Intention to Use Cryptocurrencies for Business Transactions: The Case of North Carolina

Abstract

Financial technologies and payment applications have revolutionized money flow recently, with cryptocurrencies offering decentralization, though still limited in transactional use. This study investigates the factors influencing the use of cryptocurrencies for business transactions in North Carolina (NC). This exploratory research utilizes an extended technology acceptance model (TAM) using survey data collected from 228 North Carolina residents and applying Partial Least Squares Structural Equation Modeling (PLS-SEM) to find the relationship between the independent and dependent variables. Our results indicate that perceived usefulness, social influence, and personal innovativeness significantly impact users’ intentions to adopt cryptocurrencies as a medium of exchange. A surprising finding is that ownership has a negative effect on the intention to use cryptos for business transactions. The findings imply that regulators and cryptocurrency issuers should make the system more useful, take full advantage of social media to promote cryptos, and encourage crypto holders to use cryptos for their intended utility rather than just as speculative instruments.

1. Introduction

The world has advanced in financial technology, digital payments, and the Internet of Things (IoT) (Allioui & Mourdi, 2023). One such innovation is using cryptocurrencies (Chohan, 2023) that seek to offer security, transparency, and anonymity (Alzoubi, 2024). Cryptocurrencies are especially useful in the remittance sector of emerging economies, where a significant portion of the population is employed in other countries and sends money home through cryptos because it is fast and cheap (El Hajj & Farran, 2024; Vincent & Evans, 2019).

Cryptocurrencies were initially created as a mechanism enabling people to pay for goods and services with the help of digital currencies (Panda et al., 2023). However, in the real world, the primary application of cryptocurrencies is in trading and investment, with minimal application in acquiring goods and services (Luo & Yu, 2024). This slow adoption of cryptocurrencies as a payment method may be attributed to fluctuating prices, risk propensity, lack of information, and regulatory uncertainty (Sridharan et al., 2023). Assessing the future use of cryptocurrencies as an instrument in the world economy is crucial for businesses, investors, and governments (Sandua, 2023; Sestino et al., 2024). Thus, it is relevant to ask whether cryptocurrencies will become the means of regular business transactions or remain an exclusively speculative instrument (Molina, 2023), and this is the question we try to answer, especially in the context of North Carolina, which is midway along the East Coast of the United States and away from the financial and business hubs of the country.

Previous research has identified factors affecting the intention to use cryptocurrencies, including perceived usefulness, Perceived Ease-of-Use, and perceived risk (Ecer et al., 2024; Hayashi & Routh, 2024; Kim, 2021). As defined by Nadeem et al. (2021), cryptocurrencies’ adoption is relatively low, and consumers need to learn more about trust, security, and perceived value as the primary influencers in adopting cryptocurrencies for business transactions (El Hajj & Farran, 2024). Most of the prior research on the adoption of cryptocurrencies is at the global or national level, and little is known about regional- or local-level adoption (Jegerson et al., 2024). This research is relevant to the existing literature in that it investigates the adoption and usage of cryptocurrencies in the United States, focusing on North Carolina. This study aims to establish the factors that affect the uptake of cryptocurrencies for daily and business transactions on a regional basis in the United States. Further, although prior research has examined the different factors that influence consumers’ intention to use cryptocurrencies, the literature still needs to be more conclusive. This research, therefore, seeks to fill this gap by establishing the key factors affecting consumers’ intention to use cryptocurrencies in North Carolina. Some researchers have investigated the antecedents of consumers’ cryptocurrency usage intention (Kim, 2021; Pillai et al., 2024), identifying certain behavioral factors, such as perceived usefulness and ease-of-use, for customers’ intention to use (Ecer et al., 2024; Namahoot & Rattanawiboonsom, 2022). However, these studies have been conducted in different geographical regions, and there needs to be more research on this in the United States. Even different states in the US have their unique demographic dynamics, and we argue that these behavioral aspects need to be investigated at the state level. Thus, this study identifies the factors that may be significant to North Carolinians regarding accepting cryptocurrencies for business transactions (Hayashi & Routh, 2024).

Survey data were collected from 228 respondents in North Carolina, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to analyze relationships among the constructs of the extended technology acceptance model (TAM), including perceived usefulness, Perceived Ease-of-Use, social influence, perceived trust, ownership, financial literacy, transparency, innovativeness, perceived risk, and intention to use cryptocurrencies.

This paper is organized into the following sections. Section 2 provides a literature review and hypothesis development; Section 3 explains the methodology; Section 4 presents the results; Section 5 discusses the findings; Section 6 summarizes the conclusions; and Section 7 provides implications, limitations, and suggestions for further research.

2. Literature Review

Blockchain was invented in 2008 as a technology for Bitcoin, the first decentralized digital currency, which sought to remove the intermediaries in financial transactions (Panda et al., 2023). Blockchain was introduced as a public ledger to record all the transactions of Bitcoin transparently and unalterably (Lisdorf, 2023; Nakamoto, 2008). Cryptocurrencies are not backed by conventional financial bodies such as banks or a central bank (Podder, 2023). However, they are developed, traded, and protected by intricate numerical formulas and computer-distributed systems (Alam, 2024; Yadav et al., 2023). Transactions are validated and documented by a network of nodes or participants, not a central agency or middleman (Howell et al., 2023). This makes having a much more secure system more accessible and less prone to fraud or manipulation (Pham et al., 2024; Potla, 2023). The independence from a central authority or regulation gives freedom and decentralization to cryptocurrencies that cannot be availed in the traditional financial system (Kayani & Hasan, 2024). Although cryptocurrencies have already influenced the financial sector in a rather significant way, many professionals believe that the use of cryptos will expand to routine business transactions (Hmimnat & El Bakouchi, 2023). Because of cryptocurrencies’ complexity, firms specializing in cryptocurrencies, like cryptocurrency brokerages and investment funds, have also been established to assist individuals and institutions in investing in cryptocurrencies (Danial, 2023; Olabanji et al., 2024).

