FACTORS INFLUENCING THE DECISION TO ADOPT BLOCKCHAIN-BASED CRYPTOCURRENCIES USING TECHNOLOGY ACCEPTANCE MODEL

FACTORS INFLUENCING THE DECISION TO ADOPT BLOCKCHAIN-BASED

CRYPTOCURRENCIES USING TECHNOLOGY ACCEPTANCE MODEL

Introduction

Overview

The study examined the factors influencing the decision to adopt Blockchain-based

cryptocurrencies in the United States. Blockchain technology is one of the most hyped

decentralized innovations with a bright future (Khan et al., 2021). Cryptocurrencies are not the

only application of blockchain technology, but in this dissertation, the researcher focused on

cryptocurrencies. Cryptocurrency can be described as digital money secured with the

al., 2021).

The cryptocurrency market showed remarkable growth throughout 2017 (Vasin, 2018). At

the beginning of 2017, the total market cap of cryptocurrencies was 17.7 billion US dollars; at

the end of the year, it skyrocketed to 592 billion US dollars, more than 30 times than the

beginning of the year (Vasin, 2018). As of March 2022, there are more than 18,000

cryptocurrencies, and their total market cap is more than US 2 trillion US dollars, according to a

well-known cryptocurrency analysis platform, CoinMarketCap.com. The price of

cryptocurrencies and high volatility concern consumers and policymakers (J. Liu & Serletis,

2019). Blockchain has revolutionized and internationalized the funding process and allowed the

trading of tokens (Boreiko & Sahdev, 2018). Initial Coin Offerings (ICOs) have completely

ledger with identical copies on multiple computing nodes maintained by various entities

blockchain developer, published a white paper titled “Bitcoin: A peer-to-peer electronic cash

system” in 2008 (Nakamoto, 2008). Satoshi Nakamoto proposed a peer-to-peer network that uses

a decentralized proof-of-work concept to log all transactions and solve double-spending issues;

since then, thousands of cryptocurrencies and tokens have been created based on blockchain

technology (Hellani et al., 2018; Schatsky & Muraskin, 2015).

Bitcoin was the first cryptocurrency based on the blockchain, but it has yet to be used

daily for payments because of the longer block creation time (Vasin, 2018). Arias-Oliva et al.

(2019) mentioned that Blockchain-based cryptocurrencies would transform our transaction

adoption of Blockchain-based cryptocurrencies in the United States. The limitations and

assumptions of the study are mentioned, followed by the definitions in chapter one.

The research problem derives from a gap in the literature, conflict in the research results

in the literature that have been ignored in the literature, requires raising a concern of

blockchain technology was introduced in research by Haber and Stornetta (1990). Later,

Blockchain captured more attention when Satoshi Nakamoto (2008) published his research paper

Bitcoin: A peer-to-peer electronic cash system. Bitcoin’s growth and dramatic rise in price in

2017 are causing financial institutions like the Wall Street Journal to take notice, while

wellknown publications like Fortune and The Economist have issued cautionary warnings about

cryptocurrencies (Madey, 2017; Stavros, 2017).

McCorry (2018) mentioned that Bitcoin was introduced in the aftermath of the worst

financial crisis since the Great Depression in the 1930s, which led to subsequent bailouts for both

banks and governments. Nakamoto posted the Bitcoin whitepaper in November 2008, and the

is a decentralized technology, a person can transfer money confidentially without the

involvement of any third-party services that eliminate the transaction fee from the third party,

auditability, and immutability (Zheng et al., 2018). Blockchain technology attempts to solve

existing problems of transferring money via the conventional method (Madey, 2017). There are

some technical challenges and limitations like low throughput, higher latency, smaller block size,

and bandwidth, its usage in illegal activities, tax evasion, and money laundering, some security

concerns like 51% attack, high energy usage in the mining process, limited usability to existing

API, versioning, and forking issues (Yli-Huumo et al., 2016). Among the many hindrances to

Blockchain-based cryptocurrencies, scalability is one of the critical issues in implementing

public blockchains (Khan et al., 2021). Khan et al. (2021) stated that more literature resources

are needed for scalability issues. Public blockchain scalability is a growing concern and is

becoming a focus of research topics across the industry, where many sectors are trying to adopt

1990s regarding innovation. Cryptocurrencies’ unregulated characteristics are skeptical to many

people, while some believe that they are the future and investing in them is an excellent

opportunity (Piedade, 2018). Decentralized Finance (DeFi) is also being discussed by various

stakeholders of the financial market; it utilizes cryptocurrency and blockchain technology to

services (Silva & Mira da Silva, 2022). Silva and Mira da Silva (2022) recognized that

cryptocurrency adoption and transaction volumes are the factors that threaten the market and

price stability.

The scalability trilemma described by Vitalik Buterin, the co-founder of Ethereum, is

well-known in blockchain (Hafid et al., 2020). It is first described by the co-founder of

Ethereum, Vitalik Buterin (Yuan & Wang, 2018). According to Vitalik, tradeoffs between three

properties, 1) decentralization, 2) scalability, and 3) Security, are inevitable. Decentralization is

the core and nature of blockchain technology; scalability is the main challenge, whereas security

is an essential blockchain property (Hafid et al., 2020). It is crucial to find the right balance

among all three aspects, decentralization, scalability, and security of the blockchain, as the

scalability trilemma states that a blockchain solution can only have two out of either

discussed factors in blockchain technology. Most of these factors are interdependent and linked

directly or indirectly to a consensus mechanism (Khan et al., 2021). Poon and Dryja (2016)

revealed that the payment network Visa achieved 47,000 transactions per second (TPS) on its

network, whereas Bitcoin supports less than seven transactions per second with a 1-megabyte

block limit.

as a challenging issue. Despite the characteristics like transparency, decentralization,

critical limitations of blockchain is scalability (Hafid et al., 2020; Khan et al., 2021; Kumar

Bhardwaj et al., 2021); Swan, 2017). Hafid et al. (2020) revealed that scalability needs to be

better defined in the literature. Kamble et al. (2021) demonstrated that scalability is a significant

source of complexity in a blockchain system. Kamble et al. (2021) mentioned that companies

should verify compatibility while deciding to implement blockchain and distributed ledger

technology and whether these various financial services products and services meet regulatory

requirements. Swan (2017) revealed that the development of consensus algorithms that are

scalable, efficient, and secure is a challenge for the long-term development of blockchain

technology.

Silva and Mira da Silva (2022) observed the research gap in cryptocurrency adoption and

constructs like social influence and compatibility. Grover et al. (2019) suggested more research

to understand the perceived usefulness and perceived ease of use for blockchain technology

applications. Conrad (2009) mentioned a research analysis gap in how information technology

innovations are productively adopted at the individual level and in identifying the critical success

ease of use is not explored because of errors, so they suggested exploring their relationship in

future studies.

