ABSTRACT. More and more users are spontaneously deleting content they had posted on social media, which impacts the data resources and sustainability of social media. This study aims to clarify the influencing factors of social media users' information deletion behavior, explore the formation mechanism of this behavior, and make suggestions for avoiding its negative effects. In this study, semi-structured interviews were used to obtain raw data materials, which were coded and analyzed based on the qualitative research paradigm of grounded theory, and a theoretical model of influencing factors of social media users' information deletion behavior was constructed by drawing on the theory of context collapse. Through three-level coding of interview materials, 7 main categories under 3 dimensions are finally obtained, which are social dimension (perceived context collapse, social anxiety, role conflict, privacy concerns), user dimension (information organization habit, information value), and platform dimension (perceived ease of use). The findings also indicate that perceived context collapse can cause information deletion behavior by aggravating social anxiety, role conflict, and privacy concerns. The theoretical contributions and practical implications of this study are also discussed.

ABSTRACT. Cyberchondria refers to the compulsive behavior of excessively search for health-related information online, which can heighten anxiety about one’s health. While previous studies have identified numerous cognitive and emotional factors that contribute to cyberchondria, how individuals suffer from cyberchondria when they face complicated online system features warrants further investigation. This study introduces a moderated mediation model to explore the relationships between online system complexity, health advertising intrusiveness, health risk perception, health information skepticism and cyberchondria. Drawing on data from an online survey of 437 participants, the results indicate that system complexity is positively related to cyberchondria through health advertising intrusiveness and risk perception. Additionally, health information skepticism negatively moderates the effect of system complexity on health advertising intrusiveness. This study concludes with a discussion of its implications and limitations.

ABSTRACT. Abstract. This paper investigates cybersecurity awareness amongst rural communities in Indonesian, aiming to develop strategies for community cybersecurity capacity building. As a starting point of our study regarding cyber capacity building in Indonesia, we conducted a national survey span-ning 172 participants from South Sulawesi, situated in Sulawesi island, Eastern Indonesia. The aim of the survey is to identify crucial factors influ-encing cybersecurity awareness, including personal information protection, reporting mechanisms, and media education on cybersecurity issues. Find-ings indicate a moderate level of cybersecurity awareness with gaps in un-derstanding anti-virus use, firewall programs, and the importance of not sharing others' personal information online. The study suggests that tai-lored educational campaigns and increased collaboration across government and private sectors are essential to enhance cybersecurity capabilities in these communities. The findings are important to inform strategies for a more inclusive and resilient cybersecurity capacity building and supporting infrastructure across Indonesia's diverse socio-economic landscape.

ABSTRACT. Background. Interorganisational Data Sharing (IDS) has always been consid-ered extremely essential. Despite decades of intense research, IDS in practice is still quite problematic. This paper reports on a meta-analysis that aims to analyse and understand how data quality influence IDS. Method and Analysis. This study adopts a meta-analysis approach. A total of 69 interview transcripts gathered in two completed research projects in Chi-na were analysed using a Grounded Theory analysis approach. Results. Data quality can strongly influence IDS and can become either an enabler or a barrier. The analysis showed that good, well-structured, and well-maintained data are more likely to be required and shared through IDS, whereas bad (incomplete, unreliable, unmaintained) data is mostly unwanted by other organisations. Ugly data refers to those manipulated data that should not be shared. Conclusion. This is one of the early research projects investigating the rela-tionship between data quality and IDS. The theoretical implications could be important and worth further exploring.

ABSTRACT. With the rapid development of artificial intelligence (AI) in recent years, generative AI, represented by ChatGPT, Gemini and Sora has demonstrated its powerful capabilities in many fields. Such AI tools are “penetrating” scientific research as well. The attitudes and reactions of early-career researchers (ECRs), as a new generation of digital natives and those who just start academic career, to this new technology are crucial, as their behaviors and habits may influence the shaping of scientific communication patterns and the development of order in the scientific field in the future. To explore how Chinese ECRs are doing in this regard, longitudinal qualitative interviews were conducted with twenty-two Chinese ECRs. As the results show, Chinese ECRs use AI tools to assist their academic work to some extent, and make the most of it within certain self-ethical constraints. They are also able to recognize its shortcomings and propose ideas for optimization and control of AI development.

