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This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

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.

This compact case provides a relatively large data set that students explore using visualization and a Tableau dynamic dashboard that they create. Students were asked to describe what the data set contained in relation to employee attrition experience of Baca Beverage Distributors (BBD). The application and managerial questions are set in human resources and a company that is facing high attrition during the pandemic.

BBD shared their data and problem scenario for this compact case. The protagonist, Morgan Matthews, was the authors’ contact and provided significant clarification and guidance about the data. Both the company and the protagonist have been disguised. Some of the job positions have been rephrased. All names of employees, supervisors and managers have been replaced with codes.

During the 2020–2022 pandemic years, BBD experienced, like many companies, a higher than usual employee turnover rate and Morgan Matthews, Director of People, was concerned. Not only was it time-consuming, expensive and disruptive but the company had prided itself on being a good place to work. Were they hiring the right people, people that fit the company culture and people that fit the positions for which they were hired? The company had been using the Predictive Index [1] when on-boarding employees. In addition, there were results from self-reviews and manager reviews that could be used. Morgan wondered if data visualization and visual analytics would be useful in describing their employees and whether it would reveal any opportunities to improve the turnover rate. Before seeking a solution for the high turnover, it was important to step back and learn what the data said about who was leaving and the reasons they gave for leaving.

This compact case can be used in courses that include visualization using Tableau and dashboards. As it is a compact case, it requires less preparation time from the students and less class time for discussion. The case is for students who have been recently introduced to business analytics, specifically visualization and data storytelling with Tableau. For this reason, significant guidance has been provided in the case assignment. The level of the case can be adjusted by the amount of guidance provided in the case assignment. Courses include introduction to business analytics, descriptive analytics and visualization, communication through data storytelling. The case can be used for all modalities – in person, hybrid, online. The authors use it here for visualization and dynamic dashboards but using the same data set and compact case description, exploratory data analysis could be assigned.

Supplementary material for this article can be found online.

This paper aims to investigate the literacy corpus regarding the potential of big data to improve public decision-making processes and direct these processes toward the creation of public value. This paper presents a map of current knowledge in a sample of selected articles and explores the intersecting points between data from the private sector and the public dimension in relation to benefits for society.

A bibliometric analysis was performed to provide a retrospective review of published content in the past decade in the field of big data for the public interest. This paper describes citation patterns, key topics and publication trends.

The findings indicate a propensity in the current literature to deal with the issue of data value creation in the private dimension (data as input to improve business performance or customer relations). Research on data for the public good has so far been underestimated. Evidence shows that big data value creation is closely associated with a collective process in which multiple levels of interaction and data sharing develop between both private and public actors in data ecosystems that pose new challenges for accountability and legitimation processes.

The bibliometric method focuses on academic papers. This paper does not include conference proceedings, books or book chapters. Consequently, a part of the existing literature was excluded from the investigation and further empirical research is required to validate some of the proposed theoretical assumptions.

Although this paper presents the main contents of previous studies, it highlights the need to systematize data-driven private practices for public purposes. This paper offers insights to better understand these processes from a public management perspective.

The study aims to explore digital transformation from the viewpoint of human resource management to uncover possible threads of relationship using bibliometric analysis. It also aims to identify the trending research themes within the domains of digital transformation (DT) and human resource management (HRM) collectively.

The research employs a mix of quantitative bibliometric techniques and qualitative content analysis. A corpus of 227 articles retrieved from the Scopus database was analyzed using the R-based Biblioshiny and VOS viewer.

The study shows publication trends, influential authors, leading journals, highly productive institutions, and, countries in the domain of DT and HRM. Co-citation and co-occurrence analysis was undertaken to identify the research clusters, depicting trending research themes that extensively dominate the research under this domain.

This study will serve as a ready reckoner for academicians and business leaders, giving them useful insights to make their road towards digital transformation less challenging with the assistance of human capital.

This study is one of the initial efforts to quantitatively synthesize the results of earlier publications using bibliometric techniques in the domain of DT and HRM together. It will aid researchers in locating research gaps and filling those gaps in the future.

