The digital transformation of commercial banks (DTCB) has altered the way financial institutions collect, process, and use information, with potential implications for firms’ investment behaviour. This study examines whether and how DTCB affects corporate investment efficiency using panel data on Chinese listed companies from 2013 to 2023. The results indicate that a higher level of DTCB is associated with a statistically significant improvement in corporate investment efficiency. Further analysis suggests that this effect operates primarily through two channels: a reduction in financing constraints and a decline in agency costs. The heterogeneity analysis shows that the positive effect of DTCB on investment efficiency is concentrated among privately owned firms, while no significant effect is observed for state-owned enterprises (SOEs). These findings provide evidence that the DTCB reshapes firms’ financing and governance environments in ways that influence investment outcomes. The study contributes to the literature on digital finance and corporate investment by offering firm-level empirical evidence on the economic consequences of banking digitalisation.
Endometriosis remains underdiagnosed due to reliance on invasive laparoscopy. Artificial Intelligence (AI) using either imaging or structured clinical data have shown promise, but single modality approaches face limitations in sensitivity, calibration, and clinical reliability. This work seeks to evaluate whether decision-level multimodal fusion of Magnetic Resonance Imaging (MRI)-based and clinical data-based AI systems improves diagnostic performance, calibration, and net clinical benefit, compared with single-modality models. Two previously validated models were combined with retrospective data from 1,208 patients with suspected endometriosis: a Dual U-Net trained on pelvic MRI with Gradient-weighted Class Activation Mapping (Grad-CAM) interpretability and a dense neural network trained on structured clinical features with SHapley Additive exPlanations (SHAP). This study tested weighted averaging, stacking via logistic regression, and confidence-gating. Performance was assessed using accuracy, precision, recall, F1-score, and area under the curve (AUC). Calibration was evaluated using the Brier score, expected calibration error (ECE), and reliability diagrams. Clinical utility was quantified with decision curve analysis (DCA). Statistical significance was tested with McNemar’s test for accuracy and DeLong’s test for AUC. Multimodal fusion outperformed both single modality models. Weighted averaging accuracy was 0.89, precision was 0.89, recall was 0.87, and F1-score was 0.86, thus improving on either modality alone. Stacking further enhanced calibration (ECE reduction from 0.8 to 0.04) and yielded higher net benefit across clinically relevant probability thresholds (20 to 60%). DCA indicated fusion would avoid 12 to 18 unnecessary surgical investigations per 100 patients, compared with single modality strategies. Confidence-gating maintained performance under simulated distribution shifts to support robustness. Decision-level multimodal fusion enhanced non-invasive diagnosis of endometriosis by improving accuracy, calibration, and clinical utility. These results demonstrated the value of integrative AI gynecological care and justify prospective validation in real-world clinical settings.
This research addresses the rapid advancements and exponential growth in academic research on the use of augmented reality (AR) and the metaverse in e-commerce. Through comprehensive bibliometric analysis, the research evaluates the performance of publications and citation metrics, uncovers influential works and collaborative networks, and explores thematic trends and researcher sentiments in this domain. Data from Scopus was analyzed using tools such as R, RStudio, BiblioShiny, VOSviewer, Tableau, and Python. In addition, sentiment analysis was conducted via Hugging Face’s DistilBERT model. The research findings highlight key themes, including the integration of AR and the metaverse in retail, online shopping, and mobile commerce, emphasizing the role of immersive technologies in transforming consumer experiences. This study identifies emerging trends and gaps, providing a roadmap for future research and strategic implementation. Sentiment analysis reveals a balanced outlook among researchers, with both enthusiasm for technological advancements and concerns over implementation challenges. The research offers valuable insights for researchers, publications, and the e-commerce industry, guiding informed decision-making, fostering innovation, and enhancing consumer experiences in the evolving landscape of AR and the metaverse in e-commerce.
