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The purpose of this paper is to accomplish a bibliometric analysis, investigate the underlying knowledge structure, founding and development, and evolution of the Journal of Knowledge Management (JKM) through its articles published between 1997 and 2020.

A total of 1,346 research papers from JKM were selected and VantagePoint^® software was used to generate bubble maps, auto-correlation maps, and matrix maps through techniques such as principal component analysis (PCA) and natural language processing (NLP). The analysis gives insights about the foundation of knowledge structure, its evolution and the development of JKM.

The systematic mapping of research illustrates topics emerging as new offshoots, global favourites, saturated and plateaued and reached academic maturity. The USA, the UK, Australia, Spain, Italy, China, Canada, Germany, and France have contributed the most to JKM. This paper provides a robust roadmap for future research investigation of JKM.

The authors humbly admit the possibility of overlooking some research papers while evaluating and filtering the database of JKM. The research outcome summarizes 23 years, subject to information retrieval from archival files.

This research is a detailed bibliometric analysis explaining paradigm shifts in the body of knowledge of JKM. The bibliometric outcomes can act as beacons for future researchers and academicians to revisit the current trends that shape the domain of knowledge management, particularly for the JKM audience with a focus on contemporary topics of research interest.

This is a unique endeavour to accomplish a systematic bibliometric analysis of the JKM for two decades, offering insights about its structural body of knowledge through an overview of the chronology of scholarly development in the field of knowledge management.

Despite the concept of energy hub becoming widely signed and used in recent literature for addressing energy geopolitics, policies and relationships, it is still being confused interchangeably with other concepts. Hence, the present paper aims to shed light on the concepts of an energy hub, energy corridor, energy transit and energy center to clarify the main differences among these concepts through conducting a systematic review for the previous studies using the VOSviewer software targeting the identification of the main research gaps and clearing any confusion related to these concepts.

This paper conducted a systematic review based on a qualitative synthesis for the available publications from Web of Science and Scopus databases. It also followed strict search and filtration criteria based on the preferred reporting items for systematic reviews and meta-analyses guidelines. In this review, a total of 47 papers, which met the inclusion criteria were selected and identified in the synthesis, and a bibliometric visualization approach were implemented by using VOSviewer software.

The bibliometric analysis presented the growth of the research topic recently. Besides, text mining and keyword analysis revealed the key themes that address energy hubs issues such as security, geopolitics, relationships, policies and transit of energy. The main research gaps identified in this review will provide a reference point that will encourage and open new avenues for other interested researchers for future studies. Furthermore, this review contributes to our understanding of the literature related to energy hub area through bibliometric analysis and visualization of publications data.

According to the authors’ knowledge, this review may be the first bibliometric analysis and systematic review of the energy hub domain.

The purpose of this study is to map the intellectual structure of the research concerning conflict and conflict management in virtual teams (VT), to contribute to the further integration of knowledge among different streams of research and to develop an interpretative framework to stimulate future research.

A data set of 107 relevant papers on the topic was retrieved using the Web of Science Core Collection database covering a period ranging from 2001 to 2019. A comparative bibliometric analysis consisting of the integration of results from the citation, co-citation and bibliographic coupling was performed to identify the most influential papers. The systematic literature review complemented the bibliometric results by clustering the most influential papers.

The results revealed different intellectual structures across several types of analyses. Despite such differences, 41 papers resulted as the most impactful and provided evidence of the emergence of five thematic clusters: trust, performance, cultural diversity, knowledge management and team management.

Based on the bibliometric analyses an interpretative research agenda has been developed that unveils the main future research avenues. The paper also offers important theoretical contributions by systematizing knowledge on conflict in identifying VTs. Managerial contributions in the form of the identification of best practices are also developed to guide conflict management in VTs.

The uniqueness of this paper is related to its effort in studying, mapping and systematizing the knowledge concerning the topic of handling conflicts in VTs. Considering the current contingencies, this research is particularly timely.

Recently, much research about the effects of simulation on an organization’s operational performance and efficiency has been carried out. But still, there is a need for lean-based simulations rather than just modelling the system. However, these studies still lack systematization. Consequently, the purpose of this paper is to systematize the literature on lean simulations in the area of production and operations management.

A systematic analysis was performed on 93 articles extracted from the Clarivate Analytics Web of Science (WoS) Core Collection database. The analysis was limited to only articles that come under the “Engineering industrial,” “Engineering Manufacturing” and “Operations Research and Management” subject category in WoS Core Collection database. Later, a bibliometric analysis was integrated with the literature review for deeper literature categorization.

A bibliometric analysis displayed the relevance of selected literature (keywords, authors and sources). The systematic literature review helped to clarify each clusters’ content and purpose.

This study portrays the gaps in the lean simulation integration methods and techniques through a detailed systematic and bibliometric review.

Lean managers can get various insights and ideas on implementing lean and simulation integration methods in real-time process improvement scenarios.

This study demonstrates the best future recommendations for lean based modelling for both consumers and organizations that shall address the social and economic issues.

