Perceived impact of fintech on financial inclusion in South Africa
Leonard Runyowa
WITS Business School
Thesis presented in partial fulfilment for the degree of Master of
Business Administration to the Faculty of Commerce, Law, and
Management, University of the Witwatersrand
ii
DECLARATION
I, Leonard Runyowa declare that this research report entitled “Perceived
impact of fintech on financial inclusion in South Africa” is my own unaided work.
I have acknowledged, attributed, and referenced all ideas sourced elsewhere.
I am hereby submitting it in partial fulfilment of the requirements of the degree
of Master of Business Administration at the University of the Witwatersrand,
Johannesburg. I have not submitted this report before for any other degree or
examination to any other institution.
Leonard Runyowa
Signed at Johannesburg
Name of candidate Leonard Runyowa
Student number 720361
Telephone numbers 0712966241
Email address runyowa@gmail.com
First year of registration 2019
Date of proposal
submission
30-11-2020
Date of report submission 09-06-2021
Name of supervisor Dr Chengete Chakamera
mailto:runyowa@gmail.com
iii
ABSTRACT
Author: Leonard Runyowa Supervisor Chengete Chakamera
Thesis
title:
Perceived impact of fintech on financial inclusion in South
Africa
Globally, financial inclusion has been recognised as a critical pillar to alleviate
poverty, reduce inequality, and increase economic growth. As an emerging
economy, South Africa is still facing challenges of poverty and inequality and
is ranked one of the unequal countries in the world (World Bank, 2013).
Financial inclusion by leveraging fintech has been identified as a critical
enabler for delivering affordable financial services to under-served population
segments (National Treasury, 2020).
The primary purpose of this study is to determine the perceived impact of
financial technology on financial inclusion in the South African context. The
study's specific objectives are (i) to determine if the adoption and usage of
fintech influences financial inclusion in South Africa; (ii) to determine if fintech
can influence financial inclusion barriers and (iii) to determine if awareness of
the benefits and risks associated with fintech influence the adoption and usage
of fintech.
The study used a quantitative research strategy with a cross-sectional or
survey design. An online self-completion questionnaire was used as a data
collection instrument and non-probability sampling with convenience sampling
method to select the respondents. One hundred twenty-five respondents
completed the online survey, and data were analysed using descriptive
statistics, correlation, and regression analysis.
The findings indicate that fintech adoption and usage has a positive influence
on financial inclusion. The study also found that fintech influences financial
inclusion barriers like distance to the nearest branch, transport costs,
transaction costs, and lack of proper documentation. Consumers who use
fintech products and services no longer perceive these barriers as a hindrance
to financial access. Lastly, it was also found that awareness of the benefits
iv
associated with fintech influences fintech adoption and usage. In contrast,
awareness of the risks associated with fintech does not influence fintech
adoption and use among the respondents.
This study will contribute to the fewer studies that have looked at the impact of
fintech on financial inclusion in the South African context. The study can help
the government and financial institutions understand the impact of fintech on
financial inclusion and improve the current strategies implemented to increase
financial inclusion in South Africa.
Johannesburg, June 2021
Keywords: digital financial inclusion, financial technology, financial inclusion,
technology acceptance model
v
TABLE OF CONTENTS
DECLARATION .............................................................................................. ii
Abstract .................................................................................................. iii
TABLE OF CONTENTS .................................................................................. v
List of Tables................................................................................................ viii
List of figures.................................................................................................. ix
List of Tables and figures in the appendices ................................................... x
ACKNOWLEDGEMENTS .............................................................................. xi
Definition of key terms and concepts ............................................................ xii
ABBREVIATIONS ........................................................................................ xiii
1. Introduction to the research ..................................................................... 1
1.1 Context of and background to the study .......................................... 1
1.2 Research conceptualisation ............................................................ 3
1.2.1 The research problem statement .......................................... 3
1.2.2 The research purpose statement .......................................... 4
1.2.3 The research questions and research hypotheses ............... 4
1.3 Delimitations and assumptions of the research study ..................... 5
1.4 Significance of the research study .................................................. 5
1.5 Preface to the research report ........................................................ 6
2. Literature review ...................................................................................... 7
2.1 Research problem analysis ............................................................. 7
2.1.1 Definition of key terms ......................................................... 7
2.1.2 Financial exclusion in detail .................................................. 8
2.1.2.1Dimensions of financial exclusion ........................................ 9
2.1.3 Financial inclusion in detail ................................................. 10
2.1.3.1 Measuring financial inclusion – dimensions. .................... 10
2.1.4 Digital Financial Inclusion ................................................... 12
2.1.5 Consequences of poor financial inclusion........................... 14
2.2 Research knowledge gap analysis ................................................ 15
2.2.1 Existing knowledge gaps ...................................................... 17
2.3 Framework for interpreting research findings ................................ 17
2.3.1 Technology acceptance model - TAM ................................ 17
2.3.2 Technology acceptance model 2 – TAM2........................... 19
2.3.3 Diffusion of innovation theory ............................................. 20
2.3.4 Integrated TAM and TPB with “perceived risk and perceived
benefit.” ............................................................................... 21
2.3.5 Integrated TAM2 with perceived cost, perceived risk, and
trust..................................................................................... 22
2.4 Conclusion .................................................................................... 24
3. Research strategy, design, procedure and methods ............................. 26
3.1 Research strategy ......................................................................... 26
vi
3.2 Research Design ........................................................................... 27
3.3 Research procedure and methods ................................................ 28
3.3.1 Research data and information collection instrument(s) .... 28
3.3.2 Research target population and selection of respondents .. 28
3.3.3 Ethical considerations when collecting research data. ....... 29
3.3.4 Research data collection procedure ................................... 30
3.3.5 Research data processing and analysis ............................. 30
3.4 Research strengthens – reliability and validity measures applied. 34
3.5 Research weaknesses – technical and administrative limitations . 35
3.6 Conclusion .................................................................................... 36
4. Presentation of research results ............................................................ 37
4.1 Reliability and Validity ................................................................... 37
4.2 Descriptive statistics ...................................................................... 38
4.2.1 Response Rate ................................................................... 38
4.2.2 Demographic information .................................................... 39
4.3 Effect of fintech adoption and usage on financial inclusion. .......... 42
4.3.1 Descriptive statistics ........................................................... 42
4.4.1 Statistical hypothesis testing ............................................... 46
4.4.2 Comparison of research findings ....................................... 48
4.4 Effect of fintech on barriers to financial inclusion .......................... 48
4.4.1 Descriptive statistics ........................................................... 48
4.4.2 Statistical hypothesis testing ............................................... 51
4.4.3 Comparison of research findings .......................................... 54
4.5 Effect of fintech benefits and risk awareness on fintech adoption
and usage ..................................................................................... 54
4.5.1 Descriptive statistics ........................................................... 55
4.5.2 Statistical hypothesis testing ............................................... 56
4.5.3 Comparison of research findings ....................................... 59
5. Discussion of research findings ............................................................. 60
5.1 Effect of fintech adoption and usage influence on financial inclusion
60
5.1.1 Hypothesis testing discussion ............................................. 62
5.2 Effect of fintech on barriers to financial inclusion .......................... 63
5.2.1 Hypothesis testing discussion ............................................. 66
5.3 Effect of fintech benefits and risks awareness on fintech adoption
and usage. .................................................................................... 67
5.3.1 Hypothesis testing discussion ............................................. 68
5.4 Conclusion .................................................................................... 69
5. Summary, conclusions, implication, limitations, and recommendations 70
6.1 Summary ....................................................................................... 70
6.2 Conclusions ................................................................................... 72
6.3 Implications of the results .............................................................. 72
vii
6.4 Limitations ..................................................................................... 73
6.5 Policy Recommendations .............................................................. 73
6.6 Recommendations for future research .......................................... 74
References ................................................................................................ 75
Appendix 1.1: Data collection instrument(s) ............................................... 82
Appendix 2.1: One-page bio of the researcher including declaration of
interest in the research and funders, if any ............................. 88
Appendix 2.2: Ethic documentation ............................................................. 89
viii
LIST OF TABLES
Table 4.1: Reliability statistics ...................................................................... 38
Table 4.2: Sub-scale reliability statistics ...................................................... 38
Table 4.3: Pearson correlation - fintech usage, adoption, and financial
inclusion ................................................................................................ 46
Table 4.4: Regression coefficients – fintech usage and adoption ................ 47
Table 4.5: Model summary – fintech usage, adoption, and financial inclusion
.............................................................................................................. 48
Table 4.6: Correlations - fintech and financial inclusion barriers .................. 52
Table 4.7: Association between fintech and financial inclusion barriers ....... 53
Table 4.8: Model summary - fintech and financial inclusion barriers ............ 54
Table 4.9: Correlations - fintech adoption, usage, benefits, and risks
awareness ............................................................................................ 57
Table 4.10: Association between fintech usage, and benefits and risk
awareness ............................................................................................ 58
Table 4.11: Model summary – fintech usage, benefits, and risks awareness
.............................................................................................................. 58
Table 4.12: Association between fintech adoption and benefits and risks
awareness ............................................................................................ 59
Table 4.13: Model summary – fintech adoption, benefits, and risks awareness
.............................................................................................................. 59
ix
LIST OF FIGURES
Figure 2.1: Technology acceptance model .................................................. 18
Figure 2.2: Technology acceptance model 2 ............................................... 19
Figure 2.3: Diffusion of innovation theory ..................................................... 21
Figure 2.4: Integrated TAM and TPB model with perceived risk and benefit 22
Figure 2.5: Integrated TAM2 with perceived cost, perceived risk, and trust . 23
Figure 4.1: Gender responses ..................................................................... 39
Figure 4.2: Race responses ......................................................................... 39
Figure 4.3: Age responses ........................................................................... 40
Figure 4.4: Educational qualification responses ........................................... 40
Figure 4.5: Employment status responses ................................................... 41
Figure 4.6: Frequently used fintech products and services .......................... 42
Figure 4.7: Flexibility and control of costs .................................................... 43
Figure 4.8: Technology adoption.................................................................. 43
Figure 4.9: Online financial products and services usage ............................ 44
Figure 4.10: Safety and convenience ........................................................... 44
Figure 4.11: Access to financial products and services ............................... 45
Figure 4.12: Access to financial products and services - family and friends 45
Figure 4.13: Geographical distance barrier .................................................. 49
Figure 4.14: Transport costs barrier ............................................................. 49
Figure 4.15: Transaction costs or fees barrier ............................................. 50
Figure 4.16: Documentation barrier ............................................................. 50
Figure 4.17: Online benefits awareness ...................................................... 55
Figure 4.18: Online risk awareness .............................................................. 56
x
LIST OF TABLES AND FIGURES IN THE APPENDICES
Appendix 1.1: Data collection instrument(s) ............................................... 82
Appendix 2.1: One-page bio of the researcher including declaration of
interest in the research and funders, if any ............................. 88
Appendix 2.2: Ethic documentation ............................................................. 89
xi
ACKNOWLEDGEMENTS
I want to thank the following people for the tremendous support throughout my
MBA study journey:
➢ My Supervisor, Dr Chengete Chakamera, for the patience, guidance
and valuable support throughout the writing of this study.