Due to the financial disturbances created by the COVID-19 pandemic and the burden on the government exchequer, it is expected to see a rise in the knowledge base of the research corpus so far as the government's fiscal sustainability is concerned. Therefore, the present research examines a systematic quantitative analysis of public debt sustainability research by applying a bibliometric approach. Research also analyzes journals, institutions, countries and authors contributing to public debt sustainability.

This paper scrutinizes the published scientific research on public debt sustainability based on the dataset of 535 articles from 1991 to 2021 obtained from the Scopus database. Biblioshiny (R-based application) and VoSviewer software were used to perform bibliometric analysis through Performance analysis and science mapping techniques. The authors combined co-citation analysis (CCA), bibliometric analysis, keyword co-occurrence analysis (KCA) and a conceptual thematic map of the most cited articles to find the intellectual structure.

The research identified three dominating clusters, e.g. fiscal sustainability and policy rules, empirical sustainability testing and debt and growth dynamics. Another finding was that most articles were analytical and empirical and few descriptive articles were found. Owing to the empirical nature of the domain, the issues concerning public debt sustainability have continued to change over the past decades for different economies, reflecting the complexity and diversity of economic structures of different economies at different times.

The insight of this article provides academicians and researchers with a more refined comprehension of the conceptual and intellectual structure of the research corpus. The present research complements the existing literature review studies by pushing the research towards emerging or less developed issues such as financial and debt crises.

The purpose of this study is to depict the value added by digital transformation to intellectual capital (IC) by virtue of the studies reached by the literature review on different databases are examined.

Journal of Intellectual Capital, which has the highest number of records from the resources included in the “Web of Science” content and covering the title of “intellectual capital” has been selected in this study. Research using bibliometric analysis has been conducted and it has been determined that the terms “digital transformation” and “intellectual capital” should be searched for simultaneously in each and every article published in the journal between the years 1975 and 2022.

A bibliometric analysis and citation mapping process are carried out considering all dimensions to reach the results and interpretation of findings. VOSviewer is used to visualize the bibliometric networks of results and findings in the form of scientific mapping, as well as to visualize the co-authorship analysis of keywords, co-authorship analysis and citation networks.

Bibliometric analysis is a method that can be used to evaluate the performance of a single journal. However, it is important to note that bibliometric analysis has some limitations when it comes to assessing the validity of a single journal. This circumstance is elaborately described as a limitation of this study. Bibliometric analysis is a method that can be used to evaluate the performance of a single journal. However, it is important to note that bibliometric analysis has some limitations when it comes to assessing the validity of a single journal. One limitation is that bibliometric analysis is based on quantitative metrics, such as citation counts, which do not take into account the quality of the research. Therefore, bibliometric analysis alone may not provide a complete picture of the validity of a single journal. In addition, bibliometric analysis is based on the number of times a paper is cited, which can be influenced by factors such as the prestige of the journal, the field of research and the time since the publication. In conclusion, bibliometric analysis can be used to evaluate the performance of a single journal, but it is important to consider its limitations.

This study identified contributions, gaps and limits based on the results of a bibliometric analysis. Italy is the most influential country and the issue is structured around four clusters: IC; digital transformation; human capital; and knowledge management. As previously unexplored issues are addressed in an innovative manner, it is acceptable to underline the paper’s originality.

Health-care marketing typically entails a coordinated set of outreach and communications designed to attract consumers (patients in the health-care context) who require services for a better health outcome and guide them throughout their health-care journey to achieve a higher quality of life. The purpose of this study is to understand the progress and trends in healthcare marketing strategy (HMS) literature between 2018 and 2022, with a special emphasis on the pre- and post-Covid-19 periods.

The authors examine 885 HMS-related documents from the WOS database between 2018 and 2022 that were extracted using a keyword-based search strategy. After that, the authors present the descriptive statistics related to the corpus. Finally, the authors use author co-citation analysis (ACA) and bibliographic coupling (BC) techniques to examine the corpus.

The authors present the descriptive statistics as research themes, emerging sub-research areas, leading journals, organisations, funding agencies and nations. Further, the bibliometric analysis reveals the existence of five thematic clusters: Cluster 1: macroeconomic and demographic determinants of healthcare service delivery; Cluster 2: strategies in healthcare marketing; Cluster 3: socioeconomics in healthcare service delivery; Cluster 4: data analytics and healthcare service delivery; Cluster 5: healthcare product and process innovations.

