User-generated content in academic weblog platforms fosters collaborative learning and knowledge dissemination. However, traditional weblog systems often lack transparent and verifiable mechanisms for recognising contributors, which limits the credibility of acknowledgement processes and the ability to provide authors with immutable proof of their accomplishments. In this paper, SBTBlogCert, a hybrid distributed data management framework that integrates centralised and decentralised infrastructures to acknowledge academic weblogs' user-generated content contributors, is introduced. For traditional Web 2.0 blog content management, a Supabase and PostgreSQL database is used. For Web 3.0 technologies, such as blockchain networks, Soulbound Tokens (SBTs), and the InterPlanetary File System (IPFS), are used to make sure that contributions are recognized in a transparent and decentralised way. A proof-of-concept prototype is designed and developed to evaluate the effectiveness of the hybrid distributed data management solution. This included performance metrics for web and blockchain, such as transaction latency, gas consumption, and transaction fees across multi-chain deployments. The results contribute to ongoing research in data and network science, especially in improving hybrid architectures that combine Web 2.0 cloud databases with Web 3.0 decentralised storage and verification networks.

This study seeks to systematically evaluate a range of electronic transportation planning tools and their impact on tele-transport planning efficiency with respect to temporal performance. To achieve this objective, an extensive literature review was first conducted to formulate a mathematical model of the transportation problem based on the transportation path length (TPLW). The developed model was subsequently coded and implemented as a web-based TPLW application using HTML. Following model implementation, four categories of digital devices including smartphones, tablets, laptop computers, and desktop computers were employed to systematically assess their effects on transportation planning efficiency in terms of processing time. The evaluation procedure incorporated computational randomization of supplier and customer locations, supply capacities, and demand orders, along with route mapping between each location pair using Google Maps. The comparative efficiency of these electronic transport planning tools in facilitating tele-transport operations was then statistically analyzed using a one-way ANOVA. The empirical findings revealed significant variations in time-based efficiency across different electronic devices. Notably, desktop computers demonstrated superior effectiveness, exhibiting the shortest processing durations among all tested devices. These results suggest that desktop computers hold a distinct advantage in enhancing firms’ transport planning efficiency, thereby contributing to improved operational competitiveness and long-term sustainability within an increasingly dynamic and digitally driven global marketplace.

This study explores consumer behavior in purchasing mobile phones through e-commerce platforms. It focuses on how lenient online return policies, customer reviews, and transaction security contribute to building consumer trust, which subsequently affects their decisions to make purchases online. Data were obtained from an online survey completed by 385 consumers aged 18 until 35 who reside in Jakarta Province and actively engage with e-commerce platforms. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the study finds that lenient return policies, helpful customer reviews, and secure transactions all play an important role in boosting consumer trust. On the other hand, lenient return policies, positive customer reviews, and secure transactions also have a strong positive impact on consumers’ decisions to purchase through e-commerce platforms. Consumer Trust has been proven to have a strong and positive influence on E-Commerce Purchase Decisions. In addition, Consumer Trust effectively mediates and amplifies the impact of return policy leniency, customer reviews, and transaction security on e-commerce purchase decisions. These findings highlight the importance of providing flexibility in return policies, displaying positive customer reviews, and ensuring advanced transaction security in building consumer trust and encouraging purchasing behavior on e-commerce platforms.

Business competition puts pressure on companies to improve their competitive advantage. Companies utilize technology to develop innovative products that offer added value and benefit. This study aims to determine the impact of supply chain integration (internal, supplier, and customer integration) in building company competitiveness through the adoption of artificial intelligence. Data collection was conducted on 252 manufacturing companies in Java, Indonesia, the center of the manufacturing industry, using direct distribution and Google Forms. Data processing was carried out using SmartPLS 4, with the results indicating that supply chain integration, specifically internal integration, has a significant impact on supplier integration, customer integration, and the adoption of artificial intelligence. Supplier integration has a significant effect on customer integration, the adoption of artificial intelligence, and a company's competitive advantage. Customer integration is having a substantial impact on the adoption of artificial intelligence and the competitive advantage it provides. The results offer a practical contribution to company management, enabling them to invest wisely in technologies that enhance competitiveness. Companies need to involve cross-functional and external partners in supporting business activities to support technology implementation, allowing rapid adaptation and improved performance.