While earlier attempts at increasing the usage of cryptocurrencies were made with DigiCash and Hashcash, neither of these currencies gained much usage until a mysterious author launched Bitcoin through a paper known as the white paper in 2008 (Hanl, 2018; Lee et al., 2018). Bitcoin is an electronic currency that was introduced by an unknown person (Hanl, 2018).

The disparity in emerging and developed economies has identified varied reasons for holding cryptocurrencies (Koziuk, 2022). In emerging economies, cryptocurrencies are a development enabler that helps make transactions and protect against volatile local currencies, while in developed countries, cryptocurrencies are viewed more as an instrument of investment (Rubanov et al., 2022). These use cases are not separate; some people in emerging economies are trading cryptocurrencies as an investment asset, while some people in developed economies are using them as a store of value or for other things (Gupta et al., 2023; Levulytė & Šapkauskienė, 2021).

In the following subsections of the literature review, we develop our hypotheses based on the extended TAM, starting with the original TAM variables and then adding the extended variables that the author recognized as uniquely applicable to cryptocurrencies.

2.1. Perceived Usefulness and Intention to Use Cryptocurrencies

Perceived usefulness is a person’s belief that using a particular technology will help them perform better or benefit them (Romero-Rodríguez et al., 2023). In cryptocurrencies, this perception is critical in determining users’ intention to adopt and use these digital currencies (García-Monleón et al., 2023). Research has shown that the perceived usefulness of cryptocurrencies is a significant predictor of the intention to use cryptocurrencies (Islam et al., 2023). Arias-Oliva et al. (2019) found that perceived usefulness is critical in the technology acceptance model. They found that individuals who think cryptocurrencies are suitable for practical uses are more likely to use them, suggesting that demonstrating the practical benefits of cryptocurrencies to potential users is essential (Ter Ji-Xi et al., 2021).

Furthermore, a study conducted by Mishra et al. (2024) shows that perceived usefulness plays a significant role in the intention to adopt cryptocurrencies. This aligns with the broader technology acceptance literature, which holds that users are more likely to use technologies as long as they are helpful (Granić, 2023). These findings have implications for how the perceived usefulness of cryptocurrencies can increase users’ adoption rates (Al-Omoush et al., 2024).

People will more likely adopt cryptocurrencies when they believe they can obtain tangible benefits, such as investment opportunities or increased transaction efficiency (Sukumaran et al., 2023). The work of Rahardja et al. (2023) also strengthens this relationship by showing that perceived usefulness is mediated by perceived trust and perceived risk in the intention to use cryptocurrencies. This implies that perceived usefulness is essential but only with an overall context of trust and risk perceptions that may affect users’ intentions (Ventre & Kolbe, 2020), and we assume this will also hold in the context of North Carolina. Thus, the following hypothesis is proposed:

H1. 

Perceived usefulness (PU) positively influences the intention to use cryptocurrencies in NC.

2.2. Perceived Ease-of-Use and Intention to Use Cryptocurrencies

As a foundational framework, the technology acceptance model (TAM) explains how users’ Perceived Ease-of-Use and usefulness of a new technology, such as cryptocurrencies, affect their intention to adopt it (Namahoot & Rattanawiboonsom, 2022). For instance, research from Jariyapan et al. (2022) finds that the perceived benefits of cryptocurrencies are positively related to perceived value and behavioral intentions to use cryptocurrencies.

Furthermore, the results obtained by Saif Almuraqab (2020) also support the idea that the Perceived Ease-of-Use is a strong predictor of the intention to use digital currencies. A study by Avcı et al. (2023) also shows that ease-of-use is one of the main factors influencing consumers’ attitudes toward cryptocurrency adoption. In the case of North Carolina, we also expect that if people find cryptos easy-to-use as a payment method, they most likely will. Hence, the following hypothesis is proposed:

H2. 

Perceived Ease-of-Use (PEU) positively influences the intention to use cryptocurrencies in NC.

2.3. Financial Literacy and Intention to Use Cryptocurrencies

Financial literacy consists of understanding and effectively using various financial skills, such as personal financial management, budgeting, and investing (Hasanuh & Putra, 2020). This competence is increasingly critical in adopting innovative financial technologies, including cryptocurrencies (Kumari et al., 2023). According to research, people who know more about finance are more likely to make informed decisions about investments—including cryptocurrencies (Sa’diyah et al., 2024). For example, Alomari and Abdullah (2023) showed that financial literacy plays a significant role in accepting and using cryptocurrencies, as people who are more financially literate are better at understanding the risks and benefits of investment in cryptocurrencies. Likewise, Dabbous et al. (2022) discovered that financial technology awareness is an essential determinant of users’ willingness to adopt cryptocurrencies and diminish the perceived risks of these digital assets. In addition, research has indicated that financial literacy is associated with significantly higher financial confidence in financial decision-making and decision-machination to invest in cryptocurrencies (Jariyapan et al., 2022).

Furthermore, Kumari and Kumar (2023) indicate that although financial literacy plays a part in shaping users’ behavioral intentions toward cryptocurrencies, it might not be the sole determiner, as other factors, such as technological awareness and personal innovativeness, are also very important in determining users’ behavioral intentions towards cryptocurrencies. The work of Maleh et al. (2024) further illustrates this complexity by showing a positive correlation between financial literacy and the intention to use cryptocurrencies while insisting on the necessity of further explorations of the underlying mechanisms. We assume that the educated North Carolina population will be more inclined to use cryptos as a medium of exchange rather than just a speculative instrument. Hence, we develop hypothesis H1.

H3. 

Financial literacy (FL) positively influences the intention to use cryptocurrencies in NC.

