Information asymmetry remains a fundamental constraint on the allocation of external finance, as the organisational capabilities and internal quality of firms cannot be fully observed by external financiers. Although managerial quality, human capital, innovation, digitalisation, relational networks and financial transparency have each been linked to financing outcomes, their interrelated nature has received considerably less attention. To address this gap, a unified theoretical framework is developed in which these observable organisational attributes are conceptualised as indicators of a latent construct, termed Organisational Quality (OQ). Drawing on information asymmetry theory and signalling theory, the framework is tested using longitudinal data from 2,017 Vietnamese manufacturing small and medium-sized enterprises (SMEs), comprising 6,051 firm-year observations from the SMEs Survey. OQ is operationalised as a reflective latent construct within a structural equation modelling (SEM) framework, through which its associations with access to external finance, productive investment and firm performance are examined. Strong empirical support is obtained for the proposed framework. Higher OQ is found to be positively associated with access to external finance, and this association is significantly stronger under conditions of greater information asymmetry. Access to external finance is, in turn, positively associated with productive investment, while productive investment is positively associated with firm performance. The mediation results further indicate that OQ is associated with firm performance through both direct and indirect pathways, with a sequential pathway operating through improved access to external finance and subsequent productive investment. These relationships remain stable across alternative measurement approaches, model specifications and measures of firm performance. The findings provide three principal contributions. First, organisational characteristics that have traditionally been examined separately are integrated into a common latent organisational dimension. Second, OQ is operationalised and empirically validated as a reflective latent construct within an integrated SEM framework. Third, evidence is provided that OQ is associated with improved access to external finance, greater productive investment and enhanced firm performance, while its financing-related association becomes more pronounced as information asymmetry increases.
Multi-tier supply chains are exposed to operational disruptions that can spread through interconnected supplier–customer relationships. Existing risk assessments often rank individual firms or links without considering whether these relationships form continuous routes of concentrated vulnerability. This study investigates critical-path identification as a decision-analytics problem in which relationship-level performance and network structure are considered jointly. A two-phase framework was developed by integrating the Criteria Importance Through Intercriteria Correlation (CRITIC) method with evolutionary path optimization. In the first phase, CRITIC was used to derive objective weights for 14 operational performance criteria and to calculate a criticality index for each supplier–customer link. In the second phase, two optimization models were formulated to identify the path with the highest cumulative criticality and the path with the highest average link criticality subject to a minimum path length. The framework was applied to an automotive supply chain consisting of 19 enterprises and 35 directed links. The cumulative model identified a five-link path with a total criticality of 3.274 and an average criticality of 0.655, whereas the average-criticality model identified a four-link path with a total criticality of 2.738 and an average of 0.684. Both models selected the same initial relationship but produced different subsequent routes. The results indicate that link-level rankings alone cannot identify the most critical continuous route because path selection also depends on connectivity, topological position, and the optimization objective. The framework provides a reproducible basis for prioritizing supplier relationships, directing monitoring resources, and selecting risk-mitigation measures across multi-tier supply networks.
Vietnam’s post-pandemic tourism recovery raises the question of how renewed visitor growth relates to economic value, climate vulnerability, and mobility-related carbon performance. This study assesses Vietnam’s tourism recovery under climate change and the Net Zero transition using secondary data for 2019–2023. It applies descriptive analysis, recovery indices, system-level emission-to-activity ratios, passenger-aviation Tapio decoupling analysis, and a national-transport system-level proxy elasticity for 2022–2023. National transport and passenger-aviation emissions are treated as system-level proxies for mobility-related carbon pressure and do not constitute a tourism emissions inventory. The results indicate a scale–value–carbon mismatch. In 2023, total tourist volume reached 117.3% of its 2019 level, whereas nominal tourism revenue and average nominal revenue per reported tourist visit reached 89.8% and 76.5%, respectively. Contextual climate evidence indicates vulnerability channels affecting destinations, infrastructure, transport connectivity, business operations, and service continuity. National transport and passenger-aviation emissions reached 104.1% and 93.8% of their 2019 levels. During 2022–2023, national transport emissions grew more slowly than measured passenger activity, while passenger aviation exhibited weak decoupling. Absolute emissions nevertheless increased in both systems. The results indicate short-run relative improvement in emissions performance rather than structural decarbonisation. Three policy priorities emerge: strengthening tourism carbon accounting, integrating climate adaptation into destination and transport planning, and coordinating measures to reduce mobility-related emissions. The diagnostic framework may also be relevant to tourism transitions in emerging economies facing similar climate, mobility, and data constraints.
