Search Results (3,395)

The growing complexity of public policies and socio-ecological transitions demands evaluation tools that integrate environmental, economic, and social dimensions across multiple governance contexts. However, the main evaluation impact methods are fragmented and sector-specific, limiting their ability to capture cross-scale interdependencies and sustainability of trade-offs. While existing reviews address methods in isolation, no updated systematic framework is built upon joint epistemological and participatory dimensions, proposing an integrated and transformative framework. Through a common analytical framework—encompassing the type of impact assessed, territorial scale, methodological approach, degree of stakeholder participation, and operational complexity—the paper presents a systematic comparative review of eleven widely used evaluation methods, covering the environmental, economic, social, and multidimensional dimensions. These methods are selected through screening of peer-reviewed literature published between 2022 and 2025 using an AI-assisted text-mining protocol to support keyword extraction, methodological classification, and manual validation. Its focus is on the analysis and critique of their characteristics, complexity, and epistemological limitations, particularly focusing on measurability and participation. The comparative analysis reveals a structural epistemological gap between qualitative bottom-up perspectives and quantitative, top-down approaches, as well as structural fragmentation across evaluative dimensions. They also draw attention to the approaches’ inadequate ability to manage non-linear dynamics, uncertainty, and transformative scenarios, which affects the legitimacy, comparability, and general efficacy of decision-making processes. Building on these, the paper advances a paradigmatic shift toward three systemic integrated principles: (i) systemic evaluation grounded in methodological and epistemological integration; (ii) transformative evaluation oriented toward collective learning and the redefinition of development trajectories; and (iii) co-produced evaluation based on the active engagement of territorial actors. Full article

This conceptual paper revisits the learning styles debate in the context of generative and adaptive artificial intelligence in higher education. It does not seek to rehabilitate classical learning style taxonomies, whose prescriptive claims have been widely challenged as a neuromyth. Instead, it argues that AI-mediated learning creates new conditions under which patterns of regulation, delegation, verification, iteration, epistemic control, and ethical responsibility may become more observable and pedagogically relevant. Drawing on research on learning styles, neuromyths, AI literacy, adaptive learning, assessment, self-regulated learning, metacognition, and epistemic agency, the paper proposes a conceptual framework for regulation and agency styles in AI-mediated learning. In this framework, style is not treated as a fixed psychological trait or as a category into which learners should be sorted, but as a situated and modifiable profile of decisions and actions distributed across learners, tasks, and intelligent systems. Three analytical dimensions are proposed: epistemic control and metacognitive orchestration, adaptive regulation and generative iteration, and socio-algorithmic agency and ethical governance. The paper concludes with implications for task design, assessment, teacher education, and equitable AI integration. Full article

The increasing integration of renewable energy sources into microgrid systems has driven significant advances in energy management system (EMS) design, yet the diversity of proposed approaches makes systematic comparison challenging. This paper presents a comprehensive review of decision-making approaches for microgrid EMSs, organized along three dimensions: architecture-based, decision-method-based, and application-context classification. The review covers deterministic mathematical programming; heuristic and meta-heuristic optimization; stochastic and robust optimization; data-driven intelligent and agent-based methods, including fuzzy logic, machine learning, reinforcement learning, and multi-agent systems; and model predictive control and its variants. For each category, representative studies are analyzed with respect to optimization objective, uncertainty handling, key components, and control architecture. The results show that classical methods offer transparency and optimality guarantees but are limited under uncertainty and nonlinearity conditions, while AI-based and MPC approaches provide adaptability and real-time performance at the cost of higher data requirements. This comparative analysis aims to guide researchers and practitioners in selecting appropriate EMS strategies for microgrid applications. Full article

