Behavioral and Psychological Determinants of Cryptocurrency Investment: Expanding UTAUT with Perceived Enjoyment and Risk Factors

Abstract

With their potential for high returns and expanding role in the financial landscape, cryptocurrency investments have garnered the attention of the financial press and investors. Applying an integrated research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study investigates the factors influencing individual investors’ attitudes toward cryptocurrency investments and their intention to continue investing. The model incorporates constructs such as performance expectancy, effort expectancy, social influence, perceived risk, perceived privacy, technology competency, perceived enjoyment, and prior experience. Data from 506 cryptocurrency investors located in the United States were collected through a 50-item questionnaire. The findings indicate that performance expectancy and perceived enjoyment positively impact attitudes toward cryptocurrency investments, which, in turn, influence the intention to continue investing. Perceived privacy positively affects performance expectancy, while technology competency enhances effort expectancy. These results offer valuable insights for policymakers and cryptocurrency exchanges to foster sustainable growth in the cryptocurrency market. Despite its contributions, the study acknowledges limitations, including a focus on current investors in the US and the exclusion of factors such as optimism and innovativeness. Future research should explore these aspects across different populations and regions to gain a more comprehensive understanding of cryptocurrency investment behavior.

1. Introduction

Many industries worldwide, including the financial industry, have been significantly reshaped over the past two decades by advances in technology (Bland et al. 2024). These technological advances have merged with the finance industry, giving rise to a series of new products, including online banking, mobile banking, online payments, mobile payments, online commerce, mobile commerce, and peer-to-peer financing (Changchit et al. 2024). The benefits of these innovations have been experienced by individuals and organizations alike, including reduced transaction times and costs, increased flexibility and financial inclusion, and enhanced user experiences through anytime, anywhere interaction.

The combination of technology and finance has given rise to the FinTech industry, within which cryptocurrencies have emerged as alternatives to traditional currencies (Tariq 2024). Cryptocurrencies such as Bitcoin, Ethereum, Litecoin, and Ripple were created using cryptographic algorithms and serve as mediums of exchange and stores of value (Iqbal et al. 2021; Hasan et al. 2022). Unlike traditional fiat currencies, cryptocurrencies are not physical notes or coins; they exist entirely in digital form, with ownership and all cryptocurrency transactions recorded in a digital ledger called the blockchain, protected by cryptographic algorithms (Miraz et al. 2022; Ter Ji-Xi et al. 2021).

The earliest and most famous cryptocurrency is Bitcoin, created in 2008 by a person or group under the alias Satoshi Nakamoto (Jariyapan et al. 2022). Bitcoin and other cryptocurrencies enable transactions without the need for financial intermediaries. As of this study, the current market capitalization of Bitcoin is approximately USD 1.28 trillion, while the total market capitalization of all cryptocurrencies is around USD 2.42 trillion (CoinMarketCap 2024). The ability to convert cryptocurrencies into traditional currency is still in its early stages. Currently, Bitcoin remains a subject of ongoing debate, with discussions centered on whether its overall impact is more positive or negative (Hairudin et al. 2022). Cryptocurrencies are attracting significant public interest, and the cryptocurrency market is expected to grow at a rate of over 60% annually, with the potential to account for about 10% of the world’s GDP. The growth of cryptocurrencies offers benefits to both individuals and organizations by facilitating quick, convenient, and low-cost transactions that are not limited by space or time and do not require financial intermediaries (Alomari and Abdullah 2023; Ramachandran and Stella 2022). Consequently, companies such as Overstock.com, eGifter, Newegg, Shopify, Dish, Roadway Moving Company, Microsoft, and CheapAir accept cryptocurrencies as a form of payment (Almajali et al. 2022).

Despite the benefits offered by cryptocurrencies, governmental perspectives on the topic vary (Namahoot and Rattanawiboonsom 2022). Some governments, such as those in Bulgaria, El Salvador, Ukraine, and Finland, are investing in cryptocurrencies, while many others express concerns about the use of digital currencies, particularly when they are used to finance money laundering, drug trafficking, or activities related to international terrorist organizations (Li et al. 2023). Nevertheless, governments are studying the intangible aspects of cryptocurrency technology in an effort to develop appropriate monetary policies, with some even considering launching government-regulated cryptocurrencies.

Although many scholars believe that cryptocurrencies offer advantages over traditional fiat currencies (Gil-Cordero et al. 2020), a surprising reluctance among individuals to invest in them remains. Previous studies have primarily focused on the technical aspects of cryptocurrencies, often neglecting the behavioral and psychological factors that influence cryptocurrency investment attitudes (Hariguna et al. 2023). Moreover, the relationship between attitudes toward cryptocurrency investment and the intention to continue investing remains relatively unexplored.

This study aims to address these gaps by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) to explain individual investors’ attitudes toward cryptocurrency investments. Additionally, the factor of perceived enjoyment is introduced to the base UTAUT model, given that cryptocurrency is considered a groundbreaking FinTech innovation with high potential profitability. Given the inherently risky nature of cryptocurrency investments, this research also integrates factors such as perceived risk, previous experience, perceived privacy, and technology competency to enhance the model’s predictive power.

The objectives of this study are to examine the influence of perceived privacy and perceived risk on performance expectancy; to explore the impact of technology competency on effort expectancy; and to investigate how performance expectancy, effort expectancy, social influence, previous experience, and perceived enjoyment affect attitudes toward cryptocurrency investment. Additionally, the study seeks to understand the relationship between these attitudes and the intention to continue investing in cryptocurrencies. This study provides both theoretical and practical implications for policymakers constructing laws and regulations to support the healthy and sustainable growth of cryptocurrencies. The results and implications of the study may provide insights into cryptocurrency exchanges to develop strategies to expand their investor base and enhance market liquidity.

The paper is structured as follows: The next section provides an overview of the basic characteristics of cryptocurrencies, including their development, functionality, and the technological advancements that support them. A detailed discussion of the underlying theoretical framework, specifically the Unified Theory of Acceptance and Use of Technology (UTAUT), and a review of existing research related to cryptocurrency investments follow. Subsequent sections introduce the integrated research model used in the study, outline the methodology employed to gather and analyze data, and present the results of the research. The paper then offers a comprehensive discussion of the findings, highlighting their implications for both theoretical understanding and practical applications. The paper concludes with a discussion of potential limitations of the study and suggests directions for future research to further explore and validate the insights gained.

