Search Results (443)

Background: The alarming trend of rising depression and insomnia among the world’s population, especially in young adults, demands urgent attention from both clinical and lifestyle medicine perspectives. This study aimed to evaluate whether habitual physical activity (PA) could improve sleep quality and depressive symptoms. It explores whether sleep parameters mediate the relationship between training intensity and depression. Methods: This cross-sectional study examined the relationships between PA, sleep quality, and depression in 349 participants, predominantly young adults, aged 16–54 (Median: 21, Q1–Q3: 20–23). Depressive symptoms and sleep quality were assessed using the Beck Depression Inventory (BDI) and Pittsburgh Sleep Quality Index (PSQI). PA parameters, including training intensity and frequency, were evaluated via a study-specific self-report tool. Mediation effects were assessed utilising Structural Equation Modelling (SEM) to evaluate direct, indirect, and total effects. Results: Higher training intensity and training volume were associated with lower depressive symptoms. Except for the daytime dysfunction domain, the current data did not support sleep as a mediator in the relationship between physical activity and depressive symptoms. Participants with 3–5 years of training experience exhibited lower BDI scores compared to those with less than 12 months of experience. Interestingly, the type of PA did not influence BDI or PSQI scores, suggesting that consistency and intensity are more critical than the kind of exercise itself. Conclusions: Systematic, high-intensity PA, especially for a prolonged period of time, is significantly associated with lower depressive symptoms. The exploratory nature of this study highlights training frequency and intensity as potential factors that need further research. These findings highlight the mind–body connection and demonstrate that PA might be an intervention supporting mental well-being. Full article

Systematic multidimensional assessment of commercial complex spatial efficiency has become essential for sustainable urban governance as cities globally transition from spatial expansion to stock renewal. Most existing research, however, evaluates commercial space from a single economic dimension, failing to integrate social, economic, and ecological dimensions simultaneously. Informed by synergetics and urban morphology theory as conceptual lenses (not as tested causal mechanisms), this study develops an integrated multidimensional evaluation framework as its primary contribution. To characterize within-sample efficiency levels and explore directional associations among subsystems, we combine a spatial compactness model, functional diversity index, AHP-entropy hybrid weighting, and partial least squares structural equation modeling (PLS-SEM), applied to 20 purposively selected commercial complexes across eight administrative districts of Guangzhou. All results are within-sample and exploratory. Three descriptive patterns emerge: (1) comprehensive efficiency scores vary moderately across the sample (mean = 0.566, SD = 0.067); (2) efficiency scores follow a core–semi-periphery–periphery gradient (Tianhe: 0.665; Huadu: 0.451); and (3) functional synergy shows the largest positive directional association with overall comprehensive efficiency among the four predictors examined. Three exploratory policy directions for stock-renewal governance in high-density Asian megacities are proposed; the reference benchmarks are derived from this 20-case sample and require large-N validation before any regulatory application. Full article

The increasing integration of artificial intelligence (AI) into organizational processes has accelerated the adoption of algorithmic management, reshaping work structures across labor-intensive service organizations. However, empirical evidence remains limited regarding the simultaneous control-oriented and developmental dimensions of AI-enabled management systems. This study examines how algorithmic control and AI-supported skill development influence perceived autonomy and career security among employees in hospitality organizations. Data were collected from 441 employees working in hotel organizations in Bavaria, Germany. The data were analyzed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM), bootstrapped mediation analysis, and hierarchical regression. The results suggest that control-oriented and developmental dimensions of AI are associated with different aspects of employees’ work experiences. Algorithmic control was negatively associated with perceived autonomy, whereas AI-supported skill development was positively associated with both perceived autonomy and career security. Perceived autonomy did not mediate the relationships between AI-related workplace conditions and career security, and digital literacy did not moderate the relationship between algorithmic control and perceived autonomy. The study contributes to research on algorithmic management and AI-enabled workplaces by highlighting the importance of analytically distinguishing between the control-oriented and developmental dimensions of AI when examining the organizational implications of AI-enabled management. Full article

