Abstract

This study analyzed 534 academic papers published in Korea on K-SDGs (Korean Sustainable Development Goals) from 2015 to 2024 to identify overall research trends and key thematic characteristics of K-SDGs studies. To achieve this objective, a comprehensive quantitative and qualitative integrated analysis was conducted, employing keyword network analysis, SBERT (Sentence-BERT)-based SDG automatic mapping, and manual mapping by researchers. The keyword network analysis revealed “Sustainable Development Education,” “ODA (Official Development Assistance),” “ESG (Environmental, Social, and Governance),” and “VNR (Voluntary National Review)” as prominent keywords. Specifically, the co-occurrence network analysis of keywords appearing four or more times indicated that “Sustainable Development Education” served as a central hub. The SBERT-based SDG automatic mapping showed that “Goal 17: Partnerships for the Goals” and “Goal 4: Quality Education” accounted for the highest proportion of research topics. Furthermore, comparison with manual mapping results revealed a matching rate of 58.1% for representative SDG targets. When including cases where at least one SDG overlapped among all SDGs, the expanded matching rate reached 82.8%. These findings suggest that SBERT-based automatic analysis can capture the major themes of K-SDGs research with considerable accuracy and can be utilized in a complementary manner with manual analysis by human experts. This study illuminates both the practicality and limitations of automation-based analysis while emphasizing the importance of expert interpretation. It proposes the necessity of adopting an integrated approach that combines quantitative and qualitative analysis in future K-SDGs-related research and policy development processes.

Abstract

Although health information encountering is a significant aspect of information behavior on social media, it has been understudied in the literature. The study aims to investigate the factors that influence health information encountering on social media in three contexts: environmental characteristics, personal characteristics, and network characteristics. Online surveys were conducted with social media users at a university in Seoul, South Korea. A total of 316 responses were collected, and hierarchical regression analysis was performed to test the hypotheses. Significant predictors affecting health information encountering included environmental characteristics, such as unexpected leads and trigger connections in social media; personal characteristics, such as frequency of health information seeking and perceived health status of users; and network characteristics, such as users’ self-disclosure on social media. It contributes to the literature on health information-seeking behavior by shedding light on unintentional health information behavior, which is a more common behavior among users. The study highlights social media as a potential health information-seeking channel to effectively cope with emergent health issues and identifies the major factors affecting health information encountering on social media. This study draws on survey data collected in September 2021, during the COVID-19 pandemic, and the findings should be interpreted within this temporal context.

Abstract

This study explores intercountry adoptees’ information-seeking as an identity-based meaning-making process and examines the structural constraints shaping their access to adoption records. Twelve adoptees participated in semi-structured interviews, and the data were analyzed using Dervin’s Sense-Making framework of situation, gap, use, and barriers. The findings show that adoptees’ information needs stemmed from racialized identity confusion, curiosity about biological family members, and the desire for medical and genealogical information. Their searches followed multi-stage strategies, including reviewing records held by adoptive parents, contacting adoption and government agencies, and visiting hospitals or childcare facilities. However, incomplete records, inconsistent disclosure practices, staff discretion, restrictive interpretations of privacy laws, and language barriers significantly hindered their ability to form coherent understandings of their origins. This study provides a structured account of adoptees’ identity-driven information-seeking and offers empirical grounding for standardizing archival information services and improving institutional support for identity reconstruction.

Abstract

The aim of this study was to identify the major topics that can inform collection development and curation practices in university libraries. To achieve this, it applied co-occurrence and network analysis to examine books borrowed together with introductory course textbooks, using two analytical units—title keywords and KDC classification. The title keyword analysis revealed that Introduction to Psychology emphasized themes such as self-development, society, and history beyond its disciplinary scope, indicating that students’ interests extend beyond their majors. Introduction to Education encompassed various education-related subfields, while Introduction to Childcare and Introduction to Social Welfare showed strong thematic connections. In contrast, Introduction to History exhibited a consistent focus on history-related topics, suggesting limited topic diversity. The KDC-based analysis identified ‘Education,’ ‘Sociology,’ ‘Psychology,’ ‘History,’ ‘Geography,’ and ‘Home Economics’ as central nodes, highlighting common intellectual structures across courses. In the subject analysis based on title keywords, specialized topics related to each course were identified, whereas in the subject analysis based on classification numbers, topics common across courses appeared. Therefore, title keyword analysis is more suitable for course-linked curation, while KDC-based analysis provides a sound basis for general collection development.