➢ All the respondents who took part in the online survey.
➢ My mother Mrs B. Runyowa, my sister, and my brothers for always
encouraging me throughout this study journey. This is for you Mom.
➢ My cousin Cosmas Runyowa and his family for the constant support
throughout the study period
➢ My sister-in-law Fortune Gowera-Makamanzi and her family for the
constant support throughout the study period
➢ My two special sons, Tinotendaishe and Tadiwa Runyowa for always
keeping me company during those long study nights.
➢ Last but not least, I would like to thank my wife, Tsitsi Joyleen Runyowa,
for her tremendous support throughout this MBA study journey. Words
alone cannot express my appreciation for everything you have done for
the family and me during this study period. Your valuable support is
forever appreciated. I Love You.
xii
DEFINITION OF KEY TERMS AND CONCEPTS
Word in Title Meaning using
English Dictionary
Meaning according to academic articles
Impact have a strong effect
on someone or
something
“The positive and negative changes
produced by a development intervention,
directly or indirectly,
intended or unintended” (Chianca, 2008, p.
43).
Technology the application of
scientific knowledge
for practical
purposes, especially
in industry
“A system created by humans that uses
knowledge and organization to produce
objects and techniques for the attainment
of specific goals” (Volti, 2009, p. 6)
Sustainable the ability to be
maintained at a
certain rate or level.
“Securing the needs of the present without
compromising the ability of future
generations to meet their own needs.”
(Brundtland, 1987, p. 16)
Inclusion the action or state of
including or of being
included within a
group or structure.
“A philosophy where the belief is that
everyone has a basic right to participate
fully in society” (Peters, 1999, p. 15)
xiii
ABBREVIATIONS
Acronym Full description
ABSA Amalgamated Banks of South Africa
AFI Alliance for Financial Inclusion
ATM Automated teller machine
CGAP Consultative Group to Assist the Poor
FNB First National Bank
ICASA Independent Communication Authority of South Africa
IMF International Monetary Fund
MTN Mobile Telephone Network
OECD Organization for Economic Co-operation and Development
SABRIC South African Banking Risk Information Centre
SPSS Statistical Package for Social Sciences
TAM Technology acceptance model
TPB Theory of planned behaviour
1
1. INTRODUCTION TO THE RESEARCH
This research determines the perceived impact of fintech on financial inclusion
in South Africa. Before getting to research conceptualisation in Section 1.2,
the following section introduces the context and background of the study. First,
the research conceptualisation Section 1.2 discusses the research problem
statement Section 1.2.1, research purpose statement Section 1.2.2, research
questions and research hypothesis Section 1.2.3. Next, section 1.3 discusses
the delimitations and assumptions of the research study. Finally, section 1.4
discusses the significance of the research study, and the chapter closes with
a preface of the research study in Section 1.5.
1.1 Context of and background to the study
Globally issues relating to financial inclusion, providing affordable financial
products and services to all population segments have been a top priority for
governments worldwide and different organisations (World Bank, 2014). Yet,
more than 1.7 billion adults worldwide remain unbanked without a transaction
account at a bank or a mobile money services provider (Demirguc-Kunt,
Klapper, Singer, Ansar, & Hess, 2017).
In 2010 at the G20 Summit in Seoul, financial inclusion was recognised by G20
leaders as one of the critical pillars to increase economic growth, end poverty
and inequality around the world (Timmermann 2017). Research has shown
the positive impact of financial inclusion in increasing national economic
growth, reducing income inequality, poverty alleviation and raising productivity
growth (Barajas, Beck, Belhaj, & Naceur, 2020; Demirguc-Kunt, Klapper, &
Singer, 2017). Access to formal financial systems allow people to make
payments, remittances, savings, insurance, credit, wealth management
efficiently and safely without fear of losing their money (Demirguc-Kunt,
Klapper, & Singer, 2017).
The Global Financial Crisis of 2008 was the catalyst that saw the sudden rise
of fintech companies worldwide. Fintech companies are “financial service
2
firms whose product or service is built upon technology, often resulting in highly
innovative, pioneering services” (Gelis & Woods, 2014, p. 3). Governments
can utilise these innovative technologies in the financial services industry to
expand financial inclusion to previously excluded segments of the population
by reducing the barriers to financial access in areas like mobile payments,
remittances, credit access, insurance, savings etc (World Bank, 2014).
Since the advent of democracy in 1994, which saw the end of apartheid rule
in South Africa, significant gains have been achieved in social equity and
reduction of extreme poverty; however, the country still has challenges of
poverty and inequality (Philip, Tsedu, & Zwane, 2014). South Africa is ranked
one of the unequal countries in the world, with a highly polarised society where
the gap between the rich and the poor is very high (World Bank, 2013).
Furthermore, with over 55 million people (StatsSA, 2016), the country is still
facing severe challenges addressing historical economic imbalances. Slow
economic growth, high unemployment has hampered government efforts to
reduce poverty and inequality (NationalTreasury, 2020).
The country boasts of a sophisticated, well-regulated, and well-developed
financial sector with proper infrastructure; however, the biggest challenge has
been the slow pace by the private sector to engage with low-income segments
and tap into this market (National Treasury & AFI, 2014). In 2004 the
government of South Africa, in partnership with four big private banks - FNB,
Nedbank, ABSA, Standard Bank, working closely with Postbank, launched the
Mzansi account, which was a low-cost entry-level transactional account
(Kostov, Arun & Annim, 2015).
The Mzansi account initiative was a success in promoting the adoption of bank
accounts and extending financial services to the previously excluded adults in
South Africa; however, the usage of the bank accounts was shallow (National
Treasury & AFI, 2014). In its draft financial inclusion policy framework, the
National Treasury has identified the leveraging of fintech to promote and
support financial inclusion in South Africa as one of its key priorities (National
3
Treasury, 2020). The main objective of this study is to determine the perceived
impact of fintech on financial inclusion in the South African context.
1.2 Research conceptualisation
1.2.1 The research problem statement
South Africa is one of the world's most unequal countries, with a highly
polarised society where the poor and the rich gap is very high (Dessus &
Hanusch, 2018). The country's economy has a dual economy, with a well-
developed high-end economy co-existing with a less developed low-end
economy functioning in the townships, informal settlements, and rural areas
(Bojabotseha, 2011).
Among the several strategies taken by the South African government to
address the problems of poverty and inequality, financial inclusion through
leveraging fintech has been identified as a critical enabler to deliver quality and
affordable financial services to the previously disadvantage and under-served
segments of the population (National Treasury, 2020). However, fintech has
not been influential in South Africa, as evidenced by the failure of M-PESA
launched by Vodacom SA in 2010 in partnership with Nedbank (Rouse &
Verhoef, 2016). Despite this viewpoint, studies such as Kinsman (2019),
Finscope (2017), Nyoka (2019) and Chigada and Hirshfelder (2017) have
presented an opposite view that the use of technology has made progress
towards increasing financial inclusion in South Africa. In addition, limited
studies focused on the impact of fintech on financial inclusion in the South
African context.
Given the above, the main problem is whether the implied financial inclusion
in South Africa is associated with the adoption and usage of fintech on the
ground. For example, the Mzansi account initiative managed to increase the
adoption of transactional bank accounts but failed to address usage issues,
propensity to save and sustainability (National Treasury & AFI, 2014).
4
An accompanying sub-problem of interest in this study is whether fintech
influences the barriers to financial inclusion in the South African context. Some
of the reasons why most South Africans are not using banks are high bank
fees, mistrust of the formal financial sector in fear of exploitation, fraud, sense
of trust with community groups like stokvels, too much documentation required
by banks, and most people run their businesses informally (Kessler et al.,
2017a)
Another sub-problem of interest is whether awareness of the benefits and risks
associated with the use of fintech can influence the adoption and usage of
online financial products and services in South Africa. The use of digital
technology is creating benefits for consumers to engage with financial
services; however, some risks come with using these technologies (National
Treasury, 2020).
1.2.2 The research purpose statement
The primary purpose of this study is to determine the perceived impact of
fintech on financial inclusion in South Africa.
The specific objectives of the study are:
• To determine if adoption and usage of fintech can influence financial
inclusion.
• To determine if fintech can influence barriers to financial inclusion.