2.4. Ownership and Intention to Use Cryptocurrencies

One might argue that people who own cryptocurrencies for various reasons, like investment purposes, are more likely to use them in actual business transactions. Therefore, it is important to understand the relationship between ownership and the intention to use cryptocurrencies. A growing body of literature exploring different psychological, social, and economic factors that may influence cryptocurrency adoption has been used to support the hypothesis that ownership (OW) positively influences the intention to use cryptocurrencies.

Sachitra and Rajapaksha (2023) suggest that those who value innovation and achievement are more likely to embrace cryptocurrencies and that ownership may increase these values, increasing the intention to use cryptocurrencies (Al-Omoush et al., 2024).

In addition, social factors have a significant role in cryptocurrency adoption (Steinmetz et al., 2021). Alzahrani and Daim (2019) state that social norms affect people’s decision to adopt cryptocurrencies. These social dynamics reveal that ownership can increase an individual’s social capital and lead to a positive relationship between cryptocurrencies and ownership and the intention to use (Siqueira et al., 2020).

In addition, the relationship between ownership and the intention to use cryptocurrencies is affected by economic factors (Steinmetz et al., 2021). Kovalchuk et al. (2024) showed that more cryptocurrency trading is correlated with economic growth and that ownership can bring about confidence in cryptocurrencies as a medium of exchange. This is also consistent with the work of Mazambani (2024), who found that a positive attitude towards cryptocurrencies is significantly correlated with the behavioral intention to adopt them. It was found that individuals who gain ownership are more likely to have favorable attitudes toward cryptocurrencies and more likely to intend to use them (Steinmetz et al., 2021).

Some researchers suggest that ownership may mitigate perceived risks, which leads to a more favorable intention of using cryptocurrencies (Sagheer et al., 2022). Based on the literature, we assume a positive relationship between the ownership of cryptos and their use as a medium of exchange in North Carolina. Hence, the following hypothesis is proposed:

H4. 

Ownership (OW) positively influences the intention to use cryptocurrencies in NC.

2.5. Personal Innovativeness and Intention to Use Cryptocurrencies

Personal innovativeness is the extent to which an individual is willing to adopt new ideas and technology (Mendoza-Tello et al., 2019). It has been found that personal innovativeness has a significant effect on willingness to adopt cryptocurrencies (Hasan et al., 2022). Dilanchiev et al. (2024) noted that innovativeness is positively associated with adopting cryptocurrencies, as those who are more innovative are more likely to seek out and use new financial technologies. According to their findings, innovative people are more likely to embrace new financial products and services, which would help cryptocurrency adoption (Kumari et al., 2023). This is consistent with Kumari et al. (2023), who found that performance expectancy mediates between personal innovativeness and the intention to use cryptocurrencies. This means that innovative people also have a higher intention to use cryptocurrencies and perceive these technologies as beneficial and valuable, encouraging their behavior of adoption (Mendoza-Tello et al., 2019). As with Wongsunopparat and Nanjun (2023), research on personal innovativeness also supports the idea that personal innovativeness is positively related to consumers’ attitudes toward adopting cryptocurrencies. Hence, we believe that North Carolinians who consider themselves more innovative are more likely to use cryptos not just for trading or investment but also for the actual purchase and sale of goods and services. Therefore, the following hypothesis is proposed:

H5. 

Personal innovativeness (PI) positively influences the intention to use cryptocurrencies in NC.

2.6. Perceived Risk and Intention to Use Cryptocurrencies

The risk dimension includes financial, legal, and security risks, which can be perceived as very high and can deter people from engaging with cryptocurrencies (Huang et al., 2023). Maheta and Mehta (2024) note that regulatory barriers are a significant concern for potential users because regulatory uncertainty can lead to an uncertain environment. This is consistent with the findings from Dabbous et al. (2022), who claim that perceived risk can significantly impede using cryptocurrencies for financial transactions in high-risk contexts. The results of their research suggest that the lack of consensus on the dangers of cryptocurrencies is one of the reasons why users are hesitant and that perceived risk negatively affects the intention to use (Huang et al., 2023).

Additionally, the existing literature has highlighted the role of perceived trust in mitigating perceived risk (Qalati et al., 2021). According to Rahardja (2023), individuals who are more open to new technologies are more likely to perceive lower risks regarding cryptocurrencies. In this sense, increasing trust might mitigate perceived risks and encourage a more favorable intention to use cryptocurrencies (Dabbous et al., 2022). In contrast, Arias-Oliva et al. (2019) did not find perceived risk to significantly influence the intention to use cryptocurrencies for electronic payments, suggesting that there is not a universal relationship. Furthermore, the results of Soomro and Ghumro (2024) supported the belief that perceived risk plays a pivotal role in adopting cryptocurrencies. According to their research, perceived risk trust and social norms significantly impact users’ intention to adopt cryptocurrencies (Ögel & Ögel, 2021). This shows how perceived risk is multifaceted and interacts with other psychological factors (Li & Li, 2023). Based on this discussion, we assume that North Carolinians who consider cryptos riskier will be less likely to use them as a medium of exchange. Hence, the following hypothesis is proposed:

H6. 

Perceived risk (PR) negatively influences the intention to use cryptocurrencies in NC.

2.7. Perceived Trust and Intention to Use Cryptocurrencies

Trust in technological attributes is crucial for users’ confidence, especially in cryptocurrencies, as there are so many scams in this area (Marella et al., 2020). Rahardja et al. (2023) confirm that trust is essential because users tend to distrust cryptocurrencies because of their complexity and the controversies around cryptocurrencies.