Abstract

In recent years, the concept of “records” has expanded beyond institution-centered discourse to encompass personal life and cultural experience. This study begins with the recognition that records easily discarded and forgotten can be captured as ephemera, and seeks to restore the fragmented and disappearing contexts of performing arts records in the digital environment. As the first step toward establishing a digital performing arts ephemera archive, this research aims to design and validate a faceted metadata schema for musical performances that restores the contextual value of ephemera and enables their integrated management. To this end, the study examines the characteristics of digital ephemera and the structural features of performing arts data, and analyzes major music ontologies to derive eight key facets—Work, Movement/Sub Work, Person, Group, Performance, Ephemera, Textual Media, and Recorded Media. The Performance facet is positioned as the central hub of information organization, interconnecting all entities through unique identifiers. Two case studies were conducted: a Work-centric analysis of Debussy’s Clair de Lune and an Ephemera-centric analysis based on a recital poster. The results demonstrate that the proposed classification system effectively reconstructs the holistic context of performances from fragmented records, reduces data redundancy, and supports multidimensional retrieval. Moreover, through standardized structures and identifier systems, it ensures interoperability and scalability, suggesting applicability not only to other musical genres such as K-POP and Korean traditional music but also to broader performing arts—including exhibitions, musicals, and plays—and to other time- and context-based fields such as sports events and academic conferences.

Abstract

Digital platforms have become essential infrastructures that facilitate problem-solving through interaction and support social connections and value creation. This study examines how dynamic capabilities influence the operational performance of library digital platforms and analyzes the mediating effects of environmental uncertainty and organizational culture. Digital platforms were defined across infrastructure, services, and governance, while dynamic capabilities included environmental sensing, organizational responsiveness, and organizational transformation. A nationwide survey of librarians across national, public, academic, and special libraries was conducted, and regression analyses were performed. The results show that dynamic capabilities significantly affect all dimensions of digital platform performance. Organizational responsiveness had the strongest impact on infrastructure and governance, whereas environmental sensing most strongly influenced service performance. Mediating effects were also identified: abundance and adhocracy culture mediated infrastructure outcomes, dynamism mediated service outcomes, and clan culture mediated governance. These findings highlight the need for libraries to adopt adaptive strategies to meet evolving user expectations in volatile environments.

Abstract

This study analyzed the effect of critical media literacy on information sharing, mediated by consumer innovativeness, utilizing data from the 2024 Korean Media Panel Survey. Consumer innovativeness was first divided into four dimensions—functional, hedonic, social, and cognitive innovativeness—and structural equation modeling was utilized to verify the respective paths. The results showed that critical media literacy had positive (+) effects on all four types of innovativeness, with the strongest effect observed on cognitive innovativeness. However, the effects of consumer innovativeness on information sharing varied: hedonic innovativeness and social innovativeness significantly facilitated information sharing, while cognitive innovativeness showed a negative (-) effect. These findings suggest that critical media literacy competency enhances consumers’ innovative tendencies, whereas the patterns of information sharing can differ depending on the type of innovativeness. Specifically, the inhibitory effect of cognitive innovativeness on information sharing suggests a dual consequence: the positive effect of preventing indiscriminate information proliferation and the concurrent negative risk of avoiding sharing altogether.