• To determine if awareness of benefits and risks associated with fintech
influence the adoption and usage of fintech.
1.2.3 The research questions and research hypotheses
• Does fintech adoption and usage influence financial inclusion?
H0: Fintech adoption and usage does not influence financial inclusion.
H1: Fintech adoption and usage can influence financial inclusion.
• Does fintech influence barriers to financial inclusion?
H0: Fintech does not influence barriers to financial inclusion.
5
H1: Fintech can influence barriers to financial inclusion.
• Does awareness of benefits and risks associated with fintech influence
adoption and usage?
H0: Awareness of benefits and risks associated with fintech does not
influence adoption and usage.
H1: Awareness of benefits and risks associated with fintech influence
adoption and usage.
1.3 Delimitations and assumptions of the research study
During the data collection process, the researcher assumed the respondents
answered all the questionnaire questions truthfully. Unfortunately, due to the
Covid-19 pandemic, time constraints and limited resources, data collection for
this study was limited to adults living in South Africa who could read and write
with access to an electronic device connected to the internet.
The researcher chose to focus on fintech ahead of other factors that can
impact financial inclusion because this is a new trend. Due to technology, it is
easier and quicker for people to adopt it than other factors mentioned below.
Previous studies related to the use of technology and financial inclusion have
focused more on the impact of mobile money on improving financial inclusion
in developing economies and not fintech.
The study did not focus on other factors that impact financial inclusion like
demographic factors, financial literacy, language barriers, religious beliefs,
legal framework, and institutional factors like governance.
1.4 Significance of the research study
According to the World Bank (2018), financial inclusion is considered one of
the key enablers to reduce poverty and inequality and boost developing
economies like South Africa. South Africa is among the world's unequal
countries, where the poor and the rich gap is very high (Dessus & Hanusch,
2018). Thus far, the South African government's measures to address these
6
issues have benefited only a few black elites and left the general population in
extreme poverty (Ponte, Roberts, & Sittert, 2007). Therefore, the impact of
fintech in promoting and supporting financial inclusion in the South African
context is one of the key priorities in the recently drafted financial inclusion
policy framework for South Africa by the National Treasury department
(National Treasury, 2020).
This study aims to investigate the perceived impact of fintech on financial
inclusion in South Africa. This study is essential to the government and the
financial services sector as it addresses priority 16 in the draft financial
inclusion policy framework by National Treasury, “Leveraging fintech
disruptors to promote and support financial inclusion” (National Treasury,
2020, p. 4). Studying the impact of fintech on South Africa's financial inclusion
is of great significance, provide insights into the country's battle to reduce
poverty and inequality through financial inclusion. The study will also contribute
to the already limited academic literature on the perceived impact of fintech on
financial inclusion in the South African context.
1.5 Preface to the research report
To this end, the report has six chapters. Following this introductory chapter,
Chapter 2 provides a literature review covering the research problem analysis,
the previous studies, the explanatory framework, research knowledge gap
analysis and the conceptual framework. Chapter 3 discusses the research
strategy, design, procedures, reliability, validity measures, and limitations.
Chapter 4 presents the research results, statistical analysis, reliability, and
validity. Chapter 5 presents the research findings, and finally, Chapter 6 closes
off the research report by presenting the research summary, conclusions, and
recommendations for future studies.
7
2. LITERATURE REVIEW
This chapter has three broad objectives: understanding the research problem,
identifying the knowledge gap, and developing a framework for interpreting the
results and research findings. Specifically, in Section 2.1, we detail the
research problem. Then, in Section 2.2, we review the literature on studies that
have attempted similar research. Finally, with this knowledge, identify and
describe a framework that we will use to interpret our research findings in
Section 2.3.
2.1 Research problem analysis
South Africa is facing severe challenges in trying to addressing the post-
independence challenges of high inequality and poverty. The country has
about 2.9 million adults still excluded from the formal financial systems, still
not fully utilising the financial access benefits (National Treasury, 2020). On
the surface, the country looks inclusive, but the high adoption of transaction
accounts is not matched by increased usage of the products and services
(Kessler et al., 2017a).
In partnership with the private sector, the government of South Africa has
made initiatives like the implementation of the Mzansi account to try and
increase the country's financial inclusion. However, these initiatives have
managed to increase the adoption of transactional bank accounts but failed to
address usage issues, propensity to save and sustainability (National Treasury
& AFI, 2014). In addition, lack of awareness and experience with technology
are significant challenges that impact the adoption of digital services among
low-income groups; technology can help reduce some financial inclusion
barriers (Tobbin, 2012).
2.1.1 Definition of key terms
Financial exclusion
It is “a process whereby people encounter difficulties accessing and or using
financial services and products in the mainstream market that are appropriate
8
to their needs and enable them to lead a normal social life in the society in
which they belong” (Anderloni, Bayot, Blędowski, Iwanicz-Drozdowska, &
Kempson, 2008, p. 9). Individuals can be financially excluded when they
cannot access financial products and services at an affordable cost and
appropriate way (Kodan & Chhikara, 2013).
Financial inclusion
“It refers to all initiatives that make formal financial services available,
accessible, and affordable to all segments of the population. This requires
particular attention to specific portions of the population that have been
historically excluded from the formal financial sector either because of their
income level and volatility, gender, location, type of activity, or level of financial
literacy” (Triki & Faye, 2013, p. 25).
In the South African context, National Treasury defines financial inclusion as
“the delivery of financial services at an affordable cost to vast sections of the
population that are historically excluded or under-served by the formal financial
sector” (National Treasury, 2020, p.1).
Financial technology - fintech
Financial technology provides or delivers financial and banking services using
technological innovation driven by computer software and complex algorithms
(Ozili, 2018).
2.1.2 Financial exclusion in detail
It's essential first to comprehend financial exclusion to understand financial
inclusion fully (Abrahams, 2017). Financial exclusion can be categorised as
voluntary self-exclusion; individuals who have access to financial services
choose not to use them because there is no need, cultural or religious reasons
(Cámara & Tuesta, 2014). The second category is involuntary exclusion,
where barriers like physical distance, lack of proper infrastructure, low income,
discrimination, high costs, the product does not meet the customer needs
comes into play (Demirgüç-Kunt, Beck, & Honohan, 2008). Finally, most
financially excluded individuals are the youth, elderly, illegal migrants, who
9
struggle with proper documentation, unemployed, and minorities (Bimha,
2015).
2.1.2.1 Dimensions of financial exclusion
Many factors cause people to be financially excluded from using formal
financial systems, as discussed below:
Geographical exclusion - Geographical location can impact access to financial
services in many ways, and it can lead to the closure of bank branches in
remote areas, poor infrastructure like roads and telecommunication systems
which forces financial services to shun these isolated areas (Kempson, 2000;
Kempson, Whyley, Caskey, & Collard, 2000). In addition, the lack of financial
services in these remote areas deprives these communities of a chance of
participation in the mainstream economy (Corr, 2006).
Access exclusion - Risk assessment by financial institutions might lead to
financial exclusion as many people in low-income communities might not meet
the standards required by these institutions to grant access to credit (Kempson
et al., 2000).
Condition exclusion - Financial service providers sometimes can attach certain
conditions to access their financial products, and these conditions might not
be appropriate for the low-income consumer's needs (Kempson et al., 2000).
In addition, the profit nature of these institutions forces them to create
sophisticated products that are only suitable for their profitable customers
(Corr, 2006).
Price exclusion - The bank charges or fees paid to maintain these financial
products can be unaffordable for many people, leading to using traditional
channels that are cheaper or at no cost (Kempson et al., 2000).
Marketing exclusion - Target marketing and sales strategies by financial
service providers can lead to financial exclusion, and these institutions could
10
target only their high income or profitable customers. In addition, marketing
exclusion can lead to information exclusion or blackout for low-income
customers (Corr, 2006).
2.1.3 Financial inclusion in detail
2.1.3.1 Measuring financial inclusion – dimensions.
Data is available from various sources for the supply-side and demand-side
data to measure financial inclusion in a country or region. The supply-side
information is available from multiple sources like CGAP, World Bank, African
Development Bank, IMF. At the same time, Finscope, OECD, World Bank -
Global Findex provide demand-side data (Triki & Faye, 2013).
To understand financial inclusion deeper, we need to look at it from a multi-
dimensional perspective rather than looking at it from the included or not
included view (Triki & Faye, 2013). Financial inclusion should not only be about
the availability of financial services, but it should also consider if other financial
systems dimensions are working appropriately to meet the supply and demand
side, and it should be sustainable (Arcdic, Chen, & Latortue, 2012).
Various indicators determine the dimensions below from the demand side -
consumers and supply-side - financial service providers (Cámara & Tuesta,
2014).
• Access - Availability of regulated financial service providers with little or
no potential barriers like distance to the physical branches, automated
teller machines and agent locations (Arcdic et al., 2012; Cámara &
Tuesta, 2014).
• Usage – Usage of the available financial products and services,
frequency and duration of use (Arcdic et al., 2012).
• Quality – Product-market fit, do the products address customers' needs
and segmentation (Arcdic et al., 2012).
• Sustainability - The whole financial services ecosystem is beneficial to
all stakeholders, and the consumers have access to affordable financial
11
products. At the same time, the financial service providers also profit
from their products and services (Kessler et al., 2017).
When analysing data to get the extension of financial inclusion, it is vital to
combine the data from all the dimensions because access does not imply
usage, and usage does not mean the product or service meets the customer
needs (Cámara & Tuesta, 2014).