Tree-ring chronologies are essential proxies for investigating ecosystem dynamics and reconstructing environmental variability, yet integrative approaches for assessing chronology quality and sampling representativeness across heterogeneous regions remain limited. We analyzed 190 tree-ring chronologies distributed across Mexico and developed two composite indicators: the Signal Quality Index (SQI), integrating internal coherence, interannual sensitivity, common growth signal strength, and the Sampling Representativeness Index (SRI), quantifying the statistical adequacy of sampling efforts. Both indices were standardized and evaluated using Moran’s I, Local Indicators of Spatial Association (LISA), Getis–Ord Gi* hotspot analysis, and correlations with climatic, hydrological, and edaphic variables. Results revealed a marked decoupling between chronology signal quality and sampling representativeness. SQI exhibited significant positive spatial autocorrelation, with clusters of high and low values associated with hydroclimatic gradients. It was strongly related to indicators of water availability and atmospheric evaporative demand, suggesting greater growth coherence under water-limited conditions. In contrast, SRI displayed weak spatial structure and largely non-significant relationships with environmental variables, indicating that representativeness is driven primarily by methodological decisions and sampling design. These findings highlight complementary ecological (SQI) and methodological (SRI) dimensions of dendrochronological networks and provide a practical framework for improving chronology evaluation, comparability, and network development across environmentally heterogeneous regions. Full article

The leadership, personality, and behavioral characteristics of senior managers relate to organizational performance in construction firms, yet these dimensions have rarely been examined together in relation to firm size. This study investigates senior managers across Turkish construction firms of different sizes. Data came from 42 directors, coordinators, and project managers representing 42 firms (response rate: 23.3%). The 23-item Turkish questionnaire was developed through theory-informed item generation, qualitative expert review, and cognitive pre-testing. It included six leadership-style indicators, five personality-trait indicators based on the Five-Factor Model, and seven managerial-behavior frameworks: Likert’s System 4, Path–Goal Theory, the Vroom–Yetton Decision-Making Model, the Ohio State Leadership Studies, the Managerial Grid Model, McGregor’s Theory X and Theory Y, and Fiedler’s Contingency Model. Because most leadership and personality dimensions were measured using single-item indicators, analyses were conducted at the item level. Small- and medium-sized firms showed higher descriptive ratings for servant and democratic leadership and more frequent consultative and participative orientations. Large firms showed higher ratings for transformational and charismatic leadership and more frequent directive and achievement-oriented tendencies. Across both groups, concern for production exceeded concern for employees, and information gathering was the most frequent decision-making approach. Democratic leadership, agreeableness, and Likert’s System 4 produced the most notable unadjusted group differences, with medium-to-substantial effect sizes, but none remained significant after Bonferroni correction. Firm size therefore appears to provide relevant context, but not a standalone explanation of senior managerial profiles. The study offers an integrated, contextual, and hypothesis-generating framework for examining senior management in construction firms. Full article

Access to safe, nutritious, and sustainable food remains a major challenge in many Low- and Middle-Income Countries (LMICs), where food insecurity, malnutrition, and foodborne diseases frequently coexist. Despite growing recognition of the interconnections between food safety, nutrition security, and sustainability, monitoring systems often assess these dimensions separately, limiting the effectiveness of policy interventions. This study conducted a structured methodological review and framework development study of indicators used to monitor food safety, nutrition security, and sustainability within food systems, with particular emphasis on LMIC contexts. Peer-reviewed literature and internationally recognized monitoring frameworks, drawn from Scopus, Web of Science, PubMed, Google Scholar, and institutional repositories including FAO, WHO, World Bank, and GAIN, were systematically reviewed, yielding 75 sources encompassing 54 peer-reviewed articles and 21 institutional reports, from which 47 indicators were identified and assessed. Indicators were evaluated according to relevance, data availability, methodological robustness, and policy actionability. The review identified a core set of ten operationally relevant indicators, including the Global Diet Quality Score (GDQS), Minimum Dietary Diversity for Women (MDD-W), Food Insecurity Experience Scale (FIES), foodborne disease burden indicators, cost of a healthy diet, and post-harvest loss rates. Building on these findings, an integrated indicator selection framework is proposed to support context-specific monitoring and decision-making in LMICs. The results suggest the value of a One Health approach that links human, animal, and environmental health, and three One Health interface indicators, antimicrobial resistance prevalence in food chain isolates, zoonotic disease incidence attributable to food, and pesticide residue exceedance rate in fresh produce, are explicitly integrated into the proposed framework with LMIC-specific implementation pathways. A key limitation of this review is that it did not employ formal systematic methods and may not have exhaustively identified all relevant indicators across the full breadth of the published literature. Full article

