PurposeThis study investigates the application and scholarly impact of Reference Publication Year Spectroscopy (RPYS), a bibliometric technique that identifies historically significant references in scientific fields. By analyzing RPYS citation trends and patterns using Scopus data, the study seeks to understand the frequency and disciplinary impact of RPYS, the document types that cite it most frequently and the defining characteristics of these publications.Design/methodology/approachA structured, four-step retrospective search strategy was used to compile a comprehensive dataset from Scopus. An initial keyword search identified relevant documents, which were refined and expanded by extracting and analyzing author keywords. An advanced search incorporated synonyms and variations of RPYS terms, yielding a final set of 448 documents. Data were analyzed using R Studio and visualization tools like VOSviewer and pyBibx to uncover patterns in RPYS usage across disciplines.FindingsRPYS citations demonstrate exponential growth, with the number of citing publications doubling approximately every 2.5 years, surpassing general scientific literature growth rates. Review articles are identified as the most common document type referencing RPYS, with significant impacts in social sciences and decision sciences. Notably, China shows a lower-than-expected representation in RPYS citations. Five prominent research clusters are identified, including research mapping, clinical studies and citation analysis.Practical implicationsThe findings reinforce RPYS as a valuable bibliometric tool for tracing the historical underpinnings of research fields, with implications for its use in interdisciplinary studies and knowledge management.Originality/valueThis study highlights RPYS’s potential as a bibliometric method, encouraging broader application in scientific research for its unique capability to reveal foundational literature and trends across disciplines.

PurposeBrowsing library resources is a method long used by researchers to expand their understanding of ill-defined projects and to have serendipitous research discoveries. This study sought to understand the browsing activities of students in an academic library.Design/methodology/approachThe study used two methods. First, a link to an online survey was posted on posters throughout the library inviting students to share their browsing experience. Second, library security officers (student employees) were asked to count the number of people browsing within the stacks as they conducted their hourly walk-throughs of the library. Browsing was broadly defined as anyone who is between the ends of each stack, regardless of their activity. Data from the library security counts was compared to data from an unpublished report on browsing activity in 2017 that used similar data collection methods.FindingsStudents indicated that they used a blend of online and in-person browsing to find resources needed for their research and learning, followed by using only online browsing. The mean number of in-person browsers in the library has declined since 2017.Research limitations/implicationsThis study had several limitations. First, the survey was a voluntary sample and had only 325 responses. It would have been better to obtain a larger more representative sample of all students, but current university policy does not allow for this. Having a larger sample might produce different results. Additionally, the survey data was self-reported, and self-reported data may not always be accurate. The same definition of browsing was used in both library security counts, but the semester and length of data collection varied. It would have been better to have the library security counts in the same semester, but different years, and for the same duration. While library security did their best to count browsers, there were more counts missed in 2017 than in 2023. Had these counts not been missed, observed browsing activity would be higher in 2017. Finally, the library is undergoing several renovation projects. Between 2017 and 2023, some stacks were weeded and removed from the open stacks. Others were moved from one area of the library to another or condensed to make room for student study spaces. These changes may have affected browsing patterns. We did not examine the effect of shifting collections. Nor did we determine changes in stacks resulting from programs that were discontinued at the university at both the undergraduate and graduate levels.Practical implicationsBrowsing is a tried-and-true method for gaining better understanding of ill-defined research projects and to have serendipitous discoveries. Students need to be taught how to browse so they understand the benefits of browsing.Originality/valueThe research is original because the research team partnered with library security to conduct extensive observations and counts of browsers within the library. This collaboration provided data that determined browsing activity within an academic library. Since the baseline data was from 2017, the data provided a pre-pandemic and post-pandemic look at browsing activity.

PurposeThe goal of this study is to assess the degree of resemblance between machine-generated terms provided by two major indexing systems: Web of Science Keywords Plus and Dimensions Concepts.Design/methodology/approachA thorough analysis examines the distributional characteristics and similarities between these two terms. The study utilizes the rank frequency distribution of terms and comparisons of their forms using goodness-of-fit measures to assess distributional properties. Whereas to evaluate the similarities, the study utilized Jaccard similarity measures between high-frequency terms as well as overall terms (i.e. KW Plus and Dimensions Concepts).FindingsThe findings demonstrate that these two terms differ significantly in both distributional forms and similarities, thus representing different kinds of information related to the publication. The findings further indicate that the algorithms used by both databases for term generation/extraction are quite different from each other.Research limitations/implicationsThe implications of this study will enhance scholarly indexing and retrieval practices, supporting effective information access, organization and interdisciplinary research within academic databases and knowledge systems.Originality/valueThe novelty of the study is that it focuses on revealing the characteristics, similarities and differences between major indexing terms that were previously argued to be useful for performing various text analysis and scientometric exercises.

