By combining social cognitive theory (SCT) and capability approach (CA), this study aims to explore Indonesian youths’ intentions regarding responsible consumption shaped by their awareness and capability. An online survey was conducted among youths aged 18–24 in Indonesia. By developing distinct theoretical models, our study then utilised structural equation modelling to explore cognitive, normative, and structural pathways that shape youths’ sustainable behavioural intentions. This research finds that the three models capture different behavioural mechanisms underlying responsible consumption intentions. It concludes that youth behavioural intention emerges through the interplay between internal awareness and strong external enabling conditions. This research implies that high awareness does not directly lead to committed action when structural barriers such as affordability or limited access persist. The gap between values and behaviour reflects how unequal capability across youth groups can limit the realisation of sustainable choices. This paper offers a novel theoretical contribution by bringing together SCT and CA within a complementary SCT-CA framework, addressing the empirical gap between awareness and action in pro-environmental consumer research. It also introduces a scenario-based measurement of behavioural intention, offering contextually grounded insights into youth responses to real-life sustainability dilemmas. Ultimately, it presents a further policy reformulation agenda for social re-engineering that addresses environmental-legal and infrastructural gaps, enabling motivated youth to translate their intentions into responsible consumption practices.
Digital railway transport systems increasingly integrate online ticketing, real-time passenger information, digital payment, onboard connectivity, and customer support into the passenger journey. Their effectiveness depends not only on the availability of these functions but also on how passengers experience and evaluate their interactions with them. This study investigates the relationship between digital experience and railway passenger satisfaction (RPS) and examines the parallel mediating roles of perceived value and digital trust in the Vietnamese railway context. Survey data were collected from 348 passengers who had recently used at least one digital railway service and were analysed using partial least squares structural equation modelling. Digital experience was positively associated with passenger satisfaction ($\beta$ = 0.452, $p <$ 0.001) and accounted for significant variation in perceived value and digital trust. Both mediating pathways were statistically significant, although the indirect effect through perceived value ($\beta$ = 0.175, $p <$ 0.001) was stronger than that through digital trust ($\beta$ = 0.113, $p$ = 0.001). Together, digital experience, perceived value, and digital trust explained 62.2% of the variance in RPS. These findings indicate that the passenger-side performance of digital railway transport systems rests on the quality of integrated interactions across digital touchpoints, particularly their convenience, reliability, security, and service value. The study provides a passenger-centred framework for evaluating the implementation of digital railway services and identifies the user-related factors that railway operators should consider when planning and prioritising digital system improvements.
Obesity represents a substantial public health burden in Saudi Arabia, yet the predictive contribution of anthropometric and lifestyle characteristics beyond body mass index remains insufficiently characterized. An interpretable machine-learning framework was developed to classify obesity among Saudi adults using anthropometric, demographic, health, and lifestyle variables and to assess whether predictive performance was retained after excluding variables directly related to the body mass index-defined outcome. Of 294 survey responses, 279 were retained after consent and data-completeness criteria were applied. Numerical variables were median-imputed, categorical variables were mode-imputed and one-hot encoded, and obesity was defined as a body mass index $\geq$30 kg/m$^2$. Random forest performance was evaluated using stratified five-fold cross-validation with fixed tuned hyperparameters. Sensitivity analyses excluded body mass index alone and body mass index, height, and weight simultaneously. Model interpretability was assessed using Shapley additive explanations. With body mass index included, all evaluated performance metrics reached 1.000 ± 0.000, reflecting target leakage because body mass index directly defined the outcome. After body mass index exclusion, accuracy was 0.911 ± 0.038, recall 0.624 ± 0.168, F1-score 0.701 ± 0.125, and area under the receiver operating characteristic curve 0.972 ± 0.023. After simultaneous exclusion of body mass index, height, and weight, accuracy decreased to 0.842 ± 0.023 and area under the receiver operating characteristic curve to 0.815 ± 0.056. Waist circumference, hip circumference, waist-to-hip ratio, age, and selected health and lifestyle characteristics retained predictive information, although sensitivity to obesity decreased substantially after removal of the defining anthropometric variables. Shapley additive explanation analyses clarified feature contributions to individual predictions. These findings demonstrate that the exceptional performance of the complete model was predominantly attributable to target leakage. Complementary characteristics retained meaningful discriminatory information, but reduced sensitivity warrants cautious interpretation. External validation in larger, representative cohorts with independently measured anthropometric data is required before clinical or population-level screening applications are considered.