Water distribution network (WDN) pipelines are essential infrastructure, and their failures cause significant economic and operational losses. Existing condition prediction models often struggle with high-dimensional non-linearities, severe class imbalance, and a lack of decision-centric interpretability. To address these challenges, this study presents a Stacking-based ensemble learning framework that integrates Random Forest, XGBoost, and LightGBM base learners with a Logistic Regression meta-learner under a spatially constrained group sampling scheme. Model performance is evaluated alongside an independent CatBoost benchmark using macro-averaged metrics, an operational cost-loss function for high-risk assets, and Copeland ranking, supplemented by global and class-specific SHAP interpretability analyses. Evaluated on a pipeline dataset, the Stacking framework achieved superior overall performance and recorded the lowest operational cost loss by minimizing severe misclassifications of critical pipelines. SHAP analysis identified pipe age, material, physical dimensions, temperature, and soil moisture as the primary risk drivers. This integrated framework delivers a robust, transparent, and decision-centric decision support tool for proactive municipal pipeline maintenance and risk mitigation. Full article

Public-sector sustainability and climate reporting increasingly address environmental exposure, governance, and financial effects, yet existing frameworks do not adequately disclose preparedness for low-probability, high-impact systemic risks whose probabilities, timing, thresholds, and transmission channels remain deeply uncertain. This article develops a Public-Sector Resilience Reporting Standard (PSRRS) as a pre-standard architecture for government preparedness disclosure. The design has three bounded objectives: diagnose cross-framework disclosure gaps, translate these gaps into a theoretically grounded capability-to-disclosure architecture, and demonstrate its analytical use through an illustrative Florida application and two hazard-neutral stress tests. The documentary corpus includes international sustainability and public-sector reporting standards, ISO and UNDRR resilience and continuity instruments, three Florida resilience documents, and peer-reviewed literature on resilience governance, decision-making under deep uncertainty, critical infrastructure interdependency, catastrophic uncertainty, climate-risk disclosure, public finance, climate-risk pricing, local-government credit risk, investor attention, and ransomware service disruption. A structured interpretive coding protocol classifies each framework as explicit, partial, or not explicit across nine disclosure dimensions; a codebook appendix identifies the assessment criteria, the a priori and inductively refined dimensions, and the validation boundaries. Florida is not treated as a basis for statistical or jurisdictional generalization. Instead, it illustrates how a comparatively developed resilience architecture may disclose statutory continuity, critical-asset data, project ranking, and output metrics while leaving systemic dependencies, adaptive triggers, long-horizon fiscal exposure, residual service risk, distributional effects, and assurance mechanisms insufficiently visible in the reviewed reporting corpus. AMOC and case-grounded cyber-fiscal stress tests show how the PSRRS shifts reporting from hazard inventories and funded projects toward auditable evidence of institutional capacity, adaptive readiness, and public-value protection. The article specifies mandatory, recommended, and optional clauses, evidence requirements, indicator examples, a disclosure index, a sample report structure, and a three-tier pilot conformity model. The contribution is conceptual and operational, but not yet a validated formal standard; cross-jurisdictional piloting, inter-rater coding, cost testing, assurance testing, and stakeholder consultation are identified as the next stage of standardization. Full article

Coastal marinas and high-traffic nearshore sites accumulate pollution from vessel movements, tourism, and shifting weather, yet routine monitoring rarely operates at the temporal resolution needed to catch emerging risks before they become acute. This study developed an AI-enabled decision support system and tested it across three European coastal sites over a 90-day window in summer 2025: an urban marina (Site A), a tourism marina (Site B), and a mixed-use port channel (Site C). A composite Water Quality Risk Index (WQRI), combining five normalised environmental and vessel-traffic stressor dimensions, fed a two-layer AI framework in which a gradient boosting model estimated short-term traffic-related stress and a random forest model classified next-day risk. Vessel traffic was heaviest at Site B, but water quality risk followed a different pattern: Site C returned the highest mean WQRI and logged the most hours under red alert despite intermediate traffic volumes, indicating that sustained moderate traffic mattered more than peak volume. Vessel intensity and WQRI were positively correlated at all three sites, most strongly at Site B, and the next-day random forest risk classifier, trained across all three sites, achieved strong discriminative performance (AUC 0.93). When the system indicated elevated risk, managers responded by deploying inspections, issuing traffic advisories and increasing monitoring activity. The pilot shows that connecting vessel tracking, environmental sensing, and ML-based classification into a single decision loop can move coastal pollution management from reactive to anticipatory. Full article

