Comprehending the acceptance of cryptocurrencies for financial transactions within Saudi context

1 Introduction

Rapid technological innovation has fundamentally reshaped multiple dimensions of modern society, particularly the mechanisms through which financial transactions are executed (Glebova and Desbordes, 2020; Poongodi et al., 2020). Across the globe, a significant shift is occurring from conventional paper-based monetary systems toward digital and electronic modes of value exchange, most prominently exemplified by the rise of cryptocurrencies (Kavanagh and Ennis, 2020). Cryptocurrency represents a digital alternative to conventional currencies, characterized by strong security features, rapid transaction processing, autonomy from financial intermediaries, and user anonymity (Kim, 2021). As a digital medium of exchange, it can be stored, transferred, and traded by individuals and organizations (Abdul-Hamid et al., 2019). The underlying technology employs advanced cryptographic mechanisms to facilitate secure and efficient financial transactions, while also offering the advantage of lower transaction costs (Omane-Adjepong and Alagidede, 2020). The increasing adoption of cryptocurrency among businesses and retailers reflects growing acceptance and suggests a promising future for this technological innovation (Almajali et al., 2022). Its expanding use as a method of payment is driven by widespread recognition of its benefits, with potential to strengthen national economies particularly in developing nations (Patil et al., 2020). However, the realization of these benefits depends on achieving broader user acceptance (De Filippi, 2014).

In the context of Saudi Arabia, the country has significantly accelerated cryptocurrency adoption, positioning the Kingdom as one of the fastest-growing digital asset markets in the Middle East and North Africa (MENA) region. Saudi Arabia recorded approximately US$47.1 billion in crypto inflows, representing a 153% year-on-year increase, the highest growth rate in the Gulf Cooperation Council region (Anis, 2023). According to Gulf Business around 3 million Saudis are actively engaged in cryptocurrency trading or ownership, with youth participation (ages 18–35) being particularly prominent, supported by high digital penetration and smartphone usage rates exceeding 95% (Anis, 2023). Based on the world bank (2021), approximately 15% of Saudi Arabia’s population owns cryptocurrency, placing the Kingdom at levels comparable to advanced economies such as the United States (15.5%) and above several developed nations including Germany (8.3%) and Canada (10.2%), as indicated in Table 1. Likewise, institutional-sized transfers (above US$10,000) account for a substantial share of regional crypto transaction volume, indicating increasing market maturity and strategic capital participation (Sheela et al., 2023). Although cryptocurrencies are not recognized as legal tender and remain subject to regulatory oversight by the Saudi Central Bank, the rapid growth in transaction volume, user participation, and institutional engagement reflects a structurally expanding digital asset ecosystem aligned with the Kingdom’s broader digital transformation agenda (Benhamlaoui, 2025).

TABLE 1. Global percentage of population owning cryptocurrency (Source: world bank, 2021).

Equally significant, the large-scale adoption of cryptocurrency in Saudi Arabia aligns closely with the objectives of Saudi Vision 2030, which seek to transition the Kingdom from an oil-dependent economy toward a knowledge and innovation-driven model (Nurunnabi, 2017). By fostering a robust digital asset ecosystem, cryptocurrencies can stimulate financial sector innovation, enhance fintech development, and expand digital entrepreneurship, all of which support Vision 2030s focus on economic diversification and technological advancement (Kim, 2021). Correspondingly, widespread crypto adoption encourages the development of human capital in blockchain, cybersecurity, and digital finance, promoting the skills and expertise necessary for a knowledge-based economy (Ter Ji-Xi et al., 2021). The growth of institutional and retail engagement in cryptocurrencies also attracts foreign investment and strategic partnerships, strengthening Saudi Arabia’s position as a regional fintech hub (Sheela et al., 2023). Essentially, the integration of digital assets into the Kingdom’s financial and technological infrastructure represents not only an avenue for market modernization but also a tangible mechanism for achieving the broader Vision 2030 goals of innovation, economic diversification, and sustainable development.

Despite this potential, cryptocurrency utilization remains relatively limited among users in Saudi Arabia (Mukherjee et al., 2022; Al-Amri et al., 2019). Furthermore, while cryptocurrency offers substantial advantages, users must recognize associated risks particularly the irrecoverable loss of assets due to cyberattacks or human error (Ermakova et al., 2017), and potential devaluation that may adversely affect investment returns (Al-Hatmi et al., 2018). Previous research on cryptocurrency adoption has predominantly focused on Western countries (Ter Ji-Xi et al., 2021; Walton and Johnston, 2018). Empirical studies examining users’ perceptions and attitudes toward cryptocurrency in developing nations, such as the Kingdom of Saudi Arabia (KSA), remain limited (Al-Amri et al., 2019). As a result, users in emerging economies like the KSA have been slower to adopt cryptocurrency compared to their counterparts in more developed regions (Khwaji et al., 2021). Therefore, an in-depth investigation is necessary to better understand the factors that could promote cryptocurrency adoption among users in Saudi Arabia (Bhimani et al., 2022). In addition, determinants such as user awareness and technological readiness, which may influence cryptocurrency utilization, have not been sufficiently explored (Al-Amri et al., 2019). Prior studies (Hasan et al., 2022; Miraz et al., 2022; Patil et al., 2020; Shaw and Sergueeva, 2019) have reported inconsistencies regarding the relationships between the factors of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and users’ behavioural to accept cryptocurrency. To address these gaps, the present study investigates the insightful drivers affecting the acceptance of cryptocurrency. In order to address this, the study is guided by the following objective:

• To explore the key drivers impacting the acceptance of cryptocurrencies for financial transactions within Saudi context.