PurposeThe outbreak of COVID-19 has posed a significant public health threat, prompting the need for effective prediction and tracking of the virus’s spread. This study focuses on leveraging social media data, particularly Arabic Twitter posts, to predict and monitor COVID-19-related incidents. We aim to explore how natural language processing (NLP) techniques can be applied to extract meaningful information from Arabic tweets, offering valuable insights for health scientists and policymakers.Design/methodology/approachGiven the challenges associated with Arabic NLP, including its morphological richness and ambiguity, traditional word embedding models often fail to capture the context accurately. To address this issue, we propose a deep learning-based approach utilizing AraBERT, the state-of-the-art language representation model for Arabic. By fine-tuning AraBERT, we aim to extract named entities and identify key events related to COVID-19, enabling better understanding and tracking of the disease’s spread through social media. The data are sourced from Arabic-language Twitter posts, focusing on the COVID-19 pandemic, and processed using deep learning techniques.FindingsOur approach demonstrates that AraBERT, when fine-tuned for COVID-19-related tweets, can effectively capture context-rich entities and events, providing a reliable framework for real-time monitoring of the pandemic. This model outperforms traditional NLP techniques, showcasing its ability to handle the complexities of the Arabic language and improve the accuracy of event extraction in the context of social media.Originality/valueThis research introduces a novel method for monitoring COVID-19 using Arabic social media, highlighting the potential of deep learning models, particularly AraBERT, in enhancing information extraction from complex, context-heavy languages. The study contributes valuable insights into how social media can be used as a tool for epidemics.

PurposeThis study aims to address the challenge of generating accurate and engaging product descriptions for e-commerce platforms, particularly in the fashion domain. It seeks to alleviate the labor-intensive and time-consuming process of manual description writing by leveraging advanced natural language processing (NLP) techniques.Design/methodology/approachThe proposed solution integrates GPT-Neo, a transformer model, with the word-embedding model word2vec to automate product description generation. A dataset comprising 14,000 product titles and descriptions was sourced from Noon, a prominent Arabic e-commerce platform, and used to fine-tune the models for specific fashion categories.FindingsThe results demonstrate that the developed system effectively generates product descriptions based on product titles, achieving a recall rate of 67% and a precision of 72%. These findings validate the system’s potential to reduce manual effort while maintaining description quality.Originality/valueThis research offers a novel approach to automating product description generation for Arabic e-commerce platforms. It combines state-of-the-art NLP techniques to address a significant bottleneck in the e-commerce industry, contributing to enhanced operational efficiency and scalability. The study’s outcomes also pave the way for further advancements in multilingual NLP applications.

PurposeThe Open University Library’s Enquiries team developed and implemented a peer support and review process within its service. The aim was to develop a mechanism for staff that is supportive, shares good practice and informs learning and service improvement.Design/methodology/approachIn this case study, members of the Enquiries team were randomly assigned into small groups that met every other month to provide constructive feedback on how they responded to customer enquiries. Enquiries were evaluated based on criteria such as the use of template answers, tone, referral procedures and consideration of EDI principles. Feedback collected from the groups was shared with the whole team during the Enquiries team meetings.FindingsOur team has already seen benefits from the process which confirms it as a promising and useful tool we can use to improve our service. It allows the team to identify training gaps and areas for improvement as well as share best practice. Adjustments had to be made to the sessions’ frequency based on staff workload capacity. Concerns regarding the exposure of confidential information were also addressed.Originality/valueWe feel our approach is innovative and unique because it is tailored to the needs of the Enquiries team of a distance-learning institution. Our approach emphasises collaboration, reflective practice and continuous improvement.

