Search Results (634)

This study synthesizes the rapidly expanding evidence on how digital technologies reshape international trade, with a particular focus on small and medium-sized enterprises (SMEs). Guided by two research questions—(RQ1) How do digital tools influence the volume and composition of cross-border trade? and (RQ2) How do these effects vary by countries’ development level and firm size?—we conducted a PRISMA-compliant systematic literature review covering 2010–2024. Searches across eight major databases yielded 1857 records; after duplicate removal, title/abstract screening, full-text assessment, and Mixed Methods Appraisal Tool (MMAT 2018) quality checks, 86 peer-reviewed English-language studies were retained. Findings reveal three dominant technology clusters: (1) e-commerce platforms and cloud services, (2) IoT-enabled supply chain solutions, and (3) emerging AI analytics. E-commerce and cloud adoption consistently raise export intensity—doubling it for digitally mature SMEs—while AI applications are the fastest-growing research strand, particularly in East Asia and Northern Europe. However, benefits are uneven: firms in low-infrastructure settings face higher fixed digital costs, and cybersecurity and regulatory fragmentation remain pervasive obstacles. By integrating trade economics with development and SME internationalization studies, this review offers the first holistic framework that links national digital infrastructure and policy support to firm-level export performance. It shows that the trade-enhancing effects of digitalization are contingent on robust broadband penetration, affordable cloud access, and harmonized data-governance regimes. Policymakers should, therefore, prioritize inclusive digital-readiness programs, while business leaders should invest in complementary capabilities—data analytics, cyber-risk management, and cross-border e-logistics—to fully capture digital trade gains. This balanced perspective advances theory and practice on building resilient, equitable digital trade ecosystems. Full article

In response to the low efficiency of collaborative processing of sewing machine cases at the part level in network collaborative manufacturing, this paper proposes a sewing machine cases manufacturing service composition optimization method based on an improved multi-objective artificial hummingbird algorithm. The structure and production process of sewing machine cases are analyzed; a framework for service composition optimization in the sewing machine cases manufacturing service platform is established; the required manufacturing resource service composition is determined; and a dual-objective service composition optimization mathematical model that considers Quality of Service (QoS) indicators and flexibility indicators is constructed. Opposition-based learning strategies, roulette wheel selection strategies, and improved differential evolution strategies are embedded in the multi-objective artificial hummingbird algorithm, and the improved artificial hummingbird algorithm (ORAHA_DE) is used to solve the sewing machine cases manufacturing service composition optimization model. The experimental results show the effectiveness and superiority of this composition optimization method in solving the sewing machine cases manufacturing composition optimization problem while avoiding entrapment in a local optimum during the solution process, thereby achieving the composition optimization of sewing machine cases collaborative manufacturing services. Full article

Cloud computing offers a broad spectrum of services to users, particularly in multi-cloud environments where service-centric features are introduced to support users from multiple endpoints. To improve service availability and optimize the utilization of required services, cloud brokerage has been integrated into multi-cloud systems. The primary objective of a cloud broker is to ensure the quality and outcomes of services offered to customers. However, traditional cloud brokers face limitations in measuring service trust, ensuring validity, and anticipating future enhancements of services across different cloud platforms. To address these challenges, the proposed intelligent cloud broker integrates an intelligence mechanism that enhances decision-making within a multi-cloud environment. This broker performs a comprehensive validation and verification of service trustworthiness by analyzing various trust factors, including service response time, sustainability, suitability, accuracy, transparency, interoperability, availability, reliability, stability, cost, throughput, efficiency, and scalability. Customer feedback is also incorporated to assess these trust factors prior to service recommendation. The proposed model calculates service ranking (SR) values for available cloud services and dynamically includes newly introduced services during the validation process by mapping them with existing entries in the Service Collection Repository (SCR). Performance evaluation using the Google cluster-usage traces dataset demonstrates that the ICB outperforms existing approaches such as the Clustering-Based Trust Degree Computation (CBTDC) algorithm and the Service Context-Aware QoS Prediction and Recommendation (SCAQPR) model. Results confirm that the ICB significantly enhances the effectiveness and reliability of cloud service recommendations for users. Full article

