Search Results (920)

Background: As Information and Communication Technology (ICT) advances, the healthcare market is shifting toward a consumer-centered paradigm. This study analyzes the structural relationships between medical information quality, medical service quality, and Technology Acceptance Model (TAM) variables to determine their impact on patient satisfaction and revisit intentions in a general hospital context. Methods: An online survey was conducted with 376 consumers who had experience using general hospitals in South Korea between June and December 2024. Data were analyzed using structural equation modeling (SEM) with SPSS 29.0 and AMOS 29.0. Ethical measures, including informed consent and anonymity, were strictly followed. Results: Findings indicate that among medical information quality factors, accuracy significantly enhanced perceived usefulness, while timeliness positively influenced perceived ease of use. Regarding medical service quality, both accessibility and responsiveness significantly improved both usefulness and ease of use, with responsiveness being the most powerful predictor of ease of use (β = 0.655). While both TAM variables significantly increased patient satisfaction, only perceived ease of use and satisfaction directly drove revisit intentions; perceived usefulness influenced revisit intention only through the mediation of satisfaction. Conclusions: Patient satisfaction is a paramount factor directly influencing loyalty. Healthcare administrators should prioritize the accuracy and timeliness of digital health information while improving service responsiveness to enhance long-term hospital competitiveness in the proactive consumer era. Full article

This study investigates whether volatility-related variables and traditional financial predictors can explain and forecast Taiwan stock returns. Using the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and eight major Taiwan industry indices, we examine the predictive ability of long-term adjusted volatility (LVadj), short-term volatility (SV), the volatility index (VIX), idiosyncratic volatility (IVOL), the earnings-to-price ratio (EP), the book-to-market ratio (BM), and the turnover ratio (TURN). The sample period spans from January 2007 to December 2025. Univariate and bivariate predictive regression models are estimated using ordinary least squares (OLS) and exponentially weighted least squares (EWLS). The empirical results show that SV exhibits the strongest in-sample explanatory power for TAIEX returns, whereas TURN plays a more important role in explaining industry-level returns. Out-of-sample forecasting performance varies considerably across industries. For TAIEX returns, the combination of LVadj and TURN provides the strongest forecasting performance, while models incorporating SV and VIX perform relatively well in several industry sectors. Overall, the results suggest that volatility-related variables provide useful predictive information under certain model specifications and industry sectors, with EWLS generally outperforming conventional OLS estimation. 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

This article uses Jacob Gorender’s theory of colonial slavery to challenge a core premise of modern social theory—that labor inherently generates freedom. In Hegel and Marx, productive activity enables recognition, consciousness, and ultimately emancipation. The article argues that this mechanism depended on a historically specific condition: the worker’s juridical possession of labor-power. Plantation slavery abolished that condition. The enslaved laborer did not sell labor but was owned as labor, making alienation, recognition, and class formation structurally impossible. Building on Gorender’s claim that colonial slavery constituted a distinct mode of production integrated into global markets, this article shows that plantation economies produced accumulation without proletarianization, commercialization without citizenship, and economic modernization without social emancipation. Drawing on institutional theory, it further argues that the legal and political arrangements created to manage coerced labor persisted after abolition and continue to structure inequality and coercive governance across the Americas. American modernity therefore followed a trajectory different from the European one: rather than emerging from the emancipation of labor, it developed from its permanent subordination, indicating the existence of multiple modernities. Full article

Ensuring sustainable management of water, energy, and food (WEF) resources requires governance frameworks capable of addressing cross-sectoral interdependencies and policy fragmentation. This study evaluates the performance and coherence of national water, energy, and agricultural policies in Mali, South Africa, Malawi, and Tanzania, with a focus on their contribution to WEF nexus integration and energy transition pathways. A mixed-methods approach is applied, combining qualitative policy analysis, stakeholder consultations (n = 52), and a composite policy coherence index to assess cross-sectoral policy alignment across three river basins: the Bani River Basin (Mali), the Songwe River Basin (Malawi–Tanzania), and the Inkomati–Usuthu Water Management Area (South Africa). The results indicate that key water policy dimensions such as conservation, pollution control, and stakeholder participation demonstrate high performance (mean = 1.0) and strong coherence (SD = 0.0–0.1) across all countries. However, these values primarily reflect the presence of policy instruments rather than their effective implementation. Stakeholder evidence highlights persistent gaps in enforcement, coordination, and institutional capacity. In the energy sector, core infrastructure and participation policies exhibit high performance (mean = 1.0; SD = 0.0), while critical market instruments—including feed-in tariffs (FITs) and power purchase agreements (PPAs)—show moderate performance (mean = 0.6–0.8) and high variability (SD = 0.4–0.5), indicating regulatory inconsistency. In the agricultural sector, economic incentives achieve high performance (mean = 1.0; SD = 0.0), whereas sustainable practices such as agroecology, crop rotation, and organic fertilization remain weakly integrated (mean = 0.1–0.4; SD up to 0.5). Overall, the findings reveal that WEF nexus governance is characterized by strong structural policy alignment (mean = 0.8–1.0) but limited functional integration, reflecting a gap between policy design coherence and implementation effectiveness. Strengthening regulatory frameworks, improving cross-sectoral coordination, and enhancing investment mechanisms are critical for advancing resource efficiency and accelerating energy transition. The study provides a reproducible framework for assessing policy coherence and offers policy-relevant insights for integrated resource governance in Sub-Saharan Africa. Full article