2. Literature Review, Theoretical Framework, and Research Hypothesis

2.1. Prior Studies on Cryptocurrency

Bitcoin was introduced in 2008 and is considered the first cryptocurrency (Al-amri et al. 2023). As the leading examples of cryptocurrencies, Bitcoin, Ethereum, Litecoin, and Ripple each showcases unique features and functions (Wu et al. 2022). For instance, Ethereum operates on a decentralized blockchain platform designed to execute smart contracts and support various decentralized applications. Litecoin is seen as an improved version of Bitcoin, offering faster transactions at a lower cost. Ripple was developed to streamline cross-border payments and transactions between financial institutions.

Despite their distinctive features, cryptocurrencies share a common foundation in blockchain technology, which differentiates them from traditional currencies (Saputra and Darma 2022). One key difference is that cryptocurrencies operate within a decentralized system and are not subject to the financial regulatory oversight that applies to fiat currencies issued by central banks (Abbasi et al. 2021). Another difference is that the blockchain technology underlying cryptocurrencies utilizes asymmetric encryption, time validation, consensus mechanisms, and peer-to-peer communication to foster trust among network participants (Abbasi et al. 2021).

Blockchain technology is essentially a database maintained consistently across nodes, ensuring it is tamper-resistant (García-Monleón et al. 2023). It can be described as a distributed online ledger that records transactions transparently and publicly, making them easy to trace. These transactions are processed through computers and peer-to-peer communication and are secured by encryption algorithms without central control (Sukumaran et al. 2022a). Due to the nature of blockchain, altering a transaction requires changing not only the specific block but also all subsequent blocks, an action that is nearly impossible to accomplish (Gupta et al. 2023).

Building on the foundational understanding of blockchain technology, this study can now examine the increasing adoption and investment in cryptocurrencies. The adoption and investment in cryptocurrencies are generating significant public interest, as reflected in the growing number of individual accounts on cryptocurrency exchanges (Al-Omoush et al. 2024; Treiblmaier et al. 2021). Specifically, the number of cryptocurrency investment accounts increased from 203 million in 2021 to 402 million in 2023. However, investing in cryptocurrencies remains highly risky due to the extreme market volatility influenced by factors such as supply, demand, risk tolerance, and investor sentiment (Foka Nzaha et al. 2022).

Based on recent data, Bitcoin’s market capitalization is approximately USD 1.28 trillion, Ethereum’s is USD 313.4 billion, Tether’s is USD 115.8 billion, and Dogecoin’s is USD 15.3 billion (CoinMarketCap 2024). The total market capitalization of all cryptocurrencies is about USD 2.42 trillion (CoinMarketCap 2024). For comparison, the capitalization of gold worldwide is about USD 18 trillion (CompaniesMarketCap 2024). This indicates that while cryptocurrencies are a newly emerging investment asset, they represent approximately one-ninth of gold’s market value. Given these figures, although cryptocurrencies may not yet fully replace gold, they have the potential to challenge gold and traditional currencies like the Euro and the US dollar as investment assets and stores of value.

While most research has concentrated on the technical aspects of cryptocurrencies, some empirical studies have investigated the factors influencing the acceptance and intention to use cryptocurrencies. For instance, Platt et al. (2022) conducted a survey of cryptocurrency users in Nigeria. Their findings reveal that very few users purchase Bitcoin to evade government control; instead, most view cryptocurrencies primarily as long-term investment tools.

Veerasingam and Teoh (2023) explored emerging Islamic markets and reported that attitudes toward risk and perceived behavioral control are significant factors influencing cryptocurrency adoption. In Malaysia, Sukumaran et al. (2022b) identified several factors, including compatibility, trialability, usefulness, observability, and perceived value, that significantly impact the intention to engage with the cryptocurrency market. Almajali et al. (2022) found that, in Jordan, subjective norms, perceived threat, perceived usefulness, perceived enjoyment, perceived ease of use, trust, and motivating conditions affect the intention to use Bitcoin.

In India, Chary et al. (2022) highlighted education, occupation, product pricing, social media, brand ambassadors, social status, workplace, and applications as determinants of the intention to accept cryptocurrency. Osakwe et al. (2022) expanded on this by demonstrating that subjective norms, self-efficacy, trust, perceived risk, and pessimism influence Bitcoin investment.

2.2. Theoretical Foundation

Venkatesh et al. (2003) developed the Unified Theory of Acceptance and Use of Technology (UTAUT) by synthesizing eight popular technology acceptance models. UTAUT identifies four key factors that influence individuals’ behavioral intentions toward technology: performance expectancy, effort expectancy, social influence, and facilitating conditions. UTAUT has demonstrated significant explanatory power in various empirical studies focusing on technology adoption behaviors within organizational contexts (Venkatesh et al. 2011). It has also been applied to help explain consumer technology adoption behaviors (Arfi et al. 2021).

The UTAUT framework builds upon previous models and includes four fundamental constructs: performance expectancy (PE), which refers to the perceived benefits of using technology to enhance job performance; effort expectancy (EE), which pertains to the perceived ease of use; social influence (SI), which involves the impact of others’ opinions on an individual’s technology use; and facilitating conditions (FC), which covers the availability of resources and support needed to use the technology effectively. UTAUT is renowned for its robust ability to elucidate intentions to use information technology (Venkatesh et al. 2016).

In the context of cryptocurrency investment, UTAUT is particularly relevant. UTAUT provides a comprehensive and methodical approach to understanding technology acceptance. As an emerging form of financial technology, cryptocurrencies present unique challenges and opportunities that align well with the constructs of UTAUT. Performance expectancy is crucial to keep in mind as investors evaluate how cryptocurrencies can potentially enhance their financial gains. Effort expectancy is significant given the technical complexity of managing cryptocurrency transactions and understanding blockchain technology. Social influence plays a role as investors in cryptocurrencies, and many other goods and services, are often influenced by peers, trends, and social media. Facilitating conditions are essential, as they relate to the accessibility of platforms and tools needed for cryptocurrency trading.

Previous research has shown that UTAUT can explain over 70% of new technology usage intentions (Dwivedi et al. 2019), highlighting its relevance for understanding cryptocurrency investment behaviors. This study extends that research trail and fills some of the existing gaps in the literature by applying UTAUT to explore how its key components—performance expectancy, effort expectancy, and social influence—affect attitudes toward cryptocurrency investment and intentions to continue investing. The simplicity of the UTAUT has led to it becoming a widely accepted framework used for investigating technology acceptance. Nevertheless, the simplicity of the UTAUT falls short in explaining the multifaceted nature of user behavior (Bagozzi 2007; Benbasat and Barki 2007) in emerging technologies such as cryptocurrency. This study’s addition of factors such as perceived risk, privacy concerns, and technological competency address the need to explore the UTAUT at a deeper level beyond its original simplicity. The inclusion of the perceived enjoyment and previous experience factors in the UTAUT framework takes a step in addressing missing emotional and motivational aspects (Bagozzi 2007) that may form investors’ attitudes toward cryptocurrency.

