Search Results (4,150)

Digital transformation is reshaping industries, business models, and investment opportunities, creating new challenges for international portfolio management. The European communication services sector has become a strategic component of the digital economy, driven by advances in artificial intelligence (AI), digital platforms, 5G infrastructure, cloud computing, cybersecurity, and data-driven business models. Despite its importance, limited evidence exists regarding the effectiveness of portfolio optimisation strategies within digitally transforming sectors. This study investigates international portfolio optimisation using constituent firms of the MSCI Europe Communication Services 35/20 Capped Index. Drawing upon Modern Portfolio Theory and the Treynor–Black framework, an actively managed portfolio is constructed and evaluated against the SPDR^® MSCI Europe Communication Services UCITS ETF and an equal-weight portfolio. Using daily market data, the analysis estimates asset returns, alpha and beta coefficients, portfolio weights, and risk-adjusted performance measures, including the Sharpe and Treynor ratios. Paired-samples t-tests are employed to assess the statistical significance of performance differences among investment strategies. The findings show that the Treynor–Black portfolio generated the highest annual return (27.32%), outperforming both the benchmark and equal-weight portfolios, and the highest percentage of Sharpe ratios (1.2159), suggesting that diversification benefits outweighed the advantages of active security selection. Hypothesis testing indicates no statistically significant difference between the Treynor–Black and equal-weight portfolios, and no statistically significant difference exists between the proposed and benchmark portfolios. The study extends the international portfolio management literature by applying the Treynor–Black model to a digitally transforming sector. The findings suggest that portfolio performance is influenced not only by firm-level financial characteristics but also by broader digital and institutional environments. Firms operating within digitally advanced and well-governed economies appear better positioned to exploit technological innovation and generate sustainable long-term value. Overall, the results demonstrate that successful international portfolio optimization requires balancing active security selection with diversification while recognizing the role of digital transformation, governance quality, and innovation ecosystems in shaping investment performance within the digital economy. Full article

Against the backdrop of increasingly stringent global environmental constraints and rising environmental cost pressures on businesses, artificial intelligence offers a new approach to green cost-reduction and transformation. However, due to constraints such as transformation costs, technological compatibility, and industry standards, the extent to which it can effectively reduce costs and empower businesses remains uncertain. Based on this, this paper uses panel data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2018 to 2024 as a sample to systematically empirically examine the impact, transmission mechanisms, boundary conditions, and spatial spillover characteristics of AI on corporate environmental cost pressures. The study finds that AI can significantly alleviate corporate environmental cost pressures, a conclusion that remains robust after multiple robustness and endogeneity tests. Moderating effects indicate that corporate willingness to engage in green governance and the regional digital regulatory environment can positively reinforce its cost-reduction effects. At the mechanism level, AI can indirectly reduce corporate environmental costs through two pathways: promoting green technological innovation and optimizing the allocation of production factors. Further research confirms that AI exhibits distinct positive spatial spillover effects, which can help regional firms achieve coordinated reductions in environmental costs. This paper enriches the theoretical framework of corporate environmental cost governance from a digital empowerment perspective, providing empirical references and practical insights for corporate green digital transformation, the refinement of government digital-green support policies, and low-carbon development in emerging economies. Full article

This study examines the role of strategic flexibility (SF) in supporting sustainable growth among small and medium-sized enterprises (SMEs) operating in an emerging economy context. Specifically, it investigates the relationships between SF, business model innovation (BMI), competitive advantage (CA), and firm performance (FP) in Iranian SMEs. Drawing on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), the study empirically examines a capability–innovation–performance model and contributes by extending existing theoretical relationships to the context of SMEs in an emerging economy. Using structural equation modeling (SEM), data were collected through 391 validated questionnaires from SMEs across different sectors in Iran. The findings reveal that SF has a direct and significant effect on both BMI and CA, while BMI positively influences CA and FP. However, CA does not show a significant direct effect on FP, suggesting that competitive positioning alone may be insufficient to generate performance outcomes in uncertain and resource-constrained environments. The results indicate that adaptive and innovation-oriented capabilities play a central role in enhancing long-term organizational resilience and sustainable performance among SMEs operating under institutional and market volatility. Full article