This study provides an in-depth analysis of the advancements made in HMS-related research between 2018 and 2022. In addition, this study describes the evolution of research in this field from before to after the Covid-19 pandemic. The findings of this study have both research and practical significance.

To the best of the authors’ knowledge, this is the first study of its kind to use bibliometric analysis to identify advancements and trends in HMS-related research and to examine the pattern before and after Covid-19 pandemic.

This study aims to know the tactician role of financial technology (FinTech) in the field of accounting and auditing through contextualized systematic literature review by using bibliometric analysis.

The qualitative bibliometric analysis includes studies from 2017 to 2021 using the Scopus and Web of Science databases, which yielded 277 published papers with the keywords, FinTech accounting and auditing. The contextualized systematic literature review greatly helped in clarifying the content within each cluster.

The study identified the tactician role of fintech primarily in the accounting and auditing professional field. Fintech is still in its inception, with continual development and implementation taking place especially, in the auditing field. The findings also confirm that FinTech can produce a confluence between various research areas, including accounting, auditing, business finance, economics, management and business field.

The study describes the tactician role of FinTech and its huge possibility for future study in the accounting and auditing field among professionals, academics and regulators.

This study be able to help accounting professionals, policymakers and government regulators to establish policy development, as this research emphasizes the tactician role of FinTech in the accounting and auditing field.

FinTech in accounting and auditing might add to the existing field of FinTech in the IR4.0 era that give benefits to different players such as policymakers, governments, researchers, FinTech entrepreneurs and practicing professionals.

To the best of the author’s knowledge, little focus has been given about FinTech in the accounting and auditing field using bibliometric analysis. The insights of systematic literature review provide researchers on FinTech among practicing professionals and offer opportunities for further scientific endeavours.

This study aims to highlight the theoretical foundations and future research directions in religious tourism and related topics from 2003 to 2023.

A total of 4,143 documents from Web of Science and Scopus databases related to religious tourism and pilgrimage were analysed using VOSviewer software.

The analysis illuminates a steady rise in religious tourism research. The most influential countries are the USA, the UK and Israel. Gendered studies, tourists’ perceptions and technology in religious tourism will be research hotspots, as predicted with keyword co-occurrence analysis.

This study thoroughly evaluates two decades of religious tourism literature through bibliometric and network analysis. It can help researchers comprehend religious tourism study more thoroughly and determine where to focus in future research.

本研究强调了 2003–2023 年宗教旅游及相关主题的理论基础和未来研究方向。

使用 VOSviewer 软件分析了来自 Web of Science 和 Scopus 数据库的与宗教旅游和朝圣相关的总共 4143 份文件。

分析表明宗教旅游研究稳步上升。 最具影响力的国家是美国、英国和以色列。 正如关键词共现分析预测的那样, 宗教旅游中的性别研究、游客感知和宗教旅游技术将成为研究热点。

本研究通过文献计量和网络分析彻底评估了二十年的宗教旅游文献。 它可以帮助研究人员更透彻地理解宗教旅游研究, 并确定未来研究的重点。

Este estudio destaca los fundamentos teóricos y las futuras direcciones de investigación en turismo religioso y temas relacionados entre 2003 y 2023.

Se analizó un total de 4143 documentos de las bases de datos Web of Science y Scopus relacionados con el turismo religioso y la peregrinación utilizando el software VOSviewer.

El análisis ilumina un aumento constante en la investigación del turismo religioso. Los países más influyentes son Estados Unidos, Reino Unido e Israel. Los estudios de género, la percepción de los turistas y la tecnología en el turismo religioso en el turismo religioso serán puntos críticos de investigación como se predice con el análisis de co-ocurrencia de palabras clave.

Este estudio evalúa minuciosamente dos décadas de literatura sobre turismo religioso a través de análisis bibliométrico y de redes. Puede ayudar a los investigadores a comprender el estudio del turismo religioso más a fondo y determinar dónde enfocarse en futuras investigaciones.