Engineered cementitious composites (ECCs) are increasingly considered for protective structures because their fiber-bridging mechanism, tensile ductility, and energy absorption can restrict crack growth under severe loading. Their compressive response at high strain rates, however, remains difficult to represent using conventional concrete constitutive models, particularly when rate-dependent deformation and progressive damage occur concurrently. This study investigated the dynamic compressive behavior of ECC containing 2.0% polyvinyl alcohol fibers and developed a constitutive framework that couples nonlinear viscoelasticity with statistical damage evolution. Split Hopkinson pressure bar (SHPB) tests were conducted at four average strain-rate levels ranging from approximately 15 to 200 s$^{-1}$, with three specimens tested at each level. Relative to the lowest strain-rate level, the dynamic peak stress and peak strain increased by up to 41.0% and 98.6%, respectively, while their rates of increase gradually declined at the higher loading rates. A rate-dependent constitutive model was then formulated by combining the Zhu–Wang–Tang (ZWT) nonlinear viscoelastic model with a Weibull damage function. The model reproduced the ascending branch and peak region of the measured stress–strain curves, although larger discrepancies remained in the post-peak softening stage. After implementation in Livermore Software for DYnamic Analysis (LS-DYNA), the model reproduced the strain-rate-dependent stress–strain response and the transition from localized cracking to extensive fragmentation. The maximum deviations between the simulated and experimental increases in peak stress and peak strain were 8.2% and 6.5%, respectively. The proposed framework provides a physically interpretable representation of the coupled rate-dependent deformation and damage of ECC and supports numerical analysis of ECC components subjected to impact-type loading.
This paper presents a unique Modified Differential Evolution Optimization Algorithm (MDEOA) for the intelligent modification of proportional–integral–derivative (PID) controller settings. The proposed MDEOA is developed with particular emphasis on future PID controller tuning applications. Benchmark evaluations demonstrate significant improvements in convergence speed and optimization quality compared with Classical Differential Evolution (CDE), indicating its potential suitability for intelligent control parameter optimization. The study tackles the enduring drawbacks of CDE in control parameter optimization, including its inadequate exploration-exploitation balance and sluggish convergence when adjusting dynamic or nonlinear systems. The suggested MDEOA addresses these issues by introducing improved crossover, mutation, and adaptive control techniques that increase population diversity and hasten convergence toward ideal PID gains. To give a methodical comparison analysis, the PID tuning problem is subjected to both the traditional CDE and the suggested MDEOA. The MDEOA-based PID controller offers noticeably better dynamic performance, as shown by analytical simulations and experimental validation. In particular, it completely eliminates the maximum peak and steady-state error, corresponding to 100% reduction in both parameters. The MDEOA-based PID controller reduces the rise time by 10.18%, the peak time by 49.70%, and the settling time by 93.47% compared with the conventional PID controller. These enhancements are a direct result of the algorithm's modified operators' efficacy. Overall, the findings show that MDEOA is a strong and dependable substitute for conventional CDE in intelligent control system parameter optimization. MDEOA is a viable tool for future applications in sophisticated automation and real-time control contexts because of the large improvements in response characteristics, which demonstrate that the suggested alterations greatly increase the resilience, accuracy, and flexibility of PID tuning.
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.
For Authors
For Reviewers
For Editors
Open Access