The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions. Full article

In the context of green development, promoting the development of data elements is crucial for advancing the green and low-carbon transition and achieving China’s “dual-carbon” targets. This study quantitatively evaluates China’s data element policies to identify their strengths and weaknesses and to assess their alignment with green development objectives. In this study, we examine 15 representative data element policy texts, evaluating their quality by integrating the Latent Dirichlet Allocation (LDA) topic model with the PMC-Index model. The LDA analysis identifies five core themes within the policy texts: the data element industry, data resource management, data element trading systems, service platform construction, and e-governments. The evaluation results show an average PMC-Index score of 6.03 for the 15 policies, with 9 rated as “Good” and 6 as “Acceptable”. This indicates that while the overall design of the current policy system is acceptable, there remains substantial room for improvement. Based on the average scores for the primary indicators, the policies perform relatively poorly in terms of green development assessment, policy timeliness, policy nature, and policy guarantee. Drawing from these findings, we propose recommendations to enhance China’s data element policies, offering insights for policymakers. Full article

AI-powered health assistants offer promising opportunities to enhance health management among older adults. However, real-world uptake remains limited, not only due to individual hesitation, but also because of complex interactions among users, platforms, and public policies. This study investigates the dynamic behavioral mechanisms behind adoption in aging populations using a tripartite evolutionary game model. Based on replicator dynamics, the model simulates the strategic behaviors of older adults, platforms, and government. It identifies evolutionarily stable strategies, examines convergence patterns, and evaluates parameter sensitivity through a Jacobian matrix analysis. Results show that when adoption costs are high, platform trust is low, and government support is limited, the system tends to converge to a low-adoption equilibrium with poor service quality. In contrast, sufficient policy incentives, platform investment, and user trust can shift the system toward a high-adoption state. Trust coefficients and incentive intensity are especially influential in shaping system dynamics. This study proposes a novel framework for understanding the co-evolution of trust, service optimization, and institutional support. It emphasizes the importance of coordinated trust-building strategies and layered policy incentives to promote sustainable engagement with AI health technologies in aging societies. Full article

Background: The outbreak of the COVID-19 pandemic has highlighted the importance of online health consultations, as they can help reduce the risk of contagion and infection. However, due to limited trust, these services have not yet gained widespread adoption and usage among patients. Objective: This research aims to examine the impact of perceived quality on patients’ adoption and usage of online health consultations from three perspectives: emotional support, responsiveness, and service continuity. Additionally, this research further explores the moderating effects of online service prices on these relationships. Methods: Based on trust theory, this research constructs theoretical models and empirically tests them by using a panel dataset that comprises 1255 physicians and 65,314 physician–patient communication records. Results: The empirical results confirm that emotional support, responsiveness, and service continuity positively influence patients’ adoption and usage behaviors. Additionally, higher online service prices negatively moderate the impact of emotional support and responsiveness on adoption behavior. Moreover, increased online service prices weaken the positive relationship between emotional support and usage behavior while strengthening the positive relationship between service continuity and usage behavior. Conclusions: This research extends the existing literature on online health services and provides practical guidance for platform managers, physicians, and policymakers to improve overall service acceptance. Full article

This study investigates consumer satisfaction in e-learning services by addressing a specific gap in the literature: the limited integration of sustainability principles and behavioral modeling in understanding satisfaction drivers in online education. While existing studies have explored engagement and usability, few have considered how sustainability-related factors influence satisfaction in digital learning environments. Based on a conceptual model involving system quality, service quality, motivation, and cognitive engagement, we applied structural equation modeling (WarpPLS) to a sample of 312 university students from Romania, using mainstream learning management systems (LMS). Data were collected from students at the Bucharest University of Economic Studies using a convenience sampling method. The results show that service quality and cognitive engagement are the strongest predictors of satisfaction. This study offers practical recommendations for improving sustainable digital marketing strategies in e-learning, such as enhancing support services and aligning platform features with eco-conscious consumer expectations. Full article

