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The purpose of this empirical study is to enable a better understanding of the construct sustainability-oriented entrepreneurial intentions (SEI) and thereby promote sustainable entrepreneurship. It aims to examine the significance of work values (extrinsic rewards, intrinsic rewards and job security) as antecedents of SEI and to test the mediating effect of three constructs derived from the theory of planned behaviour – attitude towards sustainability, perceived entrepreneurial desirability and perceived entrepreneurial feasibility on the relationships between work values and SEI.

Confirmatory factor analysis and exploratory factor analysis were performed using analysis of moment structures v27 and statistical package for social science v28 on data obtained from the survey of young individuals of India. The respondents were students enrolled in higher education programmes.

All the identified antecedents (extrinsic rewards, intrinsic rewards, job security and theory of planned behaviour constructs) were found to be statistically significant. The partial mediating effect of the theory of planned behaviour constructs was also reported.

This empirical work leads to the theoretical advancement of the emerging construct, SEI, by presenting evidence of the significant individual-level antecedents of the construct. The results lead to recommendations for policymakers and educators to design strategies to strengthen SEI, thereby expanding the adoption of sustainable entrepreneurship.

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.