2.1.3.2 Barriers to financial inclusion
The barriers to financial inclusion vary from region to region, and these barriers
are sometimes complex and interlinked (Abrahams, 2017). Lack of money,
transaction costs, transport costs, physical distance to the nearest bank
branch, and lack of proper documentation are the most common barriers
among African adults (Zins & Weill, 2016). Globally lack of money is the most
common barrier to financial inclusion for adults, and some self-reported
barriers like distance to the nearest branch, high bank costs, lack of trust in the
financial system and lack of proper documentation (Allen, Demirguc-Kunt,
Klapper, & Peria, 2012; Zulfiqar, Chaudhary, & Aslam, 2016).
Financial inclusion barriers can be demand or supply barriers. The demand-
side barriers are related to the customer perceptions that influence their choice
of using the formal financial systems like financial service provider reliability,
product design, product simplicity, transparency, fees, and distance to the
nearest branch (Imaeva, Lobanova, & Tomilova, 2014). On the other hand,
supply-side barriers include distance to the nearest branch, inappropriate and
complex products, limited product range, policy regulation, lack of proper
technology infrastructure (Bhuvana & Vasantha, 2016). In addition, for many
financial service providers, high operating costs due to outdated technology,
legacy business models and limited innovations make the processing of small
transactions unprofitable, leading to the neglect of creating products that meet
low-income consumers' needs (Pazarbasioglu et al., 2020a).
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2.1.3.3 Sustainable financial inclusion
Sustainable financial inclusion in an economy must address all facets of the
financial ecosystem, the demand side - consumer needs, supply-side -
financial service providers operating models, and the regulatory environment -
public-private sector cooperation in creating a conducive environment for
business (Kessler et al., 2017). Financial services providers do not create
products and services that meet the needs of low-income segments because
it is not profitable (Corr, 2006).
According to Kessler et al. (2017), when financial inclusion is sustainable in a
country, it means the following:
• Transaction accounts - Bank fees are affordable to the consumer, and
a bank's operating costs are compatible with those fees.
• Credit - Loan repayments are affordable to consumers, and the service
providers’ cost structure is compatible with the payments.
• Insurance - Consumers are paying affordable premiums, and insurance
providers operating costs are compatible with the premiums.
• Savings - Savings must benefit the consumer, and the operating costs
of the service provider are compatible with the interest rates.
2.1.4 Digital Financial Inclusion
Digital financial inclusion is providing formal financial services to low-income
consumers, unbanked and the underbanked using digital technology channels
(Duoguang et al., 2016). This process ensures that all economy members have
easy access, availability, and usage of financial systems through digital
technologies (Kesuh, Ngong, & Manasseh, 2020). In addition, digital
innovations in the financial services industry have reduced access and costs
barriers among the financially excluded population in various areas like digital
payments and remittances, digital savings, digital wealth management, digital
credit service, and digital insurance (Patwardhan, Singleton, & Schmitz 2018).
Notable digital technology success stories worldwide include mobile money
transfer M-PESA in Kenya, mobile banking in India, and peer-to-peer lending
platforms in China (Arner, Buckley, & Zetzsche, 2018; Guild, 2017). According
13
to Zhang and Yang (2019), the integration of finance, innovation and digital
technology has ushered in a new era of inclusive finance, which presents
opportunities to promote economic growth and poverty alleviation in a
sustainable way. In addition, the use of various technologies like the internet
of things, big data, cloud computing, blockchain, and artificial intelligence can
help reduce costs, increase efficiency, and optimise different operations in
financial services (Zhang & Yang, 2019).
2.1.4.1 Digital financial inclusion opportunities
Digital technologies can increase financial inclusion through the rapid growth
in mobile payments, mobile banking and the use of biometric data, which can
reduce costs, physical distance barriers and documentation barriers (Bimha,
2015). These technologies offer cost-effective, easy, convenient, and efficient
ways for poor households and small businesses to save, access credit,
insurance and make secure payments (Pearce, Michaels, Kachingwe, &
Iravantchi, 2017). Digital technologies are transforming financial business
models in payments, remittances, lending, insurance, wealth management and
financial planning, all providing an opportunity to create cost-effective products
that meet the needs of all segments of society (Pazarbasioglu et al., 2020).
The use of digital technologies can also help improve the government
efficiency in the payment of grants or social benefits to the poor, which can
also help to reduce crime, corruption (Shipalana, 2019). According to
Pazarbasioglu et al. (2020), digital finance combined with technology can help
financial service providers reduce cost, increase efficiency, security,
optimisation, transparency, eliminate physical distance barriers and tailor
products for the poor sustainably. It has been evident during the Covid-19
pandemic, where mobile money payments and online transactions have
enabled individuals to transact with little or no physical contact (Pazarbasioglu
et al., 2020).
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2.1.4.2 Digital financial inclusion risks and challenges
Despite all the benefits of digital finance, technology to improve financial
inclusion comes with risks and challenges to the consumers and service
providers. The use of digital finance poses data governance and privacy
protection challenges, cybersecurity challenges, financial integrity challenges,
and fair competition issues where big companies can monopolise the industry
(Pazarbasioglu et al., 2020). According to Shipalana (2019), service providers
can use unethical practices to increase profits and lure unsuspecting
customers, and heavy regulation can impact investment in the industry which
can reduce competition. This can severely impact both consumers and the
service providers, and consumers will lose trust in the system and digital
technology, leading to further exclusion (Shipalana, 2019).
The profit nature of corporations can lead to further exclusion through
marketing exclusion and withdrawal of services to remote geographical areas
like rural areas with poor infrastructure (Ozili, 2018). Other challenges might
include consumer over-indebtedness through digital credit, data security and
confidentiality issues, discrimination via digital credit scoring, and those will
literacy issues will be excluded (Pazarbasioglu et al., 2020).
2.1.5 Consequences of poor financial inclusion
Poor financial inclusion can lead to financial, social, and economic
repercussions. Financially excluded individuals might suffer from over-
indebtedness (Gloukoviezoff, 2007; Russell, Maître, & Donnelly, 2011), social
exclusion and mental health issues (Hajaj, 2002). Financially excluded
individuals have difficulties finding employment, accessing credit, meeting
some of the financial commitments that might need services of financial
services providers like paying for electricity bills and rentals. They end up
resorting to risk and expensive channels like cash payments or accessing
credit via informal systems with high interests rates (Kodan & Chhikara, 2013;
Smyczek & Matysiewicz, 2014).
15
2.2 Research knowledge gap analysis
First, evidence from China includes Huang, Wang, and Wang (2020)
investigated the impact of mobile payments on China's financial inclusion. The
study used a sample of 15926 household observations, and their findings
suggest that the use of mobile money payments systems improves risk-
sharing among the consumers, increases small and medium business or
entrepreneurship opportunities, and has a positive impact on financial
inclusion (Huang, Wang, & Wang, 2020).
Second, in terms of evidence from India, Nandhi (2012) conducted a study to
explore how mobile banking affects the savings behaviour of low-income
consumers in Dehli, India. The study revealed four key findings. First mobile
banking helped the customers to save compared to the traditional way of
keeping their money in cash. Second, customers found saving through mobile
banking to be effective, simple, secure, and trustworthy and dependency on
risky saving methods reduced drastically among the customers. Third,
customers found mobile banking to be a viable substitute to traditional banking
and informal ways of savings. Fourth, customers were now using mobile
banking in conjunction with the other saving practices (Nandhi, 2012). These
results agree with Kozma (2016) analysing mobile money's impact on financial
inclusion in Sub-Saharan Africa (Kozma, 2016). Suri and Jack (2011) also
found similar results that mobile money is not a competitor but a complimentary
service for making savings for M-PESA customers in Kenya (Jack & Suri,
2011).
Third, evidence from Africa excluding the SADC region include Wakaba and
Wepukhulu (2019), who conducted a study to find the effect of mobile money
on Kenya's financial inclusion. The quantitative research study used secondary
data between 2013 and 2018 collected from a sample of 4 telecoms
companies in Kenya. The study showed a positive and significant relationship
between Kenya's financial inclusion and the use of mobile money for deposits,
savings, payment of bills and agency banking (Wakaba & Wepukhulu, 2019).
Findings from this study concurred with studies like Vong, Fang and Insu
16
(2012) on mobile deposits, who noted that the high number of mobile deposits
indicates financial inclusion (Vong, Fang, & Insu, 2012). Furthermore, Ouma,
Odongo and Were (2017), on the issue of savings, noted that when unbanked
and underserved have access to mobile savings service, it increases financial
inclusion (Ouma, Odongo, & Were 2017). In addition, Kya, Rugemintwari and
Sauviat (2017) showed that mobile money encouraged a saving culture among
the low-income groups (Kya, Rugemintwari, & Sauviat, 2017).
Fourth, evidence from the SADC region excluding South Africa. Mutsonziwa
and Maposa (2016) investigated mobile money in a developing country using
Zimbabwe as a case study. The study used credible and reliable Finscope
national survey data collected in Zimbabwe in 2011 and 2014, with a sample
size of 4000 adults each year. The study found that mobile money was popular
among the adult population, with more than 45% using it, over 65% saying it's
convenient, and 36% saying it's affordable (Mutsonziwa & Maposa, 2016).
Mago and Chitokwindo (2014) conducted a qualitative research study in
Zimbabwe to investigate the impact of mobile banking on financial inclusion in
the country. Using data collected through a descriptive survey from four
districts in Masvingo province, the study used a sample size of 270
respondents. The results showed that low-income households previously
excluded from the financial system have accepted and adopted mobile
banking because they find it easily accessible, convenient, cheaper, easy to
use and secure to send and receive money (Mago & Chitokwindo, 2014).
Fifth, in South Africa, Kinsman (2019) examined the underbanked consumers'
perceptions and intentions on card-less banking in Nelson Mandela
Metropolitan. The quantitative research study used a sample of 175
respondents of underbanked consumers in the province who reside in
townships. The results revealed if card-less banking is simpler to use and less
complex, more unbanked consumers would be willing to adopt it. These results
show that underbanked consumers can embrace technology for their day to
day transactions as long as it's easily accessible, cheaper, simpler and
convenient to them (Kinsman, 2019).
