This study investigates employees’ intentions to adopt Low-Code/No-Code (LCNC) platforms in Thai organizations by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with two psychological constructs: Perceived Trust and Technology Anxiety. Survey data from 411 employees were analyzed using Structural Equation Modeling to examine how UTAUT2 determinants—Performance Expectancy, Effort Expectancy, Social Influence, Hedonic Motivation, Habit, Facilitating Conditions, and Perceived Cost—together with Perceived Trust and Technology Anxiety influence Behavioral Intention to adopt LCNC platforms and its association with employee well-being. The results show that Hedonic Motivation, Habit, Social Influence, Perceived Cost, and Perceived Trust significantly predict Behavioral Intention. Effort Expectancy and Facilitating Conditions exhibit statistically significant negative relationships with Behavioral Intention, indicating unexpected directional effects in the organizational LCNC context. Performance Expectancy and Technology Anxiety do not show statistically significant effects. The extended model explains 70.4% of the variance in Behavioral Intention and 41% of the variance in employee well-being. These findings provide an empirically supported extension of UTAUT2 to the LCNC context and highlight the importance of psychological and experiential factors in shaping human-centered digital transformation within organizations.

Objective. The objective of this study was to provide a comprehensive bibliometric review of research at the intersection of retrieval-augmented generation (RAG) and knowledge graphs (KGs), to map the intellectual structure of the field, and to quantify scholarly activity on system architectures, evaluation venues, and emerging application domains from 2021 to 2026. Design/Methodology/Approach. A bibliometric review was conducted in accordance with the PRISMA 2020 reporting framework. A combined Scopus and Web of Science (WoS) search yielded 1,604 records (Scopus, n = 1,098; WoS, n = 506). After the removal of 444 cross-database duplicates using the KKU-BiblioMerge toolkit and the screening of 1,604 unique records against four exclusion criteria, 313 records were excluded, leaving 1,291 publications for analysis. A variety of bibliometric techniques were employed in the analysis, including descriptive bibliometrics, co-authorship analysis, country collaboration mapping, keyword co-occurrence, thematic mapping, co-citation analysis, and latent Dirichlet allocation topic modeling (k = 8, u_mass coherence = −1.46). The analyses were conducted in Python, utilizing the libraries pandas, NetworkX, and gensim. Results/Discussion. The findings indicated a rapid expansion of the field, with annual publication output increasing from 1 paper in 2021 to 700 papers in 2025 (average annual growth rate (AGR) of 235.49%). When author affiliations were aggregated at the unique-publication level and the two WoS variants “China” and “Peoples R China” were merged, Mainland China (n = 485 publications) and the United States (n = 139) emerged as the leading contributors. The most-cited publication was “Knowledge Graph Prompting for Multi-Document Question Answering,” with 110 citations. A total of eight major thematic clusters were identified, with “Retrieval-Augmented Generation Systems” and “Embedding & Semantic Representation” demonstrating the highest average citation impact. The findings suggested a growing scholarly focus on GraphRAG architectures and KG-enhanced large language model pipelines to enhance factual accuracy and knowledge grounding. Conclusions. Research on the integration of RAG and KGs is rapidly maturing and consolidating around hybrid architectures that combine structured knowledge retrieval with neural text generation. However, significant challenges persist, particularly regarding standardized evaluation benchmarks, cross-lingual RAG–KG systems, and scalable metadata generation frameworks. Originality/Value. This study offers the inaugural comprehensive bibliometric review of the RAG–KG research landscape. It provides a comprehensive intellectual framework for the field, identifies emerging research directions, and offers practical insights for the development of intelligent metadata-generation assistants and knowledge-enhanced AI systems.

