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Understanding the attributes and characteristics of 

cryptocurrency ownership: A South African study 
  

 

 

 

Hasita Jetha 

1374810 

Ethics clearance number: SOA-2022-07-02 

Supervisor: Yogesh Brahmbhatt 

University of the Witwatersrand 

School of Accountancy  

 

 

 

 

 

 

A research project submitted in partial fulfilment of the requirements for the 

degree of Master of commerce by coursework and research report in the field of 

accountancy in the faculty of commerce, law, and management, University of the 

Witwatersrand, Johannesburg, 31 March 2023. 

 

 



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Declaration 

I, Hasita Jetha, hereby declare that this is a submission of a research report in partial 

fulfilment of the requirements for the degree of Master of Commerce at the University 

of the Witwatersrand, Johannesburg. I further state that this report represents my own 

original and unaided work and has not been submitted elsewhere for examination 

purposes or for purposes of being awarded a degree at any other university within 

South Africa or abroad. The report, which has been assessed via Turnitin for 

plagiarism, contains a reference list where the necessary citations are made using the 

APA Harvard referencing style. 

 

Signed: Hasita Jetha 

Student number: 1374810 

Date: 31/03/2023 

  



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Abstract 

Background: This study investigates the attributes and characteristics of 

cryptocurrency investors in South Africa and the attributes of cryptocurrencies that 

drive investment or non-investment.  

Objectives: This study aims to explore the demographics and sociodemographic 

factors of cryptocurrency investors as well as the emotions and biases that impact 

investors’ decisions to invest in cryptocurrency in order to investigate the individuals 

who invest in cryptocurrency and the reasons why they invest in cryptocurrency.  

Methods: A sample of 298 South African residents aged 18 and above completed an 

online survey that assessed their cryptocurrency ownership, demographics, motives 

for investment, attitudes toward cryptocurrency, and other relevant variables. 

Descriptive statistics and logistic regression analyses were conducted to examine the 

relationship between these variables. 

Results: The results showed that cryptocurrency investors are more likely to be 

males, under the age of 35, who are currently employed and have higher income 

levels. The individuals’ main motives for investing in cryptocurrency were the 

opportunity to obtain high returns and the new technology that cryptocurrency 

encompasses. In addition, the results showed that attitudes toward cryptocurrencies 

significantly impact their decision to invest in cryptocurrency. 

Conclusion: These findings suggest that more information relating to the risks 

involved in cryptocurrency investment as well as the potential of cryptocurrency to be 

used as a medium of exchange is required among individuals to protect themselves 

against losses and simultaneously allow them to take advantage of the lucrative 

benefits that cryptocurrencies offer. Furthermore, policymakers, the government, and 

businesses require more information regarding cryptocurrencies in order to have the 

necessary policies in place and to stay competitive. 

Keywords: Behavioural finance, Bitcoin, cryptocurrencies, digital currencies, fintech, 

financial literacy, herding behaviour, investment, optimism, overconfidence, trust, risk 

 

 



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Acknowledgements 

Dedication 

I dedicate this research report to God, who has blessed me with the intellectual 

capacity, perseverance, and strength to complete this research project. I am grateful 

for the guidance and wisdom that He has provided me throughout my academic 

journey. I also devote this research to my parents, who have always supported and 

encouraged me to pursue my dreams. Their unwavering love and dedication have 

been a constant source of strength throughout my academic journey.  

Gratitude 

I would like to express my deepest gratitude to my research supervisor, Mr. Yogesh 

Brahmbhatt, for his constant support, guidance, and mentorship throughout this 

research project. I would also like to thank my family and friends for their emotional 

support and encouragement. I am grateful for the funding provided by the University 

of the Witwatersrand, which made this research possible. Finally, I would like to thank 

the study participants who generously gave their time and effort to contribute to this 

research.  



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Table of contents 

 

Understanding the attributes and characteristics of cryptocurrency ownership: A 

South African study .................................................................................................... 1 

Declaration .............................................................................................................. 2 

Abstract ................................................................................................................... 3 

Acknowledgements ................................................................................................. 4 

Table of figures .......................................................................................................... 9 

List of tables ............................................................................................................. 11 

Chapter 1 – Introduction ........................................................................................... 12 

1.1 Rationale ..................................................................................................... 12 

1.2 Research problem ....................................................................................... 14 

1.3 Research purpose ....................................................................................... 14 

1.4 Significance of the research ........................................................................ 14 

1.5 Research question ...................................................................................... 15 

Chapter 2 – Literature review ................................................................................... 16 

2 Introduction ........................................................................................................ 16 

2.1 What is cryptocurrency? .............................................................................. 16 

2.1.1 Bitcoin ...................................................................................................... 17 

2.2 Behavioural finance theory .......................................................................... 18 

2.2.1 Herding ................................................................................................. 20 

2.2.2 Overconfidence bias/ self-attribution .................................................... 21 

2.2.3 Optimism bias ....................................................................................... 22 

2.2.4 Loss aversion ....................................................................................... 23 

2.2.5 Confirmation bias .................................................................................. 24 

2.2.6 Sentiment ............................................................................................. 24 

2.3 Awareness and knowledge of cryptocurrency ............................................. 25 



6 
 

2.4 The attributes and characteristics of cryptocurrency investors .................... 26 

2.5 Trustworthiness ........................................................................................... 27 

2.6 The attributes of cryptocurrency that drive investment ................................ 27 

2.7 The attributes of cryptocurrency that drive non-investment ......................... 28 

2.8 Conclusion .................................................................................................. 29 

Chapter 3 – Method.................................................................................................. 30 

3 Introduction ........................................................................................................ 30 

3.1 Research design ......................................................................................... 30 

3.2 Population and study sample ...................................................................... 30 

3.3 Sampling technique ..................................................................................... 31 

3.4 Data collection ............................................................................................. 31 

3.5 Instruments ................................................................................................. 31 

3.6 Data analysis ............................................................................................... 32 

3.7 Reliability and validity of the study .............................................................. 37 

3.8 Ethical consideration ................................................................................... 37 

Chapter 4 – Results and Discussion ........................................................................ 39 

4 Introduction ........................................................................................................ 39 

4.1 Demographic variables of the sample ......................................................... 39 

4.1.1 Ownership and awareness of cryptocurrency of the sample ................ 43 

4.1.2 Risk appetite of the sample .................................................................. 44 

4.1.3 Financial literacy and Bitcoin knowledge of the sample ........................ 45 

4.1.4 Attitude toward cryptocurrency ............................................................. 46 

4.1.5 The attributes of cryptocurrency that drive the investment in 

cryptocurrency ................................................................................................... 47 

4.2 The attributes and characteristics of cryptocurrency investors in South Africa

 48 

4.3 What is the level of awareness of cryptocurrency in South Africa? ............. 53 



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4.4 The attributes of cryptocurrency that drive individuals to invest in 

cryptocurrency ...................................................................................................... 54 

4.5 The attributes of cryptocurrency that drive the non-investment in 

cryptocurrency ...................................................................................................... 56 

4.6 Trust in the South African rand, the Central Bank, and domestic banks ..... 57 

4.7 The impact of an individual’s emotions and other biases on their investment 

decision ................................................................................................................. 59 

4.7.1 Overconfidence .................................................................................... 60 

4.7.2 Herding and Optimism bias .................................................................. 63 

4.8 Logistic regression results ........................................................................... 64 

4.8.1 The attributes and characteristics of cryptocurrency investors in South 

Africa 64 

4.8.2 The level of awareness of cryptocurrency in South Africa .................... 66 

4.8.3 The impact of an individual’s emotions and other biases on their 

decision to invest in cryptocurrency ................................................................... 68 

4.9 Discussion ................................................................................................... 69 

4.10 Does a person’s emotions and other biases impact their decision to invest in 

cryptocurrency in South Africa? ............................................................................ 70 

4.10.1 Herding ................................................................................................. 70 

4.10.2 Overconfidence .................................................................................... 70 

4.10.3 Optimism .............................................................................................. 72 

4.10.4 Attitude toward cryptocurrencies .......................................................... 72 

4.11 What is the level of awareness of cryptocurrency in South Africa and do 

investors understand the risks involved in cryptocurrency investment? ................ 73 

4.12 What are the attributes and characteristics of cryptocurrency investors in 

South Africa? ........................................................................................................ 74 

4.12.1 Ownership ............................................................................................ 74 

4.12.2 Trustworthiness .................................................................................... 76 



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4.13 What are the attributes of cryptocurrency that drive the investment of 

cryptocurrency in South Africa? ............................................................................ 77 

4.14 What are the attributes of cryptocurrency that drive non-investment of 

cryptocurrency in South Africa? ............................................................................ 79 

Chapter 5 – Conclusion ............................................................................................ 81 

5 Introduction ........................................................................................................ 81 

5.1 Conclusion on the main findings of this study ............................................. 81 

5.2 Limitations of this study ............................................................................... 83 

5.3 Areas for further research ........................................................................... 83 

References ............................................................................................................... 85 

Appendix B – Binomial logistic regression .............................................................. 104 

 

  



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Table of figures 

 

Figure 1 The market capitalisation of the top ten cryptocurrencies .......................... 18 

Figure 2 A bar graph showing the share of cryptocurrency ownership of the sample

 ................................................................................................................................. 44 

Figure 3 A donut graph showing the risk appetite of the sample .............................. 45 

Figure 4 A bar graph showing the financial literacy of the sample ........................... 46 

Figure 5 A bar graph showing the respondent's basic cryptocurrency knowledge ... 46 

Figure 8 A pie graph showing the gender of cryptocurrency investors ..................... 48 

Figure 9 A bar graph showing the age of cryptocurrency investors .......................... 48 

Figure 10 A bar graph showing the highest level of education of cryptocurrency 

investors ................................................................................................................... 49 

Figure 11 A bar graph showing the employment status of cryptocurrency investors 49 

Figure 12 A bar graph showing the financial wealth status of cryptocurrency 

investors ................................................................................................................... 50 

Figure 15 Funnel graphs showing cryptocurrency investors’ main use for 

cryptocurrency .......................................................................................................... 53 

Figure 16 Pie graphs showing the investors’ basic Bitcoin knowledge ..................... 54 

Figure 17 A bar graph showing the attributes of cryptocurrency that drive investment 

among those who have an interest in cryptocurrency investment ............................ 55 