Abstract

This study analyzes the characteristics of research articles supported by research funding in the field of Library and Information Science (LIS) in Korea. Using data from the Korea Citation Index (KCI), a total of 7,456 LIS articles were examined, including 796 funded papers supported by the National Research Foundation of Korea and 6,660 non-funded papers. The analysis focused on annual publication trends, including the number of authors, references, and citations, as well as the distribution of journals, subject areas, and author keywords. The results show that funded papers tend to have more co-authors, include more references, and receive higher citation counts than non-funded papers, while also exhibiting a gradual thematic expansion toward records management and archival studies. Over time, funded research has focused on themes such as information inequality, community-based archiving, and data-driven analysis. In contrast, unfunded papers have continued to focus on traditional topics such as library management, information services, and reading education. These findings suggest that research funding can be an important factor influencing topic selection, research methodology, and the overall structure of scholarly communication. This study also discusses the importance of understanding how research funding affects academic diversity, researcher autonomy, and scholarly communication structures, emphasizing that such understanding is essential for developing policies and institutional and cultural foundations that support the advancement of scholarly communication.

Abstract

This study aims to quantitatively analyze collaborative research structures among research institutions and major corporations in the water sector based on bibliographic information. To achieve this, we established a protocol to collect and augment usable paper data by linking metadata available from OpenAlex, Crossref, and ROR (Research Organization Registry). Paper metadata was collected via APIs provided by these institutions and organized primarily around the affiliated institutions of paper authors. Missing DOI and RORID values were supplemented using Crossref and ROR. The journal targeted in this study is Water Research. For 20,135 data points spanning 25 years (2000-2024), divided into five periods in five-year increments, three centrality metrics were selected and compared to examine the major institutions with high publication intensity and changes in co-author networks per period. The results of this case study indicate that the selection based on the eigenvector metric is reasonable. By presenting a protocol that systematically examines the collaborative structure among research institutions for specific fields using authors' institutional affiliation information from bibliographic data, this study suggested the potential for utilizing foundational data to identify research topics based on collaboration hubs across diverse fields.

Abstract

Abstract

This study aims to examine elementary school teachers’ intention to use AI digital textbooks and the factors influencing those intentions, based on the Unified Theory of Acceptance and Use of Technology (UTAUT). To this end, a survey was conducted with teachers at public elementary schools in Seoul, Gyeonggi, and Incheon. The collected data were analyzed using descriptive statistics, reliability and validity testing, and regression, while responses to open-ended questions were qualitatively analyzed using NVivo. Through these analyses, the study identified teachers’ perceptions, expected benefits, challenges experienced during use, and attitudes, and verified the effects of performance expectancy, effort expectancy, social influence, and facilitating conditions on their intention to use AI digital textbooks. The results indicated that all four factors had positive effects on behavioral intention, with performance expectancy emerging as the most influential predictor. In addition, teachers’ perceptions and behavioral intentions varied according to grade level and years of teaching experience. The findings offer meaningful implications for the effective implementation of AI digital textbooks, the development of teacher training programs, and future educational policy planning.

Abstract

This study is an exploratory research that applies generative AI-based automated assessment to public library performance evaluation and examines its feasibility for adoption. To this end, we compared the evaluation results produced by a human expert in library and information science and by a generative AI system. The comparison focused on four domains of the current evaluation indicators that are scored by humans on the basis of submitted documents: space, collaboration, management planning, and best practices, and examined changes in reliability according to different prompt-engineering techniques. Using ChatGPT 5.1, we conducted automated evaluations on the documents submitted by 164 public libraries in Seoul for the 2024 public library performance evaluation. The results indicated that for domains with relatively simple content and clearly defined rating scales—space, collaboration, and management planning—the agreement between expert and AI scores was high. In contrast, in the best practices domain, which requires qualitative judgment, the discrepancy between expert and AI evaluation results was substantial. Furthermore, the highest level of reliability between expert and AI scores was observed under the condition that combined Task Information (TI) prompts, which provide structured input of the information required for evaluation, with Demonstration Information (DI) prompts, which offer illustrative examples. In particular, in the qualitative assessment domain, reliability improved significantly when DI prompts were added.