This study develops and evaluates an integrated Linked Open Metadata (LOM) framework for an ASEAN Galleries, Libraries, Archives, and Museums (GLAM) aggregation platform. Using a qualitative, exploratory design with a documented coding protocol, the study synthesises practices from Europeana, the Digital Public Library of America (DPLA), and Trove, and contextualises them within ASEAN’s socio-technical landscape. A hub-and-spoke prototype was built with a curated set of 100 records from ten ASEAN member states, aligned to Dublin Core, EDM and CIDOC CRM, exposed via OAI-PMH, and evaluated using a Normalised Open Access Performance Index. Results show functional OAI-PMH compliance, a 25 percentage-point improvement in metadata alignment accuracy after semantic enrichment, and demonstrable cross-border discoverability across four languages. Findings position aggregation as a socio-technical governance problem and provide a transferable framework for regions with heterogeneous institutional capacity and multilingual heritage descriptions.

This study presents the design, implementation, and evaluation of the Greater Mekong Subregion (GMS) Ethnic Groups Knowledge Graph (KG) and its accompanying web application-an innovative, culturally inclusive platform for modelling the ethnographic diversity of 375 ethnic groups across Thailand, Laos, Myanmar, Cambodia, Vietnam, and China. Addressing the limitations of traditional Knowledge Organization Systems (KOS), the project integrates semantic technologies, local ethnographic data, and community-informed classification frameworks to represent complex relationships involving language, religion, cultural practices, geography, and historical migration of ethnic groups in the GMS. This study employs structured data transformation, entity-relationship modelling, Neo4j-based graph construction, and interactive visualization using React.js and D3.js. Evaluation results, based on extrinsic performance benchmarks and domain-specific expert validation, demonstrate substantial improvements in usability, task efficiency, and data interpretability compared with conventional databases. The findings support using knowledge graphs for ethically grounded, context-sensitive knowledge infrastructures that foster epistemic justice, cultural sustainability, and inclusive access to ethnographic data. In addition, this work contributes a replicable digital humanities model that blends KOS, knowledge management, and semantic web principles to empower interdisciplinary research and community engagement.

The adoption of artificial intelligence in library services introduces significant ethical and privacy challenges, particularly in contexts shaped by emerging data protection regulations. This study examines how librarians in Thailand perceive and navigate ethical issues related to artificial-intelligence-based information services, with particular attention to transparency, fairness, accountability, and data privacy under the Personal Data Protection Act. Using a qualitative approach, semi-structured interviews were conducted with 18 librarians from academic and public libraries. The findings reveal that while artificial intelligence tools enhance service efficiency, librarians face persistent challenges related to limited transparency, unclear governance structures, and insufficient institutional guidance. Privacy concerns—intensified by requirements to comply with the Personal Data Protection Act—emerge as a dominant ethical issue influencing decision-making and user trust. The study contributes empirically by demonstrating how global ethical principles, particularly UNESCO's framework, are interpreted and operationalized in a developing-country context. Based on these findings, the study proposes a contextualized implementation model to support the responsible adoption of artificial intelligence in libraries. The results highlight the need for clearer governance mechanisms, enhanced ethical literacy, and context-sensitive policy development.

This study aims to develop and evaluate advanced machine learning (ML) models for accurate and scalable early detection of cervical cancer, addressing critical limitations in current diagnostic practices. In leveraging exploratory data analysis (EDA), rigorous data preprocessing, and multiple ML techniques—including Random Forest, ANN, SVM, XGBoost, and ensemble models—we systematically analyzed a comprehensive dataset from the UCI repository comprising demographic, clinical, and behavioral features. Results indicated that the Random Forest model achieved the highest performance, with an accuracy of 98.4 %, a sensitivity of 99.3 %, and a specificity of 97.6 %, substantially surpassing the other evaluated models. Despite limitations related to dataset homogeneity and potential biases introduced by synthetic oversampling methods, these findings represent significant methodological and practical advancements. By offering an interpretable and robust diagnostic tool, the study significantly contributes to the improvement of cervical cancer detection, particularly benefitting low-resource clinical environments where effective, scalable screening methods are urgently needed. The proposed framework—developed and evaluated solely on the UCI tabular cervical cancer dataset—achieved high discriminative performance with the Random Forest model (accuracy = 98.4 %, sensitivity = 99.3 %, specificity = 97.6 %). A previously published imaging-based ResNet-50 model (AUC = 0.97) is referenced for contextual comparison only and was not part of our experimental work. However, deployment in resource-constrained environments will require further optimization and cost-efficiency analyses to confirm feasibility.