Figure 18 A bar graph showing the attributes of cryptocurrency that drive investment 

among those who have an interest in cryptocurrency investment ............................ 56 

Figure 19 A bar graph showing the attributes of cryptocurrency that drive non-

investment among those who do not have an interest in cryptocurrency investment57 

Figure 20 A bar graph showing cryptocurrency investors’ trust in the South African 

rand, the SARB, and domestic banks....................................................................... 58 

Figure 21 A bar graph showing non-investors' trust in the South African rand, the 

SARB, and domestic banks ...................................................................................... 58 

Figure 22 Bar graphs showing the beliefs of cryptocurrency investors regarding 

cryptocurrency .......................................................................................................... 60 

Figure 23 A bar graph showing the financial literacy of cryptocurrency investors .... 61 

Figure 24 A bar graph showing the financial literacy of those who have not invested 

in cryptocurrency ...................................................................................................... 61 

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Figure 25 A bar graph showing the Bitcoin knowledge of cryptocurrency investors . 62 

Figure 26 A bar graph showing the Bitcoin knowledge of those who have not 

invested in cryptocurrency ........................................................................................ 62 

Figure 27 A bar graph showing the risk appetite of cryptocurrency investors .......... 63 

Figure 28 A bar graph showing the risk appetite of those who have not invested in 

cryptocurrency .......................................................................................................... 63 

Figure 29 A pie graph showing the percentage of those interested in cryptocurrency 

investment who are likely to invest due to herding behaviour .................................. 64 

  



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List of tables 

 

Table 1 Summary of variables included in this study ............................................... 34 

Table 2 Frequency table for the gender of the sample ............................................. 39 

Table 3 Frequency table for the age of the sample .................................................. 40 

Table 4 Frequency table for the highest qualification of the sample ......................... 40 

Table 5 Frequency table for the employment status of the sample .......................... 41 

Table 6 Frequency table for the taxable income of the sample ................................ 42 

Table 7 Frequency table for the financial wealth status of the sample ..................... 42 

Table 8 Frequency table for the profession of the sample ........................................ 43 

Table 9 Overall statistical significance of the model ................................................. 65 

Table 10 Explained variation .................................................................................... 65 

Table 11 Observed and predicted classification ....................................................... 66 

Table 12 Overall statistical significance of the model ............................................... 67 

Table 13 Explained variation .................................................................................... 67 

Table 14 Observed and predicted classification ....................................................... 67 

Table 15 Overall statistical significance of the model ............................................... 69 

Table 16 Explained variation .................................................................................... 69 

Table 17 Observed and predicted classification ....................................................... 69 

Table 18 Displays the results for regression 1 ....................................................... 104 

Table 19 Displays the results for regression 2 ....................................................... 108 

Table 20 Displays the results for regression 3 ....................................................... 110 

  



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Chapter 1 – Introduction 

1.1 Rationale 

Cryptocurrency has revolutionised the world of digital finance and has lured much 

attention from regulators, the media, academics, businesses, and consumers around 

the world (Ballis and Verousis, 2022; Hidajat, 2019; Ionescu and Radulescu, 2019; 

Stix, 2021). Cryptocurrency has the potential to be used in a variety of ways namely, 

as a medium of exchange (either for legal or illegal activities), to transfer funds 

anonymously, as a store of wealth (similar to gold), or as a speculative investment with 

the possibility of capital appreciation (Stix, 2021). Bitcoin, the first cryptocurrency, was 

created in 2009 to allow users to transfer money over the internet. However, it is 

interesting to note that investors have instead, mainly adopted cryptocurrency for 

speculative purposes ( Al-Mansour, 2020; Baek & Elbeck, 2015; Glaser, Zimmermann, 

Haferkorn, Weber, & Siering, 2014; Hidajat, 2019; Henry, Huynh, Nicholls, & 

Nicholson, 2019; Zouhair and Kasraie, 2019; Nakamoto, 2008).  Bianchetti, Ricci, and 

Scaringi (2018) studied the risk of financial bubbles related to two cryptocurrencies, 

Bitcoin and Ether, and reported that both cryptocurrencies displayed evidence of 

bubble behaviour. Financial bubbles spawn conditions prone to a significant crash in 

the price of the cryptocurrencies. This poses the question of why individuals invest in 

cryptocurrency. Whilst past research has focused on the technical aspects of 

cryptocurrency, there is limited research relating to the underlying driving force of 

cryptocurrency becoming what it is today, being the users (Glaser et al., 2014; Lo and 

Wang, 2014).  

Cryptocurrencies may be attractive to users for several reasons as they provide an 

attractive alternative method of payment for individuals who do not trust the traditional 

financial system, the anonymity trait that it possesses is a desirable feature, and it may 

be intriguing to those interested in new technology, or the transparency that it provides 

and the low costs involved may be attractive to users, and consumers may perceive a 

reduced risk of fraud or online theft (Glaser et al., 2014; Grinberg, 2012; Stix, 2021; 

Weber, 2014; Nurbarani and Soepriyanto, 2022; Nakamoto, 2008). However, 

cryptocurrency has no fundamental value. The value is most presumably determined 

by the attention gained from the public, which explains the extreme volatility in 

cryptocurrency prices (Baek and Elbeck, 2015; Bolt and Van Oordt, 2020; Hidajat, 

2019; Ionescu and Radulescu, 2019; Venter, 2016). This results in the need for an 



13 
 

understanding of cryptocurrency investment to determine how its usage could impact 

the conventional financial system. This is because cryptocurrency competes with the 

government and other businesses for the same customers, resulting in a threat to the 

government and the conventional financial system. Moreover, cryptocurrency could 

be a threat to the users themselves as many may not understand the risks they are 

exposed to when investing in cryptocurrency (Steinmetz, von Meduna, Ante, & Fiedler, 

2021). 

 

The efficient market hypothesis, one of Eugene Fama’s financial theories developed 

in 1965, assumes that stocks are always priced at their fair value as stock prices reflect 

all available information at any point in time. The theory assumes that investors always 

make rational decisions based on all available information (Fakhry, 2016). In 

considering the highly volatile nature of cryptocurrency, many researchers have 

differing standpoints relating to whether the cryptocurrency market is efficient or 

inefficient (Bui, 2022). Under the assumptions of the efficient market hypothesis, 

investors cannot realise abnormal returns because the market reflects all available 

information at all times and price movements are arbitrary (Hidajat, 2019).  

 

On the other hand, in the 1970s Amos Tversky, Daniel Kahneman and Richard Thaler 

developed the theory of behavioural finance, which assumes that human behaviour is 

not always flawless and rational due to bounded rationality. In other words, investors’ 

decisions are also bounded by psychological barriers, namely emotional and cognitive 

biases (Hidajat, 2019).  

 

In the context of the cryptocurrency market, the occurrence of financial bubbles is not 

possible under the assumptions of the efficient market hypothesis (Hammond, 2015; 

Hidajat, 2019). Given that cryptocurrency has no fundamental value and the fact that 

the price of cryptocurrency is driven by the investors themselves (Al-Mansour, 2020; 

Bianchetti et al., 2018; Ionescu and Radulescu, 2019), this research paper aimed to 

study the cryptocurrency market from a behavioural finance perspective to determine 

the factors that impact South Africans who invest in cryptocurrency as well as the 

motives that influence their decision to invest to determine whether any cognitive or 

emotional biases contribute to the volatility of cryptocurrency prices.  



14 
 

1.2 Research problem  

The growing interest in cryptocurrency could be a threat to our current financial 

systems, the government, businesses, and the general public (Baek and Elbeck, 2015; 

Fujiki, 2020; Henry et al., 2019; Stix, 2021; Zouhair and Kasraie, 2019). This is 

because digital currencies have the potential to be used in similar ways to conventional 

financial systems which poses a threat to conventional payment systems due to the 

competition arising between them to attract the same customers (Zouhair and Kasraie, 

2019). Moreover, the growing demand for cryptocurrency may hurt domestic banks 

and other businesses as cryptocurrency offers lucrative benefits that the conventional 

system cannot such as low transaction costs, anonymity, and privacy (Glaser et al., 

2014; Grinberg, 2012; Zouhair and Kasraie, 2019). Cryptocurrency is a relatively new 

concept that many of its investors do not understand well resulting in them 

experiencing significant losses, in turn leading to a major social impact (Fujiki, 2020).  

These threats are significant and with the little information known by regulators – 

partially due to the anonymity of digital currencies – regarding the investment of 

cryptocurrency, the repercussions could be detrimental to a range of different parties 

(such as the government, domestic banks, or individuals).  

1.3 Research purpose 

The purpose of this study was to investigate the attributes and characteristics of South 

African cryptocurrency investors as well as their motives for cryptocurrency investment 

and the impact of an individuals’ emotions or biases on their decision to invest in 

cryptocurrency. The study focused on the population that were cryptocurrency 

investors and those who intended to invest in cryptocurrency in the future. The study 

also aimed to gain an understanding of the attributes of cryptocurrency that were most 

attractive to cryptocurrency investors as well as the attributes of cryptocurrency that 

drove away non-investors who had no interest in cryptocurrency investment. 

1.4 Significance of the research 

The study makes a valuable contribution to South African literature by providing 

insights into the factors that impacted cryptocurrency investment in South Africa. The 

results of the study benefits regulators and the financial sector by helping them gain 

an understanding of the factors that impacted cryptocurrency investment for future 

policy setting and to avoid the destruction of the conventional system. The 



15 
 

understanding of why people purchased cryptocurrency determined whether people 

were purchasing cryptocurrency as a store of wealth or whether they were merely 

purchasing cryptocurrency due to public pressure, which helped to better understand 

the high price volatility of cryptocurrency. 

The study also has the potential to expedite awareness among society which could 

then cause a transformation in the current financial system used in South Africa. This 

awareness and a spread of knowledge regarding cryptocurrency benefits the public 

who could adopt cryptocurrency as an alternative medium of exchange, thereby 

benefitting from the low transaction costs and other perceived benefits that 

cryptocurrency offers. A culmination of knowledge and awareness put investors at a 

lower risk of financial losses. 

Similar studies had been carried out in developed countries such as Canada, Japan, 

America, Austria, and Germany. However, to the best of the researcher’s knowledge, 

this study had not been carried out in South Africa – a developing country. 

