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The purpose of this paper is to conduct a structured literature review using advanced bibliometric tools to understand the existing knowledge base, understand the trends in omni-channel (OC) research and identify emerging research topics.

More than 500 articles selected through a keyword combination search from reputed databases of peer-reviewed academic sources from period 2009–19 are analyzed for the purposes of this study. The study first presents an exploratory analysis to determine influential authors, sources and regions, among other key aspects. Second, several network analyses including co-citation and dynamic co-citation network analyses are conducted to identify themes. These allow identifying research clusters and emerging research topics algorithmically. Both centrality and modularity-based clustering are employed. A content analysis of the most influential groups within OC literature for each cluster is included.

The findings of this paper make unique contributions by using advanced tools from network analysis along with the standard bibliometric analysis tools to explore the current status of OC research, identify existing themes and the guidance for potential areas of future research interest in OC.

This research provides a comprehensive view of the range of topics of importance that have been discussed in the literature of OC management. These research trends can serve as a quick guide to researchers and practitioners to improve decision making and also develop strategies.

The paper employs advanced tools for the first time to review the literature of OC retailing. The sophisticated tools include co-citation and dynamic co-citation network analysis.

This research aims to examine the intellectual structure of iMetrics through author co-citation analysis.

This research uses common techniques in bibliometrics and social network analysis. It analyses 5,944 records from the Web of Science in the field of iMetrics that are published between 1978 and 2014.

Findings indicated that researchers including “Garfield”, “Egghe”, “Glanzel”, “Leydesdorff” and “Price” have received many co-citations. The author co-citation analysis in iMetrics resulted in eight thematic clusters, including “theoretical foundations and citation analysis”, “sociology of science”, “science mapping and visualization”, “network analysis”, “classic laws of bibliometrics”, “webometrics”, “technometrics” and “miscellaneous”. “Theoretical foundations and citation analysis” is the biggest cluster which comprises 59 authors. The results suggest the crucial role of price medallists in shaping the intellectual structure of knowledge in iMetrics.

Extracting the patterns embedded in the knowledge structure of iMetrics studies provides beneficial information for both researchers and policymakers. This research study is valuable that used an appropriate set of records regarding both recall and precision. Furthermore, this study helps us better understand the characteristics of iMetrics, its subject areas, and the prominent authors in those areas.

Entrepreneurship is the driving force of countries for sustainable economic development. The importance of this issue is to the extent that in recent years, countries have made great efforts to develop their entrepreneurial ecosystem. But the starting point for entrepreneurship is when an opportunity is identified and the entrepreneur rises to use it. Accordingly, opportunity recognition will be the foundation of entrepreneurship and ultimately sustainable development. Given the importance of this topic, this paper attempts to provide a large picture of the studies conducted in this field.

Given the importance of this topic, this paper attempts to provide a large picture of the studies conducted in this field by reviewing 868 articles published on the Web of Science database in the field of opportunity recognition. Accordingly, using statistical descriptions of articles, analyzing the communication network among elements such as authors, countries, institutions, keyword analysis in articles and examining their trends over time, identifying the most important articles using co-citation analysis and finally this macroimage has been mapped, clustered and identified in leading articles in the last decade by co-citation clustering.

The results of the clustering show that the five main clusters of recent decades have included entrepreneurial characteristics and opportunity recognition, macroeconomic opportunity recognition cluster (community and impact on economic development of the country), opportunity recognition process cluster, opportunity recognition cluster in serial and intra-entrepreneurship and opportunity recognition cluster in new venture internationalization.

Using a bibliometric analysis and co-citation analysis in the field of opportunity recognition and making a big picture of studies in this field of study is a contribution that can be used for future studies and researchers and managers in this field.

The paper introduces bibliometrics to the research area of knowledge organization – more precisely in relation to construction and maintenance of thesauri. As such, the paper reviews related work that has been of inspiration for the assembly of a semi‐automatic, bibliometric‐based, approach for construction and maintenance. Similarly, the paper discusses the methodical considerations behind the approach. Eventually, the semi‐automatic approach is used to verify the applicability of bibliometric methods as a supplement to construction and maintenance of thesauri. In the context of knowledge organization, the paper outlines two fundamental approaches to knowledge organization, that is, the manual intellectual approach and the automatic algorithmic approach. Bibliometric methods belong to the automatic algorithmic approach, though bibliometrics do have special characteristics that are substantially different from other methods within this approach.

As the main factor for sustainable development of countries, entrepreneurship is a difficult path only chosen by those who have a high level of risk-taking. On this path, entrepreneurship requires an ecosystem that welcomes this type of thinking and eliminates the barriers on the path as much as possible. This ecosystem comprises various components that attempt to pave the way in a private and public manner. The entrepreneurial ecosystem still has many latent aspects after several years. This study aims to provide a big picture of all studies published in the Web of Science database to help future researchers.