Yeoh (2017) revealed that blockchain technology is a relatively new innovation that must

overcome critical hurdles for broad adoption in the financial services sector. Ghonimy (2021) and

Yeoh (2017) mentioned that the adoption of blockchain technology has been growing

considerably over recent years, so it is indispensable to investigate the factors influencing the

decision to adopt it. Owusu (2022) mentioned that the lack of adequate knowledge about the

impact of cryptocurrency adoption would make investors prone to cyber thefts and financial

losses linked to the crypto market volatility. Owusu (2022) called for academic work and

research on factors affecting cryptocurrency adoption in the United States as the merits and

demerits of cryptocurrency adoption by the corporate world, financial, and individuals are

pool of scholarly knowledge and assist stakeholders in identifying factors that influence the

adoption of the technology (Ghonimy, 2021).

Purpose of the Study

The purpose of this non-experimental quantitative correlational study was to examine the

Blockchainbased cryptocurrencies. Ghonimy (2021) mentioned that studying the factors that

influence the decision to adopt Blockchain technology could give companies in the United States

an advantage in predicting the growth of new technologies in the future. In contrast,

the United States failed to adopt Blockchain technology. In this research, Perceived Usefulness

(PU), Perceived Ease-ofUse (PEOU), Compatibility (COMPB), Complexity (COMPX), and

Social Influence (SI) were the independent variables. In contrast, the Intention to Use (IU)

cryptocurrencies is the dependent variable. The purpose statement is the most critical in the entire

research study and needs to be clearly and specifically presented for the respective study. The

purpose statement contains a study's objectives, intent, or main idea (Creswell & Creswell,

2018). According to Locke et al. (2007), the purpose statement shows “Why you want to do the

study and what you intend to accomplish” (p. 9). The online survey using the SurveyMonkey tool

was conducted to collect the data of participants residing in the United States. The

SurveyMonkey Target

Significance of the Study

This quantitative study tests the factors influencing the decision to adopt blockchainbased

application cryptocurrency, including potential benefits and challenges. This study provided

organizations and technology enthusiasts with an understanding of different factors influencing

blockchain-based cryptocurrency adoption. This study conveyed the importance of implementing

Blockchain enthusiasts, decentralized autonomous organizations (DAOs), and policymakers will

benefit from this dissertation.

Blockchain technology has emerged as a potentially disruptive, general-purpose

technology for governments and companies to support information exchange and transactions

that require authentication and trust (Ølnes et al., 2017). Wokke and Rodenrijs (2018) observed

that there was a lack of knowledge, interest, and affinity about the cryptocurrency topic, which

indicated that cryptocurrency did not reach the mainstream market in terms of acceptance,

to expand their knowledge. The research for identifying the factors influencing cryptocurrencies

is supported by surveying participants from various fields and education levels. The result of this

compatibility, complexity, and social influence variables. This dissertation aimed to add to the

knowledge about TAM theory, as most of the literature studies utilized UTAUT.

Following is the Technology Acceptance Instrument:

Table 1

Technology Acceptance Instrument

cryptocurrencies and around 460 trading platforms according to the well-known cryptocurrency

analysis website CoinMarketCap.com (as of March 6th, 2022); only a few are being examined

this dissertation are time and context-dependent. At any point in the future, the technical, social,

financial, and law and regulation factors may not be in the same state as at the time of this

dissertation. This model is designed regarding cryptocurrency, which could limit its usability to

have a fundamental knowledge of blockchain technology. The small sample size is one of the

study's limitations, which is not generalizable to any other population. The research result may

not apply to a country other than the USA. The researcher used the third-party survey platform

SurveyMonkey for an online survey. No identifiable data was collected during the procedure to

ensure the anonymity of the participants, which may introduce response bias as some participants

may submit multiple responses. To participate in an online survey, the participants must have

basic knowledge of cryptocurrencies. However, it is also essential to note that the knowledge and

perception of the technology may vary widely.

participants voluntarily participated in an online survey and did not receive gifts apart from a pre-

defined small incentive to participate. The fourth assumption was that the participant's sample

size represented the population. The theoretical instrument-related assumption was that the

technology acceptance model's validity, reliability, and robustness were correct (Venkatesh &

and disadvantages. The researcher has analyzed only a few top cryptocurrencies in this research

study.

Definitions

research in this study. It was crucial to establish a common ground of understanding regarding

key concepts to navigate the complexities of the investigation effectively. This section provides

concise and accurate explanations of the fundamental terms that underpin the study's framework,

enabling readers to engage with the research and its findings with clarity and comprehension.

Address: It consists of a string of letters and numbers. In the conventional method, just like a

sender needs the receiver’s account number to send a cryptocurrency, a sender needs the address

of a receiver (Antonopoulos, 2014).

2014).

DAO: Decentralized Autonomous Organization. A company or an organization operates without

hierarchical management (Antonopoulos & Wood, 2018).

once. Bitcoin protects against double spending by verifying each transaction added to the

2014).

Miner: A network node finds valid proof of work for new blocks by repeated hashing

(Antonopoulos, 2014).

Network: A peer-to-peer network that propagates transactions and blocks to every Bitcoin node

on the network (Antonopoulos, 2014).

Proof of Work: A piece of data requiring significant computation. For example, Bitcoin miners

must find a mathematical solution to the SHA256 algorithm that meets the difficulty target across

Public Key: A number derived using a one-way function from the private key, which anyone can

(Antonopoulos & Wood, 2018).

hypotheses, the methodology used to answer these research questions, assumptions, and

limitations of the study.

Chapter Two's literature review.

Chapter Two consists of a literature review of Blockchain technology and cryptocurrency

as its application, including the studies used to identify the research gaps. Chapter two includes

Chapter Two

studies with the reader and provide a framework for establishing the importance of the study as

well as a benchmark for comparing the results with other findings (Creswell & Creswell, 2018).

Cooper (2015) mentioned four types of literature reviews that integrate what other researchers

have done and said, criticize previous scholarly works, build bridges between related topics, and

identify the central issues in a field. Quantitative research includes substantial literature to guide

the research questions or hypotheses (Creswell & Creswell, 2018). Wang et al. (2020) stated that

the research methodology contains two main parts: examining the literature and conducting a

pilot study. Hence, an extensive literature review was conducted on blockchain technology, its

characteristics, and cryptocurrency as its application.

Chapter two included the background of blockchain, its characteristics, brief information

about some cryptocurrencies, and research variables. The theoretical details of the technology

technology and its applications as a cryptocurrency.