Abstract

This study investigates the core competencies required for professionalism in public-sector data management and proposes directions for a convergent curriculum from the perspective of Library and Information Science(LIS). In-depth interviews were conducted with five practitioners responsible for data management in public institutions, and the data were analyzed using qualitative content analysis. The findings reveal that public-sector data management exhibits a hybrid form of professionalism in which technical expertise and administrative coordination are simultaneously required. Six key themes emerged: gaps between legal-institutional frameworks and actual practice, staffing and organizational constraints, ambiguity surrounding job professionalism, pressures from rapid technological change, difficulties in data standardization and quality management, and the dominance of evaluation-centered administrative practices. Practitioners further emphasized the need for convergent competencies that integrate technical, administrative, and policy-oriented skills, particularly strategic data planning capabilities. These results indicate that professionalism in data management involves navigating tensions among professional control, autonomy, and responsibility, rather than relying solely on technical skills. Accordingly, LIS-based education should be restructured into a convergent curriculum that incorporates administrative management, policy understanding, and collaborative governance, alongside technical components such as metadata, standardization, and quality management. This study contributes to redefining the role of information professionals in the data-driven public sector.

Abstract

This study examines acknowledgment practices in Korean social science journals by extending an acknowledgment classification framework originally developed in library and information science. Focusing on whether existing typologies adequately capture social science practices and how acknowledgment patterns differ across disciplines, this study analyzes 922 articles published between 2017 and 2021 in KCI-listed journals in psychology, journalism and communication, and sociology. Acknowledgment statements were manually collected from full-text articles and analyzed using qualitative content analysis with an open coding approach. Based on this process, the original classification scheme was revised and expanded to reflect disciplinary characteristics of the social sciences. The results indicate that 74.7% of social science articles included acknowledgments, a higher proportion than that observed in library and information science during the same period. The findings also show that the existing classification framework was insufficient to capture key features of social science acknowledgment practices, leading to the development of an expanded scheme with five major categories and twenty-five subcategories. In particular, peer interactive communication emerged as a prominent acknowledgment type, especially in sociology, despite being rarely observed in library and information science journals. These results highlight substantial disciplinary variation in acknowledgment practices and provide empirical evidence for developing field-sensitive acknowledgment classification and data construction in the Korean academic context.

Abstract

This study proposes a user-profiling framework for scholarly information services that integrates research stages, behavior levels, and semantic analysis to overcome the limits of keyword-based systems. The framework unifies static attributes, dynamic behavioral signals, semantic topic representations, and contextual research states to support adaptive, researcher-centered information services. Five core components structure the model: research stage modeling (SE-1), behavior-level classification (SE-2), embedding- and knowledge-graph-based semantic analysis (SE-3), log-driven dynamic profiling (SE-4), and a modular architecture for interoperability (SE-5). Structural validation confirmed that the framework satisfies ten requirements derived from prior studies, including semantic linkage, quality filtering, adaptive interfaces, and predictive recommendations. Expert evaluation by three specialists yielded an average score of 4.6 out of 5, highlighting strong theoretical grounding and practical usefulness. The main challenge identified was the scarcity of labeled data for inferring contextual user states. To address this, heuristic bootstrapping and active-learning strategies are proposed.

Abstract

This study aims to evaluate the usability of the book search function in the “Dokseoro” system and propose improvement measures. To accomplish this, a heuristic evaluation by experts based on Nielsen’s principles and a user evaluation involving 10 high school students were conducted. Expert assessment revealed high levels of severity in the “Help users recognize, diagnose, and recover from errors” and “Help and documentation” domains among the ten heuristic categories, while “Aesthetic and minimalist design” and “Consistency and standards” showed excellent results. The user evaluation recorded an average accuracy rate of 92.7%, with efficiency differing by search type, and a user satisfaction score of 3.8 out of 5. Based on these findings, the study identified key problems in the book search function of the Dokseoro system and proposed the following improvements: establishing error prevention and handling systems, supporting diverse search techniques, providing terminology guidance and help documents, and strengthening visual elements to improve usability. In addition, the study suggests actively presenting subject index terms and detailed material type information contained in the bibliographic data within the interface. It also proposes the development of customized interfaces tailored to different school levels.