An example of the extension this paper adds to the body of research on the addition of perceived risk and perceived enjoyment. Given the high-risk nature of cryptocurrency investments (Arias-Oliva et al. 2021; Islam et al. 2023), this study integrates perceived risk into the UTAUT model. Perceived risk refers to the extent to which individuals believe that unfavorable outcomes could occur, potentially impacting their investment decisions. This factor is critical in financial decision-making and is especially pertinent in the volatile cryptocurrency market. Additionally, perceived enjoyment is included to capture the excitement and intrinsic satisfaction some investors derive from engaging with cryptocurrencies.

To trade or exchange cryptocurrencies, investors are required to open exchange accounts and provide personal and financial information (Pham et al. 2021). This raises concerns about the potential misuse of this information, making perceived privacy an important factor in the research model. Previous experience is also considered. Positive experiences can reinforce the decision to continue cryptocurrency investments. In addition, understanding blockchain technology is essential for cryptocurrency investors (Quan et al. 2023), which prompts including technology competency in the model. This measure will gauge how familiarity with the technology impacts investment behavior.

2.3. Research Model and Hypotheses

The 4.0 Industrial Revolution is marked by the massive volume of data transmitted over the Internet (Nadeem et al. 2021). These data encompass a broad spectrum, from everyday conversations to sensitive personal and financial information involved in economic transactions. On the one hand, businesses seek to leverage customer data for various purposes. Customers, on the other hand, are concerned about the potential misuse of their private information, including unauthorized collection, processing, storage, sharing, and distribution by businesses. Therefore, addressing perceived privacy concerns has become critical to maintaining customer trust (Alkhwaldi et al. 2023).

Figure 1 illustrates the integrated research model used in this study. It illustrates the incorporation of the main components of the Unified Theory of Acceptance and Use of Technology (UTAUT), with perceived enjoyment, perceived privacy, perceived risk, previous experience, and technology competency. This model is designed to explore how these factors influence attitudes toward cryptocurrency investment. Additionally, it examines the relationship between these attitudes and the intention to continue investing in cryptocurrencies. By integrating these variables, the model provides a comprehensive framework for understanding the complexities surrounding cryptocurrency investment behavior.

Before an individual investor can invest in cryptocurrencies, one must open at least one account with a cryptocurrency exchange (Joshi et al. 2023). These accounts require and must then store sensitive personal and financial information. Once an account is set up, cryptocurrency transactions are updated and reported by these exchanges. Cryptocurrency exchange investors, like e-commerce platform patrons, are concerned about the potential leakage or misuse of their private information. Previous studies in e-commerce, online banking, and mobile payment have shown that perceived privacy positively impacts performance expectancy (Mollick et al. 2023). Consistent with these findings, the following hypothesis is proposed:

H1. 

There is a positive relationship between perceived privacy and performance expectancy in the context of cryptocurrency investment.

Perceived risk is a well-established concept in consumer behavior research (Bland et al. 2024). It is defined as the degree of uncertainty a consumer faces when making purchase or sale decisions, reflecting the potential for unfavorable outcomes such as dissatisfaction, poor quality, or overpayment (Van et al. 2021). In cryptocurrency investment, individual investors face risks including hacking of their accounts or lower-than-expected returns (Almarashdeh et al. 2021). Inadequate digital infrastructure can also disrupt transactions. Previous research in e-commerce, online banking, and mobile payment indicates that perceived risk negatively affects performance expectancy (Van et al. 2020). Hence, the following hypothesis is proposed:

H2. 

There is a negative relationship between perceived risk and performance expectancy in the context of cryptocurrency investment.

An individual’s ability to effectively use technology to complete tasks is referred to as technology competency (Almaiah et al. 2022). Those with high technology competency are generally more adept at using various technologies and are more open to adopting new ones. Cryptocurrency is an example of a breakthrough technology based on blockchain and distributed ledger technology. It should benefit from technology competency, as it enables individuals to understand and engage with its mechanisms more effectively (Cheng 2020; Sukumaran et al. 2022b). Prior studies have found that technology competency positively influences effort expectancy (Bui et al. 2022). Therefore, the following hypothesis is proposed:

H3. 

There is a positive relationship between technology competency and effort expectancy in the context of cryptocurrency investment.

Performance expectancy is defined as the degree to which an individual believes that using a technology enhances their ability to perform tasks (Rahi et al. 2019). This variable combines elements such as perceived usefulness, extrinsic motivation, job fit, and relative advantage from various technology acceptance models (Chao 2019). In cryptocurrency investment, performance expectancy reflects investors’ satisfaction with investment outcomes, including risk levels and expected returns (Wongsunopparat and Nanjun 2023; Bhuvana and Aithal 2022). Previous research indicates that performance expectancy positively influences attitudes toward technology acceptance. Thus, the following hypothesis is proposed:

H4. 

There is a positive relationship between performance expectancy and attitudes toward cryptocurrency investment.

Effort expectancy refers to the ease or difficulty of using technology (Bharadwaj and Deka 2021). It encompasses factors such as complexity and ease of use (Arias-Oliva et al. 2019). Regarding cryptocurrency investment, effort expectancy pertains to how straightforward or challenging it is to invest in cryptocurrencies. The investment decision is influenced by attributes that include user friendliness and convenience (Jariyapan et al. 2022). Research has shown that effort expectancy affects attitudes toward technology adoption (Li et al. 2023). Therefore, the following hypothesis is proposed:

H5. 

There is a positive relationship between effort expectancy and attitudes toward cryptocurrency investment.

Influences from family, peers, and social networks can shape attitudes toward technology (Chávez Herting et al. 2023). Such social influence, derived from subjective norms, social environment, and social image in technology acceptance models, reflects how strongly individuals feel that others expect them to use a technology (Gagarina et al. 2019; Beldad and Hegner 2018). Previous studies have demonstrated that social influence positively influences attitudes toward technology adoption. Hence, the following hypothesis is proposed:

H6. 

There is a positive relationship between social influence and attitudes toward cryptocurrency investment.