Ghonimy (2021) revealed that identifying the main factors influencing the new

technologies would assist organizations in smooth transitions to adopt new technologies like

Blockchain technology. Conrad (2009) acknowledged that studying the factors influencing

individual attitudes toward using new technology begins with examining the existing literature

regarding these factors to gain insight into blockchain technology and cryptocurrency as its

application, advantages, and challenges. The Grover M. Hermann Library from the University of

the Cumberland was the leading resource for the academic literature review articles and research

papers, which consists of 105 Databases such as APA PsysARTICLES, APA PsycBOOKS, APA

PsysINFO, APA PsysTESTS, IEEE Electronic Library (IEL), Statista, Purdue OWL – APA,

ProQuest Dissertation and Theses. The additional resources include but are not limited to

Figure 4 shows the workflow of blockchain transactions. Fang et al. (2022) defined a

blockchain as a series of immutable data records with timestamps managed by a cluster of

account (Fang et al., 2022). For example, when person A pays X Bitcoins to person B, a

transaction gets signed by person A using its private key, and transactions are broadcast on the

network.

Figure 5 shows an example of the digital signature used in blockchain. Khan et al. (2021)

defined the digital signature as asymmetric cryptography deployed in a trustless environment.

The signing and verification phases are the two typical digital signature phases involved (Zheng

public key cryptography used in Bitcoin. It verifies that a message created by the owner of the

key is only accessible to its owner for signing in the transactions, and the public key is visible to

function Alice used while signing the transaction (Zheng et al., 2018).

Figure 5

Blockchain digital signature example

Note. Adapted from “Blockchain challenges and opportunities: A survey” by Zheng, Z., Xie, S.,

Dai, H. N., Chen, X., & Wang, H., (2018), International journal of web and grid services, 14(4),

p. 356. Copyright 2018 by Inderscience Enterprises Ltd.

Nakamoto provided an initial workflow on how the Bitcoin system will prevent

doublespending attacks (Garay et al., 2020). A double-spending attack can be prevented by

introducing a confirmation lag into transactions by waiting for some blocks before completing

the transactions, making it difficult to alter the transactions (Chiu & Koeppl, 2017; Hellani et al.,

2018). The two types of consensus mechanisms are proof-based and voting-based mechanisms

(Zheng et al., 2018). The proof-based consensus mechanism is utilized in public or

permissionless blockchains, whereas the voting-based consensus mechanism is used in the

private blockchain (Zheng et al., 2018). Proof-of-work (PoW) and Proof-of-stake (PoS) are

examples of the proof-based mechanism, whereas the Practical byzantine fault tolerance (PBFT)

is an example of the voting-based mechanism (Zheng et al., 2018). Miners compete to solve a

cryptographic puzzle to receive a Bitcoin reward, resulting in a new block to add to the Bitcoin

blockchain called a proof-of-work (Bratspies, 2018).

consumption, Proof-of-work blockchain protocols suffer from their low throughput; therefore,

the blockchain community has proposed various consensus methods like Proof-of-Stake (PoS),

Delegated Proof-of-Stake (DPoS), Stellar Consensus Protocol (SCP), Proof of Authority (PoA),

Proof of Elapsed Time (PoET), and Proof of Retrievability (PoR) (Hafid et al., 2020).

Cryptocurrency

In the following section, the main aim is to understand cryptocurrency, its characteristics,

advantages, and disadvantages. Understanding the difference between virtual currency, digital

currency, and cryptocurrency is essential (Piedade, 2018). While elaborating on the difference,

Piedade (2018) stated that a virtual currency is a type of unregulated digital money issued and

created and stored. Doran (2014) defined cryptocurrency as a decentralized medium of exchange

that uses cryptographic functions to conduct financial transactions. Cryptocurrencies use

blockchain technology to gain decentralization, transparency, and immutability features (Fang et

Cryptocurrencies are digital exchanges based on cryptography to secure ownership and

transactions and create new means of currency (Stavros, 2017). Bitcoin was the first

cryptocurrency launched in 2009 and is still one of the most widely accepted and circulated

cryptocurrencies (Stavros, 2017). Cryptocurrencies are decentralized, as these cryptocurrencies

are not controlled by any government or financial institutes like other currencies (Janssen et al.,

2020). Although Blockchain technology proliferates across different industries, like logistics

operations, manufacturing, public sectors, and financial services, Cryptocurrency is the most

dominated the cryptocurrency market (Piedade, 2018). Stavros (2017) mentioned that the Bitcoin

network, a distributed payment system, contains all valid transactions with additional details of

software license validation methods can protect software copyright (Herbert & Litchfield, 2015).

One vital advantage cryptocurrency provides is that the transactions are instantaneous and

borderless; unlike traditional transaction methods, which restrict users by business hours,

or amount of their transactions (Huang, 2015).

Bitcoin uses cryptography features like public and private keys and the cryptographic

validation of the transactions (Stavros, 2017). Public keys are known in the whole network,

whereas private keys are only known by their owners, and both keys are used for message

encryption (Stavros, 2017). Participants' private and public keys are needed for transactions to

occur in the Blockchain-based cryptocurrency ecosystem (Bugbee, 2019). Litecoin, also called

the silver to Bitcoin’s gold, was launched in 2011 due to its higher supply, and Ripple was

the countries working on issuing their own national cryptocurrencies. The economic reasons

include enhancement of the overall financial situation, establishing a digital analog of their

current money that would allow them to circumvent sanctions, take cash turnover under control,

tracking the movement of digital money, fight with black market cash transactions and tax

integrate smart contracts in the infrastructure, which would reduce the costs for intermediaries,

make the process faster to make deals between two entities without excessive bureaucracy, and

ICOs (Initial Coin Offering) help in raising venture capital for the startup companies in the form

Cryptocurrency businesses raise money through an initial coin offering (ICO), an

unregulated crowd-funding alternative to the regulated capital-raising process performed by

venture capitalists, banks, or stock exchanges (Silva & Mira da Silva, 2022). Initial Coin

Offerings (ICO) can be described as a non-regulated means to raise funds for a new

cryptocurrency venture, which involves the opportunity for individual investors to exchange

currency such as US dollars or cryptocurrencies in return for a digital asset labeled as a coin or

token (Piedade, 2018). The typical procedure for a start-up is to publish a white paper with the

& Mira da Silva, 2022). The unregulated characteristics of cryptocurrency markets lead to

volatility in the market, which results in significant theft, fraud, and price manipulation (Silva &

Mira da Silva, 2022). McCorry (2018) mentioned that Bitcoin and Ethereum are the most widely used

and successful cryptocurrencies, with market capitalizations of 37 billion US Dollars and 21 billion US

Dollars, respectively, in June 2017.

Cryptocurrencies have been described as “one of the greatest technological breakthroughs

Road, a well-known online Black-market site, exploited the anonymity of Bitcoin to engage in

illegal activities (Huang, 2015). Huang (2015) mentioned that it has become very important for

government regulatory organizations to update with the rise of cryptocurrencies. Although the

government has shut down Silk Road, the other black-market websites have replaced its place

and continue to operate and exploit cryptocurrencies for illegal activities (Huang, 2015). Bugbee

(2019) stated that limiting factors must be addressed for cryptocurrencies to achieve widespread

acceptance and usage; however, the very design of the blockchain system is currently of

detriment to scalability without altering fundamentals of consensus protocol and anonymity.