Abstract

This study analyzes the utilization of generative AI in the context of Library and Information Science education through the framework of Activity Theory. Focusing on a 10-week team project conducted by undergraduate students enrolled in a “Program Design and Evaluation” course at a university in Seoul, South Korea, the research qualitatively examines the impact of generative AI on the learning process, student responses, and contradictions within the activity system. The analysis reveals that generative AI served as a tool to reduce cognitive load and enhance learning efficiency across multiple domains, including idea generation, drafting and structuring a program proposal, summarizing information, development of interview questions, etc. However, it also triggered various levels of contradictions, such as limitations in reliability, contextual inappropriateness, and tensions with traditional learning practices. These contradictions acted as catalysts for expansive learning, leading to the emergence of new collaborative structures and learning strategies. This study provides insights into both the pedagogical potential and educational challenges of integrating generative AI into LIS education and offers guidance for the future direction of AI literacy instruction and curriculum design.

Abstract

Craft breweries around the world are striving to create a higher quality and diversity of beer in markets dominated by mass market or big beer. While the information needs and behaviors of craft brewers are understudied in library and information science, research in other disciplines has described the phenomenon of ‘coopetition’ in the craft brewing industry, in which craft brewers collaborate with competing craft brewers for their mutual benefit. This study employs the theory of information worlds to explore the social contexts of craft brewers’ information behaviors using semi-structured interviews with a purposive sample of craft brewers in the United States, one of the most developed craft brewing markets. The findings of this study indicate that craft brewers promote and enforce social norms related to safety, professionalism and self-control, a “rising tide raises all ships” philosophy, and kindness that motivate craft brewers to share information and resources with other craft brewers, even competitors, and foster ‘a shared community identity’ that further unifies against the shared enemy of mass market beer. However, collaboration is not guaranteed. Factors that impact potential collaborations, as well as emergent themes, such as quality, sustainability, innovation, accessibility, and inclusion, are identified and discussed. Theoretical implications for library and information science research, as well as practical applications for craft brewers and librarians, are discussed.

Abstract

This study aims to identify the knowledge structure and patterns of interdisciplinary convergence of Digital Humanities (DH) in South Korea. To this end, citation frequency analysis and journal co-citation network analysis were conducted on 563 DH-related articles and 14,068 references indexed in the Korea Citation Index from 2005 to 2025. The study revealed that despite quantitative growth, the field remained concentrated in traditional humanities such as history, classical literature, and Korean literature. While research on classical text translation and database construction formed a distinct domain, direct linkages with technological fields were relatively weak. These findings indicate a priority on humanities data compilation over the methodological application of digital technologies. The study revealed strong internal cohesion within the humanities and limited technical convergence, indicating a need for active integration with technological disciplines.

Abstract

Despite the transition of administrative work from a paper-based management system to a digital media management system, the Public Records Management Act is still based on “electronic management of paper records”. In particular, production records are classified through the record management standard including the retention period and disclosure status for each unit task of the Government Functional Classification System (BRM), but this system does not properly reflect the complex record production environment. As a result, despite the efforts of the central government and various methods of preceding researchers, due to the lack of a business operation method that can sufficiently reflect the subjective judgment of the record producer and the lack of feedback for sustainable operation, all institutions still face many difficulties in the record classification process, which is the beginning stage of record management. Therefore, the purpose of this study is to automatically check and apply the record management standard table based on the document registration ledger data of public institutions. As a method of inspection and application, first, the Levenstein distance algorithm can be applied to check whether the compilation of records is appropriate within the unit task. Second, it is possible to identify unused unit task cards, check the retention period for the same unit task name, and check the appropriateness of disclosure by analyzing the correlation between the unit task of the record management standard and the document registration ledger data. Third, this method is applied to actual work, and general user groups are recruited for performance evaluation and research methods are evaluated. The conclusion of the study is, first, because the record management reference table is automatically checked based on data accumulated by the institution, It shows high accuracy in practice. it shows high accuracy. Second, high results were obtained for efficiency and ease, but somewhat negative results were obtained for informativity. Third, it is possible to expand and convert thinking to data-based record information management, where most of the subjective opinions of practitioners were applied.