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This paper aims to assess whether the current eResearch Knowledge Centre’s (eRKC) research support practices align with researchers’ requirements for achieving their research objectives. The study’s objectives were to assess the current eRKC research support services and to determine which are adequate and which are not in supporting the Human Sciences Research Council (HSRC) researchers.

This study uses interviews as part of the qualitative approach. The researcher chose to use interviews, as some aspects warranted further explanation during the interview. The interviews were scheduled using Zoom’s scheduling assistant. The interviews were semi-structured, guided by a flexible interview procedure and supplemented by follow-up questions, probes and comments. The research life cycle questions guided the interviews. The data obtained were coded and transcribed using MS Excel. The interview data were analysed, using NVivo, according to the themes identified in the research questions and aligned with the theory behind the study. Pre-determined codes were created in line with the six stages of the research life cycle and applied to group the data and extract meaning from each category. Interviewee responses were assigned to groups in line with the stages of the research life cycle.

The current eRKC research support services are aligned with the needs of HSRC researchers and highlight services that could be expanded or promoted more effectively to HSRC researchers. It proposes a new service, data analysis, and suggests that the eRKC could play a more prominent role in research impact, research data management and fostering collaboration with HSRC research divisions.

This study is limited to assessing the eRKC’s support practices at the HSRC in Pretoria, South Africa. A more comprehensive study is needed for HSRC research services, capabilities and capacity.

Assessment of eRKC followed a comprehensive interviewee schedule that followed Raju and Schoombee’s research life cycle model.

Zoom’s scheduling assistant may have generated Zoom fatigue and reduced productivity. Technical issues, losing time, communication gaps and distant time zones may have affected face-to-face interaction.

eRKC research support practices are rare in South Africa and most parts of the world. This study bridges the gap between theory and practice in assessing eRKC research support practices.

In today's global and competitive corporate environment characterised by rapidly changing information, knowledge and technology (IKT), researchers must be upskilled in all aspects of research data management (RDM). This study investigates a set of capabilities and competencies required by researchers at selected South African public universities, using the community capability model framework (CCMF) in conjunction with the digital curation centre (DCC) lifecycle model.

The post-positivist paradigm was used in the study, which used both qualitative and quantitative methodologies. Case studies, both qualitative and quantitative, were used as research methods. Because of the COVID-19 pandemic rules and regulations, semi-structured interviews with 23 study participants were conducted online via Microsoft Teams to collect qualitative data, and questionnaires were converted into Google Forms and emailed to 30 National Research Foundation (NRF)-rated researchers to collect quantitative data.

Participating institutions are still in the initial stages of providing RDM services. Most researchers are unaware of how long their institutions retain research data, and they store and backup their research data on personal computers, emails and external storage devices. Data management, research methodology, data curation, metadata skills and technical skills are critically important RDM competency requirements for both staff and researchers. Adequate infrastructure, as well as human resources and capital, are in short supply. There are no specific capacity-building programmes or strategies for developing RDM skills at the moment, and a lack of data curation skills is a major challenge in providing RDM.

The findings of the study can be applied widely in research, teaching and learning. Furthermore, the research could help shape RDM strategy and policy in South Africa and elsewhere.

The scope, subject matter and application of this study contribute to its originality and novelty.

This study aims to examine the state of embedded librarianship in African academic libraries by exploring the extent of librarian embedment in research processes in selected academic libraries in Africa.

A survey was conducted to collect data using an online questionnaire. The study population was comprised of librarians who directly support researchers throughout the research process in the scholarly communication and information services sections. Purposive sampling was used to select librarians, and 63 responses were received from the target of 100. The collected data were analysed using the statistical package for the social sciences (SPSS) software, and content analysis was used to analyse open-ended questions.

The study results showed that the extent and nature of librarians' embeddedness in the research process vary across different university contexts in Africa. Challenges and areas of research skill gaps among librarians were identified, and propositions to address the research skill gaps were provided.

The study findings can be used as a framework for libraries to embed librarians in the research process. The study made recommendations with practical implications for library practitioners in academia, and institutions offering library and information science education, and the library management responsible for the research process.