In this research, 765 scientific papers published in the database were analyzed using 3 main approaches of network analysis, co-occurrence analysis of keywords and co-citation clustering.

In the end, four major clusters were identified for articles in this field in the clustering section, including the entrepreneurial ecosystem, academic entrepreneurship, innovation ecosystem and institutional entrepreneurship.

This paper used a new approach for reviewing the entrepreneurial ecosystem and made a big picture of all previous research studies. In the end, an unsupervised machine learning approach was used to clustering the research studies and four major clusters were identified.

This study aims to provide an objective analysis of the state-of-the-art and intellectual development of publications related to event study methodology in business research.

The sample includes 1,219 papers related to event study methodology, covering all business disciplines and spanning 34 years from 1983 to 2016.

Through three stages of primary analysis, namely, initial sample, citation and co-citation analyses, the authors identified the publication trends, supplementary techniques, influential publications and intellectual clusters in the area of event study methodology in business.

The findings serve as a benchmark for the extensive literature related to event study methodology in business and may facilitate the transference of the amassed useful techniques among disciplines and the identification of future research directions.

The current study represents as a pioneering effort to review event study-related publications using 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.

This study aims to identify the trends that have changed in the field of construction management over the last 20 years.

In this study, 3,335 journal articles published in the years 2000–2020 were collected from the Web of Science database in construction management. The authors applied bibliometric analysis first and then detected topics with the latent Dirichlet allocation (LDA) topic detection method.

In this context, 20 clusters from cluster analysis were found and the topics were extracted in clusters with the LDA topic detection method. The results show “building information modeling” and “information management” are the most studied subjects, even though they have emerged in the last 15 years “building information modeling,” “information management,” “scheduling and cost optimization,” “lean construction,” “agile approach” and “megaprojects” are the trend topics in the construction management literature.

This study uses bibliometric analysis. The authors accept that the co-citation and co-authorship relationship in the data is ethical. They accept that honorary authorship, self-citation or honorary citation do not change the pattern of the construction management research domain.

There has been no study conducted in the last 20 years to examine research trends in construction management. Although bibliometric analysis, systematic literature reviews and text mining methods are used separately as a methodology for extracting research trends, no study has used enhanced bibliometric analysis and the LDA topic detection text mining method.

The purpose of this paper is to present a quantitatively supported explanation of the intellectual development, the schools of thought and the sub-areas of the food cold chain (FCC) research to derive meaningful avenues for future research.

This study builds on bibliometric analysis and network analysis to systematically evaluate a sample of 1,189 FCC articles published over the past 25 years. The descriptive statistics and science mapping approaches using co-citation analysis were performed with VOSviewer software.

The findings reveal a state-of-the-art overview of the top contributing and influential countries, authors, institutions and articles in the area of FCC research. A co-citation analysis, coupled with content analysis of most co-cited articles, uncovered four underlying research streams including: application of RFID technologies; production and operation planning models; postharvest waste, causes of postharvest wastage and perishable inventory ordering polices and models; and critical issues in FCC. Current research streams, clusters and their sub-themes provided meaningful discussions and insights into key areas for future research in FCC.

This study might reshape practitioners’, researchers’ and policy-makers’ views on the multifaceted areas and themes in the FCC research field, to harness FCC’s benefits at both strategic and tactical level. Finally, the research findings offer a roadmap for additional research to yield more practical and modeling insights that are much needed to enrich the field.

Quantitative bibliometric approaches were used to statistically and objectively explore patterns in the sharing economy literature.

Journal (co-)citation analysis, author (co-)citation analysis, institution citation and co-operation analysis, keyword co-occurrence analysis, document (co-)citation analysis and burst detection analysis were conducted based on a bibliometric data set relating to sharing economy publications.

Sharing economy research is multi- and interdisciplinary. Journals focused upon products liability, organizing framework, profile characteristics, diverse economies, consumption system and everyday life themes. Authors focused upon profile characteristics, sharing economy organization, social connections, first principle and diverse economy themes. No institution dominated the research field. Keyword co-occurrence analysis identified organizing framework, tourism industry, consumer behavior, food waste, generous exchange and quality cue as research themes. Document co-citation analysis found research themes relating to the tourism industry, exploring public acceptability, agri-food system, commercial orientation, products liability and social connection. Most cited authors, institutions and documents are reported.

The study did not exclusively focus on publications in top-tier journals. Future studies could run analyses relating to top-tier journals alone, and then run analyses relating to less renowned journals alone. To address the potential fuzzy results concern, reviews could focus on business and/or management research alone. Longitudinal reviews conducted over several points in time are warranted. Future reviews could combine qualitative and quantitative approaches.

We contribute by analyzing information relating to the population of all sharing economy articles. In addition, we contribute by employing several quantitative bibliometric approaches that enable the identification of trends relating to the themes and patterns in the growing literature.