In addition, Yassin (2023) shows that perceived trust is an essential predictor of the intention to use cryptocurrencies, confirming that trust is a critical factor in cryptocurrency adoption. Jariyapan et al. (2022) also find that consumer protection measures in cryptocurrency exchanges can increase adoption rates by enhancing trust. Just as in Arli et al. (2021), trust is vital for potential adopters to trust the credibility and reliability of cryptocurrencies. Furthermore, Maheta and Mehta (2024) show that the volatility of cryptocurrencies makes investors hesitant. This is supplemented by Ahsan and Gupta (2024), who explore how perceived trust influences the likelihood of cryptocurrency adoption in high-risk contexts. Therefore, people in North Carolina who trust cryptos as a sound alternative to fiat currency will most likely use them for business transactions. Hence, the following hypothesis is proposed:

H7. 

Perceived trust (PT) positively influences the intention to use cryptocurrencies in NC.

2.8. Social Influence and Intention to Use Cryptocurrencies

Social influence refers to the impact of the opinions of peers, societal norms, and other people on an individual’s choice of the technology adoption process (Wolske et al., 2020). In cryptocurrencies, social influence can come through family, friends, social media, and so on (Subramanian, 2021). According to Mansoor et al. (2024), social factors are the dominant driving force in the desire to use cryptocurrencies. This is consistent with the larger picture of adopting technology, where intention is so important. Sachitra and Rajapaksha (2023) corroborate this further by pointing to social factors such as subjective norms and influencers’ influence on cryptocurrency adoption in emerging markets. Social influence plays a huge role among younger demographics and is significantly correlated with behavioral intention to adopt cryptocurrencies in Saudi Arabia among university students (Ter Ji-Xi et al., 2021).

Additionally, Chhillar et al. (2024) also point out that social influence is a significant predictor of cryptocurrency acceptance and that investors are swayed by the opinions and actions of their peers. Nurbarani and Soepriyanto (2022) show that demographic factors can moderate subjective norms’ influence on cryptocurrency investment decisions, whereby social pressure may push or restrain individuals’ investments. The integration of social influence into the existing models of technology acceptance has improved the predictability of users’ intention to adopt cryptocurrencies (Alaklabi & Kang, 2022; Jariyapan et al., 2022). We assume our findings will align with the existing literature and that social influence will make people in North Carolina more inclined to use crypto for business transactions. Thus, the following hypothesis is posited:

H8. 

Social influence (SC) positively influences the intention to use cryptocurrencies in NC.

2.9. Transparency and Intention to Use Cryptocurrencies

Trust and credibility in the cryptocurrency ecosystem are primarily based on transparency; therefore, building confidence in an uncertain regulatory environment is critical (Ibrahimy et al., 2023). For user trust and confidence to grow, cryptocurrency transactions must be transparent (Handayani et al., 2023). According to Alsmadi et al. (2023), clear and well-defined rules significantly affect users’ intention to adopt cryptocurrencies. This study shows that transparent legal frameworks can reduce the uncertainty regarding cryptocurrencies’ use, which increases users’ willingness to use such digital assets (van der Linden & Shirazi, 2023). Gil-Cordero et al. (2020) also show that the immediacy and transparency inherent to cryptocurrencies make them more attractive than traditional financial services. Other studies also found that the transparency of cryptocurrency transactions can make users more likely to have a more positive attitude toward using cryptocurrencies and their intentions (Miraz et al., 2022).

Additionally, Dabbous et al. (2022) add that the need for more consensus on risks involved in cryptocurrency adoption can be resolved by higher transparency. According to their findings, if users know the risks and benefits of using cryptocurrencies, they are more likely to intend to use these digital currencies (Al-Omoush et al., 2024). This is consistent with the broader literature on technology adoption that consistently indicates that transparency increases user confidence and decreases perceived risk (Soomro & Ghumro, 2024). Additionally, there is an essential interaction between transparency and user attitudes (Zhang et al., 2019). Trust, closely related to transparency, has a robust role in predicting users’ intention to adopt cryptocurrencies (Soomro & Ghumro, 2024). This relationship further emphasizes the significance of transparency as a standalone factor as well as a vehicle for trust within the cryptocurrency ecosystem (ur Rehman et al., 2019). We assume that if the NC population believes crypto transactions are more transparent, at least to them, they will be willing to use them. Hence, the following hypothesis is proposed:

H9. 

Transparency (TP) positively influences the intention to use cryptocurrencies in NC.

2.10. Theoretical Model

Based on the literature review above and the identified hypotheses, we propose the following theoretical framework shown in Figure 1.

3. Methodology

We use the Technology Adoption Model (TAM) to test our theoretical framework and the relationship between variables. Data were collected through an online questionnaire from the residents of North Carolina in the United States. North Carolina (NC) was selected because of the funding agency’s requirement for this research to be conducted in NC and its diverse economic landscape, providing a microcosm for cryptocurrency adoption trends. The Institutional Review Board (IRB)’s approval was received before embarking on the data collection process, meaning that all steps were taken to safeguard participants, including receiving consent.

Participants were required to have prior knowledge of cryptocurrencies to ensure informed responses. It was assumed that participants who did not know about cryptocurrencies were not in a good position to gauge the risks and benefits of using them in transactions. An initial screening questionnaire was used for this purpose. A negative response disqualified them from taking part in this research. Data were collected over a four-month period from February 2024 to May 2024 using convenience sampling. Participants were recruited through online forums, social media, and email invitations. After discarding 2 incomplete responses due to missing data, 228 responses were used in the final analysis. Before proceeding with the statistical analysis, data screening was conducted to ensure accuracy and integrity. The mean replacement method was used to replace some missing values, as the proportion of missing data was below 5%, adhering to the recommendations of Little and Rubin (2014). The mean replacement method replaces missing values with the average of that variable’s observed values, ensuring that the overall mean remains unchanged. Thus, the dataset was appropriate for further statistical analysis.

We used an extended version of the TAM, adding variables such as financial literacy, ownership, personal innovativeness, perceived risk, social influence, and transparency to make this research more relevant to the dynamics involved in the cryptocurrency space. These additional variables were built into hypotheses in the literature review section and supported by appropriate citations.