Hence, the study aims to answer the following research questions:

• 2. What are the underlying drivers impacting the acceptance of cryptocurrencies for financial transactions within Saudi context?

This study offers several key contributions. To start with, it examines the perspectives of users in the Kingdom of Saudi Arabia (KSA) regarding their accepting cryptocurrency through a comprehensive research framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT), extended with the constructs of perceived risk, technology awareness, and government support. The findings are expected to deepen the understanding of cryptocurrency adoption behavior and provide valuable insights and practical implications for cryptocurrency developers, policymakers in the KSA government, and potential cryptocurrency stakeholders within the country. The structure of this paper is organized as follows. First, a comprehensive review of the relevant literature is presented, followed by an explanation of the proposed model and the development of research hypotheses. Next, the research methodology is described, and the study’s results are discussed. Finally, the paper concludes by highlighting its limitations, offering theoretical, practical implications, and recommendations for future research, and presenting the overall conclusion.

2 Literature review

The determinants influencing cryptocurrency acceptance (CCA) have attracted academic interest; however, available data on this emerging field remain limited, and existing research often presents inconsistent results (Arias-Oliva et al., 2021; Al-Amri et al., 2019). Several studies (Albayati et al., 2020; Zamzami, 2020; Mazambani and Mutambara, 2019; Schaupp and Festa, 2018) generally assert that behavioral intention to use cryptocurrencies is primarily driven by a positive attitude toward their usage. This attitude, however, is shaped by multiple underlying factors. For example, Jankeeparsad and Tewari (2018) identified trust as the most influential determinant of a favorable attitude toward Bitcoin usage within the South African context. Similar findings were reported in other regions, including South Korea (Lee et al., 2018), China (Shahzad et al., 2018), Malaysia (Sas and Khairuddin, 2017), and Cyprus (Zarifis et al., 2014). Moreover, users’ attitudes toward cryptocurrency have been shown to depend on satisfaction (Alaeddin and Altounjy, 2018), optimism and innovativeness (Sohaib et al., 2019), as well as knowledge and technical familiarity with cryptocurrencies (Sun et al., 2020; Ostern, 2018). Accepting decisions related to Bitcoin are also often influenced by significant others such as family, friends, or social groups whose positive attitudes can encourage adoption (Kim, 2021; Boxer and Thompson, 2020; Gazali et al., 2018; Jankeeparsad and Tewari, 2018; Schaupp and Festa, 2018; Walton and Johnston, 2018). On the contrary, other studies have stated that subjective norms do not play a decisive role in influencing behavioral intention, as demonstrated in research conducted in Indonesia (Zamzami, 2020), South Africa (Mazambani and Mutambara, 2019), and Pakistan (Ullah et al., 2021). In contrast, a study in Spain by Arias-Oliva et al. (2021) found that subjective norm acts as an enabling factor positively associated with cryptocurrency usage intention. Perceived risk has often been determined as having a strong negative effect on the intention to adopt cryptocurrency (Gil-Cordero et al., 2020; Sun et al., 2020; Gazali et al., 2018; Sohaib et al., 2019; Abramova and Böhme, 2016). Nonetheless, contrasting evidence from other studies (Nadeem et al., 2021; Ter Ji-Xi et al., 2021; Yoo et al., 2020; Nuryyev et al., 2018) claims that perceived risk can also have a positive or context-dependent influence, as observed by Arias-Oliva et al. (2021), who noted that its impact may vary according to social and situational factors. Similarly, perceived usefulness and ease of use have been reported to positively affect behavioral intention toward cryptocurrency adoption (Nadeem et al., 2021; Albayati et al., 2020; Arias-Oliva et al., 2019; Nuryyev et al., 2018).

However, some studies such as (Shahzad et al. (2018), Walton and Johnston (2018)) only found indirect relationships between perceived usefulness and intention to use. Janssen et al. (2015) further suggested that the influence of perceived usefulness may differ depending on consumer segments. Additionally, behavioral intention to use cryptocurrencies has been linked to facilitating conditions (Jankeeparsad and Tewari, 2018; Ter Ji-Xi et al., 2021) and perceived enjoyment (Nadeem et al., 2021). Overall, these findings indicate that numerous interrelated factors contribute to the behavioral intention to adopt cryptocurrencies. Due to variations in cultural, regulatory, and contextual conditions, the effects of these factors may differ across regions. In particular, cryptocurrency adoption in Saudi context remains underexplored despite evidence suggesting that Saudi Arabian demonstrate a general openness toward technological innovations (Alomari and Abdullah, 2023). Google Trends (2022) data further show rising awareness of cryptocurrencies among the users of KSA, though participation remains insufficient, largely due to distrust, restrictive government policies, and the absence of supporting financial institutions. Consequently, the existing body of evidence presents conflicting insights into the determinants of cryptocurrency acceptance, with limited applicability to the KSA. This highlights the need for more comprehensive, context-specific research to better understand what motivates or deters Saudi from embracing cryptocurrencies.