The rapid integration of artificial intelligence (AI) into information services has raised critical questions about ethics, privacy, and user trust. This study explores the ethical and privacy concerns surrounding AI-based information services. It investigates their implications for perceived ethical legitimacy, which is conceptualized as a precursor to user trust in AI-based information services. A quantitative research design was employed, utilizing a structured survey distributed to 278 library users in Thai universities. The instrument, validated through expert review and reliability analysis (Cronbach’s alpha = 0.870), measured perceptions of 10 ethical constructs, including data responsibility and privacy, fairness, security, transparency, and accountability. Data were analyzed using correlation and multiple regression techniques. The results revealed that while users overwhelmingly perceived AI in libraries as beneficial (mean score = 4.34/5), perceptions of ethical legitimacy were strongly associated with ethical safeguards, which may influence trust formation. Specifically, data responsibility & privacy, fairness, and ethics & regulations emerged as significant predictors of overall perceptions of AI ethics, collectively explaining 26.6% of the variance. Other constructs, such as security, accountability, and trust, while valued, did not demonstrate unique predictive power within the model. The findings underscore that ethics are not peripheral but central to technology acceptance in information services. This study contributes to theory by extending technology adoption frameworks to include ethical dimensions and to practice by offering evidence-based guidance for policymakers and library administrators to prioritize privacy, fairness, and regulatory clarity in AI deployment. Although limited to a single national context and cross-sectional design, these findings provide a foundation for future comparative and longitudinal research. Ultimately, the study highlights that sustainable adoption of AI in academic libraries depends not only on technical innovation but also on embedding robust ethical practices that foster user trust and confidence.

In China, developing the digital literacy of rural farmers is important to rural revitalisation, which requires developing and implementing a digital literacy framework for rural farmers. This study first designed a digital literacy framework built on the Digital Literacy Global Framework (DLGF), customised to Chinese rural farmers based on the research literature. Referring to the primary indicators of DLGF and its 26 secondary indicators, as well as to DigComp 2.2 framework from the European Commission, we designed more granular third-level indicators for the digital literacy of rural farmers in China, which was also informed by Maslow's Hierarchy of Needs. Next, the framework was tested with 13 experts in the area using the Delphi method, followed by interviews with 11 rural farmers in Guangdong Province. Together they helped refine the proposed framework and adjust its constituent indicators. Finally, the paper discusses the limitations and implications of the framework, and its applicability for relevant entities in China and developing countries elsewhere for improving the digital literacy of rural farmers, including governments, enterprises, university outreach and for public services such as public libraries.

Background/purpose. This study investigates the evolving role of the Technological Pedagogical Content Knowledge (TPACK) framework in teacher education. It aims to explore the development of TPACK scholarship, identify key contributors, and evaluate its academic and societal impact, thereby offering a comprehensive understanding of its influence on technology-integrated teaching. Materials/methods. A multidimensional analytical approach was employed, combining bibliometric analysis, content analysis, and Altmetric Attention Score (AAS) evaluation. Data were sourced from the Web of Science and Scopus databases, with analyses focusing on publication trends, collaboration patterns, and methodological characteristics of highly cited TPACK studies. Results. Findings reveal a substantial increase in TPACK-related research since 2010, underscoring its growing significance in teacher preparation. The study highlights influential authors, institutions, and thematic shifts, along with regional collaboration patterns. It also identifies limited societal impact, methodological concentration in survey-based studies, and underrepresented regions in TPACK discourse. Conclusion. The study aligns TPACK scholarship with the goals of Education 5.0, particularly in fostering creativity, collaboration, and critical thinking. It underscores the need for more diverse, context-sensitive methodologies and stronger connections between research and practice. By integrating academic and societal impact assessments, this research contributes new insights into TPACK literature and supports global efforts to enhance curriculum design and teacher professional development for equitable and effective technology integration in education.

