Enhanced Features. Clinical Course Workshop. Technical Course Workshop. Advanced Course Workshop. Contact Us! Your Name. Your E-mail. Your phone number. This could be because most of the respondents considered operating with cryptocurrencies to be risky; the lack of variability in their responses to the questions about perceived risk would explain this lack of explanatory power.
However, willingness to manage cryptocurrency risk could be a precondition for adoption. The performance expectancy for a given cryptocurrency was the most important factor for its success. The research was conducted in Spain with college-educated adults with basic knowledge of the Internet. Cryptocurrencies are based on blockchain but are not the only possible application. There is a dangerous relationship between blockchain and cryptocurrencies Carson et al. Although blockchain is expected to dramatically impact and have applications in most economic sectors and activities, at present cryptocurrencies remain more important.
The World Bank defines a non-fiat digital currency as a digital currency that is not backed by any underlying asset, has zero intrinsic value, and does not represent a liability on any institution Natarajan et al. Digital currencies based on blockchain technology, which employs cryptographic techniques, are considered cryptocurrencies.
The U. Federal Reserve considers the current payment system to be slow, insecure, inefficient, uncollaborative, and non-global Federal Reserve System, Cryptocurrencies are seen as a potential instrument for solving all these problems Deloitte, From the start of this revolution with the launch of bitcoin, the first cryptocurrency, the business and economic worlds have sought to adapt and integrate the new financial technology into their activities.
In , the first retail purchase was made with Bitcoins. Laszlo Hanyecz paid 10, bitcoins for two pizzas Bort, Nor does that number include all cryptocurrencies, just the ones quoted on the market to be bought and sold. Today, any business can create its own cryptocurrency using blockchain technology and determine its use through an initial coin offering ICO.
The new cryptocurrency can be used as an internal business ecosystem payment method to grant access to the products or services the ecosystem offers; it can represent a right to an asset or liability; or it can be used as a speculative cryptocurrency whose value is based on market expectations.
The range is very wide and will only grow wider in the coming years. Illegal activities with cryptocurrencies are a fact, especially with bitcoin, the first and most frequently used Turner et al. For instance, cryptocurrencies have been used for tax evasion, money laundering, contraband transactions, extortion, and the theft of bitcoins themselves Bloomberg, Another drawback is that cryptocurrencies are not an easy technology to use; operating with bitcoins is a major challenge for many users Krombholz et al.
One qualitative study found that non-users of bitcoin felt incapable of using it Gao et al. In addition to the lack of technological know-how, financial literacy can also constrain the development of cryptocurrencies. Given this low level of financial literacy, explaining financial concepts related to cryptocurrencies could be difficult CCN, Social perception will also be key to cryptocurrency development.
In short, cryptocurrencies open up many opportunities, such as fast, efficient, traceable, and secure transactions, but also have drawbacks, such as their inherent risk, the technological and financial difficulty of using them, and the uncertain social perception of owning them. The complexity and consequences of the blockchain and cryptocurrency revolution make it imperative to analyze its impacts and challenges from an interdisciplinary perspective.
Although some research has been done on bitcoin, as the most widely used and important cryptocurrency today Holub and Johnson, , the literature on cryptocurrencies in general is scarce, mainly due to their novelty. This paper focuses on the critical factors that any cryptocurrency must consider to succeed in the emerging and chaotic cryptocurrency market. Specifically, it uses technology acceptance models to analyze the influence of perceived risk, performance expectancy, facilitating conditions, effort expectancy, social influence, and financial literacy on the intention to use cryptocurrencies.
Determining the key factors for customer acceptance of cryptocurrencies would let current and future market players focus on the most important features a cryptocurrency should have. The research was conducted in Spain with a sample of college-educated adults with basic knowledge of the Internet.
UTAUT models define a direct and positive influence of performance expectancy, social norm, and facilitating conditions on the intention to use a technology. Performance expectancy is defined as the degree to which a person considers that using a specific technology would be useful to enhance his or her performance.
Effort expectancy is defined as the degree of ease associated with the use of a specific technology. Social influence is defined as the degree to which a person perceives that others believe that he or she should use a specific technology. Facilitating conditions are defined as the degree to which a person believes that he or she has the necessary organizational and technical infrastructure to use a specific technology Venkatesh et al. Several studies have looked at the influence of these variables on the acceptance of financial technologies, or fintech, but no consensus has been reached regarding their influence on the intention to use them.
On the contrary, important differences have been found depending on the type of technology and target segment. For instance, Moon and Hwang show that effort expectancy and social influence positively affect the intention to use crowdfunding, but find no evidence that performance expectancy and facilitating conditions do.
