While open government data (OGD) is increasingly recognized as a critical resource for economic growth and data-driven innovation, methods for proactively evaluating the potential utilization of these datasets remain underdeveloped. This study addresses this gap by investigating two key methodological questions: first, whether automated machine learning (AutoML) is an appropriate tool for measuring and evaluating OGD utilization, and second, how the composition of training data affects the performance of models designed to predict such utilization. This research specifically compares the efficacy of two distinct data strategies: models trained on integrated datasets spanning multiple domains versus those trained on domain-specific datasets. Using metadata from the South Korean government’s extensive OGD portal, this study employs AutoML to systematically build and evaluate predictive models under these different training conditions. The findings reveal that the training data strategy is a critical determinant of predictive accuracy, with the integrated-domain approach frequently yielding superior performance over domain-specific models. This research provides empirical evidence on the impact of data integration strategies in this context and establishes a methodological framework for the prospective assessment of OGD value, offering a more robust alternative to traditional retrospective evaluation metrics.
This article attempts to present the entire research output of a scientist in terms of his publication activity and its impact as comprehensively as possible. To this end, a bibliometric overview of the scope and structure of his research output is first provided. Furthermore, the visibility of this output in the various information services is analyzed. Christian Schlögl authored a total of 177 publications, including 77 journal articles. This set of publications is only visible through the use of personal publication lists in an institutional repository (Uni Graz Online). In contrast, only a fraction of these publications are included in the common information services; in the Web of Science, for example, not even a third exist. The publication output is then examined in more detail, including preferred topics, co-authors, and journals. Next, a citation analysis is conducted, revealing, for example, the temporal distribution of citations and the most frequently cited publications. Finally, the seven most important research areas are briefly presented. For each of these areas, a co-author was asked to comment on Christian Schlögl’s working methods and collaboration. Overall, this article could serve as a good illustrative example for analyzing the research performance of an individual researcher.
This study examines the predictors and consequences of artificial intelligence (AI) tool adoption among university students in Saudi Arabia, integrating the technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), as well as Cognitive Social Theory with AI-specific trust and ethical concern. Using a cross-sectional survey of 317 students from four geographically distributed public universities, we employed partial least square-structural equation modeling to test ten hypotheses. The results reveal that perceived usefulness, trust in AI, and social influence significantly predict AI adoption, while perceived ease of use and ethical concerns show no significant effects. Self-efficacy partially mediates the relationships between perceived usefulness and perceived ease of use with adoption, but not for trust. Notably, AI adoption significantly enhances academic performance and cognitive skill development. The findings contribute to technology acceptance literature by validating the combination of TAM, UTAUT, and social cognitive theory in AI adoption contexts, highlighting trust as a critical factor beyond traditional TAM constructs, and demonstrating AI’s positive educational outcomes. Practically, universities should emphasize AI’s usefulness, foster trust through transparency, and integrate self-efficacy training to maximize adoption benefits.
Biomechanics is an interdisciplinary field with varying citation patterns and centrality of journals where researchers publish. The audit culture of scientific publication has driven persistent, inconsistent, and questionable interpretation of citations and metrics in selecting and evaluating journals. This article extends the understanding of bias in citation patterns and journal metrics for eight biomechanics journals. Citations from three databases and several journal metrics were examined from 2019 to 2023. Long-term changes (1999-2023) were documented for the most prestigious journal in the field (Journal of Biomechanics). There was large variation and positive skew in citations and citation rates to top cited articles in the biomechanics journals from 2021 in all databases. The skew and variation in citations and subject categories/areas assigned by databases contributed to biased journal usage metrics for biomechanics journals. For the Journal of Biomechanics, citation patterns led to opposing changes in overall usage (Journal Impact Factor) and field-normalized usage (Source-Normalized Impact per Paper) journal metrics. Evidence-based interpretations of journal metrics are illustrated and recommendations made to limit bias in planning, publishing, and evaluating research in interdisciplinary fields such as biomechanics.
This study explores the applications and developments of the theory of information worlds (TIW), developed by Burnett and Jaeger (2008), Jaeger and Burnett (2010), in peer-reviewed research published between 2008 and 2023. Building on the previous systematic review by Park et al. (2022), a qualitative content analysis was conducted on 27 articles that employed TIW at substantial theoretical levels, including theory application, theory conversation, and theory generation. The findings reveal that TIW serves as a robust and scalable theoretical framework, applied across diverse contexts and methodological approaches, often integrated with qualitative and mixed methods. The core concepts of normative information behavior and information value were most frequently employed and concepts such as social types were less frequently applied, indicating potential areas for further theoretical refinement. TIW has also informed new theoretical contributions, including a framework of information access in local communities and a theory of local information landscapes, and has been used in conjunction with complementary theories across disciplines. While criticisms regarding its limited treatment of individual agency and affective dimensions have been raised, recent theoretical developments incorporating cognitive and signification domains into the framework have sought to address these limitations. This study contributes to understanding the role and evolution of LIS theory and highlights the potential of TIW for future theoretical advancement and interdisciplinary application.
