In recent years, technological advancements have significantly transformed the financial and business sectors. These innovations have introduced new payment methods, reshaping consumer perceptions of money and influencing their payment behaviors. Among these innovations, cryptocurrencies—particularly Bitcoin—have emerged as novel alternatives with strong growth potential in retail and consumer services. This study investigates the factors influencing Bitcoin adoption by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The extension includes users’ predisposition to adopt new technologies, their risk acceptance, and the moderating role of familiarity. Data were gathered through an online survey from a validated sample of 515 active Bitcoin users, and the findings were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results show that performance expectancy, social influence, hedonic motivation, and risk predisposition significantly impact the intention to use Bitcoin, with familiarity as a partial moderator. The study offers practical insights for companies and retailers integrating cryptocurrency payments.

Advancements in technology coupled with shifts in consumer behavior have precipitated a notable surge in the utilization of Food Delivery Apps (FDAs). This study endeavors to scrutinize the contemporary landscape of these delivery services and dissect the factors influencing user recommendations. To achieve this objective, an online survey was administered to a cohort of 210 respondents with prior experience in employing FDAs. In the initial phase, Structural Equation Modeling (SEM) was employed to discern the determinants underpinning the endorsement of these apps. The subsequent phase concentrated on assessing the most impactful predictors. The findings underscore that Autotelic experience, Ability/challenge, Perceived control, and Attitude stand out as significant influencers in the recommendation of FDAs. The study not only sheds light on managerial implications but also proffers avenues for prospective research.

This study aims to shed light on the role of social media in building consumer engagement and examines the impact of Islamic brand personality traits on electronic word-of-mouth and brand trust in the context of social media engagement behaviour within Islamic online banking. To assess the proposed model a sample of 665 users was recruited. The results show that the brand personality traits of Islamic brands within the context of the Islamic banking sector have a positive impact on brand trust and electronic word of mouth when mediated by brand love. This study addresses this gap in research by examining the impact of Islamic brand personality traits and brand love on electronic word-of-mouth and brand trust in Palestine. These results underline the importance of considering the alignment of brand personality traits with Islamic principles in fostering engagement and trust towards the brand.

Abstract In recent years, research on sustainable consumption has been particularly relevant, highlighting the importance of the collective over the individual to reduce pollution. This study focuses on the study of the perceived marketplace influence (PMI) concept in its organizational and consumer dimensions, together with the financial incentives that exist in the adoption of electric cars and their effect on green customer engagement. A sample of 382 potential buyers of electric vehicles was obtained. A new hybrid analytical approach was taken structural equation modelling and artificial neural network. The research found the most significant variables affecting purchase intention were financial incentives, followed by PMI Organization and finally PMI Consumer. The results of artificial neural network analysis confirmed all the findings of the structural equation modelling, although the importance of each PMI dimension is different for each technique used. The conclusions point to new business opportunities that can be exploited by companies selling this green technology.

Although mobile payment systems offer countless advantages, they do present certain drawbacks, mainly associated with security and privacy concerns. The inclusion of biometric authentication technologies seeks to minimise such drawbacks. The aim of this article is to examine the effect of key antecedents of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and perceived risk on the intention to use a mobile payment system featuring biometric identification. A new hybrid analytical approach is taken. A sample of more than 2500 smartphone users was obtained through an online panel-based survey. Two techniques were used: first, structural equation modelling (PLS-SEM) was conducted to determine which variables had a significant influence on the adoption of the mobile payment system, and second, an artificial neural network (ANN) model was used, taking a deep learning approach, to rank the relative influence of significant predictors of use intention obtained via PLS-SEM. The study found that the most significant variables affecting use intention were performance expectancy, effort expectancy, facilitating conditions, hedonic motivation and risk. In contrast, subjective norms, price value and habit were found to be weak predictors of use intention. The results of the ANN analysis confirmed almost all SEM findings but yielded a slightly different order of influence among the least significant predictors. A review of the extant scientific literature revealed a paucity of published studies dealing with the adoption and use of mobile payment systems featuring biometric identification. The conclusions and managerial implications point to new business opportunities that can be exploited by firms through the use of this technology.

COVID-19 pandemic, the foodservice industry has had to modify the way it offers its services. The aim of this paper is to examine the drivers of intention to use and recommendation of online food delivery (OFD) using the SOR model, to analyze the perceived risk of COVID-19 and its relationship with the perceived risk for online purchase of OFD as well as to analyze the cultural effect between Spain and India. For this purpose, an online questionnaire was developed by obtaining a sample of 422 users and structural equation modeling (PLS-SEM) was used to determine which variables had a significant influence on the adoption of the OFD. The results confirm that attitude is the main antecedent of intention to use and recommendation, in contrast to the subjective norm relationships, where it was only confirmed by recommendation. This finding demonstrates how individuals’ attitude toward intention and recommendation is more favorable than influence of third parties on decisions.