Previous experience with technology significantly affects attitudes and behaviors toward new technologies (Dindar et al. 2021). For instance, experience with e-commerce makes m-commerce adoption easier, and experience with online payment facilitates mobile payment use (Montford and Goldsmith 2016). Similarly, experience with traditional financial investments can ease the adoption of cryptocurrency as an investment asset (Chen et al. 2022; Tenkam et al. 2022). Thus, the following hypothesis is proposed:

H7. 

There is a positive relationship between previous experience and attitudes toward cryptocurrency investment.

Investors in the cryptocurrency market often seek high returns despite substantial risks (Agustina 2019). Beyond risk and return considerations, investors are motivated by the novelty and excitement of new technologies (Stix 2021). Enthusiastic and innovative investors are eager to experience cutting-edge technologies (Huang et al. 2023). Therefore, the following hypothesis is proposed:

H8. 

There is a positive relationship between perceived enjoyment and attitudes toward cryptocurrency investment.

Attitude encompasses an individual’s feelings and perceptions about an object, event, or situation that influence behavioral intentions across various contexts (Albayati et al. 2020; Choi 2021). In technology adoption contexts such as e-commerce, online banking, and mobile payment, attitudes significantly impact behavioral intentions. Consistent with these findings, the following hypothesis is proposed:

H9. 

There is a positive relationship between attitudes toward cryptocurrency investment and the intention to continue investing in cryptocurrencies.

3. Research Methodology

The survey instrument for this study was developed by adapting items from various previous studies. Specifically, survey items related to performance expectancy, effort expectancy, social influence, attitude toward cryptocurrency investing, and intention to continue investing in cryptocurrency were adapted from Venkatesh et al. (2003). Items for perceived risk and perceived privacy were adapted from Pavlou (2003) and Nguyen et al. (2020), respectively. Items for technology competency were adapted from Cutshall et al. (2022), while items for perceived enjoyment and previous experience were adapted from Yan et al. (2023) and Changchit et al. (2017), respectively.

The survey instrument consisted of 50 items, with responses measured on a 5-point Likert scale. Additionally, participants were asked to provide basic demographic information through ten questions. Data collection was conducted by a commercial data collection firm in the United States, targeting 506 current cryptocurrency investors. Only current investors were included in the sample, as the study aims to examine factors relevant to the intention to continue investing in cryptocurrency. Descriptive demographic data of the participants are presented in

Table 1. Subjects’ demographics.

Gender
	Male	Female	Other
	258	248	0
	50.99%	49.01%	0.00%
Education
	Less than high school	High school	Associate’s degree	Bachelor’s degree	Master’s degree	Doctoral degree
	8	162	123	136	59	18
	1.58%	32.02%	24.31%	26.88%	11.66%	3.56%
Age
	18–25	26–35	36–45	46–55	56–65	Above 65
	73	89	97	94	73	80
	14.43%	17.59%	19.17%	18.58%	14.43%	15.81%
Ethnicity
	African American	Anglo	Asian	Hispanic	Native American	Other
	117	318	21	41	5	4
	23.12%	62.85%	4.15%	8.10%	0.99%	0.79%
Annual Income
	Under USD 20,000	USD 20,000–40,000	USD 40,001–60,000	USD 60,001–80,000	USD 80,001–100,000	Over USD 100,000
	53	109	108	75	62	98
	10.50%	21.58%	21.39%	14.85%	12.28%	19.41%
Employed
	Full-time		Part-time		Not employed
	304		66		136
	60.08%		13.04%		26.88%
Student
	Undergraduate		Graduate		Not a student
	54		105		347
	10.67%		20.75%		68.58%
Cryptocurrency trading pattern
	Day trader (trade multiple times a day)		Short-term investor (hold for not more than one year before selling it)		Long-term investor (hold for more than one year before selling it)
	97		213		196
	19.17%		42.09%		38.74%
Cryptocurrency investment transactions per month
	0	1–2	3–5	6–9	10–20	>20
	62	180	129	69	44	22
	12.25%	35.57%	25.49%	13.64%	8.70%	4.35%
Cryptocurrency investment transactions per year
	0	1–20	21–50	51–100	>100
	23	260	125	66	32
	4.55%	51.38%	24.70%	13.04%	6.32%

.

4. Data Analysis

Multiple statistical tests were performed on the data, using SPSS 27 and AMOS 26. This section will describe the tests performed on the dataset and present the results of the tests.

4.1. Reliability Test

To determine the internal consistency of the survey item constructs used in this study, the Cronbach’s alpha reliability test was performed on each of the constructs in the proposed research model. The results of the reliability tests are shown in

Table 2. Reliability statistics.

Constructs	Cronbach’s α
PP1, PP2, PP3, PP4, PP5	0.896
PR1, PR2, PR3, PR4, PR5	0.902
TC1, TC2, TC3, TC4, TC5	0.847
PE1, PE2, PE3	0.778
EE1, EE2, EE3, EE4	0.807
SI1, SI2, SI3, SI4, SI5	0.928
PEX2, PEX3, PEX4	0.717
PEN1, PEN2, PEN3, PEN4, PEN5	0.903
ATT1, ATT2, ATT3, ATT4	0.874
INT1, INT2, INT3, INT4	0.907

. Nunnally (1978) recommends a minimum threshold of 0.70 for the Cronbach’s alpha test. The results of the analysis for the factors in this study all exceed the recommended value. These results provide evidence supporting the internal consistency of the factors in this study.

4.2. KMO and Bartlett’s Test

Following the assessment of internal consistency, the unidimensionality of the measurement scales was evaluated using the Kaiser–Meyer–Olkin (KMO) and Bartlett’s test of sphericity. The results of these tests are displayed in

Table 3. KMO and Bartlett’s tests.

KMO and Bartlett’s Test
KMO sampling adequacy measurement		0.939
Bartlett’s test of sphericity	Approx. Chi-Square	14,208.409
	Degree of Freedom	903
	Significance	0.000

. Bartlett’s test produced a p-value of 0.000. This means that the null hypothesis that states the variables lack intercorrelations can be rejected. The KMO test was used to assess the sampling adequacy. The result of the KMO test was 0.939 demonstrating support for the suitability of factor analysis for assessing the sample data.

