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The findings of this study have lightened the focal research areas in maintenance planning and scheduling. These also served as effective guidelines for future studies in this area. This research, therefore, contributes in fulfilling the gap by carrying out an SLR of contemporary research studies in the area of models for maintenance planning and scheduling. At present, SLR rooted in BA has not been carried focusing on a survey over models for maintenance planning and scheduling. SLR uses advanced scientific methodologies from BA tools to unveil thematic structures.

We have systematically reviewed over 1,021 peer-reviewed journal articles. Advanced contemporary tools from Bibliometric Analysis (BA) are used to perform a Systematic Literature Review (SLR). First, exploratory analysis is presented, highlighting the influential authors, sources and region amongst other key indices. Second, the large bibliographical data is visualized using co-citation network analyses, and research clusters (themes) are identified. The co-citation network is extended into a dynamic co-citation network and unveils the evolution of the research clusters. Last, cluster-based content analysis and historiographical analysis is carried out to predict the prospect of future research studies.

BA tools first outlined an exploratory analysis that noted influential authors, production countries, top-cited papers and frequent keywords. Later, the bibliometric data of over 1,021 documents is visualized using co-citation network analyses. Later, a dynamic co-citation analysis identified the evolution of research clusters over time. A historiographical direct citation analysis also unveils potential research directions. We have clearly observed that there are two main streams of maintenance planning and scheduling applications. The first has focused on joint maintenance and operations on machines. The second focused on integrated production and maintenance models in an echelon setting for unrealizable production facilities.

There are many literature review-based research studies that have contributed to maintenance scheduling research surveys. However, most studies have adopted traditional approaches, which often fall short in handling large bibliometric data and therefore suffer from selection biases from the authors. As a result, in this area, the existing reviews could be non-comprehensive. This study bridges the research gap by conducting an SLR of maintenance models, which to the best of our knowledge, has not been carried out before this study.

This study research contributes in fulfilling the gap by carrying out a systematic literature review (SLR) of contemporary research studies in closed-loop supply chain (CLSC). To the best of the author’s knowledge, an SLR rooted in bibliometric analysis has not been carried focusing on advent developments in CLSC. SLR employs scientific methodologies to select papers from standard databases. The SLR using advanced bibliometric and network analysis enables unveiling the key features of the contemporary literature.

The author has analyzed over 333 documents published from 2008 and onward. Using the contemporary tools from bibliometric analysis tools, the author presented an exploratory analysis. A network analysis is utilized to visualize literature and create clusters for the cocited research studies, keywords and publication sources. A detailed multivariate analysis of most influential works published based top 100 articles via a cocitation matrix is done. The multivariate analysis used k-means clustering in which optimal number of clusters are estimated. The analysis is further extended by using a factor analysis, which enables determining the most influential clusters in the k-means clustering analysis.

The SLR using a bibliometric and network analysis enables unveiling the key features of the contemporary literature in CLSC. The author examined published research for influential authors, sources, region, among other key aspects. Network analysis enabled visualizing the clusters of cocited research studies, cowords and publication sources. Cluster analysis of cocited research studies is further explored using k-means clustering. Factor analysis extends findings by identifying most contributing grouping of research areas within CLSC research. Each clustering technique disclosed a unique grouping structure.

CLSC has received considerable attention, and its core areas start with focusing on reverse logistics concepts relating reuse, recycling, remanufacturing, among others. Contemporarily, the studies have enhanced reverse logistics core functionalities interfaced with the other interesting avenues related to CO₂ emission reduction, greening and environmental protection, sustainability, product design and governmental policies. Earlier studies have presented a literature review of CLSC; however, these reviews are commonly conducted in the traditional manner where the authors select papers based on their area of expertise, interest and experience. As such these reviews fall short in utilizing the advanced tools from bibliometric analysis.

This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.

Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.

Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.

Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

The findings of this paper throw light on the focal research areas within RFID in the supply chain, which serves as an effective guideline for future research in this area. This research, therefore, contributes to filling the gap by carrying out an SLR of contemporary research studies in the area of RFID applications in supply chains. To date, SLR augmented with BA has not been used to study the developments in RFID applications in supply chains.