Innovative small and medium-sized enterprises (SMEs) in European Widening Countries face persistent financing gaps, yet empirical evidence on how modern financing instruments shape post-investment performance trajectories remains scarce. This study applies a decision tree classification approach to examine post-investment revenue growth of 57 innovative SMEs across twelve European Widening Countries that received alternative financing grants, venture capital, business angel investment, or crowdfunding—between 2020 and 2022. Two research questions are addressed: which pre-investment firm characteristics predict revenue growth following modern financing, and how does the initial revenue level shape post-investment performance trajectories over a three-year observation window. Variable importance analysis indicates that pre-investment revenue level accounts for 83.9% of the predictive importance in the CART model, with development stage as the only secondary predictor (16.1%). Financing type was not identified as a discriminative predictor within the present sample. Low-revenue firms benefit most consistently from alternative financing, advancing an average of 1.51 revenue categories over three years, with 82.9% of firms exhibiting a growth trajectory. Medium-revenue firms exhibit a delayed growth pattern; and higher-revenue firms show persistent stagnation, suggesting a possible ceiling effect in post-investment revenue growth. The RQ1 model achieved cross-validated accuracy of 65.0%; the RQ2 model achieved 38.3%, reflecting the complexity of predicting four trajectory categories from a limited sample. These findings suggest that pre-investment firm characteristics may warrant greater attention alongside financing type when interpreting post-investment SME performance in Widening Country ecosystems. Complementary Logistic Regression and Random Forest analyses yielded broadly consistent results, providing additional support for the robustness of the reported findings. Full article

Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated with perceived firm performance through innovation capability and logistics efficiency, with managerial support treated as a secondary boundary condition. Cross-sectional survey data from 254 middle- and senior-level managers in Chinese logistics firms were analyzed using IBM SPSS Statistics 27 and IBM SPSS Amos 29 (IBM Corp., Armonk, NY, USA), and the PROCESS macro version 4.2 (Andrew F. Hayes, Calgary, AB, Canada), with Model 83 and 5000 bootstrap samples. Perceived AI utilization was positively associated with innovation capability, logistics efficiency, and perceived firm performance. Both mediators showed significant indirect effects, and their sequential indirect effect was also significant. The two individual indirect effects did not differ significantly, but both exceeded the sequential indirect effect. The proposed sequential, reverse-sequence, and parallel-mediation models produced identical fit indices, whereas the restricted direct-effects model showed weaker fit. Neither the AI utilization–managerial support interaction nor the moderated mediation indices was significant. Exploratory item-level analyses showed differentiated associations for demand forecasting and order allocation and for AI infrastructure; the pattern remained stable among 194 respondents involved in AI- or digital transformation-related activities. Innovation capability and logistics efficiency appear to function as complementary mechanisms, with a smaller capability-to-process pathway. Their relative ordering cannot be determined from the cross-sectional data. As the data are self-reported, the findings represent associations among managerial perceptions rather than objective causal effects. Sustainability implications are limited to operational efficiency because environmental outcomes were not directly measured. Full article

Background: Technological innovation in logistics (TIL), logistics capability (LC), and lean logistics (LL) are receiving wider attention in logistics and supply chain research. They are often examined in separate models and less often in relation to both business performance (BP) and market performance (MP). Their joint effects on organizational performance remain insufficiently examined in previous research. Methods: This study examined the effects of TIL, LC, and LL on BP and MP through a quantitative survey conducted among enterprises in Serbia, a transition economy with limited empirical evidence on these relationships. Data from 129 valid responses were analyzed through descriptive statistics, correlation analysis, linear regression analysis, and multicollinearity diagnostics. Results: The findings show that LC has a positive and significant effect on both BP and MP. LL has a positive and significant effect on BP, while TIL has a positive and significant effect on MP. The model explains a larger share of variance in BP than in MP. Conclusions: The results support a differentiated view of logistics transformation and indicate that BP and MP should be examined as related but distinct outcome dimensions. The study integrates TIL, LC, and LL within a unified performance framework. Full article