Large language models (LLMs) are changing how students approach problem solving, code interpretation, and software development. Successful task completion with AI assistance, however, does not necessarily indicate conceptual understanding of underlying programming principles, making it difficult to determine how programming skills develop over time. This study examines whether students’ critical engagement, collaborative learning practices, and exploratory use of LLMs are associated with self-reported programming competence, coding practices, and longer-term knowledge retention. Survey data from 189 students with varying levels of LLM use in educational and coding-related contexts were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings suggest that students perceive LLMs as more educationally valuable when they actively question, reinterpret, and adapt AI-generated responses rather than accept them as final answers. Students who interacted more reflectively with AI outputs reported stronger perceived competence and more deliberate attention to code organization, readability, and maintainability. Collaborative use corresponded to broader development of programming abilities, whereas creative experimentation was more closely related to stylistic refinement and perceived benefits for longer-term retention. Active engagement with AI-generated material may therefore promote analytical reasoning and deeper conceptual involvement instead of merely accelerating code production. By moving beyond technology adoption and productivity-oriented perspectives, the study highlights the role of learner agency in shaping LLM-supported programming education. Full article

Background: Whether congenital cataract-related visual deprivation and subsequent rehabilitation are associated with retinal structural and vascular network differences remains unclear. This study evaluated superficial retinal vascular network descriptors and retinal nerve fiber layer (RNFL) thickness in children after bilateral congenital cataract extraction and secondary intraocular lens implantation. Methods: Age-matched children served as controls. Participants underwent spectral-domain optical coherence tomography (OCT) and 6 × 6 mm optical coherence tomography angiography (OCTA). Metrics were magnification-corrected, and comparisons used generalized estimating equation models adjusted for age, eye side, axial length, and spherical equivalent. Results: Eighteen pseudophakic children (36 eyes; median age, 8 years) and 17 controls (34 eyes; median age, 9 years) were included. In adjusted models, macular mean vessel diameter was higher in pseudophakic eyes (β = 11.16 µm; p = 0.003), as was macular mean tortuosity (β = 0.032; p = 0.001). Branchpoint density was lower in direction but not independently significant (p = 0.125). Choroidal thickness, choroidal vascularity index, and foveal avascular zone area did not differ significantly. Temporal RNFL thickness was greater in pseudophakic eyes (β = 13.37 µm; p = 0.007); other RNFL parameters were not significant. Conclusions: These findings suggest exploratory differences in superficial vascular network morphology and temporal RNFL thickness. However, because the groups differed clinically in best-corrected visual acuity, refractive status, and axial length, residual confounding cannot be excluded despite magnification correction and adjusted modeling. Full article

Artificial intelligence (AI) is reshaping tourism service operations, smart tourism ecosystems, and digital customer engagement, yet many tourism SMEs still struggle to convert AI readiness into operational capability and firm performance. This study develops a staged capability-conversion model that integrates the Technology-Organization-Environment (TOE) framework, Social Cognitive Theory, the Resource-Based View, and Dynamic Capabilities Theory. Using a quantitative cross-sectional design, data were collected from employees and managers of tourism SMEs, particularly small and medium-sized travel agencies in mainland China. After data screening, 433 valid responses were analyzed using exploratory factor analysis, confirmatory factor analysis, covariance-based structural equation modeling, and 5000-resample bootstrap mediation testing. The results suggest that organizational readiness is the most consistent readiness-related antecedent, digital self-efficacy is positively associated with AI adoption and operational capability, and operational capability shows the strongest direct association with perceived firm performance. These findings are consistent with the view that AI-enabled value in tourism SMEs is associated less with technology adoption alone than with the conversion of readiness into behavioral and operational capabilities. Full article

Problem: Entrepreneurial decision-making is widely recognized as central to startup outcomes, yet how founders make decisions during the startup execution phase remains underexplored. Prior research rarely distinguishes between strategic decisions (e.g., market entry, scaling) and operational decisions (e.g., coordination, problem-solving), even though these two decision types differ in their uncertainty, reversibility, and cognitive demands. Objective: This study investigates how founder attributes relate to self-reported decision-making styles across strategic and operational decision contexts during startup execution. Methodology: Drawing on Dual-Process Theory, decision-making is viewed as an interplay between intuitive (System 1) and analytical (System 2) cognitive processes. A sequential exploratory mixed-methods design was employed, beginning with semi-structured interviews with 20 Indian startup founders to develop the conceptual framework, followed by quantitative examination using Partial Least Squares Structural Equation Modelling (PLS-SEM) on data from 350 funded startup founders, with separate structural models estimated for strategic and operational decision contexts. Results: The findings revealed context-specific patterns of association between founder attributes and self-reported decision-making styles across strategic and operational decision contexts. In the strategic model, cognitive orientation, domain experience, and risk appetite were significantly associated with decision-making style, explaining 49.7% of the variance (R² = 0.497). In the operational model, only risk appetite remained significant, with substantially lower explanatory power (R² = 0.125). Taken together, the findings indicate stronger patterns of association between founder attributes and decision-making style in the strategic context than in the operational context. Conclusions: The study contributes to entrepreneurial cognition research by demonstrating that founder attributes exhibit context-specific patterns of association with decision-making styles. These findings underscore the importance of considering decision context when examining entrepreneurial decision-making. Full article