1.5 Research question 

The research question for this study is:  

1. What are the attributes and characteristics of cryptocurrency investors in South 

Africa? 

The sub-questions include: 

1.1. What is the level of awareness of cryptocurrency in South Africa? 

1.2. Do cryptocurrency investors in South Africa understand the risks involved in 

cryptocurrency investment? 

1.3. What are the attributes of cryptocurrency that drive the investment and non-

investment of cryptocurrency in South Africa? 

1.4. Does a person’s emotions and other biases impact their decision to invest 

in cryptocurrency in South Africa? 

  



16 
 

Chapter 2 – Literature review 

2 Introduction 

This literature review provides an overview of the previous research on 

cryptocurrency. Specifically, previous literature on what cryptocurrency is, behavioural 

finance theory, awareness and knowledge of cryptocurrency, ownership of 

cryptocurrency, trustworthiness of cryptocurrency investors, as well as the benefits 

and disadvantages of cryptocurrency will be examined in this chapter. Furthermore, 

behavioural finance has been utilised as the theoretical framework to understand the 

impact of an individual's emotions and other biases on their decision to invest in 

cryptocurrency. 

2.1 What is cryptocurrency? 

The conventional financial system is characterised by trust, accountability, or oversight 

by a third party (for example, a central bank) (Chohan, 2017). It is centralised and far 

less transparent in comparison to digital currencies (Glaser et al., 2014). Distrust in 

the conventional financial system has arisen amongst the public due to breaches of 

trust such as the 2008 financial crisis, fraudulent activity, data breaches and higher 

than expected inflation (Böhme, Christin, Edelman, & Moore, 2015; Stix, 2021). The 

involvement of third parties in the conventional financial system provides the required 

mediation between a buyer and a seller, resulting in increased transaction costs. The 

increased transaction costs to third parties, make small transactions using the 

traditional payment system impractical (Nakamoto, 2008).  

 

Cryptocurrencies propose a deviation from the traditional medium of exchange present 

in the financial structure (Alzahrani and Daim, 2019; Glaser et al., 2014; Weber, 2014). 

This proposed deviation is attractive considering global economic crises, wherein the 

public becomes apprehensive about the stability of government-backed currencies 

(Glaser et al., 2014; Kinney, 2021; Lammer, Hanspal, & Hackethal, 2020; Stix, 2021; 

Weber, 2014). Cryptocurrency is a digital currency, which has also been termed virtual 

currency (Nakamoto, 2008; Schuh and Shy, 2016; Venter, 2016). Digital currencies 

are a relatively new concept that has not yet been widely adopted, resulting in 

unfamiliarity regarding the actual meaning of the term. To understand what digital 

currency is, it is important to understand what is meant by the word “currency” in the 

conventional sense of the word. Currency, in its simplest form, is a medium of 



17 
 

exchange used in exchange for the provision of goods and services. Whereas digital 

currency is an entirely different form of exchange that is not linked to any physical or 

digital cash (Schuh and Shy, 2016; Velde, 2013; Venter, 2016;). This means that 

electronic forms of cash, such as money in an online bank account, are not digital 

currency. As seen with traditional currency, the value of a digital currency is derived 

from the supply and demand for it. In contrast to traditional currency, however, it is not 

backed by any government or authority but solely by the community that are 

participants of the virtual currency system (Venter, 2016). The first cryptocurrency, 

Bitcoin, will now be explored in further detail to understand what cryptocurrency is. 

2.1.1 Bitcoin  

The pseudonymous, Satoshi Nakamoto, revolutionised the world of digital currency 

with the development of the first cryptocurrency, Bitcoin, in 2009. Since the creation 

of Bitcoin, thousands of other digital currencies (known as Altcoins) have been created 

such as Ethereum, Ripple, Dash, Litecoin and Monero (Gurdgiev and O’Loughlin, 

2020; Osterrieder, Lorenz, & Strika, 2016; Nurbarani and Soepriyanto, 2022). Bitcoin, 

however, continues to be the leading digital currency holding the largest market 

capitalisation among cryptocurrencies, with a market capitalisation of 

$543,022,924,352 on 30 March 2023 (Bolt and Van Oordt, 2020; Chohan, 2017; 

CoinMarketCap, 2022). Initially, the price of bitcoin ranged between $0 and $1 when 

it first entered the market, and it is currently priced at $28,085.59.20 on 30 March 2023 

(CoinMarketCap, 2022). Since inception, the price of bitcoin has experienced extreme 

volatility and has displayed notable financial bubbles over the years (CoinMarketCap, 

2022, Yanardağ, 2019). Figure 1 below shows the market capitalisation of the top ten 

cryptocurrencies. 



18 
 

 

 

Figure 1 The market capitalisation of the top ten cryptocurrencies 

Nakamoto described Bitcoin as a payment instrument that relies on blockchain 

technology. Blockchain is a peer-to-peer system wherein users validate transactions 

using cryptographic proof thereby minimising the risk of fraudulent counterfeits 

(Nakamoto, 2008, Walton and Johnston, 2018). Cryptographic proof, one of Bitcoin’s 

primary benefits, relies on peer-to-peer technology whereby users validate 

transactions on a public ledger (a record of all transactions). All transactions are then 

secured using a unique cryptographic hash and stored on a blockchain – which 

represents all verified transactions on the network (Nakamoto, 2008). New bitcoins 

are created by mining which will become more difficult over time as the total supply of 

bitcoin is fixed at 21 million bitcoins (Bui, 2022, Yanardağ, 2019).  

2.2 Behavioural finance theory 

This section looks at the theories underpinning this study, first, by explaining the flaws 

of the efficient market hypothesis, and then the behavioural finance theory is explained 

in more detail. The efficient market hypothesis (EMH) developed by Eugene Fama in 

1965, represents the basis upon which more recent financial theories have been 

developed (Poyser, 2018). The EMH argues that investors are rational and their 

investment decisions are not affected by any emotional biases but rather based on 



19 
 

maximising their expected utility (Bui, 2022; Safarli, 2022). The EMH states that a 

stock will always be priced at its fair value as the stock price immediately reflects all 

available information, thereby implying that the price of a stock on an exchange is 

never overvalued or undervalued (Fakhry, 2016; Joo and Durri, 2015). Furthermore, it 

is argued under the assumption of the EMH that stock price movements are 

completely random and unpredictable leaving no room for investors to outperform the 

market using technical or fundamental analysis – any profits realised by investors are 

merely a result of coincidence (Hidajat, 2019; Safarli, 2022). Another fundamental 

aspect of an efficient market is that stock prices remain relatively stable in the long 

term (Safarli, 2022).  

 

Research has continuously shown that the EMH has numerous flaws in explaining the 

irrationality present in certain markets (Ballis and Verousis, 2022; Poyser, 2018). In 

such instances where irrational behaviour presents itself, behavioural finance – a mix 

of economics and psychology – has been useful in explaining why investors make 

decisions. The behavioural finance theory argues that investors do not always act 

rationally when making investment decisions, but rather that investors make financial 

decisions based on emotions and other cognitive errors (Fakhry, 2016; Ionescu and 

Radulescu, 2019; Joo and Durri, 2015; Youssef, 2022). The behavioural finance 

theory suggests that market prices are impacted by the emotional and cognitive biases 

present in the financial decisions of investors. This explains the deviation of stock 

prices away from their fundamental value in a market, known as market inefficiencies 

(Bassidi, Elkabbouri, Abdeljalil, & Sassi, 2013; Bikas, Jurevičienė, Dubinskas, & 

Novickytė, 2013). 

 

Whilst the EMH has been the main premise of neoclassical finance for many years, it 

has ample pitfalls when it comes to interpreting the extreme fluctuations in 

cryptocurrency prices (Poyser, 2018). It has been argued that cryptocurrencies are 

merely speculative assets with no underlying fundamental value meaning that the 

value of cryptocurrencies is ultimately driven by behavioural biases and speculation 

on their future value (Coskun, Lau, & Kahyaoglu, 2020; Poyser, 2018). As a result, the 

price of cryptocurrencies undoubtedly displays extreme volatility and bubble-like 

behaviour as a result of herding and other biases, namely overconfidence, optimism, 

loss aversion, and confirmation bias as well as investor sentiment (Al-Mansour, 2020; 



20 
 

Hidajat, 2019; Poyser, 2018). Using behavioural finance to understand the impact of 

an individual’s behavioural biases on their decision to invest in cryptocurrency and 

understand the attributes of cryptocurrency that drive the investment of 

cryptocurrency, this research paper will help to better understand the high price 

volatility of cryptocurrency (Baek and Elbeck, 2015; Bolt and Van Oordt, 2020; Venter, 

2016). The following sections detail concepts of behavioural finance that have been 

utilised to assess the impact of an individual’s emotions and other biases on their 

decision to invest in cryptocurrency. 

 

2.2.1 Herding  

As defined by Youssef (2022) herding behaviour is a concept used in behavioural 

finance that takes place "when investors ignore their private information and base their 

investment decision on their peers’ ones”. Herd behaviour in a market reduces the 

interconnection between information and the market due to a large number of 

investors following the crowd, causing the price of the stock to deviate from its 

fundamental value (Poyser, 2018). In the cryptocurrency market, there is an influx of 

noise traders which heightens the possibility of investors distorting the price of 

cryptocurrencies. Noise traders refer to the group of investors who trade based on 

heuristics rather than following the advice of rational and sophisticated investors 

(Poyser, 2018). It has been reported that noise traders present substantial herd 

behaviour as volatility increases (Bui, 2022). In times of declining economic conditions, 

cryptocurrency markets experience increased herd behaviour as traders make use of 

cryptocurrencies as a hedge against conventional financial assets (Bui, 2022).  

Youssef (2022) investigated herding behaviour in the cryptocurrency market over a 

period using cross-sectional absolute deviation under static and time-varying models. 

Results from the study showed anti-herding behaviour under the static model and 

herding behaviour under the time-varying version for the duration of the period under 

study. In a similar study,  Bouri, Gupta, and Roubaud (2018) concluded that substantial 

herding behaviour was present, and fluctuated over the period under investigation. 

