In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.

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The aim of this paper is to empirically examine the effect of emotional solidarity, stakeholders’ attitude, stakeholders’ commitment, perceived economic benefit, and cost on the sustainable tourism development in the Iranian tourism sector. Data were collected from surveying 258 Iranian stakeholders. The analysis was completed by using Partial Least Squares—Structural Equation Modeling (PLS-SEM). The findings show that there is a direct effect of emotional solidarity and stakeholders’ attitude on perceived economic benefit. Stakeholders’ attitude and commitment influence perceived cost, and perceived economic benefits and sustainable tourism development were highly associated. Moreover, perceived economic benefit plays the mediator role between emotional solidarity, stakeholders’ attitude, and supports sustainable tourism development. This study makes significant contributions to the body of tourism literature by confirming the link between emotional solidarity, stakeholders’ attitude, stakeholders’ commitment, perceived economic benefit and cost on support in sustainable tourism development. Furthermore, this study offers several practical implications for local authorities and tourism policies aiming to improve support and engagement in tourism planning for aiding sustainable tourism development in Iran.

How much environmental pollution can be reduced by the efficient use of financial, natural, and energy resources in the current globalization. Thus, this study provides empirical evidence in support of the theoretical argument by investigating the impact of financial development, environmental assets, globalization, coal, natural gas, and sustainable carbon emissions in 32 developed countries from 1990 to 2018. Ecological degradation (estimated by carbon dioxide emissions) experienced a structural shift that was considerably more pronounced in 2000–2011 than in 1991–1998. A broad variety of econometric methodologies (such as the Chow test, Cross-country regression, and the Generalized Method of Moments (GMM)) were applied. As a consequence, environmental deterioration is strongly linked to economic development and urbanization, according to the findings. These nations’ ecological footprints are favorably influenced by financial development, environmental assets, and non-renewable energy, whereas globalization and sustainable sources have a negative impact. Environmental degradation may be slowed by combining globalization’s impact on financial growth with the conservation of natural resources such as renewable energy sources. In order to improve their economic and ecological resource frameworks, these nations will need to increase their use of solar and other renewable energy.

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