transmitted into organisations by 

individuals”. These are made up of the regulatory, cognitive, and 

normative dimension.  

o Regulatory dimension- involves aspects like government rules, 

regulations, and policies that support businesses, as well as 



 
8 

facilitating entrepreneurship effort (Busenitz, Gomez, & 

Spencer, 2000). 

o Cognitive dimension- consists of the knowledge and skills of 

individuals regarding business start-ups (Farashah, 2015) 

o Normative dimension- it measures the degree to which 

entrepreneurship is valued by society (Busenitz et al., 2000) 

1.8 Assumptions  

In order to complete this research, the following assumptions were raised: 

i. The respondents have enough knowledge about entrepreneurship and 

their responses are truthful and representative of their intentions.  

ii. The respondents have some knowledge of and the workings of the digital 

environment. 

1.9 Report Structure 

The rest of this report is organised as follows:   

Chapter 2: This is the literature review in which the key terms and concepts 

pertaining to the study are elaborated. The key terms for this study are 

entrepreneurship, institutions, institutional dimensions, entrepreneurial intension 

and digital ecosystem. At the end of the literature review hypotheses are stated 

as possible solutions to the research objectives posed in Chapter 1. 

Chapter 3: This division of the report is about the methodology of the study, it 

aims to address the hypotheses that arose from the literature review and that 

were put forward as possible solutions to the research objectives. In this segment, 

the research approach, research design, population and sample, research 

instrument, procedure for data collection as well as procedures for data analysis 

and interpretation are outlined.  

Chapter 4: In this chapter, the results of the research methodology outlined in 

chapter 3 are presented in line with the objectives of the study. 



 
9 

Chapter 5:  This section discusses and explains the results within the context of 

the literature reviewed in chapter 2. Thus, in this chapter, a detailed discussion 

pertaining to the hypotheses is presented and the results are compared and 

contrasted with the literature.  

Chapter 6: In this last chapter, the results of the study are integrated into the 

original research objectives outlined in Chapter 1, and answers to each research 

objective are provided. Thereafter, policy recommendations are drawn from the 

conclusions and areas for further research are suggested towards the end of the 

chapter.  

     



 
10 

CHAPTER 2. LITERATURE REVIEW 

2.1 Introduction 

This chapter focuses on reviewing theoretical and empirical literature on the topic 

of study. In this chapter, key constructs of the study are discussed, the theoretical 

foundation of the problem is laid out, and hypotheses are formulated. The chapter 

begins by defining entrepreneurship and entrepreneurial intention; this is followed 

by a review of the theory of planned behaviour, the institutional theory of 

entrepreneurship and the digital ecosystem. The final part of the chapter outlines 

each objective and hypotheses and the conceptual model is presented. 

2.2 Background discussion 

Entrepreneurship has become increasingly important in the modern world as it 

plays a crucial role in economic development (North, 1990). It helps resolve  

economic burdens like unemployment, poverty and low economic growth 

(Busenitz et al., 2000). Traditionally, it was implicitly believed that most 

entrepreneurs would be men, this is mainly due to the fact that most businesses 

in the past were male dominated (Estrin & Mickiewicz, 2009). However, the 

situation has changed; policy makers and governments across the globe are now 

paying attention to gender issues in entrepreneurship in line with Sustainable 

Development Goals (SDGs) which aims to achieve gender equality by 2030. As 

stipulated in the SDG 2030 Agenda for Sustainable Development, gender 

equality and women’s empowerment in entrepreneurship is now at the centre of 

its efforts to speed up progress towards development goals (SDGfund, 2020). 

The phenomenon of female entrepreneurship is increasing globally, leading to 

the emergency of a large body of literature analysing aspects such as 

entrepreneurial intentions, barriers for female entrepreneurial success and the 

role of institutions in female entrepreneurship (Chinomona & Maziriri, 2015; Estrin 

& Mickiewicz, 2009; Kamberidou, 2020; Meyer & Mostert, 2016). Women across 

the world face several barriers preventing them from realizing their full potential 

(Okeke-Uzodike et al., 2018). Kamberidou (2020) posits that women in 



 
11 

entrepreneurship continue to face challenges in terms of financial capital, skills 

and limited access to business networks and digital devices and access to 

connectivity.  

The socio-economic environment of a country shapes the entrepreneurial 

behaviour of individuals in that particular country (Kostova, 1997). Literature 

acknowledges that the entrepreneurial ecosystem is largely affected by the 

external environment of a country (Busenitz et al., 2000; Kostova, 1997), which 

includes the institutions of a country. The three dimensions of the institutional 

profile of a country were introduced by Kostova (1997) and the following section 

discusses the relationship between institutional dimensions and 

entrepreneurship. 

In literature, entrepreneurship is defined in different ways. Spencer and Gómez 

(2004) highlight aspects of risk and uncertainty as key aspects of venture creation 

whereas Schumpeter and Redvers (1934) identify innovation and ground 

breaking ideas, new products, and new markets as part of the entrepreneurship 

process. In the same vein, Eckhardt and Shane (2003) develop the idea further 

by adding the exploiting, identifying, and evaluating of opportunities to make new 

products for profit.  

There are also various operational definitions of entrepreneurship in literature 

including Engle, Schlaegel and  Dimitriadi (2009) who see entrepreneurship as 

the commencement of a business venture. This definition is widely used in 

entrepreneurship literature especially for emerging economies (Shaw & Urban, 

2011). This study therefore follows the Engle et al. (2009) definition, thus 

regarding entrepreneurship as the formation of a new business venture by 

females in South Africa.  

2.3 Theory of Planned Behaviour (TPB) – Entrepreneurial 

Intentions (EI)  

Successful entrepreneurship is regarded as an offshoot of entrepreneurial intent 

(Naushad, Faridi & Malik, 2018).  Entrepreneurial intention is regarded as a state 

of mind that seeks to create new ventures and develop new business concepts  



 
12 

(Bird, 1988). Davidsson (1995) points out that the decision to start a business is 

planned over time and is preceded by intention. Intention however may not lead 

to action, implying that it may be an imperfect predictor of entrepreneurial activity. 

The definition of entrepreneurial intention used in this study is given by Engle et 

al. (2009) and Nguyen (2018) who define it as an individuals’ openness to venture 

into self-employment. This is in line with the view adopted in this report and 

coincides with the definition of entrepreneurship adopted.  

Several different measures of entrepreneurial intention categorised into short- 

and long-term intentions were suggested in literature (Reitan, 1996). Short term 

intentions are measured by the likelihood of engaging in new business in two 

years, whereas long term intentions are longer (Reitan, 1996).  This study, 

however, adopts GEM’s definition of entrepreneurial intention, regarding it as the 

latent, non-entrepreneur population aged between 18- 64, who aspire to engage 

in entrepreneurial activity within the next three years (Herrington & Kew, 2018).  