Well-known Cryptocurrencies

The main objective of this section was to introduce emerging cryptocurrencies and how

the well-known cryptocurrencies were analyzed in detail, allowing us to understand how these

cryptocurrencies have emerged and have been evolving, and have their unique characteristics.

Bitcoin was introduced by the pseudonym developer Satoshi Nakamoto in 2008, and in

same funds to two receivers, and it is seen as one of the most critical issues in cryptocurrencies

(Antonopoulos, 2014). A person can buy Bitcoin using real money or exchange other

cryptocurrencies exchange for selling products or services or through mining (Huang, 2015).

The decentralized characteristic of cryptocurrencies is the main differentiating factor

from traditional currencies (Bugbee, 2019). The Bitcoin ecosystem allows transactions to be

conducted quickly, globally, and pseudo-anonymously (Bugbee, 2019). Bugbee (2019) provided

an example of how two entities may utilize Bitcoin technology to perform transactions compared

to the traditional way. If Company A, a Canadian business, wants to perform the transaction with

Company B in Great Britain, there is an urgency on both sides to get this transaction done, as

delay will cost each company (Bugbee, 2019). The traditional way of performing the transaction

taxation, currency conversion costs, and there’s no anonymity; also, this transaction would take a

few days to weeks to be fully authorized and executed (Bugbee, 2019). On the contrary, If

maintaining anonymity, and the transaction would be executed in 15-20 minutes (Bugbee, 2019).

Basically, the Bitcoin ecosystem provides the ability to conduct transactions freely and globally

(Bugbee, 2019).

computing power of a computing node. Anyone with sufficient computing power can join the

mining network to mine Bitcoin and leave it anytime (Sterry, 2012). Mining Bitcoin is a core

function in the governance of the Bitcoin blockchain. Participants who provide computing power

to help solve the cryptographic problem on the blockchain are called miners (Sterry, 2012).

Consensus protocol affirms that when 51% of miners agree on the nonce and hash of the block,

the block is accepted (Bugbee, 2019). Once the consensus protocol is attained, all the

transactions for that block are executed and recorded permanently within the block (Bugbee,

(Huang, 2015). This mining procedure for cryptocurrencies is based on the Proof-of-Work

consensus mechanism, which depends on the cryptocurrencies, as different cryptocurrencies use

different consensus mechanisms. Transactions are verified through mining, avoiding

doublespending issues, and miners collect transaction fees as a reward (Sterry, 2012).

given in 2009 as a reward to miners who helped solve complex mathematical problems to verify

(Sterry, 2012). This incentive process helps to generate 210,000 blocks of Bitcoin every four

years. Powerful GPUs, FPGA, and ASIC systems are being used for Bitcoin mining, making it a

very energy-consuming procedure (Sterry, 2012). In 2009, when the first Bitcoin block was

created, the mining process was easy as there was no need to go over previous ledgers. As more

users started utilizing the system, the mining process became more complex and expensive as

time passed (Sterry, 2012). McCorry (2018) revealed that Bitcoin does not support the creation

of smart contracts due to its limited scripting feature, which led to the rise of Ethereum, the

second most popular cryptocurrency in terms of market capitalization.

Ethereum came to life after Vitalik Buterin’s white paper and crowd-funding campaign

(Boucher, 2017). Ethereum provides the ability to develop decentralized software protocols by

creating custom software contracts on the Ethereum blockchain. Ethereum is a decentralized

computing platform with its Turing-complete programming language (Hileman & Rauchs,

per second (TPS), which is comparatively less compared to other transaction processing

alternatives like VISA (2000 TPS typical, 10000 TPS peak), and the Twitter network performs

with high-frequency trading. All blocks are stored on every node in the distributed system of the

blockchain, which also creates the issue of speed and scalability. To resolve this limitation, the

size of each block should be increased, leading to other problems like size and blockchain bloat

block is 10 minutes, meaning it will take a minimum of 10 minutes for the transaction to be

confirmed, while for a VISA, it takes just a few seconds. However, it will take longer for large

transactions as it must outweigh the cost of a double-spend attack- the same coins are spent

multiple times (Swan, 2015). Therefore, the tradeoff between block size and security has become

another challenge (Zheng et al., 2018). The current size of a Bitcoin blockchain is 160GB, which

already takes a long time to download. If processing speed increases to 2000 TPS (VISA

standards), it would be 1.42 PB/year. As a result, it will lead to a problem called “bloating”

(Swan, 2015).

prioritize transactions with a high transaction fee. A larger block size would eventually slow the

propagation speed, leading to blockchain branches, so the scalability problem is critical (Zheng

et al., 2018).

As a blockchain grows, the system's scalability can be compromised because only users

with large storage spaces and computational power can partake in the blockchain as miners or

every block in the blockchain. On the other hand, light nodes only store block headers, including

the previous block's hash, the Merkle Root, and the nonce (Zheng et al., 2018). By storing the

block header, the light node can verify that certain transactions have not been altered without

as required (Zheng et al., 2018).

Rustem et al. (2019) highlighted the usage of cryptocurrencies in criminal activities and

why there’s a need to introduce criminal laws to prevent unlawful encroachment in

cryptocurrency (Rustem et al., 2019). The anonymity of using cryptocurrencies, decentralized

functionality, and lack of legal laws and regulations are a few factors that fuel the participation of

cryptocurrencies in criminal activities. The use of malicious tools and software to generate

cryptocurrency illegally, DDoS attacks, laundering, illegal trafficking of narcotics, drugs,

factors influencing the decision to adopt the new technology. This section provided an

abbreviated description of these three frameworks. The main objective of this study is to measure

the influence of the five factors, the perceived usefulness, ease of use, social influence,

understanding how users adopt new technology and information systems. Park and Park (2020)

variables to examine tweets using social media analytics with data mining and statistical

approaches. Hebron (2008) reviewed the technology acceptance model, unified theory of

utilized the extended technology acceptance model to measure students’ beliefs about using the

online learning system.

Unified Theory of Acceptance and Use of Technology (UTAUT)

Venkatesh et al. (2003) theorized that four constructs, performance expectancy, effort

voluntariness of use are four key moderators. The Unified Theory of Acceptance and Use of

(UTAUT) is based on TAM (Wibowo, 2019).

