Search Results (2,798)

The growing availability of large-scale online data has created new opportunities for analysing public discourse on climate change, although the reconstruction of structured social representations within digital environments remains methodologically challenging. This study proposes a computational framework for adapting the Hierarchical Evocation Method (HEM), grounded in Social Representation Theory, to large-scale social media discourse. Using a continuously updated Reddit dataset on climate change, the approach combines theory-informed lexical anchoring with data-driven semantic expansion to construct a renewable energy subcorpus comprising 91,817 comments published between 2018 and 2026. Representational structures are reconstructed through user-level lexical diffusion, positional salience, rhetorical foregrounding, and co-occurrence analysis, enabling the identification of central, peripheral, and contrastive components within online discourse. The results reveal a relatively stabilised representational core centred on climate transition, fossil dependency, renewable infrastructures, and socio-economic transformation, while peripheral zones display greater contextual variability and evaluative fragmentation. Longitudinal analyses further suggest a progressive consolidation of renewable energy discourse despite high user turnover and sustained growth in participation. The framework additionally highlights the relevance of affective and interactional dimensions, particularly through the widespread use of ironic and sceptical emoji configurations. Methodologically, the study provides a transparent and reproducible computational pipeline that extends classical evocation-based approaches to large-scale, dynamic corpora. More broadly, the findings contribute to sustainability communication research by showing how renewable energy is collectively framed and negotiated within English Reddit-based digital discussions. Full article

Digital and intelligent transformation reshapes manufacturing ecosystems, and the synergy between technological innovation and sustainable business upgrading drives high-quality industrial development. Based on knowledge interaction theory, this paper adopts a longitudinal single-case design and the Gioia analytical framework to study BYD covering the period 2003–2025. With data triangulation realized through internal corporate archives, public industrial materials and five semi-structured interviews, this paper explores the staged evolution and value allocation mechanism of sustainable business model innovation driven by firms’ proprietary core technologies. Three sequential phases of technological value circulation are summarized: value creation enabled by single-point core technologies, value addition realized through generic product technologies, and cross-industry value sharing facilitated by industrial technology openness. The traction, utilization and recombination of knowledge generate synergistic advantages of core technologies across the innovation chain, industrial chain and value chain, reconstructing a new value operation logic centered on value creation, value addition and cross-boundary value sharing. The extant literature decouples technological evolution and business model innovation, resulting in prominent theoretical gaps. This study improves relevant theoretical explanations and proposes operable industrial strategies, offering references for manufacturing enterprises to achieve long-term sustainable development relying on core technological capabilities. Full article

The intelligent transformation of ports has become a critical pathway for advancing sustainable regional development in the context of rapid globalization and digitalization. While existing studies primarily focus on evaluating port intelligence, limited attention has been given to its role in fostering regional economic sustainability. To address this gap, this study examines the Yangtze River Delta (YRD) region as a representative case to explore the mechanisms through which port intelligent upgrading influences regional economic systems. An integrated analytical framework is developed in which the Analytic Hierarchy Process (AHP) is used to construct a composite index of port intelligence, and Principal Component Analysis (PCA) is employed to measure regional economic development. Furthermore, a Panel Vector Autoregression (PVAR) model is applied to capture the dynamic interactions between these two systems. The results indicate that port intelligence has increased significantly across the sampled ports, with the Port of Shanghai and the Port of Ningbo–Zhoushan exhibiting particularly notable growth, rising by 28% and 44%, respectively, over the period of 2011–2021. The PVAR results further reveal a sustained upward trend in regional economic development, suggesting a positive and persistent association between port intelligence and regional economic performance. These findings provide both theoretical and empirical support for understanding the role of port intelligent transformation in promoting regional economic sustainability and offer policy-relevant insights for fostering coordinated development between port systems and regional economies. Full article

This closing editorial summarizes and introduces the Special Issue, Applied Industrial Metrology: Methods, Uncertainties, and Challenges, highlighting recent advances in traceability, uncertainty evaluation, intelligent metrology, sensor technologies, flow measurement, and standardization. The collected contributions demonstrate how digitalization, artificial intelligence, and advanced sensing are transforming industrial measurement while reinforcing the enduring importance of measurement traceability, comparability, and uncertainty for reliable, sustainable industrial applications. Full article