Gurdgiev and O’Loughlin (2020) carried out a study to determine the impact of investor 

sentiment on the prices of cryptocurrencies, results showed that herding behaviour 

and anchoring have a direct impact on the price fluctuation of cryptocurrency. Jalal, 

Sargiacomo, Sahar, and Fayyaz (2020) studied herding behaviour in cryptocurrencies 



21 
 

in bullish and bearish markets using a quantile regression method among leading 

cryptocurrencies. The study revealed that herding behaviour is present in bullish 

markets as the results displayed that herding was prominent in the upper quantile. 

Another study carried out by Haryanto, Subroto, and Ulpah (2020) investigated the 

disposition effect and herding in Bitcoin. Results showed that herding behaviour was 

present in a bullish market trend when the price of bitcoin increases and in bearish 

market trends when bitcoin prices decrease.  

Herding factors are important to understand as these may impact an individual’s 

decision to invest in cryptocurrency and in turn cause the rapid price fluctuations in 

cryptocurrency prices. 

 

2.2.2 Overconfidence bias/ self-attribution 

Behavioural finance encompasses over-confidence as another major concept that 

impacts investors’ financial decisions (Fakhry, 2016; Poyser, 2018). Overconfidence 

bias refers to the ideology of those who assume they have better information, and that 

their judgement is more reliable than that of others (Hidajat, 2019). Overconfident 

investors are more likely to participate in risky investment activities, such as 

cryptocurrency trading ( Hidajat, 2019; Kim, Hanna, & Lee, 2022). Barber and Odean 

(2001) found that men are more inclined to be overconfident and generally trade more 

frequently and trade in more risky investments than women (Barber and Odean, 2001; 

Poyser, 2018).  

Sudzina, Dobes, and Pavlicek (2021) investigated the impact of overconfidence and 

self-control on cryptocurrency investment using a binary logistic model. The study 

concluded that early cryptocurrency investors are more likely to be male, display 

overconfidence, and have less self-control. Tran (2019) examined the presence of 

overconfidence in the cryptocurrency market and specifically Bitcoin, Ethereum and 

Ripple. Using vector autoregression along with Granger causality and Impulse 

Response Function, the study found evidence of overconfidence bias in the 

cryptocurrency market as well as in the bitcoin and ripple investments. In an 

Indonesian study on university students, Syarkani and Tristanto (2022) investigated 

the impact of overconfidence, financial literacy and attitude on an individual’s decision 



22 
 

to invest in cryptocurrency. The authors conclude that overconfidence and financial 

literacy has a positive impact on attitude and investment decision. 

Overconfidence in relation to cryptocurrency investment is an important concept to 

gain an understanding of as overconfident investors may not have the relevant 

knowledge required to invest in cryptocurrency, thereby exposing themselves to 

unnecessary risk. Furthermore, this understanding will provide policymakers with 

more information regarding the categories of individuals who may need to be protected 

against these risks.  

 

2.2.3 Optimism bias  

Anamika, Chakraborty, and Subramaniam (2021) argue that investors have an 

optimistic sentiment toward cryptocurrencies. In turn, this optimistic sentiment 

increases investors’ confidence concerning cryptocurrency returns, thereby trading 

volumes and cryptocurrency prices. Optimism bias refers to an individual’s inclination 

to misperceive the probability of experiencing positive and negative outcomes (Hidajat, 

2019). Optimism bias results in investors neglecting the possibility of negative returns 

and overemphasising the possibility of positive returns. This behavioural bias 

contributes to the price crashes experienced in the cryptocurrency market as investors 

tend to overreact to incidents in the market (Al-Mansour, 2020).  

Younger investors with lower earnings and education levels tend to have a greater 

sense of optimism regarding the future of cryptocurrency (Benetton and Compiani, 

2021). In a study investigating the behaviour of investors in Bitcoin and Ethereum 

markets,  Aspembetova, Feng, and Chew (2021) found that Ethereum investors exhibit 

a pessimistic attitude toward the future of Ethereum and that Bitcoin investors display 

greater optimism concerning considerable systemic events. Using cross-sectional 

standard and cross-sectional absolute deviation, Caferra (2020) investigated the 

relationship between sentiment resulting from online news and behaviour in the 

cryptocurrency market. The author found that increased or decreased optimism 

impacts the dispersion of cryptocurrency returns. Anamika and Subramaniam (2022) 

studied the impact of investors’ feelings derived from news headlines on 

cryptocurrency returns using a natural language processing technique. Results show 

that optimism based on news headlines resulted in cryptocurrency prices increasing 

following herding behaviour in the market. The study also revealed that new, small, 



23 
 

and more volatile cryptocurrencies are influenced by investor sentiment to a greater 

extent.   

2.2.4 Loss aversion  

Loss aversion refers to an individual’s preference to avoid making losses rather than 

realising a profit (Nagel, 2018). This can hamper the investor’s potential to realise a 

gain if the investor closes a trade too early fearing that the upward trend may turn 

against them (Ionescu and Radulescu, 2019). On the other hand, the investor may 

hold an unprofitable trade rather than selling, generating a greater loss. Loss aversion 

can be analysed through the lens of Prospect Theory (Abdellaoui et al., 2007; Hidajat, 

2019). Prospect theory disputes the theory of expected utility – a theory which 

assumes that in conditions of uncertainty a rational investor will make investment 

decisions by ranking each investment based on the expected utility and selecting the 

option that provides the highest level of expected utility (Abdellaoui, Bleichrodt, & 

Paraschiv, 2007). However, the theory of expected utility does not hold due to the 

individual’s tendency to feel losses more heavily than gains (Abdellaoui et al., 2007; 

Bui, 2022). In the 1970s, Kahneman and Tversky developed the prospect theory which 

states that individuals are willing to accept risks to avoid enduring a loss and risk-

averse concerning gains. It assumes that individuals evaluate choices in terms of 

gains and losses and make decisions based on the deviation from a reference point, 

rather than in terms of their net assets (Abdellaoui et al., 2007; Levy, 1992). Al-

Mansour (2020) examined the influence of behavioural finance factors on an 

individual’s decision to invest in cryptocurrency using a survey with 112 participants. 

The results of the study showed that prospect theory, herding and heuristic theory 

considerably influenced investors’ cryptocurrency investment decisions. Thoma 

(2020) studied the risk and return determinants in the cryptocurrency market under 

prospect theory using an extensive set of cryptocurrencies during the period 2014 to 

2020. The results revealed that with high prospect theory value – an opinion of the 

investors on the value of a stock based on past return distribution and evaluated using 

prospect theory – cryptocurrencies tend to realise low returns in comparison to 

cryptocurrencies with low prospect theory value.  

Using a quantitative technique Gupta and Shrivastava (2021) investigated whether 

loss aversion and herding influence an individual’s investment decision in the Indian 

stock market using data collected through questionnaires from 323 participants 



24 
 

through questionnaires. The authors found that an investor’s investment decision is 

heavily impacted by loss aversion, herding and the fear of missing out. In the 

cryptocurrency market, among other hypotheses, Nagel (2018) investigated whether 

loss-averse investors are less willing to take on risk in comparison to non-loss-averse 

investors. The results confirmed the author’s hypothesis thereby proving that loss 

aversion impacts investors’ investment decisions.  

2.2.5 Confirmation bias  

Confirmation bias impacts the way that individuals collect and interpret information. 

This bias causes an individual to subjectively follow the information that advocates for 

their beliefs rather than information that conflicts with their belief (Hidajat, 2019). For 

example, when an investor with the opinion that cryptocurrency is a good investment, 

experiences a price decrease, they will look for information that supports the idea that 

cryptocurrency is a good investment and assume that this is a momentary decline and 

the price will increase shortly (Tokarchuk and Donkohlova, 2018). Information-

processing biases influence the price of cryptocurrencies due to the impact of traders’ 

confidence in short-term price developments (Craggs and Rashid, 2016; Hidajat, 

2019).   

In a Chinese study, cryptocurrency investors showed evidence of confirmation bias 

when interpreting news from authorities, such as the government and central bank, as 

investors tend to increase trading volume in response to such news (Ballis and 

Verousis, 2022). In the Thai cryptocurrency market, Tangwattanarat (2017) 

investigated the perception of investors concerning their decision to invest in 

cryptocurrency and digital currencies. The study revealed that cryptocurrency 

investors are influenced by confirmation bias, overconfidence, optimism, and herding 

behaviour. 

2.2.6 Sentiment  

Investor sentiment in the cryptocurrency market influences investors’ investment 

decisions as well as the returns realised by investors (Ballis and Verousis, 2022). In 

the context of the stock market, sentiment refers to the beliefs formed by investors 

regarding the riskiness of investing in stocks and future price developments of the 

asset (Baker and Wurgler, 2007).  Rognone, Hyde, and Zhang (2020) investigated the 

influence of news related sentiments on the returns, volatility and volume of Bitcoin 



25 
 

and conventional currency. The authors found that Bitcoin responded differently in 

comparison to the conventional currencies – the return and volume of Bitcoin 

immediately rise in response to positive news sentiment and experience a delayed 

increase in response to negative news sentiment, whereas negative news sentiment 

negatively impacts conventional currency returns more rapidly. Anamika et al., 2021 

examined how investor sentiment influences cryptocurrency prices using a 

questionnaire to collect data regarding investors’ feelings regarding Bitcoin. The 

results revealed that Bitcoin returns increase when investors have an optimistic 

sentiment towards Bitcoin and that investor sentiment towards Bitcoin also impacts 

the prices of other cryptocurrencies.  

2.3 Awareness and knowledge of cryptocurrency  

The heightened media attention on cryptocurrency has resulted in an increased level 

of awareness among consumers. Stix (2021), who conducted 3 surveys among 

Austrian residents aged 14 or older, using a sample of 1 400 residents for each survey, 

found that (at the time of the survey) 64 per cent of the Austrian population were aware 

of cryptocurrency and of this, 37 per cent of respondents have no interest in 

cryptocurrency. In another study, a sample representative of German internet users, 

Steinmetz et al. (2021) who conducted an online survey reported that 83 per cent of 

respondents had heard of cryptocurrency with a low level of knowledge about 

cryptocurrencies whereas cryptocurrency owners were found to have a higher level of 

cryptocurrency knowledge. The Bank of Canada uses the Bitcoin Omnibus Survey 

(BTCOS) to determine Canadians’ level of awareness of cryptocurrency, the 2018 

BTCOS reported an increase of around 44 per cent in the awareness of Bitcoin from 

2016 to 2018. The level of awareness among Canadians was 89 per cent in 2018 

(Henry et al., 2019). Schuh and Shy (2016) performed a survey among U.S. 

consumers and reported that as of October 2015, about 47 per cent of their survey 

population were aware of any cryptocurrencies, the majority of whom were only slightly 

familiar with cryptocurrency. Another internet survey, conducted among 10 857 

Japanese citizens, by Fujiki (2020) reported that in March 2018, 80 per cent of 

respondents were aware of cryptocurrency at the time of the survey.  

