Determinants of organisational 

blockchain usage behaviour within 

the South African financial services 

industry 

 

Ashley J. Paul 

Student number: 692930 

 

 

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

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

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

Digital Business 

 

 

Johannesburg, 2023 

 

 

 



 
ii 

ABSTRACT 

This report presents findings on individual blockchain usage behaviour at the 

organizational-level in the South African financial services industry.  

The study examined the influences of perceived usefulness, motivational factors, 

design and implementation, and perceived ease of use, on blockchain adoption 

behaviour.  

Empirical results from quantitative analyses following a survey of technical and 

non-technical managers within the South African Banking and Financial Service 

Industry (BFSI) (n = 158) revealed key insights. Perceived usefulness, while 

important, had a negative effect on blockchain usage behaviour, indicating that 

managers prioritize other organizational drivers over perceived usefulness. 

Motivational factors were insignificant, requiring further investigation with aligned 

respondent profiles. Design and implementation emerged as a highly significant 

factor, emphasizing the need for well-designed systems, user-friendly interfaces, 

and integration with existing processes. Perceived ease of use was insignificant, 

potentially due to managers' assumed background knowledge.  

The report concluded by highlighting the complexities and challenges of 

blockchain adoption in the South African financial services industry and 

recommended comprehensive education and training, well-designed 

implementations, and assessment of unique adoption factors. The study 

contributed to the existing literature by focusing on organisational-level 

blockchain usage behaviour and extending the Technology Acceptance Model 

(TAM).  

Future research must involve the examination of digital maturity, delivery 

methodologies, and the impact of non-technical skills on blockchain adoption 

behaviour. Overall, the study provides valuable insights for practitioners and 

researchers seeking to enhance blockchain adoption in organizational contexts. 

 

 



 
iii 

KEYWORDS 

Blockchain, Adoption Factors, Banking and Financial Services Industry (BFSI), 

Individual Usage Behaviour at the Organisational Level, South Africa 

 

 

 

  



 
iv 

DECLARATION 

 

I, Ashley James Paul, declare that this research report is my own work except as 

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

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

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

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

university. 

 

Name:  Ashley James Paul   Signature: 

 

Signed at: Johannesburg 

 

On the 19th day of June 2023 

 

 

 

 

 

 

 

 

 



 
v 

DEDICATION 

To my beloved son, Joash James Paul. 

With joy and humility, I dedicate this blockchain research study to you. Your 

goodness and moral compass can make a lasting impact. Use your talents to 

build a more equitable society with technology. Blockchain's potential is vast – 

will reshape systems, empower individuals, and foster inclusivity. May your 

compassion guide you, uplifting the marginalized and amplifying silenced voices. 

May this dedication remind you of my unwavering love and belief in your 

boundless potential. May you find fulfilment in using your God-given gifts to create 

a kinder, fairer, and more compassionate world. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



 
vi 

ACKNOWLEDGEMENTS 

First and foremost, I humbly acknowledge the Lord Jesus Christ for His 

unwavering strength and guidance, enabling me to complete this research study 

on blockchain.  

I extend my heartfelt gratitude to my supervisor, Dr. Maradona Gatara, whose 

exceptional support, unwavering belief, and invaluable guidance have been 

instrumental in shaping the outcome of this study.  

I also want to express my appreciation to my supervisor, Professor Mjumo 

Mzyece, for his insightful feedback and valuable contributions, which have 

enriched the quality of this research.  

Lastly, I am grateful to my family for their unwavering support and encouragement 

throughout this journey. 

Philippians 4:13: "I can do all this through Him who gives me strength." 



 
vii 

TABLE OF CONTENTS 

LIST OF ACRONYMS ..................................................................... x 

LIST OF TABLES .......................................................................... xv 

LIST OF FIGURES ...................................................................... xvii 

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

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

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

1.3 RESEARCH PROBLEM ......................................................................... 2 

1.4 STUDY OBJECTIVES ........................................................................... 5 

1.5 SIGNIFICANCE OF THE STUDY .............................................................. 6 

1.6 SCOPE AND DELIMITATIONS OF STUDY ................................................. 7 

1.7 DEFINITIONS OF TERMS ...................................................................... 7 

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

1.9 CHAPTER OUTLINE ............................................................................. 8 

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

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

2.2 DEFINITION OF TOPIC ....................................................................... 10 

2.3 AN OVERVIEW OF BLOCKCHAIN TECHNOLOGY .................................... 10 
2.3.1 THE IMPORTANT FEATURES OF BLOCKCHAIN ......................................................... 11 
2.3.2 TYPES AND GENERATIONS OF BLOCKCHAIN ........................................................... 13 
2.3.3 THE APPLICATIONS OF BLOCKCHAIN ...................................................................... 15 
2.3.4 BLOCKCHAIN EVOLUTION – IMPORTANT MILESTONES ............................................. 19 
2.3.5 REVIEW OF SURVEYS ........................................................................................... 21 
2.3.6 REVIEW OF JOURNAL ARTICLES ............................................................................ 25 

2.4 ANALYTICAL FRAMEWORK ................................................................. 35 

2.5 CONCEPTUAL FRAMEWORK .............................................................. 39 
2.5.1 PERCEIVED USEFULNESS ..................................................................................... 43 
2.5.2 MOTIVATIONAL FACTORS ...................................................................................... 43 
2.5.3 DESIGN AND IMPLEMENTATION .............................................................................. 43 
2.5.4 PERCEIVED EASE OF USE ..................................................................................... 44 

CHAPTER 3. RESEARCH METHODOLOGY .............................. 46 

3.1 INTRODUCTION ................................................................................ 46 

3.2 RESEARCH DESIGN .......................................................................... 46 
3.2.1 RESEARCH APPROACH ......................................................................................... 46 

3.3 CONTEXT OF THE STUDY AND SAMPLE ............................................... 47 
3.3.1 UNIT OF ANALYSIS ................................................................................................ 47 



 
viii 

3.3.2 STUDY POPULATION AND SAMPLING METHOD ........................................................ 47 

3.4 DATA COLLECTION ........................................................................... 49 
3.4.1 RESEARCH INSTRUMENT CONSTRUCTION .............................................................. 49 
3.4.1 ADMINISTRATION OF THE RESEARCH INSTRUMENT (SURVEY) ................................. 54 

3.5 DATA ANALYSIS AND INTERPRETATION ............................................... 55 

3.6 VALIDITY AND RELIABILITY ................................................................ 55 
3.6.1 VALIDITY .............................................................................................................. 55 
3.6.2 RELIABILITY ......................................................................................................... 56 

3.7 LIMITATIONS OF THE STUDY ............................................................... 56 

3.8 ETHICAL CONSIDERATIONS (CLEARANCE PROTOCOL) ......................... 57 

CHAPTER 4. PRESENTATION OF RESULTS ............................ 58 

4.1 INTRODUCTION ................................................................................ 58 

4.2 DATA SCREENING, OUTLIER, AND MISSING VALUES ANALYSIS ............... 58 
4.2.1 RESPONSE RATE.................................................................................................. 58 
4.2.2 RESPONDENT’S PROFILE ...................................................................................... 59 
4.2.3 MISSING VALUE ANALYSIS .................................................................................... 60 
4.2.4 OUTLIER ANALYSIS ............................................................................................... 62 
4.2.5 NORMALITY .......................................................................................................... 62 

4.3 PREPARATION FOR FACTOR ANALYSIS ............................................... 63 
4.3.1 FACTORABILITY .................................................................................................... 63 
4.3.2 TEST FOR COMMON METHOD BIAS ........................................................................ 64 

4.4 EXPLORATORY FACTOR ANALYSIS (EFA) .......................................... 65 

4.5 RELIABILITY AND VALIDITY ................................................................ 70 

4.6 DESCRIPTIVE STATISTICS FOR COMPOSITES ...................................... 72 

4.7 PEARSON’S TEST OF CORRELATION .................................................. 72 

4.8 EVALUATION OF THE STRUCTURAL PATH MODEL ................................ 73 
4.8.1 TESTING FOR COLLINEARITY ................................................................................. 73 
4.8.2 ESTIMATION OF THE STRUCTURAL PATH COEFFICIENTS ......................................... 74 
4.8.3 COEFFICIENTS OF DETERMINATION (R2 VALUE) ..................................................... 78 
4.8.4 EFFECT SIZE (F2) .................................................................................................. 79 
4.8.5 PREDICTIVE RELEVANCE (Q2 VALUE) .................................................................... 80 
4.8.6 EFFECT SIZE (Q2) ................................................................................................. 81 

CHAPTER 5. DISCUSSION OF THE FINDINGS ......................... 83 

5.1 INTRODUCTION ................................................................................ 83 

5.2 THE RELATIONSHIP BETWEEN PERCEIVED USEFULNESS AND INDIVIDUAL 

BLOCKCHAIN USAGE BEHAVIOUR AT THE ORGANISATIONAL-LEVEL ...... 83 

5.3 THE RELATIONSHIP BETWEEN MOTIVATIONAL FACTORS AND INDIVIDUAL 

BLOCKCHAIN USAGE BEHAVIOUR AT THE ORGANISATIONAL-LEVEL ...... 84 

5.4 THE RELATIONSHIP BETWEEN DESIGN AND IMPLEMENTATION AND 

INDIVIDUAL BLOCKCHAIN USAGE BEHAVIOUR AT THE ORGANISATIONAL-
LEVEL ............................................................................................. 84 

5.5 THE RELATIONSHIP BETWEEN PERCEIVED EASE OF USE AND INDIVIDUAL 

BLOCKCHAIN USAGE BEHAVIOUR AT THE ORGANISATIONAL-LEVEL ...... 85 



 
ix 

5.6 CONCLUSION ................................................................................... 86 

CHAPTER 6. CONCLUSIONS & RECOMMENDATIONS ........... 87 

6.1 INTRODUCTION ................................................................................ 87 

6.2 SUMMARY OF THE STUDY ................................................................. 87 

6.3 IMPLICATIONS FOR STAKEHOLDERS .................................................... 88 
6.3.1 PRACTICAL CONTRIBUTION ................................................................................... 88 
6.3.2 ACADEMIC CONTRIBUTION .................................................................................... 89 
6.3.3 RECOMMENDATIONS FOR FUTURE RESEARCH ....................................................... 89 

6.4 CONCLUSION ................................................................................... 90 

REFERENCES .............................................................................. 91 

Appendix A – Participant Information Sheet .............................. 97 

Appendix B - Instrument ............................................................. 98 

Appendix C – Ethical Clearance ............................................... 100 

 