The rising volume of electronic waste generated by public organisations in the Thi-Qar province of Iraq adversely affects the environment due to inadequate recycling and disposal methods, thereby intensifying pressures on enterprises. This research seeks to identify the factors influencing organisational managers’ adoption of environmentally sustainable e-waste disposal methods. A research model was established to connect three components: coercive pressure, normative influence, and mimicry. It investigates the impact of adopting environmentally responsible e-waste disposal and analyses the mediating role of attitude. The model underwent validation through data collected from a field survey involving 302 managers of public organisations in the Thi-Qar province. A questionnaire was created to gather data, consisting of five primary variables represented by twenty-one items. The seven-dimensional scalar form was employed for measurement and underwent reliability and validity assessments. Partial least squares were utilised to analyse the survey responses. The findings demonstrated that coercive, mimetic, and normative pressures significantly influenced attitudes and the adoption of environmentally responsible e-waste disposal practices. Attitudes regarding the environmental disposal of e-waste influence the adoption of eco-friendly disposal methods and mediate the effects of coercive, mimetic, and normative pressures on this adoption. This study advances the green information technology literature by identifying critical institutional factors influencing environmental behaviour and provides practical recommendations for policymakers and organisations to improve e-waste management strategies.
As political engagement is increasingly migrating to social media platforms, understanding the emotional dynamics embedded in online reactions has become critical to interpreting voter behavior. This study explores how Facebook’s reactions function as affective signals of voter sentiment and predictive indicators of electoral outcomes, using the 2022 Kathmandu metropolitan mayoral election. Drawing on emotion theory and affective information behavior, the research analyzes 322 Facebook posts related to the campaign from three leading candidates: Balen Shah, Keshav Sthapit, and Sirjana Singh, focusing on emoji-based reactions, shares, and comments. Sentiment scores were computed using a weighted classification of emojis, and predictive potential was assessed through rank-order analysis and correlation with actual vote shares. The result reveals a positive correlation between pre-election sentiment indicators and electoral outcomes. Balen Shah, who led in both emotional engagement and final vote count, demonstrated how affective online support can signal electoral strength. Time-truncation and unweighted scoring further validated the robustness check. Additionally, text analytics highlight Shah’s resonance with majority voters and his alignment with calls for systemic change, contrasting with mixed sentiment toward the latter two. This study contributes to the growing literature on digital political engagement by demonstrating that emoji reactions can serve as reliable proxies for public sentiment in emerging democracies. The findings suggest practical implications for political strategists, campaign managers, and communication agencies seeking to understand and respond to digital sentiment in real time.
This study employed a convergent mixed-methods approach to examine the development of learning communities among longan farmers in Chiang Mai Province, Thailand, and the role of information professionals in community engagement. Quantitative and qualitative data were collected simultaneously and synthesized to provide a comprehensive understanding. The qualitative phase involved structured interviews with ten longan farmers, with six participating, and data were analyzed descriptively to assess learning community development. In the quantitative phase, surveys were distributed to 144 stakeholders, with 136 completed responses (94.44%). Descriptive and inferential statistical analyses were conducted using IBM SPSS Statistics 30.0 (IBM Co., Armonk, NY, USA), including the Shapiro-Wilk test, Friedman test, and Wilcoxon Signed-Rank test, to evaluate the significance of community engagement in agricultural development. Findings from the qualitative analysis highlight three key aspects of learning center operations: (1) management, (2) farmer skills development, and (3) knowledge creation and transfer. The quantitative results identify four areas in which information professionals contribute to community engagement: (1) Outreach (Information Providing), (2) Collaboration (Information Evaluation), (3) Involvement (Information Analysis), and (4) Consultation (Information Consulting). Qualitative insights complement the statistical findings, providing a deeper understanding of the learning community development process. This integration clarifies the role of information professionals and supports policy development initiatives aimed at strengthening the agricultural capacity of longan farmers, with potential applicability to other agricultural communities. Furthermore, the study offers a framework for information professionals, librarians, and agricultural specialists to enhance learning communities and contributes to the advancement of curricula in library and information science.
