Search Results (12,945)

As governments pursue digital transformation to improve service delivery and administrative efficiency, AI chatbots have emerged as a promising innovation in smart public administration. However, their adoption remains limited, particularly in transitioning countries where institutional, organizational, and technological conditions are complex and evolving. This study aims to empirically examine the key aspects, challenges, and strategic implications of AI chatbots’ adoption in public administration of Uzbekistan, a transitioning government in Central Asia. The study offers a novel contribution by employing an extended technology–organization–environment (TOE) framework. Data were collected through a survey among 501 public employees and partial least squares structural equation modeling was used to analyze data. The results reveal that perceived usefulness, compatibility, organizational readiness, effective accountability, and ethical AI regulation are key enablers, while system complexity, traditional leadership, resistance to change, and concerns over data management and security pose major barriers. The findings contribute to the literature on effective innovation in public administration and provide practical insights for policymakers and public managers aiming to effectively implement AI solutions in complex governance settings. Full article

This study examines the intersection of environmental, social, and governance (ESG) disclosure (operationalized through sustainability reporting), corporate financial performance, and the behavioral dynamics of herding in capital structure decisions among non-financial firms in five ASEAN countries. As ESG and sustainability finance gain prominence in addressing climate change and climate risk, understanding the behavioral factors that relate to ESG adoption is crucial. Employing a quantitative approach, this research utilizes a purposive sample of 125 non-financial firms from Indonesia, Malaysia, the Philippines, Singapore, and Thailand, gathered from the Bloomberg Terminal spanning 2018–2023. Managerial Herding Ratio (MHR) is used to assess herding behavior, while Sustainability Report Disclosure Index (SRDI) measures ESG disclosure. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA) were applied for data analysis. This research finds that while sustainability reporting enhances return on assets (ROA) and Tobin’s Q, it does not significantly relate to net profit margin (NPM). The findings also confirm that herding behavior—where companies mimic the financial structures of peers—moderates the relationship between sustainability reporting and performance outcomes, with leader firms gaining more from transparency efforts. This highlights the double-edged nature of herding: while it can accelerate ESG adoption, it may dilute the strategic depth of climate action if firms merely follow rather than lead. The study provides actionable insights for regulators and corporate strategists seeking to strengthen ESG finance as a driver for climate resilience and long-term stakeholder value. Full article

High-pressure carbon dioxide (HPCD) has been widely used in the extraction of high-quality bioactive compounds. The flavor profiles of cold brew coffee (CBC) prepared by HPCD, traditional cold brew (TCB), and ice drip (ID) were comprehensively evaluated by chromatographic approaches, and their variations were investigated by multivariate statistical methods. ID produced the lightest coffee color while HPCD produced the darkest. No significant difference was found in pH among the three coffee processes. The concentrations of chlorogenic acids and caffeine were the highest in ID but the lowest in HPCD. Seventeen of the forty-eight volatiles were identified as key aroma compounds, contributing nutty, cocoa, caramel, baked, and other coffee flavors to all CBCs. Among them, linalool (OAV = 100.50) was found only in ID and provided ID with unique floral and fruity notes; 2-methyl-5-propylpyrazine (OAV = 17.70) was found only in TCB and gave a roasted aroma. With significantly lower levels of medicine-like and plastic off-flavors, HPCD had a refined aroma experience featuring nutty, cocoa, and caramel notes, though their contents were not the highest. Orthogonal partial least squares discriminant analysis (OPLS-DA) identified 36 aromas that could differentiate three cold brew methods, with TCB and HPCD being the most similar. Aroma sensory tests showed that no significant difference was perceived between TCB and HPCD. These findings provide a profound understanding of CBC flavor produced by cold brew methods from the aspect of composition, indicating that HPCD has great potential to realize TCB-like flavor characteristics in a shorter time. Full article

Understanding watershed-scale interactions among landscape patterns, river morphology, and water quality is essential for effective water management. However, quantitative assessment of their coupled effects remains challenging. Utilizing water quality observation data, this study analyzed the independent and interactive influences of landscape pattern and river structure on the water quality of inlet rivers in the Chaohu Lake Basin (CLB) using correlation analysis and partial least squares structural equation modelling (PLS-SEM). The main conclusions are as follows: (1) The river water quality showed significant spatial distribution characteristics, and the northwestern part of the CLB formed a pollution aggregation area. (2) Ammonia nitrogen correlated positively with impervious surfaces but negatively with forest cover and patch cohesion, permanganate index linked positively to water surface but negatively to forest cover, and water temperature exhibited a significant negative correlation with network connectivity. (3) PLS-SEM results showed that both river structure (path coefficient = 0.877, p < 0.001) and landscape pattern (path coefficient = 0.177, p < 0.05) significantly influenced CLB water quality, with river structure having a stronger effect. This study supports source-based water quality control for Chaohu Lake Basin. Full article