Grain storage is vital for a country within a framework of food sovereignty and security. It helps stabilize markets, prices, and imbalances between supply and demand. In Mexico, public storage infrastructure is almost nonexistent, having been transferred to the private sector. The objective of this article is to analyze the relationship between public storage infrastructure and distribution problems that maize producers face in Mexico. A mixed-methods analysis procedure was implemented. Semi-structured interviews were conducted with small, medium, and large distributors, selected using the snowball sampling technique. The analysis identifies a positive association between references to storage infrastructure and distribution problems in the interview materials. Additionally, Spearman’s rank correlation coefficient was applied to the counts to strengthen the analysis. The results indicated a positive and significant relationship between the variables “storage infrastructure” and “distribution problems”, but also that, around the latter, there are others: lack of government support, price fixing, guaranteed price, insecurity, production costs, and inconveniences that require attention to stabilize the maize market. Inadequate infrastructure limits storage capacity, affects grain quality, increases costs, reduces producers’ bargaining power, and contributes to price volatility. It also impacts logistics, transportation, and marketing, especially in less developed regions. Evidence suggests that public storage infrastructure is a strategic element for food security; however, its concentration and predominantly private nature generate territorial inequalities. Full article

Stable interactions among electricity, carbon allowance, and fossil fuel markets are essential for sustainable energy transition, because excessive cross-market risk transmission may affect energy affordability, carbon-price credibility, and low-carbon investment signals. This study provides comparative evidence on dynamic connectedness, tail-state shock responses, and return-based complexity in representative Chinese and European benchmark markets. Using daily market data from the Wind database for November 2021–January 2026, the empirical framework combines time-varying parameter vector autoregression (TVP-VAR), quantile vector autoregression and quantile impulse response functions (QVAR/QIRFs), and rolling multifractal detrended fluctuation analysis (MFDFA). The results show that the European benchmark system has a higher absolute connectedness level than the Chinese benchmark system: the full-sample mean total connectedness index (TCI) is 18.75 in Europe and 5.63 in China, while the crisis-period mean TCIs are 25.19 and 12.12, respectively. Post-peak adjustment depends on the reversion metric used: China shows a faster initial half-life decline from the crisis peak, whereas reversion to lower region-specific connectedness thresholds depends on the selected benchmark. Natural-gas-shock QIRFs indicate stronger upper-tail persistence in Europe, whereas China is characterized mainly by short-run directional divergence; supplementary coal-, oil-, and carbon-shock checks show that response patterns are shock-source-dependent. Electricity-return multifractal spectrum width (MFW) does not show stable full-sample explanatory power for TCI, but it provides stage-dependent auxiliary diagnostic information. These findings provide a comparative diagnostic framework for monitoring cross-market systemic risk and supporting sustainability-oriented energy-market governance under low-carbon transition. Full article

At present, photovoltaic (PV) systems are becoming the core of low-carbon power systems, but their large-scale integration is still limited by weather-driven intermittency, heterogeneous data, equipment failures, operational uncertainty, and life-cycle sustainability requirements. Unlike specific task reviews that only focus on photovoltaic forecasting, fault diagnosis, or general artificial intelligence applications in renewable energy, this review develops an integrated data-driven perspective for machine learning and artificial intelligence in photovoltaic power generation systems. It links data governance, feature engineering, prediction, and uncertainty quantification, fault diagnosis and predictive maintenance, energy management, market participation, and carbon-aware optimization within a framework for photovoltaic systems. This review indicates that traditional machine learning, deep learning, graph learning, reinforcement learning, generative artificial intelligence, and physics-based artificial intelligence are suitable for different photovoltaic tasks based on data structure, time range, operational constraints, and deployment maturity. The main contribution is cross-task integration, which links the output of artificial intelligence models, including scheduling, storage scheduling, maintenance planning, virtual power plant operation, and low-carbon management, with actual decision-making. The review further identified the most critical deployment barriers, such as incomplete benchmarks, weak cross-site generalization, insufficient uncertainty calibration, limited interpretability, network security risks, and computational costs. The resulting methodological approach emphasizes data management, uncertainty awareness, physical constraints, decision orientation, and sustainability-driven photovoltaic intelligence. Full article