  



 
x 

LIST OF ACRONYMS 

ATTI Attitude 

AVE Average Variance Extracted 

AW Awareness 

BFSI Banking and Financial Service Industry 

BI Behavioural Intention 

BR Business Model and Regulation 

CC Customs Clearance 

CR Construct Reliability 

CS Cost Savings 

CSE Computer Self Efficacy 

DAPPS Decentralised Applications 

DI Design and Implementation 

DISC Discomfort 

DLT Distributed Ledger Technology 



 
xi 

DOI Diffusion of Innovation Theory 

DP Digitalizing and ease paperwork 

DV Dependent Variable 

EEXP Effort Expectancy 

ERP Enterprise Resource Planning 

FCON Facilitating Conditions 

HTMT Heterotrait-Monotrait Ratio of Correlations 

INN Innovativeness 

INSC Insecurity 

ISS Information System Success 

IT Information Technology 

ITF Individual Technology Fit 

IV Independent Variable  

JR Job Relevance 

KYC Know your customer 



 
xii 

M Mean 

Mdn Median 

NE  Network Externality 

OPT Optimism 

OQ Output Quality 

PC Perceived behavioural control 

PCON Privacy Concern 

Penj Perceived Enjoyment 

PEOU Perceived Ease of Use 

PEU Perceived Ease of Use 

PEx  Process Excellence 

PEXP Perceived Expectancy 

POC Proof of Concept 

PR Perceived Risk 

PS Perceived Safety 



 
xiii 

PT Perceived Trust 

PU Perceived Usefulness 

QOS Quality of System 

RD Results demonstrability 

RR Risk and Regulatory 

SARB South African Reserve Bank 

SI Social Influence  

SN Subjective Norms 

SP Standardisation and platform 

SRMR Standardised Root Square Residual 

TAM Technology Adoption Model 

TAMO Technology Adoption Model Organisational 

TAMO-1 (Blockchain)  Technology Adoption Model Organisational - Blockchain 

TPB Theory of Planned Behaviour 

TRA Theory of Reasoned Actions 



 
xiv 

TT Tracking and tracing 

TTF Task Technology Fit 

UDMM Unified Digital Maturity Model 

UI User Interface 

UK United Kingdom 

UTUAT Unified Theory of Acceptance and Use of Technology 

UX User Experience 

WQ Web quality 

 



 
xv 

LIST OF TABLES 

 

Table 1 - Blockchain Application Across Industry - Chapter 2 .......................... 15 

Table 2 - Key Milestones - Blockchain Evolution (Sanka et al., 2021) - Chapter 2

 ......................................................................................................................... 20 

Table 3 - Overall Findings of Review – Chapter 2 ............................................ 22 

Table 4 - Results of Blockchain Adoption Research by Industry – Chapter 2 .. 26 

Table 5 - Constructs and Variables – Chapter 3 .............................................. 49 

Table 6 - Response Rate – Chapter 4 .............................................................. 58 

Table 7 - Demographic Information of the Respondents – Chapter 4 .............. 59 

Table 8 - Missing Data Analysis – Chapter 4 ................................................... 61 

Table 9 - Descriptive Statistics – Chapter 4 ..................................................... 62 

Table 10 - Results of Harman's (1976) Single-Factor Test – Chapter 4 ........... 64 

Table 11 - Items Excluded after PCFA – Chapter 4 ......................................... 65 

Table 12 - Results Principal Component Analysis (PCA) - Perceived Usefulness 

– Chapter 4 ...................................................................................................... 67 

Table 13 - Results of Total Variance Explained by the Dimensions (variables) of 

Perceived Usefulness – Chapter 4 ................................................................... 67 

Table 14 - Principal Component Analysis (PCA) Results: Motivational Factors – 

Chapter 4 ......................................................................................................... 68 

Table 15 - Results of Total Variance Explained by the Dimensions (variables) of 

Motivational Factors – Chapter 4 ..................................................................... 68 



 
xvi 

Table 16 - Principal Component Analysis Result: Design and Implementation – 

Chapter 4 ......................................................................................................... 68 

Table 17 - Results of Total Variance Explained by the Dimensions (variables) of 

Design and Implementation – Chapter 4 .......................................................... 69 

Table 18 - Principal Component Analysis Result: Perceived Ease of Use – 

Chapter 4 ......................................................................................................... 69 

Table 19 - Results of Total Variance Explained by the Dimensions (variables) of 

Perceived Ease of Use – Chapter 4 ................................................................. 70 

Table 20 - Instrument (Construct) Validities and Reliabilities – Chapter 4 ....... 71 

Table 21 - Descriptive Statistics for Composites – Chapter 4 .......................... 72 

Table 22 - Results of Pearson's Test of Correlations – Chapter 4 ................... 72 

Table 23 - Test for Collinearity – Chapter 4 ..................................................... 74 

Table 24 - Results of Significance Testing of the Structural Path Model 

Coefficients – Chapter 4 ................................................................................... 77 

Table 25 - Summary of Results of Hypotheses Testing – Chapter 4 ................ 77 

Table 26 - Result of R2 Value of Dependent (Criteria) Variable – Chapter 4 .... 78 

Table 27 - Results of f2 Effect Sizes for Dependent Variables – Chapter 4 ...... 79 

Table 28 - Result of Q2 Values of Dependent Variable – Chapter 4 ................. 81 

Table 29 - Results of q2 Effect Sizes for Dependent (Criteria) Variables – Chapter 

4 ....................................................................................................................... 82 

 



 
xvii 

LIST OF FIGURES 

 

Figure 1 - Blockchain Structure (Sanka et al., 2021, p. 181) – Chapter 2 ........ 11 

Figure 2 - Breakthroughs on the Blockchain Journey (Sanka et al., 2021, p. 190). 

– Chapter 2 ...................................................................................................... 20 

Figure 3 - Adoption Supporting Factors (Kawasmi et al., 2020, p. 134) – Chapter 

2 ....................................................................................................................... 33 

Figure 4 - Adoption Barriers (Kawasmi et al., 2020, p. 135) – Chapter 2 ......... 33 

Figure 5 - Adoption Circumstantial Factors (Kawasmi et al., 2020, p. 136) – 

Chapter 2 ......................................................................................................... 34 

Figure 6 - Ranked Blockchain Factors (Kawasmi et al., 2020, p. 140) – Chapter 

2 ....................................................................................................................... 35 

Figure 7 - Adapted  Blockchain TAM (Kawasmi et al., 2020, p. 142) – Chapter 2

 ......................................................................................................................... 36 

Figure 8 - TAMO - Organisational Adaptation (Mohapatra et al., 2015, p. 254) – 

Chapter 2 ......................................................................................................... 37 

Figure 9 - Unified Digital Maturity Model (Armstrong & Lee, 2021, class slide)  - 

Chapter 2 ......................................................................................................... 38 

Figure 10 - Conceptual Process – Chapter 2 ................................................... 39 

Figure 11 - Conceptual Model – Chapter 2 ...................................................... 41 

Figure 12 - Analysis of Blockchain Adoption Factors – Chapter 2.................... 42 

Figure 13: Structural Path Model (n = 158) – Chapter 4................................... 76 

 

 



 
1 

CHAPTER 1. INTRODUCTION 

1.1 Purpose of the Study 

The past decade has seen a slow adoption of blockchain technology. However, current 

research shows that it has reached an inflection point which will see its rapid 

implementation across industries. In this study, blockchain usage behaviour is 

investigated by identifying and examining the effects of its individual adoption in the 

organisation.   

1.2 Context of the Study 

Since the introduction of the Bitcoin cryptocurrency electronic cash system in 2009, its 

underlying technology blockchain, has become a topic of much discussion and debate 

(Nakamoto, 2008). Blockchain is essentially a distributed ledger that uses a peer-to-

peer network of computers to store data cryptographically. This results in all nodes in 

the network being updated using cryptography to ensure immutability against 

tampering of records (Almekhlafi & Al-Shaibany, 2021).  

There have been many studies, research papers, and articles within academic circles 

and industry consulting houses that predict the significant disruption of most industries 

driven by blockchain technology (Almekhlafi & Al-Shaibany, 2021; Budman, 2020; 

Iansiti & Lakhani, 2017). However, its adoption has been slow over the past decade 

(Sanka et al., 2021). Many executives have been dabbling with experimentation with 

limited large-scale deployments. Towards the end of 2019, it has been observed that 

the hype surrounding Bitcoin has subsided, as more executives are ranking blockchain 

technology within their top five priorities in solving large-scale business challenges 

(Budman, 2020). 

The Banking and Financial Service Industry (BFSI) is touted as one sector that will 

face tremendous disruptions (Alt et al., 2018). This stands to reason since the initial 

application of blockchain was based on cashless electronic payments (Nakamoto, 

2008). Given the attributes of blockchain such as security, immutability, consensus, 

and faster settlement, it questions the need for intermediaries such as banking 



 
2 

institutions, as trust is built into the system (Iansiti & Lakhani, 2017). Furthermore, the 

application of blockchain in “smart contracts” also sets up opportunities for auto-

payments and self-executing contracts, which will significantly impact many business 

processes (Iansiti & Lakhani, 2017). 

Although interest in blockchain has started to move from experimentation to more 

purposeful projects and implementation, some significant barriers, and risks impact 

blockchain adoption. These include the ability to scale, system integration, lack of 

standardisation, the complexity associated with blockchain applications, regulatory 

uncertainty, and lack of knowledge and skills (Prewett et al., 2020). Additional barriers 

that have been identified include architectural and design risk, end-point and oracle 

risk (a general IT risk), data security and confidentiality, storage, smart contract risk, 

compliance, vendor risk, contractual risk, and private key management (Prewett et al., 

2020). Blockchain adoption also referred to as blockchain usage behaviour measure 

the extent to which individuals or organisations are influenced to utilise the technology 

(Sanka et al., 2021)..    

In the South African context, the South African Reserve Bank (SARB) initiated project 

Khokha in 2018 as a  Proof of Concept (POC) to stimulate collaboration between the 

settlement banks, and by introducing a tokenised South African Rand as a mechanism 

with which to facilitate wholesale settlements (Bank, 2018). The project was deemed 

successful and laid the foundation for future research and experimentation. It further 

highlighted the need for a closer investigation of technological practicalities such as 

integration into various legacy systems, the economic impacts thereof, and 

implications of legal and regulatory factors (Bank, 2018). 

1.3 Research problem 

Since the end of 2019, research indicated that organisations were moving from 

experimentation to a more strategic priority by applying blockchain technology 

solutions to large-scale business processes (Budman, 2020). There has been a slow 

adoption of blockchain technology over the past decade, which has been 

characterised by  the topic  being widely researched and subjected to a large amount 

of experimentation (Woodside et al., 2017).  



 
3 

The current body of research in blockchain usage behaviour highlights that the 

Technology Adoption Model (TAM) and  Unified Theory of Acceptance and Use of 

Technology (UTUAT) are the most popular models utilised within blockchain adoption 

research (Almekhlafi & Al-Shaibany, 2021).  