This research analyzes and develops an essential dataset for creating a community information system (CIS) for managing cultural capital. The study has three primary objectives: (1) to identify and define key data categories required for a CIS focused on cultural capital management; (2) to assess the relevance and necessity of each data category through expert evaluation, particularly from individuals with practical experience in utilizing cultural capital in communities; and (3) to propose a dataset that can be adopted by local administrative organizations in Thailand to support cultural capital management and policy-making. The methodology employed in-depth interviews with six key informants: two cultural practitioners from local administrative organizations, two experts in dataset development, one expert in arts, culture, and local wisdom, and one user of cultural data for professional, educational, or research purposes. Data were analyzed using content analysis to identify standardized datasets, based on concepts of CIS, cultural capital information, and datasets, with verification through triangulation. Findings revealed that the essential data for developing the community cultural capital information system comprises two main datasets: community data, consisting of 117 data points with an overall high necessity rating, and cultural capital data, comprising 127 data points with an overall very high necessity rating. These findings establish the crucial data required for effective utilization of the system in managing cultural capital at the community level.
Thailand’s digital industry is experiencing rapid growth, driven by government initiatives and the widespread adoption of digital technologies. In response to this dynamic development, the present study proposes a predictive model to forecast future workforce requirements across five key segments of the digital industry: hardware and smart devices, software and software services, digital services, digital content, and telecommunications. Data were collected via web scraping from ten job advertisement websites between 2023 and 2024, resulting in a dataset comprising 24,494 job positions. The collected data underwent comprehensive preprocessing through natural language processing techniques—including text cleaning, punctuation removal, tokenization, stop word removal, and feature extraction using term frequency-inverse document frequency—to prepare it for analysis. Four supervised machine learning models (logistic regression, decision tree, k-nearest neighbors, and Naïve Bayes) were constructed and evaluated using performance metrics such as accuracy, precision, recall, and F1-score, alongside receiver operating characteristic-area under the curve analysis. The results demonstrate that the k-nearest neighbors model outperformed the other methods, achieving an accuracy (AUC) of 0.792, precision of 0.793, recall of 0.731, and an F1-score of 0.751, with all models yielding AUC values greater than 0.5. These findings indicate an upward trend in digital service job demand and underscore the model’s potential utility in guiding workforce planning and human resource strategies in the digital sector. The study offers a robust, data-driven framework that can be adapted to forecast workforce needs in other rapidly evolving industries.
This study represents the first systematic analysis of research related to the National Science & Technology Information Service (NTIS)—South Korea’s comprehensive national Research and Development (R&D) information portal—from 2008 to 2024, utilizing bibliometric analysis, thematic analysis, and selective literature review. The study aims to identify key research trends, collaborative patterns, and thematic evolution in NTIS-related research to understand its knowledge structure and provide directions for scholarly and system development. The analysis reveals that despite limited publications (169 articles), NTIS research demonstrates steady growth with 13.9% annual growth. Distribution analysis using Herfindahl-Hirschman Index (HHI) indicates low concentration levels across authors, institutions, and journals, though citations show higher concentration. Korea Institute of Science and Technology Information (KISTI) leads in all performance indicators and serves as the central hub in inter-institutional collaboration networks. Research scope has expanded into diverse academic fields, with growth in interdisciplinary studies, business administration, and engineering. While studies using NTIS data have increased, research on system improvement has declined. Thematic evolution analysis reveals nine research clusters and shows a shift from system development towards R&D trend analysis, performance evaluation, and advanced analytical methodologies, including Artificial Intelligence (AI) technologies. Major research themes include NTIS system implementation, data quality management, R&D investment-performance analysis, economic impact assessment, and analytical techniques. This study indicates that NTIS’s function is evolving beyond R&D information service into a data-driven academic analysis infrastructure supporting evidence-based knowledge production across disciplines. To address limited research volume and domestic concentration, this study recommends activating NTIS-related academic communities, expanding international participation through compatibility with global systems, and building an intelligent platform for customized services.
This study addresses information management and absorptive capacity in the context of open innovation projects—essential topics for public and private organizations. The construction of the conceptual model is based on the consolidated literature on these topics, also incorporating recent empirical studies that highlight their complementarity and confirm existing gaps in the area. The proposed theoretical model considers the stages of innovation development, represented by the innovation funnel, integrating the information process model and the absorptive capacity model. The proposed model aims to support managers in systematizing the innovation process, using information as a foundation for absorptive capacity and promoting the integration of information management, absorptive capacity, and open innovation. The implementation of open innovation can be carried out through the effective management of information flow and the application of new knowledge, facilitating decision-making and enhancing innovation outcomes. It is expected that this study will contribute to the advancement of these topics and offer practical instruments that strengthen the agenda of innovation managers.