To the best of the authors’ knowledge, minimal attention has been paid to systematizing the literature on lean simulation in industrial, manufacturing and operations management. This study contributes to close this gap.

Teleworking, working from home and flexible work have gained popularity over the last few years. A shift in policies and practices in the workplace is required owing to the COVID-19 pandemic accelerating current trends in work-from-everywhere (WFE) research. This article presents a systematic literature review of WFE research from 1990 to early 2023 to understand the transformation of the field.

The Web of Science database was used to conduct this review based on rigorous bibliometric and network analysis techniques. The prominence of the research studied using SPAR-4-SLR and a collection of bibliometric techniques on selected journal articles, reviews and early access articles. Performance and keyword co-occurrence analysis form the premise of cluster analysis. The content analysis of recently published papers revealed the driving and restraining forces that help define and operationalize the concept of WFE.

The major findings indicate that the five established and accelerated trends from cluster analysis are COVID-19 and the pandemic, telework(ing), remote working, work from home and well-being and productivity. Driving and restraining forces identified through content analysis include technological breakthroughs, work–life integration challenges, inequality in the distribution of jobs, gender, shifts in industry and sector preferences, upskilling and reskilling and many more have been published post-COVID in the restraining forces category of WFE.

A key contribution of this pioneering study of “work from everywhere” is the linking of the bibliometric trends of the past three decades to the influencing and restraining factors during the pandemic. This study illustrates how WFE could be perceived differently post-COVID, which is of great concern to practitioners and future researchers.

A wide range of publications on WFE and multiple synonyms can create confusion if a systematic and effective system does not classify and associate them. This study uses both bibliometric and scientometric analyses in the context of WFE using systematic literature review (SLR) methods.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.

Artificial neural networks (ANNs), which represent computational models simulating the biological neural systems, have become a dominant paradigm for solving complex analytical problems. ANN applications have been employed in various disciplines such as psychology, computer science, mathematics, engineering, medicine, manufacturing, and business studies. Academic research on ANNs is witnessing considerable publication activity, and there exists a need to track the intellectual structure of the existing research for a better comprehension of the domain. The current study uses a bibliometric approach to ANN business literature extracted from the Web of Science database. The study also performs a chronological review using science mapping and examines the evolution trajectory to determine research areas relevant to future research. The authors suggest that researchers focus on ANN deep learning models as the bibliometric results predict an expeditious growth of the research topic in the upcoming years. The findings reveal that business research on ANNs is flourishing and suggest further work on domains, such as back-propagation neural networks, support vector machines, and predictive modeling. By providing a systematic and dynamic understanding of ANN business research, the current study enhances the readers' understanding of existing reviews and complements the domain knowledge.

This chapter aims to analyse the evolution of research in the Indian tourism and hospitality domain from 1976 to 2021. By employing the bibliometric and systematic literature review, the chapter highlights the prominent authors, institutions, methods, emerging and explored research themes in the tourism and hospitality field. Accordingly, the authors collected 458 articles from core tourism and hospitality related journals using PRISMA and evaluated them using VOSViewer. The findings revealed an emic perspective of research contribution in Indian tourism and hospitality. While some topics such as religious tourism, sustainability, cultural and heritage tourism products have attained more attention, topics such as tourism entrepreneurship, technology and human resource development failed to seek attention in the eyes of journals and researchers. Although the literature has expanded significantly, there is a need to build global academic networks to examine the state of Indian tourism and hospitality. The chapters suggest that future research should critically evaluate the schemes and initiatives undertaken by the government to promote Indian tourism, expand research to western and eastern parts of the country, and follow the mixed-method research to contribute holistically to the topic.

This paper aims to quantify the trend of variables used for building a theoretical model with the help of analysing bibliometrics data of inventory management (IM) and Total Quality Management (TQM) practices and their effects on firm performance.

Scopus' research database and the Web of Science (WoS) (including Elsevier, Emerald Group Publishing, Taylor and Francis, Wiley, IEEE, Informs and SAGE) were used to find relevant articles. The articles, review papers and conference proceedings were screened from 1993 to 2021. The articles were analysed to explain the different types of IM practices, TQM practices and their effect on the firm's performance. Thematic analysis was done using a bibliometric package of “R” (Biblioshiny) and VOSviewer to identify the key trends, approaches and research agenda.

The research covered 28 years of publications and summarised 497 articles, review papers and conference papers. Researchers concluded that IM highly correlates with the inventory turnover ratio and has no relation to firm performance. Further, TQM positively affects firm performance, but integrating IM and TQM will be a research scope for future study as none of the researchers previously covered this.

Due to the systematic literature review (SLR) and Bibliometric review, the study is limited to the Scopus and WoS (including Elsevier, Emerald Group Publishing, Taylor and Francis, Wiley, IEEE, Informs and SAGE) databases.

Being the research on a bibliometric and systematic review, relating IM practices with TQM practices would be the novelty of this paper.

The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM).

In the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing.

The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices.

A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.