Blockchain very much denotes an underlying technological infrastructure that is not 

directly experienced by individual users (Almekhlafi & Al-Shaibany, 2021; Kawasmi et 

al., 2020). Individual users are typically unaware of the technology’s underlying 

characteristics and interact with it through a User Interface (UI) that enables the User 

Experience (UX) through an application. This poses a problem in the research, since 

most research applies technology acceptance models based on theories that underpin 

individual-level adoption, whereas blockchain adoption should be examined at the 

organisational-level (Kawasmi et al., 2020). The decision to adopt the technology does 

not necessarily occur at the individual end-user level but rather lies with decision 

makers at the organisational-level. The current state of research lacks a technology 

adoption model that specifically considers individual user adoption from the 

organizational-level perspective. Existing technology adoption models primarily focus 

on individual-level adoption user environments, which may not adequately capture the 

complexities of blockchain adoption in the organizational context (Kawasmi et al., 

2020). Consequently, there is a need to develop and apply individual user technology 

adoption models at the organizational-level to address this emergent research gap 

and provide a more comprehensive understanding of blockchain adoption in the 

financial services industry in South Africa. 

As described throughout blockchain literature, the technology is characterised as a 

system of distributed databases that is connected by an encrypted cryptographic 

enabled chain, and this highlights the technology as an underlying technological 

infrastructure (Almekhlafi & Al-Shaibany, 2021; Kawasmi et al., 2020; Sanka et al., 

2021; Wang et al., 2020).  

Although there have been various theoretical extensions of the TAM, it does not 

necessarily incorporate the potential broader organisational-level adoption factors that 

impact blockchain usage behaviour. Organisational blockchain usage behaviour can 

be associated with the organisation’s digital maturity. Digital maturity is defined as the 

organisations ability to embrace profound technological change whilst achieving 



 
4 

desired strategic and operational outcomes (Armstrong & Lee, 2021). As highlighted, 

the problem with the current body of research is that the assessment of individual 

usage behaviour becomes less relevant since blockchain is adopted at the 

organisation-level.  

In the context of the South African financial services industry, the relevance of this 

problem becomes evident. The industry faces unique challenges and dynamics that 

require tailored approaches to technology adoption. By developing individual user 

technology adoption models at the organizational-level, we can specifically address 

the industry's needs and overcome the limitations of existing models. 

The specific problem that individual user technology adoption models at the 

organizational-level will address in the South African financial services industry is the 

lack of a comprehensive framework that considers the holistic aspects of digital 

maturity and its impact on successful technology adoption. Digital maturity, defined as 

an organization's ability to embrace profound technological change while achieving 

desired strategic and operational outcomes, plays a crucial role in the successful 

implementation of technologies like blockchain (Armstrong & Lee, 2021). By 

incorporating dimensions and factors from the Unified Model for Digital Maturity, we 

aim to provide a more robust and contextually relevant model for organisational-level 

blockchain adoption. 

In their study of SAP ERP adoption, Mohapatra et al. (2015) adapted the Technology 

Adoption Model (TAM) to Technology Adoption Model Organisation (TAMO) to better 

cater to organisational technology adoption (Mohapatra et al., 2015). The TAM model 

was developed to predict technology adoption and usage at the individual-level  and 

states that perceived usefulness and perceived ease of use are determinants of 

individual technology adoption (Davis, 1989). Mohapatra et al., argued the TAM should 

not apply to organisational technology adoption, since there are additional factors such 

as design and implementation, and motivational factors that determine organisational 

technology adoption (Mohapatra et al., 2015). They extended the TAM to the TAMO. 

The TAMO still falls short in measuring organisations’ digital maturity which ultimately 

determines success in adopting technologies such as blockchain. In their research, 

Armstrong et al. (2021) analysed global advisory firms, popular businesses, and 

academic literature, and proposed the unified model for digital maturity which 



 
5 

comprises two dimensions with six factors each (Armstrong & Lee, 2021). The 

opportunity within the current body of blockchain adoption research is to move towards 

a more holistic blockchain organisational adoption model focusing on organisational-

level blockchain individual usage behaviour. This is achieved by furthering the current 

technology user specific adoption factors to include  organisational-level adoption 

factors incorporated from the unified model for digital maturity (Armstrong & Lee, 

2021). The research gap that prior studies have not adequately addressed is the 

integration of individual user adoption factors at the organizational-level and the 

assessment of digital maturity within the context of blockchain adoption. While there 

have been theoretical extensions of technology adoption models, they do not fully 

encompass the broader individual user factors that are essential for understanding 

and promoting successful blockchain usage behaviour at the organisational-level. 

Therefore, the focus of this study is on individual blockchain usage behaviour at the 

organisational-level. By bridging this research gap, we can offer valuable insights and 

recommendations for the South African financial services industry to enhance their 

adoption strategies and effectively leverage the benefits of blockchain technology. 

1.4 Study Objectives 

The overall study objective is to investigate the adoption factors influencing individual 

blockchain usage behaviour at the organizational-level, examining their effects, and 

exploring the relationships between these factors to provide a comprehensive 

understanding of how they collectively influence individual blockchain usage 

behaviour in an organisational context. The following two (2) study objectives are, 

therefore, specified: 

1. Identify the adoption factors that influence individual usage behaviour at the 

organisational-level.  

2. Examine the effects of these adoption factors on individual blockchain 

usage behaviour at the organisational-level. 

These objectives were specified to answer the following formulated research 

questions: 



 
6 

1. What are the adoption factors that influence individual usage behaviour at the 

organisational-level? 

2.  What are the effects of these adoption factors on individual blockchain usage 

behaviour at the organisational-level? 

1.5 Significance of the Study 

Implementation of blockchain takes on average 25 months from Proof of Concept 

(POC) to implementation, with many being discontinued due to a lack of understanding 

of the technology and regulatory concerns (Rauchs et al., 2019). The significance of 

this study lies in its contribution to the body of knowledge in blockchain 

implementation. By investigating usage behaviour at the organizational level, this 

research aims to provide a comprehensive understanding of the significant factors that 

impact blockchain adoption, thus informing managers in their proactive planning from 

technical, risk, digital transformation, and change management perspectives. This 

would ensure holistic, proactive planning, and faster execution of projects aligned to 

business strategic objectives.    

 This study is also relevant to practice and policy directions, particularly in the Banking 

and Financial Services Industry (BFSI). As this sector faces disruptive changes, the 

innovative implementation of blockchain technologies can reshape business 

processes(Alt et al., 2018). By exploring the potential benefits of blockchain, such as 

leveraging crowdfunding, peer-to-peer lending, and digital Know Your Customer 

Initiatives (KYC) to address the unbanked population, this research contributes to 

practical implications for financial institutions." 

With the recognition that blockchain implementation may lead to job losses through 

optimization, digitalization, and automation, understanding its impact in the South 

African context becomes crucial. This study sheds light on the concerns within the 

BFSI sector, which is one of the largest employers in a country grappling with high 

unemployment rates. Thus, the findings of this research have policy implications for 

employment strategies and workforce planning. 



 
7 

1.6 Scope and Delimitations of Study 

This research paper will be limited to: 

1. Organisations that fall under the financial services sector in the context of 

South Africa. 

2. Managers across front office, back office, and support services, in these 

institutions. 

3. The existing and pending implementations of blockchain technology to 

understand individual usage behaviour at the organisational-level.  

1.7 Definitions of Terms 

a) Blockchain – A “chained” set of blocks through a cryptographic signature that 

stores digitally recorded data. Through cryptographic consensus, it becomes a 

tamper-proof digital ledger (Consensys, 2021) 

b) Cryptocurrency – Electronic currency, independent of Central Banks, that uses 

Mathematics and Cryptography to regulate the creation of units (Consensys, 

2021). 

c) Decentralisation – The transfer of authority from intermediaries to the network 

(Consensys, 2021). 

d) Distributed Ledger – Database spread across multiple sites. Data can be 

permission-based to control viewing (Consensys, 2021). 

e) Immutability – A key attribute of blockchain that provides the basis for its 

commercial application. Once records have been written into the chain, they 

cannot be altered (Consensys, 2021). 

f) Smart Contracts – The terms of a contract are written into computer 

programmable language enabling self-execution once terms are met 

(Consensys, 2021). 

g) Tokens – Are unique, transferable, and secure assets built on an existing 

blockchain (Consensys, 2021). 

 



 
8 

1.8 Assumptions 

Since being introduced in 2009 through the application of Bitcoin cryptocurrency 

(Nakamoto, 2008), blockchain has been topical, especially in the BFSI, a sector that 

is set to be disrupted the most (Alt et al., 2018). In banking and financial services, 

managers would have had some level of exposure, either in the form of awareness to 

direct experimentation and implementation and would therefore have a reference 

context. Considering the slow pace of adoption, it would be beneficial to connect 

current skills with attitudes towards implementing the technology.   

As indicated by the global Deloitte Survey of 2020 (Budman, 2020), blockchain has 

taken its place within the top five strategic priorities on executive dashboards 

(Budman, 2020). A thorough understanding of organisational blockchain usage factors 

will inform key programs from a digital transformation perspective, that will have the 

desired impact on blockchain adoption. This also forms a basis for quality use-case 

selection, business model innovation, and new value streams (Armstrong & Lee, 

2021). 

1.9 Chapter Outline 

1. Chapter 1 – Introduction  

This chapter constitutes an introduction to blockchain technology and the context of 

the South African financial services industry. The purpose, context, research objective, 

significance, delimitations, and assumptions of the study, are also described. 

 

2. Chapter 2 – Literature Review 

This chapter constitutes a review of the existing literature on blockchain adoption, and 

the application of technology acceptance theory. The blockchain technology’s 

evolution to the current state and its advancements, are reviewed. 

 

3. Chapter 3 – Research Methodology 

This chapter constitutes a detailed description of the research approach, data 

collection, analysis method, validity, reliability, and limitations of the study. 

 



 
9 

4. Chapter 4 – Analysis and Presentation of Results  

This chapter presents the results of the analysis undertaken on the responses from 

the research questionnaire. 

 

5. Chapter 5 Discussion of Results 

This chapter presents a discussion of the results of previous chapters in relation to 

chapter two (literature review of blockchain adoption research). 

 

6. Conclusions and Recommendations 

This chapter presents recommendations to academics and practitioners, embarking 

on or undertaking their blockchain journeys. 

 



 
10 

CHAPTER 2. LITERATURE REVIEW 

2.1 Introduction 

In this literature review, the features that make blockchain a disruptive technology are 

considered. The evolution of blockchain and key application of the technology is 

highlighted. Key findings on the current state of blockchain are discussed and factors 

and measurement indicators of blockchain usage behaviour are extracted. These were 

contrasted in relation to organisational technology adoption factors for a more 

appropriate model of technology adoption as the current literature is dominated by 

individual technology adoption models. Various technology acceptance theories and 

their extensions applied to blockchain technologies are discussed. These extensions 

are challenged as they are based on individual-level adoption. However, through the 

literature review process, it is evidenced that blockchain adoption occurs at the 

organisational-level.  

2.2 Definition of Topic 

Organisational blockchain usage behaviour is adapted from the TAM and will be used 

to described usage behaviour or intention to use. Factors are determinants that drive 

organisational blockchain usage behaviour. 