4.3. Common Method Bias

To inspect for the potential for common method bias, Harman’s single factor test was conducted using SPSS. The test involves conducting an un-rotated, single-factor constraint factor analysis, specifically designed to determine whether a predominant factor accounts for the majority of variance within the dataset. If more than fifty percent of the variance can be explained by one variable, then that would imply the existence of common method bias. In the case of common method bias, the responses may not necessarily reflect the distinct constructs proposed in the study but rather be artifacts of the data-collection methodology itself. The results of this test revealed that the maximum variance accounted for by a single factor was 33.661 percent. Thus, no single factor appears to dominate the explanation of the variance, and that the variation is due to the distinct constructs proposed in this study.

4.4. Analysis of Factor Loadings

The convergent validity of the constructs was assessed using factor analysis conducted in SPSS. Principal component analysis (PCA) with equamax rotation was employed to optimize the variance of the squared loadings within factors and across variables. This methodological approach was selected to reveal the underlying structure of the dataset, enabling the extraction of principal components that capture the maximum variance from the original survey items. PCA is essential for reducing the dataset’s dimensionality, thereby identifying key factors that explain the observed variations within the variables.

The analysis involved examining the factor loadings for each survey item to evaluate their magnitude and statistical significance, which reflect the strength and direction of the associations between each item and its corresponding underlying factor. According to Hair et al. (2019), factor loadings above 0.5 were considered indicative of adequate item representation within their respective factors.

The results, presented in

Table 4. Factor analysis.

Rotated Component Matrix
Component
	1	2	3	4	5	6	7	8	9	10
PP 1	0.074	−0.068	0.742	0.183	0.062	0.114	0.133	0.142	0.160	0.171
PP 2	0.102	−0.002	0.766	0.120	0.126	0.108	0.130	0.149	0.146	0.202
PP 3	0.134	−0.019	0.737	0.076	0.107	0.099	0.142	0.173	0.087	0.124
PP 4	0.142	−0.066	0.750	0.157	0.126	0.132	0.114	0.183	0.094	0.197
PP 5	0.081	−0.058	0.740	0.099	0.124	0.111	0.173	0.160	0.172	0.172
PR 1	−0.011	0.864	0.035	−0.054	−0.142	−0.028	−0.046	−0.003	−0.075	−0.010
PR 2	−0.081	0.842	−0.052	−0.106	−0.012	0.002	−0.044	−0.035	−0.129	0.027
PR 3	0.094	0.761	−0.081	0.004	−0.141	−0.128	−0.114	0.000	0.025	−0.114
PR 4	0.070	0.825	−0.032	0.004	−0.132	−0.047	−0.098	−0.003	0.032	−0.036
PR 5	−0.034	0.881	−0.047	−0.064	−0.005	0.001	−0.060	−0.023	−0.077	−0.020
TC 1	0.121	−0.014	0.128	0.101	0.148	0.702	0.109	0.104	0.217	0.190
TC 2	0.082	−0.109	0.032	0.025	0.107	0.658	0.071	0.157	−0.055	0.133
TC 3	0.076	−0.014	0.152	0.179	0.167	0.759	0.105	0.101	−0.008	0.189
TC 4	0.089	−0.050	0.108	0.243	0.143	0.717	0.034	0.061	0.206	0.046
TC 5	0.098	−0.002	0.129	0.254	0.111	0.731	0.098	0.031	0.189	0.113
PE 1	0.162	−0.027	0.158	0.128	0.215	0.194	0.285	0.100	0.647	0.113
PE 2	0.144	−0.035	0.131	0.130	0.112	−0.009	0.135	0.146	0.778	0.163
PE 3	0.175	−0.075	0.055	0.186	0.275	0.157	0.355	0.085	0.561	0.156
EE 1	0.164	0.024	0.214	0.103	0.033	0.116	0.167	0.720	0.116	0.091
EE 2	0.115	0.002	0.074	0.060	0.015	0.022	0.014	0.860	0.066	0.064
EE 3	0.046	−0.061	0.147	0.069	0.131	0.179	0.000	0.679	−0.030	0.318
EE 4	0.239	−0.002	0.128	0.130	0.065	0.025	0.109	0.661	0.251	0.126
SI 1	0.753	−0.013	0.112	0.168	0.060	0.124	0.087	0.144	0.211	0.087
SI 2	0.835	0.007	0.066	0.097	0.122	0.091	0.103	0.178	0.126	0.123
SI 3	0.802	0.009	0.113	0.119	0.084	0.096	0.135	0.153	0.157	0.110
SI 4	0.817	0.031	0.143	0.137	0.095	0.113	0.157	0.161	0.147	0.085
SI 5	0.831	0.013	0.115	0.106	0.098	0.088	0.126	0.174	0.125	0.102
PEX 2	0.132	−0.011	0.108	0.059	0.069	0.117	0.052	0.183	0.111	0.785
PEX 3	0.079	−0.123	0.099	0.207	0.207	0.004	0.169	0.064	0.433	0.607
PEX 4	0.003	0.041	0.225	0.126	0.093	0.173	0.128	0.130	0.048	0.702
PEN 1	0.150	−0.124	0.161	0.662	0.189	0.226	0.230	0.124	0.186	0.178
PEN 2	0.164	−0.062	0.088	0.615	0.255	0.148	0.268	0.168	0.188	0.217
PEN 3	0.142	−0.038	0.129	0.743	0.199	0.155	0.192	0.121	0.138	0.214
PEN 4	0.085	−0.043	0.150	0.689	0.206	0.216	0.212	0.081	0.257	0.186
PEN 5	0.133	−0.021	0.147	0.719	0.202	0.167	0.187	0.146	0.162	0.081
ATT 1	0.075	−0.130	0.100	0.150	0.256	0.102	0.766	0.066	0.178	0.162
ATT 2	0.089	−0.078	0.175	0.183	0.182	0.086	0.744	0.083	0.252	0.118
ATT 3	0.115	−0.060	0.219	0.258	0.230	0.083	0.627	0.095	0.218	0.134
ATT 4	0.222	−0.110	0.114	0.223	0.186	0.032	0.686	0.126	0.244	0.158
INT 1	0.127	−0.085	0.088	0.223	0.770	0.123	0.169	0.072	0.188	0.124
INT 2	0.075	−0.131	0.147	0.193	0.728	0.151	0.238	0.119	0.120	0.242
INT 3	0.091	−0.135	0.097	0.180	0.804	0.177	0.210	0.071	0.173	0.082
INT 5	0.045	−0.107	0.111	0.169	0.734	0.157	0.240	0.041	0.270	0.173

Extraction method: principal component analysis. Rotation Method.