This study identifies the strategic determinants of intention to use m-payment and explores how the information and availability of a mobile payment service impact on the intention to use it from a holistic perspective. Specifically, it is studied the effect of perceived ease of use, perceived security, perceived usefulness, perceived compatibility, and subjective norms in a context where mobile payment was not yet available. The research model is empirically tested through a field experimental design with 437 potential users in Spain. The results show that perceived security, perceived usefulness, perceived compatibility, and subjective norms have a significant effect on the intention to use. Especially, subjective norms play a key role if the user has the opportunity to use the mobile payment and receive adequate information. This paper also provides implications to mobile payments industry and to theoretical field of technology adoption. According to the results, it is recommended that FinTechs and companies dedicated to the development of payment systems devote efforts to increasing the compatibility of these systems with users’ lifestyles not only from a digital but also from a physical perspective. In addition, they should continue to look for ways to make users feel secure and see in this means of payment some utility that fits their needs. Finally, all these efforts must be transmitted to the consumer so that they can generate positive word-of-mouth among their acquaintances to encourage mobile payment adoption.

Customers today benefit from online grocery shopping (OGS) since it makes their lives easier and more pleasant. This study examines the extension of the technology adoption model with some new variables like visibility, economic values, habit, informativeness, and website trust and their influence on the intention to use online grocery shopping by Indian customers. A structured questionnaire was developed, and 342 responses were used for analysis. Two techniques were used: firstly, structural equation modelling (PLS-SEM) was used to determine which variables had a significant influence on the intention to use OGS; in the second phase, artificial neural network modelling (ANN) was used, under a deep learning approach, to classify the relative influence of the significant predictors obtained by PLS-SEM. The study’s findings suggest that the habit has the highest impact on users’ intention to use OGS, followed by perceived usefulness, perceived ease of use and website trust. On the other side, the results of neural network analysis confirmed many SEM findings. The study will help all online grocery service providers to build their services and strategies based on the tastes and needs of their customers.

The present study intends to explore intermittent continued adoption behaviour of users of digital payment services (forms of contactless, cashless and paperless payment methods) during the COVID-19 pandemic. For this, the study explores the drives of adequate level of expectations and measures their impact on confirmation, leading to neutral satisfaction and intermittent continued adoption behaviour. Expectation confirmation model is used to support the hypothesized model. The study surveyed 413 adopters of digital payment services during the pandemic. Smart PLS 3 was used to test mediating effects of neutral satisfaction and perceived COVID-19 risk on intermittent continued adoption behaviour. Findings indicate that adequate level of expectations, once confirmed, leads to neutral satisfaction and intermittent continued adoption behaviour of users. The study also confirms the significant positive direct and mediating impact of perceived COVID-19 risk on intermittent continued adoption behaviour. The study only discusses adequate (minimum) level of expectations of users.

The relevance of social networks in recent years has been demonstrated at the business level. However, selling through social networks and more specifically on Instagram constitutes an emerging field of knowledge. Instagram commerce is part of the new means of social commerce, in which various companies all over the world sell their products and services, and Instagram is the second most preferred social media platform globally. The aim of this research is twofold: on the one hand, to demonstrate the influence of social networks on the purchasing decisions of individuals by focusing more on Instagram commerce, and on the other hand, to analyze the factors that drive purchases in the context of the development of sales in social businesses, more specifically, on Instagram in Palestine. For this purpose, an experimental situation was developed in which users answered a survey after watching a video. From this survey, the present study obtained 200 valid responses. The research model is evaluated through structural equation modeling (SEM). Results reveal the huge impact of social networks on consumer purchase intention. In this sense, there are several factors that mediate the growth of sales in social commerce, particularly on Instagram. The research has important implications for the theory and practice of Instagram commerce.

The purpose of this paper is to identify the factors affecting the intention to use peer-to-peer (P2P) mobile payment. Although mobile technology has become part of everyday life, certain actions and services, such as mobile payments, are still used relatively infrequently. In this paper, we analyse consumers’ adoption of P2P mobile payment services. Following a review of previous literature in this field, we identify the main factors that determine the adoption of mobile payments, and then perform a logistic regression (LR) analysis and propose a neural network to predict this adoption. From the logistic regression results obtained we conclude that six variables significantly influence intentions to use P2P payment: ease of use, perceived risk, personal innovativeness, perceived usefulness, subjective norms and perceived enjoyment. With respect to the nonparametric technique, we find that the multilayer perceptrons (MLP) prediction model for the use of P2P payment obtains higher AUC values, and thus is more accurate, than the LR model. This paper is a pioneer study of intention to use with mobile payment using these methodologies. The outcome of this research has important implications for the theory and practice of the adoption of P2P mobile payment services.

The rapid growth of social networks has led many companies to use mobile payment systems as business sales tools. As these platforms have an increasing acceptance among the consumers, the main goal of this research is to analyze the individuals’ use intention of these systems in a social network environment. The data used were collected after performing an experiment using the Zong Payment tool. The paper includes a comparative analysis of several machine learning methods applied to the variable selection problem considering strategic, sociodemographic, and behavioral variables. The methods were applied isolatedly and, in a novel approach, using multiobjective optimization criteria. The results obtained by these algorithms were validated by an expert committee, who ranked them on the basis of their quality. The proposed methodology obtained the best results in terms of optimization as well as the highest scores from the experts.