2.3 An Overview of Blockchain Technology 

Blockchain, also referred to as Distributed Ledger Technology (DLT), is essentially a 

group of peer-to-peer blocks of data (nodes) connected via a chain and updated or 

protected through a consensus-based process (Sanka et al., 2021). Blockchain is 

tamper-proof as each block contains an encrypted code (hash) of the previous block, 

and if a record is changed it becomes detectable to the rest of the network (Wang et 

al., 2020). This attribute enables decentralised trust built into the blockchain 

technology, and eliminates the need for a centralised intermediary to govern the chain 

(Sanka et al., 2021). A blockchain structure is depicted in Figure 1. 

 



 
11 

Figure 1 - Blockchain Structure (Sanka et al., 2021, p. 181) – Chapter 2 

  

Figure 1 serves as an example, depending on the type of blockchain network, of the 

information and structure stored in a chain. In this example, the structure has a  Merkle 

root, which is a list of transactions stored in the blockchain (Sanka et al., 2021). 

2.3.1 The Important Features of Blockchain 

Blockchain is described as an exponential technology that will profoundly impact how 

we operate as a society, as was the advent of the Internet in the 1990s (Beck & Müller-

Bloch, 2017). The technology contains fundamental attributes that present 

opportunities to reimagine the way in which processes work across most industries. 

a) Distributed Nature 

Data loss and record tampering are prevented due to all blocks on the network 

simultaneously containing a replication of the information. Any block that is added will 

download a copy of the blockchain from other blocks (Iansiti & Lakhani, 2017).  

b) Data Integrity and Security 

Due to the cryptography where a mechanism adds a timestamp string that is 

generated by random hash function is applied to each block. Each one carries the 

previous block’s hash, any changes to a block alters the hash, rendering it unmatched 

with the next block. Any attempt to tamper with it will require every block to be modified 



 
12 

down from the genesis block for all computers. Practically, an adversary will be 

impeded, hence the data is secured from tampering (Wang et al., 2020). 

c) Anonymity 

Personal details of participants are hidden due to scrambled hex digits. However, there 

are still concerns about privacy since this can be deciphered using statistical software. 

In private blockchain, identity could be exposed to prevent fraud. Privacy can be 

enhanced through further encryption (Sanka et al., 2021). 

d) Transparency and traceability 

Audit and tracking capabilities are positively enhanced due to all transactions or 

records being stored on all the nodes. Any changes are therefore tracked, and this 

assists in the detection of fraudulent transactions (Sanka et al., 2021). 

e) Decentralised nature 

Although private blockchain and blockchain consortiums may allocate control to a few 

if not all its member nodes, blockchain effectively eliminates the need for 

intermediaries and, in so doing, caters to a system with in-built trust where contracting 

parties can transact confidently as rules are executed within the code of the system 

(Wang et al., 2020). 

f) Cost-Savings 

Effective implementation of blockchain will result in huge cost savings since the need 

to rely on intermediaries will be eliminated. (Van Steenis et al., 2016). As a result, it is 

observed that blockchain is a top-five strategic priority for most leading executives in 

the current context (Budman, 2020). 

g) Speed 

The settlement, especially of international remittances, is much faster as there is no 

need for intermediaries and latencies to be removed, and no back office function is 

required (Sanka et al., 2021). 

 



 
13 

h) Efficiency 

Through blockchain, system autonomy is achieved, and there is no reliance on sub-

intermediary systems (transitioning systems within a system architecture), thereby 

increasing efficiency through self-executing capabilities.(Sanka et al., 2021). 

i) Interoperability  

Blockchain creates better client experiences since organisations can share and 

synchronise data across traditional silos. This benefits multi-sided business platform 

strategies across organisations such as banking and insurance (Narayanan & Clark, 

2017). 

j) Verifiability 

The cryptography capability of blockchain ensures that records can be verified. This 

cannot be achieved in traditional or current databases as it requires digital signatures 

(Sanka et al., 2021). 

k) Right to be forgotten 

Blockchain stores information immutably across various locations which can contain 

personal information such as passwords, medical records, and other information-

types. Further, records could contain information of an illegal nature. Under these 

circumstances, the data will need to be removed by enforcement of a court order. 

Given that the record is stored in many different locations, there lacks a method to 

deal with such complexity (Sanka et al., 2021).  

2.3.2 Types and Generations of Blockchain  

Based on the way the network is operated or managed, three types of blockchain exist, 

namely Private, Public, or Consortium (Almekhlafi & Al-Shaibany, 2021). 

a) Private Blockchain 

Private blockchain requires permission to join the network. Thus, it is referred to as a 

Permissioned blockchain. It is characteristic of rules that control which node may 



 
14 

transact and view certain transactions. They are typically used in private enterprises 

and considered undeletable or permanent (Almekhlafi & Al-Shaibany, 2021). 

b) Public Blockchain 

One does not require permission to access this network. Just the correct specialised 

software is required. They are fully transparent and typically meant to operate 

cryptocurrency such as Bitcoin, the first public blockchain (Almekhlafi & Al-Shaibany, 

2021). 

c) Consortium Blockchain 

Consortium blockchains can be described as a hybrid between private and public 

blockchains, where selected nodes may be assigned to participate in consensus 

processes. It is centralised partially with limited public use. They are typically used in 

networks involving multiple organisations (Almekhlafi & Al-Shaibany, 2021). 

The literature highlights four generations of blockchain: 

a) First Generation Blockchain (Blockchain 1.0) 

The first generation of blockchain is represented by cryptocurrency such as Bitcoin 

and Litecoin, among others (Almekhlafi & Al-Shaibany, 2021). 

b) Second Generation Blockchain (Blockchain 2.0) 

The arrival of Smart Contract, a self-executable software based on stated conditions, 

marks second generation blockchain technologies, such as Ethereum (Almekhlafi & 

Al-Shaibany, 2021). 

c) Third Generation Blockchain (Blockchain 3.0) 

Regarded as a general-purpose technology spanning multiple industries and applied 

to various processes such as contract management, Internet of Things (IoT), supply 

chain management, healthcare, insurance, and Internet payments (Almekhlafi & Al-

Shaibany, 2021). 

 



 
15 

d) Fourth Generation Blockchain (Blockchain 4.0) 

This will integrate Artificial Intelligence (AI) to combine cognitive capability, which will 

further enhance innovative application and further reduce human dependency 

(Almekhlafi & Al-Shaibany, 2021). 

As the technical advances in blockchain continue to reduce the technological barriers 

such and scalability, energy consumption and speed as example, these cannot 

advance blockchain adoption in isolation. A more holistic view of blockchain adoption 

factors, extending from individual blockchain usage behaviour at the user level, to 

individual blockchain usage behaviour at the organisational-level is required to drive 

successful adoption.     

2.3.3 The Applications of Blockchain  

The literature highlights the adoption of blockchain across many industries such as 

inter alia banking and financial services, government and the public sector, the 

automotive industry, telecoms, e-commerce and retail, healthcare, the logistics and 

supply chain sector, real estate, music, media, and gaming (Nuryyev et al., 2020; 

Rauchs et al., 2019; Sanka et al., 2021; Wang et al., 2020).   

Blockchain has been applied to use cases across a variety of sectors or industries as 

highlighted by Table 1 (Sanka et al., 2021).    

Table 1 - Blockchain Application Across Industry - Chapter 2 

Area of 

Application 

Discussion 

Cryptocurrency As the first application of blockchain technology, Bitcoin, 

the most successful cryptocurrencies, has a market 

capitalisation of $726 billion as of 14 June 2021 

(Bitcoin.com, 2021). 



 
16 

It is estimated that there are between 2.9 million and 5.8 

million cryptocurrency wallet users globally 

(Kakushadze & Serur, 2018). It is estimated that 36% of 

small businesses accept Bitcoin as payment in the US, 

whereas 56% purchase Bitcoin for their own use. Bitcoin 

is accepted by Microsoft, Expedia, Wikipedia, Burger 

King, KFC, Subway, Norwegian Air, and more (Beigel, 

2020). 

Smart contract A Smart contract is essentially a contract that is 

governed by computer software that becomes self-

executing when respective conditions of the contract are 

fulfilled. It was introduced in 1994, run on blockchain 

such as Ethereum, Hyperledger, Corda and Namecoin 

for Decentralised Applications (Dapps), and doesn’t 

require intermediary governance (Szabo, 1997). There 

are over 14 million contracts on Ethereum, and Solidity 

is the most popular language for the design of smart 

contracts (Pinna et al., 2019).   

Stock Exchange Secondary share markets leveraging blockchain have 

come into existence and do not have the high costs of 

intermediaries (Almatarneh, 2020). Developments 

within the London Stock Exchange and the Australian 

Security Exchange are all set to integrate blockchain 

into their systems (Crosby et al., 2016). 

Healthcare 

Management 

Four broad categories within healthcare are leveraging 

blockchain as a solution. These are records 

management, medical insurance, clinical and 

biomedical research, and applications connecting 

various healthcare providers (Mazlan et al., 2020). The 

challenges of data inconsistency, poor records 



 
17 

management, duplicate records etc., can be eliminated 

by leveraging blockchain (Griggs et al., 2018). 

Insurance 
Blockchain applications in insurance enable faster and 

seamless claims processes that provides audit trail and 

transparency, thereby mitigating fraud. Digitisation of 

assets also further enhances business models and 

creates client-centric ecosystems (Sanka et al., 2021). 

The application for certification history of diamonds in 

companies such as Everledger and use cases across 

companies such as Etherisc, Insurwave, and MedRec 

are other examples (Crosby et al., 2016; Raikwar et al., 

2018) 

Banking and 

Finance 

The first banking transaction of blockchain was carried 

out between the Commonwealth Bank of Australia and 

Wells Fargo in 2016. Since the very attributes of 

blockchain place banks under pressure from an 

intermediary perspective, banks have been trying to 

improve their systems leveraging blockchain (Zheng et 

al., 2018). 

Internet of Things 

(IoT) Industry 

The application of Blockchain to the IoT includes IBM 

ADEPT, where home appliances troubleshoot and 

upgrade themselves. Further application involves the 

integration with IoT to monitor inventory on the cold 

storage supply chain (Zheng et al., 2018). 

Blockchain-based 

DNS services 

Blockchain is utilised to administer domain name 

services as a mechanism to protect against cyber-

attacks and misuse (Ali et al., 2017). 



 
18 

Decentralised Data 

Storage 

Traditional centralised storage of data provides a single 

point of failure, and by utilisation of blockchain, data 

storage can be decentralised, thereby mitigating this 

risk. Storj is a decentralised cloud storage network 

leveraging blockchain (Storj, 2021).  

Intellectual 

Properties and 

Document 

Stamping 

Blockchain is used to protect Intellectual Property (IP) 

where documents are stored on the blockchain once it 

is digitally signed. Companies such as Stampery, Block, 

Notary, and Microsoft utilise blockchain for this purpose 

(Crosby et al., 2016). 

Voting Many developing countries face election-rigging and 

fraud, which can be eliminated with blockchain. Agora 

voting, Bitcongress, and Remotengrity, are projects that 

provide good frameworks for blockchain voting 

(Foroglou & Tsilidou, 2015).  