, indicate that forty-three of the fifty survey items had factor loadings significantly exceeding the 0.5 threshold, confirming that each item effectively measured its intended construct. The total variance explained by these factors was 72.002%, demonstrating that the identified factors accounted for a substantial portion of the data’s variability. This explained variance confirms that each factor contributes uniquely and significantly to the measurement model.

These findings not only validate the convergent validity of the factors in this study but also enhance confidence in their ability to represent the underlying constructs of interest.

4.5. Average Variance Extracted (AVE)

As illustrated in

Table 5. Correlation matrix and Average Variance Extracted (AVE).

Constructs	Composite Reliability	AVE	Square Root of AVE	PP	PR	TC	PE	EE	SI	PEX	PEN	ATT	INT
PP	0.8634	0.5583	0.7472	1
PR	0.9202	0.6980	0.8355	−0.143 **	1
TC	0.8386	0.5101	0.7142	0.412 **	−0.146 **	1
PE	0.7038	0.4464	.06681	0.462 **	−0.176 **	0.419 **	1
EE	0.8222	0.5389	0.7341	0.465 **	−0.068	0.336 **	0.387 **	1
SI	0.9039	0.6532	0.8082	0.384 **	−0.039	0.354 **	0.485 **	0.448 **	1
PEX	0.7419	0.4920	0.7014	0.506 **	−0.136 **	0.427 **	0.521 **	0.439 **	0.349 **	1
PEN	0.8167	0.4723	0.6872	0.496 **	−0.190 **	0.549 **	0.607 **	0.412 **	0.454 **	0.529 **	1
ATT	0.7996	0.5009	0.7077	0.494 **	−0.250 **	0.387 **	0.667 **	0.354 **	0.425 **	0.492 **	0.646 **	1
INT	0.8450	0.5772	0.7597	0.421 **	−0.279 **	0.476 **	0.588 **	0.305 **	0.347 **	0.483 **	0.621 **	0.630 **	1

** indicates significance level < 0.001.

, the Average Variance Extracted (AVE) values range from 0.4464 to 0.6980. Three of these AVE values fall below the commonly accepted threshold of 0.5, which may raise concerns about construct validity (Hair et al. 2019). However, it is important to consider the perspective of Fornell and Larcker (1981), who argue that AVE might represent an overly stringent criterion for evaluating measurement model validity. They suggest that “on the basis of composite reliability alone, the researcher may conclude that the convergent validity of the construct is adequate, even though more than 50% of the variance is due to error” (p. 46).

This viewpoint highlights that while AVE values below the recommended threshold often indicate a variance issue due to measurement error, the constructs’ composite reliability, significantly exceeding the benchmark of 0.60 (Hair et al. 2019), can offset this limitation. Therefore, the internal reliability of the measurement items, as demonstrated by strong composite reliability figures, remains satisfactory. This approach underscores the complexity of validating measurement models and suggests that both AVE and composite reliability should be considered together when evaluating the constructs’ validity.

4.6. Multicollinearity Test

Given the potentially detrimental effects of multicollinearity on statistical analyses (Cenfetelli and Bassellier 2009), this was rigorously evaluated within the context of the research model. As demonstrated in

Table 6. Multicollinearity test.

Coefficients a
Model	Unstandardized Coefficients		Stand. Coefficients	t	Sig.	Collinearity Statistics
	Beta	Std. Error	Beta			Tolerance	VIF
(Constant)	0.865	0.207		4.174	0.000
PP	0.005	0.041	0.005	0.125	0.900	0.591	1.692
PR	−0.085	0.025	−0.110	−3.437	0.001	0.925	1.081
TC	0.160	0.043	0.143	3.759	0.000	0.649	1.541
PE	0.176	0.046	0.174	3.806	0.000	0.451	2.215
EE	−0.031	0.035	−0.033	−0.885	0.377	0.659	1.517
SI	−0.018	0.030	−0.023	−0.604	0.546	0.652	1.535
PEX	0.100	0.045	0.091	2.256	0.024	0.575	1.738
PEN	0.216	0.047	0.219	4.636	0.000	0.425	2.353
ATT	0.268	0.047	0.264	5.648	0.000	0.433	2.310

^a Dependent variable: Intention to continue investing in cryptocurrency.

, the variance inflation factor (VIF) analysis across the dataset produced values ranging from 1.164 to 2.237. All VIF values are well below the critical threshold of 10, indicating that multicollinearity is not a significant concern in this study’s dataset. This finding confirms the independence of the explanatory variables, thereby enhancing the credibility of the regression estimates. Consequently, the research findings are likely to reflect genuine associations rather than artifacts arising from inter-correlated predictors. This confirmation of minimal multicollinearity is essential for affirming the robustness of the research model’s explanatory power and the reliability of the conclusions drawn from the empirical data.

4.7. Structural Equation Model

To validate the study’s theoretical framework, structural equation modeling (SEM) through SPSS—AMOS was used. SEM is especially useful for models with mediating variables and latent constructs that are measured with multiple items. It is widely considered the best method for testing mediation effects. Bollen (1989) recommends using several goodness-of-fit measures to assess the overall model fit. The following goodness of fit indices and their results were the CMIN/df = 1.711 (acceptable at values no more than 3), goodness of fit = 0.885 (GFI, no less than 0.80), adjusted goodness of fit = 0.867 (AGFI, no less than 0.80), root mean square error of approximation = 0.038 (RMSEA, no more than 0.06), comparative fit index = 0.958 (CFI, no less than 0.90), Tucker Lewis index = 0.953 (TLI, no less than 0.90), and the normed fit index = 0.904 (NFI, no less than 0.90).

As detailed above, all the fit indices fell within their acceptable ranges as defined by foundational research (Black and Babin 2019; Bentler and Bonett 1980; Bollen 1989; Hu and Bentler 1999; Tucker and Lewis 1973). This adherence to established normative standards confirms that the model strongly aligns with the empirical data. This provides evidence of the ability of the model to accurately reflect the underlying structure of the dataset, thereby enhancing the credibility of the derived relationships and reinforcing the overall reliability of the research findings.

4.8. Hypothesis Testing

Figure 2 displays the characteristics of the causal paths, including the standardized path coefficients. The results of the hypothesis testing are shown in

Table 7. Hypothesis testing and results.