This study aims to design, develop, and evaluate a semantic knowledge graph (KG) and web application that ethically and effectively represents the ethnographic diversity of 375 ethnic groups across Thailand, Laos, Myanmar, Cambodia, Vietnam, and China. Addressing the limitations of conventional Knowledge Organization Systems (KOS), the project integrates semantic web technologies with community-informed data to capture complex relationships involving language, religion, cultural practices, geography, and historical migration. This study employed structured data transformation, entity-relation modeling, and knowledge graph construction using Neo4j. It further incorporated interactive data visualization through React.js and D3.js. The dataset was collaboratively developed by over 20 regional scholars, ensuring cultural relevance and accuracy. Evaluation was conducted through extrinsic and domain-specific validation involving 24 usability experts and 17 ethnographers, who assessed task performance, data completeness, and cultural appropriateness. The findings of this study reveals that the knowledge graph significantly improves usability, interpretability, and task efficiency, exceeding traditional database performance by over 25% in expert evaluations. Additionally, the semantic model supports multilingual access, temporal migration tracking, and culturally contextualized identities. This research concluted that semantically enriched, ethically designed knowledge infrastructures can enhance representation and accessibility of marginalized knowledge. Key limitations include manual data curation and gaps in representing hybrid cultural practices. Future work should focus on scalability through automation and sustained community collaboration. The implications extend to digital humanities, education, and policymaking, offering a replicable model for equitable, culturally sensitive knowledge systems worldwide.

Purpose The existing metadata frameworks are limited to fully capture unique characteristics of Chinese paper-cutting identified as traditional art and intangible cultural heritage (ICH). Therefore, the paper aims to develop a multi-dimensional metadata description framework tailored specifically for Chinese paper-cutting works. The proposed framework emphasizes both the tangible attributes and cultural significance within the broader context of ICH, covering physical objects, cultural context and technical skills. Design/methodology/approach The proposed framework encompasses six dimensions: basic attribute, connotative feature, creator, ICH project, inheritor and derived innovation. Elements from existing metadata standards or vocabularies, such as DC terms, CDWA Lite, VRA Core and FOAF are reused in this study. The ICH project and inheritor dimensions draw elements from the “application form of representative projects of national ICH” and “application form for the fifth batch of representative inheritors of national ICH representative projects.” The derived innovation elements are creatively designed to describe derived works from the original paper-cutting work. The framework’s usability was validated by generating Resource Description Framework (RDF) metadata for two specific paper-cutting works named “Flower Cutting Lady” and “Happiness” in Turtle syntax. Findings The proposed metadata description framework supports a detailed description of the connotative features of Chinese paper-cutting works. It indicates that the designed multiple dimensions assist the comprehensive and cultural description of paper-cutting works, which would promote digital inheritance through the enrichment of the interpretable and machine-readable cultural heritage metadata. Originality/value This paper introduces an innovative application of metadata description framework design and develops a specialized framework that integrates established methodologies with new dimensions tailored specifically for Chinese paper-cutting works. By emphasizing both the tangible attributes and cultural contexts, this study addresses gaps in existing metadata frameworks and proposes a metadata description framework uniquely suited to the nuanced description requirements of Chinese paper-cutting as traditional art.

E-Governance has become a pivotal mechanism for achieving transparent and efficient public administration. This study investigates the evolution of e-governance research, focusing on its role in fostering public trust. The study uses Scopus and Web of Science data to identify key trends, influential contributions, and thematic developments in the field through bibliometric analysis, content analysis, and the Altmetric Attention Score (AAS). Findings reveal that e-service quality, technology adoption, and smart city initiatives dominate the discourse, with a growing emphasis on citizen engagement and sustainability. The analysis underscores the increasing importance of integrative frameworks in designing citizen-centric governance systems, addressing existing gaps in the literature. These frameworks aim to enhance trust and transparency by aligning technological advancements with citizens' needs and expectations. The study provides actionable insights for policymakers and practitioners, highlighting the transformative potential of e-Governance in promoting societal well-being. By bridging the divide between technology and public administration, the research contributes to a deeper understanding of how e−governance can drive trust, transparency, and sustainable development in modern governance systems.