The level of awareness of cryptocurrencies has increased at an expeditious rate 

around the globe (Stix, 2021). Gaining more information regarding the individuals who 



26 
 

are aware of cryptocurrencies and whether these individuals are aware of the risks 

related to cryptocurrency investors is important for policymakers.  

2.4 The attributes and characteristics of cryptocurrency investors 

With regards to demographic factors, owners are more likely to be young males, have 

a higher level of education, have higher pre-tax income, and have better financial 

knowledge than those with only an awareness of cryptocurrency (Fujiki, 2020; Henry 

et al., 2019; Lammer et al., 2020; Schuh and Shy, 2016; Stix, 2021;). In contrast to the 

aforementioned high level of financial knowledge of owners, Henry et al. (2019) found 

that 7.3 per cent of owners have lower financial knowledge while only 4.1 per cent of 

owners have greater financial knowledge despite a greater number of respondents 

with higher financial knowledge being aware of cryptocurrency. 

 

In Austria, Stix (2021), reported that only 1.6 per cent of Austrian residents owned 

cryptocurrencies and a further 1.1 per cent had owned it in the past, but not at the time 

of the survey. Austrian residents with an intention to invest approximate a further 5 per 

cent of additional potential cryptocurrency owners (Stix, 2021). Among the German 

population, 9.2 per cent of German internet users owned cryptocurrency at the time 

that a survey was carried out, with Bitcoin being the most well-known cryptocurrency. 

A further 9.1 per cent of the German population owned cryptocurrency in the past but 

not at the time of the survey. A possible reason why consumers who have owned 

cryptocurrency in the past but do not currently hold any may be due to their perceived 

trust in the stability of the cryptocurrency system or the volatility of the price of 

cryptocurrency (Steinmetz et al., 2021, Henry et al., 2019). In another study, Henry et 

al. (2019) reported that approximately 5 per cent of the Canadian population owned 

Bitcoin, which increased from 3 per cent in 2016. Another study carried out by Schuh 

and Shy (2016) reported that the share of ownership among U.S. consumers in 2015 

was less than 2 per cent. A further study carried out on Japanese citizens reported 

that 2.2 per cent of the population owned cryptocurrency at the time of the survey 

(Fujiki, 2020). It is interesting to note that among Japanese cryptocurrency owners in 

2018, Fujiki (2020) reports that 39 per cent of the owners had little to no understanding 

of cryptocurrencies and only about half of these owners made a profit from their 

investment. 



27 
 

2.5 Trustworthiness  

In relation to cryptocurrency, trust refers to an individual’s trust in blockchain 

technology as well as the individual’s trust in the government and the current financial 

system. Cryptocurrency is a virtual payment system that is decentralised thereby 

diminishing the need for users to place their trust in third parties (Grinberg, 2012; Lo 

and Wang, 2014; Schuh and Shy, 2016). According to a survey carried out by the 

Bank of Canada, Henry et al. (2019) reported that 19 per cent of Canadian Bitcoin 

owners’ main reason for investment was due to their distrust of conventional currency 

or banks. Stix (2021) reported similar findings regarding the motives for the decision 

to invest in cryptocurrency, with about 25 per cent of owners investing in 

cryptocurrency due to their distrust of banks or the Euro. In contrast to this, only about 

10 per cent of consumers in the U.S. are cryptocurrency investors due to their distrust 

of banks or conventional currency (Schuh and Shy, 2016). Some individuals are rather 

reluctant to purchase cryptocurrency due to their lack of trust in the relatively new 

cryptocurrency system as a whole, or because they do not trust the stability of the 

price of cryptocurrency ( Henry et al., 2019; Steinmetz et al., 2021). 

 

Gaining an understanding of the level of trust that individuals have in the new 

technology, the government and conventional currency will help identify the attributes 

and characteristics of cryptocurrency investors.  

2.6 The attributes of cryptocurrency that drive investment 

Cryptocurrencies can be seen as a means to reduce the transaction costs incurred by 

users (Walton and Johnston, 2018).  Ermakova, Fabian, Baumann, Izmailov, and 

Krasnova (2017) and Stix (2021) found that 12 per cent and 25 per cent of consumers 

(in their respective studies) chose to invest in cryptocurrency to benefit from the low 

transaction costs that cryptocurrency provides. Most users reported that the 

speculative opportunity or the store of value provided by the cryptocurrency was their 

main motive for owning cryptocurrency (Henry et al., 2019; Steinmetz et al., 2021; Stix, 

2021). Another motive for owning cryptocurrencies includes the anonymity and privacy 

of the blockchain system as users are identified as public keys rather than by name. 

This allows participants to transact or transfer funds anonymously. In studies carried 

out by Steinmetz et al. (2021) and Schuh and Shy (2016), the anonymity and privacy 

feature has been reported as the main motive of approximately 14 to 32 per cent of 



28 
 

German and American cryptocurrency owners. Other incentives that cryptocurrencies 

offer to consumers is the innovative technology, the ability to administer irreversible 

transactions (a transfer can be sent back to the payer only once it has been accepted 

by the payee), the transparency in the way that new coins are created (a process 

known as ‘mining’), as well as the removal of inflation due to the predetermined total 

supply of 21 million coins (Alzahrani and Daim, 2019; Walton and Johnston, 2018, 

Stix, 2021, Nakamoto, 2008; Weber, 2014).  

 

The benefits of cryptocurrencies will provide more information relating to the motives 

that drive an individual’s cryptocurrency investment. Gaining an understanding of 

these motives will be useful to the government and domestic banks in order to remain 

competitive.  

2.7 The attributes of cryptocurrency that drive non-investment  

A study performed by Arias-Oliva et al. (2019) reported that the risks inherent in 

cryptocurrency are not found to be a relevant factor for investors regarding their 

decision to invest in cryptocurrency. In saying this, it does not mean that the risks 

attached to investing in cryptocurrency do not play a part in consumers’ intention to 

invest in cryptocurrency (Arias-Oliva et al., 2019; Stix, 2021). Zhao and Zhang (2021) 

found that investment experience was found have a more significant impact on the 

decision to purchase cryptocurrency than the financial knowledge of investors. In 

studies performed by Schuh and Shy (2016), Fujiki (2020) and Steinmetz et al. (2021) 

it was found that owners of cryptocurrency are more willing to take on a higher level 

of risk to reap the benefit of higher returns. Furthermore, Fujiki (2020) reported that 

approximately 81 per cent of cryptocurrency owners hold other risky investments in 

comparison to approximately 58 per cent of nonowners holding other risky 

investments.  

 

There is a substantially higher level of risk involved when investing in cryptocurrency 

due to the volatile prices, with cryptocurrencies having considerably higher annualised 

volatility than any conventional currency or other financial assets (Osterrieder et al., 

2016; Steinmetz et al., 2021). This high level of volatility, however, creates an 

opportunity for owners of cryptocurrency to earn quick money (Fujiki, 2020). Other 

risks associated with cryptocurrency noted by past research that impact consumers’ 



29 
 

intention to invest in cryptocurrency is the fact that blockchain technology is a fairly 

new phenomenon resulting in consumers being reluctant to trust the new technology, 

or that consumers fear that cryptocurrency is susceptible to cyber hacking wherein 

investors are exposed to the risk of losing their money (Henry et al., 2019; Steinmetz 

et al., 2021). Many cryptocurrency users have also reported being a victim of 

fraudulent cryptocurrency exchanges as a result of the deregulated market, resulting 

in another reason why people are reluctant to invest in cryptocurrency (Weber, 2014). 

Although the anonymity feature of cryptocurrency presents as a key benefit of the 

digital currency platform, it has also provided an opportunity for illegal activities such 

as the sale of illegal goods, money laundering, and tax evasion (Zouhair & Kasraie, 

2019).  

 

Gaining an understanding of the motives that drive the non-investment in 

cryptocurrency sparks the need for governments to impose policies to regulate the 

world of digital currencies (Fujiki, 2020; Schuh and Shy, 2016). 

 

2.8 Conclusion 

The heightened media attention on cryptocurrency caused a considerable number of 

consumers to invest in cryptocurrency, which could have been driven by consumers 

feeling that they may be missing out on an investment opportunity. These individuals 

may not be aware of the risks they are exposed to as cryptocurrency owners (Angerer, 

Hoffmann, Neitzert, & Kraus, 2021; Chohan, 2017). Gaining a comprehensive 

understanding of the demographic and behavioural characteristics of individuals who 

invest in cryptocurrency can guide policymakers in determining which categories of 

individuals may need protection against the risks associated with these investments 

(Steinmetz et al., 2021; Zouhair and Kasraie, 2019). 

  



30 
 

Chapter 3 – Method 

3 Introduction 

The purpose of this study was to identify the level of awareness of South Africans 

regarding cryptocurrency, the share of ownership among the South African population, 

their demographics, and their motives to invest in cryptocurrencies. This chapter 

consists of the methodology that was structured to meet the purpose of this study. The 

chapter includes the following: the research design, followed by the population and 

study sample, instrumentation, data collection and data analysis. Thereafter, followed 

by a discussion of the reliability and validity of the study and the ethical considerations. 

3.1 Research design 

This study is within the positivist worldview. This approach supports the belief that 

objective facts can be quantified to explain an individual’s behaviour (Firestone,1987). 

The study adopted a quantitative research method which set out to interpret what 

caused a change in social facts using objective measurement and quantitative 

analysis (Firestone, 1987). This approach allowed the primary researcher to form, 

confirm, or validate relationships between variables in order to determine the attributes 

and characteristics of cryptocurrency investors (Leedy, 2021). A non-experimental 

design was used which was suitable as the variables in the research could not be 

manipulated (Radhakrishnan, 2013). 