In contrast, Kim et al. Makanyeza and Mutambayashata show that while performance expectancy and effort expectancy positively influence the behavioral intention to adopt plastic money, social influence and facilitating conditions do not significantly affect it. Khan et al. Several studies have likewise looked at the adoption of mobile banking m-banking.
For instance, Farah et al. Warsame and Ireri show that for some consumer segments based on age, gender, and religion performance expectancy and effort expectancy significantly influence the intention to use mobile microfinance services, while for others these factors do not affect acceptance.
These authors further demonstrate that social influence affects the intention to use mobile microfinance services in all segments. In their study of mobile payment adoption specifically by the base-of the-pyramid BoP segment, i. Focusing on m-banking in Bangladesh, Mahfuz et al. Additionally, they find that while performance expectancy and facilitating conditions do not significantly affect the intention to use this technology, facilitating conditions do affect actual use of it.
Similarly, in a study conducted in Karnataka, in rural India, Kishore and Sequeira show that performance expectancy, effort expectancy, and social influence have significant explanatory power with regard to the adoption of m-banking. As for the literature specifically on cryptocurrencies and bitcoin, Mendoza-Tello et al.
According to another study on cryptocurrency adoption based on the TPB, subjective norms social influence and perceived behavioral control how easy or difficult it is to use cryptocurrencies are significant Schaupp and Festa, : people who perceive cryptocurrencies as easy to use and people receiving a positive social influence regarding their use are more likely to use them. Bitcoin has also been analyzed as a cryptocurrency. In an acceptance study in China, Shahzad et al. Based on these findings regarding the acceptance of financial technologies, the following hypotheses are proposed:.
Performance expectancy regarding the use of cryptocurrencies positively influences the intention to use them. Effort expectancy regarding the use of cryptocurrencies positively influences the intention to use them. Social influence regarding the use of cryptocurrencies positively influences the intention to use them.
Facilitating conditions for the use of cryptocurrencies positively influences the intention to use them. Perceived risk has been considered a determinant of consumer behavior in the context of purchase intention e. Several recent studies analyze the influence of perceived risk on the intention to use financial technologies with contradictory results.
In their study of the intention to use online banking, Khan et al. Kishore and Sequeira show that perceived risk has significant moderate explanatory power with regard to the adoption of m-banking in rural areas. Shaikh et al. Farah et al.
Likewise, Moon and Hwang find no evidence that perceived risk negatively affects the intention to use crowdfunding. With regard to the literature on cryptocurrencies in particular, Mendoza-Tello et al. Based on the understanding of cryptocurrencies as an emerging fintech entailing potential risk, the following hypothesis is proposed:.
The perceived risk of using cryptocurrencies negatively influences the intention to use them. Stolper and Walter define financial knowledge as the degree of knowledge a person has about key financial concepts and their capacity to apply that knowledge to their financial decision-making. Several studies demonstrate that financial knowledge is a predictive variable of financial behaviors. Van Rooij et al. Their research includes papers from the United States and other countries. Likewise, Stolper and Walter argue that higher levels of financial knowledge are associated with more saving planning, more saving behavior, more stock market participation, and smarter choices when it comes to the selection of financial products; at the same time, lower levels of financial knowledge are associated with poorer financial decisions, more expensive loans, costly credit card practices, and excessive debt accumulation.
In their literature review, Hastings et al. Stolper and Walter report similar findings, showing that many research papers demonstrate that people with a higher level of financial knowledge are more cautious about their financial decisions. Lam and Lam demonstrate the important influence of financial knowledge on problems related to online shopping, such as addiction or compulsive shopping behaviors. Given that cryptocurrencies are a technological financial product, and based on the above findings regarding the influence of financial literacy on the use of financial products, the following hypothesis is proposed:.
Financial literacy positively influences the intention to use cryptocurrencies. Figure 1 shows the proposed model for analyzing the intention to use cryptocurrencies. We used a structured and self-administered online survey to sample people over the age of 20, living in Spain, who had a university degree. We sent invitations to people with this profile without making any distinctions for age, gender, or household income until we achieved the desired sample size and composition to enable reliable research.
Due to the online nature of the survey, the sample is limited to people with a basic command of the Internet. As noted in the introduction, because cryptocurrencies are based on blockchain technologies, a minimum level of both technological and financial knowledge is needed to have a basic understanding of how to operate with them.