This study examines how client ESG performance affects supplier total factor productivity (TFP) from a knowledge spillover perspective, using matched client–supplier–year data for Chinese A-share listed firms from 2010 to 2023. The results show that client ESG significantly improves supplier TFP; specifically, a one-unit increase in client ESG is associated with an average increase of approximately 8.3% in supplier TFP. These results remain robust across a series of robustness tests. Mechanism analysis indicates that client ESG enhances supplier productivity through three knowledge spillover channels: technical assistance, management sharing, and innovation induction. Heterogeneity analysis further shows that this positive effect is more pronounced in long-term cooperative relationships, among clients with stronger market power, for state-owned suppliers, and when clients and suppliers have aligned ownership structures. Further analysis shows that the positive effect of client ESG persists for at least three fiscal years and is more pronounced in industries characterized by lower volatility. These findings suggest that policymakers and firms should strengthen supply chain ESG governance to promote knowledge spillovers and improve productivity. Full article

Household livelihood resilience is an important foundation for sustainable rural development, particularly in rural areas exposed to climate risks, market fluctuations, and demographic pressures. This study examines whether participation in new rural collective economic organizations (NRCEOs) is associated with household livelihood resilience and explores the mechanisms and contextual heterogeneity underlying this association. Using survey data from 837 rural households in Jiangxi Province, China, we construct a multidimensional livelihood resilience index and apply ordinary least squares, propensity score matching, and lasso Regression, together with an exploratory IV-2SLS sensitivity analysis. The results show that participation in NRCEOs is positively associated with household livelihood resilience, and this relationship remains stable across alternative estimation strategies. Mechanism analysis provides evidence consistent with two pathways: land, labor, and capital allocation support the resource-allocation pathway, while production efficiency and agricultural income support the agricultural production pathway; the sales channel estimate remains inconclusive because online sales are rare in the sample and statistical power is limited. Overall, the findings indicate that the relationship between collective economic participation and household livelihood resilience varies across mechanism dimensions and local development contexts. Full article

The rapid proliferation of electric vehicles (EVs) has positioned Vehicle-to-Grid (V2G) technology as an important enabler for mitigating grid congestion, accelerating the energy transition, and supporting the sustainable transition of regional energy systems. However, recent incentive mechanisms often fail to balance EV users’ willingness to participate with the economic viability of intermediary operators, thereby hindering effective multi-party collaboration in Regional Integrated Energy System (RIES). To address this challenge, this paper proposes a novel commission-based service fee mechanism for V2G incentive mechanisms to dynamically regulate revenue distribution among Integrated Energy System Operator (IESO), Energy Supplier (ES), Charging Station Operator (CSO), and Electric Vehicle Aggregator (EVA). The study further examines how different incentive strategies affect V2G market liquidity. Case studies indicate that the proposed strategy significantly increases effective V2G transaction power while preserving CSO profit margins and encouraging EV participation. The results also indicate that the reward rate, commission rate, and subsidy have nonlinear effects on V2G transaction performance and should be set within reasonable ranges. The proposed model also exhibits superior performance in enhancing system economic benefits and promoting multi-agent coordination. It provides an actionable framework for sustaining CSO participation under upper-level subsidy mechanisms while improving the long-term commercial viability and ecological sustainability of smart-grid ecosystems. These findings provide practical guidance for designing incentive policies that facilitate the low-carbon energy transition and sustainable smart-grid development. Full article

This study investigates whether shareholder value creation, defined as beta-adjusted outperformance relative to a market benchmark, can be effectively predicted in an emerging market using a sequential machine learning framework. While prior research has predominantly focused on profitability forecasting or stock return prediction, the prediction of risk-adjusted shareholder value creation remains relatively underexplored, particularly in emerging economies such as Morocco. To address this gap, the study develops a predictive framework that combines market-based indicators, macroeconomic variables, and accounting fundamentals using only information realistically available to investors at each decision date. These variables are organized into firm-level temporal sequences based on a monthly decision-date panel of non-financial firms listed on the Casablanca Stock Exchange over the period 2010–2024. To capture nonlinear relationships and temporal dependencies in financial data, the empirical analysis compares baseline models with deep learning architectures, including GRU, LSTM, and CNN1D. The results indicate that deep learning models consistently outperform naïve and linear benchmark models, suggesting that shareholder value creation exhibits a measurable degree of predictability. With an AUC of 0.700 and a PR-AUC of 0.727, CNN1D achieves the strongest performance in the final evaluation setting and ranks as the best-performing model according to the primary AUC criterion. The findings also reveal that macroeconomic variables generate the strongest standalone predictive signal, whereas market-based variables exhibit comparatively weaker predictive power when considered in isolation. By extending financial prediction toward a risk-adjusted, benchmark-based, and investor-oriented framework, and by providing new empirical evidence on the value of temporal modeling and multi-source financial information for forecasting shareholder value creation in an emerging market context, this study contributes to the growing literature at the intersection of financial forecasting and artificial intelligence. Full article