3.2 Population and study sample 

The target population for this study comprised the adult South African population aged 

18 and older who used the internet. The target population was limited to internet users 

as the use of blockchain technology required the use of the internet. 

In accordance with Leedy (2021), a sample size of 400 was required to conduct a 

meaningful analysis when the target population exceeded 5,000. This study used a 

sample size of 400 participants with the expectation of a 50% response rate yielding 

200 participants. The primary researcher believed that 200 participants would be 

representative of South African internet users above the age of 18. This is supported 

by previous research conducted by Ermakova et al. (2017), wherein a sample size of 

98 participants was acquired. Another similar study carried out by Walton and 

Johnston (2018) yielded a sample size of 237 participants. This project acquired a 

sample size of sample size of 298 responses, from which 5 responses were removed 



31 
 

due to the respondents failing to answer all the questions. As a result, the final sample 

size was 293 participants, which is well above the desired minimum sample size of 

200 responses.  

3.3 Sampling technique 

The study adopted a non-probability sampling technique as the researcher needed to 

use judgement in determining the participants in the study. The need for the primary 

researcher to apply judgement when selecting the sample used in this study arose 

due to the large population size and the need to focus on South Africans above the 

age of 18 who used the internet (Etikan et al., 2016). As such, purposive sampling was 

used for a focus to be placed on a sample of individuals with the characteristics. 

Purposive sampling limited the likelihood of survey participants who did not conform 

to the purpose of this study (Etikan et al., 2016). Thereafter, snowball sampling was 

used whereby participants were able to send the link to the survey to their friends and 

family. 

3.4 Data collection 

In this study, primary data was used as the primary researcher administered an online 

survey, using Google Forms, and sent to South African internet users to collect the 

data. A link to the survey was shared on the social media platform, LinkedIn, and was 

also shared with colleagues and friends via email. A link to the survey was also shared 

with students at the University of the Witwatersrand. The advantage of using an online 

survey was that it enabled the researcher to reach a geographically spread sample. 

Surveys were also easy to utilise, time and cost efficient, and convenient for 

participants (Van Selm and Jankowski, 2006). 

3.5 Instruments 

An online survey (see Appendix A for the survey) was designed, based on past 

literature, and incorporated questions taken from the published literature that used a 

similar approach to this study. Answers to the survey questions were marked on a 

Likert scale from 1 to 5, with 1 being of least importance/strongly disagree and 5 being 

of most importance/strongly agree. 

Part 1 included questions regarding the awareness of cryptocurrency. To avoid 

confusion among participants regarding what cryptocurrency refers to, a list of the top 

10 cryptocurrencies, according to market capitalisation, was inserted into the online 



32 
 

survey. If a participant was not aware of cryptocurrency, they were diverted to part 9 

of the survey. Part 2 included questions regarding the ownership of any 

cryptocurrency. Part 3 of the survey was conditional upon non-investment in 

cryptocurrencies and contained questions regarding the participants' interest in 

purchasing cryptocurrency, such as whether they intended to purchase cryptocurrency 

or never intended to purchase cryptocurrency. Participants with no intention to invest 

in cryptocurrency were diverted to part 8 of the survey. Part 4 was conditional upon 

ownership and comprised questions regarding the respondents' main use of 

cryptocurrency. Part 5 covered questions relating to the participant’s trust in the South 

African Rand, the South African Reserve Bank, as well as domestic banks within South 

Africa. Part 6 of the survey contained questions regarding the participants' attitudes 

towards cryptocurrency, such as whether cryptocurrency offered an advantageous 

alternative to the conventional payment system. Part 7 comprised questions relating 

to a participant's motives that drove their decision to invest in cryptocurrency or their 

intention to invest. Part 8 of the survey was conditional upon non-ownership of 

cryptocurrency and no intention to invest in cryptocurrency and included questions 

regarding the participants' reasons for not wanting to invest in cryptocurrency. Part 9 

included questions regarding the financial literacy of the participants. The questions in 

part 9 had been taken from similar past research and had been used by Henry et al. 

(2019) and Stix (2021). More specifically, questions 9.4 – 9.6 had been adapted from 

the "Big Three" questions developed by Lusardi and Mitchell (2011), which had been 

used in many academic papers to study the financial literacy of respondents (Henry et 

al., 2019). Part 10 comprised questions relating to the respondents' risk appetite as 

well as their holdings of other financial assets. Finally, part 11 incorporated questions 

regarding the participants' sociodemographic information. Fessler and Schürz (2018) 

had found that information regarding an individual's ownership or rental of real estate 

and ownership of a business indicated the individual's net wealth. As such, the 

questions in part 11 included questions regarding the ownership of a home to classify 

individuals into their respective financial wealth categories (Henry et al., 2019; Fessler 

and Schürz, 2018; Stix, 2021). 

3.6 Data analysis  

The data was inserted onto an Excel spreadsheet with the required labels and codes 

and then reviewed for any missing data and summed up. The level of statistical 



33 
 

probability was set at 0.05 when analysing the data (Leedy, 2021).  Statistical Package 

for the Social Sciences (SPSS) version 28.0.1.0 was used to analyse the data and 

identify relationships among the variables. The research questions of this project were 

analysed using descriptive and inferential statistics (Steinmetz et al., 2021). 

Descriptive statistics were calculated for all variables used in this study to identify and 

relationships and meaningful information. Specifically, descriptive statistics were used 

to determine the motives for investment and non-investment in cryptocurrency by 

interpreting the frequencies of the independent variables, such as the investor’s 

investing due to the fear of missing out on an investment opportunity or their lack of 

trust in the South African Rand. To assess whether investors understand the risks 

involved in cryptocurrency investment, frequencies were calculated between investors 

(dependent variable) and Bitcoin knowledge (independent variable). Descriptive 

statistics were suitable for this study as it required summarising large datasets which 

helped in demonstrating the patterns and trends found in the datasets (Leedy, 2021). 

In accordance with the central limit theorem, a sample size greater than 30 can be 

regarded as normally distributed (Field, 2013). The sample size acquired for this study 

was 293 participants and as a result, normally distributed. All the relevant assumptions 

required for binomial logistic regressions were met. Thus, logistic regression analysis 

was utilised to identify any meaningful information and address the research questions 

of this study.  

Logistic regressions were performed between investment (dependent variable) and 

demographics, sociodemographic factors, and trust (refer to table 1 for the various 

independent variables within these categories). This was to determine the attributes 

and characteristics of cryptocurrency investors. A second regression was run to 

determine the relationship between cryptocurrency awareness (dependent variable) 

and demographic factors, and Bitcoin knowledge (independent variables). A third 

regression was performed to determine the impact of behavioural factors such as an 

individual’s attitudes and biases toward cryptocurrency on their decision to invest. The 

dependent variable for this regression was investment and the independent variables 

utilised for this regression were demographic factors, financial literacy, attitude 

towards cryptocurrency, and risk appetite (refer to table 1 for the details relating to 

these variable categories). The equations for the regressions are as follows: 



34 
 

Regression 1: 

Investment = β0 + βDemographics + β2Sociodemographics + β3trust + εi 

Regression 2: 

Awareness = β0 + β1Demographics + β2Bitcoin knowledge + εi 

Regression 3: 

Investment = β0 + βDemographics + β2Financial literacy + β3Attitude + β4Risk 

appetite + εi 

Where, 

ε: Error term 

β0: Constant 

Table 1 Summary of variables included in this study 

Variable Description 

Dependent 

variables  

 The two dependent variables are used separately to 

run different logistic regressions 

Awareness 

Awareness of cryptocurrencies was derived by asking 

whether participants had heard of Bitcoin or other 

cryptocurrencies. Participants who were aware of 

cryptocurrency were given a score of 1, and a score of 0 

was given to participants who were unaware of 

cryptocurrencies. 

Investment 

Cryptocurrency investment was derived by asking 

participants whether they (i) currently owned bitcoin, (ii) 

currently owned another cryptocurrency, (iii) owned 

cryptocurrencies in the past but not anymore, (iv) never 

owned cryptocurrency but had an interest in purchasing 

cryptocurrency, (v) merely knew of cryptocurrency, or (vi) 

knew of cryptocurrency but had no interest in purchasing 

cryptocurrency. A score of 1 was given for answers (i) and 



35 
 

(ii), and a score of 0 was given for answers (iii), (iv) and 

(v). 

Independent 

variables  
  

Main use for 

cryptocurrency 

Participants were given questions regarding their main use 

of cryptocurrency (store of wealth, a speculative 

investment, medium of exchange, to transfer funds 

anonymously) and were required to select from the options 

"strongly agree, agree, neutral, disagree, strongly 

disagree". Responses were scored from 0 to 4 with 4 

being strongly agree and 0 being strongly disagree.  

Trust 

Participants were given questions regarding their trust in 

the South African rand, the South African Reserve Bank, 

domestic banks in South Africa, and financial advice 

provided by their bank. Participants were required to select 

from the options "strongly agree, agree, neutral, disagree, 

strongly disagree". Responses were scored from 0 to 4 

with 4 being strongly agree and 0 being strongly disagree.  

Motives for 

cryptocurrency 

investment 

Participants were given questions regarding their main 

motive for investing or intention to invest in cryptocurrency 

(low transaction costs, anonymity feature, high returns, to 

make quick money, to avoid missing out on an investment 

opportunity, lack of trust in South African banks/or the 

government, transparency, expectation to outperform 

traditional investments, it is the future of online spending, 

interest in new technology) and were required to select 

"yes" or "no". A score of 1 was given to participants who 

answered yes, and a score of 0 was given to those who 

answered no. 



36 
 

Motives for non-

investment 

Participants were given questions regarding their main 

motive for not wanting to invest in cryptocurrency (do not 

understand it, it is often not accepted for payment, content 

with the conventional financial system, excessive risks, not 

regulated, do not trust the new technology, insufficient 

funds to invest, high level of fraud or online theft) and were 

required to select "yes" or "no". A score of 1 was given to 

participants who answered yes, and a score of 0 was given 

to those who answered no. 

Attitude toward 

cryptocurrency 

Participants were given questions regarding their attitude 

towards cryptocurrency (it offers advantages, positive 

returns are very likely, losses are very likely, there is a 

great risk of fraud and online theft, and high exposure to 

volatility against the rand) and were required to select from 

the options "strongly agree, agree, neutral, disagree, 

strongly disagree". Responses were scored from 0 to 4 

with 1 being strongly agree and 5 being strongly disagree. 