Consequently, in order to survey people likely to have a reasonable understanding of these technologies, we focused on college-educated adults. This allowed us to ensure that the respondents would have the minimum required knowledge. This decision regarding the sample was based on other studies that justify the choice of a highly educated sample as a means of making suring that respondents have a higher level of financial knowledge in order to ensure that the collected data will fit the research purpose Hastings et al.
The sample consisted of people, over the age of 20, living in Spain and with a university degree and a basic grasp of the Internet. The data were collected between August 1 and September 10, The innovative blockchain-based financial and insurance services emerging today reduce intermediation and transaction costs, but they could also be insecure and risky if used incorrectly.
Cryptocurrencies such as bitcoin are a perfect example of blockchain-based financial innovation, offering inalterable, anonymous, and traceable transactions. Today, the technology suffers from significant legal gaps, enabling it to be used for illegal and opaque operations, including tax evasion, money laundering, illegal transactions such as purchasing weapons or drugs, corruption, etc.
In addition, it poses other risks, such as the fact that losing your password entails losing your money or that heirs who do not have the key will not be able to access their inheritance. We based our measurement scales on scales that are widely accepted and used in the literature on technology acceptance. Table 1 shows the constructs, items, and theoretical foundations of each one. We decided to use a self-assessment approach because we consider that people make decisions based on their perception of reality, not reality itself.
From a consumer behavior point of view, this means that people will behave according to their perceptions of their financial knowledge, not their actual financial knowledge. The self-conception of financial literacy would thus be the influential factor in relation to the intention to use cryptocurrencies.
As already noted, the sample consisted of people over the age of 20, with a university degree and a basic grasp of the Internet. People under the age of 21 were not included because of the very high unlikelihood that they would already have a university degree. The largest segment of respondents was people between the ages of 41 and This is similar to the distribution of the Spanish population as a whole. Therefore, we believe the sample is adequate and representative of the population.
The breakdown of net monthly household income for the sample was as follows: 6. As can be seen, income levels were quite high, which is reasonable given that the sample consisted of college-educated adults, who are more likely to earn higher salaries. This distribution is similar to that of the Spanish population as a whole. Measurement model analysis.
Principal component exploratory factor analysis with Varimax rotation was performed to check for the possible existence of dimensions in the scales. Reliability and convergent and discriminant validity analyses of the scales were then performed. The removal of items from the scales based on these analyses was decided at this stage. Explanatory model of the intention to use cryptocurrencies analysis of the structural model.
We analyzed the proposed explanatory model for the intention to use cryptocurrencies, calculating R 2 , Q 2 , path coefficients, and their estimated degree of significance. This analysis is also recommended when data do not follow a normal distribution or it is uncertain that they do. The intention to use cryptocurrencies was low. The arithmetic mean of the intention to use them was a 3 on a scale of When respondents were asked about their use in the near future, the score increased to an average of 4, very close to the breaking point between using or not using cryptocurrencies 5.
Standard deviations were high the coefficient of variation was 1. Given the dispersion in the intention to use, it was highly advisable to develop an explanatory model to understand cryptocurrency acceptance behaviors. With this aim, we proposed the aforementioned model based on variables accepted by the scientific and academic community with high explanatory power regarding variability in the intention to use new technologies and products.
We performed an exploratory factor analysis to test the number of dimensions included in each scale. Each scale was found to have only one dimension. From an exploratory perspective, it was confirmed that the scales did not include any mental structures with more than one dimension. Regarding the evaluation of the measurement mode, according to Hair et al.
One of the observed variables showed a standardized loading slightly less than 0. In that case, we kept the variable based on Chin because the standardized loading rule of 0. The scales also showed an average variance extracted AVE greater than or equal to 0.
Table 3. Consistent PLS bootstrapping with resamples was used to evaluate the relevance of the path coefficients. Figure 4. Graphical model of the influence of the explanatory variables path coefficients on the intention to use cryptocurrencies and R 2. The Q 2 obtained with PLS predict was greater than 0, and Q 2 values greater than zero indicate that the exogenous constructs have predictive relevance. It is thus confirmed that the model strongly explains the intention to use cryptocurrencies.
The average variance explained by each antecedent variable of the intention to use is shown in Table 5. Table 5. Goodness of fit of the model, direct effects, p -value, correlation with the dependent variable and variance explained by the explanatory variables. The results indicate that performance expectancy and facilitating conditions significantly influence the intention to use cryptocurrencies.
Support was thus found for hypotheses H 1 and H 4. Therefore, although support was also found for H 2 , this support was less clear. No support was found for the rest of the hypotheses H 3 , H 5 , and H 6. With the objective of producing valid predictions of behavioral intention to use cryptocurrencies, we used PLS predict Shmueli et al. This research sought to test an explanatory model of the intention to use a new financial technology, namely, blockchain-based cryptocurrencies.