Digital systems increasingly require real-time mechanisms that can detect interaction risks and regulate interface responses under variable user behaviour. However, behavioural sensing, probabilistic error prediction, intervention control, and user experience (UX) evaluation are rarely integrated within a single experimentally validated system. This study investigates a systems engineering framework for predicting user errors and governing adaptive UX interventions. A four-week controlled crossover experiment was conducted with 84 users stratified equally by interface experience. The experiment comprised 168 sessions, 1,008 task instances, and 161,616 validated interaction events. Logistic regression, XGBoost, recurrent neural network, and transformer models were evaluated through participant-isolated nested cross-validation. The transformer achieved the strongest predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.941 (95% confidence interval (CI): 0.937–0.945), an F1-score of 0.889, and a Brier score of 0.110. Model-triggered intervention reduced the mean task-level error rate from 0.280 ± 0.059 to 0.160 ± 0.050 and shortened task completion time from 145.2 ± 13.1 s to 117.8 ± 12.0 s. The intervention also improved the System Usability Scale (SUS), User Experience Questionnaire (UEQ), and Net Promoter Score (NPS) by 16.1, 0.23, and 21.43 points, respectively, while reducing the NASA Task Load Index (NASA-TLX) by 11.8 points. Mean end-to-end system latency was 65.4 ms, with a 95th-percentile latency of 93.8 ms. Decision-cost analysis identified an error probability of 0.70 as the preferred intervention threshold within the tested sensitivity corridor. The results indicate that user-error prediction can be incorporated into a closed-loop monitoring and control architecture without disrupting real-time interaction. The framework provides an experimentally grounded basis for managing predictive interventions in adaptive digital systems.
This paper discusses the effects of the sustainable digital marketing approach on the green purchase intentions in the renewable energy market in Jordan and in particular the behavioral processes that mediate the formation of the environmentally responsible consumption. The quantitative research design was utilized to obtain data based on 237 consumers and processed with the help of the Partial Least Squares Structural Equation Modeling, to determine the direct, mediating, and moderating relationships. The results indicate that sustainable digital marketing initiatives have a significant positive impact on the environmental awareness and green purchase intentions. The environmental awareness can be identified as one of the main explanatory factors, and it mediates the connections between sustainable digital marketing and green purchase intentions, which shows that it is the key influential factor of pro-environment consumer behavior. In contrast, the moderating effect of perceived consumer effectiveness on the relationship between sustainable digital marketing strategies and green purchase intentions is found to be statistically insignificant. However, the Standardized Root Mean Square Residual (SRMR) values of the saturated and estimated models slightly exceeded the recommended 0.08 threshold, suggesting that the structural estimates should be interpreted with caution and that future research should validate the model using larger samples and additional fit indicators. These findings imply that the digital sustainability messages are effective in increasing awareness and behavioral intentions, but the individual perceptions of personal impact might not have a significant effect on strengthening this relationship in the context of the study. On the whole, the research finds that sustainability of digital marketing through the use of eco-friendly practices is a very crucial channel via which sustainable marketing strategies can predict green consumption behavior. The results advance the existing knowledge of the effects of sustainability-focused digital communication on consumer behavior in the developing markets and offer valuable practical recommendations to policymakers and marketers who may want to encourage the use of renewable energy by leveraging the behavioral-based digital communication approaches.
Whether foreign direct investment (FDI) inflows provide predictive information for stock market returns at the sectoral level remains insufficiently understood, particularly in emerging markets. This study examines the short-term predictive relationship between sectoral FDI inflows and stock market returns across major sectors of the Turkish equity market. Monthly data covering January 2010 to November 2025 were analysed for the banking, finance and insurance, services, manufacturing, industrial, and wholesale and retail sectors. Separate Vector Autoregression (VAR) models were specified for each sector, with Borsa İstanbul 100 (BIST 100) index returns and USD/TRY exchange-rate returns included as control variables to account for broad market and exchange-rate conditions. Lagged predictive relationships were assessed using Granger causality tests, while the dynamic responses of the variables to shocks were examined through impulse response analysis. Model adequacy was evaluated using standard diagnostic tests, and substantive interpretation was restricted to the banking and finance and insurance models that satisfied the required diagnostic criteria. No statistically significant Granger-predictive relationship was identified in either direction between sectoral FDI inflows and the corresponding sectoral stock market returns in either of these diagnostically adequate models. The impulse response results likewise provide limited evidence of a persistent or systematic transmission from sectoral FDI inflows to sectoral stock market returns. Overall, the findings suggest that sector-specific FDI inflows should not be regarded as a robust short-term predictor of sectoral equity returns in Türkiye over the sample period. The results also indicate that sectoral stock market dynamics may be driven more strongly by broader market conditions and other macro-financial factors than by contemporaneous changes in sector-specific FDI inflows.