Digital Identity 

Management 

The management of identity through physical 

documents such as passports is open to fraud. Many 

countries such as the United States (US), Japan, 

Switzerland, India, and Finland are conducting 

blockchain-enabled identity trials (Sanka et al., 2021). 

Cyber Security In cybersecurity, blockchain is used to store network 

history, configuration, log files and other network files 

preventing attacks through its immutable attribute 

(Bouckaert et al., 2010). 

Asset Registry and 

Tokenisation 

Blockchain provides secure tokenisation of assets and 

prevents theft and asset fraud. Sweden, Russia, the 



 
19 

United Kingdom (UK), and India are conducting trials in 

this regard (Sanka et al., 2021). 

Supply Chain and 

Trade 

Management 

Supply chain management is enhanced in terms of 

improved transparency, efficiency, cost, speed through 

the adoption of blockchain. Maersk founded Tradelens, 

a blockchain-enabled supply chain company (Sanka et 

al., 2021). 

Energy Trading 

and Management 

Blockchain is currently being used for the trade of 

electricity and data management across smart grids. 

Powerledger is an Australian company facilitating 

transactions between suppliers and consumers of 

energy (Monrat et al., 2019). 

Contract 

Management 

Contract management carries high operational costs 

and risks. Blockchain enables contract management 

free of intermediaries. Numerous companies offer 

contract management as a solution, such as Monax, 

Corda, Oracle, Konfidio, and Incertis (Sanka et al., 

2021). 

2.3.4 Blockchain Evolution – Important Milestones 

The significant breakthroughs experienced in blockchain technology adoption during 

2018 and 2019 has termed the current era as the years of enterprise blockchain. Table 

2 and Figure 2 highlight key breakthroughs along the blockchain journey (Sanka et al., 

2021).  

 

 



 
20 

Figure 2 - Breakthroughs on the Blockchain Journey (Sanka et al., 2021, p. 190). 

– Chapter 2 

 

Table 2 - Key Milestones - Blockchain Evolution (Sanka et al., 2021) - Chapter 2 

Period Discussion 

2012 - 2014 The primary use of blockchain was for the administration of 

cryptocurrencies such as Coinbase, Coinsetters, and Peercoin 

(Sanka et al., 2021). 

2014 - 2016 Organisations start to experience the early benefits of 

blockchain use since the launch of Ethereum. Stock exchanges 

like Nasdaq implement blockchain, and collaborations give rise 

to payments with Citi. Autonomous Decentralised Peer-to-Peer 

Telemetry (ADEPT), unveiled by Samsung and IBM, allows 

devices to communicate for autonomous upgrades and 

maintenance (Sanka et al., 2021). 

2016 - 2018 A transaction for 88 bales of cotton worth $35,000 became the 

world’s first international blockchain payment between Wells 

Fargo and Commonwealth Bank of Australia. Microsoft 

embedded the Application Programming Interface (API) of 

Stampery, which could be used for the certification of 

documentation. Since 2016, the R3 consortium (200 plus 



 
21 

financial institutions) has been using blockchain for trading, 

issuance, and redeeming fixed-income products. There has 

been greater acceptance of cryptocurrencies globally (over 

100 000 entities)  (Sanka et al., 2021). 

2018 - 2020 Major adoption in the automotive sector, encompassing 

companies such as BMW, Ford, Renault, and GM. Mobility 

Open Blockchain Initiative (MOBI) implements blockchain for 

data sharing between vehicles. Oracle integrates blockchain 

into its cloud offerings, including contract management. FedEx 

is exploring IoT and blockchain integration for tracking 

purposes. There have also been implementations of innovative 

use cases on tracking unused cancer medication. LG 

announced Uplus for international payments, leveraging 

blockchain. Implementations of blockchain in healthcare and 

contract management. Australian Security Exchange 

announces plans to implement blockchain as a clearing and 

settlement system. Maersk implements blockchain as part of its 

supply-chain management through Tradelens. Facebook 

cryptocurrency receives much publicity and widespread 

adoption of blockchain across multiple industries (Sanka et al., 

2021). 

2.3.5 Review of Surveys 

The literature indicates that blockchain has been significantly researched both in 

academic and industry circles. Sanka et al. (2021) analysed 12 surveys with the 

following studies highlighted: 

a) Cambridge University: 67 deployed use cases in 25 countries, 60 blockchain 

vendors, 56 blockchain network operators, and 45 public sectors from 25, 22, 

and 33 countries, respectively (Sanka et al., 2021). 

b) Stanford University: 110 Organisations in 6 sectors (Agriculture, Finance, 

Environment, Governance, Digital Identity and Health) (Sanka et al., 2021). 

c) Deloitte: 1386 top executives in 12 countries and 31 organisations. 



 
22 

d)  Price Waterhouse Coopers (PWC) – 600 big business executives in 15 regions 

(Sanka et al., 2021). 

e) IPSOS - Centre for International Governance Innovation (CIGI): Over 10,000 

people from 25 countries (Sanka et al., 2021). 

The methods of data gathering included inter alia direct survey by invitation, email, 

social networks, phone interviews, and digital surveys (Sanka et al., 2021). The 

findings of the research were categorised into 14 dimensions as highlighted in Table 

3.  

Table 3 - Overall Findings of Review – Chapter 2 

Dimensions Finding 

Analysis of Current State of 

Blockchain adoption 

There was a marked increase in 

blockchain adoption showing a 

significant increase of about 86% 

towards the end of 2019 (Budman, 

2020). 

Survey of Blockchain Platforms in Use In terms of enterprise blockchain 

applications, Hyperledger Fabric is the 

most widely used. However Ethereum 

is the most popular platform used for 

both permission and permissionless 

networks (Galen et al., 2019). 

Multi-party Blockchain Vs Blockchain 

Meme Networks 

A multiparty blockchain (Distributed 

Ledger Technology) works on 

consensus and shared record 

keeping. A blockchain meme works on 

only some components such as 

cryptography. 

 

77% of 67 surveyed are blockchain 

memes. 20% are considering 



 
23 

multiparty DLTs and 3% are full DLT 

networks (Rauchs et al., 2019). 

Analysis of Sectors using Blockchain The sectors with the most  

implementations were the Insurance 

and Financial industries in the public 

sector (China, 2018; Rauchs et al., 

2019). 

Analysis of Blockchain Use Cases Supply chain management had the 

most amount of enterprise blockchain 

networks, whilst most use cases 

deployed were for records storage and 

verification (China, 2018). 

Analysis of the Smart Contract 

languages in Use 

General purpose programming 

languages such as Java and Solidity 

are the most popular at 69% (Rauchs 

et al., 2019). 

Enterprise Blockchain Consensus 

Algorithms Analysis 

The most popular used consensus 

algorithm is The Practical Byzantine 

Fault Tolerance (PBFT) (Rauchs et 

al., 2019). 

Privacy and Confidentiality Methods 

Analysis 

In terms of enterprise blockchain 

privacy methods, pseudonymous 

addresses, and restricted transaction 

visibility are the most popular (Rauchs 

et al., 2019). 

Key Motivation/Driver of Enterprise 

Blockchain Networks 

72% of the blockchain use cases were 

implemented or in development target 

cost reduction (Carson et al., 2018; 

Rauchs et al., 2019). 

Duration of Blockchain Project 

Completion 

The surveys showed that it took about 

25 months from POC (Proof of 

Concept) to full scale deployment of 

the solution. The major obstacles 



 
24 

being regulatory and lack of 

understanding of the technology. 

(Rauchs et al., 2019). The Deloitte 

survey highlights that 47% of the 

respondents expect a return within 1 – 

3 years (Budman, 2020). 

Criteria for Platform Selection Choosing the correct platform is 

critical to success. The most important 

selection criteria was vendor maturity 

(Rauchs et al., 2019). 

Cause of Blockchain Project 

Discontinuation 

The main causes of stopping projects 

were high cost and lack of 

understanding of the technology. 

Another significant contributing factor 

was that respondents could not extract 

benefits (Rauchs et al., 2019). 

Overall Satisfaction of Existing 

Blockchain 

The results show that most of the 

respondents are satisfied with the 

results of implemented use cases 

(Sanka et al., 2021). 

Survey of Obstacles impeding wider 

Blockchain Adoption 

The main obstacles were regulatory in 

nature and characterised by a lack of 

understanding (China, 2018; Rauchs 

et al., 2019). 

Other significant obstacles:  

• Compelling use case, vendor 

immaturity (China, 2018; 

Rauchs et al., 2019). 

• Lack of standardisation (China, 

2018; Rauchs et al., 2019) 

• Shortage of skilled blockchain 

developers (China, 2018; 

Rauchs et al., 2019). 



 
25 

• Reluctance to replace the 

current systems (China, 2018; 

Rauchs et al., 2019). 

(Sanka et al., 2021) recommend further blockchain research in the areas of scalability, 

big data analytics, blockchain verification, interoperability, secure consensus 

protocols, post-quantum cryptosystems, and integration of blockchain with other 

systems (Sanka et al., 2021). 

2.3.6 Review of Journal Articles 

Published in March 2021, Almekhlafi et al., (2021) reviewed journal articles from 2017 

to 2021 from 7 Scopus databases (ScienceDirect, Springer, IEEE, Emerald, Taylor & 

Francis, MDPI, and Wiley), with a view to determining factors influencing users to 

adopt blockchain, the adoption models, industries, and methodologies (Almekhlafi & 

Al-Shaibany, 2021). 

In their review, they investigated 21 studies on blockchain adoption and being a new 

topic, 68% of the articles examined were published in 2020, with 28% in 2019 

(Almekhlafi & Al-Shaibany, 2021). Most of the studies were quantitative with only one 

being mixed-method-based (interview and survey). The countries covered were Brazil, 

Italy, Pakistan, Spain, Taiwan, India, United Arab Emirates (UAE), Malaysia, and 

Australia (Almekhlafi & Al-Shaibany, 2021). In the studies, researchers used seven 

adoption models, the Technology Acceptance Model (TAM), Theory of Reasoned 

Action (TRA), Theory of Planned Behaviour (TPB), Task-Technology Fit (TTF), Unified 

Theory of Acceptance and Use of Technology (UTAUT), Information Systems Success 

Model (ISSM), and Diffusion of Innovation Theory (DOI). The TAM and UTUAT models 

were the most popular accounting for 71% and 28% of the studies sampled, 

respectively (Almekhlafi & Al-Shaibany, 2021). It was further noted that the rest 

combined the TAM and UTUAT Models, and in one study, the DOI was exclusively 

used (Almekhlafi & Al-Shaibany, 2021). Although blockchain emerged as the 

technology driving cryptocurrency, the studies focused on service and manufacturing 

industries, energy, tourism and hospitality, small micro-industries, maritime, logistics, 

auditing, and health care. Supply chain management received the most attention as 



 
26 

an area of focus, accounting for 33% of the studies (Almekhlafi & Al-Shaibany, 2021). 

Table 4 highlights the results of blockchain adoption research per industry. 