Digital technologies are reshaping economies, labor markets, social security systems, and societal well-being across the European Union, increasing the need for decision-support methods capable of systematically analyzing complex survey evidence. This study proposes B-MIDIA, a parametric extension of the belief-structure TOPSIS framework that integrates the MIDIA (Multi-Criteria Method Integrating Distances to Ideal and Anti-Ideal Points) aggregation mechanism with belief-structure-based multi-criteria decision analysis. The framework preserves the ordinal structure of survey responses and follows the standard belief-structure representation adopted in B-TOPSIS, in which uncertain (“Don’t know”) responses are incorporated into the evaluation through the normalization procedure. Using published country-level response distributions from Special Eurobarometer 554, the study evaluates public perceptions of recent digital technologies, including artificial intelligence (AI), across EU Member States in five domains: the economy, society, quality of life, current job, and social security benefits. Sensitivity analyses performed for alternative values of the aggregation parameter α, together with comparisons with the benchmark B-TOPSIS rankings, demonstrate the robustness of the proposed aggregation mechanism within the adopted belief-structure framework. Across most dimensions, Lithuania and Malta achieved the highest evaluations, whereas France, Italy, and Romania generally ranked among the lowest. Economic and employment-related evaluations formed the central perception structure and were strongly associated with quality-of-life assessments. The proposed framework extends the analytical capabilities of belief-structure TOPSIS by enabling systematic sensitivity analysis of aggregation assumptions while preserving methodological compatibility with the original framework. Full article

There are a wide range of possible applications for the collection and analysis of statistical data in sports, although their potential has not yet been fully exploited. This study focuses on the areas in which cognitive computing systems can offer advantages for sports organizations. Furthermore, it explores the extent to which media companies can use artificial intelligence to evaluate unstructured data and provide better services. Six semi-structured interviews with experts from the fields of sports, media, and information technology were evaluated using qualitative content analysis. This revealed the need for companies in both industries to adapt to rapidly changing market conditions. The speed of decision-making can be increased by collecting and analyzing large amounts of data in real time. Furthermore, relationships can be derived that were previously hidden due to the cognitive limitations of the human brain. Based on the analysis of all dimensions of athletic ability and the facets of a player’s character, team performance can be improved. In addition, it is possible to assess the extent to which an athlete’s character is compatible with a team and with which teammates he or she is likely to be a better or worse fit. Media companies are enabled to provide sports organizations with insights from the use of cognitive applications and are transforming from media companies to service providers. Full article

In the current era, the tourism sector faces interconnected challenges that require a rapid transition toward sustainable and smart management models. Within the framework of the project ‘Tourism as a Service’ (TAAS), this paper examines the transferability potential of good practices across nine diverse European areas. The study adopts a dual methodological approach that integrates technical feasibility with local relevance. First, the identified practices are clustered into seven thematic pillars. Second, a Multi-Criteria Decision Analysis (MCDA), employing the PROMETHEE method, is applied to assess transferability based on five key dimensions: institutional complexity, financial requirements, technical infrastructure, human resources capacity and regulatory constraints. This quantitative evaluation is complemented by qualitative insights from local stakeholders, ensuring that the proposed solutions align with regional priorities. The findings indicate that communication-oriented practices, such as online marketing campaigns, demonstrate high transferability across most contexts, whereas infrastructure-dependent tools encounter significant implementation requirements. By reconciling constraints rankings with stakeholder perspectives, this paper provides a roadmap for policy improvements aimed at fostering digital transformation in European tourism. Full article