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2.2.1 Existing knowledge gaps
The studies discussed above all agree that mobile money has a positive impact
on financial inclusion. However, there is a gap in the literature, especially for
research studies that looked at other technologies other than mobile money
for financial inclusion like fintech in the South African context. For example,
Dragoş (2017) looked at how blockchain technology impacts financial inclusion
in the United States. Danho (2019) investigated the perceived usefulness of
blockchain technology on mobile money services in Sub-Saharan Africa.
Yermack (2018) looked at fintech products that have succeeded and failed in
Africa. However, literature on the impact of fintech on financial inclusion in
South Africa is very scarce. Studies that have attempted to investigate the
effect of technology on financial inclusion in South Africa include Kinsman
(2019). He examined the impact of card-less banking on financial inclusion in
Nelson Mandela province. Abrahams (2017) reviewed financial inclusion in
South Africa looked at technologies that have succeeded in the country. This
research study intends to narrow this gap by investigating the perceived impact
of fintech on financial inclusion in the context of South Africa.
2.3 Framework for interpreting research findings
This section looks at the theories and models for interpreting the research
findings. Theories and models play a critical role in research as they provide
frameworks for designing and interpreting the results in research studies (Kim
& Crowston, 2011).
2.3.1 Technology acceptance model - TAM
Davis (1989) developed the technology acceptance model, influenced by
reasoned action theory and reasoned behaviour theory (Kim & Crowston,
2011). TAM is used to forecast how an organisation will adopt new technology
(Aiolfi & Bellini, 2019). In addition, Davis (1989) added two new constructs,
“perceived usefulness and perceived ease of use” (p.320). Davis (1989)
defined the two constructs as follows:
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• Perceived usefulness – “the degree to which a person believes that
using a particular system would enhance his or her job performance”
(Davis, 1989, p. 320)
• Perceived ease of use – “the degree to which a person believes that
using a particular system would be free of effort” (Davis, 1989, p. 320)
Davis (1989) claimed if a user believed the system was beneficial to their
performance at work and easier to use, they are more likely to adopt and
accept it.
Figure 2.1 depicts the technology acceptance model.
Figure 2.1: Technology acceptance model
Source: (Davis & Venkatesh, 1996, p. 20)
The technology acceptance model has utilised in several studies. It was first
used at IBM in Canada to test the adoption of email service, and a file editor
and the findings on perceived usefulness validated the model (Sharma &
Mishra, 2014). In addition, King and He (2006) found it to be a “valid and
robust” model that can be beneficial in many areas (King & He, 2006).
However, many researchers have criticised the technology acceptance model
for being constantly modified, and it's not easy to understand which version is
acceptable considering the fast-changing technology environment (Benbasat
& Barki, 2007; Otieno, Liyala, Odongo, & Abeka, 2016). It has also been
criticised for being suited to be used only in an organisational setting and not
in other everyday settings (Lule, Omwansa, & Waema, 2012; Venkatesh &
Davis, 2000).
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2.3.2 Technology acceptance model 2 – TAM2
Technology acceptance model 2 (TAM2) is an extension of the original
technology acceptance model. According to Venkatesh and Davis (2000),
“TAM2 incorporates additional theoretical constructs spanning social influence
processes (subjective norm, voluntariness, and image) and cognitive
instrumental processes (job relevance, output quality, result demonstrability,
and perceived ease of use)” (Venkatesh & Davis, 2000, p. 187).
Figure 2.2 depicts the technology acceptance model 2.
Figure 2.2: Technology acceptance model 2
Source: (Venkatesh & Davis, 2000, p. 188).
When consumers perceive advantages and serviceability when using fintech,
they are more likely to adopt and use these products and services. Additionally,
when fintech products and services are easier to understand and use,
consumers will be more inclined to adopt and use these products and services.
The technology acceptance models - TAM and TAM2 will help this study make
sense of the observations and results. The constructs “perceived usefulness”
and “perceived ease of use” will help us understand how they influence
“behavioural intention” to adopt and use fintech products and services. The
20
model will be used to hypothesise the correlation or linear relationship between
the dependant and independent variables (Pigott, 2017).
2.3.3 Diffusion of innovation theory
Rogers (1995) defined diffusion as the process of communicating an
innovation over a certain period among the population, where innovation can
be an idea or behaviour the people perceive as new. The theory focuses on
reinventing the product or idea to meet the people's needs than persuading
the people to adopt the change (Robinson, 2009). According to Rogers (1995),
the adoption rate is the speed of adoption or the number of people who adopt
the innovation over a specified period. The following variables determine the
rate of adoption:
• Relative advantage – the degree to which the new idea, technology or
behaviour is perceived as better than its predecessor by the audience
regarding its performance, convenience, cost, or risk. The audience
should view it as advantageous (Rogers, 1995).
• Compatibility – The degree to which the new idea or innovation is
consistent with the current norms, previous experiences, and the
audience's needs (Rogers, 1995).
• Complexity – The level of difficulty to use or understand the innovation
(Rogers, 1995).
• Trialability – the extent to which the new idea or innovation can be
experimented with (Rogers, 1995).
• Observability – the extent to which the audience can see the new idea
or innovation (Rogers, 1995).
“Innovations that are perceived by individuals as having a greater relative
advantage, compatibility, trialability, observability, and less complexity will be
adopted more rapidly than other innovations” (Rogers, 2002, p. 990). Figure
2.3 depicts the diffusion of innovation theory.
21
Figure 2.3: Diffusion of innovation theory
Source: (Rogers, 1995, p. 207).
The diffusion of innovation constructs “relative advantage, compatibility,
trialability and observability” will help understand how they influence users’
perceptions to adopt and use fintech products and services.
2.3.4 Integrated TAM and TPB with “perceived risk and perceived
benefit.”
The model was developed by Lee (2009) after integrating the technology
acceptance model and the theory of planned behaviour with the perceived risk
theory in a mobile banking adoption study. The perceived risk theory is always
used to explain consumer behaviour. Perceived risk has the following
dimensions:
• Performance risk – the potential loss due to the product not working as
designed or advertised, hence failing to deliver the promised benefits
(Lee, 2009).
• Social risk – potential loss or diminished social standing results from
adopting the product or service (Lee, 2009).
• Financial risk – potential money lost after buying the product or
transactional errors (Lee, 2009).
22
• Security risk – potential loss due to security breach on the system, fraud
or hacking leading to loss of personal information (Lee, 2009).
• Convenience/time risk – potential time lost learning or using the system
only for the product failing to perform as expected (Lee, 2009).
• Physical risk – potential harm resulting from purchasing the product.
Perceived benefit – According to Lee (2009), the perceived benefits can be
direct or indirect advantages. Direct advantages are tangible benefits that
users get from using the system like improved performance, increased
security, lower transaction fees, automation of tasks etc. Indirect advantages
are the benefits that are intangible and not easy to measure, like convenience
(Lee, 2009). Figure 2.4 depicts the integrated TAM and TPB model.
Figure 2.4: Integrated TAM and TPB model with perceived risk and
benefit
Source: (Lee, 2009, p. 133)
Studies that have utilised the perceived risk construct in technology adoption
studies include (Masinge, 2010; Tan & Teo, 2000; Wu & Wang, 2005).
2.3.5 Integrated TAM2 with perceived cost, perceived risk, and trust
Masinge (2010) proposed a new model that used original TAM2 constructs of
perceived usefulness and perceived ease of use, combined with the five
23
characteristics of perceived risk, and added two more constructs, perceived
cost, and trust. Figure 2.5 depicts the integrated model.
Figure 2.5: Integrated TAM2 with perceived cost, perceived risk, and
trust
Source: (Masinge, 2010, p. 27)
Perceived cost – defined as the degree to which a user believes using a
system will cost them money, including fees, transaction costs, mobile data
costs, and device costs (Masinge, 2010).
The more consumers understand the perceived benefits and risks associated
with the use of fintech products and services, the more likely they are to make
an informed decision to adopt and use them. Furthermore, when the perceived
benefits and perceived costs outweigh the perceived risks, they will be more
inclined to use and adopt fintech products and services.
The constructs perceived risk, perceived benefit, perceived cost discussed in
the integrated model above in Section 2.3.4 and Section 2.3.5 would help us
understand how they influence users' behavioural intention to adopt and use
fintech products and services.
24
2.4 Conclusion
A literature review was presented in this chapter which examined three general
objectives for understanding the research problem, identifying the knowledge
gap, and developing an interpretation framework for the research results and
findings.
The literature has shown that barriers to financial inclusion vary from region to
region, and these barriers can be complex and interrelated (Abrahams, 2017).
Lack of money, geographical distance to the nearest bank branch, lack of
proper documentation, high transaction and transport costs, and lack of trust
in the financial systems are some of the barriers to financial inclusion (Allen,
Demirguc-Kunt, Klapper, & Peria, 2012; Zulfiqar, Chaudhary, & Aslam, 2016).
According to literature, leveraging financial technologies in the areas of
payments, remittances, lending, insurance, wealth management, etc. can be
helpful for financial inclusion and reduce the barriers to financial inclusion
(Bimha, 2015). Despite the opportunities, financial technologies come with
risks and challenges for both consumers and service providers, such as data
governance and privacy protection, cybersecurity, fraud, etc. (Pazarbasioglu
et al., 2020). Research studies on financial technology and financial inclusion
are important as they can provide strategy recommendations and policies that
will help improve financial inclusion in the country for the government, financial
institutions, and other organizations.