The study of EIs is generally built from  the TPB by Ajzen (1991) which is 

considered a benchmark model for studying intention across populations of 

different characteristics (Bird, 1988) . A person’s intention to perform an action is 

a central factor in this theory (Ajzen, 1991).  Intention is influenced by attitude 

towards behaviour, perceived behavioural control and the subjective norm (Autio, 

Keeley, Klofsten, Parker & Hay, 2001). The theoretical model is depicted in Figure 

1 and discussed in detail thereafter.  



 
13 

 

 

Figure 1: Illustration of TBP (Ajzen, 1991) 

 Attitude towards behaviour 

This element considers the extent to which an individual favours or dislikes the 

behaviour in question. Thus, to decide on a course of action, an individual will 

consider all the available information and the consequences which influence their 

behaviour. Positive outcomes emanating from behaviour will lead to performance 

of action.  

 Subjective Norm 

Subjective norms refer to perceptions and opinions of others, in society, regarding 

the proposed behaviour. This in turn has an influence on whether the behaviour 

will be executed or not (Farashah, 2015). 



 
14 

 Perceived behavioural Control  

Perceived performance capability may hinder or encourage behaviour depending 

on available resources whether these are skill-related, physical or financial. 

Normally, people pursue behaviours they believe they will be able to execute in 

the case of extenuating circumstances. 

Several studies have applied the TPB in studying entrepreneurial intention. Liñán 

and Chen (2009) and Sivarajah and Achchuthan (2013) went further to test a 

model adapted from the TPB and concluded that the model was adequate for 

studying entrepreneurial behaviour across all nations. This was  supported by 

Nabi, Linan, Iakovleva, Kolvereid, and Stephan (2011) who concluded that the 

theory can be applied in countries of all income levels, arguing that it is reliable 

and consistent. This report, however, does not intend to test the validity of this 

theory but rather use it as a conceptual model which encompasses the three 

predictors of intention from the theory. It embeds them into an institutional 

framework in the analysis of female entrepreneurial intentions in South Africa.   

2.4 Institutional theory of entrepreneurship – Institutional 

factors 

The theoretical basis of this study attempts to explain the link between the 

institutional environment and entrepreneurship. Institutional theory is of 

considerable importance when it comes to studying entrepreneurship (Kazumi & 

Kawai, 2017). Applying institutional theory to entrepreneurship has become 

increasingly important because entrepreneurship is an economic behaviour 

embedded in the institutional framework of a country (Baumol, 1990).  Institutions 

are viewed as “rules of the game” that shape the course of an individual’s 

behaviour or beliefs.  These institutions have been broadly categorized  into 

formal and informal institutions (North, 1990).  

Formal institutions consist of government policy and regulatory guides that shape 

entrepreneurial behaviour (North, 1990). Government policies and regulations 

have direct and indirect impacts on business activity, for example, high level taxes 

may deter investment. High compliance costs and bureaucracy in obtaining 



 
15 

business licences may also act as a barrier to entrepreneurship. Informal 

institutions, on the other hand, consist of the normative and cognitive dimension 

norms, traditions, customs, value systems and religions, and knowledge that 

govern human interaction (Scott, 1995).  The way society values 

entrepreneurship and society’s knowledge and skills have a direct influence on 

entrepreneurial behaviour.  

The GEM 2017/2018 report acknowledges that entrepreneurial intentions in 

South Africa are adversely influenced by institutional factors such as red tape, 

labour regulations, corruption, lack of education among others (Herrington & 

Kew, 2018). When applying institutional theory to entrepreneurial intentions, 

Spencer and Gómez (2004), Engle et al. (2009) and Shahid, Imran and Shehryar 

(2018) introduced the three institutional dimensions that govern human 

behaviour, which are the normative, regulative and cognitive dimensions.  

Prior studies have examined the link between a country’s institutional framework 

and entrepreneurial intention in general. Institutional factors like lack of belief in 

oneself, inexperience, poor education, financial constraints, and poor business 

networks have been found to inhibit aspiring entrepreneurs from starting their 

own business ventures (Herrington & Kew, 2018; Urban, 2008). There are 

previous empirical studies that have attempted to establish the influence of 

institutional factors on female entrepreneurship (Kazumi & Kawai, 2017), 

concluding that informal institutional support for women has a positive impact on 

entrepreneurial self-efficacy in Japan. Yousafzai, Saeed and Muffatto (2015) 

found that the regulatory institutional dimension has a positive influence on 

women entrepreneurship. This study differs in that it applies institutional theory 

on entrepreneurial intentions of females in the digital ecosystem. The three 

dimensions of institutions are analysed in detail below. 

 Regulative dimension  

The regulative institutional dimension is made up of government rules, 

regulations and policies that incentivise, constrain and regularize human 

behaviour (Spencer & Gómez, 2004). Government has at its disposal various 

tools that it can use to regulate business activity in a country. These tools include 



 
16 

aspects of the macroeconomic policy environment such as tax policy, labour laws 

and businesses legislation enacted through parliament. Taxes levied on 

businesses have an effect on business start-ups. High taxes may discourage new 

investments and also strict labour market regulations discourage start-ups as 

entrepreneurs struggle to cope with the costs associated with such regulations. 

The regulative environment offers physical and emotional incentives that remove 

the negative perception regarding entrepreneurship (Farashah, 2015). 

  However, in instances where the environment is hostile to particular 

entrepreneurial activities, entrepreneurship is discouraged due to increased 

difficulties in obtaining the required business licences and permits (Garcia-

Cabrera, Garcia-Soto, & Dias-Furtado, 2018). Government may implement 

support policies for new businesses, and reduce risks associated with starting a 

new business by offering tax incentives and financial support in the form of grants 

and protection of property rights to promote investment.  For instance, the Broad 

Based Black Economic Empowerment (BBBEE) Act 53 of 2003 was enacted to 

accelerate economic transformation by supporting previously disadvantaged 

groups (BBBEC, 2017), and in this group, women are top on the list. The 

regulatory dimension has a strong influence on an individual’s decision to venture 

into entrepreneurship (Kujinga, 2016).  

Research has indicated that a favourable regulatory environment has positive 

implications on the formation and growth of new ventures in South Africa (Bosma, 

Wennekers, & Amorós, 2011). Countries with strong regulatory institutions 

usually have higher levels of entrepreneurial intentions (Farashah, 2015). 

Research has found evidence that resources offered by government for 

entrepreneurial support, tax incentives, business development assistance, 

universities, and export incentives can aid individual entrepreneurial efforts 

(Herrington & Kew, 2018; Spencer & Gómez, 2004). As such, the regulatory 

dimension has a strong effect on an individual’s EI (Kujinga, 2016).  

 Despite the various support programs under the regulatory dimension that are 

offered to women in South Africa under the DTI,  the gender gap remains wide, 

with fewer women expressing entrepreneurial intention (Herrington & Kew, 2018). 

The GEM and the Entrepreneurial Dialogues reports highlight that accessing 



 
17 

finance and government support sponsored start-up capital is not easy in South 

Africa and many people are not aware that such government support initiatives 

exist (Herrington & Kew, 2018).  Other impediments to female entrepreneurs in 

South Africa include lack of education training, lack of access to finance, and 

inadequate resources (Chinomona & Maziriri, 2015).  