The quality of machined marine propellers plays a critical role in underwater propulsion performance. Precision casting is the predominant manufacturing technique; however, deformation of wax models and rough blanks during manufacturing frequently cause deviations in the dimensions of final products and, thus, affect propellers’ performance and service life. Current inspection methods primarily involve using coordinate measuring machines and sampling. This approach is time-consuming, has high labor costs, and cannot monitor manufacturing quality in real-time. This study developed a real-time online automated measurement system containing a high-resolution CITIZEN displacement sensor, a four-degree-of-freedom measurement platform, and programmable logic controller-based motion control technology to enable rapid, automated measurement of blade deformation across the wax model, rough blank, and final product processing stages. The measurement data are transmitted in real time to a cloud database. Tests conducted on a standardized platform and real propeller blades confirmed that the system consistently achieved measurement accuracy to the second decimal place under the continual measurement mode. The system also demonstrated excellent repeatability and stability. Furthermore, the continuous measurement mode outperformed the single-point measurement mode. Overall, the developed system effectively reduces labor requirements, shortens measurement times, and enables real-time monitoring of process variation. These capabilities underscore its strong potential for application in the smart manufacturing and quality control of marine propellers. Full article

In increasingly competitive digital markets, software firms must strategically balance cybersecurity investments and pricing decisions to attract consumers while safeguarding their platforms. This study develops a game-theoretic model in which two competing firms choose among three cybersecurity strategies—no action, bug bounty programs, and in-house protection—before setting prices. We demonstrate that cybersecurity efforts and pricing are interdependent: investment choices significantly alter market outcomes by influencing consumer trust and competitive dynamics. Our analysis reveals that a bug bounty program is preferable when consumer sensitivity to security and the probability of ethical vulnerability disclosures are high, while in-house protection becomes optimal when firms must rebuild credibility from a weaker competitive position. Furthermore, initial service quality gaps between firms critically shape both investment intensity and pricing behavior. By jointly endogenizing security efforts and prices, this study offers new insights into strategic cybersecurity management and provides practical guidance for software firms seeking to integrate security initiatives with competitive pricing strategies. Full article

Understanding and promoting guest satisfaction is central to the economic sustainability of the hospitality industry. Satisfaction influences consumers’ booking intentions, hotel choice, loyalty, and the reputation and performance of accommodation establishments. Thus, accurate decision making by hotel managers relies on trustworthy and easily accessible information on the variables that affect guest satisfaction. Nowadays, this information is available through reviews and ratings provided by online platforms, such as TripAdvisor. Indeed, much research into guest satisfaction uses TripAdvisor reviews. However, this study aims to analyse guest satisfaction using only TripAdvisor ratings. These ratings can be more succinct and tractable indicators than reviews. A sample of 118 hotels in Cape Verde and the Azores, two archipelagos belonging to Macaronesia, and a descriptive, predictive, and discriminant methodology are employed for this purpose. Four main results are obtained. First, the rated items on TripAdvisor are consistent with the scientific literature on this topic. Second, TripAdvisor ratings are valid and reliable. Third, TripAdvisor ratings can predict guest satisfaction based on the perceived quality of hotel services. Fourth, there are significant differences in ratings depending on the tourism destination chosen. These results are of interest to researchers, tourists, as well as hotel, destination, and platform managers. Full article

The integration of lithium-ion (Li-ion) battery energy storage systems (LiBESSs) with photovoltaic (PV) generation offers a promising solution for powering auxiliary services (ASs) in high-voltage power stations. This study conducts a systematic literature review (SLR) to evaluate the feasibility, benefits, and challenges of this integration. The proposed SLR complies with the PRISMA 2020 statement, and it is also registered on the international PROSPERO platform (ID 1073599). The selected methodology includes the following key steps: definition of the research questions; search strategy development; selection criteria of the studies; quality assessment; data extraction and synthesis; and discussion of the results. Through a comprehensive analysis of scientific publications from 2013 to 2024, trends, advancements, and research gaps are identified. The methodology follows a structured review framework, including data collection, selection criteria, and evaluation of technical feasibility. From 803 identified studies, 107 were eligible in accordance with the assessed inclusion criteria. Then, a custom study impact factor (SIF) framework selected 5 out of 107 studies as the most representative and assertive ones on the topics of this SLR. The findings indicate that Li-ion BESSs combined with PV systems enhance reliability, reduce reliance on conventional sources, and improve grid resilience, particularly in remote or constrained environments. The group of reviewed studies discuss optimization models and multi-objective strategies for system sizing and operation, along with practical case studies validating their effectiveness. Despite these advantages, challenges related to cost, regulatory frameworks, and performance variability remain. The study concludes that further experimental validations, pilot-scale implementations, and assessment of long-term economic impacts are necessary to accelerate the adoption of BESS-PV systems in high-voltage power substations. This study was funded by the R&D program of the Brazilian National Electric Energy Agency (ANEEL) via project number PD-07351-0001/2022. Full article