The Mediterranean region faces severe water scarcity exacerbated by climate change. The reuse of treated industrial wastewater for industrial processes or agricultural irrigation offers a sustainable mitigation strategy, yet its implementation is hindered by low social acceptance and institutional fragmentation. This study aims to enhance treated wastewater reuse (TWWR) uptake within the textile and pharmaceutical sectors in Türkiye and Tunisia through structured stakeholder engagement, hydrological modelling, simulation, and co-design of preferred strategies. Data were collected from regional water authorities, industries, farmers, and civil society, chosen based on their influence, interest, and capacity regarding integrated water management. A Participatory Adaptive Planning framework was applied across three co-designed workshops per site, and interviews. Data were charted using the Natural Assurance Schemes Business Canvas and a conceptual visual support framework. Stakeholders identified key barriers, including legislative gaps in Türkiye and financial capabilities and farmer reluctance in Tunisia. Through the conceptual visual support framework, holistic strategies combining TWWR, water savings, urban treated wastewater quality improvement by tertiary treatment, desalination, and Managed Aquifer Recharge were co-created, simulated and ranked. Technological innovation in wastewater treatment is insufficient without robust “social readiness”. Participatory governance effectively aligns environmental engineering solutions with societal needs, establishing a foundation for resilient and integrated water management action plans. Full article

Despite rapid global growth in renewable energy capacity, fossil fuels still dominate the energy mix. Renewable energy firms often face limited access to bank credit because their asset-light, technology-intensive business models provide little collateral, constraining investment in clean energy deployment. This study examines whether China’s Fintech Reform Pilot Zones, which introduce digital technology-based credit evaluation, can alleviate these financing constraints and accelerate corporate energy transition. Using a staggered difference-in-differences design on a panel of Chinese listed renewable energy firms, we find that pilot zone designation significantly improves firms’ access to external financing and increases Energy Transition Depth (ETD) by approximately 3.6 percentage points, equivalent to 24.7% of the sample mean, indicating economically meaningful improvements in corporate energy transition. The strongest effects are observed in solar photovoltaic deployment and battery storage penetration. Greater energy transition is also associated with lower firm-level greenhouse gas emission intensity, suggesting potential environmental benefits. Mediation analysis identifies two complementary pathways: an innovation-accumulation route which advances renewable energy technology, and a capital-deployment route which supports renewable energy capacity expansion by relaxing firms’ general financing constraints. Regions with more developed renewable energy industries also exhibit lower fossil energy consumption and carbon emissions, suggesting potential regional spillover effects. These findings demonstrate that Fintech-enabled financial reform can facilitate renewable energy deployment and support broader energy transition and decarbonization, with important implications for emerging economies. Full article

Enhancing renewable energy penetration (REP) is essential for accelerating the low-carbon transition of the power system. The evolution of REP is driven by the interaction of multiple factors, including policy, technology, market demand, and environmental constraints. Based on the system dynamics (SD) method, this study constructs a model to simulate the evolution of REP in China during 2012–2060 under business-as-usual (BAU), single-factor, and synergistic scenarios. The results show that, by 2060, REP reaches 77.17% under the BAU scenario. REP improvement is most pronounced under the policy support scenario, reaching 80.71%, while REP increases by 4.58%, 1.94%, 2.36%, and 1.30% relative to BAU under the policy support, technological innovation, market demand expansion, and environmental constraint scenarios, respectively. After 2030, the effect of market demand expansion gradually strengthens and surpasses environmental constraints and technological innovation, with the crossover points corresponding to REP levels of 41.69% and 57.19%, respectively. The synergistic scenario analysis further shows that policy–market synergy is the most effective pathway, with REP reaching 81.21% by 2060, followed by policy–technology synergy at 80.89%. In contrast, policy–environment synergy (80.48%) does not outperform the single policy support scenario. This suggests that environmental constraints need to be coordinated with market-based consumption and technological support to effectively promote REP. Full article