The widespread adoption of mobile applications has led to a substantial increase in user feedback. Systematically extracting actionable insights from large volumes of unstructured user feedback presents a significant analytical challenge, particularly in mobile learning (m-learning) environments. This study addresses this gap through a mixed-method analysis of 36,625 Duolingo application reviews by integrating machine learning and thematic analysis. The primary objectives are to determine the predictability of user satisfaction from review text and to identify specific thematic factors that underpin user sentiment. Multiple machine learning classifiers were trained using the Bag-of-Words (BoW) model and transformer-based document embeddings to predict the binary satisfaction sentiment label. The results demonstrated that user satisfaction was highly predictable, with logistic regression and neural network models achieving accuracy exceeding 90% (AUC up to 0.968, MCC up to 0.818). Thematic analysis revealed a clear bifurcation in sentiment; core pedagogical features (e.g., internationalization, personalization, and usability) were the primary drivers of satisfaction, whereas significant dissatisfaction stemmed from technical instability (e.g., bugs and performance issues) and monetization strategies (e.g., pricing and ad intrusiveness). This mixed-method provides an interpretable satisfaction-prediction framework and delivers evidence-based recommendations for app developers aiming to maintain competitiveness through continuous user-centered enhancements.

Digital transformation has emerged as a cornerstone of economic development, enhancing competitiveness and accelerating financial inclusion through technology-enabled systems. Fintech inclusion, in particular, is critical for broadening access to financial services and fostering industry growth through innovation. Drawing on growth theory and the principles of open innovation, this paper investigates the impact of digital transformation on Fintech inclusion and introduces inter-rationality as an organizational capability that shapes open innovation processes. While prior research underscores the importance of digital technologies and collaborative innovation, few empirical studies have examined these constructs within a unified analytical framework. Data were collected from 300 innovation managers, product development specialists, R&D directors, and senior executives from Fintech-enabled organizations. Hypothesized relationships were tested using Structural Equation Modeling via the PLS-SEM approach. Results indicate that digital transformation significantly increases Fintech inclusion (β = 0.366, p < 0.001), underscoring the role of robust digital infrastructures in enhancing the availability of technology-driven financial services. Entrepreneurial growth mechanisms similarly exhibit a substantial impact (β = 0.389, p < 0.001), confirming the importance of entrepreneurial renewal in financial innovation. Furthermore, open innovation dynamics positively influence Fintech inclusion (β = 0.412, p < 0.001), highlighting the value of cross-organizational collaboration in developing inclusive financial solutions. Inter-rationality, manifested through integrative thinking, cross-functional coordination, and collaborative decision-making, emerges as a potent moderator that amplifies the effects of digital transformation, growth, and open innovation on Fintech inclusion. This moderating role suggests that organizations with higher inter-rationality achieve greater alignment between digital capabilities and inclusive financial outcomes. The study offers strategic implications for managers, researchers, and policymakers. Firms are advised to invest in digital infrastructure, open innovation strategies, and cross-functional knowledge integration to advance Fintech inclusion. Policymakers can leverage these insights to cultivate enabling regulatory and technological environments that promote inclusive finance and innovation-driven economic development.