The field of competitive strategy has attracted growing academic interest in recent years; however, the intellectual framework and thematic evolution of this research area remain fragmented. This study aims to systematically map the evolution of competitive strategy research using an unsupervised machine learning framework. Drawing on a dataset of approximately 3900 journal articles indexed in the Scopus database between 2015 and 2025, the study employs probabilistic topic modeling, specifically Latent Dirichlet Allocation (LDA), together with keyword co-occurrence network analysis, thematic mapping, and community detection techniques to identify the latent thematic structure of the field. The findings reveal a modular and interconnected conceptual landscape in which capability-based strategic perspectives, particularly dynamic capabilities and the resource-based view, continue to occupy central positions in the literature. At the same time, themes related to digital transformation, artificial intelligence, supply chain resilience, environmental, social, and governance (ESG)-oriented management, and sustainability-focused strategic capabilities demonstrate substantial growth and emerging prominence. Temporal analyses further indicate a gradual reconfiguration toward digitally integrated, sustainability-oriented, and capability-driven strategic frameworks. By integrating topic modeling with network-based bibliometric analysis, the study provides a comprehensive and data-driven mapping of the field’s intellectual evolution. The study contributes to competitive strategy research by synthesizing the latent thematic structure of the field and by showing how complementary computational and bibliometric techniques can support large-scale literature mapping in a rapidly evolving research domain. Full article

In the era of carbon neutrality, urban competition has evolved from single-dimensional economic rivalry to multidimensional competition emphasizing green and sustainable development. However, it remains unclear whether digital infrastructure, the physical backbone of the digital economy, can externally enhance urban green competitiveness. Building on the measurement of urban green competitiveness through the super-efficiency SBM model, this study employs the “Broadband China” policy as an exogenous shock and uses a Staggered difference-in-differences (SDID) model to systematically analyze the impact and mechanisms of digital infrastructure on urban green competitiveness. The SDID model is adopted in this study for the following reason: the Broadband China strategy constitutes a phased rollout policy shock. By treating this policy as a quasi-natural experiment, the SDID framework enables more accurate identification of its causal impact on urban green competitiveness. The results show that the construction of digital infrastructure significantly promotes urban green competitiveness. Further analysis reveals that digital infrastructure enhances green competitiveness primarily through green technological innovation, industrial upgrading, and economic agglomeration, with the effect of economic agglomeration being stronger than that of the other two channels. Heterogeneity analysis shows that moderate government intervention amplifies the green effects of digital infrastructure. Moreover, the direct effect of digital infrastructure on green competitiveness is more significant in western cities than in eastern and central cities. This study provides empirical evidence for optimizing digital infrastructure development and achieving a “dual drive” of digitalization and green transformation. Full article

In response to intensifying environmental pressures and the rapid pace of digital transformation, firms are increasingly turning to artificial intelligence (AI) as a tool to support sustainable development. Using panel data from Chinese A-share-listed manufacturing firms from 2016 to 2023, this study examines the relationship between AI adoption and two forms of green innovation: green product innovation and green process innovation. The results reveal that AI adoption is positively associated with both forms of green innovation, with a stronger association observed for green product innovation. The relationship between AI adoption and green innovation also varies across organizational and market contexts. CEO turnover weakens the association between AI adoption and green product innovation but strengthens its association with green process innovation. Market competition further strengthens the positive association between AI adoption and both types of green innovation. Further heterogeneity tests indicate that these associations tend to be more pronounced among high-tech firms and smaller firms. This study provides new evidence on the relationship between AI adoption and different forms of green innovation. It further clarifies the organizational and market conditions under which AI is more closely linked to corporate green transformation. Full article

With the deep integration of artificial intelligence technology into education, AI literacy has emerged as a core competence indispensable for pre-service teachers. However, its formation mechanisms and sustainable cultivation pathways remain to be further explored. This study integrates the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) to construct a theoretical model, in which Individual Innovation (II) and Self-Efficacy (SE) serve as antecedents, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) as mediators, Behavioral Intention (BI) as a proximal variable, AI literacy as the outcome variable, gender and major as moderating variables, and grade and AI exposure time as control variables, exploring the influencing factors and mechanisms of pre-service teachers’ AI literacy. Through a questionnaire survey of 778 pre-service teachers, Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) were employed for sequential empirical analysis. The PLS-SEM results reveal that II and SE were significantly and positively associated with AI literacy through the serial mediation of PU, PEOU, and BI. The fsQCA further identified four distinct equifinal configurations associated with high AI literacy: “High-efficacy Practice-Oriented”, “High-Behavioral-Intention-Oriented”, “High-Innovativeness-Oriented”, and “Long-Term-Development-Oriented”. The findings demonstrate that the improvement of pre-service teachers’ AI literacy follows multiple equifinal mechanisms, necessitating a shift beyond the single-training mindset. Accordingly, this study proposes differentiated cultivation pathways, providing theoretical foundations and practical references for normal universities to deliver targeted and sustainable AI literacy training. It also offers empirical evidence and strategic support for the sub-goals of SDG 4 concerning teacher capacity-building and the digital transformation of education, which aim to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Full article