In lithium-ion battery management systems (BMSs), accurate state of charge (SOC) estimation is essential for the stable operation of BMSs. Furthermore, the accuracy of SOC estimation is significantly influenced by the precision of battery model parameters. To improve the SOC estimation accuracy, this paper focuses on the second-order RC equivalent circuit model, firstly designs a simple and reliable improved adaptive forgetting factor (IAFF) regulation mechanism, and proposes the improved adaptive forgetting factor recursive least squares (IAFFRLS) algorithm, which not only improves the accuracy of parameter identification, but also exhibits excellent performance in anti-interference. Secondly, based on the identified model, a weighted multi-innovation improved Sage–Husa adaptive extended Kalman filter (WMISAEKF) algorithm is proposed to solve the problem of filter divergence caused by noise covariance updating. It fully utilizes historical innovations to reasonably allocate innovation weights to achieve accurate SOC estimation. Compared with the VFFRLS algorithm and AFFRLS algorithm, the IAFFRLS algorithm reduces the root mean square error (RMSE) by 29.30% and 19.29%, respectively, and the RMSE under noise interference is decreased by 82.37% and 78.59%, respectively. Based on the identified model for SOC estimation, the WMISAEKF algorithm reduces the RMSE by 77.78%, compared to the EKF algorithm. Furthermore, the WMISAEKF algorithm could still converge under different levels of noise interference and incorrect initial SOC values, which proves that the proposed algorithm has good stability and robustness. Simulation results verify that the parameter identification algorithm proposed in this paper demonstrates higher identification accuracy and anti-interference performance. The proposed SOC estimation algorithm has higher estimation accuracy and good robustness, which provides a new practical support for extending battery life. Full article

Pale lager dominates global beer markets. However, rising living standards and changing consumer expectations have reshaped sensory preferences, highlighting the importance of understanding consumers’ true sensory priorities. In this study, a twenty-eight-item questionnaire, refined through multiple rounds of optimization, was distributed across China and yielded 1837 valid responses. Spearman correlation analysis and partial least-squares regressions showed that educational background and spending willingness exerted the strongest independent effects on sensory priorities. A hybrid analytic hierarchy process–entropy weight method–Delphi procedure was then applied to quantify sensory attribute importance. Results indicated that drinking sensation (30.92%) emerged as the leading driver of pale lager choice, followed by taste (26.60%), aroma (24.77%), and appearance (17.71%), confirming a flavor-led and experience-oriented preference structure. Weighting patterns differed across drinking-frequency cohorts: consumers moved from reliance on overall mouthfeel, through heightened sensitivity to negative attributes, to an eventual focus on subtle hedonic details. Based on these findings, a new sensory evaluation scale was developed and validated against consumer preference rankings, showing significantly stronger alignment with consumer preferences (ρ = 0.800; τ = 0.667) than the traditional scale. The findings supply actionable metrics and decision tools for breweries, supporting applications in product development, quality monitoring, and targeted marketing. Full article

This study examines the comparative impact of energy policy uncertainty, geopolitical risk, and R&D expenditures on renewable and fossil fuel consumption in China from 2002m1 to 2022m12, using Fourier ADL, fully modified and dynamic ordinary least squares methods. The analysis aims to clarify how these key factors shape the country’s energy transition. The results show that energy policy uncertainty significantly promotes renewable energy but has no significant impact on fossil fuel consumption. Geopolitical risk increases the adoption of renewables, while fossil fuel consumption decreases, reflecting concerns about energy security. R&D expenditure contributes to the growth of both renewable and fossil fuel consumption, indicating a dual investment path in China’s energy strategy. These findings underscore the importance of consistent energy policies, reduced reliance on imported fossil fuels, and targeted R&D investment to support China’s transition to a low-carbon energy future. To effectively promote renewable energy and reduce dependence on fossil fuels, China should stabilize its energy policy environment, redirect R&D funding to clean technologies, and treat geopolitical risks as a strategic driver to accelerate domestic renewable energy capacity and energy self-sufficiency. Full article