Research on AI-supported EFL learning has largely focused on learning outcomes, with limited attention to the cognitive mechanisms underlying creativity and the role of AI experience in shaping this process. This study examined how AI cognitive stimulation relates to EFL learners’ cognitive creativity, as measured by divergent thinking, with attention to the conditioning role of AI experience. Using a quantitative, correlational, cross-sectional design, data were collected from 878 undergraduate EFL learners recruited through stratified cluster sampling across four public universities in Saudi Arabia. Following instrument validation through exploratory and confirmatory factor analysis, structural equation modelling was employed to test direct, indirect, and conditional relationships among the study variables. The findings show that AI cognitive stimulation was strongly associated with divergent thinking (β = 0.52), and divergent thinking was strongly associated with cognitive creativity (β = 0.46). Although AI cognitive stimulation was directly associated with cognitive creativity, this effect was relatively limited. Mediation analysis showed that divergent thinking accounted for the primary pathway linking AI cognitive stimulation to cognitive creativity. Moderated mediation analysis further showed that learners’ duration of AI experience modestly but meaningfully conditioned this indirect pathway without altering its structural form. These results indicate that creative cognition in AI-supported EFL learning depends primarily on learner-controlled ideational processes rather than exposure to AI-generated content alone. The study contributes a process-oriented account of creativity in EFL contexts by specifying the cognitive pattern through which AI-related stimulation is associated with creative capacity and by clarifying how accumulated AI experience conditions this process. Full article

Postharvest quality deterioration of Korla fragrant pear (Pyrus sinkiangensis Yu) severely constrains its market value, yet the regulatory role of preharvest soil management in shaping postharvest performance remains poorly understood. Although green manure is widely adopted to ameliorate orchard soil degradation, species-specific modulation of postharvest storage trajectories and the quantitative fidelity of soil-to-fruit nutrient transmission have rarely been resolved for climacteric pear species. This study investigated how green manure species modulate fruit quality at harvest and during postharvest storage life and their underlying soil–fruit linkages. Three preharvest treatments were imposed, as follows: control (CK), sweet clover (CM), and alfalfa (MX). Fruits were harvested and stored at 4 °C, with samplings at 1, 5, 10, 15, and 20 d. A critical quality transition was identified at 15 d, characterized by the concurrent peaking of soluble sugars, organic acids, vitamin C, and anthocyanins alongside an optimal sugar–acid ratio. Beyond this inflection point, CM and MX diverged markedly: CM enhanced soluble sugar accumulation, anthocyanin retention, and ester volatile production—most notably hexyl acetate, which increased over 14.4-fold—thereby generating a pronounced fruity aroma bouquet. Conversely, MX sustained higher amino acid and vitamin C levels and conferred superior late-storage stability, evidenced by a three-fold lower coefficient of variation in the sugar–acid ratio relative to CK. Partial-least-squares structural equation modeling (PLS–SEM) revealed soil fertility as the principal exploratory associative factor of fruit quality, but the fidelity of soil-to-fruit transmission was species-dependent. MX exhibited the highest observed associative strength (R² = 0.971), whereas CM exhibited attenuated transmission fidelity (R² = 0.777), with network analysis further indicating that CM exhibited divergent associative patterns of key soil–fruit correlations. These findings suggest that green manure identity is linked to postharvest quality through divergent soil–fruit coupling pathways: alfalfa shows nutrient transmission efficiency and stabilizes nutritional quality, whereas sweet clover promotes sugar-aroma accumulation at the cost of reduced soil–fruit conversion fidelity. Species-specific green manure selection thus offers a viable strategy for targeted modulation of postharvest traits in Korla fragrant pear. Full article