The systemic shift in the energy consumption structure from high-carbon fossil fuels to low-carbon clean energy constitutes a critical pathway toward global climate governance and carbon neutrality. However, this sustainable transition is consistently impeded by deep-seated institutional frictions and structural barriers, such as governance fragmentation and carbon lock-in effects embedded in traditional industrial organization. Whether digital government development can overcome these barriers by nurturing resilient business ecosystems and thereby promote a systemic low-carbon energy transition remains an urgent question within sustainable development research. To address this issue, this study integrates digital government development, regional e-commerce ecosystem competitiveness, and the low-carbon transition of the energy consumption structure into a unified analytical and sustainable governance framework. Using panel data from 30 Chinese provinces from 2012 to 2022, we exploit the institutional reform of provincial big data administrations as a quasi-natural experiment to identify the impacts of digital government. Regional e-commerce ecosystem competitiveness is comprehensively evaluated across four sustainable dimensions: ecological innovation capacity, market connectivity, ecological global integration, and inclusive infrastructure. Methodologically, we employ a spatial difference-in-differences model to capture geographic interdependencies alongside a double machine learning framework to handle high-dimensional confounding and nonlinear disturbances. The empirical findings reveal that both digital government development and regional e-commerce ecosystem competitiveness significantly drive the low-carbon transition of the energy consumption structure. The institutional effect of digital government exhibits strong regional embeddedness with localized impacts, whereas e-commerce ecosystem competitiveness generates positive spatial spillovers that accelerate energy optimization in neighboring regions. Crucially, regional e-commerce ecosystem competitiveness serves as a significant partial mediator, constructing a reliable transmission channel from institutional design to market-based decarbonization. Further pathway analysis indicates that market connectivity and inclusive infrastructure function as the primary transmission channels, effectively mitigating transportation energy intensity and bridging the digital-green divide, while the mediating contribution of ecological innovation capacity is relatively constrained due to cross-organizational coordination thresholds. This study clarifies the interactive mechanism between public digital governance and market ecosystem competitiveness in advancing environmental sustainability, thereby offering fresh theoretical insights and actionable policy implications for emerging market economies striving for economic growth and decarbonization. Full article

Background: Current ablation strategies for the treatment of cardiac arrhythmias remain suboptimal. Treating cardiac arrhythmias using epicardial pulsed field ablation (PFA) selectively targets ganglionated plexi (GPs) within epicardial fat, offering a promising alternative to thermal ablation. Previous computational studies lacked physiological realism, excluding catheter geometry, fluid flow and post-PFA thermal latency. This study aimed to develop a realistic 3D epicardial PFA model integrating a clinical catheter, clinical PFA parameters and a sequentially coupled electro-thermal-fluid dynamics model, including thermal latency, to assess electrical and thermal collateral effects near stented coronary arteries. Methods: The model included epicardial fat, myocardium, blood, and the left circumflex artery containing a metallic stent positioned 0.25 mm beneath the catheter electrodes. Pulses of 1000, 2000, and 2500 V (60 pulses × 100 µs, 1 Hz) were simulated to analyze electric field distribution, PFA-induced lesion volume, temperature evolution, and Arrhenius-based thermal damage, including a 90 s post-pulse period to account for thermal latency. The PFA-threshold of 1000 V/cm was considered. Results: The artery reduced PFA-induced lesion size mainly by occupying fat tissue volume, while the stent shielded the lumen without altering fat lesion volume. The presence of a stent produced localized electric field enhancement at the arterial wall, with up to 3.83% of the arterial wall volume affected by PFA in the worst-case configuration. At clinical settings (1000 V), temperature remained below 40 °C and no collateral damage occurred. Voltages > 2000 V increased arterial wall heating, with thermal damage expanding up to five-fold during latency in the epicardial fat. Myocardium remained unaffected in all cases. Conclusions: The computational model developed in this study indicates that clinically relevant PFA parameters (1000 V) produce localized electric field enhancement at the stent–artery interface, resulting in limited collateral electrical effects in the arterial wall, while avoiding collateral thermal effects and preserving the myocardium. However, the use of higher pulse voltages can lead to delayed thermal damage within the epicardial fat. Full article