Leadership is a key element in diverse working environments, contributing to the construction of more competitive and efficient institutions. Its impact transcends different sectors, including non-profit organizations, where it is essential to improve management and achieve institutional objectives. This research aimed to analyze whether leadership styles influence learning culture and dynamic capacity. An explanatory study was carried out involving 300 workers from nine Latin American countries who declared that they carried out work activities in a non-profit institution, aged between 19 and 68 years old (M = 34.10 and SD = 8.88). They were recruited through non-probabilistic sampling for convenience. The theoretical model was evaluated using the Partial Least Squares Structural Equation Model (PLS-SEM). A measurement model with adequate fit was obtained (α = between 0.909 and 0.955; CR = between 0.912 and 0.956; AVE = 0.650 and 0.923). Based on the results, it was observed that there was a positive impact of servant leadership on learning culture (β = 0.292), of empowering leadership on learning culture (β = 0.189), and of shared leadership on learning culture (β = 0.360). Likewise, there was a positive impact of culture of learning on dynamic capacity (β = 0.701). This research provides valuable insight for leaders in this sector who are seeking to achieve higher levels of learning culture and increase dynamic capability among their workers. Full article

Stable crop yields require an appropriate supply of essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) based on the accurate diagnosis of soil nutrient status. Traditional laboratory analysis of soil nutrients is often complicated and time-consuming and does not provide real-time nutrient status. Visible–near-infrared (Vis-NIR) spectroscopy has emerged as a non-destructive and rapid method for estimating soil nutrient levels. Vis-NIR spectra reflect sample characteristics as the peak intensities; however, they are often affected by various artifacts and complex variables. Since Vis-NIR spectroscopy does not directly measure nutrient levels in soil, improving estimation accuracy is essential. For spectral preprocessing, the most important aspect is to develop an appropriate preprocessing strategy based on the characteristics of the data and identify artifacts such as noise, baseline drift, and scatter in the spectral data. Machine learning-based modeling techniques such as partial least-squares regression (PLSR) and support vector machine regression (SVMR) enhance estimation accuracy by capturing complex patterns of spectral data. Therefore, this review focuses on the use of Vis-NIR spectroscopy for evaluating soil properties including soil water content, organic carbon (C), and nutrients and explores its potential for real-time field application through spectral preprocessing and machine learning algorithms. Vis-NIR spectroscopy combined with machine learning is expected to enable more efficient and site-specific nutrient management, thereby contributing to sustainable agricultural practices. Full article

This study explores the relationship between digital human resource management (HRM) practices, organizational culture, and employees’ perceived digital competence within Greek organizations. While digitalization has become a central priority in human resource management (HRM), there is still limited understanding of how cultural context shapes the effectiveness of digital HR interventions. Using a quantitative approach, data were collected via an online questionnaire from 257 employees across various sectors. The research employed the method of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (MGA) to examine the structural relationships between digital HRM practices—such as e-learning, onboarding, and performance management—and digital competence, taking into account different organizational culture profiles. The results show that digital HRM practices have a positive, but modest, impact on employees’ digital skills, with e-learning emerging as the most influential factor. Importantly, the effect of HRM practices varies significantly according to the cultural environment: supportive and innovative cultures foster stronger development of digital competence compared to hierarchical settings. The findings underline the necessity for organizations to adapt digital HR strategies to their specific cultural context and not to rely solely on technological solutions. This research contributes to the growing literature by demonstrating the interplay between technology and culture in shaping employees’ digital capabilities and suggests that a balanced focus on both is essential for successful digital transformation. Full article

In recent years, the rapid growth of artificial intelligence (AI) has significantly transformed higher education, particularly among Generation Z students who are more open to new technologies. Tools such as ChatGPT are increasingly being used for learning, yet empirical research on their acceptance, especially in Hungary, is limited. This study aims to explore the psychological, technological, and social factors that influence the acceptance of AI among Hungarian university students and to identify different user groups based on their attitudes. The methodological novelty lies in combining two approaches: partial least-squares structural equation modelling (PLS-SEM) and cluster analysis. The survey, based on the TAM and UTAUT models, involved 302 Hungarian students and examined six dimensions of AI acceptance: perceived usefulness, ease of use, attitude, social influence, enjoyment and behavioural intention. The PLS-SEM results show that enjoyment (β = 0.605) is the strongest predictor of the intention to use AI, followed by usefulness (β = 0.167). All other factors also had significant effects. Cluster analysis revealed four groups: AI sceptics, moderately open users, positive acceptors, and AI innovators. The findings highlight that the acceptance of AI is shaped not only by functionality but also by user experience. Educational institutions should, therefore, provide enjoyable and user-friendly AI tools and tailor support to students’ attitude profiles. Full article

