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This paper aims to present a summary of the green consumer behaviour (GCB) research conducted during the 2001–2021 period using the bibliometric analysis and to carry out a thematic and content analysis on the three clusters which comprise 57 articles resulting from the co-citation analysis and identify the significant green purchasing factors.

The three-pronged methodology applied to this research analysis includes performance analysis of the literature using biblioshiny and R Studio; network mapping analysis using VOSviewer and Gephi; thematic analysis using word clouds generated with R Software and content analysis of each paper with the aid of within and between-study analyses.

Cluster one acted as a base for the theoretical foundations of GCB which aids in understanding the basic concepts of green marketing, its evolution and the methodologies, whereas cluster two determined the predictors of everyday green behaviour, which helps in gaining knowledge about the everyday sustainable activities the consumers indulge and the factors motivating to do so. Cluster three mainly focused on the psycho-socio demographic determinants of GCB, which assists in segmentation and predicting the purchase behaviour of the various consumer segments.

The significant variables and major gaps in each of the clusters were identified and authors have drawn the implications for future researchers and marketing managers.

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.

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.

This paper aims to improve decision making in credit portfolio management through analytical data-mining methods, which should be used as data availability and data quality of credit portfolios increase due to (semi-)automated credit decisions, improved data warehouses and heightened information needs of portfolio management.

To contribute to this fact, this paper elaborates credit portfolio analysis based on cluster analysis. This statistical method, so far mainly used in other disciplines, is able to determine “hidden” patterns within a data set by examining data similarities.

Based on several real-world credit portfolio data sets provided by a financial institution, the authors find that cluster analysis is a suitable method to determine numerous multivariate contract specifications implying high or, respectively, low profit potential.

Nevertheless, cluster analysis is a statistical method with multiple possible settings that have to be adjusted manually. Thus, various different results are possible, and as cluster analysis is an application of unsupervised learning, a validation of the results is hardly possible.

By applying this approach in credit portfolio management, companies are able to utilize the information gained when making future credit portfolio decisions and, consequently, increase their profit.

The paper at hand provides a unique structured approach on how to perform a multivariate cluster analysis of a credit portfolio by considering risk and return simultaneously. In this context, this procedure serves as a guidance on how to conduct a cluster analysis of a credit portfolio including advices for the settings of the analysis.

The purpose of this paper is to explore and map the intellectual structure of biofuel research.

The study attempts to present the structure of biofuel research through document co‐citation patterns of core references. Document co‐citation analysis was performed using the Web of Science of the Thomson‐ISI database. A sample of 26 cited references was identified and the co‐citation frequencies were analyzed and represented them systematically within groups of similar researched topics.

The study shows the co‐citation analysis method suitable for depicting structure of biofuel research in document clusters by performing multivariate analysis: cluster analysis, factor analysis, multidimensional scaling and network analysis.

The study is limited to research articles and co‐citation data for the first author only. For the co‐citation analysis, the cited references rather than the cited authors were used as the units for analysis.

Co‐citational analysis using multivariate tools provides a useful technique to explore and document the development of the field supplementing the insights normally available from the routine co‐citational analysis.

Specialties in biofuel research are identified and this may provide a valuable building block for future research.

The objective of this paper is to propose an approach to comparatively analyze the performance of drugs and consumable products warehouses belonging to different healthcare institutions.

A Cluster Analysis is completed in order to classify warehouses and identify common patterns based on similar organizational characteristics. The variables taken into account are associated with inventory levels, the number of SKUs, and incoming and outgoing flows.

The outcomes of the empirical analysis are confirmed by additional indicators reflecting the demand level and the associated logistics flows faced by the warehouses at issue. Also, the warehouses belonging to the same cluster show similar behaviors for all the indicators considered, meaning that the performed Cluster Analysis can be considered as coherent.

The study proposes an approach aimed at grouping healthcare warehouses based on relevant logistics aspects. Thus, it can foster the application of statistical analysis in the healthcare Supply Chain Management. The present work is associated with only one regional healthcare system.

The approach might support healthcare agencies in comparing the performance of their warehouses more accurately. Consequently, it could facilitate comprehensive investigations of the managerial similarities and differences that could be a first step toward warehouse aggregation in homogeneous logistics units.

This analysis puts forward an approach based on a consolidated statistical tool, to assess the logistics performances in a set of warehouses and, in turn to deepen the related understanding as well as the factors determining them.

This literature review explores the definitions and characteristics of cluster analysis, a machine-learning technique that is frequently implemented to identify groupings in big datasets and its applicability to library and information science (LIS) research. This overview is intended for researchers who are interested in expanding their data analysis repertory to include cluster analysis, rather than for existing experts in this area.