The current state of scholarly publishing is marked by the dominance of commercial publishers, the expansion of open access (OA) models, and the persistent challenges faced by independent, non-commercial journals. The aim of this paper is to critically examine the 12-year developmental course of the Journal of Information Science Theory and Practice (JISTaP), analyzing how an Asia-based library and information science journal with global aspirations has navigated academic publishing challenges. By tracing four distinct developmental epochs from 2013 to the present, the research explores its adaptation strategies, institutional support mechanisms, and bibliometric performance. An analysis of publication trends and citation patterns revealed significant growth, with steady increases in submissions and published articles despite systemic challenges for independent journals. A strengths–weaknesses–opportunities–threats analysis identified JISTaP’s key strengths, including robust institutional support from the Korea Institute of Science and Technology Information (KISTI) and an innovative OA model. However, critical challenges remain, such as limited Social Sciences Citation Index visibility and modest citation impact. The findings illuminate external pressures confronting non-commercial scholarly journals, including predatory publishing threats, technological disruptions, and the emerging complexities of AI-driven editorial systems. This research presents JISTaP’s 12-year trajectory as a critical case study of how a scholarly journal from an underrepresented region can strategically navigate global scholarly communication barriers, demonstrating that success lies in reimagining academic influence through institutional support, digital innovation, and a commitment to OA principles that transcend commercial publishing constraints.
This study investigates the practices of Western YouTube content creators’ cross-posting behavior, i.e., uploading their content on Bilibili—a Chinese video-sharing platform. A qualitative content analysis is employed to analyze videos of 14 popular YouTubers to compare the content of videos posted on both platforms. The findings indicate that the content differs for both platforms, possibly to accommodate Chinese viewers. The results show that Western YouTubers have made multiple changes to their original content before reuploading them to Bilibili, including the addition of Chinese subtitles, cut-out sponsorship content, shorter and translated descriptions, altered thumbnails, and exclusive content for Bilibili. The results also show a less frequent uploading schedule on Bilibili than on YouTube. While existing studies have explored content creators’ cross-posting behavior within the same social media ecosystem, the study significantly contributes to advancing our understanding of online creators’ content distribution practices across platforms in different cultural domains, especially in a Chinese-Western context.
Many descriptions and evaluations of research institutions apply publication and citation indicators, which can also be found in most of the popular rankings of universities all over the world. When comparing different institutions, the question arises whether scientometric indicators, and institutional rankings derived from them, are really valid. In our paper we discuss various dimensions of scientometric analyses which have a more or less strong impact on the results of research evaluations on the scientometric meso-level (e.g., department or university level). Concerning research output (based upon publications), we found nine different dimensions, namely time period, size of the institution, representatives of the institution, data source, language, document types and their weighting, co-authorship, document length, and access option. Concerning research impact (based upon citations), there are 14 different dimensions. Using the simple example of two university departments, we present different rankings while varying a few attributes of the suggested dimensions. As the results show, there are large differences between the performed rankings. Each ranking stresses a different aspect; hence there is not the one and only valid ranking. All the popular global university rankings work with arbitrary selections of indicator combinations, making the results more or less arbitrary. The greatest issues are the rankings’ incomplete empirical bases, the non-consideration of fractional counting of authors, and – most importantly – the disregarding of the institutions’ sizes.
Augmented reality (AR) offers significant potential for inclusive design by merging virtual and physical environments to support equitable interaction and engagement. This study explores the role of AR in facilitating inclusive play, focusing on how it can enhance information accessibility and collaboration among children with and without disabilities. Using participatory design methodologies, we engaged 21 children aged 8-13, including children with and without disabilities, in co-design sessions to identify barriers and opportunities within AR-based activities. The study included both online and offline sessions, ensuring diverse participation and perspectives. We employed a thematic analysis approach to examine patterns in children’s interactions with AR, focusing on engagement, accessibility, and collaborative play. Findings reveal that AR can blur social and physical barriers, foster engagement through interest-driven interaction, and provide adaptive tools to support diverse user needs. The study also highlights the importance of autonomy, tailored technological support, and the role of facilitators in designing equitable AR environments. By positioning AR as a tool for inclusivity, this research contributes to the broader field of information science, offering insights into designing systems that prioritize information accessibility, user engagement, and collaborative interaction. Practical implications for developing AR-based information systems and environments are discussed.