This study aimed to develop and validate a psychometric instrument to assess intercultural mediation among public employees of security forces in Spanish border contexts (CMIEPFS). An initial pool of 42 items was created and refined through expert judgment, resulting in a final 38-item questionnaire. The instrument was administered to two independent samples (n = 426 for exploratory factor analysis; n = 512 for confirmatory factor analysis). Construct validity was examined using structural equation modeling, and reliability was assessed through Cronbach’s alpha, intraclass correlation coefficient, and Pearson correlation. Results supported a three-factor structure: (1) acceptance of intercultural environments, (2) intercultural competences in the workplace, and (3) training in intercultural mediation. Exploratory factor analysis indicated adequate sampling adequacy (KMO = 0.916) and a significant Bartlett’s test (p < 0.001), explaining 63.08% of the variance. Confirmatory factor analysis showed an acceptable model fit (RMSEA = 0.07). Internal consistency was high (α = 0.91), and test–retest reliability was satisfactory (ICC = 0.82; r = 0.84, p < 0.001). The instrument shows adequate validity and reliability for assessing intercultural mediation in public security personnel. It provides a useful tool for research, training evaluation, and institutional diagnosis in multicultural professional contexts. Full article

Rapid urbanization has intensified transportation challenges in many metropolitan areas, increasing reliance on private vehicles and contributing to congestion and environmental concerns. Although transit-oriented development strategies aim to promote sustainable mobility by improving public transport accessibility, the behavioral mechanisms linking built environment characteristics to travel behavior remain insufficiently explored. This study examines the influence of transit accessibility (TA) and parking availability on mode choice preference and travel behavior. A structured questionnaire survey was conducted, and the data were analyzed using covariance-based structural equation modeling after validating the measurement constructs through exploratory and confirmatory factor analyses. The results indicate that TA positively influences mode choice preferences, whereas parking availability negatively affects them. Furthermore, mode choice preferences significantly influence travel behavior, underscoring their mediating role between built environment factors and mobility outcomes. These findings provide empirical insights for urban planners seeking to promote sustainable transportation through enhanced TA and effective parking management strategies. Full article

Background/Objectives: Although Hospital Information Systems (HIS) are essential to the provision of contemporary healthcare, clinical professionals’ use of HIS is still uneven. Robust healthcare decision-making is based on the systematic collection, storage, and analysis of health data, and it is crucial to comprehend the elements that promote or impede adoption. In a tertiary-care multi-specialty hospital in Chennai, India, this study sought to evaluate the role of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—on the Behavioural Intention of healthcare professionals to adopt HIS. Methods: 140 medical professionals (physicians, nurses, and hospital technicians) from a 750-bed teaching hospital where HIS had been in use for at least 24 months were chosen by stratified random sampling to participate in a descriptive, cross-sectional study. The original UTAUT instrument was modified into a structured, self-administered questionnaire using a validated 5-point Likert scale. Expert review was used to demonstrate face validity, while Cronbach’s Alpha (α > 0.70) was carried out. Statistical analysis methods included Pearson correlation, multiple linear regression, one-way ANOVA with Tukey’s HSD post hoc analysis, and Structural Equation Modelling (SEM). Results: The majority of responders in the sample were female (51.5%), primarily nurses (47%), and had less than five years of work experience (36%). All four UTAUT constructs were found to be significantly correlated with Behavioural Intention by Pearson correlation, with Performance Expectancy showing the strongest association. The structural model explained a significant proportion of the variance in technology adoption. Multiple regression analysis indicated that Performance Expectancy (β = 0.480, p < 0.01) and Social Influence (β = 0.180, p < 0.05) were significant positive predictors of Behavioural Intention. Confirmatory Factor Analysis verified acceptable measurement boundaries (χ²/df = 1.42, RMSEA = 0.043, SRMR = 0.062, CFI = 0.94. An exploratory one-way ANOVA revealed that perceptions of Facilitating Conditions differed significantly by professional designation (F (2, 137) = 6.42, p = 0.002), with nurses scoring significantly lower than physicians (p = 0.002) and technicians (p = 0.011). Conclusions: Performance Expectancy is the main driver of healthcare professionals’ Behavioural Intention to adopt HIS. Compared to doctors and technical professionals, nurses reported considerably lower perceptions of Facilitating Conditions, indicating a substantial support gap. In order to close the clinical digital gap and enhance patient safety, these findings advocate for role-specific infrastructure investments and focused implementation techniques. Full article