The measurement and structural models were assessed for convergent and discriminant validity following the PLS-SEM methodology. Confirmatory Factor Analysis (CFA) evaluated the relationships between items and their corresponding constructs. The internal consistency of the constructs was measured using Cronbach’s alpha and Composite Reliability (CR), which is a more robust alternative to Cronbach’s alpha (Paiva et al., 2014).

Convergent validity was assessed using the Average Variance Extracted (AVE), which evaluates how much variance is explained by the indicators of a construct. All AVE values exceeded the minimum recommended value of 0.50 (Cheung et al., 2024), indicating that the constructs accounted for more than half of the variance in their indicators.

Discriminant validity was tested using the Fornell–Larcker criterion and the Heterotrait–Monotrait (HTMT) ratio. According to the Fornell–Larcker criterion, the square root of each construct’s AVE was higher than the correlations with other constructs, confirming that each construct was distinct from the others. The HTMT ratio further supported this, with values below the recommended threshold of 0.85, ensuring satisfactory discriminant validity.

The structural model assessment involved testing the significance of relationships between variables using path coefficients, t-values, and standard errors using the bootstrapping technique in Smart PLS 4. The coefficient of determination (R²) was calculated to measure the model’s predictive accuracy. The R² value of 0.518 indicates that the model has a moderate level of explained variance which can be interpreted as the fact that independent variables explain 51.8% of the variance in the dependent variable (Hair et al., 2019). This is also supported by the Q-Square value of 0.456, as shown in Table 8. To further assess the strength of the relationships in the model, the effect size (f²) was calculated and is shown in Table 7. According to Cohen (2013), f² values of 0.02, 0.15, and 0.35 indicate small, medium, and large effects, respectively.

We also conducted a Common Method Bias (CMB) test using the Variance Inflation Factor in SmartPLS4. The results are given in

Table 1. Variance Inflation Factor (VIF).

Construct	VIF	Construct	VIF	Construct	VIF	Construct	VIF
FL1	2.82	OW1	2.15	PI3	2.11	SC1	2.96
FL2	2.79	OW2	2.15	PR1	1.5	SC2	3.54
FL3	1.5	PEU1	2.1	PR2	1.8	SC3	2.85
IU1	3.48	PEU2	2.24	PR4	1.37	TP1	1.44
IU2	4.18	PEU3	1.35	PU1	2.29	TP2	1.27
IU3	3.2	PEU4	1.2	PU2	2.51	TP3	2.61
IU4	2.56	PI1	1.49	PU3	2.2	TP4	2.2
IU5	1.62	PI2	1.74	PU4	2.25	TR1	2.01
				TR3	1.86	TR2	2.13

, which shows that all values for the independent variables are less than the recommended threshold of 3.3. This confirms that CMB does not exist. The VIFs for the dependent (endogenous) variable are greater than 3.3, reflecting the strong explanatory power of the model.

4. Results and Discussion

Before concluding, we present several tests we conducted to check the model’s reliability and validity.

Table 2. A convergent validity test.

Construct	Items	Factor loading	Cronbach’s Alpha	Composite Reliability (CR)	Average Variance Extracted (AVE)	Construct	Items	Factor Loading	Cronbach’s Alpha	Composite Reliability (CR)	Average Variance Extracted (AVE)
Intention to Use	IU1	0.98	0.87	0.905	0.659	Perceived Usefulness	PU1	1	0.88	0.916	0.733
	IU2	1.01					PU2	1
	IU3	1.14					PU3	1.04
	IU4	1.03					PU4	0.97
	IU5	0.76				Social Influence	SC1	1	0.91	0.943	0.847
Ownership	OW1	0.99	0.85	0.928	0.865		SC2	0.99
	OW2	1.01					SC3	1.01
Perceived Ease-of-Use	PEU1	1.11	0.76	0.842	0.577	Transparency	TP1	0.93	0.76	0.848	0.59
	PEU2	1.06					TP2	0.58
	PEU3	0.77					TP3	1.18
	PEU4	0.9					TP4	1.2
Personal Innovativeness	PI1	0.96	0.78	0.874	0.698	Perceived Trust	TR1	0.92	0.84	0.902	0.755
	PI2	0.96					TR2	1.01
	PI3	1.06					TR3	1.06
Perceived Risk	PR1	1.06	0.73	0.789	0.574	Financial Literacy	FL1	1.02	0.84	0.901	0.752
	PR2	0.88					FL2	1.05
	PR4	0.5					FL3	0.93

Note: CR stands for Composite Reliability, and AVE stands for Average Variance Extracted.

shows the analysis of the constructs, assessed with factor loadings, Cronbach’s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE) to determine the reliability and validity of these constructs. The strong factor loadings across all constructs indicate that items represent the constructs well. Most constructs have high reliability according to the Cronbach’s alpha above 0.7, which is acceptable. Composite Reliability (CR) also supports the constructs’ reliability, with values consistently above 0.8, indicating the items’ ability to measure their respective constructs. The AVE values are all above 0.5, indicating that the constructs explain a large portion of the variance in the items, and thus, convergent validity is achieved. Overall, the results are reliable and valid for using the constructs to measure their concepts in a structural equation model.

The Heterotrait–Monotrait (HTMT) ratios, presented in

Table 3. Heterotrait–Monotrait (HTMT) ratios for Construct Validity Assessment.