We analyze 556 articles from years 2001 to date using Systematic Literature Review (SLR). Contemporary bibliometric analysis (BA) tools are utilized. First, an exploratory analysis is carried, out revealing influential authors, sources, regions, among other key aspects. Second, a co-citation work analysis is utilized to understand the conceptual structure of the literature, followed by a dynamic co-citation network to reveal the evolution of the field. This is followed by a multivariate analysis is performed on top-100 cited papers, and k-means clustering is carried out to find optimal groups and identify research themes. The influential themes are then pointed out using factor analysis.

An exploratory analysis is carried out using BA tools to provide insights into factors such as influential authors, production countries, top-cited papers and frequent keywords. Visualization of bibliographical data using co-citation network analysis and keyword co-occurrence analysis assisted in understanding the groups (communities) of research themes. We employed k-means clustering and factor analysis methods to further develop these insights. A historiographical direct citation analysis also unveils potential research directions. We observe that RFID applications in the supply chain are likely to benefit from the Internet of Things and blockchain Technology along with the other machine learning and visualization approaches.

Although several researchers have researched RFID literature in relation to supply chains, these reviews are often conducted in the traditional manner where the author(s) select paper based on their area of expertise, interest and experience. Limitation of such reviews includes authors’ selection bias of studies to be included and limited or no use of advanced BA tools for analysis. This study fills this research gap by conducting an SLR of RFID in supply chains to identify important research trends in this field through the use of advanced BA tools.

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 paper is to propose a new hybrid approach based on bibliometrics analysis (BA), morphology analysis (MA) and conjoint analysis (CA) to help identify new technology development opportunities.

A new hybrid approach based on BA, MA and CA has been conducted to help identify new technology development opportunities. The proposed hybrid process is illustrated with a case example of blue light‐emitting diode (LED) based on GaN.

In this paper, the proposed hybrid process is illustrated with a case example of document information from a blue light‐emitting diode (LED) based on GaN documents database. The results show that the configuration “Al₂O₃+MBE+two dimensional photonic crystal” should be given greater attention with respect to R&D activities in future.

This paper is of interest for technology opportunities analysis practitioners and policymakers at the industrial and government levels.

This paper proposes a new hybrid approach of technology opportunities analysis based on bibliometrics analysis, morphology analysis and conjoint analysis methods.

This study focuses on the role of individuals in the innovation management process, by concentrating on leaders and associated behaviors. Specifically, Entrepreneurial Leadership (EL) represent one of the most important fields of innovation management that has become increasingly multifaceted and interdisciplinary with its evolution. Thus, the purpose of this study is to examine a newly emerging research trend with a new lens that is “neuroscience”.

This paper finds an evidence-based roadmap by reviewing the literature with a quantitative Bibliometric Analysis (BA) employing Co-Citation (Co-C) and bibliographic coupling analysis (BcA) to find linkages between the leadership and entrepreneurship literature and the neuroscience literature.

This study identifies five promising groups of research areas such as the organizational approach, the biological approach, the cognitive approach, the emotional approach and it identify five future research topics such as dynamic skills in innovation exploitation process, the human aspect of leadership, the building process of leadership, the biological perspective of leadership and the application of neuroscience in the ecosystem. Moreover, we find an evidence-based roadmap for stimulating focused EL within the broad topic of innovation management research, to move the field forward.

Although the past few years have observed the necessity of review studies on the subsets of biological factors, no reviews have sought to bring those different subsets together into a broader biological perspective. This study provides important indications on the interdisciplinary developments between the neuroscience aspects and EL, as a new emerging paradigm within the broad field of innovation management.

This paper identifies, evaluates and structures research that focuses on “collaboration” (C), “trust” (T) and “performance” (P) in the agri-food supply chain (AFSC) and reveals its intellectual foundation. It aims to synthesize research published over a period of 18 years (from 2003 to the beginning of 2020) and provide a platform for practitioners and researchers in their efforts to identify the existing state of work, gaps in current research and future directions in the area of collaboration–trust–performance (CTP) in the AFSC.