This work provides a complete methodology for adopting well-established AI methods (predictive analytics, LLM agents, forecasting) into Microsoft Dynamics 365 Customer Relationship Management (CRM) for agricultural lending. While not claiming that the algorithms are novel, this work contributes a pragmatic approach to implementing these algorithms that specifically address the regulatory, seasonal, and operational characteristics of agricultural finance, as regulated by the Farm Credit System. It focuses on the real-life constraints and constraints within the regulated financial services industry, and measurable impacts that occurred. The paper provides a domain-oriented application of specific existing AI-CRM integration, with credible statistical testing including an external validation on USDA datasets and benchmarking across peer Farm Credit institutions, as well as cross-institutional analysis. By taking a reasonably conservative duration of 18 months, the Farm Credit institutions noted a statistically significant impact (operational efficiencies of the lending institution to assess member interests) where average case resolution time reduced by 28% (67.2h to 48.4h), and lead conversions improved by 35% (25.9% to 35.0%). Each methodology of implementation also included a series of validations in compliance with regulatory oversight in financial institutions that started to build data governance, model performance compliance through a proactive risk definition, and compliance standards suitable for their institution, and within regulatory standards by regulations. Beyond statistical significance (paired tests, $p <0.001$), practical impact was quantified using absolute and relative changes and bootstrap confidence intervals. The article provides the agricultural lending industry an applied methodology to adopt AI for stakeholder innovation while ensuring they are adept in their enterprise risk management requirement, and still target measurable business outcomes. Given a conservative potential implementation timetable (i.e., 18 months) and validation methodology protocols developed to ensure complete data and model validation, this approach is scalable for agricultural lending implementation and would be a useful instrument across all 72 Farm Credit System institutions.
Circularity and regenerative tourism are instruments that influence the sustainability and resilience of the settings where tourism activities take place. Despite this, these instruments fail to consolidate all the theoretical integrity that corresponds to them as key elements for achieving sustainable development in rural contexts. Hence, the purpose of this study is to theoretically and methodologically re-evaluate the guiding principles of circular and regenerative tourism as tools to guarantee the sustainability and resilience of tourism. It highlighted the tangible and intangible resources of rural communities and developing potential that has not yet been sufficiently explored. The deductive method was used along with other methods derived from practices, such as document reviews, observations, surveys, interviews, and scaling. Techniques such as synthetic analysis, abstractions, comparisons, and generalisations were used to study the potential of circularity and regenerative tourism for sustainable tourism development in the rural parishes in the province of Manabí. The impact on improving the living conditions in host communities were also revealed. To conclude, the revaluation of the theoretical and methodological elements, and principles associated with circularity and regenerative tourism as instruments could help achieve sustainable development in rural communities.
This single case study examined how globalization shaped the sustainability of strategic management in Tazweed Venture Capital in Jordan. Qualitative evidence from six senior managers, triangulated with secondary sources, identified three dominant challenges: economic (47%; including competitiveness, regulation, and supply chains), environmental (25%; including waste management, degradation, and energy), and socio‑cultural (28%; including language and time‑zone frictions, regulatory diversity, and supplier alignment). In addition, the current study identified three opportunity clusters led by culture and society (45%; including partnerships, reputation, and innovation), followed by environment (30%; including renewable integration and footprint reduction) and economic (25%; including cost efficiencies and market expansion). Based on these findings, the study recommended (1) institutionalizing supplier sustainability due diligence and traceability; (2) adopting location‑specific practices with measurable targets; (3) embedding cross‑functional governance linked to key performance indicators; (4) leveraging partnerships and blended finance for renewables and circularity; and (5) formalizing risk‑based Environmental, Social, and Governance (ESG) materiality screens for cross‑border operations. The contribution is a practice‑oriented framework that connects globalization pressures to sustainability initiatives and outcomes for venture capital actors in emerging economies.