Table 4 - Results of Blockchain Adoption Research by Industry – Chapter 2 

Industry Factors Discussion 

Bitcoin 1. Perceived Risk (PR) 

2. Perceived Usefulness (PU) 

3. Perceived Ease of Use 

(PEU) 

The study utilised the TAM model, 

involved research through 

qualitative interview and case 

study found PEU and PU to be 

positive (Folkinshteyn & Lennon, 

2016). 

Supply Chain 1. Discomfort (DISC) 

2. Insecurity (INSC) 

3. Perceived Usefulness (PU) 

4. Perceived Ease of Use 

(PEOU) 

5. Attitude (ATTI) 

6. Subjective Norms (SN) 

7. Perceived Behavioural 

Control (PC) 

8. Behavioural Intention (BI) 

The study in India, utilised the 

TAM, TRA, and TPB models, 

researched through quantitative 

instrument and found that PU, 

ATTI, and PC, had positive 

significant impacts on BI. In 

addition, SN had a weak impact on 

BI, and DISC was found to be 

insignificant (Kamble et al., 2019).  

Digital 

Currency 

1. Awareness (AW) 

2. Perceived Ease of Use 

(PEOU) 

This study in the UAE utilised the 

TAM, for which research survey 

data was collected through a 

quantitative questionnaire. It was 



 
27 

3. Perceived Usefulness (PU) 

4. Social Influence (SI) 

5. Perceived Trust (PT) 

found that PU, PT, SI, and PEOU 

had a significant positive impact 

on intention (Saif Almuraqab, 

2020). 

Maritime 

Shipping 

1. Customs Clearance (CC) 

2. Digitalising and Ease of 

Paperwork (DP) 

3. Tracking and Tracing (TT) 

4. Standardisation and Platform 

(SP) 

5. Business Model and 

Regulation (BR) 

The study in Taiwan, used the 

TAM model and for which 

research survey data was 

collected through a questionnaire, 

found that CC, DP, and SP had a 

positive significant impact on 

intention to use (Gil-Cordero et al., 

2020).  

Blockchain-

Based Smart 

Lockers 

1. Individual-Technology Fit 

(ITF) 

2. Task-Technology Fit (TTF) 

3. Perceived ease of use 

(PEOU) 

4.Perceived usefulness (PU) 

5. Attitude (ATT) 

6. Usage Intention (UI) 

7. Network Externality (NE) 

8. Perceived Safety (PS) 

The research based in Taiwan 

Utilised the TAM and TTF model 

and followed a quantitative 

approach, for which research 

survey data was collected through 

a questionnaire indicating that PU 

and PEOU had insignificant 

impacts. PS and NE were 

insignificant (Lian et al., 2020). 



 
28 

Corporate 

Governance 

1. Individual-Technology Fit 

(ITF) 

2. Task-Technology Fit (TTF) 

3. Perceived Ease of Use 

(PEOU) 

4. Perceived Usefulness (PU) 

5. Attitude (ATT) 

6. Usage Intention (UI) 

7. Network Externality (NE) 

8. Perceived Safety (PS) 

A quantitative study utilised the 

TAM and for which research 

survey data was collected through 

a questionnaire, indicating that the 

model fit validated the various 

constructs as per the theorized 

model (Singh et al., 2019) 

Supply Chain 1. Performance Expectancy 

(PEXP) 

2. Facilitating Conditions 

(FCON) 

3. Effort Expectancy (EEXP) 

4. Behavioural Intention (BI) 

5. Social Influence (SINF) 

In Brazil, a quantitative study, for 

which research survey data was 

collected through a questionnaire, 

utilised the UTUAT. It was found 

that SINF had a significant 

positive impact on UTUAT 

constructs (Wamba & Queiroz, 

2019). 

Blockchain 

based 

research data 

– Sharing 

system 

1. Perceived Ease of Use 

(PEOU) 

2. Usefulness (PU) 

3. Quality of System (QOS) 

4. Perceived Enjoyment (PEnj) 

This study extended the TAM, in a 

quantitative study conducted via 

an online survey, found that PEnj 

and QOS had stronger impacts on 

PU. However, PEOU was 



 
29 

5. Intention to use (ITU) 
insignificant for PU (Shrestha & 

Vassileva, 2019). 

Service and 

manufacturing 

industry 

1. Attitude (ATT) 

2. Behaviour Intention (BI) 

3. Innovativeness (INN) 

4. Optimism (OPT) 

5. Perceived Behavioural 

control (PBC) 

6. Perceived Usefulness (PU) 

7. Perceived Ease of Use 

(PEOU) 

8. Subjective Norms (SN) 

A Pakistan-based study, in which 

the TAM, TRI, and TPB models 

were examined and for which 

research survey data was 

collected through a questionnaire 

indicating that PEOU, ATT, PBC, 

and PU had significant positive 

impacts on BI (Ullah, 2020). 

Energy 

Management 

1. Perceived Ease of Use 

(PEOU) 

2. Perceived Usefulness (PU) 

3. Attitude (ATT) 

4. Cost Saving (CS) 

5. Innovativeness (INN) 

6. Behavioural Intention (BI) 

A quantitative study conducted via 

questionnaire utilised the TAM 

and TRA models indicating that BI 

was positively impacted by PEOU, 

ATT, PU, and CS. INN showed a 

significant positive impact on 

PEOU (Ullah et al., 2020). 

Cryptocurrency 1. Performance Expectancy 

2. Web Quality (WQ) 

Study based in Spain followed the 

quantitative approach and utilised 

extended TAM. It was found that 



 
30 

3. Trust 

4. Electronic Word of Mouth (E-

Wom) 

5. Perceived Risk (PR) 

6. Behavioural Intention (BI) 

all factors had significant positive 

impacts on BI (Gil-Cordero et al., 

2020). 

Cryptocurrency  1. Attitude (A) 

2. Perceived Behavioural 

Control (PBC) 

3. Subjective Norm (SN) 

4. Behavioural Intention (BI) 

A South African study that 

followed a quantitative approach 

through which the TPB was 

applied. It was found that A and 

PBC positively impacted intention 

to adopt. However, SN was found 

to be insignificant (Mazambani & 

Mutambara, 2019). 

Business in 

Tourism and 

hospitality 

SMEs 

1. Strategic Orientation (SO) 

2. Social Influence (SI) 

3. Innovativeness (INN) 

4. Self-Efficacy (SE) 

5. Perceived Usefulness (PU) 

6. Perceived Ease of Use 

(PEOU) 

7. Behavioural Intention (BI) 

This study extended the TAM and 

followed a quantitative approach 

through a survey. It was found that 

SO and Owners characteristics 

had a significant positive impact 

on BI. Technology Characteristics, 

Gender and Age were found to be 

insignificant (Nuryyev et al., 

2020). 

Logistics 

Industry 

1. Perceived Ease of Use 

(PEOU) 

2. Perceived Usefulness (PU) 

An online study used TAM and the 

approach was a quantitative study 

that found PEOU, PU, and ATT 



 
31 

3. Attitude (ATT) 

4. Behavioural Intention (BI) 

5. Actual Behaviour (AB) 

had a significant, positive impact 

on BI (Jain et al., 2020).  

Accounting 

and auditing 

profession 

1. Computer Self-Efficacy 

(CSE) 

2. Perception of External 

Control (PEC) 

3. Job Relevance (JR) 

4. Output Quality (OQ) 

5. Results Demonstrability (RD) 

6. Effort Expectancy (EE) 

7. Performance Expectancy 

(PE) 

8. Social influence (SI) 

9. Intention (INT) 

An Italian study that used TAM3 

and UTUAT and followed a 

quantitative approach via 

questionnaire where it was found 

that PE, SI, and EE had significant 

positive impacts on INT (Ferri et 

al., 2020).  

Healthcare 

services 

1. Privacy Concern (PCON) 

2. Perceived Utility (PU) 

3. Perceived Ease of Use 

(PEOU) 

4. Behavioural Intentions (BI) 

5. Trust (T) 

An India-based study that utilised 

the TAM and followed a 

quantitative approach for which 

research survey data was 

collected through a questionnaire 

and found that PU, PEOU, Trust, 

and PCON were significant 



 
32 

predictors of BI (Dhagarra et al., 

2020). 

(Almekhlafi & Al-Shaibany, 2021) 

Almekhlafi et al., (2021) found that perceived ease of use and perceived usefulness 

are the most important factors influencing user’s intention to adopt blockchain as a 

technology across the 21 studies within the cross-section of industries (Almekhlafi & 

Al-Shaibany, 2021).  

The limitations of their study highlight that only seven databases were used and further 

recommend future studies on organisational adoption since the studies focussed on 

individuals. It is also recommended that post-implementation research be conducted 

with a view to understand the development of adoption (Almekhlafi & Al-Shaibany, 

2021). 

In 2020, Kawasmi et al., (2020) conducted a study on establishing an adoption model 

for blockchain use in banking through research incorporating 25 selected articles 

published between 2015 and 2018 (Kawasmi et al., 2020). The documents comprised 

white papers, research papers, and reports from Central Banks as well as fintech 

companies. 

Kawasmi et al., (2020) classified three factors for blockchain adoption in banking. First, 

there are Supporting Factors (Enhanced Data Exploration, Regulatory Compliance, 

Improving the KYC Process, Improved Transactions Speed, Smart Contracts, and 

Increased Transparency). Second, there are Hindering Factors (Scalability, Energy 

Consumption, Currency Stability, Legislations, and Regulations, and Governance). 

Third, there are Circumstantial Factors (Costs, Security, and Interoperability) and 

new factors, through literature review (Kawasmi et al., 2020). They then analysed the 

documents (Figure 3) through a text mining approach involving data preparation, data 

cleaning, frequency analysis, and categorisation (Kawasmi et al., 2020). The findings 

of the research in terms of their categorisation, incorporated Adoption Supporting 

Factors, Adoption Hindering (Barriers), and Adoption Circumstantial Factors, as 

depicted in Figures 3, 4, and 5, respectively (Kawasmi et al., 2020). 



 
33 

Figure 3 - Adoption Supporting Factors (Kawasmi et al., 2020, p. 134) – Chapter 

2 

 

Figure 3 shows that the top three, namely improving KYC, improved transaction 

speed, and smart contract, account for about 85%, whilst the remaining 15% are 

allocated between regulatory compliance, enhanced data exploration, and increased 

transparency (Kawasmi et al., 2020). 

Figure 4 - Adoption Barriers (Kawasmi et al., 2020, p. 135) – Chapter 2 

 

In terms of Adoption Barriers, the finding from research conducted by Kawasmi et al., 

(2020) was that governance accounted for 32% was the highest barrier whilst energy 

consumption represented 5.8%, the lowest obstacle. Legislation and regulation, 



 
34 

currency stability, and scalability, ranged between 16% and 24% (Kawasmi et al., 

2020). 

Figure 5 - Adoption Circumstantial Factors (Kawasmi et al., 2020, p. 136) – 

Chapter 2 

 

In terms of Adoption, Circumstantial Factors as Security featured as the most 

prominent at 71% (Kawasmi et al., 2020). Kawasmi et al., (2020) further identified 

Competitive Advantage as a new factor as it stood independently (Kawasmi et al., 

2020). 