Literature also indicates that not many studies have been conducted on fintech
and financial inclusion in South Africa, and those that have been conducted
have mainly dealt with mobile banking and mobile money.
Literature on the technology acceptance model (TAM) has demonstrated that
perceived usefulness and perceived ease of use are the two main constructs
that determine intentions to adopt and use technology. TAM has been further
extended by authors by adding other constructs such as perceived risk,
perceived benefit, and trust. The constructs are important to help us
25
understand how they influence users' behavioural intentions to adopt and use
technology.
26
3. RESEARCH STRATEGY, DESIGN, PROCEDURE AND METHODS
Chapter 2 reviewed the literature and presented frameworks used to interpret
and discuss the research questions results and findings. The three research
questions posed in Section 1.2.3 are: 'Does fintech adoption and usage
influence financial inclusion?', 'Does fintech influence barriers to financial
inclusion?', and 'Does awareness of benefits and risks associated with fintech
influence adoption and usage?'. Chapter 3 discusses the research strategy in
Section 3.1; research design in Section 3.2; research procedure and methods
in Section 3.3; research strengthens – reliability and validity in Section 3.4 and
research weakness – administrative and technical limitations in Section 3.5.
3.1 Research strategy
Research strategy or research paradigm or research approach is the general
orientation, method, or plan to conduct research (Bryman, 2012). It involves
breaking down the problem into sub-problems and evaluating each sub-
problem against the data (Neuman, 2014). Research strategies can be
qualitative, quantitative, or mixed methods (Bryman, 2012).
Looking at the research questions, literature review, framework for interpreting
research findings and strategies used in similar research studies, this study
will utilise a quantitative research strategy. Quantitative research strategy
emphasises quantification or numerical data collection and analysis (Bryman,
2012), and it's “characterised by deductive approaches to the research
process aimed at proving, disproving, or lending credence to existing theories”
(Leavy, 2017, p. 9).
Wakaba and Wepukhulu (2019) in Kenya and Kinsman (2019) in South Africa
applied a quantitative research strategy. Using a quantitative research
strategy has several benefits. First, quantitative data analysis saves time
because the study can use software like SPSS (Eyisi, 2016). Second, scientific
methods to collect and analyse data generalise the results possible (Queirós,
Faria, & Almeida, 2017). Third, the researcher can perform correlation,
27
regression, multivariate analysis and other statistical tests on the data.
(Queirós et al., 2017). Fourth, hypothesis testing means the researcher does
not need to make intelligent decisions or guesswork because of clear
guidelines and set objectives (Eyisi, 2016). Lastly, surveys for data collection
in quantitative research studies eliminate researcher bias and subjectivity
(Queirós et al., 2017).
Using a quantitative research strategy in this study simplified data collection,
processing, interpreting, analysis, and it was cost-effective.
3.2 Research Design
Research designs provide a foundation, structure, or framework for collecting,
interpreting, and analysing data (Bryman, 2012). They are procedures of
inquiry found in qualitative, quantitative and mixed methods strategies that
guide a research study (Creswell & Creswell, 2018). These five types of
research designs are: “experimental design, cross-sectional or survey design,
longitudinal design, case study design and comparative design” (Bryman,
2012, p. 50).
This study utilised a cross-sectional or survey design. The cross-sectional
research design involves collecting quantitative data from more than one case
to establish variation among the variables. The researcher can also collect
data at a single point in time (Bryman, 2012).
Abor, Amidu and Issahaku (2018) also used a cross-sectional design. The
benefits of using a cross-sectional design are that the researcher can collect
data from different places quickly (Johnson & Christensen, 2014). It is simple,
cheaper, and the researcher can use collected data for other research types
(Neuman, 2014). Using a cross-sectional design enables the researcher to
examine relationships between variables, and the researcher can use it for
descriptive analysis (Bryman, 2012).
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3.3 Research procedure and methods
3.3.1 Research data and information collection instrument(s)
Data collection is one of the most critical components of a research project. A
researcher needs to decide when choosing the data collection instruments and
the type of data collected. Data can be managed using different data collection
instruments like a structured interview schedule or self-completion
questionnaire and observation schedule (Bryman, 2012).
This study utilised a fully structured interview schedule or self-completion
questionnaire, which was sent online to all respondents to complete
independently. According to Johnson and Christensen (2014), in a self-
completion questionnaire, no interviewer is involved; the participants must
read and answer the questions alone. Therefore, the questionnaire should
have more closed questions and few open-ended questions, questions should
be easy to follow and answer, and the questions should be short and precise
to avoid boring the respondent.
Kinsman (2019) in South Africa used a self-completion questionnaire in a
related study. A self-completion questionnaire has the following benefits. First,
a self-completion questionnaire is cheaper and quicker to administer; it can be
sent via post or completed online. (Bryman, 2012; Neuman, 2014). Second,
using a self-completion questionnaire eliminates interview effects and
interviewer variability when asking questions because no interviewer is present
when respondents complete the questionnaire (Bryman, 2012). Lastly,
respondents can complete the questionnaire during their own free time at their
speed, bringing many conveniences (Bryman, 2012; Neuman, 2014).
3.3.2 Research target population and selection of respondents
3.3.2.1 Target population
A target population is a large group or set of all elements which the researcher
wants to learn about or generalise for his / her sample (Johnson & Christensen,
2014). Neuman (2014) described it as “the concretely specified large group of
many cases from which a researcher draws a sample, and to which results
29
from the sample are generalised” (p.252). The target population for this study
were adults above the age of 18 who reside in South Africa with an electronic
device that can access the internet.
3.3.2.2 Sampling or selection of respondents from the target
population
A sample is a subset or segment of the population that the researcher will
select for the study (Bryman, 2012). Neuman (2014) described a sample as a
subset or part of cases selected from a large pool by the researcher, and it is
generalised to the population. Probability and non-probability sampling are two
of the most popular research studies sampling techniques (Bryman, 2012).
Due to time constraints, a non-probability sampling technique was used in this
research study. The researcher used the convenience sampling method
because of its simplicity and accessibility to the sample. Convenience
sampling means collecting data from participants conveniently available to the
researcher to participate in the study (Bryman, 2012). The researcher sent the
online questionnaire link to the respondents via different online communication
channels like WhatsApp, Telegram, email, LinkedIn, and Facebook.
3.3.3 Ethical considerations when collecting research data.
According to Leavy (2017), “ethics involves morality, integrity, fairness, and
truthfulness” (p.24). The principles and morals guide our behaviour and help
us uphold things we value (Johnson & Christensen, 2014).
For this study, the researcher followed the following ethical considerations.
The safety and security of the respondents were of paramount importance to
ensure no harm to the participants. The researcher sent out an informed
consent letter to all respondents, letting them know that their participation was
voluntary; they were free to withdraw their participation in the study at any time
with no ramifications. The researcher assured respondents of anonymity and
that all the information they provided was strictly private and confidential. The
30
data collected was only for academic purposes, and the study results would
be made available upon request. All data collected was stored in a password-
protected database application. The researcher always kept all information
extracted from the database in a password-protected word processor. A full
informed consent letter sent to all respondents is attached see Appendix 1.1.
3.3.4 Research data collection procedure
Data collection is one of the essential steps for any research, and it involves
collecting data from the respondents for analysis to answer the research
questions (Bryman, 2012). Data for the research study is collected using online
surveys, online self-completion questionnaires, and interviews (Johnson &
Christensen, 2014).
This study collected data using an online self-completion questionnaire
created on the Survey Monkey platform. The researcher shared the online link
with the respondents via different online communication channels like
WhatsApp, Telegram, email, and social media platforms like LinkedIn.
Responses collected from the respondents were only accessible to the
researcher who had the username and password to access the Survey
Monkey database. The researcher exported raw data from the Survey Monkey
database to a password protected excel sheet and prepared it for processing.
3.3.5 Research data processing and analysis
3.3.5.1 Research data processing
Once the data is collected, it needs processing for analysis. Data processing
involves “editing, coding, classification and tabulation of collected data so that
they are amenable to analysis” (Kothari, 2004, p. 122). Data coding involves
re-organising raw data systematically for easy analysis with statistical software
packages and data cleaning, looking for blank responses and assigning a
value to them (Neuman, 2014).
Data was collected using an online self-completion questionnaire created on
the Survey Monkey platform, and the data was captured and automatically
31
coded through the platform. The researcher could export survey data from the
platform in various formats such as excel, pdf, CSV, and SPSS ready format.
By default, data exported to SPSS format from the Survey Monkey platform
was already assigned with numeric codes for each response, eliminating the
need to do manual coding. Most of the survey questions were closed-ended,
and the numeric codes posted by Survey Monkey were already sufficient. Only
question 9 ('Please indicate which online financial products and services do
you use the most?') required coding because it involved entering multiple
responses.
The researcher performed data cleaning to remove or modify incorrectly
captured data, missing values or not correctly formatted. The researcher
conducted data cleaning in three steps:
• Missing value analysis - In SPSS descriptive statistics, the researcher
ran frequency tests to identify variables with missing values; the
researcher found no missing values.
• Out of the range value analysis – In SPSS descriptive statistics,
frequency tests were also run to identity the minimum and maximum
values for each variable. There were no out of range values because
Survey Monkey assigned the appropriate codes for each response
during data collection.
• Detecting and removing outliers – In SPSS descriptive statistics, the
researcher explored all variables to identify any outliers; there were no
outliers in the data collected.
3.3.5.2 Research data analysis
Data analysis is “the numerical representation and manipulation of
observations for the purpose of describing and explaining the phenomena that
those observations reflect” (Babbie, 2010, p. 422). A researcher can use
various methods to analyse data in quantitative research like correlation
analysis, regression analysis, cluster analysis, content analysis (Babbie, 2010;
Johnson & Christensen, 2014).