3 Theoretical framework

This study extends UTAUT by incorporating perceived risk, technology awareness, and government Support to better capture the unique characteristics of cryptocurrency adoption. Drawing on Innovation Diffusion Theory (Rogers, 2003), awareness represents the knowledge stage preceding adoption decisions. Perceived risk is grounded in risk theory (Bauer, 1960), which emphasizes uncertainty in decision-making, particularly relevant to volatile digital assets. Government Support is theoretically proved in Institutional Theory (DiMaggio and Powell, 1983), which posits that regulatory structures reduce uncertainty and enhance legitimacy. In addition, social influence and facilitating conditions were excluded from the model, as cryptocurrency adoption is largely voluntary and individually driven, reducing the impact of social norms on user behaviour. Moreover, the respondents show relatively homogeneous access to technological infrastructure, such as internet connectivity and smart devices, which limits the variability and explanatory power of facilitating conditions. Therefore, excluding these constructs enhances the parsimony and contextual relevance of the model while allowing greater focus on the most influential determinants of adoption behavior. Correspondingly, performance expectancy and effort expectancy are core determinants of behavioural intention in the UTAUT, their predictive strength is enhanced when contextual and technology-specific factors are considered. According to Venkatesh et al. (2003), UTAUT explains technology acceptance through performance expectancy, effort expectancy, social influence, and facilitating conditions.

However, the UTUAT allows for contextual extensions when studying emerging technologies. In the case of cryptocurrency, an innovation characterized by decentralization, volatility, and regulatory ambiguity additional constructs become theoretically necessary. The inclusion of perceived risk is grounded in risk theory and consumer behavior literature. Cryptocurrency adoption involves financial, legal, and security uncertainties, which are central determinants of behavioral intention in high-uncertainty innovations. Featherman and Pavlou (2003) showed that perceived risk significantly influences intention to adopt electronic services, particularly in online financial environments. Similarly, World Bank reports highlighted regulatory and volatility risks as key concerns surrounding crypto-assets (World Bank, 2021). In Saudi Arabia, cryptocurrency operates within evolving regulatory structures overseen by institutions such as Saudi Central Bank (SCB). Given the absence of fully formalized cryptocurrency regulation and public warnings issued regarding digital asset risks, perceived risk becomes theoretically indispensable in explaining adoption behavior. Thus, its integration extends UTAUT by incorporating risk-theoretic reasoning relevant to financial technologies. The construct of technology awareness is theoretically supported by the Diffusion of Innovations framework developed by Rogers (2003). Rogers emphasized that awareness knowledge is the first stage in the innovation-decision process. Without sufficient knowledge and understanding, individuals cannot form perceptions regarding usefulness or ease of use. In Saudi Arabia, despite rapid digital transformation under Saudi Vision 2030, empirical research on cryptocurrency awareness remains limited. Reports from Saudi Data and Artificial Intelligence Authority stated that national efforts to enhance digital literacy. Nevertheless, cryptocurrency-specific literacy is still irregular. Consequently, incorporating technology awareness reflects the innovation diffusion stage preceding attitudinal formation and strengthens the explanatory scope of UTAUT in emerging technology contexts. The addition of government support is theoretically confirmed in institutional theory, which points out that regulatory frameworks, legal structures, and normative pressures shape organizational and individual behavior. Scott (2008) deduces that regulatory and normative pillars significantly affect technology legitimacy and adoption.

In Saudi Arabia, the country plays a central role in economic and technological transformation. Initiatives led by the Ministry of Communications and Information Technology and financial authorities influence public trust in digital innovations. Empirical evidence asserts that government endorsement increases perceived legitimacy and reduces uncertainty in fintech adoption (Albayati et al., 2020). Given the centralized governance model of KSA, government support is not merely a facilitating condition but a legitimacy-conferring mechanism, thus justifying its conceptual distinction from traditional UTAUT constructs. In addition, UTAUT comprises social influence, which echoes normative pressure from others. Nonetheless, cryptocurrency decisions are often individual financial decisions rather than organizational compliance behaviors. In voluntary adoption contexts, UTUAT found social influence to be less significant when usage is not mandatory. Thus, social influence was excluded to maintain model parsimony and focus on contextually dominant determinants. In the same way, facilitating condition reflects infrastructure and technical support availability. In the current context, cryptocurrency access is largely platform-based and globally accessible. Participants were already internet users capable of completing an online survey. Infrastructure availability is relatively uniform in the study setting. Therefore, facilitating conditions was unlikely to provide additional explanatory power beyond effort expectancy. Figure 1 demonstrates the proposed model of the study. Meanwhile, a detailed discussion of the hypothesis development follows in the subsequent section.