Background: The rapid digitalization of warehouse logistics has accelerated the adoption of smart industrial robots, AI-driven autonomous vehicles, and drones to enhance operational coordination and supply chain performance. However, empirical evidence regarding the combined operational implications of these autonomous technologies within emerging-market logistics environments remains limited. This study examines the relationships between autonomous warehouse technologies and perceived warehouse operational performance in Morocco. Methods: A quantitative cross-sectional research design was employed using survey data collected from 1753 logistics professionals operating in warehouses connected to the SoftLogistics platform. Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) were conducted to evaluate the proposed relationships and interaction effects among the study constructs. Results: The findings indicate that smart industrial robots, AI-driven autonomous vehicles, and drones are positively associated with perceived warehouse operational efficiency, operational accuracy, and warehouse resilience. The results further suggest that technological interoperability, workforce readiness, organizational adaptation, and AI-enabled coordination mechanisms may enhance the operational contribution of autonomous warehouse systems. Conclusions: Autonomous logistics technologies are associated with smart warehouse transformation and improved operational performance within emerging logistics environments. The findings also highlight the importance of organizational and socio-technical readiness for the implementation of autonomous warehouse technologies. Full article

The relationship between environmental, social and governance (ESG) performance and corporate financial performance (CFP) remains an open question, particularly in capital-intensive sectors exposed to regulatory pressures and long-term transition costs. This study analyses the relationship between ESG ratings and financial performance using accounting data from 59 listed European energy companies over the period 2014–2023. ESG ratings were obtained from standardised sustainability scores available on Yahoo Finance and are therefore used as proxies for corporate sustainability performance. Financial data were extracted from Orbis Europe Full. The empirical design adopts an exploratory multi-method approach combining correlation analysis, multiple linear regression (OLS) and structural equation modelling (SEM). In the SEM model specification, ROE, ROA and ROCE are modelled as reflective indicators of a latent construct termed ‘economic performance’, whilst financial leverage is treated as a distinct observed variable representing firms’ financing structure. The results show that traditional linear models have limited explanatory power in explaining ESG ratings. The SEM analysis indicates that the latent construct of economic performance has a positive but statistically insignificant association with ESG ratings, whilst financial leverage shows a marginally significant positive association. Factor loadings confirm that ROE, ROA and ROCE consistently represent the common dimension of economic performance. Overall, the results suggest that ESG ratings in the European energy sector are explained not solely by short-term accounting profitability but also by broader strategic, organisational, governance, and financing conditions. The study contributes to research on the ESG-CFP relationship by proposing an exploratory structural equation modelling approach to modelling economic performance as a latent accounting construct. It offers food for thought for managers and policymakers evaluating sustainability strategies in capital-intensive transition contexts. Full article

Recently, the Quality by Design (QbD) principle has been implemented in the pharmaceutical industry to enhance product and process understanding through a science- and risk-based approach. This study aimed to apply QbD principles to the formulation development of felodipine push–pull osmotic pump (PPOP) capsules. The quality target product profile (QTPP) and critical quality attributes (CQAs) were established. A Box–Behnken experimental design was employed to optimize the formulation variables, including the amounts of Polyox WSR N80, Polyox WSR Coagulant, and sodium chloride, selected based on the initial risk assessment. Four responses were monitored: lag time, release rate and R² based on zero-order release kinetics, and drug release at 24 h. Results indicated that the optimal formulation consisted of 125 mg Polyox WSR N80, 26 mg Polyox WSR Coagulant, and 30 mg sodium chloride. This formulation met the predefined criteria for lag time (≤6 h) and release kinetics (R² ≥ 0.95), while drug release at 24 h remained below the target value (≥80%). Because most fitted response surface models were not statistically significant, the generated regression equations and response surfaces were interpreted qualitatively to identify formulation trends rather than as predictive models. Experimental verification showed reasonable consistency in overall response trends, although substantial deviations between predicted and observed values were observed for some responses, particularly drug release at 24 h. Therefore, the present work should be considered a formulation-development and QbD feasibility study rather than a definitive optimization study. These findings demonstrate that the QbD-based approach enabled systematic, multivariate optimization and design space establishment, providing a more structured framework for formulation refinement compared with prior exploratory development and supporting controlled drug release characteristics of felodipine PPOP capsules. Full article