Purpose This study explores the growth, trends and patterns of scholarly research by using bibliometric and computational methods. This study aims to identify prolific authors, influential journals, collaborative networks and emerging themes. This paper offers insights into how Artificial Intelligence (AI) and Machine Learning (ML) are transforming metadata practices to enhance information organisation, retrieval and interoperability. Design/methodology/approach A structured three-phase methodology was adopted. At first, appropriate keywords were identified, and the required data was extracted from the Scopus database (2010–2024) using a Boolean search string. Of 3,624 records, 3,424 articles were found relevant after removal of irrelevant articles manually. Software such as Biblioshiny and VOSviewer were used to analyse the trends in publication, citation patterns, co-occurrence of keywords and the collaborative patterns. Social Network Analysis was applied to map the co-authorship patterns, collaboration at institutional and country levels. Findings The results of this study indicate steady growth in AI and ML research in library metadata, though citation impact has declined since 2018. Kanwal Ameen is the most productive author, and the USA is the most productive country. Dominance by a few journals and authors aligns with Bradford’s and Lotka’s Laws. The collaboration pattern remains limited among authors and countries. Thematic evolution shows a shift from traditional metadata access to intelligent technologies like AI, ML and IoT. Regional collaborations are stronger than author-level links, and recent trends align with broader digital transformation Originality/value To the best of the authors’ knowledge, this is the first known study to comprehensively examine AI and ML applications in library metadata management through computational bibliometric analysis.
Purpose At present, most people use social media. Social media can be considered a double-edged sword; one is a rapid way for information dissemination at low cost, and the other is the widespread spread of misinformation or fake news. This study aims to assess the media literacy and awareness of fake news among the youth during the National General Election. The purpose of this study is to investigate the awareness of media literacy, information evaluation criteria and the perception of youth about important factors of fake news on social media. Design/methodology/approach Present study has surveyed by used structure questionnaires and collected data from 427 young voters from the western region of India who are casting their votes in the General Election 2024. The study applied MS Excel and SPSS Software and analysed the data. Findings The study reported that 65.81% were found first time voters. It is observed that Facebook and WhatsApp are frequently used social media platforms. In total, 75% of users expressed that fake news spread during the election on social media mostly through Facebook, WhatsApp and YouTube. 34% of respondents showed that they are casting their vote in line with the manifesto of political parties. The result also shows that media literacy helps to evaluate the information obtained from any source. Because of awareness about social media and fake news, people critically analyse the facts before selecting an electoral candidate. Practical implications This study offers key implications for policymakers, educationalists, election authorities, social media platforms and citizens. Improving fact-checking systems, integrating media literacy education and performing voter perception programs can assist in reducing the spread and effect of election-related misinformation. In addition, teamwork between social media companies, autonomous fact-checkers and reliable news organisations can foster informed decision-making and improve democratic participation. Originality/value This study not only is assessing the media literacy and awareness of fake news among the youth but also shows the impact of the fake news among the young generation during the National General Election. The study also conducted to understand the voters’ criteria for choosing election candidate, perception of comprehensibility of fake news, acceptance and usage of social media while casting vote.
Purpose This study aims to examine the relationship between cybersecurity knowledge, cyber threat awareness and cybersecurity practices among staff and students in Kuwait’s higher education institutions (HEIs). It addresses a critical gap in understanding the human factors that influence cybersecurity resilience within Gulf higher education contexts. Design/methodology/approach A cross-sectional quantitative survey was conducted involving 270 participants from seven Kuwaiti HEIs. Data were analyzed using Spearman’s rank correlation, nonparametric group comparison tests, and ordinal logistic regression to examine the relationships between cybersecurity knowledge, threat awareness, demographic characteristics and cybersecurity practices. Findings Cybersecurity knowledge and cyber threat awareness were both significant predictors of cybersecurity practices. Moderate positive correlations among knowledge, threat awareness and secure behavior indicate that awareness alone is insufficient to promote secure practices without institutional support and behavioral reinforcement. Gender differences were significant in the bivariate analyses but became nonsignificant after controlling for cybersecurity knowledge and threat awareness, suggesting that disparities are mediated by sociocultural and educational factors rather than inherent behavioral differences. Age and institutional role were not independently associated with cybersecurity practices, indicating that the principal barriers to cybersecurity resilience are institutional rather than demographic. Practical implications The findings highlight the need for institutionally embedded cybersecurity governance that integrates leadership commitment, continuous awareness programmes, and culturally responsive training. Kuwaiti HEIs should move beyond compliance-based awareness initiatives toward strategic, governance-led approaches that align with Kuwait’s National Cybersecurity Strategy (2022). Originality/value This study contributes empirical evidence from the Gulf region by integrating Protection Motivation Theory, the Theory of Planned Behavior, and Socio-Technical Systems Theory to explain cybersecurity behavior in higher education. It demonstrates that cybersecurity resilience depends not only on individual knowledge but also on institutional governance, organizational culture and sociocultural context, thereby extending the predominantly Western evidence base and providing a foundation for future longitudinal and comparative research across the Gulf Cooperation Council.
Purpose This study aims to examine the impact of artificial intelligence (AI) technology and metaverse on academic and personal growth of students, moderated by the components of social–emotional learning (SEL), that is, emotional intelligence and peer relationships. Design/methodology/approach The study uses a two-stage hybrid structural equation modeling (SEM) artificial neural network (ANN) methodology. The initial phase uses SmartPLS4 to perform partial least squares SEM for exploratory investigations. Further, to explore the non-linear interaction between variables, an ANN model is used. Findings Findings suggest that perceived usefulness significantly impacts academic growth. The relationship is enhanced by moderators such as peer relationships and emotional intelligence, where higher perceived usefulness weakens the contribution of peer relationships to personal growth, while emotional intelligence strengthens the contribution of perceived usefulness to personal growth. Moreover, the study identifies social–emotional variables as key predictors of enhanced personal and academic growth. Research limitations/implications This study offers major implications for higher education by demonstrating how integrating the Technology Acceptance Model and SEL frameworks can enhance students’ academic and personal growth. Practically, it urges institutions to prioritize emotional intelligence and peer relationships alongside technological adoption, promoting a more inclusive, sustainable and student-centered learning environment. Originality/value This study examines the use of emerging technologies, AI and metaverse applications in higher education. Its contribution lies in integrating them with the SEL framework. Rather than treating learner characteristics as predictors, this study positions emotional intelligence and peer relationships as moderators that influence whether technology engagement leads to academic and personal growth. By further modeling non-linear relationships among variables, this study refines the predictive understanding of technology-enhanced learning and informs the andragogical implementation of these tools.
Purpose This study aims to investigate the serial mediating roles of affective trust (AT) and willingness to cooperate (WC) in the relationship between perceived knowledge hiding (PKH) and organizational citizenship behavior directed toward individuals (OCBI) drawing on the principles of social exchange theory (SET). Design/methodology/approach A quantitative research design was used to test the proposed research model. The data were collected from professionals working in the banking sector in İstanbul, Türkiye. The analyses were conducted using SPSS 25 and AMOS 24 and hypotheses were tested using PROCESS Macro for SPSS (Model 6). Findings Findings indicate that PKH is negatively associated with OCBI and that this association is reflected in statistically significant indirect pathways through AT and WC, both individually and through the serial pathway. Originality/value This study contributes to the literature by examining knowledge hiding (KH) from a relatively underexplored perspective of the knowledge seeker. By integrating AT and WC into a serial mediation model, it provides insights into the underlying relational mechanisms linking PKH to OCBI and offers directions for future research.