4 Hypothesis testing

4.1 Performance expectancy

Performance Expectancy (PE) refers to the extent to which individuals believe that using cryptocurrency can enhance their job performance (Miraz et al., 2022; Venkatesh et al., 2012). Previous literature has identified performance expectancy as a significant positive player influencing individuals’ intention to use cryptocurrency (Gunawan and Novendra, 2017; Hutchison, 2017). Built on blockchain technology, cryptocurrency overcomes several limitations of existing payment systems, such as credit cards and PayPal, while offering additional advantages to users (Baur et al., 2015). Its adoption is expected to simplify and improve financial transactions, for example, by increasing transaction efficiency (Kim, 2021). The absence of intermediaries like central financial institutions further reduces transaction costs and enhances the transfer of funds (Patil et al., 2020). Numerous studies have found a significant positive association between performance expectancy and users’ behavioural intention to adopt cryptocurrency (Shaw and Sergueeva, 2019; Alalwan et al., 2017; Diep et al., 2016). Similarly, research in Malaysia by Ter Ji-Xi et al. (2021) identified performance expectancy as a critical driver influencing cryptocurrency acceptance. However, contrasting evidence from Miraz et al. (2022) suggests a negative influence of performance expectancy on behavioural intention to use cryptocurrency, highlighting inconsistencies in previous findings. Based on the above mentioned, further investigation into the relationship between performance expectancy and accepting cryptocurrency is needed. Therefore, this study proposes the following hypothesis:

H1 Performance expectancy is positively related to the acceptance of cryptocurrency:

4.2 Effort expectancy

Effort Expectancy (EE) refers to the degree of ease associated with learning and using a new technology (Ter Ji-Xi et al., 2021; Venkatesh et al., 2012). As stated by Raweh (2025), cryptocurrency is a relatively new and unstable technology that requires a certain level of understanding to be used effectively in financial transactions. They emphasized that possessing basic knowledge of cryptocurrency use is essential for protecting users from potential fraud. Many studies identified effort expectancy as a significant driver influencing individuals’ behavioral intention to accept cryptocurrency (Mensah and Mwakapesa, 2022; Tamphakdiphanit and Laokulrach, 2020; Alalwan et al., 2017). People often consider the amount of effort required to use a technology when deciding whether to adopt it. Conversely, Yusof et al. (2018) found that effort expectancy had an insignificant effect on cryptocurrency adoption, indicating inconsistencies in prior research. Therefore, further examination of the relationship between effort expectancy and accepting cryptocurrency is necessary. It is expected that if people in Saudi Arabia perceive cryptocurrency as easy to use, their acceptance will increase (Beh et al., 2021). Hence, this study articulates this hypothesis:

H2 Effort expectancy is positively related to the acceptance of cryptocurrency.

4.3 Perceived risk

Perceived Risk (PR) refers to user’s evaluation of uncertainty and potential adverse consequences associated with using a particular technology (Sukumaran et al., 2023). In the context of cryptocurrency, perceived risk is especially salient due to financial speculation, cybersecurity threats, and price volatility, making it a critical factor beyond performance and effort expectancy. Prior studies in financial technology adoption generally conceptualize perceived risk as a psychological barrier that reduces users’ behavioural intention (Kishore and Sequeira, 2016; Shahzad et al., 2018). This view is consistent with the dominant assumptions of the Unified Theory of Acceptance and Use of Technology and related acceptance models, where uncertainty is expected to hinder adoption. However, empirical findings in the cryptocurrency literature remain mixed. While some studies report a strong negative effect of perceived risk (Gil-Cordero et al., 2020), others stress that its influence may be positive or context-dependent (Arias-Oliva et al., 2021). These divergent results can be theoretically reconciled through a risk–return trade-off perspective and contingency logic. In highly speculative environments such as cryptocurrency markets, risk may simultaneously function as both a deterrent and an attraction (Ali and Abdullah, 2023; Anser et al., 2020; Ayedh et al., 2021; Bagozzi and Yi, 1989; Gupta et al., 2020; Faqih, 2016). For risk-averse individuals, higher perceived risk reduces adoption intention, consistent with traditional technology acceptance theories. In contrast, for risk-tolerant users, perceived risk may signal opportunities for high returns, thereby increasing their willingness to adopt. Moreover, the effect of perceived risk is likely conditional on moderating factors such as trust, user experience, and financial literacy. When trust in blockchain systems or platforms is high, the negative impact of perceived risk is mitigated, whereas low trust amplifies its deterrent effect. Similarly, experienced users may perceive risks as manageable, while inexperienced users interpret them as prohibitive barriers. Therefore, rather than exerting a uniform influence, perceived risk should be understood as a context-dependent construct, whose effect varies across user segments and situational conditions (Henry et al., 2018; Liu et al., 2022; Mendoza-Tello et al., 2018; Nakamoto, 2008; Napoleoncat, 2020; Nofer et al., 2017; Noreen et al., 2021). In this study, consistent with the dominant theoretical perspective and the characteristics of the target population, perceived risk is expected to exert a negative influence on cryptocurrency adoption intention. Therefore, this study posited the following:

H3 Perceived risk is negatively related to the acceptance of cryptocurrency.

