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This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of the existing literature, this study aims to provide valuable insights into the emerging field of big data in librarianship and its potential impact on the future of libraries.

This study employed a rigorous four-stage process of identification, screening, eligibility and inclusion to filter and select the most relevant documents for analysis. The Scopus database was utilized to retrieve pertinent data related to big data applications in librarianship. The dataset comprised 430 documents, including journal articles, conference papers, book chapters, reviews and books. Through bibliometric analysis, the study examined the effectiveness of different publication types and identified the main topics and themes within the field.

The study found that the field of big data in librarianship is growing rapidly, with a significant increase in publications and citations over the past few years. China is the leading country in terms of publication output, followed by the United States of America. The most influential journals in the field are Library Hi Tech and the ACM International Conference Proceeding Series. The top authors in the field are Minami T, Wu J, Fox EA and Giles CL. The most common keywords in the literature are big data, librarianship, data mining, information retrieval, machine learning and webometrics.

This bibliometric study contributes to the existing body of literature by comprehensively analyzing the latest trends and patterns in big data applications within librarianship. It offers a systematic approach to understanding the state of the field and highlights the unique contributions made by various types of publications. The study’s findings and insights contribute to the originality of this research, providing a foundation for further exploration and advancement in the field of big data in librarianship.

The purpose of this case study was to complement existing weeding and retention criteria beyond the most used methods in academic libraries and to consider citation counts in the identification of important scholarly works.

Using a small sample of items chosen for withdrawal from a small liberal arts college library, this case study looks at the use of Google Scholar citation counts as a metric for identification of notable monographs in the social sciences and mathematics.

Google Scholar citation counts are a quick indicator of classic, foundational or discursive monographs in a particular field and should be given more consideration in weeding and retention analysis decisions that impact scholarly collections. Higher citation counts can be an indicator of higher circulation counts.

The authors found little indication in the literature that Google Scholar citation counts are being used as a metric for identification of notable works or for retention of monographs in academic libraries.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

This study presents a review based research framework that aims to influence memory institutions in their projects on digital storytelling from digitized ancient travel records. This study aims to influence research and policymaking related to design and delivery of services based on memory institutions’ collections of historical records.

The demonstrated research framework has been synthesized using inputs from a review of existing studies on the domain accompanied by a short survey created for collecting the opinion of selected experts. Studies demonstrating utilization of semantic web technologies and those that can influence policymaking related to digital storytelling were primarily reviewed.

The core tasks behind digital storytelling vary depending on the project goals. So, a two-part framework had to be proposed that covers the generic fundamental tasks with diverse applicability and digital storytelling related specific tasks separately. Also during the review, it was found that studies demonstrating the use of travel records for digital storytelling were less in number compared to studies using digital storytelling for tourism in general.

The demonstrated research framework can guide memory institutions in exposing their travel-related holdings to a wider audience using innovative semantic web technologies and open up avenues for future empirical research thereby adding to the novelty of the presented research. Also, reviews of articles on digital storytelling or digital humanities in general exist, but, review of digital storytelling initiatives focusing specifically on tourism and travel literature is scarce.

The concept of banking reputation has gained significant attention due to its relevance in the banking industry. A strong reputation has become crucial for a bank’s success, as it affects trust, credibility and stakeholders' perceptions. However, understanding and managing reputation in the banking sector involves several challenges. This study aims to analyze the field of banking reputation research through bibliometric analysis.

It explores the evolution of research in this area, identifies key journals, articles and authors, examines the main research streams, and identifies research fronts and opportunities for future advancement.

The findings reveal that banking reputation research has evolved over time, with multiple perspectives and viewpoints. Key journals and authors in the field are identified, and leading research streams are highlighted. The study also uncovers the conceptual and intellectual structure of the research domain, providing insights into the complex and multidimensional nature of banking reputation. Furthermore, the study emphasizes the importance of corporate social responsibility, sustainability practices and gender diversity in shaping a bank’s reputation. These factors play a significant role in attracting and retaining customers, accessing financial markets and securing funding.

The results contribute to the existing body of knowledge and provide researchers and practitioners with valuable insights for further exploration.

The paper concludes by outlining potential avenues for future research in the field of banking reputation.

The study aims to analyze the synergy of artificial intelligence (AI), with scientometrics, webometrics and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields.

By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles.

(1) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis and knowledge mapping, in a more objective and reliable framework. (2) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis and recommender systems. (3) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining and recommender systems are considered as the potential of AI integration in the field of bibliometrics.

This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.

The focus of this study was to evaluate the relationship between research publications in the pesticide field, a country’s gross domestic product (GDP) and GDP per capita. The study aims to analyze pesticide use in association with a country’s population and research publications. The purpose of this study is to uncover the country’s contribution to pesticide research and assess the financial resources allocated to it as a percentage of their GDP by exploring these factors.

The Web of Science database was used to retrieve data for the period of 2001–2020. The use of scientometric indicators allowed for the analysis of the collaborative patterns and active performance of countries in pesticide research. Socio-economic analysis was used to determine the contribution of countries toward pesticide research.

This study demonstrated a strong association (0.952%) between a country’s GDP and its research publications in the field of pesticide research. Countries, such as Denmark, Belgium and Australia, have benefited from global collaboration, which has enhanced their research efforts. Despite ranking lower in pesticide utilization, India focused on pesticide research, as indicated by its high publication/GDP per capita ratio (0.26).

