Search Results (1,382)

Housing inequalities remain a major challenge for contemporary urban governance, as they combine economic, social, spatial, and demographic dynamics that are difficult to capture through single indicators. This paper develops a data-driven assessment of housing inequalities in Portugal between 2015 and 2025, drawing on official national and European statistics and applying a Business Intelligence (BI) and urban analytics framework oriented towards policy monitoring. Official data from Statistics Portugal and Eurostat are integrated through an analytical pipeline including automated extraction via public APIs, data enrichment, and visual analytics. The workflow follows a CRISP-DM-inspired structure, creating a set of normalized indicators to capture different dimensions of housing conditions. The results point to a structurally polarized housing market. Housing valuations increased across all regions, but at uneven rates, reinforcing territorial disparities rather than convergence. Metropolitan and tourism-oriented regions experienced faster appreciation and indirect effects, while year-over-year growth in completed dwellings slowed after 2021–2022, indicating an uneven supply response. Beyond its empirical findings, the primary contribution of this study lies in demonstrating how BI and data science methodologies can be operationalized to monitor housing inequalities using official statistics. The proposed framework is replicable and can be adapted to other territorial and policy contexts. Full article

Against the backdrop of global energy pattern restructuring, the advancement of dual-carbon goals and large-scale development of unconventional oil and gas, complex oil and gas fields are confronted with practical challenges including harsh geological conditions and diversified development objectives. Traditional development planning methods for oil and gas fields suffer from single evaluation dimensions, strong subjectivity in decision making and insufficient dynamic adaptability, which make them unable to meet the full-process development requirements. To realize scientific, quantitative and systematic development planning of complex oil and gas fields, a development planning evaluation method suitable for complex oil and gas fields is established by integrating multidisciplinary theories. First, a multilevel evaluation model for oil and gas field development planning is constructed according to the characteristics and difficulties of development planning evaluation for complex oil and gas fields. The model consists of five core modules: external analysis, internal analysis, corporate development strategy selection, business planning and risk assessment. Secondly, a development planning evaluation method is established through a closed-loop process including special quantitative IFE/EFE analysis, IE matrix strategic positioning, SWOT alternative strategy pool and QSPM priority ranking. Then, the strategic priority ranking is dynamically adjusted by considering the impact of stepped oil prices. Finally, combined with the analytic hierarchy process (AHP), a comprehensive risk index evaluation model is established to realize quantitative assessment and traceability of risk levels. A case application in Block M demonstrates that its strategic positioning belongs to the growth type. Under low–medium–high tiered oil prices, the strategic combinations with the highest strategic priority are W+O strategy, S+O strategy and S+O, respectively. The development risk level is moderate risk. This study fills the gap in the whole-process evaluation system of complex oil and gas fields, and realizes the transformation of development planning from qualitative analysis to quantitative decision making. It provides theoretical methods and practical references for ensuring high-quality development of complex oil and gas fields and energy security. Full article

The adoption of artificial intelligence (AI)-enabled technologies and information technology (IT) systems in entrepreneurship education has accelerated alongside the digital transformation of higher education. With a particular focus on gender-related disparities, this study examines how digital business modeling tools influence students’ entrepreneurial intentions. It conceptualizes digital tools along a continuum, ranging from non-AI solutions to AI-embedded and fully AI-driven systems. Data from 440 students taking part in entrepreneurial workshops using the AI-enabled digital tool KABADA served as the basis for empirical investigation. Changes in entrepreneurial intention and its key antecedents—attitude toward entrepreneurship, subjective norms, and perceived behavioral control—are examined by comparing the pre-workshop and post-workshop groups using structural equation modeling. According to the findings, the KABADA workshop has a statistically significant positive indirect effect on entrepreneurial intention, which is mainly mediated by perceived behavioral control. Significant gender differences are revealed by multi-group analysis: for female students, the main factor influencing entrepreneurial intention is perceived behavioral control, while for male students, the main factor is attitude toward entrepreneurship. These results emphasize the significance of IT systems that integrate guided user engagement with AI-based analytics to improve entrepreneurial self-efficacy, especially among women. Full article