4.4 Technology awareness

Technology Awareness (TA) refers to an individual’s cognitive recognition and understanding of the existence, functionality, and potential benefits of a technological innovation (Islam and Grönlund, 2011). The construct is theoretically grounded in Diffusion of Innovations, which posits that awareness constitutes the knowledge stage in the innovation-decision process (Rogers, 2003). Without adequate awareness, individuals cannot positively assess performance expectancy or effort expectancy thus technology awareness factor was added in the context of this study. According to this framework, individuals must first become aware of an innovation and acquire basic knowledge about how it functions before forming attitudes or behavioural intentions toward adoption. Without awareness, progression to persuasion, decision, and implementation stages cannot occur. Thus, awareness operates as a precursor construct in emerging digital finance contexts. From a behavioural perspective, awareness operates as a precondition to performance expectancy and effort expectancy within the Unified Theory of Acceptance and Use of Technology, as users cannot evaluate usefulness or ease of use in the absence of foundational understanding. Thus, technology awareness enhances cognitive appraisal processes, enabling individuals to assess perceived benefits and compatibility more accurately. Empirical evidence supports this theoretical linkage. Prior research demonstrates that higher levels of awareness significantly improve attitudes and adoption intentions toward emerging technologies, including blockchain and fintech systems (Chang et al., 2016; Bohr and Bashir, 2014). Conversely, limited awareness has been associated with delayed adoption in digital public services such as e-government (Reffat, 2003) and in online commercial platforms (Mohamad and Kassim, 2019).

Awareness also reduces ambiguity and information asymmetry, thereby lowering uncertainty and indirectly mitigating perceived risk (Mutahar et al., 2018). From an information processing standpoint, greater knowledge enhances individuals’ ability to evaluate technological attributes, which strengthens behavioural intention formation (Nunnally and Bernstein, 1978). Based on the technical complexity, volatility, and decentralized architecture of cryptocurrencies, a minimum threshold of financial and technological awareness is arguably essential for users to comprehend their operational mechanisms, security implications, and potential utility (Organisation for Economic Co-operation and Development, 2022; Pitafi et al., 2020; Pratama, 2021; PWC, 2020). In the absence of such awareness, users may rely on heuristics or misinformation, thereby impeding adoption (Peters et al., 2015). Consequently, consistent with DOI theory and UTAUT extensions, technology awareness is expected to exert a direct positive influence on cryptocurrency acceptance. Thus, this study constructed the following hypotheses:

H4 Technology awareness is positively related to the acceptance of cryptocurrency.

4.5 Government support

Government Support (GS) refers to the extent to which formal institutions establish regulatory frameworks, legal protections, monitoring mechanisms, and policy incentives that enable and legitimize the use of emerging technologies (Albayati et al., 2020). The construct is theoretically grounded in Institutional Theory, which posits that formal rules, regulations, and normative structures shape organizational and individual behavior by reducing uncertainty and enhancing legitimacy (Scott, 2008). The reason behind applying this factor in the context of this study, government regulation signals legitimacy and reduces perceived uncertainty, directly influencing adoption intention in highly regulated financial ecosystems (Saif Almuraqab, 2020; Steinmetz et al., 2021; Shareef et al., 2011). Within this perspective, regulatory endorsement functions as a coercive and normative mechanism that influences technology acceptance by signaling credibility and compliance. From a governance viewpoint, regulation reduces institutional voids and transaction uncertainty an argument aligned with Transaction Cost Economics (Williamson, 1980; Rana and Dwivedi, 2015; Roos, 2015). In high-risk and volatile environments such as cryptocurrency markets, clear regulatory oversight lowers perceived opportunism, information asymmetry, and enforcement ambiguity. This reduction in uncertainty enhances trust formation and strengthens behavioral intention. Government intervention operates through two principal channels, constraint mechanisms (e.g., taxation, licensing, compliance rules), and environmental structuring mechanisms, which shape the ecosystem in which technology diffusion occurs (Williamson, 1980).

For blockchain and cryptocurrencies, regulatory clarity is particularly critical due to their decentralized architecture, price volatility, cybersecurity risks, and absence from traditional monetary systems (Nadzir et al., 2025). Clear policies authenticate participating entities, authorize exchanges, and define consumer protection standards (Viriyasitavat and Hoonsopon, 2019). These institutional safeguards reduce perceived risk and enhance perceived security, both of which are central predictors of technology adoption (Lu, 2018). Empirical evidence further supports this institutional perspective. Regulatory frameworks such as New York’s BitLicense demonstrate how formalized oversight can legitimize crypto markets and increase consumer protection (Gupta et al., 2021). Studies also indicate that legal support enhances trust, facilitates e-business diffusion, and improves platform credibility (Raweh et al., 2024; Dabbous et al., 2022). Conversely, regulatory ambiguity has been shown to hinder adoption by increasing uncertainty and perceived vulnerability (Gupta et al., 2021). Within technology acceptance models, Government Support can be conceptualized as an extension of Unified Theory of Acceptance and Use of Technology facilitating conditions at the macro-institutional level. While facilitating conditions typically refer to technical infrastructure, Government Support represents formal institutional infrastructure, which indirectly enhances performance expectancy and directly strengthens trust and intention. Given that cryptocurrencies operate in a highly sensitive financial domain, institutional endorsement and regulatory clarity are likely to function as powerful signals of legitimacy and stability. Thus, consistent with Institutional Theory and Transaction Cost Economics, government support is expected to positively influence cryptocurrency acceptance by reducing uncertainty, enhancing trust, and legitimizing usage. As a result, this study proposed the following hypothesis:

H5 Government support is positively related to the acceptance of cryptocurrency.

5 Research methods

This section discusses questionnaire development, population and sampling frame, variables and measures, data collection methods, data analysis techniques, and response bias.