Harmful algal blooms (HABs) are one of the major environmental concerns, as they have various negative effects on public and environmental health, recreational services, and economics. HAB modeling is challenging due to inconsistent and insufficient data, as well as the nonlinear nature of algae formation data. However, it is crucial for attaining sustainable development goals related to clean water and sanitation. From this point of view, we employed the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, an algal toxin, utilizing dissolved oxygen as a water quality metric and evaporation as a meteorological parameter. SINDy is a novel approach that combines a sparse regression and machine learning method to reconstruct the analytical representation of a dynamical system. The model results indicate that MAPE values of approximately 2% were achieved in three out of four lakes, while the MAPE value of the remaining lake is 11%. Moreover, a model-driven and web-based interactive tool was created to develop environmental education, raise public awareness on HAB events, and produce more effective solutions to HAB problems through what-if scenarios. This interactive and user-friendly web platform allows tracking the status of HABs in lakes and observing the impact of specific parameters on harmful algae formation. Full article

Online travel agencies (OTAs) function as e-commerce platforms that facilitate transactions between accommodation providers and consumers, enabling users to efficiently search for, compare, and book travel and lodging services. As the number of OTAs continues to grow, delivering superior service quality has become essential for increasing customer repurchase intentions. Despite its significance, existing research has primarily focused on factors such as website quality, pricing strategies, brand image, and perceived value as determinants of repurchase intention. However, relatively little attention has been paid to the alignment between online information disclosure and customers’ actual offline experiences. To address this gap, the present study introduces the concept of the information disclosure gap and examines its effects on search cost, customer satisfaction, and trust, as well as the subsequent influence of these variables on repurchase intention. A questionnaire-based survey method was conducted with individuals in Taiwan who had prior experience using OTAs, yielding 365 valid responses. This study offers practical insights and recommendations for both OTAs and accommodation providers aimed at reducing the information disclosure gap and strengthening customer repurchase intention. Full article

Background/Objectives: The INTegRated InterveNtion of pSychogerIatric Care (INTRINSIC) network introduces an innovative model of psychogeriatric care, combining tertiary mental healthcare with primary care for older adults in low-resource settings in Greece via telemedicine. This study explores viewpoints of healthcare professionals on care delivery within the frames of old-age mental telehealth services in low-resource settings. Methods: All healthcare professionals, including 13 medical and 11 non-medical professionals from diverse healthcare units in urban, rural, and insular areas, participated in a semi-structured survey. Thematic analysis identified key insights. Results: Most participants (N = 19) highlighted the high usability of the INTRINSIC services and their high satisfaction for being members of the network (N = 17) was attributed to the collaborative delivery of integrated, specialized healthcare services in primary healthcare (N = 17). Further identified advantages of the services included the positive impact on timely care delivery (N = 6), cost effectiveness, and alleviation of hospital strain. Healthcare professionals valued the holistic approach of the INTRINSIC services to psychogeriatric care (N = 8) and their role in the improvement of it in communities in low-resource settings (N = 13). However, challenges were also reported, including the low openness and reluctance of service users (N = 7), difficulties in using the INTRINSIC digital platform (N = 5), and increased workload (N = 5). Conclusions: Despite these issues, the INTRINSIC services embody an innovative telehealth model for delivering high-quality, tertiary, mental, and cognitive healthcare services to older adults in underserved areas. Full article