Enterprise green innovation driven by the market is a requirement in the new era to implement the new development concept and fulfill the “dual carbon” goals, integrate them into the new development pattern of “dual circulation”, and boost sustainable economic development. Using the moderated effect regression model and selecting data on manufacturing companies listed on the Shenzhen Stock Exchange from 2014 to 2023, this paper empirically examines the impact of the most important dimension of market orientation—customer orientation—on enterprise green innovation and its internal mechanism. Research findings: Firstly, customer orientation affects corporate green innovation via demand identification, knowledge acquisition, and optimal resource allocation. Reactive customer orientation boosts incremental green innovation through economies of scale, while a proactive one drives radical green innovation via the substitution effect. The alternating effect of the two makes the impact of customer orientation on green innovation exhibit an inverted U-shaped-curve relationship, first rising and then falling. Secondly, corporate operational capacity influences the realization of customer orientation’s effect on green innovation through the following four pathways: learning capability, financial capability, managerial capability, and decision-making capability. Enterprises with a strong operational capacity exhibit a flatter inverted U-shaped curve with a right-shifted inflection point, whereas those with a weaker operational capacity demonstrate a steeper curve with a left-shifted inflection point. Thirdly, heterogeneity analysis by enterprise type reveals that customer orientation does not exert a statistically significant direct impact on green innovation in state-owned enterprises. Conversely, an inverted U-shaped relationship exists between customer orientation and green innovation in private enterprises, with operational capacity serving as a moderating effect. Finally, regional heterogeneity analysis indicates that the moderating effect of operational capacity results in a rightward shift of the inflection point in the inverted U-shaped curve for enterprises in eastern regions, while causing a leftward shift for those in central and western regions. Therefore, when enterprises choose green innovation strategies, they should clearly identify the customer-oriented type, pay attention to the trend in demands, optimize supporting measures, enhance operational capabilities, and build an innovative enterprise. Full article

Institutional governance systems increasingly rely on stakeholder surveys in strategic decision-making. Yet survey participants can act as strategic agents who shape organizational outcomes in their favor. By transforming the information content of response distributions, such behavior can systematically distort institutional decisions made under uncertainty. This study proposes a dynamic framework that detects strategic data manipulation in institutional surveys using Shannon entropy and Kullback–Leibler divergence, and converts this detection into decision support. The framework operates in three stages. First, it constructs a robust reference entropy profile from historical data. Second, it processes incoming survey responses as a sequential stream and compares them against this profile. Third, it detects manipulation at both the population and individual levels through a multi-layered anomaly scoring system. The reference profile was built from anonymized real survey data spanning 2021–2025, comprising 1233 participants and 19,728 clean observations. The framework was validated through 600 Monte Carlo scenarios derived from this profile, covering four manipulation types and five intensity levels, and was benchmarked against the Z-score and Isolation Forest methods. The findings are threefold. Straight-lining detection identifies individual suppression and inflation manipulations with perfect accuracy. KL divergence monitoring flags coordinated coalition entries before data collection is complete. Hierarchical clustering recovers a coordinated suppression group that individual scoring fails to isolate, with 76.5% cluster purity and ~59% recall. Policy impact analysis further shows that manipulation distorts dimensions in opposite directions: the gap between raw and verified means is positive in the dimension targeted by coordinated suppression but clearly negative in the dimension targeted by coordinated inflation. This bidirectional distortion shows why dimension-selective detection is necessary, as a single uniform correction cannot resolve it. The study contributes to the literature in two areas, integrating decision-making under uncertainty with strategic agent models, and survey integrity research with information-theoretic metrics. Full article

Food loss and waste (FLW) constitutes a structural challenge for agri-food systems. However, current analytical tools for assessing management and valorisation options remain fragmented across disciplines. Existing frameworks rarely capture within a single structure the environmental, social, and economic–institutional feasibility dimensions needed to compare alternative recovery pathways. This study introduces the ESG-FLW Index, a multidimensional composite index designed to support comparative evaluation of FLW valorisation strategies, with particular attention to recovery for human consumption. The index draws on a comparative analysis of eight established frameworks. It integrates Life Cycle Assessment for the environmental pillar; Techno-Economic Assessment together with compliance, data-quality, scalability, and replicability indicators for the governance pillar; and Social Return on Investment for the social pillar. These components are aggregated through Multi-Criteria Decision Analysis, following methodological guidance for robust composite indicators. The framework aligns explicitly with the Food Waste Hierarchy and SDG Target 12.3. It also includes hurdle criteria to limit the risk that high aggregate scores obscure critical shortcomings. As a proof of concept, the index was applied to the CiboAmico redistribution programme. Redistribution scored substantially higher than composting (85.1 versus 30.7). The ranking remained stable under ±20% weight variations and Monte Carlo simulations (n = 1000). The ESG-FLW Index offers a transparent and replicable decision-support tool. It is best suited to relative comparison among options and to making sustainability trade-offs explicit. Full article