ABSTRACT. With the rise of artificial intelligence (AI), research related to AI has become popular in the field of management. Whether the research outcomes containing AI-related themes or methods would have a positive impact was worth exploring. Based on the UTD 24 journals in the field of management studies, this article systematically collected over 50,000 top-tier papers from six sub-disciplines of management, identified research literature containing elements related to AI through natural language processing methods, and conducted quantitative analyses on their academic and social impact respectively. The results indicated that, in terms of academic impact, AI-related papers in the Management (General/Strategy) and Management (Operations) domains had negative effects. Specifically, the academic impact of AI-related papers significantly lower than non-AI papers in those fields. Since 2006, AI-related papers in the field of Marketing had shown a significantly positive impact on their academic influence. Regarding social impact, AI-related papers in the Management(General/Strategy) domain had a more positive influence on the social impact. Although AI-related papers in Information Systems accounted for a significant proportion (28%), they did not have significant impacts on both academic and social influence within the field. Additionally, it was noteworthy that gender played a significant performance of the journals. Female first authors demonstrated more positive academic and social impact performance in fields like Marketing. This differed from the results of previous studies that examined gender influences.

ABSTRACT. Due to different platform contexts and their distinct features, users often utilize multiple platforms to satisfy their needs. This paper concentrates on the behavioral differences among cross-platform users of academic social networking sites, with a comparison of different institution types in which the users are affiliated. The cross-platform data was collected from Academia.edu (ACA) and ResearchGate (RG), 15 institutions from Academic, Government and Corporate were selected to find their cross-platform members and to analyze their behavioral differences from perspectives on information disclosure, academic publication, and social interaction. The findings indicate that cross-platform users favor RG over ACA and are more likely to share comprehensive information, expand their social networks, and engage in RG-specific interactions. Government Institutions perform the most actively, demonstrating high levels of information disclosure, publication, and social interaction. Corporate Institutions have distinct strengths and weaknesses, while Academic Institutions display average performance. This study contributes in understanding the cross-platform behavioral characteristics and preferences of users from different institution types.

ABSTRACT. This study focuses on the significant role played by reusing datasets in pro-moting open science. This study reported the process of developing dataset reuse pattern categories and verified that these categories could be used to identify the role of reusing datasets. Articles that cited the biomedical da-taset of the Framingham Cohort were included in the sample. Two annota-tors assigned categories to the samples by checking titles, journal names, au-thor information, purpose, description of the dataset, and statements of data in the body of the articles; 12 categories were identified. The analysis of the sample articles with assigned categories indicated that this category set can contribute to verifying the increasing diversity of reuse patterns, expanding research areas, and contributing to data-driven research by reusing data. This study could serve as a testament to the advantages of data reuse and inspires researchers to consider the impact of data sharing practices.

ABSTRACT. Considering the difficulties faced by libraries in AI implementation and the research gap in users’ social-emotional aspects of AI applications in libraries, this study evaluated university students’ awareness of AI-generated versus human-created feedback (a simplified Turing test) in the scenario of reference services in university libraries. A user test was carried out on 146 Chinese university students with 5 tasks from different subject areas. Results showed students’ limited ability to distinguish AI and human agents with an accuracy rate of 49.59%. Many of them mistook human agents for AI (33.7%). The study analyzed factors affecting the judgment from three dimensions: the information about the task, AI technology, and the user. We found that the more complex the task, the more likely the students were to judge the feedback as AI-generated. When students felt the agent was knowledgeable and able to solve the task, they were more likely to judge the agent as an AI. Students also felt the AI-generated explanations were more helpful compared to the human-created ones. Students with lower AI literacy tended to judge the AI agent as a human. The study benefits AI implementation in libraries by confirming the ability of AI to provide expert services, raising the alarm bell for libraries to be replaced by AI, and calling for improvements in AI literacy cultivation.