Thus, factors that inhibit entrepreneurship include uncertainty and 

inconsistencies in government policies and burdensome procedural requirements 

(Spencer & Gómez, 2004). Bureaucracy, corruption, inadequate subsidies, high 

tax rates also increase and heighten complexity and risk associated with business 

creation. (Farashah, 2015). Conclusions were also drawn from different studies 

in different countries where researchers emphasized that females should be 

given the necessary help to start a business, pointing out that lack of access to 

bank funding is hindering their entrepreneurial prospects (Welter & Kolb, 2006).  

This study is therefore driven by the need to get more female entrepreneurs 

engaged in the mainstream economy to bridge the entrepreneurial gender divide 

by leveraging digital tools for the digital ecosystem. As such, the regulative 

institutional framework of a country should play a vital role in encouraging more 

women to participate and venture into entrepreneurship.  

In conclusion, government has a role to play in encouraging entrepreneurship 

through creation of a supportive regulatory environment, especially targeting 

women, and fostering entrepreneurship through education and training, financial 

support, and consistent government policies. A supportive regulatory 

environment would increase entrepreneurial intentions of women in South Africa.  

In recognizing the influence of the regulatory dimension in shaping 

entrepreneurial intentions, this study proposes the following hypothesis: 

 Hypothesis 1 (H1)  

Favourable perceptions of the regulative dimension have a positive influence on 

female entrepreneurial intention.  



 
18 

 Cognitive Dimension  

The cognitive dimension is made up of aspects such as information, skills and 

knowledge that individuals have that are used to interpret and evaluate situations 

and opportunities (Spencer & Gómez, 2004). This dimension describes 

ideologies and logics that are widely shared and are deep rooted in a social 

setting (Garcia-Cabrera et al., 2018).  

Entrepreneurial behaviour is realised as a function of an individual’s underlying 

cognitions (Urban, 2013b). Busenitz et al. (2000) note that information and 

knowledge sets have become institutionalised within countries and thus access 

to knowledge and skills add to one’s confidence and locus of control, thus 

becoming key determinants of entrepreneurial intention. Knowledge and skills 

may be acquired from higher level technical training programs or learning from 

experiences of those already in business (Garcia-Cabrera et al., 2018). 

Generally, the cognitive dimension also encompasses aspects of self- efficacy 

and human capital.  

Self-efficacy refers to one’s perceived ability and capability to execute a 

behaviour (van der Westhuizen & Goyayi, 2020). Farashah (2015) notes that 

entrepreneurial self-efficacy, which he defines as an individual’s judgement of his 

or her ability to successfully start a business, has become an important 

antecedent to EI. Individuals who perceive themselves as having greater abilities 

to do better in business are likely to demonstrate greater intention to venture into 

entrepreneurship (Farashah, 2015). Self-efficacy has also been found to be 

greatly influenced by availability of information and use of ICT as people are 

constantly looking for ways of utilizing digital opportunities in venture start-ups 

(van der Westhuizen & Goyayi, 2020). 

Empirical research has shown that entrepreneurship promotion programs are a 

persuasive catalyst through demonstration of possible success and good social 

and economic benefits (Farashah, 2015). As such policy frameworks under the 

cognitive dimension may focus on increasing access to information which in turn 

increases positive perceptions. Education, skills, training and experience were 

found to influence entrepreneurial intentions in emerging economies (Urban, 



 
19 

2013b). Applying a socio-cognitive model of entrepreneurial career, Farashah 

(2015) found that access to information and personal experience were positively 

related to entrepreneurial intentions.  

This study takes a gender sensitive approach in analysing the extent to which the 

cognitive dimension influences female EI, a line of research that has not received 

wide attention in South Africa. Dennis Jr (2011) acknowledges the role of 

institutional support for women, pointing out that offering institutional support 

packages like training and education empowers women, thus perceiving 

themselves as capable of starting a business venture. Westhead and Solesvik 

(2016) found that entrepreneurship education has a positive effect on intention. 

They further suggested higher levels of connection alertness skills result in a 

higher intensity of intention for female students. Huarng, Mas-Tur and Yu (2012) 

point out that the level of education, occupational and sectorial experience, 

business expertise and managerial skills affect entrepreneurial intention. 

With a number of studies agreeing that a country’s cognitive environment 

influences entrepreneurial behaviour (Busenitz et al., 2000; Farashah, 2015; 

Spencer & Gómez, 2004; Urban, 2013c) in South Africa, most people, especially 

women (Meyer & Mostert, 2016), lack skills, business knowledge and resources 

in their ability to start new business ventures (Herrington & Kew, 2018; Urban, 

2013a). From the discussion on the cognitive dimension, education, skills and 

knowledge are regarded as important determinants of individuals’ decision to 

venture into entrepreneurship. Therefore, this report hypothesises the following:  

 Hypothesis 2 (H2) 

Favourable perceptions of the cognitive dimension have a positive influence on 

female entrepreneurial intention.   

 Normative Dimension  

The normative dimension measures the extent to which residents of a country 

admire entrepreneurship, innovative thinking and value creativity (Busenitz et al., 

2000). Values, norms and cultural beliefs are some of the factors that affect 



 
20 

entrepreneurial orientation under the normative dimension. Norms and values 

encompass social definitions of what is good for society and these have an 

influence on an individual’s evaluation of entrepreneurial processes (Garcia-

Cabrera et al., 2018). Urban (2013c) argues that normative mechanisms are a 

result of a society’s structure which governs entrepreneurial behaviour. Under 

this dimension, beliefs and expectations of people influence who will and who will 

not become an entrepreneur (Krueger, Reilly, & Carsrud, 2000).  

Cognizant of the influence that the normative dimension may exert on intentions, 

there have been widespread calls for countries to reorient their values and 

behaviour towards entrepreneurship (Urban, 2013b). Therefore, this places 

greater emphasis on the need to promote a culture that values entrepreneurship 

in a society. A low value perception of entrepreneurship may be the result of 

associations of entrepreneurship with negative connotations of uncertainty and 

criminality leading to resistance in some cultures (Baumol, 1990). On the 

influence of culture, it was found that societal acceptance of entrepreneurship 

positively influences entrepreneurial activity (Krueger et al., 2000). 

Consistent with this report, a number of studies have examined the impact of 

culture, beliefs and norms, indicating that the role of women in the entrepreneurial 

ecosystem is often not appreciated (Kamberidou, 2020). A country or society that 

values women entrepreneurship, and supports them from grassroots to fruition, 

may encourage aspiring female entrepreneurs to initiate entrepreneurial activity.  

Negative attitudes towards women in business and gender disparities affect 

willingness of females to be actively involved in entrepreneurship (Vossenberg, 

2016).  

Chinomona and Maziriri (2015) found that gender discrimination and negative 

perceptions from members of the community and family members discourages 

women from taking part in business start-ups.  As such, there is a strong need 

for social support of female entrepreneurs so that their contribution is recognised, 

valued, and accepted in the country. This would invigorate entrepreneurial 

aspirations in females. 