	FL	IU	OW	PEU	PI	PR	PT	PU	SC	TP
FL
IU	0.274
OW	0.176	0.289
PEU	0.392	0.492	0.302
PI	0.486	0.556	0.212	0.542
PR	0.225	0.146	0.114	0.181	0.246
PT	0.063	0.47	0.132	0.478	0.273	0.183
PU	0.133	0.609	0.098	0.648	0.348	0.155	0.521
SC	0.313	0.654	0.326	0.415	0.392	0.135	0.368	0.438
TP	0.276	0.604	0.283	0.712	0.454	0.171	0.729	0.68	0.598

, are essential to assess the discriminant validity of different constructs in a research model. This analysis shows that intention to use (IU) has relatively strong associations with PU (0.609), SC (0.654), and TP (0.604), indicating that these factors significantly influence users’ intention to adopt the system. As with PU and TP, Perceived Ease-of-Use (PEU) shows high correlations with PU (0.648) and TP (0.712), indicating that ease-of-use is highly correlated with the perceptions of usefulness and transparency. Perceived trust (PT) also correlates highly with TP (0.729), implying a strong relationship between trust and transparency. The other constructs, perceived risk (PR) and ownership (OW), show lower HTMT values, indicating that these constructs are more distinct than others and provide good discriminant validity. Typically, HTMT values less than 0.85 are believed acceptable, and values higher than that may indicate problems with discriminant validity between the constructs. Therefore, our HTMT values establish the discriminant validity of our model because all the values are below 0.85.

To further evaluate the discriminant validity among constructs, we used the Fornell–Larcker criterion, which is a robust means of making sure constructs are sufficiently distinct from each other; the results are in

Table 4. Fornell–Larcker criterion values for assessing discriminant validity of constructs.

	FL	IU	OW	PEU	PI	PR	PT	PU	SC	TP
FL	0.867
IU	0.234	0.812
OW	−0.149	−0.252	0.93
PEU	0.303	0.419	−0.259	0.76
PI	0.403	0.454	−0.174	0.417	0.836
PR	0.167	0.159	−0.015	0.154	0.179	0.758
PT	0.034	0.409	−0.11	0.407	0.229	0.153	0.869
PU	0.115	0.533	−0.076	0.565	0.293	0.159	0.443	0.856
SC	0.275	0.588	−0.288	0.35	0.334	0.162	0.327	0.397	0.92
TP	0.236	0.511	−0.236	0.565	0.36	0.171	0.556	0.542	0.526	0.768

. The diagonal values in this context represent the square root of the Average Variance Extracted (AVE) for each construct, and the off-diagonal values represent correlations between different constructs. For a construct to show adequate discriminant validity, the square root of its AVE should be higher than its correlation with other constructs.

As all the diagonal values in Table 4 are greater than the off-diagonal values, our model satisfies the Fornell–Larcker criterion for discriminant validity. For instance, the correlation between the FL criterion and IU is 0.234, less than the square root of the AVE for the FL criterion (0.867). Other constructs, such as PU and SC, also show internal validity through their diagonal values and small off-diagonal correlations.

We also used cross-loading analysis to measure how each indicator item is related to its intended construct instead of other constructs. The cross-loadings in

Table 5. Cross-loading.

	FL	IU	OW	PEU	PI	PR	PT	PU	SC	TP
FL1	0.88	0.16	−0.12	0.23	0.31	0.16	−0.02	0.05	0.25	0.20
FL2	0.91	0.23	−0.13	0.31	0.41	0.17	0.05	0.12	0.29	0.21
FL3	0.80	0.20	−0.14	0.23	0.31	0.10	0.05	0.12	0.17	0.21
IU1	0.17	0.84	−0.19	0.27	0.30	0.08	0.38	0.42	0.57	0.48
IU2	0.16	0.87	−0.15	0.30	0.33	0.11	0.31	0.46	0.51	0.48
IU3	0.22	0.90	−0.27	0.42	0.43	0.18	0.36	0.48	0.50	0.49
IU4	0.20	0.83	−0.27	0.39	0.42	0.14	0.34	0.44	0.51	0.39
IU5	0.21	0.59	−0.13	0.34	0.38	0.13	0.26	0.36	0.25	0.17
OW1	−0.14	−0.26	0.94	−0.24	−0.20	−0.01	−0.10	−0.07	−0.28	−0.25
OW2	−0.13	−0.21	0.92	−0.25	−0.11	−0.02	−0.10	−0.07	−0.25	−0.18
PEU1	0.23	0.33	−0.28	0.86	0.37	0.10	0.32	0.51	0.23	0.44
PEU2	0.21	0.42	−0.15	0.89	0.37	0.15	0.42	0.58	0.33	0.55
PEU3	0.23	0.15	−0.01	0.62	0.31	0.16	0.12	0.22	0.20	0.26
PEU4	0.30	0.29	−0.29	0.65	0.23	0.08	0.28	0.29	0.29	0.39
PI1	0.34	0.36	−0.22	0.33	0.79	0.19	0.21	0.18	0.36	0.34
PI2	0.29	0.33	−0.11	0.31	0.81	0.13	0.12	0.24	0.18	0.19
PI3	0.38	0.44	−0.12	0.40	0.91	0.13	0.23	0.31	0.30	0.36
PR1	0.16	0.18	−0.04	0.18	0.16	0.97	0.18	0.16	0.18	0.21
PR2	0.14	0.07	0.07	0.04	0.18	0.77	0.05	0.10	0.08	0.03
PR4	0.13	0.01	−0.11	−0.08	0.11	0.45	−0.13	0.03	0.00	−0.03
PT1	0.01	0.33	−0.04	0.42	0.20	0.15	0.86	0.37	0.28	0.52
PT2	0.06	0.30	−0.15	0.35	0.16	0.07	0.86	0.41	0.24	0.46
PT3	0.02	0.42	−0.10	0.31	0.23	0.17	0.89	0.38	0.32	0.48
PU1	0.15	0.38	−0.06	0.49	0.25	0.15	0.43	0.83	0.26	0.48
PU2	0.06	0.49	0.00	0.51	0.27	0.14	0.37	0.87	0.38	0.47
PU3	0.08	0.48	−0.12	0.47	0.27	0.13	0.31	0.86	0.31	0.43
PU4	0.11	0.47	−0.07	0.47	0.21	0.13	0.42	0.86	0.40	0.48
SC1	0.26	0.53	−0.22	0.31	0.29	0.16	0.28	0.33	0.91	0.47
SC2	0.27	0.55	−0.31	0.34	0.28	0.15	0.34	0.40	0.93	0.52
SC3	0.23	0.55	−0.28	0.32	0.35	0.13	0.29	0.37	0.91	0.47
TP1	0.09	0.37	−0.10	0.40	0.20	0.22	0.56	0.45	0.37	0.74
TP2	0.03	0.26	−0.02	0.35	0.23	0.12	0.46	0.40	0.15	0.56
TP3	0.27	0.42	−0.22	0.52	0.32	0.12	0.45	0.43	0.49	0.89
TP4	0.26	0.48	−0.32	0.47	0.34	0.09	0.32	0.41	0.52	0.84