ABSTRACT. This study was geared to find out areas of knowledge possessed by staff who carry out reference and information (RIS) in Federal University libraries in the lower Southern region of Nigeria, comprising six states. The basis for the existence of university libraries was discussed and its driving force being information and communication technologies. Two structured questionnaires were deployed, one of which was to gather data from 193 staff who offer core reference and information services alongside allied reader services in the selected university libraries. The second questionnaire was for sectional/ unit heads of reference services. The first questionnaire for RIS staff was to determine the extent of their knowledge of technological applications for performing the RIS. The overall mean rating in all the responses indicated their knowledge in this respect was to a ‘great extent.’ The following rated highest: knowledge to send and receive e-mails with overall mean of 3.13, knowledge of search engines’ with overall mean rating of 3.11 followed by knowledge to provide assistance in the use of E-resources- 3.09, knowledge of the use of E-catalogues with 3.06 overall mean, knowledge of tools for E-reference service by 3.02 and knowledge of various non-print resources by 3.02 overall mean. The second questionnaire with findings from heads of sections, also indicated electronic reference and information services (ERS) carried out in the selected university libraries, was to a great extent. In view of this outcome, it was recommended among others, that social media profiles for libraries should stay active at corporate level. Also systems maintenance and other technical operations should not be relinquished to engineers only, but to librarians.

ABSTRACT. This study explores the application of generative artificial intelligence (AI) in automating the assignment of classification numbers to books acquired by li-braries, focusing on the Nippon Decimal Classification (NDC) system. Using a dataset from the National Diet Library (NDL) consisting of 1,000 to 1.2 mil-lion samples, the research evaluated the impact of training data volume on classification accuracy. Fine-tuning was conducted using OpenAI’s GPT-3.5 Turbo model with default parameter settings. Results indicated that even with 1,000 training samples, the model could correctly assign NDC division (two-digit) numbers to about half of the books, achieving over 70% accuracy for NDC class (one-digit) numbers. As the number of training samples increased, accuracy improved logarithmically, reaching 70.5% with 800,000 samples. However, when the sample size reached 1.2 million, there was only a marginal improvement observed to 70.7%. This trend was consistent across NDC class, division, and section (three-digit) levels, with more pronounced improvements observed in the more detailed classification levels. This study not only high-lights the potential of generative AI for library classification tasks, but also suggests that more training data may not necessarily lead to a substantial im-provement in accuracy. The findings indicate that while generative AI holds potential for automating library classification, the effectiveness of this ap-proach depends on the quality and volume of the data used for training.

ABSTRACT. Abstract Universities library have invested in medical library and provide clinical and pre-clinical information resources and services to encourage quality teaching, learning and research. Unfortunately, the university library are losing huge sum of money due to the non-use of medical library. Using Vygotsky’s theory of social development and his perspective on social interaction and MKO. Therefore, this study set out to explore the reasons for non-use of medical library among medical doctors in BDTH-KASU. A qualitative research methodology was adopted. Data were collected using in-depth interview with eleven (11) medical doctors, whom were selected through purposive sampling, particularly criterion sampling. Data from respondents were analyzed using thematic analysis. Findings revealed that, Lack of Time Compliance, Lack of Internet Connectivity, Inadequate Reading Space, and More Knowledgeable Others, are the major reasons for non-use of medical library. Therefore, this study posit that for the medical doctors to make use of the library there is need for the library management to provide good internet network, this will allow medical doctors to utilize the library so as to exploit the advantage of the medical library data network for their learning, teaching and researches as well as benefit from the rich databases subscribed by the university library to download current and up-to-date clinical information resources. It is also recommended that, the library management of KASU should create or provide conducive reading section for consultant doctors so as to avoid non-use of medical library. There is also need for the library management to extend the time of closing hour from 8:00am to 9:00pm so that the medical doctors can have time to utilize the library after working hours.