 
21 

In conclusion, the normative dimension concerns issues to do with social norms, 

principles and ideologies, which are related to human behaviour, which have 

been found to influence individual intentions to venture into entrepreneurship 

(Farashah, 2015; Krueger et al., 2000; Westhead & Solesvik, 2016). As such, 

acknowledging the influence of the normative institutional dimension, the 

following hypothesis is proposed:  

 Hypothesis 3 (H3) 

Favourable perceptions of the normative dimension have a positive influence on 

female entrepreneurial intention. 

2.5 The Digital Ecosystem  

The terms digital environment, economy, or ecosystem, for the purpose of this 

study are used interchangeably, consisting of organisations, processes and 

people who are transforming business through engaging digital tools, platforms, 

models, skills, methodologies and mind sets. Sussan and Acs (2017) integrated 

the concepts of entrepreneurship and digital ecosystems to develop a model for 

the digital entrepreneurial ecosystem (DEE). They note that literature has 

overlooked the role of digital technologies, overlooking how institutions and 

behaviour of entrepreneurs may change as a result of developments in the digital 

space.  

The DEE is made up of digital infrastructure, digital users, digital 

entrepreneurship and the digital marketplace (Sussan & Acs, 2017).  Digital 

infrastructure is said to include technological components and network systems 

which links users at local, national as well as at global level. Digital users refer to 

anyone who has access to digital technologies, whereas digital entrepreneurship 

involves doing business in the digital space (Sussan & Acs, 2017). 

 

 



 
22 

  

Figure 2: Conceptual model of the digital ecosystem (Sussan & Acs, 2017). 

The DEE is made up of four interrelated pillars which make it sustainable.  In the 

digital ecosystem, users and agents (entrepreneurs) utilize digital infrastructure 

innovatively and creatively. Outcomes of their work are put in the digital 

marketplace in the form of e-businesses, e-health and e-government among 

other digital outcomes (Sussan & Acs, 2017). As such, digital users and 

institutions converge in the digital ecosystem. Institutions (both formal and 

informal) enable users to participate in the digital environment by enforcing legal 

and social contracts (Sussan & Acs, 2017). Entrepreneurs, both active and 

aspiring, exploit opportunities that come as a result of participation of users in the 

ecosystem thereby initiating entrepreneurial activity. Therefore, this model of the 

digital ecosystem is helpful in this report as it sheds light on how players in the 

digital space interact and how they initiate entrepreneurial activity.  



 
23 

2.5.1 The Digital Ecosystem, Institutions and 

Entrepreneurship 

The use of digital technologies has changed the entrepreneurial ecosystem 

through leveraging the internet to execute business processes or launch a new 

business or create new business platforms to gain market traction and scalability 

(Giones & Brem, 2017). Digital technologies are considered enablers to 

entrepreneurial transformation and activity (von Briel, Davidsson & Recker, 

2018). The resulting elements take many forms including  IT enabled innovations, 

digital platforms, digital centred products and services and digital infrastructures 

(Nambisan, 2017). 

Steininger (2018) found that IT plays a key role in entrepreneurship, pointing out 

that information systems act as facilitators and mediators of venture creation. IT 

facilitates flow of business information and operations, making starting a business 

easier. Dong (2018) argues that digital entrepreneurship has been studied mainly 

in the contexts of free markets, this left a gap in studying the same in the 

regulatory environment. According to Dong (2018), digital transformation may 

enable entrepreneurs to overcome barriers that come from the regulatory 

environment. Drawing from this argument, this report considers the possibility that 

the digital ecosystem may influence perceptions of females about the regulative 

dimension of the institutional profile of a country. 

Zhang and Li (2017) presented evidence on IT access and entrepreneurship 

performance in China as well as the interaction effect between IT and social 

capital. They concluded that access to mobile communication and internet has a 

positive influence on the performance of entrepreneurs, whereas IT interacts 

positively with social capital. The digital ecosystem presents an opportunity for 

gathering information on new products and evaluating different options. Thus, it 

acts as a source of knowledge for participants, which in turn enhance 

entrepreneurial operations (Elia, Margherita & Passiante, 2020).  

The underlying drivers of entrepreneurial intention debates have received wide 

attention in literature (Autio et al., 2001; Farashah, 2015; Shahid et al., 2018).  

Again, despite the presence of literature that links the digital ecosystem to 



 
24 

entrepreneurship, there is scarcity of studies on entrepreneurial intentions in the 

digital space. Thus, the role of the digital environment and its influence on 

entrepreneurial behaviour  remains an under-researched area (Albashrawi & 

Alashoor, 2017; Dutot & Van  Horne, 2015). In addition to the institutional factors, 

this study integrates the theory of planned behaviour and institutional theory with 

the interaction effect of the digital economy and institutional dimensions on 

entrepreneurial intentions.  

By leveraging technology, digital organisations improve the customer journey and 

in turn achieve customer lifetime value and sustained business.  They achieve 

operational efficiency and reap the rewards of reduced transactional cost benefits 

of exponential networks (Dahlman, Mealy, & Wermelinger, 2016). The advent of 

the digital world has seen many businesses marketing their products on digital 

platforms and also performing all transaction digitally. This has improved 

businesses convenience and enabled firms to target a wider customer base.  

This study also recognizes the negative impact of a digital transforming economy 

in that unemployment levels soar through job losses because of digitalisation and 

automations as artificial intelligence and machine learning replace mundane 

activities (Fonseca, 2018). In the same breath, an opportunity for new jobs with 

new skill sets and more responsibility emerge, for example, in Germany for each 

job lost, 2.4 new jobs were created (Fonseca, 2018).  It is important for women 

to get involved in transforming digitally earlier on because the wider the digital 

gap, the more difficult it is to catch up (Moon, Hossain, Kang & Shin, 2012).  

Research indicates that entrepreneurial success requires a high degree of 

innovation made possible by digital skills (Kamberidou, 2020). Prior studies 

acknowledge the digital economy will help women overcome the barriers they 

face in starting business and becoming successful entrepreneurs through 

leveraging low transaction costs, access to social networks and work life balance 

(Malik, 2017). It must be noted however that all these benefits without digital 

access nor digital training or knowledge transfer may hinder entrepreneurial 

aspirations. Therefore, a deliberate conscious effort by business, society, and 

government to improve digital literacy must be a priority.  This ties in with the 

cognitive dimension discussed in the TPB as key drivers for perception, 



 
25 

embracing the digital environment to spur rather than scare aspiring female 

entrepreneurs into entrepreneurs should be the goal (Arbache, 2018). 

In summary, it can be inferred from literature that the digital ecosystem interacts 

with institutional factors through access to information, education and skills 

development, as well as facilitating ease of starting and operating new business 

ventures. Thus, the digital environment, in particular, moderates the relationship 

between institutional factors and entrepreneurial intention. With this recognition, 

the following hypotheses are formulated:  

2.5.2 Hypothesis 4a (H4a) 

Perceptions of the digital environment moderate the influence of the regulative 

dimension on EI. 

2.5.3 Hypothesis 4b (H4b) 

Perceptions of the digital environment moderate the influence of the cognitive 

dimension on EI. 

