Purpose: 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.Design/methodology/approach: 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.Findings: 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.Research limitations/implications: 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.Practical implications: 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.Social implications: 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.Originality/value: 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.

Purpose: This research investigates the factors that drive affluent customers to engage in sustainable behaviours, particularly their choice to stay in green hotels. The study aims to understand how green purchase intentions (GPI) translate into green purchase behaviours (GPB), focusing on how the scarcity of green hotels may influence this relationship. Design/methodology/approach: Using a quantitative research method, the study examines several key predictors – such as ecological concern, perceived behavioural control, social norms and green attitudes – and their impact on GPI and GPB. Data were gathered from 418 affluent consumers through a structured survey, capturing their views on ecological concern, perceived control, social norms, green attitudes and related purchasing intentions and behaviours. The data were then analysed using SPSS and AMOS, with additional validation through artificial neural networks (ANN). Findings: The findings indicate that ecological concern, perceived behavioural control, social norms and green attitudes significantly affect GPI among affluent consumers, and there is a strong positive correlation between GPI and GPB. Additionally, the study highlights that the unavailability of green hotels weakens the intention–behaviour link. Research limitations/implications: One limitation of this study is its reliance on self-reported data, which may be subject to social desirability bias and may not accurately reflect actual behaviour. Additionally, the study focuses on a specific context, green hotel purchases, which may limit the generalizability of the findings to other green consumer behaviours or industries. The cross-sectional nature of the study also prevents the establishment of causality between the constructs. Furthermore, the study may not account for other potential moderating variables, such as price sensitivity, brand loyalty or individual differences in environmental knowledge, which could also influence the relationship between green purchase intention and behaviour. Practical implications: The research emphasizes the importance of perceived behavioural control and the detrimental impact of limited green hotel availability on the intention–behaviour relationship. The study provides practical insights for hotel managers, recommending clear communication strategies, deeper integration of sustainability practices and collaboration with policymakers to encourage sustainable consumer behaviours. Originality/value: This study stands out by focusing on affluent consumers and their green purchasing habits, contributing valuable knowledge to the field of sustainable behaviour.

In the food sector, customers have been voting by their feet since ages. However, due to the technological changes clubbed with present COVID-19 pandemic, the trend has changed rapidly. Customers are confined to their home and technology has emerged as the only saviour. The number of increased downloads of food ordering app is an indication of both popularity of these apps and helplessness of the present age customer. With restricted movement, they have these apps to either get the food delivered at their doorstep or take away with pre-orders. Hence, it becomes imperative for the app designer and food service provider to brainstorm and develop an app which can focus on the customer ease rather than forcing their design and interface to customer. In this backdrop, this study is a novel attempt which primarily focuses on relationship amid customer satisfaction and app quality. The inter-related relationship among service quality, satisfaction of customer and their loyalty using the app has been explored. Collection of the data used stratified random sampling. A total of 380 usable data have been collected from customers who have been using this app for ordering since last one year.

This research seeks to investigate the influence of performance expectancy, effort expectancy, facilitating conditions, habit and hedonic motivation on behavioural intention in the context of chatbot utilization within the banking industry. Additionally, the study explores the moderation effects of age, gender and personality type on the relationships between behavioural intention and use behaviour. The study employs a quantitative survey of banking customers, and the data have been analysed using partial least squares structural equation modelling and artificial neural network. The findings suggest that the use and acceptance of chatbots in banking are influenced by a range of factors, including performance expectancy, facilitating conditions and hedonic motivation. The study also reveals that only personality types can moderate the relationship between behavioural intentions and use behaviour. The study provides insights for banks and other financial institutions that are considering the implementation of chatbots as part of their customer service strategy.