Financial literacy  

Financial literacy was derived by asking participants basic 

financial questions relating to basic compound interest, 

inflation, and investment knowledge. A score of 1 was 

given to participants who answered the questions 

correctly, and a score of 0 was given to those who 

answered incorrectly. 

Risk appetite and 

other asset holdings  

Participants were asked a question regarding their risk 

appetite. Those with a high risk appetite, meaning 

respondents who answered yes to substantially high risk 

were given a score of 0, those who stated above-average 

risk were given a score of 1, those who stated average risk 

appetite were given a score of 2, those who stated below-

average were given a score of 3 and those who were not 

willing to take on any risk were given a score of 4. 

Sociodemographic 

factors  

  



37 
 

Level of education 

Respondents whose highest level of education was grade 

12 and below were scored as a 0, respondents whose 

highest level of education was a higher certificate were 

scored as a 1, diplomas were scored as a 2, and 

respondents whose highest level of education was an 

undergraduate were scored as a 3, and postgraduate 

degrees were scored as a 4.  

Employment  

Respondents were given a score of 0 if they were 

employed, 1 if they were unemployed, 2 if they were a 

student and 3 if they are retired. 

Financial wealth 

Participants were given a score of 0 if they owned their 

place of residence and their own business, 1 if they own 

their residence, 2 if they rent their place of residence and 

own a business, 3 if they rent their residence and no don’t 

own a business, and 4 if they neither rent nor own a 

business. 

 

3.7 Reliability and validity of the study 

The questions used in the survey were based on questions asked in published 

literature that used a similar approach to this study, which gave this proposed study 

its reliability. However, there were disadvantages to using a survey, such as threats to 

internal validity and reliability, as participants who did not understand the questions or 

may not have completed all the questions could have affected the results (Mathers, 

Fox, & Hunn, 1998). To prevent this, a pilot study was performed whereby the survey 

was handed out to 5 respondents in the School of Accounting. This helped the 

researcher to determine the effectiveness of the proposed survey in capturing the 

results that were required, and adjustments could have been made if needed. The 

researcher believed that the participants provided honest information and that the 

analysis of the data collected would provide validity to the proposed study’s results 

and conclusions. 

3.8 Ethical consideration 

The researcher applied for ethical clearance due to the survey-based study requiring 

the participation of human beings. Ethical clearance was required to ensure that the 



38 
 

study did not affect the psychological wellbeing of participants and that the study led 

to beneficial results. As a result, the researcher applied for ethical clearance from the 

institution's ethics committee. Once the researcher obtained approval from the 

institution’s ethics committee, the survey was distributed to the relevant parties. The 

researcher ensured that participation in the survey was purely voluntary. All 

participants' anonymity was guaranteed in the resulting research report, and all 

information collected was kept confidential, only to be used for this study. Consent 

was obtained from participants to use the data collected in the proposed study, and 

participants were made aware of the aims of the study. The survey data was then 

accessed, transferred onto an Excel spreadsheet, and analysed by the researcher in 

SPSS. 

  



39 
 

Chapter 4 – Results and Discussion 

4 Introduction  

The results chapter displays an analysis of the data collected and results from the 

survey. Descriptive statistics have been used to summarise the characteristics of the 

data collected from the survey. Thereafter logistic regressions have been used to 

analyse the relationship between the dependent variables (ownership and awareness 

and one or more independent variables. 

The raw data yielded a sample size of 298 responses, from which 5 responses were 

removed due to the respondents failing to answer all the questions. As a result, the 

final sample size was 293 participants.  

4.1 Demographic variables of the sample 

The demographic variables include gender, age, highest qualification, employment 

status, financial wealth, and profession of the respondent. As seen in Table 2, the 

gender distribution in the sample is roughly equal with 150 (51.2%) male, 141 (48.1 

%) female, and 2 (0.7%) other gender respondents. The age of the sample population 

was dominated by younger respondents aged between 18 to 25 years old (50.9%). 

The second largest age group is 26- to 35-year-olds, with 18.1% of the sample falling 

within this category. The remaining age groups of the respondents of 36 to 45 years, 

46 to 55 years, and above 55 years comprise 8.9%, 11.6%, and 10.6% of the sample 

population respectively (Table 3).  

Table 2 Frequency table for the gender of the sample 

 

 

 

 

 

 

 

Gender 

 Frequency Percent 

Male 150 51.2 

Female 141 48.1 

Other 2 0.7 

Total 293 100.0 



40 
 

Table 3 Frequency table for the age of the sample 

 

 

 

 

 

 

 

As shown in Table 4 The results show that a significant portion of the respondents 

have achieved tertiary education, with more than half the sample population holding 

an undergraduate or postgraduate degree, with 45 (15.4%) participants who attained 

an undergraduate degree and 116 (39.6%) participants who achieved a postgraduate 

degree. This was followed by 104 (35.5%) participants whose highest level of 

education was between grades 9 to 12, however, the majority of these respondents 

are still in the progress of achieving a higher level of education. 

Table 4 Frequency table for the highest qualification of the sample 

 

 

 

 

 

 

 

Concerning the frequency distribution relating to the employment status of the sample 

population, the majority of the participants are employed or self-employed, accounting 

for 160 (54.6%) of the sample. 106 (36.2%) of the sample are students, 17 (5.8%) 

participants are unemployed and 3.4% are retired as can be seen in Table 5.  

Age 

 Frequency Percent 

18-25 149 50.9 

26-35 53 18.1 

36-45 26 8.9 

46-55 34 11.6 

Above 55 31 10.6 

Total 293 100.0 

Highest Qualification 

 Frequency Percent 

Grade 9 and below, Grade 10, 

Grade 11, Grade 12 

104 35.5 

Higher certificate 9 3.1 

Diploma 19 6.5 

Undergraduate degree 45 15.4 

Postgraduate degree 116 39.6 

Total 293 100.0 



41 
 

Table 5 Frequency table for the employment status of the sample 

 

 

 

 

 

 

The financial wealth status of the sample was determined by asking the respondents 

a question relating to whether they own or rent their place of residence and whether 

they owned a business, as well as which tax bracket they fall into. The results showed 

that a significant portion of the sample neither rent nor own a business, accounting for 

102 (34.8%). This may be due to the reason that a large portion of the sample is 

students, with limited financial resources. This is followed by 70 (23.9%) participants 

who own their place of residence, 42 (14.3%) participants who own both, their place 

of residence and a business, and 9 (3.1%) participants who rent their place of 

residence while owning a business (as shown in table 7). With regards to the taxable 

income of respondents, the largest portion of participants fall within the taxable income 

bracket of less than R216 200, accounting for 89 (30%) participants. This was followed 

by 41 (14%) of participants falling within the taxable income bracket of exceeding 

R782 200 but equal to or not exceeding R1 656 600. Those who fell within the taxable 

income brackets of exceeding R216 200 but equal to or not exceeding R337 800 and, 

exceeding R337 800 but equal to or not exceeding R467 500 accounted for 9% each. 

Those who fell within the taxable income brackets exceeding R467 500 but equal to 

or not exceeding R613 600 and, exceeding R613 600 but equal to or not exceeding 

R782 200 taxable income brackets accounted for 5% and 4% respectively (as shown 

in table 5).  

 

 

Employment status 

 Frequency Percent 

Employed or self-

employed 

160 54.6 

Unemployed 17 5.8 

Student 106 36.2 

Retired 10 3.4 

Total 293 100.0 



42 
 

Table 6 Frequency table for the taxable income of the sample 

Taxable Income 

 Frequency Percent 

Not exceeding R216200 89 30 

Exceeding R216 200 but equal to or not exceeding 

R337 800 

25 9 

Exceeding R337 800 but equal to or not exceeding 

R467 500 

26 9 

Exceeding R467 500 but equal to or not exceeding 

R613 600 

15 5 

Exceeding R613 600 but equal to or not exceeding 

R782 200 

11 4 

Exceeding R782 200 but equal to or not exceeding 

R1 656 600 

41 14 

Exceeding R1 656 600 9 3 

Total 293 100.0 

 

Table 7 Frequency table for the financial wealth status of the sample 

Financial Wealth 

 Frequency Percent 

Own residence and 

business 

42 14.3 

Own residence 70 23.9 

Rent residence and 

own business 

9 3.1 

Rent residence 70 23.9 

Neither rent 

residence nor own 

business 

102 34.8 

Total 293 100.0 

 



43 
 

The survey asked respondents to indicate their profession from a list of options and 

the majority of participants (62.1%) categorised themselves as working in business 

and administration (including finance). Education was the second most common 

profession amongst the sample, with 28 (9.6%) participants indicating that they fell 

into this field. Health and information technology and communication, each accounted 

for 17 (5.8%) participants each. While the legal profession was selected by 5 (1.7%) 

participants and science and engineering (including actuarial) was selected by 20 

(6.8%) participants. The remaining respondents (8.2%) chose “other” (as depicted in 

table 8).  

Table 8 Frequency table for the profession of the sample 

Profession 

 Frequency Percent 

Business and admin (incl. finance) 182 62.1 

Education 28 9.6 

Health 17 5.8 

ICT 17 5.8 

Legal 5 1.7 

Science and engineering (incl. 

actuarial) 

20 6.8 

Other 24 8.2 

Total 293 100.0 

4.1.1  Ownership and awareness of cryptocurrency of the sample 

A significant portion of respondents, 97%, are aware of cryptocurrency. However, as 

depicted in figure 2, only 69 (24%) participants noted that they currently own 

cryptocurrencies, and a further 15 (5%) of participants owned cryptocurrencies in the 

past but not anymore. A notable 56 (19%) participants stated that they do not own 

cryptocurrency at present but have an interest in purchasing cryptocurrency. However, 

a large portion of the sample, accounting for 145 (51%) participants, did not show any 

interest in investing in cryptocurrency.  



44 
 

Respondents who indicated that they have never owned cryptocurrency were then 

asked a question relating to their intention to purchase cryptocurrency at some time. 