H#	Hypothesis Testing			(β)	Critical Ratio	p-Value
1	PP	→	PE	1.112	11.168	**
2	PR	→	PE	−0.050	−1.346	0.178
3	TC	→	EE	1.011	9.337	**
4	PE	→	ATT	0.618	7.211	**
5	EE	→	ATT	−0.049	−1.331	0.183
6	SI	→	ATT	−0.018	−0.573	0.567
7	PEX	→	ATT	0.106	1.297	0.194
8	PEN	→	ATT	0.290	5.769	**
9	ATT	→	INT	0.840	13.692	**

** indicates significance level < 0.001.

.

5. Results and Discussions

This study examined both the direct and indirect effects of various factors—including perceived privacy, perceived risk, technology competency, performance expectancy, effort expectancy, social influence, previous experience, perceived enjoyment, and attitudes toward investing in cryptocurrency—on the intention to continue investing in cryptocurrencies.

Hypothesis 1, which proposed a significant relationship between perceived privacy and performance expectancy, received strong support with a coefficient of 1.112 (t = 11.168, p < 0.001). In the realm of cryptocurrency investment, performance expectancy is understood as the anticipated benefits, particularly the financial returns, from investing. This finding indicates that a higher level of perceived privacy can enhance investors’ expectations of performance. When investors believe their privacy is well-protected, they are likely to focus more on the potential benefits of their investments, which may lead to elevated performance expectations. Additionally, enhanced privacy could reduce investor anxiety, fostering a more favorable view of the investment’s potential. This insight is novel, as prior research has explored the impact of perceived privacy on stock investment, mobile commerce, mobile payment acceptance, and trust (Merhi et al. 2019; Kanaan et al. 2023; Wang and Lin 2017), but not specifically within the context of cryptocurrency investment.

Hypothesis 2, which suggested a relationship between perceived risk and performance expectancy, was not supported (β = −0.050, t = −1.346, p = 0.178). This outcome contrasts with findings from previous studies (Al-amri et al. 2023; Sukumaran et al. 2022b). Given that the sample comprised current cryptocurrency investors, it is plausible that these individuals have a higher risk tolerance compared to investors in other asset classes, likely due to their familiarity with the inherent volatility of cryptocurrency markets. In other technology adoption studies on mobile banking (Liébana-Cabanillas et al. 2021) and mobile payment (Wong et al. 2022), perceived risk was found to have a significantly negative influence on attitude. This could be due to consumers’ lack of familiarity of the technology behind those applications. Thus, it could be postulated that the influence of perceived risk wanes in investors that are accustomed to the high-risk environment of the cryptocurrency markets.

Hypothesis 3, which proposed a relationship between technology competency and effort expectancy, was supported (β = 1.011, t = 9.337, p < 0.001), aligning with Bharadwaj and Deka (2021). The technological nature of cryptocurrencies may attract individuals with higher technology competency. Effort expectancy, which relates to the ease of using a system (Venkatesh et al. 2003), suggests that those with higher technology competency find it easier to understand and invest in cryptocurrencies compared to those with lower technology competency.

Hypothesis 4, which indicated a significant relationship between performance expectancy and attitude toward investing in cryptocurrency, was supported (β = 0.618, t = 7.211, p < 0.001). This result is consistent with Shahzad et al. (2018), Won-jun (2018), Arias-Oliva et al. (2019), and Nadeem et al. (2021). Increased acceptance of cryptocurrency in mainstream financial markets—as evidenced by the introduction of cryptocurrency exchange-traded funds—may lead investors to anticipate greater participation and potentially higher returns. This perception of higher returns could be a key factor in retaining current investors.

Hypothesis 5, which proposed a negative relationship between effort expectancy and attitude toward investing in cryptocurrency, was not supported (β = −0.049, t = −1.331, p = 0.183). This finding diverges from studies by Almajali et al. (2022), Gupta et al. (2024), and Pilatin and Dilek (2024). The evolution of the cryptocurrency market may have made investing more straightforward, reducing the impact of effort expectancy on attitudes. Investors might view the effort required as a minor consideration in achieving the higher performance expectations associated with cryptocurrency investments. In a comparative context of smart product adoption, the more experience a consumer has with a technology, the easier that technology is perceived to be used (Schukat and Heise 2021). Thus, the influence of effort expectancy on attitude is reduced.

Hypothesis 6, which proposed a relationship between social influence and attitude toward investing in cryptocurrency, was also not supported (β = −0.018, t = −0.573, p = 0.567). This result differs from findings by Ebizie et al. (2022) and Gupta et al. (2024). Cryptocurrency investors might prioritize their own research and analysis over social influence, particularly given that many perceive cryptocurrencies as speculative rather than as traditional investments. Experienced investors may seek less social validation for their investment choices.

Hypothesis 7, which suggested a relationship between previous experience and attitude toward investing in cryptocurrency, was not supported (β = 0.106, t = 1.297, p = 0.194). This result contrasts with findings by Thaker et al. (2019). The diverse experiences within the cryptocurrency market may dilute their impact on attitudes. Additionally, the highly dynamic nature of the cryptocurrency market may render past experiences less relevant to current conditions. Unlike in other technology-adoption contexts (i.e., e-commerce, mobile payment, and mobile banking), cryptocurrency investors rely on current market conditions instead of past experiences.

Hypothesis 8, which indicated a significant relationship between perceived enjoyment and attitude toward investing in cryptocurrency, was supported (β = 0.290, t = 5.769, p < 0.001). This result is consistent with Almajali et al. (2022). Perceived enjoyment, which encompasses the pleasure and entertainment derived from the activity (Yan et al. 2023), may arise from market fluctuations or the potential for high returns. Some trading platforms also incorporate gamification elements, which could enhance enjoyment and positively influence attitudes toward investing.

Hypothesis 9, which proposed a significant relationship between attitude toward investing in cryptocurrency and intention to continue investing, was supported (β = 0.84, t = 13.692, p < 0.001). This result aligns with findings by Gupta et al. (2024) and Pilatin and Dilek (2024). According to the Theory of Planned Behavior, a positive attitude toward a behavior is a strong determinant of behavioral intent. Thus, a favorable attitude toward cryptocurrency investing is likely to increase the intention to continue investing, aligning with investors’ goals, such as diversification and wealth generation.

6. Theoretical and Practical Implications

This study aimed to examine the interplay of various factors on the intention to persist in cryptocurrency investments among current investors. The findings indicate that perceived privacy, technology competency, performance expectancy, perceived enjoyment, and attitude toward investing in cryptocurrency all exert either direct or indirect influences on the intention to continue investing. Notably, performance expectancy and perceived enjoyment had the most substantial direct impact on attitudes toward cryptocurrency investing, which, in turn, significantly influenced the intention to continue investing. Additionally, perceived privacy emerged as a crucial factor for investors in evaluating the anticipated performance of their cryptocurrency investments. Although not surprising, the perceived risk did not affect the expected performance of cryptocurrency investments, likely due to the inherent high level of risk in the cryptocurrency markets.