5.1 Questionnaire development

The questionnaire was administered in both Arabic and English languages to ensure inclusivity, clarity, and comprehension across diverse demographic groups. Arabic was used as the primary language, as it is the official language of Saudi Arabia and the native language of the majority of respondents. An English version was also provided to accommodate participants who are more comfortable with English, particularly professionals and individuals with international educational backgrounds. To ensure linguistic accuracy and conceptual equivalence, the survey instrument followed a translation and back-translation procedure. The original English questionnaire was translated into Arabic by a bilingual expert, and a second independent translator performed back-translation into English. Discrepancies were reviewed and resolved to maintain semantic consistency and construct validity. This approach aligns with established methodological recommendations for cross-language survey research (Creswell and Creswell, 2018). Providing bilingual survey options enhances response accuracy, reduces misinterpretation bias, and improves the reliability and validity of the collected data within multicultural research settings.

5.2 Population and sampling frame

This study focused on the cryptocurrency users within the Kingdom of Saudi Arabia (KSA), this study employed purposive sampling to capture essential variables and scales. To ensure robust statistical power, the sample size was calculated following the recommendation of Cohen’s recommendations (Cohen, 1992).

5.3 Sample and sampling approach

Given the anonymous nature of cryptocurrency, obtaining a complete list of its users was not feasible; therefore, purposive sampling was adopted. Initially, 600 questionnaires were chosen due to the suggestions of Cohen (1988), who reported that a high response rate is significant for generalizing the study’s findings to the studied population. And these questionnaires were distributed online in August 2025. After regular follow-up reminders, the responses were received by the end of October 2025. Following the removal of incomplete responses, there were some questionnaires with missing data, which were eliminated from the study. Therefore, a total of 412, valid questionnaires were retained for subsequent data analysis.

5.4 Variables and measure

The measurement items for each construct were adapted from established and credible sources in the existing literature. A five-point Likert scale was employed to assess the study variables, ranging from 1 (“strongly disagree”) to 5 (“strongly agree”).

5.5 Data collection producer

Primary data were collected from individual users in Saudi Arabia (KSA) through a structured, online questionnaire employing a quantitative research approach. The survey was distributed via email and social media platforms such as Telegram and WhatsApp to reach respondents across different regions of KSA.

5.6 Non-response rate and selection bias

The reported non-response rate of approximately 19% permits careful consideration. Non-response bias arises when individuals who decline participation systematically differ from those who respond (Hair Jr et al., 2021) Nonetheless, the possibility of selection bias remains. Individuals with stronger interest in digital finance or more favourable perceptions of cryptocurrency may have been more inclined to participate. This may slightly overestimate acceptance levels within the broader population.

5.7 Data analysis approach

SPSS version 27 was utilized for analyzing the demographic data, while Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS version 3.28 was employed to test the proposed hypotheses. When research models involve multiple constructs, or when the sample size is relatively small, PLS-SEM is recognized as a more suitable approach than covariance-based SEM methods such as AMOS (Hair et al., 2019). Hence, SmartPLS was chosen for analyzing the hypothesized relationships in this study.

5.8 Descriptive results

According to Table 2, 78% of the respondents were male, whereas 22% were female. Regarding age distribution, the majority (46.8%) were between 33 and 44 years old, followed by 18.8% aged 25–34, 31.4% aged 18–24, and 11% aged 45 years or older. In terms of educational attainment, 70% of respondents held a bachelor’s degree, while 14% possessed a diploma qualification. Additionally, 7% had certificates, and finally 9% had postgraduate qualifications. Concerning, cryptocurrency experience among respondents, 21% reported having less than 1 year of cryptocurrency experience, 52% had between one and 3 years of experience, and 27% had more than 3 years. This distribution indicates that a majority of participants (79%) have moderate to extensive exposure to cryptocurrencies, suggesting that most respondents are familiar with digital asset markets and have had sufficient time to develop usage patterns. In term of, transaction frequency, 22% of respondents described transacting rarely, 53% engaged in weekly transactions, and 25% transacted monthly. This pattern demonstrates that over three-quarters of the sample are actively engaged in cryptocurrency transactions, reflecting practical involvement rather than merely exploratory or passive interest. The predominance of weekly activity implies consistent interaction with digital asset platforms among users. Regarding, type of cryptocurrency owned, participants showed holding different types of cryptocurrencies, with 32% owning Bitcoin only, 51% holding altcoins, and 17% possessing multiple types of cryptocurrencies. The large proportion of altcoin owners indicates that many respondents diversify their holdings beyond the most widely recognized cryptocurrency, which may imply greater risk tolerance, market awareness, and engagement with a broader ecosystem of digital assets. Lastly, regarding awareness level, the awareness of cryptocurrency varied among respondents, with 28% stating low awareness, 49% indicating moderate awareness, and 23% demonstrating high awareness. This signifies that most participants have at least a moderate level of understanding about cryptocurrency concepts, usage, and market dynamics.

TABLE 2. Demographic’s details (Source: the author).