The results indicated that 58 participants expressed a positive attitude towards 

purchasing cryptocurrency, with 19 (6.5%) stating that it is very likely that they will 

purchase cryptocurrency at some time and a further 39 (13.3%) participants stating 

that it is somewhat likely. A significant portion of the sample stated that it was unlikely 

or highly unlikely that they would purchase cryptocurrency at some time, accounting 

for 39 (13.3%) and 27 (9.2%) participants respectively.  

 

4.1.2 Risk appetite of the sample 

The respondents were asked to state whether they are willing to take on substantial, 

above-average, average, below-average, or no risk to assess the risk appetite of 

respondents. The results showed that 171 (58.4%) participants are high-risk takers, 

82 (28%) participants are willing to take on average risk, 13 (4.4%) participants are 

only willing to take low risk, and 27 (9.2%) participants are not willing to take on any 

risk (as shown in figure 3). 

 

 

 

 

24%

5%
2%

19%

32%

0%

5%

10%

15%

20%

25%

30%

35%

Currently own
cryptocurrency

Owned in the
past but not

anymore

Never owned
cryptocurrency

but has an
interest in

purchasing

Merely know of
cryptocurrency

Knows about
cryptocurrency

but has no
interestP

e
rc

e
n

ta
g

e
 o

f 
re

s
p

o
n

d
e
n

ts

Cryptocurrency ow nersh ip

Figure 2 A bar graph showing the share of cryptocurrency ownership of the sample 



45 
 

Figure 3 A donut graph showing the risk appetite of the sample 

4.1.3 Financial literacy and Bitcoin knowledge of the sample 

As shown in figure 4, the results suggested a high level of financial literacy among the 

respondents, with some participants displaying a better understanding of concepts 

such as interest and inflation than others. This is evident as 229 (80.4%) participants 

were able to correctly calculate the interest on a savings account and 218 (76.5%) 

participants understood the concept of inflation. With regards to simple investment 

knowledge, such as the risk of investing in a single company’s shares versus a 

portfolio of shares, 242 (84.5%) participants correctly identified that purchasing a 

single company’s shares exposes them to higher risk. However, the results showed 

that a larger portion of respondents did not have much knowledge about 

cryptocurrency. When respondents were asked whether the total supply of Bitcoin is 

fixed, 167 (58.6%) participants answered incorrectly and when asked whether all 

Bitcoin transactions are recorded on a distributed ledger, 107 (37.5%) participants 

answered incorrectly (this is shown in figure 5).  

 

 

 

22%

38%

28%

4%

9%

Risk  appet i te

Substantial risk Above-average Average Below-average No risk



46 
 

Figure 4 A bar graph showing the financial literacy of the sample 

Figure 5 A bar graph showing the respondents basic cryptocurrency 

knowledge 

4.1.4 Attitude toward cryptocurrency 

Respondents were then asked about their beliefs regarding cryptocurrency where a 

significant number of participants responded negatively toward cryptocurrency as 222 

(77.9%) participants stated that they believe suffering a loss from cryptocurrency is 

very likely whereas only 129 (45.3%) participants believed that realising positive 

returns from cryptocurrency is very likely. However, a notable number of participants 

believe that cryptocurrencies offer advantages over the conventional payment system 

(as depicted in figure 6). This shows that although participants believe that 

cryptocurrency offers advantages over the conventional financial system, there is also 

a high level of concern regarding potential losses. 

0% 20% 40% 60% 80% 100%

Basic understanding of compound interest

Basic understanding of inflation

Basic investment knowledge

Percentage of respondents 

Financia l  l i te racy

Correct Incorrect

0% 20% 40% 60% 80% 100%

Total supply of bitcoin is fixed

Bitcoin is backed by a government

All bitcoin transaction are recorded on a
distributed ledger

Percentage of respondents 

Bas ic  know ledge on  c ryptocurrency

Correct Incorrect



47 
 

Figure 6 A bar graph showing the sample’s attitude toward cryptocurrency  

4.1.5 The attributes of cryptocurrency that drive the investment in 

cryptocurrency 

Respondents who showed an interest in cryptocurrency were then asked to state “yes” 

or “no” to statements relating to their motive to invest in cryptocurrency. The results 

reflected that a large portion of respondents favoured the investment opportunity due 

to the possibility of high returns as well as the new blockchain technology, with 93% 

of participants agreeing with the statements relating to the aforementioned (as shown 

in figure 7). The respondents’ trust in South African banks or the government did not 

impact their decision to invest in cryptocurrency. 

Figure 7 A bar graph showing the attributes of cryptocurrency that drive 

investment among the sample 

0% 20% 40% 60% 80% 100%

Investment opportunity with the possibility
of high returns

Interest in new technology

Alternative form of exchange with low
transaction costs

Do not trust the SARB or the government

93

93

72

46

7

12

28

54

Percentage of respondents

Att r ibutes  o f  c ryptocurrency tha t  dr ive  
inves tment  

Yes No

0

10

20

30

40

50

60

Suffering losses is very
likely

Realising positive
returns is very likely

Cryptocurrencies offer
advantages over the

conventional payment
system

18

7 9

60

39
34

19

36 40

2

16 14

1 2 4

P
e
rc

e
n

ta
g

e
 o

f 
re

s
p

o
n

d
e
n

ts
Att i tude  tow ard  c ryptocurrency 

Strongly agree Agree Neutral Disagree Strongly disagree



48 
 

4.2 The attributes and characteristics of cryptocurrency investors in South 

Africa 

From the descriptive statistics, we note that 74% of cryptocurrency investors are below 

the age of 35 (as shown in figure 9), whilst 69% of cryptocurrency investors are male 

(this is depicted in figure 8). This shows that cryptocurrency investors are more likely 

to be younger males.  

Figure 8 A pie graph showing the gender of cryptocurrency investors  

Figure 9 A bar graph showing the age of cryptocurrency investors  

In terms of education, the majority of the cryptocurrency investors held postgraduate 

degrees, accounting for 52% of cryptocurrency investors within the sample. A further 

19% of cryptocurrency investors held undergraduate degrees and cryptocurrency 

investors holding a diploma or higher certificate accounted for 6% and 4% 

respectively. It is interesting to note that a large portion of cryptocurrency owners’ 

highest level of education was high school, accounting for 19% of the sample (as 

shown in figure 10). As depicted in figure 11, with regards to the employment status 

of cryptocurrency investors, 67% of investors are employed or self-employed, while 

69%

31%

Gender  o f  c ryptocurrency inves tors

Male Female

48%

30%

10% 11%
6%

0%

10%

20%

30%

40%

50%

18 - 25 26 - 35 36 - 45 46 - 55 Above 55

P
e
rc

e
n

ta
g

e
 o

f 
in

v
e
s
to

rs

Age of cryptocurrency investors



49 
 

8% of investors were unemployed, 23% were students, and 2% were retired 

individuals.  

Figure 10 A bar graph showing the highest level of education of 

cryptocurrency investors  

Figure 11 A bar graph showing the employment status of cryptocurrency 

investors  

The survey used in this research paper asked respondents questions regarding their 

taxable income bracket and housing status to assess the financial wealth status of 

investors. The results show that cryptocurrency investors are more likely to be 

individuals who have above-average incomes in South Africa.  

 

 

0% 10% 20% 30% 40% 50% 60% 70%

Employed or self-employed

Unemployed

Student

Retired

67%

8%

23%

2%

Percentage of investors

Employment status of cryptocurrency investors

19%

4%

6%

19%

52%

0% 10% 20% 30% 40% 50% 60%

Grade 9 and below, Grade 10, Grade 11,
Grade 12

Higher certificate

Diploma

Undergraduate degree

Postgraduate degree

Percentage of investors

Highest  leve l  o f  educat ion ach ieved  by 
c ryptocurrency inves tors



50 
 

As seen in figure 13, the percentage of individuals who have invested in 

cryptocurrency varies depending on their taxable income bracket in South Africa. 

Among those with a taxable income less than R216 200, 18% had invested in 

cryptocurrency, and for individuals with taxable income exceeding R216 200, the 

percentage of cryptocurrency investment ranges from 4% to 21%, with the highest 

percentage of cryptocurrency investment being among individuals with taxable income 

exceeding R782 200 but equal to or not exceeding R1 656 600. Concerning the 

respondents’ housing status, 13% of cryptocurrency investors stated that they own 

their place of residence and a business, whilst a further 30% stated they own their 

place of residence but not a business. For those who neither own nor rent their place 

of residence nor a business, 30% of these individuals have invested in cryptocurrency, 

which could be a result of the sample population being younger individuals who may 

not have started earning an income yet. This is depicted in figure 12.  

Figure 12 A bar graph showing the financial wealth status of cryptocurrency 

investors 

 

 

 

 

 

 

13%

30%

4%

23%

32%

0%

20%

40%

P
e
rc

e
n

ta
g

e
 o

f 
in

v
e
s
to

rs

Financial wealth

Financial wealth status of cryptocurrency investors

Own residence and business
Own residence
Rent residence and own business
Rent residence
Neither rent residence nor own business



51 
 

Figure 13 A bar graph showing the taxable income bracket of cryptocurrency 

investors  

The profession of individuals among cryptocurrency investors varies, with 55% of 

investors in the business and administration (including finance) profession. Investors 

in the information and communication technology profession accounted for 13%, while 

those in science and engineering (including actuarial) also accounted for 13%. The 

professions with the lowest percentage of investment were the legal profession (1%), 

education and health (both accounted for 7% each) (this is shown in figure 14).  

 

 

 

 

 

21%

10%

11%

7%

1%

21%

7%

21%

0% 5% 10% 15% 20% 25%

Not exceeding R216200

Exceeding R216 200 but equal to or not
exceeding R337 800

Exceeding R337 800 but equal to or not
exceeding R467 500

Exceeding R467 500 but equal to or not
exceeding R613 600

Exceeding R613 600 but equal to or not
exceeding R782 200

Exceeding R782 200 but equal to or not
exceeding R1 656 600

Exceeding R1 656 600

I am not sure or prefer not to say

Percentage of investors

Taxable income of crytocurrency investors



52 
 

Figure 14 A bar graph showing the profession of cryptocurrency investors  

In assessing the attributes and characteristics of cryptocurrency investors in South 

Africa, this research assessed what these investors were more likely to use 

cryptocurrency for. As shown in figure 15, the results showed that cryptocurrency 

investors mainly use cryptocurrency as a speculative investment (81%), while a 

significant portion of