Figure 6

Unified Theory of Acceptance and Use of Technology (UTAUT) Framework

which technology could be assessed and outcomes defined based on known models of user

behaviors and past research (Hebron, 2008).

proposed a theoretical framework for the adoption of blockchain technology. Iftikhar et al. (2021)

scalability concern, top management support, competitive pressure, and regulatory policy,

categorized in the technological, organizational, and environmental context. The UTAUT model

was instrumental in identifying the rate of adoption of IT (Hebron, 2008).

innovation, communication channels, time, and social systems. Rogers (2010) defined innovation

as an idea, practice, or object that is perceived by an individual. Rogers (2010) defined

communication as a process in which participants create and share information with others in

order to get a common understanding. Rogers (2010) defined diffusion as the process by which

an innovation is communicated and implemented over time through specific channels among the

rate. Two of these five attributes, Compatibility and Complexity, are the focus of this study from

the DOI model and are discussed in detail in this chapter.

persons who adopt new technologies (Hebron, 2008). Rogers (2010) mentioned that Innovators,

terms of readiness to respond to technology. Rogers (2010) exposed that most innovations follow

an S-shaped Rate of Adoption when plotted, where the X-axis indicates the time, and the Y-axis

indicates the adoption. Conrad (2009) mentioned that many diffusion publications referenced

(2010) exposed that innovation adoption follows a sequence of processes through which an

scenarios and used in different conditions in which new technologies were integrated and user

adaptation was predicted (Hebron, 2008). Khasawneh (2021) used diffusion of innovations to

measure factors affecting technology adoption of virtual conference software in the workplace

Research Variables

This research was designed to explore the relationship between the variables influencing

and future market players in focusing on the most important features a cryptocurrency should

have. This study was expected to encourage future academics to study other research variables

beyond those described in this study.

Perceived usefulness (PU) is the degree to which an individual believes a particular

system would improve the individual’s performance (Davis, 1989). Park and Park (2020)

described the perceived usefulness as an indication of how much users believe using the new

technology improves their task achievement ability. McConnell (2009) illustrated that many

researchers agree that individual beliefs are the base for attitude toward technology acceptance,

influence the consumers’ intention to use contactless payments, and this can assist in optimizing

the user experience for all types of technology users (Carpreau, 2023).

organizational and technical infrastructure to use a specific technology (Venkatesh et al., 2003).

will enable certain behaviors to be performed with ease (Triandis, 1979). Triandis (1979) stated

that facilitating conditions directly affect the actual behavior instead of the intentions, as one may

have the intention to perform a particular behavior, but if the environment does not support this

behavior, then it will probably not be executed.

Perceived risk is defined as consumers’ perception of the degree of uncertainty and

the three most essential factors in using cryptocurrency. Brown and Douglass (2020) conducted

a study on the costs of cryptocurrencies over three days when news of cryptocurrency thefts was

published and concluded that cryptocurrency prices rose after the robbery report, which showed

Financial literacy affects a person's economic decision-making. Research studies show

that financial knowledge is an essential predictive factor in financial behaviors (Van Rooij et al.,

and the ability to utilize that knowledge and other financial skills to manage financial resources

effectively for a lifetime of financial well-being (Stolper & Walter, 2017). Life expectancy and

instruments; as cryptocurrency is considered a technological financial instrument, financial

literacy is one of the research variables that influence the adoption of the technology (Carlin et

al., 2017).

Summary

The literature review chapter provided a detailed overview of Blockchain technology, its

applications, cryptocurrencies, and factors affecting blockchain-based cryptocurrency adoption.

The literature review contained the theoretical frameworks of the technology acceptance model

(TAM), the unified theory of acceptance and use of technology (UTAUT), and the diffusion of

innovations (DOI) to study the intention to use new technologies. The literature review showed

the significance of studying different factors influencing the adoption of blockchain technology.

The research study also included information about the previously conducted studies with their

of the research designs used to evaluate the adoption of new technologies.

Chapter Two provided a detailed review of the literature and the theoretical frameworks

associated with this study. A critical analysis of theoretical and empirical literature revealed a

Hypotheses were developed to examine how specific factors affect the adoption of

cryptocurrencies. Based on the theoretical framework and research hypotheses, a conceptual

technology acceptance model was generated for this non-experimental quantitative research

collection, and data analysis using statistical methods.

Chapter Three

Procedures and Methodology

discipline (Chilisa & Kawulich, 2012). Schwandt (2001) defines a paradigm as a shared

worldview representing beliefs and values in a specific field and guiding how problems are

solved. A paradigm represents a particular way of thinking shared by a scientific community in

solving issues in their areas (Kuhn, 1974). A paradigm is the commitments, beliefs, values,

methods, outlooks, and so forth shared across a specific discipline (Schwandt, 2001, p. 183).

Creswell and Creswell (2018) explained that there are three main frameworks for

research: Philosophical worldviews, Research Designs, and Research Methods. Creswell and

Constructivism, (c) Transformative, and (d) Pragmatism. The postpositivist research paradigm

quantitative research (Creswell & Creswell, 2018).

The constructivist approach, primarily used in qualitative research, focuses on the specific

contexts in which people live and work to understand the historical and cultural settings of the

participants and replies as much as possible to the participants’ views of the situation being

studied (Creswell & Creswell, 2018). Transformative paradigm aims toward the research where

research inquiry needs to be intertwined with politics and a political change agenda to confront

blockchain-based cryptocurrencies. Surveys, experiments, and questionnaires are a few

quantitative methods used to get numerical data for a population's trends, attitudes, or opinions

(Khaldi, 2017).

The postpositivist paradigm intends to reduce the ideas into a small, discrete set to test the

variables that comprise hypotheses and research questions by developing numeric measures of

observations and studying the behavior of individuals, also by testing the theories that govern it

(Creswell & Creswell, 2018). The postpositivist paradigm begins with a theory, requires data

collection, which supports or refutes the theory, necessary revisions, and contains additional tests

(Creswell & Creswell, 2018).

This research aimed to determine the extent to which five constructs, perceived

usefulness, perceived ease of use, compatibility, complexity, and social influence, predict the

intention to adopt blockchain-based cryptocurrencies in the United States. The relationship

among variables was established, and research questions and hypotheses were posed for the

Research Design

integrating qualitative and quantitative research and data in a research study (Creswell &

Creswell, 2018).

Creswell and Creswell (2018) mentioned that selecting a research approach depends on

the nature of the research problem or issue being addressed, the researchers’ personal

experiences, and the audiences for the study. Creswell and Creswell (2018) illustrated that if the

research problem requires the identification of factors that influence an outcome, the utility of an

testing objective theories by examining the relationship among variables, where these variables

can be measured using instruments and can be analyzed using statistical procedures. Creswell

and Creswell (2018) discussed two quantitative design approaches, surveys, and experiments,

population by studying a sample of the population, which includes cross-sectional and

longitudinal studies using questionnaires for data collection to collect data from a sample

population; In contrast, experimental research determines if a specific treatment influences an

outcome.

R. Li et al. (2017) used the non-experimental quantitative research design with a

predictive approach to identify the relationship between two or more variables using appropriate

statistical analysis. The correlation research design measures relationships between variables or

which five independent variables, PU, PEOU, COMPB, COMPX, and SI, that are measurable

and quantified, predict the intention to adopt Blockchain-based cryptocurrencies’; that’s why the

non-experimental quantitative with predictive correlation approach is a more appropriate

research design for the study.