The review of the current state of the research indicates that since its introduction 

through cryptocurrency, blockchain has advanced through experimentation to a 

mature technology that is currently a top-five strategic priority for most executives 

globally (Budman, 2020). The past decade has seen slow adoption as research into 

the technical, regulatory, and business-risk components was undertaken. However, 

with the evolution towards blockchain 3.0, faster implementation is expected 

(Almekhlafi & Al-Shaibany, 2021). The review of the blockchain adoption research 

highlighted forty-nine predictors (indicators) of blockchain usage behaviour at the 

individual user level, as displayed in Figure 12 below. 



 
35 

2.4 Analytical Framework 

In developing the analytical framework of the study, it is important to critically 

investigate extensions of the TAM to accommodate blockchain usage behaviour.  

In the case of Kawasmi et al., (2020), the significant Supporting, Hindering and 

Circumstantial factors were ranked in importance based on occurrence, and then 

incorporated into their extension of the TAM. Kawasmi et al., (2020) eliminated the 

PEOU from the TAM, explaining that blockchain is not a system, but rather exhibits 

characteristics of an infrastructure and is therefore not utilised at the individual user-

level but rather at the organisational-level (Kawasmi et al., 2020). Figures 6 and 7 

highlight the ranked factors and their inclusion into their extension of the TAM.  

Figure 6 - Ranked Blockchain Factors (Kawasmi et al., 2020, p. 140) – Chapter 2 

 

 

 

 



 
36 

Figure 7 - Adapted  Blockchain TAM (Kawasmi et al., 2020, p. 142) – Chapter 2 

 

As highlighted by Kawasmi et al., (2020), users do not experience blockchain 

technology first-hand, but through an Application or user interface (UI) that delivers 

the user experience (UX). In research conducted by Sanka et al., (2021) and 

Almekhlafi et al., (2021), the shortcoming, regarding the application of technology 

adoption models to individuals rather than the organisation, is also highlighted. From 

a technological perspective it is further evidenced, throughout the blockchain literature, 

that the technology fits the profile of an underlying technology like that of an 

infrastructure. Given that in most of the studies conducted, the popular use of TAM 

and UTUAT surface a gap in the research on blockchain usage behaviour, since these 

models consider adoption at the individual user level (Almekhlafi & Al-Shaibany, 2021; 

Kawasmi et al., 2020; Sanka et al., 2021).   

The application of technology adoption models that are designed to assess individual 

adoption, to measure organisational technology adoption, will prove to be problematic. 

This is because there are significant factors impacting organisation technology 

adoption that are not included in these models. It therefore becomes necessary to 

extend individual technology adoption models, to include organisational determinants 

of blockchain usage behaviour.  



 
37 

Faced with a similar difficulty i.e., the inadequacy of TAM to assess organisational 

technology adoption, Mohapatra et al., (2015) extended the TAM from individual focus 

to an organisational focus, that is, TAMO Technology Adoption Model Organisation) 

(Figure 8), in their research on SAP ERP adoption at the National Aluminium Company 

Limited (Mohapatra et al., 2015). Their research resulted in extending the TAM by 

incorporating several constructs as per Figure 8.  

Figure 8 - TAMO - Organisational Adaptation (Mohapatra et al., 2015, p. 254) – 

Chapter 2 

 

(Mohapatra et al., 2015) 

Accordingly, Mohapatra et al., (2015) developed a useful extension of TAMO capable 

of assessing technology adoption at the organisational-level. However, within the body 

of blockchain research, it was found that there have been extensions of the TAM, but 

the direction has been purely focused on the technology and regulatory aspects 

neglecting organisational blockchain usage behaviour. It is further noted that the 

research lacks significant factors especially at the organisational-level. This requires 

further extension of TAM to include organisational technology adoption factors which 

sit within the research on organisation digital maturity.   



 
38 

Armstrong et al., (2021), through their study of global advisory firms, popular business 

and academic literature, proposed the unified model for digital maturity which 

comprises two dimensions with six factors each, as displayed in Figure 9 (Armstrong 

& Lee, 2021). Armstrong et al., (2021) describe digital transformation as not just about 

adopting exponential technology, but also about delivering on the purpose of the 

organisation (Armstrong & Lee, 2021). They further define digital maturity as the 

“measure of an organisation’s ability to achieve desired strategic and operational 

outcomes in the presence of, and embracing, profound technological change” 

(Armstrong & Lee, 2021). Since organisational digital transformation (maturity) 

involves many facets driven by organisation-wide change, it surfaces the 

interrelationships between the factors that come into play. Collectively, these factors 

not only influence business results, but also each other. Therefore, they become 

predictors of the organisation’s maturity in adapting and adopting new technology.  

In comparing the unified digital maturity model as per Figure 9 (Armstrong & Lee, 

2021), to blockchain adoption (TAM and its various extensions) and SAP ERP 

enterprise adoption (TAMO) (Mohapatra et al., 2015), it becomes clear that by 

inclusion of factors such as digital product innovation, new value streams and business 

models, innovation and investments, and digital ethics, it will make for a more holistic 

technology adoption model capable of measuring organisational blockchain usage 

behaviour more accurately. These factors have been identified to be lacking in the 

previous and current adoption models being employed to assess organisational-level 

blockchain usage behaviour.  

It is also noted through the analysis of blockchain adoption literature that the factor 

highlighted as having the most significant impacts on organisational blockchain usage 

behaviour is perceived usefulness. The factor that displays the weakest impact on 

organisational usage behaviour is motivational factors. 

Figure 9 - Unified Digital Maturity Model (Armstrong & Lee, 2021, class slide)  - 

Chapter 2 



 
39 

 

A fundamental realization that blockchain is adopted at the organisational-level and 

less by individual users, emphasises a need for deeper understanding of factors that 

drive organisational rather than individual usage behaviours. It is on this basis that a 

more appropriate analysis framework be constructed and tested, so that it accounts 

for variations in organisational blockchain usage behaviours.    

2.5 Conceptual Framework 

Through the process of analysing the blockchain adoption literature, factors 

influencing individual blockchain usage behaviour at the organisational-level were 

identified (Figure 10). The theories that underpin the development of the conceptual 

model for the purpose of the study is based on the TAM, as well as the extension 

TAMO, and the Unified Digital Maturity Model. The TAMO (Mohapatra et al., 2015) 

was compared to the Unified Digital Maturity Model (Armstrong & Lee, 2021) and a 

specific set of factors which then informed the development of the conceptual model 

to test individual blockchain usage behaviours at the organisational-level. In examining 

the TAMO (Mohapatra et al., 2015) and the Unified Digital Maturity Model (Armstrong 

& Lee, 2021) from the existing literature, a number of factors were adopted with which 

a conceptual model was developed in order to explain individual blockchain usage 

behaviour at the organisational-level as displayed in Figure 11.  

Figure 10 - Conceptual Process – Chapter 2 



 
40 

 

Figure 11 displays the conceptual framework that will be adopted in the present study 

to conduct research into organisational blockchain via individual usage behaviour. 

Incorporating Digital product innovation (PU3), New Value Streams and Business 

models (PU4), Innovation and Investments (DI5), Digital Ethics (DI6) as factors from 

unified digital maturity model (Armstrong & Lee, 2021), together with an identified forty-

nine underlying dimensions through the literature review, would provide a more holistic 

account of the factors impacting individual blockchain usage behaviour in an 

organisation. These dimensions are mapped to the constructs Perceived Usefulness, 

Motivational Factors, Design and Implementation, and Perceived Ease of Use, 

respectively.  

 

 

 

 

 



 
41 

Figure 11 - Conceptual Model – Chapter 2 

  



 
42 

Figure 12 - Analysis of Blockchain Adoption Factors – Chapter 2 
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

 

 

 

 

Code TAMO - Mohaptara 

PE Perceived Usefulness (PU)

PU1 Streamlining, standardisation of processes and practices

PU2 Benefits of the process

MI Motivational Factors (MI)

MI1 Rewards and recognition

MI2 Willingness to use the new system

MI3 User satisfaction

DI Design & Implementation (DI)

DI1 Stakeholder management in the decision process

DI2 Effective review process by the senior management

DI3 Training effectiveness and skills imparted

DI4 Risk management

PE Perceived Ease of Use (PE)

PE1 Complexity of the application

PE2 System working as per the expectations

PE3 Integration of system

Code TAMO-1 (Blockchain)

PE Perceived Usefulness (PU)

PU1 Streamlining, standardisation of processes and practices

PU2 Benefits of the process

PU3 Digital Product Innovation

PU4 New Value Streams and Business Models 

MI Motivational Factors (MI)

MI1 Rewards and recognition

MI2 Willingness to use the new system

MI3 User satisfaction

DI Design & Implementation (DI)

DI1 Stakeholder management in the decision process

DI2 Effective review process by the senior management

DI3 Training effectiveness and skills imparted

DI4 Risk management

DI5 Innovation and Investment

DI6 Digital Ethics

PE Perceived Ease of Use (PE)

PE1 Complexity of the application

PE2 System working as per the expectations

PE3 Integration of system

Notes: 

(Almekhlafi & Al-Shaibany, 2021; Kawasmi et al., 2020; Sanka 
et al., 2021) studies highlighted forty nine blockchain specific 
factors, which the researcher compared to (Mohapatra et al., 
2015) TAMO. With a view on organisational usage behaviour, 
comparisons to (Armstrong & Lee, 2021), Unified digital 
maturity model was also done. It was found that four additional 
indicators i.e., “digital product innovation, new value streams 
and business models, innovation and investments, and digital 
ethics” should be added to TAMO, thereby strengthening the 
model’s ability to predict organisations blockchain usage 
behaviour. The resultant model TAMO-1 (blockchain) 

 

# Factor Extraction - Blockchain Specific TAMO Class TAMO Sub Class Uni.Dig. M Class

1 Lack of understanding and skills DI DI3 D3

2 Privacy and confidentiality concerns DI DI4

3 Regulatory issue DI DI4

4 Possible security threat DI DI4

5 Fear of competitive information sensitivity DI DI4

6 Regulatory uncertainty DI DI4

7 Concerns for Audit/compliance DI DI4 L3

8 Regulatory compliance DI DI4 L3

9 Legislations and regulations DI DI4 L3

10 Governance DI DI4 L3

42 Not our business priority DI

43 Funding issues DI L5

44 Insufficient funding DI L5

45 Concerns for intellectual property DI L6

46 Moral hazards reduction DI L6

11 No executive buy-in MI MI2 L1

12 Users not trusting each other MI MI3 D3

13 PEOU PE PE PE

14 Technical issues PE PE1 D5

15 Scalability issues PE PE1 D5

16 Scalability PE PE1 D5

17 Energy Consumption PE PE1 D5

18 Currency stability PE PE1

19 Not suitable for business case PE PE2

20 Replacing/adapting existing legacy systems PE PE3 D5

21 Consortium formation challenges PE PE3 D5

22 Ability to integrate network PE PE3 D5

23 Interoperability PE PE3 D5

24 PU PU PU PU

25 Efficiency improvement across boundaries PU PU1 D4

26 Transparency improvement PU PU1 D4

27 Efficiency improvement within boundaries PU PU1 D4

28 Improving the KYC process PU PU1 D4

29 Improved transactions speed PU PU1 D4

30 Smart contracts PU PU1 D4

31 Increased transparency PU PU1 D4

32 New revenue generation PU PU2 L4

33 Cost savings PU PU2 D6

34 Competitive advantages PU PU2 D6

35 Assets trading PU PU2 D6

36 Failed to realise tangible benefits PU PU2 D6

37 Uncertain benefit/return PU PU2 D6

38 Enhanced data exploration PU PU2 L4

39 Costs PU PU2 D6

40 Security PU PU2 D5

41 Interoperability PU PU3 D5

47 Lack of compelling application PU D2

48 Blockchain is unproven PU D2

49 Market competition PU L4



 
43 

The proposed conceptual model shown in Figure 11 displays four constructs 

(Perceived Usefulness, Motivational Factors, Design and Implementation and 

Perceived Ease of Use) as highlighted by the TAMO in studying individual technology 

adoption and user behaviour at the organisational-level (Mohapatra et al., 2015). 