Digital transformation is increasingly recognized as an important enabler of sustainable development and competitiveness in small- and medium-sized enterprises (SMEs). However, evidence on how different dimensions of digital transformation relate to the achievement of Sustainable Development Goals (SDGs) remains limited. This study explores the relationship between digital transformation and selected Sustainable Development Goals (SDGs) using an explanatory sequential mixed-methods design, in which quantitative findings informed the subsequent qualitative exploration and interpretation of managerial perspectives. The quantitative phase combined data from Eurostat’s ICT Usage Survey (2020–2024), including 60 sustainability-related indicators, with an analysis of the relationship between the Digital Economy and Society Index (DESI) and selected SDG indicators across 27 EU member states using Spearman’s rank correlation. The quantitative analysis suggests that Slovenia performs close to the EU average in overall digitalization, while significant associations were identified between digitalization and SDG 9. The qualitative phase consisted of semi-structured interviews with managers from ten Slovenian SMEs from different sectors. The findings indicate that managers perceive digital technologies, process digitalization, data-driven decision-making, and employee digital competencies as important contributors to sustainability-related outcomes, particularly in relation to SDG 8, SDG 9, SDG 12, and SDG 13. The study contributes to the literature on sustainability-oriented digital transformation in SMEs by integrating quantitative benchmarking with managerial perspectives. The findings highlight the importance of organizational capabilities, digital competencies, and strategic alignment in translating digital transformation initiatives into sustainability-related outcomes. The results provide practical implications for SMEs and policymakers seeking to support sustainable and digitally enabled business development. Full article

The 15-min city (15minC) concept has gained popularity in policy circles as a model of sustainable transportation systems, but there has not been enough empirical study done on its application to suburban peripheries with low-density residential patterns and a significant reliance on cars. In two Southern European suburban living labs—Las Rozas de Madrid, Spain, and Alverca do Ribatejo (Vila Franca de Xira), Portugal—the study examines the impact of 15minC policy initiatives on urban social equity from four distinct angles: proximity equity, modal equity, digital equity, and participatory equity. It is clear from SWOT analysis and focus group data that both study areas’ spatial layouts are geared toward promoting the use of private vehicles, which poses significant accessibility challenges for the elderly, low-income families, and the digitally marginalized population. Modal accessibility research reveals accessibility disparities of 40–60 percentage points for key amenities between walking and driving. Because digital transformation initiatives prioritize technology advancements above expanded service accessibility, they may worsen disparities in urban accessibility. Conversely, social strata whose mobility is particularly vulnerable are still not included in participation plans. It is concluded that a more redistributive transportation strategy is necessary for a fair suburban mobility transition. Full article

This paper explores the relationship between institutional governance quality (IGQ), digital transformation, and corporate social responsibility (CSR) performance in a fragile institutional environment. The study employs an unbalanced panel dataset comprising 473 firm-year observations from firms listed on the Palestine Exchange over the period 2014–2024 and uses fixed-effects regression analysis, robustness tests, and System GMM estimation to ensure the reliability of the findings. The results reveal a significant and robust positive association between institutional governance quality and CSR performance, indicating that higher levels of governance quality are associated with greater corporate engagement in CSR activities. Furthermore, the baseline fixed-effects results show that digital transformation significantly strengthens the positive relationship between institutional governance quality and CSR performance, suggesting that technological progress enhances transparency, information exchange, and institutional monitoring, thereby improving the effectiveness of governance mechanisms in promoting CSR. Robustness tests confirm the stability of the baseline findings, while the dynamic System GMM estimation provides additional evidence after accounting for endogeneity and the persistence of CSR performance. These results indicate that CSR performance exhibits strong persistence over time and that the moderating role of digital transformation becomes more nuanced under a dynamic specification. The study contributes to the literature by providing empirical evidence from a fragile emerging economy that remains underrepresented in governance and CSR research. In addition, the findings offer important policy implications by highlighting the complementary roles of institutional governance quality and digital transformation in promoting CSR and supporting sustainable digital transformation in developing economies. Full article