The research’s target population consisted of individuals familiar with blockchain-based

cryptocurrencies who reside in the United States and must be at least 18 years older. Identifying

the recruiting method and the suitable participant for the dissertation is a critical task (Lunenburg

& Irby, 2008). Piedade (2018) stated that the estimated number of cryptocurrency users is

difficult to determine, and it is even difficult to estimate a value, as one of the most important

characteristics of cryptocurrencies is the anonymity they provide to their users. According to the

(EllardGray et al., 2015). Coinbase, one of the biggest cryptocurrency exchange platforms,

conducted an online survey in the United States and found that 20% of Americans own

cryptocurrency, translating to approximately 52.3 million American adults owning

cryptocurrency in 2022. The researcher used SurveyMonkey’s Target Audience service to get

random sample data from the population.

of the results (Sanders et al., 2018). Determining the correct sample size is one of the critical

components of the research study (Daly & Bourke, 2008). Recruiting the whole population for

model (Kelly et al., 2010). When sample sizes are too small, there’s the risk of not collecting

enough data to validate the research hypotheses. The result may show that variables aren’t

statistically significant when they might be. The researcher may miss the subjects whose answers

money if the sample size was too large, and large sample sizes don’t always guarantee the

accuracy of the result (Fowler & Lapp, 2019).

SurveyMonkey was presented with the inclusion criteria, such as users at least 18 years

old residing in the United States and familiar with blockchain-based cryptocurrencies. The

SurveyMonkey Target Audience service was used to get random sample data from the

participants, and there was no interaction with the participants. The SurveyMonkey Audience

allows one to easily select and purchase a target audience based on specific attributes. The

or quit the survey at any time without determent. All the survey questions were required to be

answered to complete the survey successfully. Multiple responses were turned off in the

SurveyMonkey collector settings to disable a survey from being taken multiple times from the

same device. To ensure all participants’ anonymity and privacy, the SurveyMonkey survey was

configured not to include the IP address of the respondent.

The power analysis for sample size determination should occur during the study planning

available statistical power analysis tool to calculate effect sizes and display graphical results of

participants’ sample size with an effect size of f 2 0.15, a power confidence interval of 1-β error

probability = 0.95, and an error probability of α = 0.05.

A two-tailed alpha value (α) is called a Type I error rate, which refers to the risk

associated with saying that we have a real non-zero correlation when this effect is not real or a

false positive effect (Creswell & Creswell, 2018). A beta value (β), also called Type II error rate,

refers to the risk associated with saying that we do not have a significant effect when there is a

significance testing, confidence intervals, and effective sizes while the results for the research

questions or hypotheses. Statistical significance testing assesses whether the observed scores

reflect a pattern other than chance (Creswell & Creswell, 2018). A confidence interval indicates a

independent variables are added (Wokke & Rodenrijs, 2018). Mertler and Vannatta (2016)

mentioned that the model summary table, ANOVA table, and coefficient table are the main

that multiple regression is the same as simple regression, except there are two or more predictors,

and each predictor has its own coefficient. The outcome variable is predicted from a combination

of all the variables multiplied by their respective coefficients plus a residual term (Field, 2013).

Yi = (b0 + b1Xi1 + b2Xi2 + . . . + bnXn) + εi

shows whether the model is significantly better at predicting the outcome than having no model

Arowolo (2017) used multiple regression analysis to test the relationship between six

technological and organizational factors to adopt cloud computing. Wokke and Rodenrijs (2018)

collected survey data using multiple regression analysis. The statistical importance of every

identified variable is easier to analyze using the multiple regression method (Iftikhar et al., 2021).

about a population based on a regression done on a sample (Field, 2013). The first assumption is

that all predictor variables must be quantitative or categorical, and the outcome variable must be

quantitative, continuous, and unbounded (Field, 2013). The second assumption is that all the

predictors should have some variation in their value or they do not have zero variances (Field,

2013). Other assumptions are the independence of observations, linear variable relationships,

Homoscedicity of residuals, no multicollinearity, no significant outliers, and normal residual

distribution, which are tested and described in more detail in the next chapter.

multicollinearity, no significant outliers, and residuals being normally distributed are the

Table 2

One of the multiple regression analysis assumptions is that the variables are independent

or not correlated (Field, 2018). The Durbin-Watson statistics test is used to determine the validity

correlated. A Durbin-Watson statistic test value of approximately 2 was obtained, which satisfies

the assumption of the independence of observation.

Table 3

variable. Figure B1 in Appendix B shows the plot of residuals of Intention to Use (IU) vs.

Perceived Usefulness (PU). Figure B2 in Appendix B shows the plot of residuals of Intention to

Use (IU) vs. Perceived Ease of Use (PEOU). Figure B3 in Appendix B shows the plot of

residuals of Intention to Use (IU) vs. Compatibility (COMPB). Figure B4 in Appendix B shows

the plot of residuals of Intention to Use (IU) vs. Complexity (COMPX). Figure B5 in Appendix

B shows the plot of residuals of Intention to Use (IU) vs. Social Influence (SI).

Figure 8 shows the plot of regression standardized residuals vs. regression standardized

predicted values. Figure 8 shows that the residuals are randomly and evenly dispersed throughout

Figure 8

The plot of Regression Standardized Residuals vs. Regression Standardized Predicted Values

variability or spread of a set of data (Field, 2018). Figure 8 shows the plot of residuals versus

predicted. The residual data points did not form the funnel shape or the curvilinear relationship.

The data points are randomly and evenly dispersed throughout the plot, indicating that the

assumption of homoscedasticity has been met. The balanced distribution of the residuals around

When there is a strong correlation between two or more predictors in the regression

model, Multicollinearity exists (Field, 2009). Multicollinearity can be identified using the

with the other predictor (Field, 2009). Bowerman and O’Connell (1992) suggested that if the

largest VIF value is greater than 10, there is cause for concern. Menard (2002) suggested that

tolerance values. Table 4 shows that the VIF values for the model are well below 10, and the

tolerance statistics are above 0.2, suggesting no collinearity within the data.

Table 4

Note. This table indicates the Collinearity Diagnostic for the predictors.

Outliners and high influence points were identified using the casewise diagnostics for

intention to use in JASP. Only one case had >3 standard residuals and was flagged as an outliner.

All cases had Cook’s distance less than 1, indicating no high influential points in the data.

Casewise Diagnostic for IU

Case Number Std. Residual IU Predicted Value Residual Cook's Distance

particular distribution's cumulative probability (Field, 2018). A Q-Q plot is very similar to a P-P

plot (Field, 2009). The P-P plot plots the quantiles of the data set instead of every individual point

Figure 4 shows that the standardized residuals fit along the diagonal, suggesting that assumptions

of linearity and normality have not been violated (Goss-Sampson, 2020).