This study examines factors influencing the use of recommendation systems for elderly research in Thailand through a quantitative research design. The target population comprises researchers experienced in elderly studies from 2012 to 2022, totaling 348 participants. Data were collected via a validated questionnaire (Cronbach’s alpha=0.955). Employing an extended the Unified Theory of Acceptance and Use of Technology 2 model, the study investigates system use behavior (SUB) based on seven core factors: Performance expectancy (PEF), effort expectancy (EFF), social influence, personal innovativeness (INN), hedonic motivation (MOT), facilitating conditions (FAC), and intention behavior (IBV), alongside three additional factors—system quality (SQU), information quality (IQU), and trust. Multiple correlation and regression analyses reveal statistically significant influences (p<0.05) from eight factors. SQU, PEF, EFF, MOT, FAC, and IBV positively influence SUB. Conversely, IQU and INN negatively affect system usage. The predictive model is expressed as: SUB=1.195+0.116 (SQU)-0.268 (IQU)+0.134 (PEF)+0.181 (EFF)-0.406 (INN)+0.137 (MOT)+0.097 (FAC)+0.866 (IBV). These findings underscore the importance of optimizing system features and recognizing the distinct needs and expectations of elderly research communities to enhance the effectiveness of these recommendation systems.
This study analyzes the roles of altmetric and citation scores in open and closed-access pesticide research journals from 2013 to 2023, revealing key insights into impact metrics across publishing models. Traditional citations predominantly favor subscription-based journals, which account for 68.03% of the total citations. In contrast, green open-access journals excel in altmetric scores, primarily driven by social media engagement on platforms such as Mendeley, Twitter, and Facebook. Green open-access documents record the highest cumulative citations (13,143) and altmetric scores (5,768), suggesting greater online visibility and broader social reach. Statistical analysis shows no significant difference between altmetrics and citations, indicating that both metrics contribute complementary perspectives on research impact. Descriptive statistics highlight variations in citation patterns, with open-access journals showing a more concentrated distribution. Toxicology journals, where much pesticide research is published, are predominantly closed access, though citation patterns and altmetric attention vary by journal. Leading journals such as Ecotoxicology and Environmental Safety emphasize citations, while Food and Chemical Toxicology focuses on altmetrics, underscoring the dual approach to research visibility and impact in pesticide studies. These findings emphasize the evolving role of altmetrics in complementing traditional citations, especially for studies with high public and social relevance.
Online social networks empower individuals with limited influence to exert significant control over specific individuals’ lives and exploit the anonymity or social disconnect offered by the Internet to engage in harassment. Women are commonly attacked due to the prevalent existence of sexism in our culture. Efforts to detect misogyny have improved, but its subtle and profound nature makes it challenging to diagnose, indicating that statistical methods may not be enough. This research article explores the use of deep learning techniques for the automatic detection of hate speech against women on Twitter. It offers further insights into the practical issues of automating hate speech detection in social media platforms by utilizing the model’s capacity to grasp linguistic nuances and context. The results highlight the model’s applicability to information science by addressing the expanding need for better retrieval of hazardous content, scalable content moderation, and metadata organization. This work emphasizes content control in the digital ecosystem. The deep learning-based methods discussed improve the retrieval of data connected to hate speech in the context of a digital archive or social media monitoring system, facilitating study in fields including online harassment, policy formation, and social justice campaigning. The findings not only advance the field of natural language processing but also have practical implications for social media platforms, policymakers, and advocacy groups seeking to combat online harassment and foster inclusive digital spaces for women.
This study aims to investigate whether the prevalence of retweets challenges the validity of Twitter mentions (tweet counts and tweeter counts) as indicators of research significance. The study first examines whether the tweeting of research papers signifies either the wisdom of crowd effect or herd-like behavior for a dataset of COVID-19 papers from The Lancet. The study then uses the Modality, Agency, Interactivity, and Navigability (MAIN) model to examine the nature of the influence involved in retweeting. The Mann-Whitney U test and multiple linear regression were used. Findings show that there was extensive evidence of herd-like behaviour, rather than the wisdom of crowd effect, in tweeting research papers, challenging the validity of Twitter mentions as indicators of research significance. Credibility heuristic cues of the original tweeters were associated with their retweet rates, suggesting that retweets are more likely influenced by perceptions of the original tweeter’s credibility rather than the quality of research papers.