Prior to carrying out a bibliometric analysis (BA), literature search was performed, identifying 69 related papers focused on CTP in the AFSC. The content of the papers was further analysed in a systematic literature review (SLR) with regard to the subject area, theoretical lenses, research methodology, supply chain (SC) category and other relevant categories.

CTP in the AFSC are based on a relationship marketing and operations management fundament but show specific particularities. AFSCM is a multi-dimensional design task, and collaboration is considered a necessity, whereas trust significantly affects the AFSC effectiveness. The paper also develops a conceptual CTP model, which shows the interrelations between all identified construct variables, where the authors were able to see also bi-directional relations. Furthermore, the paper presents viable future research opportunities, e.g. focus on organic food chains or multi-actor analysis.

Results of the conducted BA refer to the CTP discussion within a preselected number of peer-reviewed academic articles, which are provided by the WoS CC (Web of Science Core Collection) database.

CTP measurements within the AFSC context are a relevant subject with increasing academic interest in the area of agricultural economics as well as operations and supply chain management (SCM). Therefore, further studies are necessary to develop the related theory and ascertain the practical implications of collaboration, trust and performance among members in the consistently complex AFSC.

CTP have been recognized as important factors for designing a sustainable SCM strategy, particularly in the case of the AFSC. However, although previous studies have addressed the AFSC, there is insufficient knowledge regarding all three pillars (CTP) and how they enable successful AFSCM. The originality of this paper lies in systematically mapping the intellectual base of CTP research and providing path forward for research in AFSCM.

This study provides a bibliometric analysis of bank efficiency and competition over the past years (from 1993 to 2022) to (1) discover the past and current state of knowledge on bank competition and efficiency, (2) identify leading and authoritative journals and scholars who made significant contributions to the distribution of knowledge and impact, (3) identify nations that made a significant contribution and impact to the literature and (4) identify the structure of collaboration that exists between scholars in the areas of bank competition and efficiency and key thematic areas.

A total number of 868 documents made up of articles, reviews, book chapters, book and conference papers from the Scopus database were gathered. This study used a bibliometric analytic approach.

The number of documents on bank competitiveness and efficiency has increased significantly, as have their total publications, citations and national output. Additionally, the most esteemed and prestigious academic journals of eminent academics who have had a significant impact on the dissemination of knowledge on bank efficiency and competition literature champion papers on banking efficiency and competition. In terms of citation performance and collaborative efforts, the United States tops the developed countries, led by China, which is also the most productive. Additionally, single-country publications predominate in the literature, with China ranking first among the top five countries with corresponding authors. While the Lerner index, H-statistic, concentration index and market power were used to measure bank competitive behaviour, the data envelopment analysis approach predominates efficiency estimation techniques that are linked to cost, profit or revenue, scale, technical and productivity indexes.

This study is one of the first to offer bibliometric evidence of both bank competition and efficiency. It also offers proof of the distribution of knowledge and intellectual structure of the concepts and concerns in bank competition and efficiency.

The purpose of this study is to identify, recap and evaluate the state-of-the-art linkage between International Financial Reporting Standards (IFRS) and earnings management (EM).

A bibliometric analysis of 249 publications from the Web of Science (WoS) database was carried out, employing both the techniques of performance analysis and science mapping and the Bibliometrix R and VOSviewer tools.

The results of the performance analysis suggest that the publication and citation trends of the interplay of the IFRS and EM fields show an upward trend over time that most of the influential institutions emanate from the US and a significant percentage of articles published in this field emanate from high-quality journals. Science mapping via co-authorship analysis elucidates that more collaborative efforts among authors are needed in the future in this field. Bibliographic coupling analysis bifurcates the studies into six clusters and reveals the major themes and their evolution. Co-word analysis unfolds emerging trends that could be further explored, thus becoming possible future research avenues.

To the best of the authors' knowledge, no other study has attempted a bibliometric analysis of research on the relationship between IFRS and EM. This article fills this research gap and makes its contribution to the scientific community by presenting recent developments in this body of knowledge and suggesting future research avenues.