2.5.4 Hypothesis 4c (H4c) 

 Perceptions of the digital environment moderate the influence of the normative 

dimension on EI.   

2.6 Conclusion of Literature Review  

Theoretical and empirical analysis has shown that the institutional environment 

of a country may influence decisions to venture into entrepreneurship. Literature 

also acknowledges that the digital environment affects entrepreneurial 

operations, and in relation to this report, it interacts with the institutional 

dimensions. As such, the conceptual model as hypothesised in this study is 

depicted in Figure 2.  



 
26 

 

 

Figure 3: Research Model.  Source: Construction based on the ITE and TPB.   

 

 

 

 

 

 

 

 

H1 

H2 

H3 

Regulative dimension 

Cognitive dimension 

Normative dimension 

Entrepreneurial Intention 

H

Digital Environment 

H4a 

H4b 

H4c 



 
27 

CHAPTER 3. RESEARCH METHODOLOGY 

The empirical model used, research approach and design, sample size and 

sampling procedure, and definition of variables are presented in this chapter. The 

data collection procedure as well as validity and reliability of the research design 

are laid out. The chapter further outlines model estimation procedure, describing 

how objectives of the study were achieved.  

3.1 Research approach 

This study is a post positivist philosophical oriented deductive quantitative 

approach, that is scientific and comprises the use of hypotheses and research 

questions to observe and measure the study objectives (Creswell, 2014). The 

quantitative research method quantifies and analyses variables using specific 

statistical techniques (Apuke, 2017).  

Quantitative research is preferred because it achieves high levels of data 

reliability due to controlled observations and minimizes subjectivity of judgement 

(Creswell & Creswell, 2017). It also enables the development of a theoretical 

framework, presented in a model, in order to conglomerate variables and 

examine their relationship (Fischer, Boone & Neumann, 2014). This study 

undertook a deductive approach, which involves verifying theories by testing 

hypotheses or answering questions that are derived from theory (Creswell, 2014). 

This type of approach to research allows for objectivity in the analysis and 

interpretation of data, as well as data generalizability (Creswell, 2014).  

Therefore, this study addressed the following:  

a) Who was assessed? – Females across South Africa.  

b) What was assessed?  - Factors affecting entrepreneurial intention (EI) of 

females  and the moderating effect of the digital environment on the impact 

of the factors. 

c) How were they assessed?  - Structured questionnaire with closed ended 

questions. 



 
28 

3.2 Research design 

This cross-sectional study was based on a survey, which encompasses the use 

of scientific sampling methods with a designed questionnaire to measure the 

population’s characteristics through the utilisation of statistical methods. 

According to Creswell (2014), a survey is a form of quantitative research that is 

concerned with sampling, questionnaire, design, and administration for gathering 

data that enables analysis of behaviour or characteristics of respondents. The 

survey enabled the collection of demographic data, beliefs, perceptions, 

attitudes, motivations, and behaviour of respondents. Surveying a part of the 

population enables the results to be generalised to the whole population (Apuke, 

2017).  

In the field of entrepreneurship, the need for surveys arise from the desire to 

understand complex economic and social phenomenon and could effectively fill 

the void that exists due to lack of a formal method of conducting systematic 

research in design. In addition, intensive studies enable the researcher to obtain 

detailed and relevant data the researcher did not anticipate finding on the onset. 

3.3 Population and sampling frame 

 Population 

The study population consisted of females across all South African provinces, 

including tertiary level female students studying in tertiary institutions. In 

quantitative research, it is important to know the size of the population understudy 

because such information helps the researcher to determine the appropriate 

sample size. However, in this study, the researcher was not able to get 

information about the total number of adult females across the country. Therefore, 

determination of appropriate sample size was inferred from literature.  



 
29 

 Sample and sampling method 

Stratified random sampling was used to select females from different provinces 

as the respondents may not have homogenous characteristics. This type of 

sampling is used when the sample to be drawn from the population does not have 

homogeneous characteristics. It is preferred for its ability to reduce bias and 

provide a sample that is representative of the population under study (Etikan & 

Bala, 2017). In order to conduct a clear and effective survey, prior to the research 

a pilot survey was carried out where 10 females were randomly selected to 

complete the questionnaire, and necessary alterations were made to incorporate 

the feedback. 

Due to the quantitative nature of this study, the appropriate sample size should 

be selected to enable certain statistical tests to be conducted.  Field (2013)  points 

out that large sample sizes are always preferred regardless of the statistical 

technique employed. A sample size of at least 300 observations is recommended 

for factor analysis (Field, 2013). Therefore, this study targeted 300 or more 

responses, and the Kaiser- Meyer-Olkin (KMO) statistic, which ranges from 0 to 

1 (with a value closer 1 suggesting that the sample is adequate for conducting 

factor analysis), was used to test for sampling adequacy in factor analysis (Field, 

2009). The following table summarises the population and sample information 

used in this study. 

  



 
30 

Table 1: Research Techniques 

Population 
 

Females across South Africa   

Sample 
 

302 females  
 

Geographic Area South Africa 

Design of Sample Stratified  

Collection Method Online Survey  

3.4 The research instrument 

This study used an online self-administered questionnaire with structured closed 

ended questions using Qualtrics software. A research tool measuring the 

institutional environment of a country which was designed by Busenitz et al. 

(2000) was adopted for use in this study.  EI questions were adapted from the EI 

questionnaire (EIQ) developed by Liñán and Chen (2009) and used by Shaw and 

Urban (2011) in their study conducted in the South African context.  

The institutional profile and entrepreneurial intention scales were adapted for the 

purpose of this research because they have been successfully tested in South 

Africa. The scale for the digital environment, which is the moderator variable, was 

developed by the researcher, inferring from literature. Though the digital 

environment scale was never tested in prior studies, reliability and validity 

analysis indicated that the scale was reliable and consistent and thus, could be 

used for hypotheses testing. Control variables included age, province, ethnic 

group and level of education of the respondents. 

All the items in the constructs were measured on a 7-point Likert scale, with a 

score of 1 representing strong disagreement and the highest score of 7 

highlighting strongly agreement. There were no reverse coded questions in the 

questionnaire, hence the Likert scale was not reversed.  The regulative 

dimension, normative dimension, cognitive dimension, and the digital 



 
31 

environment each consisted of 4 items per construct. The EI scale consisted of 6 

items. The actual research instrument is attached in this document as Annexure 

B, and the table below summarises the research instrument as well as the 

sources from which the scales were adapted. 

Table 2: Research Instrument 

Items Construct Source 

1-4 Control variables (Shaw & Urban, 2011; 
Urban, 2013a) 

5-8 Regulative dimension (Busenitz et al., 2000; Urban, 
2013a) 

9-12 Cognitive dimension (Busenitz et al., 2000; Urban, 
2013a) 

13-16 Normative dimension (Busenitz et al., 2000; Urban, 
2013a) 

17-20 Digital Environment (Caceres-Diaz, Usero-
Sanchez, & Montoro-
Sanchez, 2019; Dong, 2018; 
Steininger, 2018; Sussan & 
Acs, 2017; Zhang & Li, 2017) 

21-26 Entrepreneurial Intention (Liñán & Chen, 2009) 

 

 

 

 Measurement of focal variables 

Dependent variable  



 
32 

Entrepreneurial Intention (EI) – The scale for measuring entrepreneurial 

intention consisted of 6 items adopted from Liñán and Chen (2009). Example 

questions included items like “My professional goal is becoming an 

entrepreneur." 