show that each item has high loading on their respective construct, indicating a strong correlation with the intended factor. For instance, FL1 has a very high loading (0.883) on its construct (FL) but a notably lower loading (0.161) on other constructs. This pattern suggests that the items are adequate at measuring their specific constructs and thus have good construct validity. The items’ discriminant validity is further reinforced by the significant differences between the loadings on the intended constructs and other factors, which do not significantly correlate with unrelated constructs. However, some items, such as IU4, have moderate cross-loading on other constructs (0.508 on SC), indicating a possible overlap, and must be carefully considered. This might also indicate that because IU is the ultimate independent variable, the items of different constructs will naturally have a higher correlation with it. Overall, the analysis validates the alignment of the indicators with their respective constructs, which supports the overall validity of the measurement model.

Hypothesis Testing

Table 6. Hypothesis testing results for factors influencing intention to use (IU).

Hypothesis	Relation	Mean	Standard Deviation	T Statistics	p-Values
H1	FL -> IU	0.007	0.061	0.119	0.453
H2	OW -> IU	−0.225	0.133	1.694	0.045
H3	PEU -> IU	−0.043	0.076	0.567	0.285
H4	PI -> IU	0.217	0.064	3.367	0
H5	PR -> IU	0.001	0.043	0.028	0.489
H6	PT -> IU	0.107	0.065	1.656	0.049
H7	PU -> IU	0.317	0.071	4.5	0
H8	SC -> IU	0.306	0.058	5.273	0
H9	TP -> IU	0.056	0.089	0.632	0.264

gives the hypothesis testing results for the relationship between the independent and the dependent variable IU. The key findings show that ownership (OW), personal innovativeness (PI), perceived usefulness (PU), and social influence (SC) have a significant effect on IU, which is in line with the previous studies identified in the literature review. The effects of the other variables are not significant.

The results show that there are positive effects of PI on IU, which indicates that innovative people are more open to playing around with cryptos in many ways, including using them as a medium of exchange. Similarly, IU is also highly affected by social influence (SC), which indicates that people are more inclined to use cryptos as a medium of exchange if their peers and social circles recommend them. Therefore, we recommend that crypto issuers focus on creating a strong community around their products, which drives investments in these cryptos and triggers their intrinsic use.

Additionally, IU is not affected by factors such as financial literacy (FL), Perceived Ease-of-Use (PEU), perceived risk (PR), and transparency (TP), as the relationships are statistically non-significant. This means that these variables, while possibly applicable in other situations, play little roles in shaping the intention to use cryptocurrencies in North Carolina.

The effect sizes, F-square (f²) values, of various independent variables in terms of the dependent variable intention to use (IU) are presented in

Table 7. The F-square (f²) values.

Construct	f2	Construct	f2
Financial Literacy	0	Perceived Risk	0
Ownership	0.012	Perceived Trust	0.015
Perceived Ease-of-Use	0.002	Perceived Usefulness	0.09
Personal Innovativeness	0.07	Social Influence	0.147
		Transparency	0.002

. They help explain how much each factor contributes to explaining the variation in IU. The higher the F-square, the stronger the effect.

Financial literacy (FL) has an F-square of 0 in this analysis, meaning that it does not significantly impact IU. Also, perceived risk (PR) does not influence IU, as measured by an F-square of 0. Combining the F-square values with the p-values, we can conclude that these constructs not only have minimal effects on IU, but they are also statistically non-significant. Therefore, we can ignore them (Table 6). The effect sizes of ownership (OW), F-square (f²) values, and perceived trust (PT) are small, with values of 0.012 and 0.015, respectively, but these are statistically significant at 5% confidence levels; hence, they are important in determining IU.

Additionally, the effect sizes of personal innovativeness (PI) and perceived usefulness are 0.07 and 0.09, respectively, indicating a moderate impact on IU, which is also significant statistically. Among all the variables analyzed, social influence (SC) has the highest F-square of 0.147, meaning that SC has the most potent effect on IU.

The R-square value of 0.518 in

Table 8. Explanation of R-square, Adjusted R-square, and Q-Square for intention to use.

R-Square	Adjusted R-Square	Q-Square
0.518	0.498	0.456

indicates that the model’s independent variables can explain 51.8% of the variance in the dependent variable, intention to use (IU). In other words, the intention to use is presented by more than half of the variation in the predictors in the model. The Q-Square value of 0.456, given in Table 8, explains the model’s strong predictive relevance, as values greater than 0.35 are strong (Hair et al., 2019). Overall, the model accounts for more than half of the variation in the intention to use, which is considered a moderately strong result.

Figure 2 shows the path coefficients of the structural equation model (SEM), indicating the strength and direction of the relationships between the latent variables. This is a graphical representation of the analysis discussed above.