6 Results

The analysis included several key metrics to evaluate the reliability and validity of the structural model within the Structural Equation Modelling (SEM) framework, namely, common method bias, convergent validity, discriminant validity, and face validity. Convergent validity was assessed using the average variance extracted (AVE), factor loadings, and internal consistency reliability, represented by Cronbach’s alpha. This procedure ensures that items measuring a given construct share a substantial proportion of variance. According to Hair Jr et al. (2021), Cronbach’s alpha value of 0.6 or higher is considered acceptable, while AVE and factor loadings of 0.5 or above indicate satisfactory convergence. As shown in Table 3, the Cronbach’s alpha values for all constructs ranged from 0.808 to 0.931, demonstrating a high level of internal consistency among the measurement items. Similarly, AVE values exceeded the minimum threshold of 0.5, ranging from 0.558 to 0.839, suggesting that the underlying constructs explain more than 50% of the variance in their respective observed variables. Furthermore, all factor loadings were above 0.5, confirming a strong relationship between the latent constructs and their indicators. In essence, these results confirm that convergent validity was successfully achieved, indicating that the items effectively capture the underlying theoretical constructs and that the measurement model demonstrates strong reliability and validity. Given that the data were collected using a single survey instrument, common method bias was assessed using Harman’s single-factor test, as recommended by Podsakoff et al. (2003). An exploratory factor analysis was conducted including all measurement items. The results indicated that the first unrotated factor accounted for 32.4% of the total variance, which is below the 50% threshold. This outlines that common method bias is unlikely to be a serious concern in this study. Additionally, procedural remedies were implemented during the survey design, including ensuring respondent anonymity and reducing item ambiguity, to further minimize potential method bias.

TABLE 3. Convergent validity (Source: the author).

Discriminant validity refers to the requirement that each construct in the model be empirically distinct from other constructs, representing a critical criterion for establishing overall construct validity. In this study, discriminant validity was evaluated using both the Fornell–Larcker criterion and the Heterotrait–Monotrait ratio (HTMT), as recommended in contemporary structural equation modelling literature. First, Fornell-Larcker Criterion, according to this criterion, the square root of the Average Variance Extracted (AVE) for each construct should exceed its correlations with all other constructs. As shown in Table 4, the square roots of AVE values for all constructs are higher than the corresponding inter-construct correlations, confirming adequate discriminant validity.

TABLE 4. Discriminant validity values using fornell-larcker criterion (Source: Fornell and Larcker, 1981).

Second, discriminant validity was also assessed using Heterotrait-Monotrait Ratio (HTMT) criterion proposed by Henseler et al. (2015). As presented in Table 5, HTMT results illustrate that it is not a concern in this study since it is a new criterion to assess discriminant validity. Additionally, no item was removed during this stage. These findings show that the constructs are distinct from each other and support the validity of the model.

TABLE 5. Heterotrait-monotrait ratio (Source: Henseler et al., 2015).

7 Discussion

The results indicate that performance expectancy, effort expectancy, technology awareness, and government support expose positive relationships with cryptocurrency adoption, whilst, perceived risk does not demonstrate a statistically robust effect. Based on Table 6, the findings revealed that performance expectancy shows a positive effect on adoption intention (β = 0.052, t = 1.142, p = 0.127). This finding illustrates that perceived usefulness does not play a critical role in influencing cryptocurrency adoption within the sample. This contrasts with the core assumptions of the Unified Theory of Acceptance and Use of Technology, where performance expectancy is a strong predictor. A possible explanation is that cryptocurrency is not primarily perceived as a productivity-enhancing technology, but rather as a speculative financial asset, thereby limiting the relevance of functional performance benefits. Also, effort expectancy is found to have a statistically significant effect (β = 0.0276, t = 5.775, p = 0.05); nevertheless, the magnitude of this relationship is small, indicating minimal practical influence.

TABLE 6. Regression analysis results.

*indicates a significant level at p < 0.01., indicates a significant level at p < 0.05 (Source: the author).

This signifies that ease of use is unlikely to be a key barrier or driver of adoption, potentially due to the respondents’ relatively high level of digital familiarity. However, perceived risk does not reveal a statistically significant effect on adoption, leading to the rejection of the corresponding hypothesis. This result contradicts conventional assumptions about technology adoption, which usually consider risk as a major deterrent. In the context of cryptocurrency, this result may be explained by the normalization of risk, where users already perceive volatility and uncertainty as inherent characteristics of the system. Thus, perceived risk may not differentiate adopters from non-adopters but instead functions as a baseline condition. Technology awareness demonstrates the strongest effect among all predictors (β = 0.098, t = 2.258, p = 0.012), however the effect size remains modest. This indicates that awareness contributes positively to adoption; but its practical impact is incremental rather than substantial. Increasing awareness alone is therefore unlikely to produce significant changes in adoption behavior without complementary factors.

Finally, government support shows a statistically significant positive effect (β = 0.084, t = 2.137, p = 0.017), nonetheless with a small effect size. While regulatory clarity and institutional backing may enhance user confidence, their overall influence on adoption remains limited. This finding can be partially interpreted through Prospect Theory (Barberis, 2012), which suggests that individuals evaluate decisions based on perceived gains and losses. Government interventions may reduce perceived losses (e.g., fraud, instability), even though the relatively small effect indicates that such measures alone are insufficient to drive widespread adoption.