This study tracks how artificial intelligence is being embedded in administrative work and the legal challenges that follow. Using 528 Web of Science Core Collection records from 2021–2025, this study combines descriptive bibliometrics with VOSviewer science mapping to analyse outlets, themes, citations, and international collaboration. Four interlocking themes dominate: governance, regulation, and accountability; privacy, security, and data-centric risk; technical and organisational enablers; and ethical and legal anchors. This field is internationalized, as evidenced by collaboration networks, with the United States and England serving as primary brokers. The study theoretically develops a socio-technical view of value that connects model performance to institutional practice and human-AI collaboration, as well as governance-by-design and a data-centric account of risk linked to purpose and lifetime. In practical terms, it distills an adoption blueprint that institutionalized risk-proportionate assurance, professionalizes operations, designs for contestability and understandable explanations, and sees privacy as continuous. The limitations of the evidence base and the research objectives for comparative, outcome-focused studies are discussed in the conclusion of the study.

The objective of this paper will be to determine how electronic human resource (E-HR) practices affect risk management performance mediated by operations strategy in the Saudi Stock Exchange (Tadawul). Under the quantitative research design, 370 managerial and human resource (HR) employees in the financial and investment institutions listed under Tadawul will be used to collect data. The research paper examines how E-HR systems, digital recruitment, performance analytics, and electronic training programs are integrated to lower institutional risk responsiveness, efficiency in operations, and strategic performance. To test the hypothesized relationships and to verify the mediating variable of operations strategy, Structural Equation Modeling (SEM) is used. The results have indicated that E-HR practices are effective in increasing the level of risk management by fostering operational agility, data-driven decision-making, and proactive financial and operational risks identification. Moreover, the relationship between operations strategy plays the mediating role by converting digital HR initiatives into operational improvements which are well-organized and provide organizational resiliency and control mechanisms. The research finds that E-HR practices are an effective enabler of proper risk management practice in the digital age that support the correspondence of technological change, human potential and operational effectiveness. In order to improve the resilience of the institutions within the Tadawul, it is advised that the listed institutions invest more in E-HR infrastructure, digital workforce development, and operational optimization systems that will maintain the sustainability of financial performance.

Intangible Cultural Heritage (ICH) is vital to cultural identity but difficult to preserve in a rapidly changing world. Artificial Intelligence (AI) and Big Data offer new opportunities to document and analyze ICH, yet the field lacks a comprehensive overview of research trends. No prior study has combined AI-driven topic modeling with bibliometric analysis to map ICH-AI scholarship. This paper addresses that gap by mining emerging topics and global trends in ICH-AI research over the past decade. We compiled ICH-related publications (2015–2024) from Web of Science and Scopus. Using BERTopic, an AI-based topic modeling technique, we extracted thematic topics from the corpus. We also analyzed publication and citation trends to gauge research growth and impact. Combining these approaches allowed us to track the evolution of research themes and identify emerging topics in ICH–AI research. ICH–AI research has grown steadily, with a surge in publications after 2018. BERTopic uncovered 18 distinct research topics, grouped into four broad thematic directions. Key emerging topics include deep learning for heritage preservation, knowledge graphs for cultural data integration, and immersive technologies (AR/VR) for intangible heritage engagement. These trends reflect a shift toward interdisciplinary, technology-driven approaches to ICH preservation. For researchers, the identified trends highlight new opportunities for collaboration and innovation; for policymakers and cultural institutions, they inform strategic support for AI-driven preservation initiatives. These results underscore the potential of AI and Big Data to enhance ICH safeguarding.

This study investigates the benefits of Generative Artificial Intelligence (GAI) in human resources operations. It focuses on several key outcomes that include reliability, validity, digitalization, relative advantage, online response, computability, and the time and money costs. Basically, the study seeks an understanding of the less-consistent benefits of using GAI in HR. Data from 290 information technology professionals in Amman Town were collected during a well-planned public examination. The analysis uses a new approach to the method with IBM SPSS version 21 integrated with smart PLS. The findings reveal that determinants such as reliability, validity, digitalization, and relative advantage are strong in terms of efficiency, while computability and online response collaboration are weak determinants. The importance of the research is that it identifies the glitches when GAI skills are used in human resource management; hence, focusing on some vital areas of reliability, validity, digitalization, relative advantage, online responsiveness, and computability, one will have a detailed analysis of the potential issues while using GAI within HR, along with the possible solutions.