Under wartime conditions, conceptual approaches to organizing the education system are changing, and the means of achieving goals are being modified. All of this affects the development of infrastructural provision for the educational network and simultaneously requires adequate management. The state, as the main subject of social management, employs management theory and practice of competent (professional) business leadership. This approach not only allows it to survive but also to develop in the objectively existing competitive environment. It has been determined that the main elements of educational management (EM) organization include the quality of intellectual resources, analysis of internal and external environments, analysis, selection and implementation of educational system (ES) development strategies and evaluation and control of their execution. Attention is focused on forming an ecologically oriented society through the lens of knowledge transfer, with a focus on the irrational use of natural resources across various spheres of human activity, energy resource deficits, and sustainable development tasks in Ukraine. A central place in this process is assigned to organizing project activities and to forming an ecologically oriented worldview among future specialists trained by educational institutions at various levels and forms of ownership. The analysis of educational management (EM) models shows that the project-investment model remains relevant. Trends in quantitative indicators of EM and ecological projects in Eastern European countries have been analyzed, based on which conclusions have been formulated that reflect the current state of ecological education development and demonstrate existing changes, challenges, and prospects. A visualized flowchart of optimizing the organization of higher education through the prism of an environmentally friendly society has been developed, with four blocks highlighted: methodological, organizational, analytical, and resultant. It has been determined that knowledge transfer from universities to communities should become a priority in the state’s post-war reconstruction, ensuring the socio-economic development of regions, including strengthening Ukraine’s energy independence. The practical significance of the obtained results lies in developing recommendations for implementing the integration of educational management (new functions) and project activities in educational institutions, which can be used when forming their development strategies, establishing international partnerships in the educational sphere, as well as for developing state programs to support the development of Ukraine’s economic, ecological, and social policy. Full article

With decarbonization, decentralization, and digitalization, energy coordination increasingly involves many actors, heterogeneous cyber–physical data, and compliance-sensitive settlement workflows. Although blockchain has been widely discussed in this domain, existing studies are still fragmented across application-specific or platform-specific narratives. As a result, it remains difficult to compare recurring mechanisms across scenarios or to determine which blockchain functions are operationally justified in deployable energy systems. We address that fragmentation through a structured narrative review of 41 representative sources, including prior surveys, foundational technical references, and scenario-specific studies. We formulate three research questions concerning architectural positioning, cross-scenario mechanisms, and deployment barriers. On this basis, we synthesize a unified five-layer reference architecture that links off-chain physical infrastructure and trusted data acquisition to protocol-level trust anchoring, reusable business services, interface and compliance functions, and application scenarios. The framework is then used to compare five recurring scenario families, namely peer-to-peer energy trading, carbon markets and renewable energy certificates, electric vehicle charging and vehicle-to-grid services, virtual power plants, and grid flexibility coordination. The analysis shows that blockchain is most defensibly positioned as an evidence-and-settlement trust layer, rather than as a replacement for real-time physical control. It also identifies three persistent adoption bottlenecks, namely scalable ledger interaction, trustworthy cyber–physical data binding, and interoperability with regulatory and operational infrastructures. By making the trust boundary explicit and by providing a common analytical lens for cross-scenario comparison, this review clarifies the scientific contribution of blockchain to energy systems and outlines stakeholder-oriented directions for deployable hybrid designs. Full article

This study develops a human-centered Artificial Intelligence (AI) framework enabling rapid ecodesign prioritization for Ecodesign for Sustainable Products Regulation (ESPR) compliance while demonstrating Large Language Model (LLM) integration in sustainability strategy. A four-stage hybrid methodology combining LLM-assisted action identification (30 ESPR-aligned interventions) with multi-criteria decision analysis with analytic hierarchy process (MCDA-AHP) is developed. Expert validation addressed LLM-driven interventions' limitations with practitioners evaluating AI suggestions based on the value chain context. The framework applied to two Italian building-integrated photovoltaic (BIPV) small-medium enterprises (SMEs) demonstrated strategic differentiation based on feasibility vs. desirability vs. affordability, producing systematically different action portfolios within regulation-aligned aggregate structures. Sensitivity analysis showed 100% priority stability under ±10% AHP variations for priority one, three, and four actions and 82% for priority two actions, validating framework robustness. The framework provides empirical evidence for augmentation-not-automation in AI-assisted strategic planning, contributing a replicable methodology for responsible LLM integration across manufacturing sectors. Results demonstrate that combining AI synthesis efficiency with human contextual judgment enable regulation-aligned, business-model-specific sustainability strategies. Full article