This study evaluates whether public-sector health financing and selected governance dimensions are associated with longevity in South Asia. The analysis uses a balanced panel of 168 country-year observations for Bangladesh, India, Maldives, Nepal, Pakistan, and Sri Lanka over 1996–2023. Life expectancy at birth (LE) is modelled against domestic general government health expenditure (HE_GDP), control of corruption (CC), political stability and absence of violence/terrorism (PS), and government effectiveness (GE).Pooled ordinary least squares (OLS), feasible generalized least squares (FGLS), panel-corrected standard errors (PCSEs), and Driscoll–Kraay standard errors are reported to assess the stability of the estimates under different error structures. Diagnostic testing indicates heteroskedasticity and cross-sectional dependence, while the test for first-order serial correlation is not statistically significant. In the FGLS specification, a one-percentage-point increase in government health expenditure as a share of gross domestic product (GDP) is associated with 1.2514 additional years of life expectancy ($p <$ 0.01). CC and political stability also show positive, statistically significant coefficients of 2.9629 and 2.3174, respectively. GE is negative but not statistically significant in the FGLS model (-1.3262, $p$ = 0.131), and its significance is sensitive to the estimator used. Across the robustness specifications, the central pattern remains that health financing, corruption control, and political stability are important correlates of longevity. The findings support a policy approach in which budget expansion is accompanied by institutional accountability and a stable environment for implementation.
This paper discusses the effects of the sustainable digital marketing approach on the green purchase intentions (GPIs) in the renewable energy market in Jordan and in particular the behavioral processes that mediate the formation of the environmentally responsible consumption. The quantitative research design was utilized to obtain data based on 237 consumers and processed with the help of the Partial Least Squares Structural Equation Modeling (PLS-SEM), to determine the direct, mediating, and moderating relationships. The results indicate that sustainable digital marketing initiatives have a significant positive impact on the environmental awareness (EA) and GPIs. The EA can be identified as one of the main explanatory factors, and it mediates the connections between sustainable digital marketing and GPIs, which shows that it is the key influential factor of pro-environment consumer behavior. In contrast, the moderating effect of perceived consumer effectiveness (PCE) on the relationship between sustainable digital marketing strategies (SDMS) and GPIs is found to be statistically insignificant. However, the standardized root mean squared residual (SRMR) values of the saturated and estimated models slightly exceeded the recommended 0.08 threshold, suggesting that the structural estimates should be interpreted with caution and that future research should validate the model using larger samples and additional fit indicators. These findings imply that the digital sustainability messages are effective in increasing awareness and behavioral intentions, but the individual perceptions of personal impact might not have a significant effect on strengthening this relationship in the context of the study. On the whole, the research finds that sustainability of digital marketing through the use of eco-friendly practices is a very crucial channel via which sustainable marketing strategies can predict green consumption behavior. The results advance the existing knowledge of the effects of sustainability-focused digital communication on consumer behavior in the developing markets and offer valuable practical recommendations to policymakers and marketers who may want to encourage the use of renewable energy by leveraging the behavioral-based digital communication approaches.
