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This study aims to investigate how fear of missing out (FOMO) mediates the relationship between cryptocurrency adoption intention and investment behavior among young Indians, using the extended unified theory of acceptance and use of technology.

The data were collected by using survey items on cryptocurrency adoption intention, investment behavior and FOMO derived from existing literature on information systems and cryptocurrencies. A total of 384 Indian participants completed an online questionnaire. The collected data was analyzed using PLS-SEM.

The findings indicate that facilitating conditions, social influence, effort expectancy and price value play important roles in cryptocurrency adoption. All hypothesized paths were significant, except for perceived risk. Furthermore, the study highlights that FOMO acts as a mediator between adoption intention and investment behavior.

This study makes a valuable addition to the literature by empirically exploring the influence of FOMO on the adoption of cryptocurrencies for investment purposes. The results provide valuable insights to crypto developers and exchanges regarding the diffusion of adoption in emerging markets. In addition, policymakers can gain meaningful insights into the influence of government regulations and FOMO on impulsive cryptocurrency behavior.

With the rise of cryptocurrency and its influence on the financial industry, this paper aims to explore cryptocurrency affordances that lead to approach–avoidance behavioral intentions for non-users (potential) and the intention to continue use for users (actual), drawing upon affordance theory and chasm theory.

The authors collected data from 480 potential and actual users in Germany and used maximum likelihood structural equation modeling (ML-SEM) to analyze it. In particular, the data consisted of 301 cryptocurrency users in Germany\ the authors used ML-SEM to test the post-adoption model. Additionally, logistic regression was utilized to determine the dominant actual usage method (store of value or medium of exchange) for various cryptocurrency coins.

According to the study's results, the perceived value benefits have a positive impact on the behavioral intention of potential users to adopt cryptocurrency, and they influence the intention of actual users to continue using it. However, both perceived volatility and financial risk tolerance are the most crucial factors hindering cryptocurrency adoption, whether in the pre-adoption or the post-adoption stage.

This is the first study to reveal cryptocurrency affordances and examine their effect on behavioral intentions toward cryptocurrency adoption based on the differences between non-users (potential) and users (actual). Furthermore, the authors explore how cryptocurrency holders perceive and invest in different coins (e.g. NFTs), which sheds light on factors such as financial risk tolerance that affect their decision making.

Using the technology acceptance model (TAM), this study investigates factors influencing the adoption of cryptocurrency in Bangladesh.

Data were collected from 346 members of the general public through a structured web survey using snowball sampling. Structural equation modeling was used to analyze the data and assess the reliability and validity of the measurement model.

The results show that knowledge of cryptocurrency, benefits of use (perceived usefulness), attitude and challenges all have a significant impact on the adoption of cryptocurrency.

This study was conducted in a single country, relied on self-reported data and used a cross-sectional design, which limits the ability to draw causal inferences. Future research could explore the factors that influence the adoption of cryptocurrency in different countries and regions and incorporate additional variables to provide a more comprehensive understanding of the drivers of intention to use cryptocurrency.

This study contributes to understanding the factors driving the adoption of and intention to use technology-based services, providing insights that can inform the design and implementation of future technology-based services.

The purpose of this study is to investigate consumers’ cryptocurrency adoption through the unified theory of acceptance and use of technology (UTAUT) and complexity theory.

By using a purposive sampling method, a configurational model was developed and a questionnaire-based survey was conducted to gather responses from a Malaysian sample. A total of 223 responses were obtained. Partial least square structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) were adopted to analyze the data.

The PLS-SEM indicated that performance expectancy, effort expectancy, social influence and affinity for technology interaction were positive cryptocurrency adoption predictors, whereas regulation was a negative predictor. Based on the fsQCA, cryptocurrency adoption could be explained by six configurational paths, which comprised combinations of the proposed causal conditions: the UTAUT factors (performance expectancy, effort expectancy, facilitating condition and social influence), environmental factor (regulation) and individual factors (financial knowledge and affinity for technology interaction).

This study offers contributions to the theoretical body of knowledge by articulating the relevance of extended UTAUT and extending the established UTAUT model by integrating external environment and personal factors, also showing the linear and nonlinear interplays of performance expectancy, effort expectancy, facilitating conditions, social influence, regulation, financial knowledge and affinity for technology interaction.