Perceived risk and financial literacy were also added, as variables specifically used in the analysis of fintech acceptance. The proposed model explains Effort expectancy also had significant explanatory power, but the influence was smaller 4. The high explanatory power of performance expectancy gives rise to the first finding: performance expectancy is the determinant variable in the acceptance of cryptocurrency financial technologies.
This finding is consistent with other studies that have found this variable to be determinant in the intention to use a given financial technology, including a biometric payment service Kim et al. Studies about cryptocurrencies and bitcoin in particular have reached the same results regarding the influence of performance expectancy on the intention to use, including in relation to electronic payments with cryptocurrencies Mendoza-Tello et al.
Perceived usefulness is also the most significant variable influencing the intention to use bitcoin Walton and Johnston, The variable with the second highest explanatory power was facilitating conditions. There is no consensus regarding the influence of facilitating conditions on the acceptance of financial technologies.
Several studies have confirmed its influence Khan et al. With regard to effort expectancy, most of the literature suggests that it does influence financial technology acceptance e. However, some authors have shown that effort expectancy does not influence fintech acceptance Khan et al. As for findings regarding cryptocurrency fintech in particular, effort expectancy has been shown to have a positive influence on cryptocurrency adoption Schaupp and Festa, and on bitcoin acceptance in China Shahzad et al.
Our results support the mainstream findings regarding the influence of effort expectancy on fintech acceptance: it is a significant factor. However, it is not the most influential one, nor is it critical to successful cryptocurrency acceptance compared to performance expectancy and facilitating conditions.
A bitcoin study in South Africa Walton and Johnston, yielded similar findings. Various factors should be considered in relation to the analyses of the variables that were not statistically significant. Given the current early stages of the development of cryptocurrency financial technologies and their technological basis blockchain , it might initially seem surprising that perceived risk was not found to be relevant to their adoption.
Because of the anonymity pseudonymity and elimination of trusted intermediaries that cryptocurrencies entail, they can potentially be used for criminal activities e. The reason for the present finding is the low variability of the explanatory variable perceived risk , which does not explain the variability in the intention to use cryptocurrencies. However, that does not mean that it is not an important factor in cryptocurrency acceptance.
Support for this argument can be found in other industries. For instance, in the hotel industry, the degree of cleanliness of a high-end hotel has no explanatory power with regard to hotel choice, because customers in general assume that a high-end hotel will be clean. This results in very low variability in the variable, such that cleanliness is not an influential variable in hotel choice. Thus, a very important variable cleanliness can play a critical role its absence would have a strong negative impact on the evaluation of the service , yet not be determinant in high-end hotel expectations and choice Medrano et al.
This same logic can be applied to the cryptocurrency acceptance decision. Thus, the arithmetic mean of the three observable variables measuring perceived risk is 7. That means that despite being a critical factor in cryptocurrency acceptance, risk does not affect the intention to use cryptocurrencies because most people assume that operating with them is risky. In their study specifically of cryptocurrencies, Mendoza-Tello et al. Likewise, Walton and Johnston show that the perceived security risk does not influence attitude toward or the intention to use bitcoin.
Another finding of our research is the non-significant role of social influence in explaining the intention to use cryptocurrencies. Previous studies reached the same conclusion: this variable does not influence the adoption of other financial technologies, such as plastic money Makanyeza and Mutambayashata, and online banking Khan et al. However, opposite findings have also been reported, as in the m-banking studies by Kishore and Sequeira ; Mahfuz et al.
The results of cryptocurrency acceptance studies are similarly contradictory. A study on electronic payments with cryptocurrencies considered the influence of social norm on acceptance to be non-significant, while other studies have found it to be significant, including one study on cryptocurrency adoption Schaupp and Festa, and another on bitcoin acceptance Shahzad et al. With regard to cryptocurrency adoption, our findings indicate that social influence will not be key.
Finally, we found that financial literacy has no power as an explanatory variable for cryptocurrency acceptance. Other studies about financial literacy have found that people with greater financial knowledge are less likely to make little-reasoned investments Lam and Lam, In that regard, Stolper and Walter , p.
As we will review shortly, the majority of papers document a positive correlation between measures of financial literacy and sound financial behavior in various domains. This is because financial literacy allows people to make better financial decisions. In some cases, the best decision could be not to invest, while in another it might be to invest. Our results contribute to previous findings.
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