Independent variables - The influence of perceptions of institutional factors were 

measured in line with previous studies. The respondents were asked to rate a 

series of statements pertaining to their perception of the institutional environment 

on all the three dimensions. 

Regulatory dimension (RD) - Respondents were asked to note their perceptions 

on whether the government employs policies and initiatives that motivate and 

support female entrepreneurship.  

Cognitive dimension (RD) – Respondents were requested to provide their views 

on whether knowledge on how to launch or manage a business is vital in 

determining entrepreneurial intention. They were assessed on their level of 

knowledge about entrepreneurship or where to find markets for their products.  

Normative dimension (ND) - Some investigators have used specific individual 

characteristics, such as achievement, to determine entrepreneurial   behaviour. 

On the other hand, other investigators have hypothesised that individuals who 

have greater willingness to take risks in cultures, where societal identity is based 

on achievement, are more entrepreneurial orientated. In this study, however, 

example questions included ‘female entrepreneurs are admired in this country” 

(Liñán & Chen, 2009), a question which interrogates support for females in 

society. 

Moderator variable 

Digital environment (DE) – The digital environment may change the way 

aspiring entrepreneurs perceive the institutional environment which in turn affects 

their EI (Caceres-Diaz et al., 2019). As such, this variable was expected to 

moderate the relationship between the institutional environment and EI. The 

respondents were asked to rate statements such as ‘The advent of the digital 

environment has made it easier for females to venture into entrepreneurship.’  



 
33 

3.5 Procedure for data collection 

Primary data was collected from a target group of females in tertiary institutions 

across South Africa. Data collection began in August 2020 after requesting 

permission from the Wits University administration. The questionnaire links were 

sent with an introductory email to the institution, articulating the purpose of the 

research.  After permission was granted, the University sent out the questionnaire 

to Wits students in the faculties of Law, Commerce, Arts, Health Sciences and 

Engineering. These links were also distributed via WhatsApp and Facebook 

networks in order to get a diverse range of respondents, including females that 

are not in tertiary institutions.   

The survey was conducted and administered using Qualtrics software which 

allows for automatic transfer of survey responses to the researcher’s database. 

For ethical considerations, consent for respondents to participate in the survey 

was first sought from Wits and from the participants themselves. The 

questionnaire was made available to the target population, through the institution, 

and both males and females participated though the targeted sample were 

females. Further, the researcher made sure that the questionnaire was 

compatible and easy to understand, and this was tested using a pilot survey of 

10 respondents who completed the questionnaire with ease.    

Online surveys have become widely used in modern day data collection due to 

their efficiency and effectiveness as compared to direct interviewing of 

participants (Sue & Ritter, 2012). This study chose online data collection because 

it was less costly, feasible, less time consuming and could cover a wider 

geographical space, thus enabling the researcher to reach out to a large number 

of participants. 

 

3.6 Validity and reliability  

There are different types of validity which include external, and internal or 

construct validity (Wetzel, 2011). Cooper and Schindler (2014) recognise that 



 
34 

designing a research procedure has its own problems and there are always 

questions about the validity of the findings. Thus, validity can be viewed as a 

question of whether a measure accomplishes its claim. The multi-item 

questionnaire was measured by different constructs for which reliability and 

validity were tested to minimise error. 

3.6.1   External validity 

Taylor, Wald and Asmundson (2007) explain external validity as the ability to 

generalize research findings, across populations, settings, and epochs. Cooper 

and Schindler (2014) note that there are threats to external validity of a measure 

because the population from which the data is collected may not be the same as 

the one to which the survey results will be generalised. However, to ensure that 

the sample was representative and that results of this survey can be generalised, 

data was collected randomly. Again, the instrumented was previously tested in 

South Africa and was found to be valid (Shaw & Urban, 2011). 

3.6.2   Internal validity  

Cooper and Schindler (2014) define internal validity as an assessment of whether 

the instrument employed actually measures what it purports to measure to the 

extent that results from the research are free from error and inferences can be 

made from them. Internal validity can be further categorized into convergent and 

discriminant validity (Salehi, 2012; Wetzel, 2011).  

Convergent validity maintains that items that are theoretically supposed to 

measure the same thing should be correlated (Salehi, 2012). On the other hand, 

discriminant validity maintains that variables that are not supposed to measure 

the same thing in theory are actually not related, thus they should be unique 

(Wetzel, 2011). As such, inter-item correlations were used to determine 

convergent validity whereas the factor correlation matrix was used to ascertain 

discriminant validity. Inter-item correlations that are greater than 0.3 suggests 

that items correlate well and they are measuring the same thing (Field, 2018). 

However, low correlations (<0.3) imply that the constructs are unique and not 

measuring the same thing. Exploratory factor analysis was used to measure 

internal validity of the instrument. 



 
35 

3.6.3   Exploratory factor analysis 

To test for construct validity, the study used exploratory factor analysis (EFA), a 

method that enhances scale reliability by identifying inappropriate items that 

should be eliminated. This method was used by Yu and Richardson (2015), they 

argued that it identifies dimensionality of constructs by examining relations 

between items and factors when the information of the dimensionality is limited. 

Field (2018) points out that the use of factor analysis arises because scientific 

research often deals with things that cannot be directly measured that are 

referred to as latent variables. Latent variables may thus be measured indirectly 

by a number of items and factor analysis is an attempt to statistically ascertain 

whether the item measures a single variable or not.  

Field (2013) identifies three main purposes of exploratory factor analysis; these 

include understanding the variable structure, designing a questionnaire in order 

to measure a certain variable and reducing large volumes of data into more 

manageable formats without losing relevant information about the data.  SPSS 

software was used to conduct the EFA process which involves correlation 

analysis and selection of an appropriate method of factor extraction and factor 

rotation.   

3.6.4   Reliability analysis 

Reliability concerns accuracy and precision to the research procedure and the 

ability of a measure to produce consistent results (Cooper & Schindler, 2014). To 

ensure research reliability and evidence sequence adherence, a research 

framework that includes objectives and procedures is key (Yin, 1994). 

Cronbach’s alpha was used to test for reliability of the scale. It is used in 

assessing the internal consistency of a survey questionnaire that involves 

multiple Likert-type items (Cronbach, 1951). If the Cronbach’s alpha is greater 

than 0.7, the scale is considered reliable.  



 
36 

3.7 Data analysis  

Following a deductive approach to research, data analysis was conducted using 

IBM Statistical Package for Social Scientists (SPSS) software.  Data collected via 

Qualtrics was cleaned for data integrity and then exported into SPSS software to 

check for missing values and test for any violations of statistical assumptions and 

then finally test for validity and reliability of measurement scales. The following 

sections outline the procedure for data analysis as conducted in this study. 