2.5.1 Perceived Usefulness 

The literature highlights that perceived usefulness influences individual 

blockchain usage behaviour (Almekhlafi & Al-Shaibany, 2021). The more a user 

perceives a technology to be useful in one’s activity the more likely they are to 

use it. Decision makers act on behalf of the organisation in adopting technology, 

therefore the extended TAM which was named the TAMO (Mohapatra et al., 

2015).  

 

Hypothesis 1 (H1): Perceive Usefulness has a positive influence on individual 

blockchain usage behaviour at the organisational-level. 

2.5.2 Motivational Factors 

Motivational factors influence individual blockchain usage behaviour at the 

organisational-level. It is likely blockchain usage behaviour will increase the 

more motivated decision makers are to use a technology. Motivational factors 

was found to be a significant factor in the study of organisational technology 

adoption (Mohapatra et al., 2015). The following hypothesis is therefore 

proposed: 

 

Hypothesis 2 (H2): Motivational Factors has a positive influence on individual 

blockchain usage behaviour at the organisational-level. 

2.5.3 Design and Implementation 

Design and implementation influences organisational individual technology 

usage behaviour (Mohapatra et al., 2015). Implementations that have been 

poorly designed and deployed have a higher risk of failure (Prewett et al., 2020). 



 
44 

Due to these failures emanating from poorly designed implementation of 

blockchain technology, it will become less desirable to adopt. The following 

hypothesis is therefore proposed: 

 

Hypothesis 3 (H3): Design and Implementation has a positive influence on 

individual blockchain usage behaviour at the organisational-level. 

2.5.4 Perceived Ease of Use 

Perceived ease of use significantly influences individual blockchain usage 

behaviour at the organisational-level, as indicated extensively throughout the 

literature (Folkinshteyn & Lennon, 2016; Gil-Cordero et al., 2020; Jain et al., 

2020; Kamble et al., 2019; Saif Almuraqab, 2020; Shrestha & Vassileva, 2019; 

Singh et al., 2019; Ullah, 2020). The easier the technology is perceived to be 

utilised, the more likely it is that its usage will increase. Across a different a 

cross-section of industry, perceived ease of use is highlighted as one of the 

most important factors for individual blockchain adoption (Almekhlafi & Al-

Shaibany, 2021). The following hypothesis is therefore proposed: 

 

Hypothesis 4 (H4): Perceived Ease of Use has a positive influence on 

individual blockchain usage behaviour at the organisational-level. 

 

Overall, the conceptual model of the study comprised of the following hypothesis:  

 

Hypotheses 1 (H1): Perceive Usefulness, has a positive influence on individual 

blockchain usage behaviour at the organisational-level. 

 

Hypotheses 2 (H2): Motivational Factors has a positive influence on individual 

blockchain usage behaviour at the organisational-level. 

 

Hypotheses 3 (H3): Design and Implementation has a positive influence on 

individual blockchain usage behaviour at the organisational-level. 

 



 
45 

Hypotheses 4 (H4): Perceived Ease of Use has a positive influence on individual 

blockchain usage behaviour at the organisational-level. 



 
46 

CHAPTER 3. RESEARCH METHODOLOGY 

3.1 Introduction 

In the previous chapter, the relevant literature was presented, a conceptual model was 

developed, and hypothesis were formulated. A set of determinants were predicted to 

influence individual blockchain usage behaviour in organisations.  

In this chapter, the data collection methods and data analysis strategy used to test the 

study’s conceptual model are described. The research approach employed in the 

context of the present study is first described.  

The sample is then described, followed by a description of the instrument development 

and administration.  

A description of data analysis methods is discussed, followed by issues of instrument 

validity and reliability. Lastly, the limitations of the study are highlighted with ethical 

considerations, and a summary of the chapter provided. 

3.2 Research Design 

The research design strategy and techniques employed in this study are discussed 

next. 

3.2.1 Research Approach 

This study used a quantitative approach. This involved using numerical primary data 

to test an empirical model. It was more appropriate to the current type of relational 

study where the aim was to examine the effect of pre-identified independent variables 

on a dependent variable. Quantitative research enables the validation of hypothesised 

relationships between variables, allowing the researcher to predict phenomena (Leedy 

& Ormrod, 2005). In using quantitative methods, the researcher is an objective 

observer independent of the research, as s(he) does not actively participate in the 

research. This assumes that reality is objective and external to the researcher (Hussey 

& Hussey, 1997). Moreover, the quantitative research approach supports the formal 



 
47 

definition of concepts, deduction of hypotheses, and prediction of outcomes (Hussey 

& Hussey, 1997; Saunders & Lewis, 2012). This deductive approach to research is 

used to test if a known theory or phenomenon is valid in given circumstances 

(Saunders & Lewis, 2012). Qualitative research, however, seeks to explain 

phenomena by interpreting emerging patterns and themes through non numerical 

examination (Bailey, 1982; Neuman, 2007) 

In the present study, data will be collected and analysed to examine the major factors 

as hypothesised, in relation to their effect on individual blockchain usage behaviour in 

organisations. This supports the use of a quantitative deductive approach that is 

explanatory in nature (Saunders & Lewis, 2012).   

 

This study will seek to explain individual blockchain usage behaviour in organisations, 

within the empirical context of the South African financial services industry. 

3.3 Context of the Study and Sample 

The research was limited to individuals employed at the managerial level at a company 

within the South African Banking and Financial Services Industry (BFSI). 

3.3.1 Unit of Analysis 

The unit of analysis is a classification of the specific unit to be sampled (Terre Blanche 

et al., 2006). Technical and non-technical managers within the South African banking 

and financial service industry (BFSI) who through their experiences provided useful 

insights into individual blockchain usage behaviour in organisations, comprised the 

target sample (Bhattacherjee, 2012) for the study. As managers within the BFSI, these 

target respondents possessed the necessary experience and expertise to contribute 

to the research study.   

3.3.2 Study Population and Sampling Method 

The study of blockchain usage behaviour in organisations requires the insights of 

those individuals who have an interest in the technology in their respective institutions.  



 
48 

Specific to the topic of this research, a master database of the total population does 

not exist, but the accessible population of desired respondents commonly found in 

blockchain and the BFSI use professional networking platforms such as LinkedIn. The 

researcher, also being well-networked within the industry, obtained through a referral 

process, respondents who would further attract their respective networks to contribute. 

This indicates the use of a snowballing method of sampling which is appropriate, given 

the specialist knowledge required of the respondents (South African financial services 

and exposure to blockchain either through the course of work or through direct 

experimentation or implementation). Although snowball sampling carries an inherent 

risk of selection bias, there is a likelihood of respondent referrals being similar in 

characteristics and knowledge (Saunders & Lewis, 2012). However, given that the 

industry and technology under study is specialised, this sampling method was justified 

for the present study.  

The researcher leveraged professional platforms such as LinkedIn to screen and 

select potential candidates as per the relevant selection criteria. Specifically, a filtration 

exercise that only displayed managers in banking and financial services within South 

Africa evidenced ninety-four thousand profiles. The researcher for the purpose of this 

research, rounded up to a threshold of one-hundred thousand managers who would 

comprise the accessible population.  

When dealing with inter-variable relationships, as a rule of thumb, the formula N > 50 

+ 8(m) (where m is the number of predictors) can be considered to represent the 

minimum sample required (Green, 1991), and was used to determine a sample size 

for this study. In this study, four individual blockchain usage behaviour predictor 

constructs were examined. Thus, 82 respondents (N > 50 + 8(4)) were targeted for the 

study’s sample. However, the researcher sought more responses to enhance the 

overall response rate.  Thus, a total number of 300 was sent from which 160 complete 

responses were received by the researcher, yielding a response rate of 53%, for data 

analysis. 



 
49 

3.4 Data Collection 

3.4.1 Research Instrument Construction 

The research instrument construction phase of the study is described. The constructs 

and the literature sources they were derived from are shown in Table 5. 

Table 5 - Constructs and Variables – Chapter 3 

Construct Hypotheses Supporting Literature Sources No of 

dimensions (all 

scale items 

measured on 5-

point Likert 

scale) 

Perceived 

Usefulness 

H1 (Almekhlafi & Al-Shaibany, 2021; 

Mohapatra et al., 2015) 

4 

Motivational 

Factors 

H2 (Mohapatra et al., 2015) 3 

Design and 

Implementation 

H3 (Mohapatra et al., 2015; Prewett 

et al., 2020) 

6 

Perceived Ease of 

Use 

H4 (Folkinshteyn & Lennon, 2016; 

Gil-Cordero et al., 2020; Jain et 

al., 2020; Kamble et al., 2019; Saif 

Almuraqab, 2020; Shrestha & 

Vassileva, 2019; Singh et al., 

2019; Ullah et al., 2020) 

3 

 



 
50 

As shown in Appendix B, existing scales were adapted from the literature. Four 

constructs were measured in this study using a questionnaire. A 5-point Likert scale 

ranging from scores of 1 = Strongly Disagree to 5 = Strongly Agree was used to 

measure each construct.  

Likert scales allow for the variation in responses to be captured, where the level of 

disagreement or agreement can be elicited. These scales are advantageous as they 

are easy to use for the respondent from which the answers can be easily coded by the 

researcher (Hussey & Hussey, 1997). 

Variables related to the Perceived Usefulness construct were reflected by 

respondents’ perceptions of the following four dimensions: 

First, Streamlining Standardisation of Processes and Policies was reflected as a belief 

that standardised, streamlined business and IT processes are more likely to be 

successful in blockchain implementations. Therefore, individuals in organisations 

driving continuous improvement methodologies and embracing latest technologies to 

automate processes are more likely to adopt blockchain.  

Second, Benefits of the Value Process was reflected as a belief that the achievement 

of positive business outcomes will positively influence individual blockchain usage