32
This study analysed data using Statistical Package for Social Sciences (SPSS)
Version 27. The researcher used descriptive statistics to describe and
summarise the data, which included frequencies and bar graphs. Statistical
inference tests done in this study included statistical significance tests,
correlation analysis. Data were also analysed using regression analysis.
Regression analysis is a set of statistical methods used to explain or estimate
the relationship between a single dependent variable based on the value(s) of
one or more independent variables (Leavy, 2017). The benefit of using
regression analysis is the flexibility to adjust for control variables
simultaneously and the ability to determine the impact of one or more
independent variables on the dependent variable (Neuman, 2014).
The following linear regression models were used to test each hypothesis:
Objective 1: To determine if the adoption and usage of fintech can influence
financial inclusion.
This study considers a multivariate linear model of the form:
𝐹𝐼𝑖 = 𝛼𝑖 + 𝛽𝐹𝑇𝑢𝑠𝑎𝑔𝑒 + 𝛽𝐹𝑇𝑎𝑑𝑜𝑝𝑡𝑖𝑜𝑛 + 𝜀𝑖 (2)
Where:
𝐹𝐼 - shows a financial inclusion variable, measured by the financial access to
transactional bank accounts, credit, insurance, and savings by all population
segments.
𝐹𝑇𝑢𝑠𝑎𝑔𝑒– shows a fintech variable, measured by the individuals who frequently
use technology for their day-to-day transactions than visiting a brick 'n' mortar
branch.
𝐹𝑇𝑎𝑑𝑜𝑝𝑡𝑖𝑜𝑛 -- shows a fintech variable, measured by individuals who have
access and have adopted technology for their day-to-day transactions –
transactional banking, credit, insurance, and savings.
𝛽 – coefficient of interest that measure the effect of FT_usage or FT_adoption on FI
𝛼 – is the constant parameter
𝜀 – is the error term
Objective 2: To determine if fintech influence the barriers to financial inclusion.
This study considers a univariate linear model of the form:
33
𝐹𝐼_𝑏𝑎𝑟𝑟𝑖𝑒𝑟𝑠𝑖 = 𝛼𝑖 + 𝛽𝐹𝑇𝑖 + 𝜀𝑖 (1)
Where:
𝐹𝐼_𝑏𝑎𝑟𝑟𝑖𝑒𝑟𝑠 - shows a financial inclusion barrier variable, which includes:
geographical distance to the nearest branch – respondents who agree or
disagree that fintech reduces the distance barrier.
Transport costs to the nearest bank branch – respondents who agree or
disagree that fintech reduces the transport costs barrier.
Transaction costs or fees – respondents who agree or disagree that fintech
reduces the transaction costs barrier.
Lack of proper documentation – respondents who agree or disagree that
fintech lowers the documentation barrier.
𝐹𝑇 – shows a fintech variable, measured by the usage of online financial
products and services for transaction accounts, credit, insurance, and savings
𝛽 – coefficient of interest that measure the effect of FT on FI_barriers
𝛼 – is the constant parameter
𝜀 – is the error term
Objective 3: To determine if awareness of benefits and risks associated with
fintech influence adoption and usage.
This study considers a multivariate linear model of the form:
𝐹𝑇𝑎𝑑𝑜𝑝𝑡𝑖𝑜𝑛 = 𝛼𝑖 + 𝛽𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠𝑖 + 𝛽𝑅𝑖𝑠𝑘 𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 + 𝜀𝑖 (3a)
𝐹𝑇𝑢𝑠𝑎𝑔𝑒 = 𝛼𝑖 + 𝛽𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 + 𝛽𝑅𝑖𝑠𝑘 𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 + 𝜀𝑖 (3b)
Where:
𝐹𝑇𝑢𝑠𝑎𝑔𝑒– shows a fintech variable, measured by the individuals who frequently
use technology for their day-to-day transactions than visiting a brick 'n' mortar
branch.
𝐹𝑇𝑎𝑑𝑜𝑝𝑡𝑖𝑜𝑛 -- shows a fintech variable, measured by individuals who have
access and adopted technology for their day-to-day transactions –
transactional banking, credit, insurance, and savings.
𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 – shows a benefits awareness variable, measured by
the individuals who are aware of benefits associated with fintech before
adoption or usage.
34
𝑅𝑖𝑠𝑘 𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠 – shows a risk awareness variable, measured by individuals
aware of risks associated with fintech before adoption or usage.
𝛽 – coefficient of interest that measure the effect benefits and risk awareness
on FTusage and FTadoption
𝛼 – is the constant parameter
𝜀 – is the error term
3.3.5.3 Description of the respondents
The respondents to be used for this study were adults above 18 who are
resident in South Africa. Since the data collection was through an online
survey, the respondents needed to have an electronic device with access to
the internet and read and write. The use of an online questionnaire made it
possible to reach people in all provinces in South Africa.
3.4 Research strengthens – reliability and validity measures
applied.
Reliability is “the dependability or consistency of the measure of a variable”
(Neuman, 2014, p. 212). If we apply the same technique on the same object
several times, we should always get the same result (Babbie, 2010). A
measure is reliable when it is always stable over time with no fluctuations. In
addition, it should have internal reliability, which means scale or index
indicators should remain consistent, and inter-observer consistency - in a case
where subjective judgement is involved, all observer decisions need to stay
the same (Bryman,2012).
Validity is “the issue of whether an indicator (or set of indicators) that is devised
to gauge a concept measures that concept” (Bryman,2012, p.171). Its more to
do with truthfulness, how close an idea is to actual reality (Neuman, 2014), the
integrity of research findings or conclusion (Bryman, 2012).
In quantitative research, the researcher can measure validity in four ways
which are: -
35
• Measurement validity – a device should measure what it claims to
measure, and its results should be consistent; the measure should
always reflect the concept it measures (Bryman, 2012).
• Internal validity – the research design has no internal errors that might
lead to false findings or conclusions (Neuman, 2014).
• External validity – degree or how far can we generalise the findings
across the population (Bryman, 2012)
• Ecological validity – the findings or results can be generalised to the
real-world setting (Leavy, 2017).
Reliability and validity are essential in research because they help us establish
how truthful, credible, and believable our research findings are, increasing our
confidence in our results (Neuman, 2014).
Face validity was conducted by sending the questionnaire to the supervisor to
check for any repeated, confusing, and leading questions. The researcher
then performed a pilot survey with a few selected respondents to ensure he
had captured all the questions correctly with no errors. The researcher used
the feedback from the respondents to ensure the questionnaire was correct
before the actual survey. However, the researcher did not include results from
this pilot survey in the final study. The researcher also used Cronbach's alpha
to test for internal reliability because it's versatile and tested in many situations
(Johnson & Christensen, 2014). A Cronbach’s alpha value greater than 0.6
was considered acceptable for internal reliability.
3.5 Research weaknesses – technical and administrative limitations
The study faced many challenges; data collection from low-income segments
was a challenge as most of these respondents did not have electronic devices
with access to the internet. In some cases, some of the respondents did not
respond to the request to complete the questionnaire. In addition, since this
was an online survey, it was impossible to capture the emotions and behaviour
of the respondents, and the reliability of data was dependent on the quality of
responses (Choy, 2014).
36
Time constraints for the data collection and presentation of findings and the
final report also challenged the researcher to collect data using a non-
probability sampling technique - convenience sampling, and 125 respondents
participated in the study.
3.6 Conclusion
This chapter presented the research strategy, design, procedure and methods.
A quantitative research strategy and a cross-sectional or survey design were
used to collect data from the participants. Non-probability sampling using the
convenience sampling method was used to collect data, where an online
questionnaire link was sent to the respondents via different online
communication channels like Whatsapp, email, social media platforms etc.
Data were processed and analyzed using SPSS. Ethical considerations,
research strengths (reliability and validity measures), weaknesses (technical
and administrative limitations) have been highlighted.
37
4. PRESENTATION OF RESEARCH RESULTS
Chapter 3 presented the research strategy, research design, research
procedure and methods, research data and information collection processing
and analysis, research strengthens (reliability and validity) and research
weaknesses (technical and administrative limitations). This chapter presents
the research results by making use of bar graphs and statistical tables. Section
4.1 presents reliability and validity tests. Section 4.2 presents the descriptive
statistics for the demographic information. Section 4.3 presents results for the
first research question- 'Does fintech adoption and usage influence financial
inclusion?'. Section 4.4 presents results for the second research question-
'Does fintech influence barriers to financial inclusion, and Section 4.5 present
results for the third research question- 'Does awareness of benefits and risks
associated with fintech influence adoption and usage?'. The researcher will
use the words “online financial products and services” interchangeably with
“fintech” in this study.
4.1 Reliability and Validity
The data collection instrument was tested for internal consistency and
construct reliability. Cronbach's alpha (α) coefficient test internal consistency.
The questionnaire consisted of 18 items related to the research questions and
nine demographic questions. The researcher used a 5-point Likert scale
ranging from 1-5 (1=Strongly Agree, 2=Agree, 3=Neither agree nor disagree,
4=Disagree, 5=Strongly disagree) to collect responses for the research
questions. After running reliability checks, the researcher deleted two items to
improve the internal consistency of the data collection instrument. A
Cronbach's alpha (α) coefficient greater than 0.6 was considered acceptable.
Research questions items tested for internal consistency. The researcher did
this to check the consistency of the indicators that make up the scale or index
to see scores if the indicators are related (Bryman, 2012). Table 4.1 presents
the overall Cronbach's alpha (α) was 0.865, which was acceptable internal
consistency.