Conventional static indicators such as passenger car equivalent (PCE) factors cannot adequately capture the dynamic bottleneck effects caused by truck speed degradation on long steep freeway upgrades. To address this issue, this study proposes an operational efficiency analysis framework integrating truck crest-speed reliability and microscopic simulation. Vehicle trajectory data were collected using unmanned aerial vehicles, and truck power-to-mass ratio data were obtained from the Chinese truck market to establish a representative truck model. Monte Carlo simulation was employed to quantify crest-speed reliability, whose complement (failure probability) characterizes the likelihood of truck bottlenecks arising. A calibrated VISSIM simulation model was then developed to reproduce truck climbing speed degradation and microscopic driving behavior on long upgrades. Finally, a response surface model was constructed using average delay as the operational efficiency indicator. The results indicate the following: (1) As grade length increases, the probability of truck bottleneck occurrence gradually rises, and the marginal effect of this increase becomes more pronounced with steeper grades. Specifically, truck crest-speed reliability exhibits a nonlinear decreasing trend with increasing grade length. For example, under a design speed of 120 km/h and a 95% reliability threshold, the corresponding grade length for a 2.5% grade is 1367 m, whereas for a 4% grade it drops to 232 m, representing a reduction of 83%. (2) Under high traffic volume conditions, an increase in truck proportion leads to a significant rise in average delay (up to 17.54 s). Although improving crest-speed reliability reduces the probability of truck bottleneck occurrence and partially mitigates delay, it cannot fully offset the traffic pressure induced by high traffic demand. Grade and grade length remain the most critical factors driving operational efficiency deterioration, with a maximum impact on average delay of 38.72 s. (3) The response surface model reveals significant interaction effects between traffic volume and truck proportion, as well as between traffic volume and crest-speed reliability, indicating that traffic demand plays a dominant role in amplifying the impact of truck bottlenecks. The framework proposed in this paper provides probabilistic quantitative decision support for sustainable longitudinal grade design and freight traffic management on mountainous freeways. Full article

This study addresses the increasing need for flexibility in the Polish Power System (PPS), particularly in the context of growing price volatility on the Day-Ahead Market (DAM) resulting from the rising share of renewable energy sources (RESs). The aim of the study was to assess the feasibility of implementing demand-driven operation in an existing linear biogas plant in Poland and to develop a Decision-Making Model (DMM) for optimizing its operation based on electricity price forecasts. A machine learning model based on Extreme Gradient Boosting (XGBoost) was developed using historical electricity price, demand and weather data and integrated into the DMM to generate hourly operating schedules. The model achieved high predictive accuracy, with a Mean Absolute Error (MAE) of approximately 51 PLN/MWh, and effectively captured nonlinear price dynamics. Based on predefined decision thresholds—biogas production rate, current biogas storage level, upper and lower limits of pressure in biogas storage capacity, maximum biogas storage duration, power quotient (PQ) and electricity price levels—optimal operating strategies were determined. The results indicate that while demand-driven operation is technically feasible and enables better alignment with market signals, its economic viability remains limited under current market and regulatory conditions. Investment in additional cogeneration capacity was not justified, as costs significantly exceeded potential revenues. Consequently, a more viable approach involves optimizing existing infrastructure through flexible production strategies. Full article

This study examines the impact of public attention on financial markets during the Israel–Hamas conflict, focusing on the TA35 stock index and the Israeli Shekel (ILS) exchange rate over the period October 2023 to April 2025. By distinguishing between global and domestic Google search activity, the analysis investigates whether the origin of attention differentially affects market performance and currency dynamics. Public attention is treated as a real-time proxy for investor sentiment and perceived risk. Methodologically, the study combines Google Trends data with EGARCH(1,1) models to capture both return effects and asymmetric volatility responses. To enhance robustness, Principal Component Analysis (PCA) is applied separately to global and domestic search datasets, generating latent indices that reflect conflict-related and humanitarian narratives. These indices are subsequently incorporated into the empirical models. The findings reveal that global search intensity related to conflict topics exerts a significant negative effect on stock returns and contributes to currency depreciation, reflecting heightened uncertainty and risk aversion. In contrast, domestic search activity is associated with stabilizing or positive effects, suggesting local resilience and confidence. PCA-based models improve explanatory power and confirm that the geographical origin of attention plays a crucial role in shaping financial outcomes. Additionally, the results indicate that attention-driven shocks influence volatility asymmetrically, amplifying downside risk during periods of intensified global concern. Overall, the study contributes to the literature by integrating behavioral indicators into financial risk modeling and providing a novel, real-time framework for assessing how digital attention transmits geopolitical risk into asset prices. Full article