Atmospheric turbulence induces severe blurring and geometric distortions in facial imagery, critically compromising the performance of downstream tasks. To overcome this challenge, a lightweight conditional diffusion model was proposed for the restoration of single-frame turbulence-degraded facial images. Super-resolution techniques were integrated with the diffusion model, and high-frequency information was incorporated as a conditional constraint to enhance structural recovery and achieve high-fidelity generation. A simplified U-Net architecture was employed within the diffusion model to reduce computational complexity while maintaining high restoration quality. Comprehensive comparative evaluations and restoration experiments across multiple scenarios demonstrate that the proposed method produces results with reduced perceptual and distributional discrepancies from ground-truth images, while also exhibiting superior inference efficiency compared to existing approaches. The presented approach not only offers a practical solution for enhancing facial imagery in turbulent environments but also establishes a promising paradigm for applying efficient diffusion models to ill-posed image restoration problems, with potential applicability to other domains such as medical and astronomical imaging.
The construction and real estate industry has been held responsible for nearly 40% of global CO2 emissions, a key focus for gathering efforts to combat climate change. Timber, a sustainable and carbon-storing building material, unravels significant potential to decarbonize the sector by replacing carbon-intensive materials such as steel and concrete. However, the full potential of timber remains underutilized, owing to a lack of knowledge, transparency, and investment opportunities in the forestry and timber industries. This paper addressed this gap by developing a comprehensive framework for investors to evaluate listed companies in the timber construction sector, based on their sustainability and financial performance. Specifically, the study sought to answer: How can investors effectively channel capital into the carbon storage capacity of timber, and what approaches are both sustainable and economically viable for timber investments? To achieve this, this paper examined how investors could invest in the CO2 storage capacity of timber, with a particular focus on the creation of Environmental, Social, and Governance (ESG) Timber Score to evaluate the sustainability of listed companies in the sector. By integrating sustainability and financial performance metrics, this study provided a robust framework that enabled investors to assess both the economic and environmental aspects of their investments. The findings revealed investment opportunities in both traditional markets (North America and Europe) and emerging markets (Asia and Africa). The current study emphasizes that investment decisions, if probable, should be tailored to individual preferences to achieve different levels of sustainability and financial goals.
The diffusion of contactless payment technologies has become a critical component of digital transformation strategies aimed at enhancing SME competitiveness in developing economies. Among these technologies, Quick Response (QR) Code Payment offers a low-cost and infrastructure-light solution, yet its adoption among SMEs remains uneven. This study investigates the determinants of QR Code Payment adoption and its subsequent effects on SMEs’ sustainability performance. Anchored in the Technology Acceptance Model (TAM) and the Resource-Based View (RBV), the proposed framework incorporates perceived usefulness, perceived ease of use, digital literacy, QR Code Payment adoption, and sustainability performance as core constructs. Integrating TAM and RBV is essential because belief-based perceptions translate into actual adoption only when supported by adequate organizational resources and capabilities, making adoption decisions the product of an interaction between what users believe and what the firm is able to execute. Survey data from 326 SMEs in the Special Region of Yogyakarta, Indonesia, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that perceived usefulness and digital literacy significantly drive QR Code Payment adoption, whereas perceived ease of use does not, suggesting that performance-oriented beliefs and capability endowments outweigh perceptions of simplicity in shaping adoption behavior. Furthermore, QR Code Payment adoption positively influences economic, social, and environmental aspects of sustainability performance. These findings highlight the strategic value of digital payment integration for advancing SME sustainability and underscore the need to strengthen digital capabilities to accelerate technological uptake. The study extends the literature by jointly applying TAM and RBV to elucidate how belief structures and firm-level capabilities interact to shape adoption outcomes and their performance implications within resource-constrained contexts. For ecosystem coordinators, aligning merchant education with simple analytics dashboards can help SMEs turn payment data into insights—underscoring the need for policy support from government, financial institutions, and payment providers to ensure QR payment adoption translates into real performance gains.
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