38
Table 4.1: Reliability statistics
Cronbach's Alpha
Cronbach's
Alpha Based on
Standardised
Items No. of Items
0,865 0,869 16
The researcher tested sub-scale internal consistency. As presented in Table
4.2, the results indicate the sub-scale Cronbach's alpha (α) - financial inclusion
barriers (0.6), fintech usage (0.704), fintech adoption (0.718), and benefits and
risk awareness (0.634).
Table 4.2: Sub-scale reliability statistics
Construct
Cronbach's
Alpha No. of items retained
Financial Inclusion Barriers 0.6 4
Fintech Usage 0.704 5
Fintech adoption 0.718 5
Benefits and risk awareness 0.634 2
Overall Cronbach’s Alpha 0.865 16
Face validity was conducted by sending the questionnaire to the supervisor to
check for any repeated, confusing, and leading questions. The researcher
then performed a pilot survey with a few selected respondents to ensure he
had captured all the questions correctly with no errors. Finally, the researcher
used the feedback from the respondents to ensure the questionnaire was
correct before the actual survey. However, the analysis and interpretation of
the final results did not include results from this pilot survey in the final
presentation.
4.2 Descriptive statistics
4.2.1 Response Rate
The researcher sent the online survey questionnaire to 200 respondents via
different online communication channels like email, social media platforms -
39
WhatsApp, LinkedIn, and Telegram. As a result, 125 responses were
completed successfully and were deemed useful for further analysis, which
gave a response rate of about 62.5%.
4.2.2 Demographic information
The results show that most of the respondents were males (71.2%), and 28.8%
of the respondents were females, as presented in Figure 4.1.
Figure 4.1: Gender responses
Most respondents were Africans (85.6%) regarding race, followed by mixed-
race (8%). Indians and whites were 3.2% and 2.4%, respectively, while Asians
were the least represented with 0.8% of the total sample, as presented in
Figure 4.2.
Figure 4.2: Race responses
As presented in Figure 4.3, the results show that the age of the respondents
ranged from 18-64 years, and the age group, 35-44, had most respondents
0
20
40
60
80
Female Male
P
e
rc
e
n
t
Gender
0
20
40
60
80
100
African Asian Colored Indian White
P
e
rc
e
n
t
Race
40
(54.3%), followed by 25-34 (24.8%). Age groups 18-24 and 55-64 had the least
number of respondents, 1.6% and 0.8%, respectively, of the total sample.
Figure 4.3: Age responses
The respondents held different academic qualifications. Figure 4.4 presents
the descriptive statistics of educational qualifications. Respondents with a
master’s degree (37.6%) were the majority, followed by honours degree
(23.2%), bachelor's degree (15.2%), diploma (12.8%), matric (6.4%) and, PhD
and other qualifications were the least represented by 2.4% each of the total
sample. These results were helpful in this study because they provided data
regarding the respondents and determine if the sample was representative of
the target population when results are generalised.
Figure 4.4: Educational qualification responses
0
10
20
30
40
50
60
18-24 25-34 35-44 45-54 55-64
P
e
rc
e
n
t
Age
0
5
10
15
20
25
30
35
40
Matric Diploma Bachelors
degree
Honors
degree
Masters
degree
PhD Other
P
e
rc
e
n
t
Highest qualification
41
In terms of employment status, as presented in figure 4.5, most of the
respondents were employed (80%), while 16.8% were self-employed,
unemployed (1.6%), student (0.8%) and other (0.8%). These results were
helpful in this study because they showed if employment status played a role
in financial access.
Figure 4.5: Employment status responses
The respondents had to indicate a list of online financial products and services
they frequently use on a day-to-day basis. As shown in Figure 4.6, the results
show the majority of the respondents use online mobile payments and
remittance applications (96.8%), e-wallets applications (48.8%) and price
comparisons applications (34.3%). The results also show that blockchain and
cryptocurrency applications (14.4%), investment and robotic advising
applications (12.8%), budgeting applications (12.8%), other applications
(10.4%), insurance technology applications (8%), crowdfunding applications
(5.6%). There were no responses for online lending applications. These results
are significant because it shows which products are popular among the
respondents for their day-to-day transactions.
0
20
40
60
80
100
Student Employed Self-employed Not employed Other
P
e
rc
e
n
t
Employment status
42
Figure 4.6: Frequently used fintech products and services
4.3 Effect of fintech adoption and usage on financial inclusion.
Section 4.3 presents the results and hypothesis testing for research question
1 – “Does fintech adoption and usage increase financial inclusion”. The
research question seeks to understand if the adoption and usage of online
financial products and services increase financial inclusion. Sustainable
financial inclusion should match the broad adoption of transaction accounts,
credit, insurance, and savings products with significant usage, which is
sustainable to consumers and service providers (Kessler et al., 2017).
4.3.1 Descriptive statistics
As presented in Figure 4.7, the results show that 44% of the respondents
strongly agree that they can control and manage their transaction fees or any
other additional charges by using online financial products and services. In
comparison, 39.2% agree, 12% could neither agree nor disagree, 4% of the
respondents disagree, and 0.8% strongly disagree.
0.0% 20.0% 40.0% 60.0% 80.0% 100.0% 120.0%
Mobile payments & remmittances…
Blockchain and cryptocurrency apps
Crowdfunding applications
InsurTech Applications
Investments and Robotic Advising…
Budgeting applications
Lending applications
E-wallets
Price Comparisons applications
Other platforms
Frequently used products and services
43
Figure 4.7: Flexibility and control of costs
As presented in Figure 4.8, the results show that 45.6% of the respondents
strongly agree they are quick to adopt new online financial products and
services on the market if they perceive them as easy to use and valuable. In
comparison, 43.2% agree, 6.4% neither agree nor disagree, 4% of the
respondents disagree, and 0.8% strongly disagree.
Figure 4.8: Technology adoption
As presented in figure 4.9, the results show that the respondents preferred to
use online financial products and services for making their payments, savings,
budgets, trading than visiting a brick 'n' mortar branch. Of the total sample,
52% of the respondents strongly agree they will transact online, 44% agreed,
3.2% neither agree nor disagree, while 0.8% disagree, no respondents
strongly disagreed.
0
10
20
30
40
50
Strongly agree Agree Neither agree
nor disagree
Disagree Strongly
disagree
P
e
rc
e
n
t
Flexibility and control of costs
0
10
20
30
40
50
Strongly agree Agree Neither agree
nor disagree
Disagree Strongly
disagree
P
e
rc
e
n
t
Technology adoption
44
Figure 4.9: Online financial products and services usage
As presented in figure 4.10, the respondents also indicated that they used
online financial products and services because it was more convenient and
safer than visiting the nearest brick 'n' mortar branch. The results show that
54.4% of the respondents strongly agree, 36% agree, 8.8% neither agree nor
disagree, 0.8% disagree, and no respondents strongly disagree.
Figure 4.10: Safety and convenience
The respondents indicated if the availability of affordable online financial and
banking products and services improved their access to financial products and
services that meet their day to day needs like payments, savings, credit, and
insurance. As presented in figure 4.11, the results indicate that 41.6% strongly
agree that online products and services improved their financial access, 55.2%
0
10
20
30
40
50
60
Strongly
agree
Agree Neither agree
nor disagree
Disagree Strongly
disagree
P
e
rc
e
n
t
Online financial products and services usage
0
10
20
30
40
50
60
Strongly
agree
Agree Neither agree
nor disagree
Disagree Strongly
disagree
P
e
rc
e
n
t
Safetly and convinience
45
agree, 2.4% neither agree nor disagree, 0.8% disagree, and no respondents
strongly agree.
Figure 4.11: Access to financial products and services
As presented in figure 4.12, the respondents indicated if they believed the
availability of affordable online financial products and services improved their
friends and family's financial access. The results show that 37.6% of the
respondents strongly agree that online products and services improved the
financial access for their family and friends, 53.6% strongly agree, 7.2% neither
agree nor disagree, 1.6% disagree, and no respondents strongly disagree.
Figure 4.12: Access to financial products and services - family and
friends
0
10
20
30
40
50
60
Strongly
agree
Agree Neither agree
nor disagree
Disagree Strongly
disagree
P
e
rc
e
n
t
Access to financial products and services
0
10
20
30
40
50
60
Strongly
agree
Agree Neither agree
nor disagree
Disagree Strongly
disagree
P
e
rc
e
n
t
Access to financial products and services - family and
friends
46
4.4.1 Statistical hypothesis testing
In this section, the researcher ran correlation and regression analysis to check
for the relationship between the variables and reject or not reject the null
hypothesis. Hypothesis H0 says, “Fintech adoption and usage does not
influence financial inclusion”.
The dependant variable was financial inclusion, measured as the financial
access to transactional bank accounts, credit, insurance, and savings by all
population segments. The independent variables were fintech adoption
measured as individuals who have adopted online financial products and
services for their day-to-day transactions – transactional banking, credit,
insurance, and savings. The other independent variable was fintech usage,
measured as the individuals who use fintech products and services for their
day-to-day transactions than visiting a physical branch.
4.4.1.1 Correlation analysis
A Pearson product-moment correlation test determined the relationship
between the dependant variable (financial inclusion) and the two independent
variables, fintech adoption and fintech usage. Table 4.3 presents the
correlations between the variables. The results show that the financial
inclusion variable and fintech usage variable have a moderate positive
correlation (r=0.438, p<0.001), which is statistically significant, the financial
inclusion variable and fintech adoption variable have a moderate positive
correlation (r=0.506, p<0.001) which is statistically significant. In addition, the
variables fintech usage and fintech adoption also have a moderate positive
correlation (r=0.583 p<0.001), which is also statistically significant.
Table 4.3: Pearson correlation - fintech usage, adoption, and financial
inclusion
fintech usage fintech adoption
financial
inclusion
fintech usage 1 - -
fintech adoption 0.583** 1 -