Figure 9

from different United States users. The SurveyMonkey assures data quality by providing features

like accurate targeting, panel calibration, survey design review, bot detection, and response

Data collection was performed once permission was granted by the University of the

description of the study, the purpose of the study, the type of data collection, and any risk

involved in the informed consent form (Appendix C). Once the participants read and agreed to

the consent form, participants were provided with instrument questions. The instrument included

prescreening questions in the SurveyMonkey survey to ensure the quality of the data collection.

The prescreening questions aimed to confirm that participants were at least 18 years old, resided

in the United States, and knew about blockchain-based cryptocurrencies. After completing the

screening questions, participants had to answer a series of Likert-based instrument questions

ranging in scale from 1 (strongly disagree) to 7 (strongly agree) for various constructs.

Depending on the reliability of the survey responses from the participants, there are instances

where a 7-point scale may perform better than a 5-point scale, respective to the selection of the

including their IP address, to protect the anonymity of the participants.

The researcher needs to protect the privacy and confidentiality of all the participants

involved in a study (Creswell & Creswell, 2018). Anonymity and confidentiality are essential as

they protect the privacy of the research participants (Coffelt, 2017). No identifiable data was

collected from the participants for the research survey instrument. The Institutional Review

The researcher provided informed consent at the beginning of the survey to learn more about the

contact the researcher if the participants had any questions. The researcher allowed the

participants to leave or close the survey at any point. All the survey responses were downloaded

and copied to the encrypted drive. After three years, the researcher will delete the stored survey

data from the drive. All the collected data was only for scholarly purposes.

Confidentiality refers to modifying any personal or identifying information provided by

information (Coffelt, 2017). Participants learn about anonymity or confidentiality through the

their institution’s IRB to protect the participants (Coffelt, 2017).

A considerable amount of effort was put into designing the survey and ensuring the

validity of the data collection. SurveyMonkey provides features like bot detection and response

The survey instrument was administered on the third-party survey tool, The

SurveyMonkey. A total of 205 responses were received; however, 64 uncompleted responses

generalizable to any other population. The statistical significance of PU, PEOU, COMPB,

COMPX, and SI on the intention to use blockchain-based cryptocurrencies was determined using

hypothesis testing. The researcher used JASP 0.16.4 to perform statistical analysis on the data

The main aim of the data analysis was to test the hypotheses and test to what extent five

independent variables, PU, PEOU, COMPB, COMPX, and SI, influence the dependent variable

IU.

Descriptive Statistics was performed to review and validate the range for all questions.

The researcher examined the data for any missing data, although all the questions were

mandatory to answer. Creswell & Creswell (2018) mentioned that descriptive statistics analysis

contains the means, standard deviations, frequencies, and range of scores for all independent and

frequency, percentage, mean, and standard deviation. Table 6 shows the descriptive statistics of

Table 6

Descriptive Statistics of Variables

distribution by education indicated that most participants either had a bachelor’s degree (n=35)

or were in some college but had no degree (n=35), approximately 24.8% in each category.

equivalent (n=32), 12.8% had an associate degree (n=18), and 10.7% had a graduate degree

Table 12

Participants Distribution by Education

26 uncompleted survey responses were identified, and 38 participants were disqualified as they

didn’t match the screen questions’ minimum criteria. Therefore, 64 survey responses were

eliminated from the data analysis. A total of 141 responses were valid for data analysis. The

minimum number of participants required for the research was 138. The hypothesis testing

method was used to identify the statistical significance of PU, PEOU, SI, COMPB, and COMPX

on the intention to use blockchain-based cryptocurrencies. The researcher used the JASP 0.16.4

tool to perform statistical analysis on the collected data set. JASP is an open-source project

supported by the University of Amsterdam. The researcher obtained the data set from the

SurveyMonkey third-party platform in an Excel spreadsheet. The main aim was to test the

hypothesis and examine to what extent the five independent variables (PU, PEOU, SI, COMPB,

SurveyMonkey’s exported Excel spreadsheet data was reviewed and validated as a sanity

data. The collected data were in the form of a seven-point Likert scale, scaling from 1 (strongly

disagree) to 7 (strongly agree) for the given survey question.

H10. Perceived usefulness has no statistically significant influence on the decision to

adopt blockchain-based cryptocurrencies.

H2a. Perceived ease of use has a statistically significant influence on the decision to adopt

blockchain-based cryptocurrencies.

H30. Compatibility has no statistically significant influence on the decision to adopt

blockchain-based cryptocurrencies.

H3a. Compatibility has a statistically significant influence on the decision to adopt

blockchain-based cryptocurrencies.

H40. Complexity has no statistically significant influence on the decision to adopt

blockchain-based cryptocurrencies.

H50. Social Influence has no statistically significant influence on the decision to adopt

blockchain-based cryptocurrencies.

H5a. Social Influence has a statistically significant influence on the decision to adopt

blockchain-based cryptocurrencies.

H10. Perceived usefulness has no statistically significant influence on the decision to

adopt blockchain-based cryptocurrencies.

H1a. Perceived usefulness has a statistically significant influence on the decision to adopt

perceived usefulness positively correlated with

the intention to use Blockchain-based cryptocurrencies (B = 0.701). Therefore, the greater the

cryptocurrencies. The p-value calculated for the perceived usefulness in the multiple regression

model was below the 0.001 significance level. Since the p-value < 0.05, the perceived usefulness

expected to increase the adoption of Blockchain-based cryptocurrencies by a factor of 0.701

when other independent variables are kept constant.

The multiple regression model results showed that perceived usefulness (PU) was a

statistically significant predictor of intention to use Blockchain-based cryptocurrencies (β =

0.624, t (134) = 7.322, p < .05). The statistically significant result for perceived usefulness

showed that the null hypothesis H1o stating that the perceived usefulness has no statistically

95% confidence level. The alternative hypothesis H1a, stating that the perceived usefulness has a

statistically significant influence on the decision to adopt blockchain-based cryptocurrencies,

was accepted.

Research Question Two

statistically significant predictor of intention to use Blockchain-based cryptocurrencies (β =

0.181, t (134) = 2.224, p < .05). The statistically significant result for perceived ease of use

showed that the null hypothesis H1o stating that the perceived ease of use has no statistically

95% confidence level. The alternative hypothesis H1a, stating that the perceived ease of use has a

statistically significant influence on the decision to adopt blockchain-based cryptocurrencies,

was accepted.

Research Question Three

Research question three asked, to what extent does compatibility influence the intention

H30. Compatibility has no statistically significant influence on the decision to adopt

blockchain-based cryptocurrencies.