Background: As supply chains undergo rapid transformation, the concept of supply chain viability (SCV) has gained increasing attention among scholars and industry practitioners. Despite this growing interest, the existing literature lacks a comprehensive and integrated understanding of readiness factors across SCV dimensions. Methods: To address this gap, this study conducted a systematic review of the literature. A total of 86 peer-reviewed articles published between 2015 and 2025 were included, synthesizing evidence across four SCV dimensions: agility, resilience, sustainability, and digitalization. Two-mode network representation was used to analyze cross-dimensional connections between readiness factors and SCV dimensions via degree centrality. Results: The analysis identified 36 readiness factors, 9 of which are cross-dimensional and connected to all four SCV dimensions: leadership and decision-making, stakeholder integration, customer relationship and engagement, partner collaboration and readiness, advanced analytics, technological/digital capabilities, real-time data visibility, information and communication technology infrastructure availability, and digital transformation/smart technologies in supply chain management. Conclusions: Readiness factors vary across SCV dimensions, with some spanning multiple dimensions and others remaining specific. SCV strategies should account for these differences rather than treating factors as uniform. Full article

The accelerating diffusion of artificial intelligence (AI) in Europe raises pressing distributional questions about employment, social cohesion, and sustainable development in disadvantaged regions. Research has concentrated on advanced urban economies, leaving the implications of AI for peripheral small and medium-sized enterprises (SMEs) operating under weak human capital, thin digital infrastructure, and constrained social capital, underexplored. We examine the interplay between AI adoption, social capital formation, workforce dynamics, and sustainable development in Eastern Macedonia and Thrace (EMT), one of the EU’s least developed regions. Regional unemployment and educational-attainment data from Eurostat and ELSTAT are incorporated as contextual evidence anchoring the qualitative findings. Drawing on Bitsani’s Biocultural City framework which treats human, social, and cultural capital as interdependent dimensions of regional sustainability, we thematically analysed twelve semi-structured interviews with SME owners and managers conducted in early 2025 using Atlas.ti, yielding 19 codes grouped into six categories. Knowledge deficits and financial constraints emerge as primary barriers, while external technology partnerships, targeted education, and economic incentives operate as enablers, all mediated by social and human capital availability. Read through this framework, AI adoption in peripheral economies emerges less as a purely technological or financial challenge than as a social and human capital one, embedded in a biocultural environment shaped by brain drain, institutional thinness, and weak civic intermediation. Without parallel investment in digital literacy, organizational culture, and inter-firm networks, AI risks reproducing rather than reducing employment inequalities. The study draws policy implications for EU Cohesion programming and Sustainable Development Goals 4, 8, 9, 10, and 17. Full article

This study proposes a BIM-integrated Eco-Digital framework for enhancing predictive maintenance and sustainability assessment in educational buildings, supporting their transition towards Digital Twin readiness. Existing facilities often rely on reactive maintenance practices, fragmented data systems, and limited integration of sustainability indicators, which hinder efficient lifecycle management and environmental performance. To address these challenges, the research develops a data-driven methodology that combines condition assessment (CA), Markov-based deterioration prediction (DP), and sustainability metrics within an interoperable BIM–facility management (FM) environment. The framework is validated through a real educational building case study, where a hierarchical asset structure and relative weighting system are established to prioritize maintenance actions based on both functional importance and condition state. The Markov model is employed to predict component deterioration under uncertainty, enabling proactive maintenance planning without reliance on real-time IoT data. Sustainability is incorporated through energy consumption analysis and lifecycle performance indicators, linking maintenance decisions to environmental impacts. Results demonstrate improved maintenance prioritization, enhanced predictive capability, and better integration of sustainability considerations within facility management workflows. The proposed framework mainly contributes to providing a practical, scalable approach for transforming conventional buildings into data-driven, sustainable assets, offering a viable pathway toward Digital Twin-enabled environments, particularly in contexts with limited digital infrastructure. Full article