Digital maturity models are widely used to support transformation; however, many remain organization-level, lack transparency, and are only weakly linked to implementation prioritization. These limitations are especially consequential in purchasing, where maturity may vary substantially across activities and sub-processes. This study develops a process-level digital maturity assessment-and-roadmapping (DEMA) artifact for purchasing. Within the broader DEMA architecture, this study develops and evaluates only the Smart Business Processes component, while retaining Digital Strategy and Infrastructure as a contextual architectural layer. Drawing on design science research and maturity model development guidance, DEMA was developed through literature synthesis, iterative expert involvement over approximately 18 months, and structured refinement conducted in approximately 20 sessions. The artifact was refined through three anonymized pilot applications in electronics manufacturing SMEs and then demonstrated through a focal case application in an electronics SME that used an ERP system but lacked a purchasing-specific digital transformation roadmap. Evaluation was utility-oriented rather than psychometric, focusing on whether the artifact could (i) generate differentiated capability profiles across purchasing subprocesses, (ii) improve item clarity, stage interpretation, and scoring logic through pilot-based refinement, and (iii) translate assessment results into feasible targets, priorities, and sequenced roadmap actions under facilitated conditions. To provide bounded but direct validation evidence, the study also included a lightweight two-rater consistency and interpretability check on a representative subset of 24 items, together with a structured diagnosis-to-roadmap traceability review of six representative items. The results showed moderate exact agreement, perfect adjacent agreement, positive weighted inter-rater agreement for ordinal ratings, and favorable interpretability scores. Together, these findings provide bounded empirical support for the artifact’s practical consistency and usability within the study’s development-oriented scope. Unlike reflective survey scales, the DEMA is evaluated here as a staged, prescriptive maturity grid. Accordingly, the methodological emphasis is on interpretability, traceability, and assessment-to-action usefulness in facilitated use rather than psychometric scale validation. The DEMA integrates a fully disclosed 70-item staged instrument with explicit scoring, dual-target setting, dependency-aware prioritization, and a structured implementation methodology. In the focal case, the artifact revealed uneven maturity profiles across capabilities, distinguished between current and target capability states, and supported the prioritization of concrete intervention areas, such as data-entry automation/RPA, digital tool budgeting, remote-access improvement, and analytics-related training. Rather than pursuing psychometric scale validation, this study presents a transparent, implementation-oriented artifact for purchasing and shows how process-level maturity diagnosis can be translated into roadmap development in guided-application settings. Therefore, the contribution is design-oriented and practice-facing rather than a claim of broad theoretical advancement or comparative superiority over existing maturity frameworks. Full article

The New Zealand construction industry, while central to national infrastructure and economic development, continues to grapple with persistent performance challenges rooted in weak strategic governance and fragmented decision-making processes. This study examines the relationship between strategic decision-making and organisational performance within the New Zealand construction sector, addressing a gap that construction management scholarship has largely left unattended. The study draws on survey data from construction professionals across diverse organisational sizes, project types, and regions in New Zealand, employing Partial Least Squares Structural Equation Modelling (PLS-SEM) as its analytical approach. The analysis identifies four significant predictors of construction business performance: strategic decision formulation, strategic decision implementation practices, strategic decision evaluation, and financial strength. Workforce capabilities, by contrast, did not demonstrate a statistically significant relationship with performance outcomes. This nuanced finding challenges prevailing assumptions about the primacy of human capital in construction performance models. The structural model achieved strong explanatory power, confirming the robustness of the proposed framework. These findings offer theoretically coherent, empirically supported insights into strategic performance determinants among mid-sized construction organisations in New Zealand. The voluntary sampling design and modest sample size of 102 respondents define the inferential boundaries of these conclusions. Full article

Business intelligence (BI) and business process management (BPM) have traditionally addressed related managerial problems from partly separate perspectives, while big data analytics, process mining, generative AI, and decision support systems are increasing the pressure toward integration. This review examines how these domains relate within a shared business-processing and decision-making context. Methodologically, the paper adopts a narrative review approach based on peer-reviewed literature published from 2015 onward, drawing on Google Scholar, Scopus, and Web of Science, and synthesizes the literature thematically across conceptual foundations, data and computational infrastructures, process intelligence, generative AI, application domains, and implementation tensions. The review finds that the literature does not support the claim that these areas have already converged into a stable, unified field. Instead, it shows a gradual movement toward a layered architecture in which BI and business analytics support organizational insight, BPM and process mining provide process intelligence, big data analytics supplies the evidentiary and computational base, generative AI functions as an interaction and augmentation layer, and decision support systems translate these elements into managerial action. The paper concludes that this emerging integration is meaningful but still uneven, with its practical value depending on interoperability, evaluation realism, governance, and the preservation of human oversight in AI-supported business processes. Full article