A study brings a novel perspective by examining the state of embedded librarianship in the African context. Its contribution generates knowledge and insights that inform the development of effective embedded librarianship practices in African academic libraries to enhance research support services and advancement of scholarly research in the region.

One of the most critical changes to academic library services in the last decade has been the development and implementation of research data services (RDS). This paper seeks to provide a comprehensive understanding of conceptual models for the RDS space.

An extensive and comprehensive review of the literature on conceptual models for RDS is performed.

Various conceptual models for RDS provide a compelling tool for guiding and evaluating RDS development and implementation within academic libraries.

An insightful review and synthesis of context and issues in RDS models will help academic libraries to plan, develop and advance their RDS.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem.

The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries.

It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries.

The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data.

The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time.

According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

This study aims to examine Big Data and the management of libraries in the era of the Fourth Industrial Revolution and its implications for policymakers in Nigeria.

A qualitative methodology was used, involving the administration of open-ended questionnaires to librarians from six selected federal universities located in Southwest Nigeria.

The findings of this research highlight that a significant proportion of librarians are well-acquainted with the relevance of big data and its potential to positively revolutionize library services. Librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

This study exclusively focuses on the Nigerian context, overlooking insights from other African countries. As a result, it may not be possible to generalize the study’s findings to the broader African library community.

To the best of the authors’ knowledge, this study is unique because the paper reported that librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

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 study aims to promote the use of qualitative methods in service research by investigating how these methods are reported in service journals, how the level of reporting has evolved and whether methodological reporting influences the downloads or citations received by qualitative articles.

Methodological reporting practices were identified through content analysis of 318 qualitative articles published in three major service research journals and comparison with prior methodological literature. Regression analysis was used to test how the level of methodological reporting influences article downloads and citations.

The study identifies 29 reporting practices related to 9 key methodological reporting areas. The overall level of methodological reporting in published qualitative articles has increased over time. While differences in the level of reporting between service journals persist, they are narrowing. The level of methodological reporting did not influence downloads or citations of qualitative articles.

Service scholars using qualitative methods should pay attention to methodological reporting as it can improve the chances of being published. Factors such as theoretical contributions are likely to have a greater influence on article impact than methodological reporting.

No prior study has explored methodological reporting practices across different qualitative methodologies or how reporting influences article impact. For authors, reviewers and editors, the study provides an inventory of reporting practices relevant for evaluating qualitative articles, which should lower barriers for qualitative methods in service research by providing practical guidelines on what to focus on when reporting and assessing qualitative research.

This research aims to analyse the current state of research on the application of Artificial Intelligence (AI) in libraries by examining document type, publication year, keywords, country and research methods. The overarching aim is to enrich the existing knowledge of AI-powered libraries by identifying the prevailing research gaps, providing direction for future research and deepening the understanding needed for effective policy development.

This study used advanced tools such as bibliometric and network analysis, taking the existing literature from the SCOPUS database extending to the year 2022. This study analysed the application of AI in libraries by identifying and selecting relevant keywords, extracting the data from the database, processing the data using advanced bibliometric visualisation tools and presenting and discussing the results. For this comprehensive research, the search strategy was approved by a panel of computer scientists and librarians.

The majority of research concerning the application of AI in libraries has been conducted in the last three years, likely driven by the fourth industrial revolution. Results show that highly cited articles were published by Emerald Group Holdings Ltd. However, the application of AI in libraries is a developing field, and the study highlights the need for more research in areas such as Digital Humanities, Machine Learning, Robotics, Data Mining and Big Data in Academic Libraries.

This study has excluded papers written in languages other than English that address domains beyond libraries, such as medicine, health, education, science and technology.

This article offers insight for managers and policymakers looking to implement AI in libraries. By identifying clusters and themes, the article would empower managers to plan ahead, mitigate potential drawbacks and seize opportunities for sustainable growth.

Previous studies on the application of AI in libraries have taken a broad approach, but this study narrows its focus to research published explicitly in Library and Information Science (LIS) journals. This makes it unique compared to previous research in the field.