PurposeThe study aims to calculate the recall ratio of selected MSEs and provide a comprehensive ranking for MSEs using features, precision and recall analysis.Design/methodology/approachThe study was divided into three consecutive sections: Keyword selections and checking demographic searchability; recall calculation among the MSEs and third calculating the Equal Weighted Score by allotting equal weight (0.25) to all MSEs to rank the MSEs based on the re-ranking aggregation approach.FindingsThe study clearly shows all the four MSEs considered—Dogpile, Metacrawler, DuckDuckGo and Startpage—Metacrawler (71%) ranked highest for recall, followed by DuckDuckGo (68%), Dogpile (63%) and Startpage (60%). The re-ranking aggregation approach results show DuckDuckGo (2) ranked 1st, followed by Startpage (2.5), Dogpile (2.75) and Metacrawler (2.75); lower scores indicate better performance. The findings indicate that DuckDuckGo is the best MSE regarding user experience (UX) and search quality.Research limitations/implicationsThe study used a re-ranking aggregation approach confined to past rankings and limited to four MSEs, limiting its generalizability.Practical implicationsThe finding helps users and developers understand the strengths and weaknesses of the different MSEs, enabling more informed decision-making and enhancing UX.Originality/valueThe study selected a novel approach for assessing the MSEs, and no similar study conducted in the past used different performance metrics to rank the MSEs.

PurposeThe purpose of this study is to investigate the integrated ICT-based library services at the Indian Institute of Technology (IIT) from the users' perspectives. By evaluating these services, the study seeks to know how ICT integration enhances academic access, user satisfaction and overall library functionality.Design/methodology/approachThe research employed a survey approach with a questionnaire as the primary data collection tool, involving a sample of 277 participants, determined through the Cochran sample size formula, with a 95% confidence level and a ±5% margin of error, drawing upon 25% of the population using a stratified random sampling technique. The collected data were analyzed using SPSS software version 23, applying statistical tests including T-tests, ANOVA and multivariate MANOVA, along with Tukey’s post hoc analysis.FindingsThe findings revealed that the library is equipped with a state-of-the-art ICT infrastructure facility, which significantly impacts users' academic performance. Research scholars (RS) perceived the highest impact with a mean score of 60.01, followed by postgraduates at 50.04 and undergraduates at 39.83. In terms of ICT-based library services, RS exhibit the highest usage. Additionally, the results indicate a high overall satisfaction level among users regarding library resources and services, with a mean satisfaction score of 4.10. However, 28.5% (N = 79) of respondents reported issue “in locating specific information.”Practical implicationsThe study demonstrates how the integration of ICT can significantly enhance service delivery, support academic advancement and improve user satisfaction in an increasingly digital and networked environment. These findings and strategies are valuable for libraries around the world, providing a roadmap for using technology to satisfy their users' changing requirements and encouraging an atmosphere of innovation and constant development for library services.Originality/valueBy focusing on user perspectives, the study provides actionable recommendations for library administrators and policymakers aiming to optimize library services in the digital age. The findings can serve as a benchmark for similar academic institutions striving to enhance academic access through technological advancements.

PurposeA usability eye-tracking study was conducted to examine how users interact with university LibGuides to find scientific data resources. During a study of DataONE’s Google Analytics, we found a high number of referrals from several university LibGuides. We wondered, “Does the high referral rate indicate that those LibGuides are more useable?”Design/methodology/approachThe study was conducted at the User-eXperience Lab at the University of Tennessee. Four LibGuides were selected for the study: Two had high referral rates, and two did not but included references to DataONE.FindingsWith people leaning more towards images than text, the idea of people preferring scanning to reading seems to be outdated thinking. Instead, people want to look at as little text as possible. Blending website design to match people’s preference for images over text while still conveying your message is becoming increasingly important. Design features such as collapsible boxes and using images to break up text blocks could be configured to help LibGuides become more modern to match the textless Internet that people seemingly prefer.Research limitations/implicationsOur study does not provide recommendations for a specific LibGuide but hopes to inform the general usability of all LibGuide interfaces. Individual universities should conduct their own usability studies when redesigning their interface. One potential limitation of the study is that our participants were not representative users of any of the university libraries’ LibGuides. We did not recruit for discipline or familiarity with any of the four universities we examined.Originality/valueUntil recently there has not been a wide range of published studies about the usability of LibGuides; however, there is a growing body of literature on the subject.