Volume 75, Issue 3

The study aimed to measure and validate the risk information seeking and processing (RISP) model among university students, exploring the factors that predict health information avoidance behaviors and their association with health risk behaviors. A cross-sectional survey was conducted to collect the data from 1,500 university students. A five-factor measure model (risk perception, informational norms, information overload, information insufficiency, and negative affective risk responses) was developed and tested using structural equation modelling (SEM). The findings revealed that risk perception, information overload, and negative affective risk responses significantly influence health information avoidance behavior, which in turn significantly impacts health risk behaviors. These results highlight the importance of health literacy programs in reducing risk perception, information overload, and negative affective responses while promoting health information-seeking behaviors. The study contributes to the existing body of knowledge by identifying key factors within the RISP model that significantly influence health information avoidance. Moreover, these findings can guide the development of targeted interventions aimed at reducing health information avoidance and promoting health-seeking behaviors.

The study aims to investigate the most important artificial intelligence (AI) applications used in the university environment, the challenges of employing AI applications, and the requirements for integrating AI into the university environment. It examines the impact of AI on higher education teaching and learning and discusses the potential benefits and challenges of integrating AI into the university setting. The descriptive analytical approach was adopted for literature review to characterize the use of AI in universities. The survey approach was also used to collect data from 240 faculty members at Saudi universities and 15 experts in AI. The survey instrument included three dimensions with a total of 47 statements. The results show that the most important AI applications used in universities include personalized learning, efficient resource management, and greater research capability. However, the study also found that universities face challenges such as insufficient knowledge of AI, lack of AI ethics, and the need for strong cybersecurity. The key requirements for integrating AI into the university environment include a comprehensive AI ecosystem, advanced technological infrastructure, and training programs for faculty and students. This study provides a comprehensive analysis of the current state of AI adoption in teaching and learning at universities and the key factors that need to be addressed for successful integration. It contributes to the growing body of research on the role of AI in higher education and offers insights for educational stakeholders on the strategies and requirements for leveraging AI to enhance the teaching and learning experience.

Library anxiety is a well-documented psychological barrier that can hinder students’ ability to access and utilize academic resources effectively. This study examines the prevalence and dimensions of library anxiety among Indian university students and explores its relationship with demographic factors, library usage patterns, and academic performance. Drawing on Self-efficacy theory and Cognitive Load Theory, the study employed a quantitative, cross-sectional design using Bostick’s Library Anxiety Scale (LAS) with 386 respondents from two public universities in Uttar Pradesh, India. Findings revealed moderate levels of library anxiety, with “comfort” and “mechanical” barriers most frequently reported. While gender, age, and level in the study showed no significant differences in anxiety, frequent library users and those with longer stays reported higher anxiety related to staff and comfort. No significant correlation was found between library anxiety and academic performance. The results highlight the importance of user-centered library design, empathetic staff training, and progressive instruction to alleviate anxiety and foster effective engagement with academic resources.

The aim of this case study was to empirically elucidate the core elements underpinning the legitimacy of twenty-first-century public libraries, with a particular focus on the New York Public Library (NYPL). We conducted a qualitative content analysis of 24 NYPL annual reports spanning the period from 2000 to 2023 to investigate the ways in which legitimacy is expressed at NYPL and explore the interrelationships among the dimensions of library legitimacy. A coding framework, grounded in the established legitimacy dimensions from Yamagishi, Koizumi, and Larsen’s (2024) research, was utilized alongside a periodization approach to assess how these dimensions interact during key historical events. We examined the NYPL’s reported practices through the lens of five primary dimensions of legitimacy. Analyzing how these practices are reported in annual reports revealed how these dimensions interact and evolve in response to significant societal challenges, including the 9/11 attacks, the global financial crisis, and the COVID-19 pandemic. The research findings highlight the critical role of both internal factors (such as librarianship) and external influences (such as economic conditions) in sustaining the overall legitimacy of public libraries. This case study provides a comprehensive framework for understanding the complex and dynamic nature of legitimacy in public libraries and demonstrates how these institutions can adapt to and reflect broader societal changes. The findings hold important implications for the development and management of public libraries on a global scale.

The study explores the role of Indian academic library professionals in advancing Open Science (OS), focusing on their familiarity with OS practices, challenges faced, and training needs. Open Science, encompassing transparency, accessibility, and collaboration in research, requires active participation from libraries as facilitators. A survey among 362 library professionals from Indian academic institutions reveals that while professionals exhibit moderate familiarity with Open Science tools and practices such as open-access publishing, data sharing, and collaborative platforms, significant gaps persist in technical expertise and institutional support. Challenges include limited resources, insufficient infrastructure, lack of awareness, and stakeholder resistance to change. The study highlights the evolving roles of librarians in promoting OS, including data curation, policy advocacy, and researcher training. However, inconsistent adoption of OS practices and barriers like inadequate funding and unclear institutional policies hinder progress. Training in technical skills, advocacy, and open data management is critical to empower librarians to support OS initiatives effectively. The findings underscore the importance of institutional support, infrastructural improvements, and targeted training programs to enhance the library profession’s contribution to the Open Science movement in India.

The concept of metaverse libraries has the potential to usher in the next phase of library development. Chinese scholars in the field of library science have engaged in extensive discussions about metaverse libraries. This study investigates the potential of metaverse libraries and aims to provide a comprehensive understanding by discussing both positive and negative topics of metaverse libraries from China. To achieve this, the research collected and analyzed literature on Metaverse Libraries in China, using sentiment analysis and topic modeling to categorize and summarize academic perspectives. The study results found that: topics extracted from the positive sentiment dimension include intelligent services, spatial scenarios, theoretical research, intelligent technologies, digital resources, digital personas, architectural design, social scenarios, big data computation, and makerspaces; and topics from the negative sentiment dimension encompass technological development, user services, ethical risks, departure from reality, user interfaces, resource construction, and user privacy. Besides, this study introduces a sentiment-topic sequential literature topic mining process based on large language model text annotation.

This study examined the health information-seeking and use behavior of patients with chronic diseases to support a change in their health behavior. We conducted semi-structured in-depth interviews and recruited fifteen patients with diabetes (ranging from twenty-five to seventy-five years of age). Thematic analysis was conducted using the NVivo software and five themes were identified under medical and lifestyle management context, including topics of health information needs, information sources, health behavior change/non-change, barriers for information seeking, and influencing factors for information use. The results extended Wilson’s Information Behaviour Model Wilson (1999) by addressing the important role of information needs, which serve, as more than a task to achieve, to ferment a context for the consequent information seeking and use. Two-thirds of the interviewees neglect topics such as disease knowledge and risk avoidance due to interviewees’ incomprehensive and misled understanding of the disease. Instructiveness of information is reported by the participants to influence their information use in behavioral changes, regardless of the trustworthiness of the information. Nearly half of the interviewees cannot apply health information to change their behavior due to perceived health conditions, absent family support, insufficient social support, and partially covered social security. This study contributes to health information behavior studies by proposing a theoretical model of change in health behavior supported by information seeking and use in a comprehensive context-sensitive process and offers implications for customized health information services.