Inclusive growth has increasingly emerged as a central framework for understanding how firms can align economic performance with social inclusion and environmental responsibility, particularly in emerging markets characterized by institutional volatility. In the context of geopolitical shocks and economic sanctions, such as those faced by Russia during 2022–2023, the normative meaning of inclusive growth is redefined toward prioritizing employment stability, industrial continuity, and strategic resilience at the firm level. This study aims to develop a systematic and transparent firm-level measure of inclusive growth that integrates strategic resilience with long-term business model potential. It further seeks to empirically assess cross-firm heterogeneity in inclusive growth performance within the Russian metallurgical and mining sector under geopolitical disruption conditions. This study constructs a composite Inclusive Growth Index using publicly available financial and non-financial disclosures, combining indicator normalization, variance-based weighting, and geometric aggregation. The index is applied to a panel of major Russian metallurgical and mining companies for the period 2021–2024 to evaluate their strategic resilience, business model potential, and industry-level dynamics under sanctions. The results reveal substantial heterogeneity in inclusive growth performance across firms, with higher index values being associated with stronger strategic resilience and more stable operational outcomes. The analysis further identifies a divergence between improving resilience and declining business model potential during 2022–2024, indicating a trade-off between short-term stabilization and long-term inclusive growth capabilities under the geopolitical stress. The findings suggest that inclusive growth at the firm level in a sanctioned emerging market context follows a distinct sovereignty-oriented logic in which employment stability and operational continuity take precedence over long-term innovation and governance enhancement. Overall, the proposed Inclusive Growth Index provides a robust analytical framework for assessing corporate adaptation to structural shocks and informing managerial and policy decisions in emerging market economies. Full article

Universities worldwide are increasingly committing to carbon neutrality; however, most institutional climate strategies treat operational emissions forecasting and ecosystem-based carbon sequestration as separate analytical domains, leading to inconsistencies in accounting boundaries, temporal alignment, and verification practices. This study develops and demonstrates an integrated LEAP–InVEST framework that explicitly links energy-system modeling with spatial ecosystem carbon accounting within a unified monitoring, reporting, and verification (MRV)-aligned structure. The framework combines the Low Emissions Analysis Platform (LEAP) for scenario-based greenhouse gas emissions modeling with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for spatial carbon storage assessment. A key methodological contribution lies in reconciling emission flows and carbon stock changes by converting carbon stock variations into annualized removal flows, thereby enabling consistent estimation of gross emissions, carbon removals, and net emissions while avoiding double counting across scopes. Using a university campus in Taiwan as a case study, a baseline inventory was established following ISO 14064-1 standards, and future emissions trajectories were simulated under Business-as-Usual and mitigation pathways through 2040. In parallel, land-use and land-cover data were used to quantify historical and projected carbon stocks across forest, grassland, agricultural, and built-up areas. Results indicate that electricity consumption constitutes the dominant emissions source, and that energy efficiency improvements, photovoltaic deployment, and green power procurement provide the largest mitigation potential. Although ecosystem carbon stocks remain substantial, their annual sequestration capacity offsets only a limited portion of projected emissions, reinforcing the importance of prioritizing emissions reduction before applying nature-based removals. The proposed framework provides a transferable methodological approach for institutional carbon neutrality planning by integrating emissions reduction and carbon sequestration within a coherent analytical system. By aligning energy modeling, ecosystem dynamics, and MRV principles, the framework enhances the transparency, credibility, and robustness of net-zero pathway assessment and is applicable to universities and compact urban systems seeking data-driven and verifiable decarbonization strategies. Full article

This study analyzes the closure, decommissioning, and abandonment (CDA) of a fuel storage and distribution facility in southern Quito, Ecuador, conceptualizing the process as a socio-technical urban transition embedded within territorial governance dynamics. While infrastructure decommissioning is commonly addressed from a predominantly technical perspective, limited research integrates reverse logistics design, stakeholder influence structures, and territorial development into a unified analytical framework, particularly in Latin American metropolitan contexts. Using a mixed-methods case study approach, the research combines documentary analysis, operational data, and 34 semi-structured interviews with public authorities, engineers, fuel marketers, business owners, and community representatives. A thematic analysis was applied to reconstruct the decommissioning logistics chain and to develop a stakeholder mapping and influence matrix assessing actor positions, economic interdependencies, and legitimacy claims. The findings show that decommissioning operates as a structured reverse logistics system embedded within asymmetric governance configurations, where economic dependency, risk perception, and urban redevelopment expectations generate competing territorial imaginaries. Technical feasibility alone proves insufficient to guide decision-making; instead, legitimacy emerges through the alignment of engineering planning, institutional coordination, and community-level expectations. The study advances an integrated socio-technical framework that articulates Engineering Management, Social Management, and Territorial Development, positioning decommissioning as a governance-driven transition rather than a purely technical operation. The results contribute to sustainability and infrastructure transition scholarship while offering practical guidance for managing urban hydrocarbon infrastructure closure in socially vulnerable territories. Full article