 Missing values analysis 

Field (2013) acknowledges that primary data analysis usually faces the problem 

of missing data and that may arise because of incomplete questionnaires due to 

reasons unknown by the researcher. An online survey was conducted, and 

several responses came back incomplete, which led to a missing value analysis 

being conducted. The steps followed in the missing value analysis process 

adopted the 10 percent rule by Little and Rubin (1987) who contend that list wise 

deletion can be performed to all cases that have more than 10 percent missing 

observation. Thus, in this study, these cases were removed from the data set and 

in cases where there were less than 10 percent missing observations, the 

expectation maximisation method was used to replace all missing values and 

ensure that the whole data set was complete.  Other values that were deleted 

from the analysis were male responses as they fell outside the targeted sample.   

 Descriptive statistics  

Descriptive statistics were used to summarise sample characteristics and to 

present demographic data. A key advantage of descriptive statistics is that large 

amounts of data are simplified and presented in a manageable format (Apuke, 

2017).  

 Correlation Analysis 

The Pearson correlation matrix was constructed to test for linear associations 

between the dependent variable and independent variables and among 



 
37 

explanatory variables themselves. Correlation analysis is important because it 

enables the researcher to have a rough idea of the underlying relationships 

between predictor variables and the outcome variable and it is also useful in 

testing for multicollinearity (Field, 2013). If the absolute value of the pairwise 

correlation coefficient is below 0.3, then the relationship is considered weak, while 

correlation coefficients above 0.9 (r>0.9) imply high correlation which suggests a 

serious problem of multicollinearity (Gujarati, 2004). As such, before carrying out 

multiple linear regression analysis, it was important to first perform correlation 

analysis. 

 Regression assumptions 

Field (2018) notes that it is important to check what certain statistical assumptions 

hold before interpreting the results to make sure that the findings are reliable for 

guiding policy and can be used by future researchers. Thus, in this study, the 

following assumptions were tested before proceeding to estimation: absence of 

outliers, normality of residuals, homoscedasticity, autocorrelation and no 

multicollinearity.  

Outliers 

Field (2018) describes an outlier as value that is different from the rest of the data 

set. The presence of outliers in a data set results in estimation bias and inflated 

standard errors which affect statistical inferences like hypothesis testing and 

confidence intervals. Outliers can be detected in several ways, chief among them 

being box and whisker plots, standard deviation rule and the Turkey outlier 

labelling rule. Box and whisker plots, also referred to as the interquartile (IQR) 

range rule, were used in this study because of its popularity and availability in 

SPSS software. In SPSS, the multiplier “k” takes two values: 1.5 (IQR) and 3 

(IQR), with k=1.5 signifying “out” values and k=3 representing extreme values. 

The IQRs will have no labelling if there are no outliers.  

 Linearity 



 
38 

The classical multiple linear regression model assumes that the model is correctly 

specified as a linear function (Gujarati, 2004). The implication of this assumption 

is that the dependent variable should be linearly related to any independent 

variables and that the combined effects of predictor variables on the outcome can 

be found by summing their effects (Field, 2013).Violation of this assumption 

results in incorrect specification of the model, thus invalidating the whole model 

(Salvatore & Reagle, 2002).The Pearson correlation matrix was used to test for 

linearity in this report, whereby statistically significant correlation coefficients 

confirm that the model is correctly specified as a linear model.  

Normality of residuals 

Greene (2003) points out that in linear regression modelling, normality of the 

shape of distribution is of importance but researchers are normally concerned 

about normality of residuals. Residuals are assumed to be normally distributed. 

Violation of this assumption affects standard errors of regression which are used 

in hypothesis testing and constructing confidence intervals, thus leading to 

incorrect conclusions about the underlying relationships between variables 

(Gujarati, 2004). In this study, normality of residuals was visually assessed on 

SPSS using the normal probability plot (P-P), with values close to the diagonal 

line implying normal distribution.  

Homoscedasticity 

This assumption states that variance of the error term generated from the least 

squares model should be equal at each level of the predictor variables (Field, 

2018). Violation of this assumption results in large standard errors of regression 

which in turn affect confidence intervals and hypothesis testing (Greene, 2003). 

The residual plot was used to test for homoscedasticity in this study, and there is 

equal variance if the scatter plot of residuals falls within the -3 to 3 range (Field, 

2013). 

 

Autocorrelation 



 
39 

 Autocorrelation, also known as serial correlation, is a problem that is common in 

time series data where observations might follow a natural ordering over time 

(Salvatore & Reagle, 2002). It becomes a problem in cross-sectional data if errors 

are correlated for any two observations (Field, 2018). The Durbin-Watson (DW) 

test was used to detect autocorrelation. The DW has critical values that range 

from 0 to 4, with a value close to 2 suggesting that there is no autocorrelation and 

a value greater than 3 and less than 1 implying that there is autocorrelation 

(Greene, 2003). 

Multicollinearity 

Multicollinearity measures the degree of linear associations among predictor 

variables in a model. In this study, the Pearson correlation matrix was used for 

testing. Multicollinearity is a serious problem in linear regression if the absolute 

value of the pairwise correlation coefficient is greater than 0.9 (Field, 2018) Using 

highly correlated independent variables in the same regression model results in 

indeterminate or invalid parameter estimates (Gujarati, 2004). A possible solution 

to the problem of multicollinearity is to drop one of the highly correlated variables 

guided by theory.  

 Hierarchical multiple linear regression 

Hierarchical multiple linear regression was utilised for hypotheses testing in this 

study. It is a form of multiple linear regression which involves sequential entering 

of additional variables into the model with the aim of finding out how explanatory 

power of the model changes as more predictor variables are added Field (2018). 

Thus, it is employed to determine whether one or a set of explanatory variables 

significantly predicts the outcome while controlling for other variables in the 

model.  

Hierarchical regression was performed using the SPSS linear regression 

command, and variables were added in blocks, beginning with control variables 

and institutional factors were added one after the other to determine the change 

in explanatory power of the model (Field, 2018). Control variables which were 

categorical in nature were entered into the regression model as dummy variables 



 
40 

after dummy coding. The R-squared measures the percentage of variation of the 

dependent variable that is explained by the combined variations in independent 

variables. In hierarchical regression, if the R-squared increases after adding a 

predictor variable, it suggests that the variable is useful to the model since it 

improves explanatory power of the model.  Since the main objective of this study 

was to investigate the influence of institutional variables on entrepreneurial 

intention in the digital environment, modelling linear regression alone would not 

be sufficient, hence moderation analysis was conducted.  

Based on the constructs described in section 3.4.1, the following linear regression 

model was estimated. 