A review of LIS articles included in the Library and Information Source (EBSCO) database that employ cluster analysis is performed. An overview of cluster analysis in general (how it works from a statistical standpoint, and how it can be performed by researchers), the most popular cluster analysis techniques and the uses of cluster analysis in LIS is presented.

The number of LIS studies that employ a cluster analytic approach has grown from about 5 per year in the early 2000s to an average of 35 studies per year in the mid- and late-2010s. The journal Scientometrics has the most articles published within LIS that use cluster analysis (102 studies). Scientometrics is the most common subject area to employ a cluster analytic approach (152 studies). The findings of this review indicate that cluster analysis could make LIS research more accessible by providing an innovative and insightful process of knowledge discovery.

This review is the first to present cluster analysis as an accessible data analysis approach, specifically from an LIS perspective.

Focussing on behaviors and attitudes of casual dining patrons, the purpose of this paper is to use a factor-cluster approach to segment patrons into market groups and attempts to determine if differences exist in motivational factors among segments.

Factor-cluster analysis is an alternative segmentation method to more traditionally used methods based on consumer demographics. Push and pull motivators were analyzed through factor analysis to determine important groupings. Then, to identify homogenous subgroups, k-means cluster analysis was conducted to segment 559 survey respondents based on factor importance.

Three diverse groups were identified: Fraternizing Kitchen Fearfuls, Functional Feasters, and Foodie Fanatics. The various push and pull factors appeared to affect segments differently, with each cluster ascribing various importance levels to each of the factors used in the clustering approach.

Limitations include the use of a convenience sample and on-campus sampling. Future research should use random sampling methods and obtain surveys from sites not associated with a college campus.

Though not often used in hospitality research, factor-cluster analysis can be useful to segment diners based on behavioral intentions and attributes, allowing marketers to more accurately target these diverse consumer segments. Marketing implications for casual dining restaurants are suggested.

Using the involvement construct with push/pull motivators, this study groups respondents though factor-cluster analysis. Though used in tourism studies, factor-cluster analysis has yet to be studied in the context of casual dining restaurant patrons.

Entrepreneurial marketing (EM) is at the brink of becoming an established discipline. To advance the field further and to better guide research efforts in different sub categories, the purpose of this paper is to examine the field's intellectual structure with the help of citation and co‐citation analysis.

This paper is based on a two‐stage research design. First a citation analysis is carried out through which thematic clusters are identified. In a second step a co‐citation analysis is conducted to determine the intellectual structure of EM research.

This study exposes the most influential authors and publications and emphasizes conjunctions among scholars and their findings. Results show three streams that are the foundation of EM research: theoretical foundations of management, entrepreneurship, and marketing; the research interface of marketing and entrepreneurship; SME and new venture marketing.

The results of a bibliometric analysis are limited by the publications that have been selected as a starting point. However, through the selection criteria chosen to identify the database for analysis, the authors are confident that the results illustrate the intellectual structure of EM research in its entirety. The authors recommend that future research should be conducted in one of the three sub‐fields identified in this study.

By laying out different research streams within EM it is hoped that future research will be guided in different directions. “Fine‐tuning” of research efforts will benefit small, new, and entrepreneurial firms.

The analyses conducted in this paper draw a picture of the field that is based on a quantitative approach and therefore sets itself apart from other literature reviews that have a qualitative core.

In the past 25 years, employer and internal branding have grown significantly. Prior reviews tended to focus on either one of these domains. This study aims to map the intellectual structure of research on both employer branding and internal branding, thereby identifying impactful authors and journals, current and evolving themes and avenues for future research.

Using VOSviewer and Biblioshiny software packages, a bibliometric analysis of 739 articles was conducted using various methods such as citation analysis, bibliographic coupling, cluster analysis, keyword analysis and three-field plot. The Scopus results were further validated using 297 articles produced by the Web of Science data set. It ensured the robustness of the results and generalizability of the findings across bibliometric data sets.

The findings first report the impactful articles, authors and institutions of employer and internal branding research, along with popular keywords used in this area. Next, the analysis reveals four major clusters and seven subthemes (i.e. employer brand and job seekers, employer brand and employees, employer brand and international human resource management (HRM), third-party employer branding, internal branding – conceptualization/review, internal branding – antecedents and consequences, internal brand management). Early research focused more on “corporate brandings,” whereas current research deals more with “employer branding: antecedents and consequences,” “employer branding conceptualization/review,” and “internal branding” and its subthemes. The employer and internal branding clusters have evolved largely independent from each other. This study offers future research directions and practical implications per cluster.

To the best of the authors’ knowledge, this study is the first comprehensive bibliometric analysis of both employer and internal branding research.