Background:In the context of global open science trends, medical open-access repositories (OARs) promote transparency in research and facilitate the sharing of scientific data. The increase in scientific output necessitates a robust infrastructure to enhance OARs in China. Objectives:This study aimed to evaluate medical open-access repositories (OARs) in China that are indexed in re3data.org and OpenDOAR.org. The study analyzed data classification, descriptions, retrieval, and the utilization of selected repositories. Methods:This study ascertained the current status of the Chinese medical OARs by visiting their respective websites and attempted to identify the disciplinary orientation of each OAR. A content analysis approach was utilized to achieve this study's objective. Twelve Chinese medical open-access repositories were selected from re3data.org and OpenDOAR.org to examine how their information is organized. The data were collected manually from May 1 to 30, 2023, and analyzed using various quantitative techniques to understand the current status of medical scientific repositories in China. Results:Based on the results, this study proposed the following recommendations: (1) implement multi-dimensional data classification, (2) use persistent data identifiers, (3) formalize the description metadata, (4) enhance advanced retrieval and result set filtering functions, and (5) optimize the preview and interaction features of data repositories. Conclusion:The scope of this study is restricted to the medical open-access repositories in China as listed on re3data.org and OpenDOAR.org. Therefore, the results of this study are only generalizable within China. The primary focus of research output in China is on medical open-access repositories. This study is essential for assessing China's current status in research data management within the medical field and its distribution infrastructure in global open science trends.

Objective. The objective of this study was to develop and validate KKU-BiblioMerge V.1.0, a bibliometric tool designed to address the limitations of single-source data in bibliometric analysis by integrating data from multiple databases, specifically Scopus and Web of Science (WoS). Design/Methodology/Approach. The tool was developed using the R Shiny framework and incorporated key functions for data deduplication, field mapping, and integrity checks to ensure effective dataset merging. The performance of KKU-BiblioMerge was assessed by testing its ability to import, merge, and export bibliometric data, focusing on the efficiency and accuracy of consolidating records from Scopus and WoS. Findings. The KKU-BiblioMerge application effectively processed and integrated 686 initial documents, eliminating 24.49% duplicate records to produce a final dataset of 518 unique entries. The tool demonstrated strong data consistency and high accuracy in field mapping, offering reliable cross-platform integration of bibliometric data compared to tools such as VOSviewer and Biblioshiny. Originality/Value. KKU-BiblioMerge V.1.0 was a user-friendly, robust solution for multi-database bibliometric analysis. It enabled a more comprehensive and unbiased understanding of research landscapes. Its capability to integrate diverse datasets laid a foundation for advancing bibliometric software, broadening the scope and accuracy of analyses across scientific domains.

This study provides a comprehensive analysis of ethnic groups research using a mixed-methods approach, integrating bibliometric analysis, content analysis, and Altmetric Attention Score (AAS) evaluation. The purpose is to identify dominant trends, emerging themes, and the societal impact of research on racial and ethnic disparities. The dataset from Scopus encompasses 16,615 publications spanning nearly eight decades, analyzed through performance metrics, citation patterns, and co-occurrence networks. Findings reveal an increasing scholarly focus on racial and ethnic disparities in healthcare, education, and socioeconomic outcomes, with significant contributions from interdisciplinary research. Content analysis of highly cited works highlights key themes such as intergroup contact theory, emotion regulation, implicit bias, and acculturation models. The AAS evaluation further underscores the broader societal influence of ethnic research beyond traditional academic citations. The study’s results emphasize the need for culturally sensitive policies and interdisciplinary collaborations to address persisting disparities. By leveraging advanced bibliometric tools and alternative impact metrics, this research offers actionable insights for policymakers, educators, and scholars, guiding future inquiries into the evolving landscape of ethnic groups research.