Low-level turbulence is a meteorological hazard that disrupts the operation of airports and is particularly pronounced at Hong Kong International Airport (HKIA), which is impacted by various sources of low-level turbulence (e.g., terrain disrupting wind flow, sea breeze, and thunderstorms). The possibility of using root-mean-square vertical acceleration (RMSVA) data from Automatic Dependent Surveillance–Broadcast (ADS-B) for low-level turbulence monitoring is studied in this paper. Comparisons are performed between RMSVA and Light Detection And Ranging (LIDAR)-based Eddy Dissipation Rate (EDR) maps and the aircraft-based EDR. Moreover, the LIDAR-based EDR map, aircraft EDR, and pilot report for turbulence reporting are compared for two typical cases at HKIA. It was found that the various estimates/reports of turbulence are generally consistent with one another, at least based on the limited sample considered in this paper. However, at very low altitudes close to the touchdown of arrival flights, RMSVA may not be available due to a lack of ADS-B data. With effective quality control and further in-depth study, it will be possible to use RMSVA to monitor low-level turbulence and to alert pilots if turbulence is reported by the pilot of the preceding flight based on RMSVA. The technical details of the various comparisons and the assumptions made are described herein. Full article

The rapid progress of generative artificial intelligence (AI) has sparked growing concerns regarding its misuse, privacy risks, and ethical issues. This study investigates the interplay between Generative AI Abuse Anxiety, trust, perceived usefulness, acceptance, and the intention to use it. Using variance-based partial least squares (PLS-SEM), we analyze 318 valid survey responses. The findings reveal that Generative AI Abuse Anxiety negatively impacts trust, perceived usefulness, acceptance, and the intention to use generative AI. Additionally, different subdimensions of trust play significant roles in influencing users’ technology acceptance and intention to use it, though the specific mechanisms differ. This research extends the applicability of the technology acceptance model to the generative AI context and enriches the multidimensional framework of trust studies. Full article

Preterm birth is a leading cause of perinatal morbidity and mortality and is particularly prevalent among socially disadvantaged female populations. This quantitative, cross-sectional study aimed to explore the prevalence of preterm birth in three segregated Roma communities in Hungary and to identify health behavior and care factors associated with its occurrence. In our study, preterm birth was defined as delivery before 37 completed weeks of gestation (i.e., <259 days). Data were collected from 231 Roma women living in three municipalities of Szabolcs-Szatmár-Bereg County, one of Hungary’s most disadvantaged regions, using a structured interview questionnaire. The participants were women aged 18–65 years. Of these, 209 had been pregnant at least once in their lifetime. The questionnaire covered socio-demographic characteristics (age, level of education, employment status, housing conditions, marital status), health behaviors (smoking, alcohol consumption, drug use, vitamin supplementation, other substance use), antenatal care attendance, and birth outcomes (preterm birth, gestational age, low birth weight, newborn status). Statistical analyses included descriptive statistics, chi-square tests, and binary logistic regression with significance set at p < 0.05. Preterm birth was significantly more common among women who smoked, consumed alcohol or drugs during pregnancy, or had vaginal infections. Drug use showed the strongest association with a 22-fold increase in risk, followed by alcohol (nearly fivefold), smoking (over threefold), and infections (threefold). Although non-attendance at antenatal care was associated with increased risk, this relationship was not statistically significant. In the multivariate logistic regression model, alcohol consumption (OR = 1.744, p < 0.01), smoking (OR = 2.495, p < 0.01), drug use (OR = 25.500, p < 0.001), and vaginal infections (OR = 4.014, p < 0.01) during pregnancy were independently associated with an increased risk of preterm birth, whereas folic acid supplementation (OR = 0.448, p < 0.05) showed a significant protective effect. These findings highlight that preterm birth is intricately linked to socioeconomic disadvantage and adverse health behaviors. Culture-specific, community-based prevention strategies are essential to reduce perinatal risks in marginalized populations. Full article

Accurate and scalable estimation of forest production is essential for quantifying carbon sequestration, forecasting timber yields, and guiding climate change mitigation strategies. While prior studies established a positive linkage between net primary production (NPP) and canopy structural complexity (CSC) metrics derived from terrestrial LiDAR, the spatial coverage of ground-based surveys is limited. Airborne laser scanning (ALS) could offer a rapid and spatially extensive alternative to terrestrial scanning, but the predictive capacity of ALS-derived CSC metrics for estimating forest production remains insufficiently explored. To address this gap, we derived a suite of three-dimensional (3D) CSC metrics from small-footprint, high-density ALS data collected by the National Ecological Observatory Network’s Airborne Observation Platform. We evaluated relationships between CSC metrics and the NPP of plots nested within seven deciduous and evergreen temperate forests. Optimal metric combinations for predicting NPP within and across forest types were identified using partial least squares regression coupled with recursive feature elimination. ALS-derived CSC metrics explained 77% (RMSE = 11%) and 76% (RMSE = 13%) of the variance in deciduous and evergreen forest plot NPP, respectively. Our findings demonstrate that 3D CSC metrics derived from high-density ALS are robust predictors of plot-level NPP, offering performance comparable to terrestrial scanners while enabling greater scalability and more efficient data acquisition. Full article