The performance of cold thermal energy storage systems is often limited by the low thermal conductivity of phase change materials (PCMs), which delays solidification and reduces charging efficiency. In the present study, the synergistic effects of hybrid nanofluids, porous metal foam, and thermal radiation on the solidification behavior of a PCM-based cold thermal energy storage unit incorporating elliptical and triangular cooling boundaries were numerically investigated. A transient numerical model was developed using the Galerkin finite element method and was coupled with an implicit time-integration scheme and adaptive mesh refinement to accurately resolve temperature evolution and the moving solid–liquid interface during the freezing process. The numerical framework was validated against benchmark results available in the literature, and excellent agreement was achieved. It was found that the addition of hybrid nanoparticles reduced the total solidification time by approximately 5.19%. When thermal radiation was incorporated, the freezing duration was further shortened by nearly 34.35%. The most pronounced enhancement was obtained through the incorporation of porous metal foam, for which the solidification time was reduced by approximately 75.25% as a result of the substantial augmentation of conductive heat transfer pathways within the PCM. Under the combined application of all enhancement mechanisms, the total freezing time was reduced by up to 84.59% relative to the baseline configuration. These findings demonstrate that the integration of porous structures, radiative cooling, and hybrid nanofluids represents an effective strategy for overcoming the thermal limitations of conventional PCM-based cold thermal energy storage systems and provides valuable design guidance for the development of high-efficiency thermal energy storage technologies in industrial cooling and sustainable energy systems.
This investigation aims to determine how the combination of Cybersecurity (CS), Digital Spending (DS), and Innovation (INN) affect Economic Growth (EG) in Jordan, Saudi Arabia, Malaysia, and the United Arab Emirates (UAE), using data collected quarterly from 2015 through 2024. The original panel is balanced (4 countries × 40 quarters = 160 observations), and it remains balanced after first differencing removes the first quarter of each country (4 × 39 = 156 observations), making it possible to study differences in short-run effects across four countries. In order to correct both non-stationarity and multicollinearity, first-differenced standardized variables (N = 156) were used, reducing VIF values below 1.04 and significantly lessening the potential for false discoveries associated with spurious regression. The study utilized Ordinary Least Squares (OLS) regressions, with heteroskedasticity-consistent covariance matrix estimator type 3 (HC3) using robust standard errors, and a fixed effects (FE) model identified by Hausman Test (χ² = 31.49; p < 0.001). Results indicate INN was statistically significant with regard to EG in the short-run (β* = 0.2508; p = 0.007), while CS and DS did not have short-term predictive value for EG. The combined model accounted for 6.5% of variations in EG (R² = 0.065; p = 0.017). Findings indicate INN was the only significant predictor of immediate productivity, while effects from CS and DS will be longer in duration before becoming evident and measurable. The non-significant effects of CS and DS should be interpreted as a lack of contemporaneous short-run predictive power in this first-differenced specification and not as evidence of long-term economic irrelevance. Robustness measures confirmed all results across alternative model specifications were consistent.
The dynamic behavior of nanoscale beam structures is strongly influenced by material gradation, geometric discontinuities, foundation characteristics, and small-scale effects, all of which play critical roles in the performance of advanced multiphysics nano-engineering systems. In the present study, the free vibration characteristics of a perforated non-homogeneous nanobeam resting on a variable elastic foundation were investigated under sliding-end boundary conditions. Particular attention was devoted to applications involving resonators of microelectromechanical and nanoelectromechanical systems, nano-sensors, smart structural components, and coupled electromechanical nano-devices, where precise control of dynamic response is essential. Spatial variations in Young’s modulus and material density were incorporated to represent non-homogeneous material properties, while perforation effects were introduced through modified geometric and mechanical characteristics. Size-dependent nanoscale behavior was captured using Eringen’s nonlocal elasticity theory. Based on Euler-Bernoulli beam theory, the governing differential equation of motion was derived. The resulting eigenvalue problem was solved using the Galerkin method in conjunction with shifted Legendre polynomial admissible functions, enabling high numerical stability, rapid convergence, and computational efficiency. Validation of the proposed formulation was performed through comparisons with available benchmark results reported in the literature, and additional convergence studies were conducted. Investigations were carried out to evaluate the effects of perforation characteristics, non-homogeneity parameters, and spatially varying foundation stiffness on the natural frequencies and mode shapes of the nanobeam. It was demonstrated that significant alterations in dynamic response may be induced by the combined interaction of material gradation, perforation geometry, and foundation variability. The developed model provides an efficient and reliable computational framework for the dynamic characterization of perforated nanoscale structures and offers valuable insights for the design, optimization, and vibration control of next-generation multifunctional nano-engineering systems operating under coupled multiphysics environments.
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