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. Greater financial knowledge allows a customer to more accurately evaluate the investment e. For this reason, financial literacy could influence the decision at the investment level, but does not have any significant influence on the decision at the technology level, which is the focus of the present research. Thus, from a financial literacy perspective, the decision of whether or not to use a cryptocurrency could be based on financial criteria, not technology acceptance ones.
Based on our findings, we propose several measures to operate with a greater likelihood of success in the cryptocurrencies and blockchain-related services market. The first recommendation concerns the risk related to operating in these markets. The perceived risk of cryptocurrency transactions is very high; given the current status of the necessary technological development, customers and investors view investing in or operating with these new technological assets as very risky.
Therefore, future cryptocurrencies should seek to solve that problem as a condition for pre-adoption. Second, the product and service design for a new cryptocurrency or the innovation efforts for current ones should focus on performance as the most critical adoption factor. Cryptocurrency must become a high-value-added proposition for customers, and considerable marketing efforts must be undertaken to ensure that potential customers perceive this value.
The more value added offered by a cryptocurrency, the more likely it is to be used. Focusing on usefulness is a recommended strategy in the cryptocurrency market. The third recommendation concerns facilitating conditions. The intention to use a current or new cryptocurrency is heavily dependent on the conditions under which potential customers can operate with them.
The fourth recommendation has to do with the effort a customer needs to make to use a cryptocurrency. Even through the effort required to learn and operate with a cryptocurrency is not one of the most important factors for acceptance, it is significant. Finally, this research has some limitations.
We focus on a very specific population segment: college-educated adults with a basic grasp of the Internet. Notwithstanding our discussion of and rationale for this decision, future studies should focus on other segments in order to gain a broader knowledge of cryptocurrency acceptance in society. Another possible limitation is that this research was circumscribed to Spain.
The results might be different if the survey had had a larger geographical scope or been conducted in another country e. Thus, future research should be conducted in other countries. Another factor that could be included in future research is sustainability of cryptocurrencies and blockchain mining. Based on this finding, sustainability factors could have an implact on cryptocurrencies development.
Cryptocurrencies are an emerging technology in constant evolution. Therefore, the findings of the present research should be interpreted with care. Consequently, future research should both include a longitudinal study to track the evolutionary adoption of cryptocurrencies and seek to update the model to future circumstances. All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ajzen, I. The theory of planned behavior. Bloomberg, J. Law Enforcement Is onto You. Google Scholar. Bort, J. Business Insider, 22 May. Business Wire Carson, B.
Digital McKinsey, June. CCN Chin, W. Issues and opinion on structural equation modelling. MIS Q. Coin Market Cap All Cryptocurrencies CoinMarketCap. Coinmap Davis, F. Perceived usefulness, perceived ease of use, and user acceptance of information technology.
Deloitte Dijkstra, T. Exton, J. Falk, R. A Primer for Soft Modeling. Faqih, K. An empirical analysis of factors predicting the behavioral intention to adopt Internet shopping technology among non-shoppers in a developing country context: does gender matter? Retailing Consum. Farah, M. Mobile-banking adoption: empirical evidence from the banking sector in Pakistan. Bank Mark.
Featherman, M. Predicting e-services adoption: a perceived risk facets perspective, International. Federal Reserve System Felipe, C. Impact of organizational culture values on organizational agility. Sustainability Fishbein, M. Boston, MA: Addison-Wesley.
Gao, X. Gefen, D. An update and extension to SEM guidelines for administrative and social science research. Gold, A. Knowledge management: an organizational capabilities perspective. Hair, J. Theory Pract. Partial least squares structural equation modeling: rigorous applications better result and higher acceptance.
Long Range Plan. Hastings, J. Financial literacy, financial education, and economic outcomes. Holub, M. Bitcoin research across disciplines. Hussain, M. M-payment adoption for bottom of pyramid segment: an empirical investigation. ICObench Decil de Salarios Del Empleo Principal.
Juels, A. Kannungo, S. Khan, I. Kim, S. Increasing the size of the pool will always decrease the variance, but at some point you will have diminishing marginal utility. What you described is a transitional proportional payout scheme. Your payout is proportional to the amount of work that you did for any given block. In your example, miner1 got That is In this example miner1 still gets Now it was Way less per hour because the block was very unlucky.
Traditional Pay-per-share PPS is a different system where you get paid by the number of shares you submit regardless of if a block is found. Say difficulty is 20, so the price per share is 2. In scenario 1, miner1 makes In PPS, your pay per hour is always the same even in the short term, but the drawback is that the pool might go bankrupt if there are too many long blocks.
In proportional, your pay per hour in any given 24 hours varies based on if blocks are lucky or unlucky and the drawback is that pool hoppers skip town on long blocks. In MaxPPS, your pay per hour in any given 24 hours may also vary if the pool is small enough that it isn't reliably finding multiple blocks per day, but the variation will lower than in proportional because the pool has withheld some of the payment you would have received from short blocks so that it can pay you more on long blocks.
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