This study has several theoretical implications. First, it confirms that the Unified Theory of Acceptance and Use of Technology (UTAUT) can effectively explain attitudes toward a new technology or innovation. Specifically, performance expectancy, a key component of UTAUT, has a statistically significant positive influence on attitudes toward investing in cryptocurrencies. Performance expectancy reflects the degree to which an individual believes a technology will enhance their ability to achieve tasks, similar to the perceived usefulness in the technology acceptance model (TAM). In the context of cryptocurrency investment, it is understood as the degree to which investors are satisfied with the investment outcome, particularly in terms of expected returns.

Second, adding perceived enjoyment to the integrated research model represents a theoretical novelty. This factor has been overlooked in previous cryptocurrency studies. Cryptocurrencies involve advanced technology, requiring investors to understand concepts like decentralized distributed ledgers. Investors not only seek high returns but also desire the excitement associated with early investment in novel assets like cryptocurrencies.

Third, the study highlights the significant role of perceived privacy. It affects attitudes toward investing in cryptocurrencies through performance expectancy. While technology acceptance models emphasize performance expectancy, few empirical studies have explored the factors that determine it. This study addresses this gap by demonstrating that perceived privacy is essential for explaining attitudes and intentions to continue investing in cryptocurrencies.

Fourth, while previous research has focused on performance expectancy, social influence, and previous experience in shaping attitudes toward technology, the cryptocurrency investment environment differs from other contexts, like online banking or e-commerce. Thus, these factors do not significantly impact attitudes toward cryptocurrency investing. Cryptocurrency investors are less influenced by social pressures or previous experiences with traditional assets. Additionally, since opening a cryptocurrency account requires minimal effort, effort expectancy does not affect attitudes toward investing. These findings challenge previous studies and theories.

In terms of practical implications, the study shows that the intention to continue investing in cryptocurrencies is influenced by attitudes toward investing, as such attitudes are shaped by performance expectancy and perceived enjoyment. Cryptocurrency exchanges should develop marketing strategies that emphasize these aspects. Highlighting performance expectancy helps investors understand that cryptocurrency is a valuable investment asset. Performance expectancy, akin to perceived usefulness, can positively influence attitudes toward cryptocurrency investments. Investors’ perceptions of the benefits, such as high yields and a better understanding of FinTech trends, will lead to a positive attitude and, consequently, a greater intention to continue investing.

In addition, financial institutions can use the findings from this study. Considering the importance of perceived privacy’s impact on investors’ attitudes and intention to continue investing in cryptocurrency from this study, financial institutions can develop specialized cryptocurrency-based investment instruments designed with privacy in mind.

Marketing strategies should also focus on the future prospects of cryptocurrencies to attract and retain investors. Despite cryptocurrencies not yet being officially recognized as a means of payment in many economies, many countries are building digital infrastructures to integrate cryptocurrency functions. In developed countries like the US, cryptocurrencies are viewed as financial assets, and many investors trade them regularly. Cryptocurrency exchanges should explore ways to generate emotional interest among potential investors to build their confidence in cryptocurrencies.

The groundbreaking blockchain technology behind cryptocurrencies requires investors to have some technological savvy. Sharing information about cryptocurrencies, such as decentralized distributed ledgers, can increase investors’ technological readiness and interest, leading to a positive attitude and a greater intention to continue investing.

Finally, governments worldwide should not ignore the growing trend of cryptocurrencies. The study’s results demonstrate the increasing number of cryptocurrency investors and suggest that cryptocurrencies represent a future development trend. Governments should devise strategies to integrate cryptocurrencies into economic activities, whether through government-created or decentralized cryptocurrencies.

7. Conclusions and Directions for Future Research

This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with additional factors, such as perceived enjoyment, perceived privacy, perceived risk, and technology competency, to create a robust model for understanding attitudes toward cryptocurrency investing and the intention to continue investing. This comprehensive model is examined within the context of the United States, a leading economic power with a substantial presence of cryptocurrency exchanges and active investors. The results reveal that performance expectancy and perceived enjoyment significantly influence attitudes toward cryptocurrency investments. These positive attitudes, in turn, have a strong effect on the intention to continue investing in cryptocurrencies. This study further finds that perceived privacy plays a crucial role in enhancing performance expectancy, while technology competency contributes positively to effort expectancy. These findings underscore the multifaceted nature of cryptocurrency investment decisions, highlighting the importance of various psychological and technological factors.

However, several limitations must be acknowledged in the study. While the integrated research model provides valuable insights, it does not encompass all potential factors that could influence attitudes and intentions regarding cryptocurrency investment. Factors such as optimism, innovativeness, discomfort, and insecurity, which might also impact investor behavior, were not included in this study. Future research should explore these additional dimensions to gain a more nuanced understanding of the full spectrum of influences on cryptocurrency investment decisions. By incorporating these variables, researchers can offer a more complete picture of what drives investment behavior in this evolving market.

The focus of this study was on current cryptocurrency investors, thus limiting the scope of the findings. Future research could broaden the examination to include potential investors who are not yet active in the market. This would enable a comparison between current and prospective investors, providing deeper insights into how attitudes, motivations, and intentions differ between these groups. Such comparative analysis could reveal important trends and factors that influence the decision to enter or remain in the cryptocurrency market.

Moreover, this study is situated within the context of the United States, which may not fully represent the global cryptocurrency investment landscape. To enhance the generalizability of the findings, future studies should extend the research to various international contexts, including Europe, Asia, Africa, and Latin America. Understanding how different regional factors, such as economic conditions, cultural attitudes, regulatory environments, and technological infrastructure, affect cryptocurrency investment behaviors can provide valuable information for policymakers and industry leaders. This cross-regional perspective would help in developing more targeted strategies and policies to foster cryptocurrency adoption and address the unique challenges faced by investors in different parts of the world.

In conclusion, while this study offers significant insights into the factors influencing cryptocurrency investment attitudes and intentions, addressing its limitations and expanding research to include additional factors, diverse investor groups, and global contexts will contribute to a more comprehensive understanding of this dynamic field. Such efforts will not only advance academic knowledge but also support the development of effective strategies for engaging with cryptocurrency investors worldwide.