This study examines the relationship between corporate financial technology (FinTech) disclosure and environmental, social, and governance (ESG) reporting performance among non-financial firms listed on the Saudi Stock Exchange (Tadawul), with a focus on the post-COVID period from 2021 to 2024. Using an ESG Disclosure Index (ESGDI) constructed from annual reports and a textual measure of FinTech adoption, the analysis provides market-level evidence on the evolution of digital transformation and ESG disclosure in Saudi Arabia. Descriptive results indicate that ESG reporting among Tadawul firms is moderate yet heterogeneous, with governance disclosure consistently stronger than environmental and social components. Correlation analysis indicates a positive association between FinTech disclosure and overall ESG disclosure, particularly within the environmental pillar. Regression results further show that the firms with stronger FinTech disclosure tend to report higher ESGDI scores. The two-way fixed effects (TWFE) model yields statistically significant results, and the direction of the relationship remains consistent with theoretical expectations. Pillar-level analysis suggests that digital transformation is most closely aligned with environmental reporting. Taken together, the results indicate that sustainability disclosure and digital capabilities appear to co-develop in the Tadawul market. Businesses may improve their ability to track, organize, and disseminate ESG-related data by investing in digital reporting systems, analytics, and technology modernization. In this way, FinTech serves as a governance-supporting instrument that improves transparency and reporting discipline in addition to being a financial innovation. This study adds to the expanding body of knowledge by providing important emerging-market-level evidence from the Saudi capital market and highlighting how FinTech can support sustainability-driven growth in an institutional context undergoing rapid transformation. Full article

Higher education plays a crucial role in equipping citizens to tackle contemporary socio-ecological challenges. However, little research has examined how different generations of university students understand socioecological transitions, or the implications of these differences for sustainability education. This study compares the perceptions of older learners (aged 55–70) enrolled in the ‘Classrooms of Experience’ programme with those of undergraduate students (aged 18–28) from the Faculty of Economics and Business at the University of the Basque Country (UPV/EHU). Qualitative data were collected from approximately 250 participants during the 2023/24 and 2024/25 academic years. The data were analysed using the Grid Elaboration Method and the IRaMuTeQ (Version 0.8 Alpha 7) text analysis tool to identify semantic structures, symbolic associations and patterns of meaning across the two age groups. The findings reveal significant generational differences in understanding socioecological transitions. Older learners tend to frame transitions as regulated processes linked to institutional action, public policy, welfare, and quality of life. In contrast, younger students interpret socioecological transitions as responses to interconnected ecological and social crises, emphasising socioecological justice, responsibility, sustainability, and technological innovation as key drivers of transformation. These results suggest the coexistence of complementary yet distinct socioecological imaginaries within the university context. The study highlights the pedagogical value of intergenerational dialogue and learning in higher education. By bringing together diverse perspectives on sustainability, universities can promote more critical, reflective, and transformative educational processes that are capable of addressing the complex challenges of contemporary socioecological transitions. 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

Accurate dynamic cost prediction is essential for budget optimization and risk mitigation in State Grid projects. However, traditional models and even recent deep learning approaches fall short, as they treat cost drivers independently, adopt simplistic concatenation that destroys sourcewise structure, or fail to handle irregularly sampled and partially missing multi-source data. This paper proposes a novel data-driven framework that integrates multi-source business data through a hierarchical tensor fusion mechanism and a hybrid spatiotemporal architecture. The problem is formalized as multivariate time-series prediction with irregular sampling and missing modalities. The framework comprises three synergistic innovations: a differentiable low-rank CANDECOMP/PARAFAC (CP) decomposition layer with adaptive attention weights that preserves cross-source structure while enabling compact dimensionality reduction; a spatiotemporal attention-based bidirectional gated recurrent unit (Bi-GRU) that captures long-range temporal dependencies; and a graph convolutional network (GCN) that explicitly learns interrelations among cost drivers, a capability absent in most existing forecasting methods. The entire system is trained end to end with a customized loss combining mean squared error, quantile loss, and temporal consistency regularization. Extensive experiments on three State Grid substation projects demonstrate that the proposed method outperforms state-of-the-art baselines by 12.7–18.4% in MAPE and maintains robust performance with up to 40% of data missing. These results confirm that explicitly modeling both temporal evolution and driver interdependencies within a unified fusion framework is the key to reliable cost forecasting in large-scale infrastructure projects. Full article