Artificial intelligence development offers new solutions for enhancing corporate carbon performance and is crucial for promoting sustainable business practices. This study investigates the dynamic impact of artificial intelligence (AI) policy on corporate carbon performance using time series panel data of Chinese A-share listed companies from 2010 to 2024. Leveraging the staggered establishment of the National New Generation Artificial Intelligence Innovation Development Pilot Zones as a quasi-natural experiment, we develop a multi-period difference-in-differences framework with time-varying treatment. Our time series-based identification strategy addresses serial correlation and time-varying confounding factors through robust clustering and event study specifications. The findings reveal that AI policy significantly improves corporate carbon performance, a conclusion that remains robust after rigorous endogeneity tests, placebo checks, and counterfactual analyses. Using dynamic panel models, this study traces the temporal evolution of policy effects and demonstrates that AI exerts indirect effects through three time-lagged pathways: micro-level technological diffusion, future industry development, and the progressive accumulation of digital infrastructure and computing resources. Heterogeneity analysis reveals differentiated impacts across micro- and macro-levels, providing granular insights for forecasting heterogeneous treatment effects. By integrating panel time series econometrics with causal inference, this study contributes to the literature on corporate carbon performance while expanding analytical frameworks for understanding AI’s enabling effects. The findings offer policy insights and empirical benchmarks for forecasting green transition trajectories, with direct implications for green finance and sustainable economic development. Full article

The transition toward circular economy (CE) systems is essential for improving resource efficiency and sustainability performance in industrial production. However, small and medium-sized enterprises (SMEs) face structural barriers that limit the adoption of circular practices and business model innovation. This study examines the systemic drivers shaping circular transitions in timber-based SMEs within an industrial cluster in northern Mexico. The research integrates the Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) structural analysis with the Circular Business Model Canvas (CBMC) to analyze influence–dependence relationships among key barriers and their implications for business model transformation. Empirical data were collected from 32 SMEs using structured surveys and expert consultation. The results suggest that financial constraints, technological limitations, and weak collaboration networks act as dominant systemic drivers. The CBMC assessment indicates an average implementation level of 45%, with high variability across firms (31–99%), reflecting fragmented and early-stage circular transition patterns. By linking structural diagnostics with business model components, the study identifies strategic leverage points and potential intervention pathways. The findings contribute to CE research by providing a systematic and replicable analytical framework, as well as insights for understanding circular bioeconomy transitions in SME-based industrial clusters. Full article

The research examines regional disparities in the diffusion of e-commerce among enterprises employing at least 10 people in Italy, using an integrated analytical framework that blends econometric modeling, machine learning, and network analysis. Instrumental Variable (IV) panel models overcome endogeneity arising from digital infrastructure, socioeconomic factors, and online business activity, with geographic slope as a suitable instrument for broadband penetration. Machine learning models—regularized regressions, random forests, and boosting—augment causal inference by registering nonlinear effects and sorting variable salience. The results, in all cases, emphasize internet use, household digital connectivity, and the prevalence of remote work as the most important predictors of the diffusion of e-commerce. Cluster analysis identifies regional digital profiles that distinguish northern-central regions from southern-insular regions, characterizing persistently distinct digital divides. The network analysis, in turn, identifies digital inclusion variables—such as internet penetration and ICT infrastructure—that occupy central positions within the entirety of the economic and technological interdependencies’ regime. Innovation and income levels, while practiced, hold peripheral positions, indicating that digital capacity, rather than economic affluence in the singular, drives online business participation. Italy’s case can particularly illustrate this beyond its national borders. Being a high-income economy with significant regional disparities, it reproduces challenges common elsewhere in the world, among which the cases of Spain, Germany, the USA, the Republic of Korea, and Japan come to mind, where regional disparities inhibit inclusive digital development. The Italian case presents, then, a transferable model for the diffusion of digital tools, the reduction in regional disparities, and the encouragement of economic integration. By synthesizing the causal, predictive, and systemic methodologies, the study offers a theoretical and practical response to digital transformation across diverse terrains. Full article