5. Discussion

This study’s findings elucidate the significant influence of perceived usefulness, personal innovativeness, and social influence on users’ intention to use cryptocurrencies in business transactions. These two variables emerged as pivotal factors in driving adoption, indicating that innovative individuals who recognize the practical advantages of cryptos are substantially more likely to embrace them. This aligns with FakhrHosseini et al. (2024) and Howard and Hair (2023).

The most interesting result is that IU is negatively related to ownership (OW) with a significant negative coefficient, indicating that people who own cryptocurrencies do not intend to use them for business transactions. This result challenges the intuitive assumption that individuals who already own cryptocurrencies would be more inclined to use them as a medium of exchange. This may indicate that the primary motivation for owning cryptocurrencies among participants may be speculative investment and capital gains rather than practical utility. The significant price volatility and the potential for high returns might attract users who treat cryptocurrencies as assets to hold rather than spend. This aligns with the broader trend in crypto ownership during market surges, such as those observed during the COVID-19 pandemic, when retail investors were drawn into the market through accessible platforms like Robinhood and Coinbase.

Social influence was found to play a crucial role in shaping users’ intentions to use cryptos as a medium of exchange. It highlighted the significance of peer experiences and recommendations in influencing individual perceptions (Liesa-Orús et al., 2023), suggesting that entities issuing cryptocurrencies should focus on creating environments that encourage sharing positive experiences, as this can significantly enhance users’ intentions to engage with the system (Wang et al., 2023). Our analysis indicates that users must first acknowledge the system’s utility; otherwise, more than ownership alone will be required to compel them to utilize the technology.

Although trust is recognized as a vital element in technology acceptance, our results imply that users prioritize immediate functionality and benefits over trust in the system (Balaskas et al., 2022). This indicates that organizations should focus on demonstrating the system’s reliability and effectiveness, as trust may develop over time through positive user experiences rather than being a primary motivator at the initial stage of adoption (Alazab et al., 2021; Amnas et al., 2023).

Respondents in North Carolina are concerned with cryptos’ volatility and usability, mirroring trends observed in studies conducted in similar regional markets. North Carolina is away from the financial hubs of New York, Washington D.C., and California, and therefore, a lesser engagement in emerging financial technologies is expected. Our findings align with L. Mazambani (2024), who noted the importance of trust in cryptocurrency adoption. However, our study uniquely highlights ownership as a potential deterrent to adoption in the US market context.

Conversely, perceived risk emerged as a negative correlate of users’ intentions to use cryptocurrencies in business transactions, indicating that higher perceived risk can deter individuals from engaging with cryptos, especially when using them for business transactions. Participants expressed concerns regarding data security, potential failures, and the system’s reliability (Hui et al., 2023). However, it is noteworthy that the influence of perceived risk appeared to diminish when users found the system beneficial. This highlights a significant trade-off. As users perceive greater usefulness and ease-of-use, their concerns about risks may decrease, allowing for a more favorable intention to use cryptocurrencies in business transactions (Schomakers & Ziefle, 2023).

6. Conclusions

This study aimed to investigate the factors that influence the intention to use cryptocurrencies in business and routine transactions among individuals and businesses in North Carolina. As cryptocurrencies become increasingly popular worldwide, companies, policymakers, and other stakeholders need to understand the main drivers and barriers to using digital currencies in different regions if they are to mainstream digital currencies into the traditional economy. This research aimed to investigate the factors that influence people to use cryptocurrencies for routine transactions other than their traditional use as an investment tool.

Some critical factors emerged as significant predictors of cryptocurrency adoption using Partial Least Squares Structural Equation Modeling (PLS-SEM). A strong driver of perceived usefulness (PU) indicated that users are more likely to adopt cryptocurrencies when they see them as useful for transactions. Social influence (SC) had the most significant impact, indicating that social networks and peer recommendations are key to influencing user behavior. This highlights the importance of the social credibility and acceptance of cryptocurrencies in user communities.

Perceived trust (PT) did affect adoption, but its effect was less than that of perceived usefulness and social influence. Positive user experiences may lead to trust development over time rather than being a primary motivator at the initial adoption stage. Finally, perceived innovativeness (PI) was positively associated with adoption, such that users who perceive cryptocurrencies as an innovative financial technology are more likely to adopt them. However, financial literacy (FL) and perceived risk (PR) did not significantly influence the intention to use cryptocurrencies. A negative effect was found in ownership (OW), meaning that people who own cryptocurrencies see them more as investment tools than routine business transaction tools.

7. Implications and Limitations

To encourage wider adoption, stakeholders should work to make cryptocurrencies seem useful, which means educating potential users about their practical benefits, like reducing transaction costs and making finances more efficient. Furthermore, regulatory oversight and technological improvements can help create trust and security in cryptocurrency platforms, which will help boost users’ confidence. Regulators should develop regulatory frameworks to enhance trust. By leveraging the endorsement of cryptocurrency through trusted individuals and influencers, adoption can be further accelerated by creating positive attitudes towards cryptocurrencies.

This study helps to clarify why cryptocurrency adoption happens in North Carolina and concludes that perceived usefulness, social influence, and trust are the most important factors. Cryptocurrencies should not be considered investment assets but practical tools for everyday transactions. Stakeholders can remove barriers, promote benefits, and create an environment that will make cryptocurrencies a vital part of the financial system. This research makes an important contribution to the growing literature on cryptocurrency adoption and lays the groundwork for future research in other regions and in other economic contexts.

This study contributes to the literature by extending the TAM framework to cryptocurrency adoption, introducing variables like ownership and perceived risk that highlight new behavioral dimensions. The findings imply that policymakers and businesses should increase the trust and usability of cryptocurrencies and financial education to promote adoption. It should be noted that this study’s geographic focus and sample size limit the findings. It should also be noted that this study is exploratory in nature, and future research should explore larger, more diverse populations and employ a larger sample size to validate the results.