This study delves into the complex landscape of social media utilization among undergraduate students in higher education institutions in Thailand, investigating the pivotal factors that shape their engagement with these platforms. Employing a quantitative research approach, the investigation utilizes a meticulously crafted multi-stage sampling methodology coupled with a robust data collection process. Through applying multi-correlation and multiple-regression analyses, the research unveils significant insights into the determinants of social media usage among Thai youth. Notably, motivation for social media use, access, creativity, and participation through these platforms emerge as substantive predictors. This aligns seamlessly with existing research, underscoring the critical roles played by motivation and accessibility in influencing online engagement. The resultant predictive equation is a pragmatic instrument for comprehending and forecasting social media engagement patterns among Thai undergraduate students. The findings underscore the importance of motivation, access, and creativity as driving forces behind social media utilization. This research equips educators, policymakers, and researchers with valuable insights, emphasizing the imperative of fostering responsible and effective use of social media within this demographic. The study's contribution to the academic landscape is noteworthy because it sheds light on unexplored facets such as cultural dynamics, peer networks, and individual traits, enriching our understanding of the intricate social media landscape among Thai undergraduate students. Doi: 10.28991/ESJ-2024-08-02-011 Full Text: PDF

Comparative study of traditional authority control and Wikidata identity management methods is of great significance for grasping the development trend of authority control and formulating effective identity management strategies.The article takes the standard specification that guides the authority control and the rule document that guides the Wikidata community as analysis samples,and analyzes and identifies the similarities and differences between traditional authority control and Wikidata identity management methods from five dimensions:entity name,identification information,information source,identifier,and entity definition.On this basis,the advantages and disadvantages of name authority control and Wikidata identity management methods are summarized,and an optimization plan for the transformation from authority control to identity management is proposed.1 tab.40 refs.

This research delves into the development of ontology, a crucial aspect of knowledge organization system studies. The primary goal is to establish a semantic search system tailored to the Khmer stone castles of the Greater Mekong Subregion (GMS), specifically addressing the challenge of semantic gaps. This paper details a novel approach to developing ontologies for Khmer stone castles in the GMS to enhance our understanding of these countries' rich and ancient cultural heritage. A systematic four-step theoretical framework for ontology development was adopted, utilizing the Hozo Ontology Editor to create the ontologies by used the Wizard for documenting ontologies (WIDOCO) framework for web ontology platform. This methodology plays a key role in differentiating relevant from irrelevant data, thus significantly improving the efficiency of semantic searches. Central to this study is developing a comprehensive ontology encompassing ten primary classes: castle, location, country, Khmer art, art type, castle size, castle type, material, religion, and condition. This ontology is foundational for developing a semantic search system designed explicitly for the Khmer stone castles in the GMS. The insights from this research offer significant implications for advancing semantic search systems in cultural heritage, providing a robust framework for future studies in this field.

Digital technologies have been used for a vast amount of bibliometric analysis research. Although these technologies have made scientific investigation more accessible and efficient, scholars now face the daunting task of sifting through an overwhelming number of documents. This study aims to identify bibliometric research analysis's primary topics, categories, and latent topics from a global perspective. This study utilized topic modeling techniques to analyze the abstracts of 16,039 eligible papers published between 1977 and 2023 in the Scopus database. Through the use of Latent Dirichlet Allocation (LDA) topic modeling, the study was able to identify four distinct research topics and observe how they have evolved over time. The research topic has shifted its focus from individual concepts and words to relationships between nodes and conceptual, intellectual, and social structures. The study’s findings have significant implications for bibliometric analysis-related research, providing valuable insights into trends and patterns in bibliometric analysis content within large digital article archives. The LDA has proven to be an efficient tool for analyzing these trends and patterns quickly. This study's novel approach considers factors for word embedding usage and optimal topic numbers. It focuses on a full understanding of the LDA results and combines statistical analysis, domain knowledge, and temporal exploration to better understand how data structures work. Doi: 10.28991/HIJ-2024-05-02-07 Full Text: PDF