ABSTRACT. As widely acknowledged, data science has been explored and applied across numerous scenarios. However, there remains limited understanding of how knowledge in data science is integrated and diffused, particularly in terms of its interdisciplinary nature. In this study, we not only adopted a citation-based strategy to define the "scope" of data science and used bibliometric methods to analyze scientific publications in the field, creating its “bibliometric cartography,” but also employed text analysis techniques to examine the themes of data science papers and their evolutionary process. This multi-layered approach allows us to gain a more comprehensive understanding of the knowledge integration and diffusion patterns of data science across various disciplines. The findings reveal that data science publications draw from various knowledge sources and significantly influence multiple disciplines. Moreover, the diffusion of data science progresses more rapidly during the virality diffusion phase compared to the broadcasting diffusion phase within the scientific community. we emphasize the importance of technical methods in data science and their application in specific domain scenarios. Through the analysis of theme evolution maps, we discovered that the evaluation of model effectiveness serves as a crucial link between the application of technical methods and specific application scenarios. This study offers diverse perspectives that can stimulate theoretical advancements and serves as a crucial resource for policymakers and funding bodies to gain a deeper understanding of data science.

ABSTRACT. The rapid integration of generative AI into academic research has raised significant ethical considerations that warrant careful examination. Young researchers, who represent an important demographic in the research community, are particularly susceptible to the influence of emerging technologies such as generative AI. As they navigate the complexities of incorporating AI into their academic work, understanding the ethical decision-making processes that guide their use of generative AI is especially pertinent for promoting responsible practices. While the existing literature has primarily focused on broader ethical frameworks or the technological aspects of AI, many studies often neglect the nuanced decision-making pathways specific to the academic environment. Additionally, the role of institutional ethical climates in shaping these decisions has not been thoroughly explored. Considering these research gaps, this doctoral project aims to propose a research model to link AI ethical awareness to responsible use of AI in academic research. The model considers the mediating roles of moral judgement, perceived accountability, and ethical self-efficacy in this process. Moreover, this project seeks to examine the moderating role of institutional ethical climate in influencing these decision-making pathways. This research is grounded in ethical decision-making theory, which provides a structured approach to understanding how individuals recognize, evaluate, and act upon moral issues. According to this theory, the decision-making process involves several stages: moral recognition, where individuals identify the ethical implications of a situation; moral judgment, where they evaluate the actions based on moral principles; moral intention, where they prioritize moral values in their decision-making; and moral behavior, where they follow through with actions that align with their ethical beliefs. To achieve these objectives, this study will employ a mixed-methods research design. The questionnaire of this study will be used to empirically validate the proposed model, which will be distributed to young researchers in China, aiming to collect approximately 800 valid responses. Partial Least Squares Structural Equation Modeling ( PLS-SEM ) will be employed to analyze the survey data, enabling the examination of complex relationships between the variables. The study will then conduct semi-structured interviews with a selected group of young researchers ( N=30 ) to gain insight into their ethical decision-making process regarding generative AI. The interview data will be analysed using grounded theory methodology, which will allow us to identify emerging themes and patterns and further validate the theoretical model, as well as gather additional useful insights. In summary, this project seeks to advance the responsible AI use among young researchers by extending the ethical decision-making model to include factors specific to the academic context. By examining the influence of institutional ethical climates and identifying key decision-making variables, the research will offer valuable insights into how ethical norms and behaviors can be cultivated within research institutions. Ultimately, the findings will provide both ethical guidance for individual researchers and support the establishment of a collective commitment to ethical AI practices in academia.