8 Limitations and future directions

Despite its several contributions, this study has certain limitations. To start with, the sample used may not fully represent the broader population of Saudi Arabia; thus, future research would benefit from incorporating a larger and more demographically diverse sample. Second, since the data were self-reported, responses may have been influenced by social desirability bias. Although the study employed a mixed-methods approach to triangulate findings and enhance analytical depth, some degree of bias cannot be entirely ruled out. Third, the conceptual framework proposed in the discussion represents an initial exploration of the key variables influencing cryptocurrency adoption and its impacts, rather than a definitive or exhaustive analytical model. Fourth, as this study focuses exclusively on the Saudi Arabian context, the generalizability of its findings to other settings is limited, as noted by Yin (2018). Nonetheless, Flyvbjerg et al. (2006) argued that context-dependent knowledge holds intrinsic value in producing concrete insights, and the authors maintain that the proposed conceptual framework and detailed results may provide valuable guidance to researchers and policymakers in comparable environments. Equally significant, several avenues for future research merit consideration. Longitudinal investigations could examine the progression of cryptocurrency adoption across different contexts to support the development of comprehensive implementation models and practical guidelines. Comparative cross-national analyses may also yield valuable insights by contrasting the Saudi Arabian findings with those from other developing economies where financial digitalization is a growing focus (Zetzsche et al., 2019). Moreover, future studies could further explore the effects of regulatory frameworks on cryptocurrency utilization and their implications for advancing financial inclusion.

9 Conclusion

This study underlines the potential of cryptocurrencies to advance financial inclusion in the KSA, while also highlighting the pivotal roles of financial literacy and robust digital infrastructure. It also aims to investigate the key factors influencing the acceptance of cryptocurrency by employing the SEM-PLS approach based on the UTAUT model, which is extended to include perceived risk, technology awareness, and government support. The findings offer a strong basis for developing policy and strategic initiatives aimed at integrating cryptocurrencies responsibly within Saudi’s financial transaction. By implementing a clear regulatory framework, enhancing public financial literacy, and strengthening digital infrastructure, the KSA could effectively harness the benefits of cryptocurrencies to promote both financial inclusion and broader socio-economic development. In doing so, this research makes a modest yet meaningful contribution to filling the existing gap in the literature concerning the role of cryptocurrencies in developing economies like KSA. It also opens new pathways for future investigations, particularly into the regulatory dimensions of cryptocurrency adoption and comparative analyses across different international contexts.

9.1 Theoretical implications

This study expands technology adoption literature by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) through the integration of perceived risk, technology awareness, and government support in the context of cryptocurrency adoption. While UTAUT has been widely validated across information systems research (Venkatesh et al., 2003), its application to decentralized financial technologies remains comparatively limited. By incorporating risk-related and institutional variables, the study aligns with insights from innovation diffusion theory thereby strengthening the explanatory power of technology acceptance models in emerging financial ecosystems. The findings assert that cryptocurrency adoption in the Kingdom of Saudi Arabia is not driven solely by underlying factors like performance expectancy and effort expectancy, but also by contextual determinants such as regulatory clarity and digital readiness. This contributes to closing a documented research gap regarding digital financial innovation in developing and emerging economies.

9.2 Practical implications

The results contend the necessity of transparent and coherent regulatory framework to reduce perceived risk and increase public trust. International evidence suggests that regulatory clarity enhances fintech innovation while mitigating systemic risk. Developing comprehensive cryptocurrency regulations covering licensing, consumer protection, anti-money laundering compliance, and cybersecurity standards would strengthen adoption confidence and align with global best practices. Furthermore, government support initiatives such as awareness campaigns, public consultations, and pilot projects could foster legitimacy and responsible innovation. Institutional endorsement has been shown to significantly influence technology diffusion in emerging markets Banks, fintech firms, and digital asset service providers should prioritize user education and risk communication strategies. Enhancing users understanding of blockchain-based financial services reduces uncertainty and improves adoption outcomes. Evidence from the World Bank (2021) indicates that digital infrastructure and connectivity are foundational for inclusive digital finance expansion. Strategic investment in secure digital infrastructure including cybersecurity frameworks, identity verification systems, and digital payment ecosystems will be critical for ensuring safe cryptocurrency transactions and maintaining systemic stability. The study underscores the importance of financial literacy programs in strengthening informed participation in digital financial systems. The OECD (2020) emphasizes that financial education improves decision-making and mitigates susceptibility to financial fraud. Integrating cryptocurrency literacy into national financial education strategies could enhance responsible adoption and reduce speculative or uninformed engagement.

9.2.1 Institutional review board statement

This study involved human participants (n = 412) who completed an anonymous structured questionnaire. Participation was entirely voluntary, and informed consent was obtained from all respondents prior to data collection. No personally identifiable information was collected, and all responses were treated with strict confidentiality and used solely for academic research purposes. The study procedures were reviewed in accordance with the research ethics policies of Prince Sattam Bin Abdulaziz University. Under the university’s institutional guidelines applicable at the time of data collection, research involving anonymous, non-interventional survey data from adult participants that does not include sensitive personal information is exempt from formal Institutional Review Board (IRB) approval. Therefore, formal ethical committee approval was not required for this study. All research procedures were conducted in accordance with internationally recognized ethical principles for research involving human participants, including the Declaration of Helsinki (World Medical Association, 2013).

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbloc.2026.1760542/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Alex Zarifis, University of Southampton, United Kingdom

Reviewed by: K. Balaji, Christ University, India

Srinidhi Vasudevan, University of Greenwich, United Kingdom