The findings facilitated practitioners’ (cryptocurrency brokers, governments and businesses) fostering of cryptocurrency adoption through the joint consideration of different factors. The factors spanned technological attributes and individual characteristics to regulation. Practitioners should acknowledge that different combinations of the aforementioned antecedents can be equally effective to increase cryptocurrency adoption. The findings suggested that these causal conditions should be considered holistically and that there is no best predictor.

In social terms, the research is expected to contribute to the dissemination of cryptocurrencies and help governments and central banks to develop, regulate and supervise digital currencies, as well as in the implementation of a digital currency ecosystem aligned with sustainable development goals. Economically, the results might foster a high cryptocurrency adoption rate and stimulate crypto-token-based business models and investment opportunities that present new means of revenue generation at individual, organizational and national levels.

This study offers unique perspectives for the body of knowledge and practice in the cryptocurrency domain, using both symmetric and asymmetric methodologies, by delineating the configurational logic involving technological capabilities, social influences, regulation and individual characteristics in facilitating more efficacious dissemination of cryptocurrencies.

This study aims to investigate the influence of perceived government control (PGC) on cryptocurrency adoption and continuance intention among Indians through an integrated model of the extended Unified Theory of Acceptance and Use of Technology (UTAUT) with the Information System Success Model (ISSM).

This study examined the items of cryptocurrency adoption, continuance intention and PGC adopted from the information systems and cryptocurrency literature. The survey was administered to 391 Indians through an online questionnaire. Partial least squares structural equation modeling was used to analyze data.

Results have shown that social influence, effort expectancy and perceived trust are the major drivers for cryptocurrency adoption. All paths leading to cryptocurrency adoption were found to be significant in the hypothesized directions. The study also found that PGC moderates the relationship between adoption and continuance intention.

This study advances existing literature by empirically verifying the integrated UTAUT and ISSM in the context of cryptocurrency adoption for investment purposes. The findings offer crypto-developers and crypto-exchange insight into how adoption is diffusing in emerging markets. The findings provide policymakers with meaningful insights into the role of government regulations in cryptocurrency continuance intention.

Financial technology innovation within the developed world is driving financial markets, yet its adoption is lagging among consumers in emerging markets. At the same time, most African economies continue to be at the tail end of global financial innovations adoption. Given lagging consumer adoption of cryptocurrency in South Africa, the purpose of this paper is to apply the theory of planned behaviour (TPB) to predict behavioural intention to adopt cryptocurrency.

A survey instrument based on the TPB was used to collect quantitative data for predicting adoption from adult distance students at the Mancosa, Cape Town campus. For data analysis, the two-step structural equation modelling approach was used.

The findings indicate that attitude and perceived behavioural control positively impact the intention to adopt cryptocurrency. Subjective norm showed a negative non-significant influence. Overall, the results of the study show that the model has a good model fit and can be used to explain the theory.

The results of this study may not be generalisable to the wider population as it is only based on a cross-sectional study of a sample of adult students at a single institute in South Africa.

The contribution of this paper is threefold: it is one of a few studies on the behavioural intention to adopt cryptocurrency in South Africa using the TPB model, it contributes towards the use of predictive behavioural economics models in understanding consumer behaviour critical to accelerating the adoption of financial innovations, and the results of the study also inform behaviour change strategies that can be applied by practitioners or policymakers to improve adoption. Studies of this nature may lead to the development of financial innovation in emerging markets through a nuanced understanding of consumer behaviour.

Financial inclusion provides access to financial infrastructure, facilitating money transfers. Therefore, blockchain and cryptocurrencies might boost worldwide financial acceptance. However, the UAE has one of the lowest cryptocurrency adoption rates. This study explores the UAE customer adoption and use of cryptocurrencies.

Using a scale, the authors created a structural model and obtained 270 responses from a snowball-distributed online questionnaire, assessed by five cryptocurrency specialists.

Performance expectations (PE), price value (PV), Hedonic motivation (HM) and consumer innovativeness (CI) were the most significant predictors of behavioural intention (BI). Surprisingly, BI is not a reliable indication of actual consumption. Facilitating conditions (FC) are the most accurate predictor of cryptocurrency usage (CU), indicating that adoption might be increased by providing the necessary structures and processes to lead users.

This research adds to the body of knowledge by examining the adoption and implementation of cryptocurrencies in the UAE and by developing and evaluating new constructs based on current notions. The study also contributes to the current understanding of cryptocurrencies and blockchain adoption.