ABSTRACT. The advent of Artificial intelligence(AI) has profoundly impacted various fields. There is no doubt that AI and libraries have a close relationship, espe-cially in the provision of high-quality research support service in university li-braries, which increasingly requires the integration of AI in the information age. This paper reports on a research study, which aims to update the existing re-search support service using AI technologies. Specifically, this study seeks to explore the evolving pattern of the research requirements in the context of the transition from traditional scientific research to those supported by AI. This study will employ a literature analysis to identify the characteristics of tradi-tional research support requirements, and conduct in-depth interviews to ex-plore the emerging requirements of researchers in an AI-driven environment. The literature analysis will encompass a broad range of sources, including poli-cy documents, industry reports, academic papers, and representative funding projects. From the analysis, six key dimensions of traditional research support will be identified: service content, service objects, service formats, service teams, outcomes, and underlying mechanisms. Building on these findings, the in-depth interviews will be conducted with representative librarians and re-searchers to summarize the new requirements of researchers operating within an AI-assisted research paradigm. Subsequently, this research will compare and analyze the changing patterns of research support requirements in light of these transformations based on the first two steps. This study will provide valuable references for detecting shifts in research requirements and optimizing the allo-cation of resources in AI-driven science environments. Furthermore, the study will offer practical recommendations that can be used by library managers, pol-icymakers and technology firms on implementing AI-based solutions to im-prove research support service in university libraries. While the case setting is focused on university libraries, its findings offer useful insights and indications that can be shared across different types of libraries and research-intensive in-stitutions.

ABSTRACT. Interorganizational Data Sharing (IDS) has always been considered extremely essential. However, the practical implementation of IDS faces significant challenges, particularly due to the lack of clarity and the imbalance of power structure among the involved departments. In response to these issues, this paper presents a research study aiming to investigate the structure of data power within IDS to address these challenges. The study adopts an inductive research approach, incorporating a pilot case study followed by a thematic analysis. The findings illuminate the conflicts that arise between data providing departments, data receiving departments, and data governance departments, particularly in relation to data ownership, data use and data management authority. Based on these insights, the study proposes a government data sharing mechanism that aligns data authority with clearly defined rights and responsibilities. The research findings provide valuable contributions to the field of government data governance, offering insights that are applicable on a global scale.

ABSTRACT. The fast-changing digital environment in this post covid era is pressured by the changing user need and behavior and this is changing the operational landscape and role of libraries in becoming more digital bound. University library management in Nigeria are now investing in emerging technology and this brings an urgent need for digital skills development of the library's workforce. This study therefore explored the current digital skills gap and the existing training programs available to library professionals in Nigeria. This study adopted a survey design using a quantitative procedure to collect information through questionnaire. A multi-stage sampling technique was used to sample 453 professionals and paraprofessionals in university libraries in Nigeria. Finding revealed that the university library professionals in Nigeria possess the basic digital skills to use technology for their day-to-day activities but lack some digital skills to carry out some operational tasks especially serials, acquisition and some cataloging tasks using z39 and OCLC. Also, this study found that in-house training is the only pathway available for digital skills development for library professionals. Other pathways for digital skills development such as conferences, workshops, self-sponsored training etc. are not expert programs geared towards digital skills development. Recommendations were made to library staff and management to re-harness the existing training programs towards expert programs geared towards digital skills development on the identified digital skills gap of this study.

ABSTRACT. Records in Contexts (RiC) provides an interoperability of archival institutions, which have undergone the variety of description practices, data quality and consistency, resource and expertise limitations, up-to-date information about archives held in institutions and user and researcher engagement. This study analyzes the transition process to apply RiC in practice using the case of M.L. Pin Malakul archives.

ABSTRACT. This study explores the applicability of Chatman's Theory of Life in the Round within Chinese "danwei communities." Using field research, we collected data on the everyday information practices of 17 participants from three distinct danwei communities. The findings indicate that, in the context of policy-driven planning, like-minded individuals with a shared worldview form small worlds and rarely seek information beyond their boundaries. The study also reveals unique information practices in danwei communities, including the overlap between work and life information, hierarchical access to information, and collective information expression. This research confirms the relevance of the theory in non-Western settings, examines the groups to which the small world theory applies, and explores the mechanisms of information interaction across boundaries. Additionally, it contributes to preserving the historical memory of China’s industrialization from an information perspective.