Research on pesticides directly impacts agricultural practices, which, in turn, influence the economic production of the agricultural sector. Changes in pesticide usage can have inference for crop yields, food price and, eventually, the GDP. Comparative analysis can assist in evaluating the efficiency of regulatory policies in balancing ecological concerns with economic interests. Changes in regulations may impact both pesticide usage and economic outcomes.

The paper aims to provide an overview of the state-of-the-art of the event industry in the context of digitalization to understand how digital technologies change the event industry and what research topics are the most promising for further exploration.

A bibliometric analysis of the existing body of knowledge on the topic was conducted and the results were visualized using CiteSpace 5.8.R3. A total of 1999 articles and proceeding papers from the Web of Science Core Collection published between 2007 and 2022 were selected for our analysis. Based on the articles and proceeding papers in the Web of Science Core Collection database, we selected a set of publications for our analysis. The data were obtained through specific keywords related to our research topic. The method involves a process of three main stages: data collection, data processing and the bibliometric analysis.

Co-citation analysis indicated that issues of crowd management and tracking human mobility during mass events are important for the event industry and that technologies such as the Internet of Things, special-purpose mobile applications and systems make it easier for an event organizer to handle the issues. The findings demonstrated a weak scientific collaboration between countries in the topic studied and shift of research hotspots to study of satisfaction, motivation and behavioral patterns of events attendees. Based on this analysis, three directions for future research were revealed.

The results should be interpreted in light of our sample, because the analysis was conducted within our sample which has boundaries. We collected data from all categories in the Web of Science Core Collection database, but we considered only articles and proceeding papers as opposed to all possible types of scientific publications and other databases. In the study, we focused on detecting the state-of-the-art of the event industry in the context of digitalization overall. More specific topics that could be analyzed remain, for example, the dependency of digital technologies from the event type, etc.

This study reflects the state-of-the-art of the event industry in the context of digitalization. It provides researchers with key developmental trends in the event industry, which assists them in more deeply understanding the evolution of research hotspots in the field during last 15 years and defining future research agenda. The paper presents an overview of digital technologies used in various types of events and describes the issues and results related to the implementing digital technologies. The results obtained were extremely important, as they can be used by event managers and organizers to enhance customers’ experience during the events.

This study reflects the state-of-the-art of the event industry in the context of digitalization. This is the first attempt to make an overall analysis of scientific papers published in the Web of Science Core Collection on the topic studied without excluding any categories. The search procedure is transparent, and the results can be reproduced in other search fields using the same approach. Based on this analysis, three directions for future research were revealed including technological aspects of online event-based social networks, issues of crowd management and security at mass events and issues of attendees’ acceptance of novel digital technologies.

This study aims to provide a comprehensive bibliometric analysis of social exclusion research, examining its evolution and identifying emerging trends and influential contributions in the field.

Using bibliometric and thematic analysis of 3,041 Scopus database documents, the study uses tools like VOSviewer for network analysis and Biblioshiny for trend analysis, focusing on publication patterns, author contributions and thematic clusters.

The findings reveal significant growth in social exclusion research since 1979, highlighting key contributions from diverse academic fields. Notable trends include the rise of digital exclusion and environmental justice themes. The study identifies leading authors, institutions and countries contributing to this field, along with highly cited documents that have shaped the discourse on social exclusion.

The study acknowledges its reliance on Scopus data and suggests incorporating other databases for future research. It highlights the need to explore emerging topics and address literature gaps.

This paper presents a unique bibliometric perspective on social exclusion research, underscoring its interdisciplinary nature and evolving focus. The study’s comprehensive approach offers valuable insights into the field’s trajectory, contributing to a deeper understanding of social exclusion phenomena.

The circular economy business models (CEBMs) provide ways for firms operating in the construction industry to move from a linear to a circular approach. Thus, this study aims to explore CEBM research within the construction sector to show the focus area of studies, highlighting new areas that require attention.

This study adopted a bibliometric approach, using the Scopus database as the data source. The keywords used for paper extraction from the database were “circular economy business” OR “circular business” AND “model” OR “models” AND “construction industry” OR “building industry”. The VOSviewer software was then used to prepare a co-occurrence and co-authorship map based on the bibliographic data gathered.

The study’s findings reveal five research clusters in the construction industry. These clusters include circular construction intelligence, modular business modelling, eco-construction, sustainable construction economics and smart energy-efficient buildings. The two most cited scholars had two publications each, while the top journals are the Journal of Cleaner Production and Sustainable Production and Consumption. This study concludes that there is a need for research within the construction sector to focus on CEBMs’ archetypes and frameworks. This will enable a smooth transition from linear to circular business models in the sector.

The information was gathered from a single database, Scopus; hence, using other databases, including Web of Science, Google Scholar and Dimensions, might produce more articles for examination and, consequently, different findings on the subject under investigation.

These findings would assist researchers in considering the areas mentioned, which are yet to receive attention, and, by extension, enhance economic development while maintaining environmental sustainability.

This paper made a significant contribution to the body of knowledge by identifying scholars and platforms that have been instrumental in advancing CEBM research and highlighting new areas that require attention in the construction sector.