𝐸𝐼𝑖 = 𝛼 + 𝜕1𝐴𝑔𝑒𝑖 + 𝜕2𝐸𝑡ℎ𝑛𝑖𝑐𝑖 + 𝜕3𝐸𝑑𝑢𝑖 + 𝜕4𝑃𝑟𝑜𝑣𝑖 + 𝛽1𝑅𝐷𝑖 +  𝛽2𝐶𝐷𝑖 + 𝛽3𝑁𝐷𝑖

+ 𝜀𝑖 … … … … … .1, 

where 𝛼 is the constant term, 𝜕𝑠 are dummy variable coefficients , and control 

variables (age, ethnic group, education level, province), 𝛽𝑠 are coefficients of 

independent variables, subscript i represents an individual element of the sample 

(cross sectional data) and 𝜀𝑖 is the residual term.  

 Moderation analysis 

Apart from estimating the direct relationship between an outcome variable and a 

predictor variable, analysis may involve estimating the combined effect of two or 

more independent variables on the dependent (Field, 2013). This combined effect 

is called moderation or the interaction effect. A moderator variable stands 

between, or moderates, the impact of the independent variable on the dependent 

variable (Creswell, 2014).  Thus, the main idea behind moderation analysis is to 

find out how the relationship between the outcome and predictor variable 

changes as a result of another variable. In this study, the digital environment 

enters the model as the moderator variable and the variable was multiplied by 

each institutional dimension to compute the interaction terms.  

To analyse the moderation effect of the digital environment on the factors 

affecting EI, three steps were followed: 



 
41 

1. The original equation was estimated without the interaction term. 

2. The moderator variable was introduced by multiplying each independent 

variable with the moderator variable. Field (2018) emphasises that for the 

interaction term to be valid, both the predictor and moderator variable 

should be included.  

3. In the final step, the change in the coefficient of determination (R-squared) 

was assessed. A significant change implies that the moderator variable is 

relevant in moderating the influence of the independent variables on the 

dependent. Therefore, in this report, it was determined how the strength 

or direction of the relationship between EI and each of the institutional 

dimensions is affected by the digital environment. If the R-squared 

changes and is significant, then there is moderation, and the reverse is 

true if it does not change.  Changes in slopes of the predictor variables 

were also assessed to determine the impact of the moderator variable.  

Based on the outlined procedure, moderation analysis was performed by 

estimating the following three equations: 

𝐸𝐼𝑖 = 𝛼 + 𝛽1𝑅𝐷𝑖 + 𝛽2𝐷𝐸𝑖 +  𝛾𝑅𝐷𝐷𝐸𝑖 + 𝜀𝑖 … … … … … 2, 

where RDDE is the interaction term and  𝛾 is the coefficient of the interaction 

term. Equation 2 tests the interaction between the regulatory dimension and the 

digital environment. 

𝐸𝐼𝑖 = 𝛼 + 𝛽1𝐶𝐷𝑖 + 𝛽2𝐷𝐸𝑖 +  𝛾𝐶𝐷𝐷𝐸𝑖 + 𝜀𝑖 … … … … … 3 

Equation 3 tests the interaction between the digital environment and the cognitive 

dimension (CDDE) 

𝐸𝐼𝑖 = 𝛼 + 𝛽1𝑁𝐷𝑖 + 𝛽2𝐷𝐸𝑖 +  𝛾𝑁𝐷𝐷𝐸𝑖 + 𝜀𝑖 … … … … … 4 

Equation 4 tests the interaction between the normative dimension and the digital 

environment (NDDE). 



 
42 

3.8 Limitations of the study 

Despite building from a strong theoretical and empirical ground, this study has its 

own limitations which are outlined in this section: 

• The study results may not be generalised to males since the focus was on 

females. 

• Krueger et al. (2000) argues that intentions are formed overtime, thus 

necessitating longitudinal analysis. However, this study is limited in this 

respect because it is cross sectional in nature, thus only covering a point 

in time, while making it difficult to infer causality 

• Limited time was also a significant constraint towards a more in-depth 

study. 

3.9 Ethical considerations 

Ethical procedures for research exist to ensure dignity and avoidance of financial 

or emotional harm to respondents. Since this study involved human participants 

it was important to protect the rights of the participants and to make sure that the 

data collection process does no harm or result in loss of privacy (Cooper & 

Schindler, 2014).  

In order to ensure that ethical practices are adhered to, the researcher explained, 

in the form of an introductory letter, the benefits of the study, and the guarantee 

that the participants’ privacy and rights would be respected. Moreover, before 

collecting data, the researcher sought consent from the participants who were 

then informed about the objectives of the research and consideration of their right 

to anonymity. Thus, to get permission to collect data from different tertiary 

institutions, an introductory was obtained from the University administration.  

3.10 Conclusion 

This chapter outlined the methodology followed in achieving the research 

objectives. The focus was on building a research model from theory as well as 

empirical studies. The research instrument used in this study was adopted from 



 
43 

previous studies that have successfully tested it in the South African context and 

other emerging countries. Sampling framework was discussed in line with 

procedure for data analysis and ethical considerations in primary research. 

Having outlined the research procedure, chapter four of this study presents the 

results of the model estimated. 

 

 

 

 

 

 

 

 

 

 

 



 
44 

CHAPTER 4. PRESENTATION OF RESULTS 

This chapter presents, analyses, and interprets results of the study as outlined in 

chapter 3. Sample characteristics are presented in the first section, followed by 

reliability and validity of measurement scales (the Cronbach’s Alpha and 

exploratory factor analysis). The final section presents hierarchical multiple 

regression analysis as well as diagnostic tests for assumptions of multiple linear 

regression. 

4.1 Sample characteristics and demographic profiles of 

respondents 

A total of 602 responses were received from the online survey. Though the target 

sample was females, male respondents participated in the survey. 235 male 

respondents were eliminated from the sample, leaving a total of 367 female 

respondents. Of the 367 females, 65 responses were eliminated from the sample 

as they had more than 10 percent missing values (Little & Rubin, 1987). Thus, 

the sample size of 302 females after screening and deletion of missing data was 

used. The following tables now present descriptive statistics for the control 

variables used in this study. 

Table 3: Age 

AGE 

 Frequency Percent Valid Percent Cumulative Percent 

Valid 

18-29 225 74.5 74.5 74.5 

30-39 57 18.9 18.9 93.4 
40-49 14 4.6 4.6 98.0 
50-59 5 1.7 1.7 99.7 
60 and above 1 0.3 0.3 100.0 
Total 302 100.0 100.0  

 

Table 3 shows that majority of the respondents (74.5 percent) were between 18 

to 29 years, followed by 30 to 39 years (18.9 percent), 40 to 49 years (4.6 

percent), 50 to 59 years (1.7 percent) and 60 years and above (0.3 percent) 



 
45 

Table 4: Ethnic group 

Ethnic group 

 Frequency Percent Valid Percent Cumulative Percent 

Valid 

Black 199 65.9 65.9 65.9 

White 57 18.9 18.9 84.8 
Indian 17 5.6 5.6 90.4 
Colored 18 6.0 6.0 96.4 
Asian 6 2.0 2.0 98.3 
Others 5 1.7 1.7 100.0 
Total 302 100.0 100.0  

 

From table 4, black people were the dominant ethnic group constituting of 65.9 

percent of the sample. White people made up 18.9 percent, followe