The conclusions of the research advise marketers on how to boost the commercialisation of cryptocurrencies in the UAE market and may pave the way for other studies to assist impending developments in the UAE cryptocurrency industry.

This research offers novel insights into significant predictors of cryptocurrency product uptake in the financial and banking business.

This paper aims to create and evaluate a model for cryptocurrency adoption by investigating how age, education, and gender impact Behavioural Intention. A hybrid approach that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was used for the purpose.

This study uses a multi-analytical hybrid approach, combining PLS-SEM and ANN to illustrate the impact of various identified variables on behavioral intention toward using cryptocurrency. Multi-group analysis (MGA) is applied to determine whether different data groups of age, gender and education have significant differences in the parameter estimates that are specific to each group.

The findings indicate that Social Influence (SI) has the greatest impact on Behavioral Intention (BI), which suggests that the viewpoints and recommendations of influential and well-known individuals can serve as a motivating factor to invest in cryptocurrencies. Furthermore, education was found to be a moderating factor in the relationship found between behavioral intention and design.

Prior studies on technology adoption have utilized superficial SEM and ANN methods, whereas a more effective outcome has been suggested by implementing a dual-stage PLS-SEM and ANN approach utilizing a deep neural network architecture. This methodology can enhance the accuracy of nonlinear connections in the model and augment the deep learning capacity.

The research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and expands upon this model by integrating elements of design and trust. This is an important addition, as design can influence individuals' willingness to try new technologies, while trust is a critical factor in determining whether individuals will adopt and use new technology.

Cryptocurrencies are a relatively new phenomenon in India, and their use and adoption have grown significantly in recent years. However, this development has not been without controversy, as the implications of cryptocurrencies for society, the economy and governance remain uncertain. The results reveal that social influence is an important predictor for the adoption of cryptocurrency in India, and this can help financial institutions and regulators in making policy decisions accordingly.

Given the emerging nature of cryptocurrency adoption in India, there is certainly a need for further empirical research in this area. The current study aims to address this research gap and achieve the following objectives: (a) to determine if a dual-stage PLS-SEM and ANN analysis utilizing deep learning techniques can yield more comprehensive research findings than a PLS-SEM approach and (b) to identify variables that can forecast the intention to adopt cryptocurrency.

This study contributes to examining the factors that drive the adoption of cryptocurrencies for financial transactions in the tourism and hospitality industries. This is crucial to develop tourism and hospitality and stimulate financial inclusion in developing and developed countries.

This research paper employs the SEM model and bootstrapping method on a sample of 417 French participants involved in tourism and hospitality industries to reveal the causal pathway between a set of independent factors and the willingness to adopt cryptocurrencies for financial transactions.

The empirical findings reveal that ease of use, perceived usefulness, social influence, and financial literacy increase the willingness to use cryptocurrencies. French hotels need to have a strategic orientation, to deal with customers, competitors and changing technological environment. The study also reveals that social influence and financial literacy reduce the level of perceived financial risk and thus, leads to increase the intention to adopt the new type of decentralized currencies.

In contrast to previous studies that focused on the volatility and risk of cryptocurrencies, this research employs a human-centric approach covering different factors that could lead to the adoption of the new type of currency for financial transactions in tourism and hospitality industries.

The purpose of this paper is to investigate the antecedents of individuals’ behavioral intention to transact in blockchain cryptocurrency through the theoretical lens of the expanded theory of planned behavior (TPB).

This study investigated the antecedents of a blockchain cryptocurrency adoption framework by adapting well-established items from the information systems (IS) and psychology literature to produce a survey instrument to measure individuals’ intention to engage in blockchain cryptocurrency transactions. The survey was administered to 492 individuals through Amazon Mechanical Turk.

This study resulted in a research model of an individual’s intention to transact with a blockchain cryptocurrency. Results indicated that the expanded TPB model explains 63.5% of the variance in intention to adopt cryptocurrency for transactional usage. In this study, all paths leading to behavioral intention were found to be significant in the hypothesized directions. In addition, all paths leading to attitude, subjective norms and perceived behavioral control were found to be significant in the hypothesized directions.

This study furthers prior literature by empirically validating the expanded TPB in the context of individuals’ intention to use cryptocurrency for transactional purposes. This study can better inform practitioners on individual attitudes and behaviors toward transactional cryptocurrency use. The findings provide regulators meaningful insights toward the development of a regulatory framework which encourages innovation while safeguarding the interests of individual citizens.