H3a. Compatibility has a statistically significant influence on the decision to adopt

intention to use Blockchain-based cryptocurrencies (B = -0.084). Therefore, the greater the

The multiple regression model results showed that compatibility (COMPB) was not a

statistically significant predictor of intention to use Blockchain-based cryptocurrencies (β =

0.058, t (134) = -0.654, p > .05). The statistically significant result for compatibility showed that

the null hypothesis H1o stating that the compatibility has no statistically significant influence on

the decision to adopt blockchain-based cryptocurrencies is accepted at 95% confidence level.

The alternative hypothesis H1a, stating that compatibility has a statistically significant influence

on the decision to adopt blockchain-based cryptocurrencies, was rejected.

H4a. Complexity has a statistically significant influence on the decision to adopt

blockchain-based cryptocurrencies.

The multiple regression revealed that the complexity negatively correlated with the

intention to use Blockchain-based cryptocurrencies (B = -0.016). Therefore, the greater the

complexity of cryptocurrencies at the user level, the less the intention to use

cryptocurrencies. The p-value calculated for the complexity in the multiple regression

the regression equation, a change in the complexity is expected to decrease the adoption of

are kept constant.

the null hypothesis H1o stating that the complexity has no statistically significant influence on

The alternative hypothesis H1a, stating that complexity has a statistically significant influence on

Research question five asked, to what extent does Social Influence influence the intention

blockchain-based cryptocurrencies.

H5a. Social Influence has a statistically significant influence on the decision to adopt

blockchain-based cryptocurrencies.

The multiple regression revealed that social influence positively correlated with the

intention to use Blockchain-based cryptocurrencies (B = 0.186). Therefore, the greater the social

influence of cryptocurrencies at the user level, the greater the intention to use cryptocurrencies.

The p-value calculated for the social influence in the multiple regression model was 0.019. Since

the p-value < 0.05, the social influence is statistically significant. In the regression equation, a

2.367, p < .05). The statistically significant result for social influence showed that the null

the decision to adopt blockchain-based cryptocurrencies, was accepted.

Multiple regression analysis was used to answer the research questions. Initially, multiple

linear regression models were assessed, including all the assumptions. Additionally, assumptions

like linearity, homoscedasticity, multicollinearity, and independence of observation were

evaluated. The results of the multicollinearity table, scatterplot, and Q-Q plot showed that all

those assumptions were met. The difference between R² and adjusted R² in Table 7 indicated that

the cross-validity of the model is very good.

The ANOVA (Analysis of Variance) table shows that the F-statistic is 81.071, p<0.05,

which indicates that the model is significant in predicting the outcome variable and that the

model is a significant fit to the data overall. The B-values for perceived usefulness (PU),

perceived ease of use (PEOU), and social influence (SI) indicated a positive relationship,

Perceived Usefulness (β = 0.624, t (134) = 7.322, p < .05)., Perceived Ease of Use (β = 0.181, t

(134) = 2.224, p < .05), and Social Influence (β = 0.177, t (134) = 2.367, p < .05) were

research questions one, two, and five was rejected, and the alternative hypothesis was accepted.

Compatibility (β = -0.058, t (134) = -0.654, p > .05) and Complexity (β = -0.010, t (134) =

the intention to adopt Blockchain-based cryptocurrencies. The null hypothesis of research

question three was retained or accepted.

by Kamble et al. (2021) found compatibility to be a statistically significant factor in Blockchain

adoption, which differs from this study’s result. Kamble et al. (2021) stated that compatibility

traditional payment system exists, which could be a possible explanation as participants find

According to Kamble et al. (2021), the degree of compatibility between blockchain

these elements, the higher the likelihood of blockchain technology being perceived as useful. As

Research Question Four

to the rate of adoption of innovation (Rogers et al., 2014). Research question four stated to what

extent complexity influences the intention to use cryptocurrencies. The null hypothesis of

significant predictor of the intention to adopt Blockchain-based cryptocurrencies. The null

hypothesis of research question four was retained or accepted.

Rogers’s (2010) Diffusion of Innovations Theory regarded complexity as a significant

complexity was statistically insignificant and negatively correlated to adopting blockchain-based

cryptocurrencies. Complexity is inversely proportional to perceived ease of use and perceived

usefulness (Gangwar et al., 2015). The study analysis aligns with the studies of “N. Liu and Ye

technologies, which could be a possible explanation as participants find blockchain-based

Research Question Five

According to UTAUT, social influence is one of the factors that influences behavioral intention

to use technology (Venkatesh et al., 2012). Research question five stated to what extent does

the social influence (B = 0.186, t (139) = 2.37, p < 0.5) was a statistically significant predictor of

the intention to adopt Blockchain-based cryptocurrencies. The null hypothesis of research

question five was rejected in support of the alternative hypothesis stating that social influence

statistically significantly influences the decision to adopt blockchain-based cryptocurrencies.

Albayati et al. (2020) proposed the technology acceptance model with new external

variables such as trust, regulatory support, social influence, design, and experience. Venkatesh et

al. (2003) mentioned that Social Influence constructs matter, and they are significant when used

behavior through compliance, internalization, and identification (Venkatesh et al., 2003).

significant factor in blockchain adoption. However, the result differs from “Arias-Oliva et al.

in blockchain technologies. Organizations can utilize Social Influence to promote the advantages

of cryptocurrencies and can strategize to leverage Social Influence by promoting

choices of important ones can influence users (Kumari et al., 2023).

Limitations of the Study

The research limitations are often related to the study methods, which include inadequate

the limitations mentioned in Chapter One. The research is quantitative and utilizes hypothesis,

Quantitative analysis aims to analyze data and measure quantifiable variables. However, it is

the third-party tool SurveyMonkey to conduct an online survey, and its service, SurveyMonkey

countries or regions might yield different results.

The SurveyMonkey might have incentivized the SurveyMonkey audience to participate

in the study, so some participants might participate only to get incentives, not to contribute to the

demographic questions might have prevented an opportunity to collect more information about

the research participants. Likert scales for this quantitative study might have limited the inclusion

data collected during the research. It is therefore recommended to take measures to ensure that

participants are adequately informed and educated about the subject matter prior to their

participation.

capturing the larger population's diversity and variability. To improve the study's generalizability,

a larger sample size could be considered, which would provide more representative and reliable

data.

Implications for Future Study

the findings for practice and future research. It is helpful to discuss the theoretical and practical

consequences of the results and highlight the potential alternative explanations for the research

cryptocurrencies. Furthermore, examining the factors influencing the decision to adopt

path to a smooth transition to replacing old and new technologies (Ghonimy, 2021).

limitations that provide future research opportunities in this field. While this research was

Summary

considered five independent variables: perceived usefulness (PU), perceived ease-of-use

(PEOU), compatibility (COMPB), complexity (COMPX), and social influence (SI), while the

blockchainbased cryptocurrencies were scarce, and a gap in the literature on the factors affecting

cryptocurrency consumers in the United States using SurveyMonkey, and the collected data were

influence were statistically significant predictors, whereas compatibility and complexity were not