The Technology Acceptance Model (TAM) is widely used in many areas but is rarely applied to determine the link between tourists’ perceptions of tourism development and environmental sustainability. Therefore, this study aimed to (1) explore the relationships among tourism development, tourists’ perceptions, and environmental sustainability, (2) examine the mediating role of blockchain technology in these relationships, and (3) analyze the use of the TAM for sustainable practices in tourism. Data were collected from tourists on their perceptions of the impact of tourism on the environment, their use of information technology (IT) during their visits, and their willingness to pay for sustainable tourism practices. The data collected from 473 respondents were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings reveal that tourism development and perceptions have a significant impact on environmental sustainability. Furthermore, blockchain technology directly affects environmental sustainability and partially mediates the relationship between tourism development and tourists’ perception constructs on environmental sustainability. This study contributes to the understanding of the relationships among tourism development, tourist perceptions, and environmental sustainability, analyzed through the lens of the TAM. Although the TAM has been used in several technology adoption and behavioral studies, this is its first application in the context of sustainable tourism, specifically used in exploring perceptions of environmental sustainability, limited to the environmentally rich Gilgit-Baltistan region in Pakistan.

This paper examines mindfulness as a costless cognitive asset in reducing stress and improving subjective well-being and psychological well-being among Malaysian bottom-forty-percent and middle-forty-percent income earners, known as B40 and M40, respectively. The participants recruited for this experimental study were divided into intervention and control groups and completed pre- and post-assessment questionnaires. The leveraging on digital technologies during pandemic times from May to June 2021 enabled participants in the intervention group (n = 95) to undergo four weekly online mindfulness intervention sessions through Google Meet and completed daily home mindfulness practices using the mobile application for mindfulness: the MindFi version 3.8.0 mobile app. Based on the Wilcoxon signed-rank test, the intervention group’s mindfulness and well-being levels increased significantly after four weeks. This outcome contrasted to those in the control group (n = 31), who exhibited lower mindfulness and well-being levels. The PLS-SEM structural model consists of mindfulness as an independent variable, subjective and psychological well-being as dependent variables, and perceived stress and financial desire discrepancies as the mediators. This model has a goodness-of-fit of 0.076, proving that it is a fit and strong model. There is a positive relationship between mindfulness and subjective well-being (β = 0.162, p-value < 0.01). This model supports the mediation effect of perceived stress between mindfulness and subjective well-being variables (β = 0.152, p-value < 0.05). The overall structural model implies that the effectiveness of mindfulness intervention training not only enhanced bottom- and middle-income earners’ well-being but also lowered the perceived stress level that, henceforth, brought the mind and body together in the present moment.

Sustainable clothing has been gaining attention worldwide as customers become more aware of the negative impacts of fast fashion on communities and the environment. The present study aims to analyze whether the factors will influence customers’ intention to purchase sustainable clothing and determine whether there is heterogeneity between gender groups in these relationships. A total of 174 respondents participated in the study through a survey link posted on social media. The model was analyzed using permutation tests and bootstrap multi-group analysis. Findings revealed that brand image and self-concept positively affect customers’ intention to purchase sustainable clothing, whereas gender does not change the strength and direction of the relationships between variables. There was a significant relationship between price and sustainable clothing purchase intention for males but not for females. This study provides insights to all those involved in the fashion industry, especially within the sustainable clothing subtype of fashion. Future research should be undertaken to explore further how customers’ intention to purchase sustainable clothing is affected by industry practices such as ethical sourcing and transparent disclosure of supply chain practices, as well as include different groups of customers to better understand the factors influencing customers’ purchase intention towards sustainable clothing.

This study examines the heterogeneous effects of key macroeconomic determinants on economic growth in selected MENA economies using a pooled quantile regression framework. Unlike conventional mean-based approaches, this method captures variation across different segments of the conditional growth distribution. Using annual data for eight MENA economies over the period 2000–2023, the analysis evaluates how foreign aid, budget deficits, foreign direct investment, and trade openness are associated with growth under different economic conditions. The results reveal that foreign aid and fiscal deficits are more strongly associated with growth at lower quantiles, indicating greater relevance during low-growth episodes, while their effects weaken or become insignificant at higher quantiles. In contrast, foreign direct investment shows a stronger and more consistent positive association at higher quantiles, whereas trade openness is mainly significant at the lower end of the growth distribution. These findings provide distribution-sensitive evidence on growth determinants in structurally diverse MENA economies. From a policy perspective, the results suggest that macroeconomic strategies should be tailored to country-specific growth conditions rather than relying on uniform policy frameworks across the region.

Household vulnerability assessment in Malaysia has traditionally relied on income-based indicators, which do not adequately capture multidimensional deprivation. To address this limitation, this study employs Random Tree–Dirichlet Process Mixture Model (RT-DPMM) to identify latent heterogeneity in spending patterns and their associated socioeconomic characteristics. Using microdata from Household Expenditure Survey (HES), this study performs clustering on 5130 stable household head samples with nine spending proportional features to model their joint distribution as mixtures of Dirichlet distributions, while five socioeconomic covariates inform cluster allocation through Random Tree embeddings. The proposed RT-DPMM identifies four distinct spending clusters: Balanced Budget Households (Cluster 1, N = 2883), Mobility and Home-Support Households (Cluster 2, N = 642), Basic Essentials-Focused Households (Cluster 3, N = 977), and Luxury Households (Cluster 4, N = 628). Cluster 1 and 3 are characterized as relatively vulnerable groups. These clusters have lower income levels and allocate a larger budget share in Food and Beverages, consistent with the Engel Law’s interpretation of higher food percentage in lower income households. Cluster 1 households primarily allocate their budget evenly across essential and non-essential spending. Cluster 3 are mostly elderly household heads with the highest budget shares in essential spending. In contrast, Cluster 2 and 4 appear relatively better off financially, given their higher income and larger spending share to non-essential categories. These findings suggest that social assistance policies should target expenditure patterns, rather than relying solely on income-based targeting.