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The purpose of this study is to examine the historical continuity of James March’s contributions to management scholarship by tracing the co-citations that appear within the textual contexts of articles in premier management journals that cite both March and Simon’s 1958 book Organizations and other works co-authored by March.

This study uses within-citation context analysis to examine 522 passages from eight premier management journals that contain co-citations to Organizations and any another work co-authored by March. This entails coding the citing passages to identify the specific knowledge claims from March’s works and how citing authors used them, which establishes linkages between the content in different works of March’s works as used by citing authors.

This study finds that 31 other works by March are co-cited within the same citation contexts along with Organizations. The vast majority (71%) of these co-citations of March’s later works are to Cyert and March’s A Behavioral Theory of the Firm. The four other most highly co-cited works are Levitt and March (1988); March (1991); Cohen et al. (1972); and Levinthal and March (1993). Of the eight summary codes used in the analysis corresponding with the contents of Organizations, two summary codes – “Routines and Programs” and “Cognitive Limits” – accounted for the clear majority (60.1%) of all co-citation contexts in this study.

This study only examined the co-citations to Organizations in eight premier journals in organization studies, and a larger selection of journals might have altered the results to some degree. A truly comprehensive analysis might consider every citation context in the published literature where citing authors jointly mention any two or more of March’s works. Given the extraordinarily large number of citations to March’s works, this was impractical and unfeasible.

A time-bound and rigorous review of co-citations in common contexts allows both scholars and practitioners to recognize the genuine threads of theory presented by leading scholars and trace them through subsequent works to see how theories have evolved both in practice – reflected in empirical work – and in conception – reflected in theoretical development.

Prior research into citation methodology has shown the proliferation of references over time. It is not uncommon for contemporary works to list 100 or more references for a single paper. This research encourages and facilitates a greater discipline in understanding and using citations by tracing the roots of citations and the extent of their importance in citing works.

This paper presents an historical perspective of the influence of James March’s body of scholarship by tracking within context co-citations that link a seminal early work of March to his most cited works in premier journals. This study tracks specific knowledge claims that have persisted throughout March’s corpus of scholarship. This historical method is a systematic approach to tracing how subsequent scholarship ties together and uses multiple works to support specific knowledge claims, enabling an objective analysis of the commonalities among a scholar’s works over time. This is the first example of research using this bibliographic method to form an historical perspective of a seminal author or a classic work.

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.

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.

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.

Previous commonly used author co-citation analysis (ACA) methods have limited the ability to deal with accidental co-citation in constructing a raw co-citation matrix. Therefore, the purpose of this paper is to propose a new method, called author tri-citation analysis (ATA), to better map knowledge domains and depict scientific intellectual structures.

Different from the previous method of using ACA that captures author co-citation relationships, the ATA method seeks tri-citation relationships among authors. Compared with ACA, ATA can ignore some accidental co-citation relationships between authors and can improve the accuracy of mapping knowledge domains.

Although ATA does not mine more sub-fields than ACA does, the results of the empirical studies show that ATA, the newly proposed method, performs better in knowledge domain maps based on publications in the field of computer science.

The definition of ATA in this article is simple and still insufficiently informative. Many other pieces of information can be involved; for example, all authors’ information, authors’ sequence in the author list, reference published time and similar. These can be enhanced in future studies.

This research will enrich the methods of mapping knowledge domains due to its new perspective.

Knowledge domain mapping is important to understand a discipline, and this research provides more potential methods for this, which benefits the performance of the maps.

ATA can provide a methodological awareness for mapping knowledge domains. This value lies in not only a tri-citation perspective, but also author bibliographic tripling and author tri-operation perspectives (“tri-” perspectives).

This paper aims to present a methodology by which future knowledge flow can be predicted by predicting co-citations of patents within a technology domain using a link prediction algorithm applied to a co-citation network.

Several methods and approaches are used: a dynamic analysis of a patent citation network to identify technology life cycle phases, patent co-citation network mapping from the patent citation network and the application of link prediction algorithms to the patent co-citation network.

The results of the presented study indicate that future knowledge flow within a technology domain can be predicted by predicting patent co-citations using the preferential attachment link prediction algorithm. Furthermore, they indicate that the patent – co-citations occurring between the end of the growth life cycle phase and the start of the maturation life cycle phase contribute the most to the precision of the knowledge flow prediction. Finally, it is demonstrated that most of the predicted knowledge flow occurs in a time period closely following the application of the link – prediction algorithm.

By having insight into future potential co-citations of patents, a firm can leverage its existing patent portfolio or asses the acquisition value of patents or the companies owning them.

It is demonstrated that the flow of knowledge in patent co-citation networks follows a rich get richer intuition. Moreover, it is show that the knowledge contained in younger patents has a greater chance of being cited again. Finally, it is demonstrated that these co-citations can be predicted in the short term when the preferential attachment algorithm is applied to a patent co-citation network.

Co-citation frequency, defined as the number of documents co-citing two articles, is considered as a quantitative, and thus, an efficient proxy of subject relatedness or prestige of the co-cited articles. Despite its quantitative nature, it is found effective in retrieving and evaluating documents, signifying its linkage with the related documents' contents. To better understand the dynamism of the citation network, the present study aims to investigate various content features giving rise to the measure.

The present study examined the interaction of different co-citation features in explaining the co-citation frequency. The features include the co-cited works' similarities in their full-texts, Medical Subject Headings (MeSH) terms, co-citation proximity, opinions and co-citances. A test collection is built using the CITREC dataset. The data were analyzed using natural language processing (NLP) and opinion mining techniques. A linear model was developed to regress the objective and subjective content-based co-citation measures against the natural log of the co-citation frequency.

The dimensions of co-citation similarity, either subjective or objective, play significant roles in predicting co-citation frequency. The model can predict about half of the co-citation variance. The interaction of co-opinionatedness and non-co-opinionatedness is the strongest factor in the model.

It is the first study in revealing that both the objective and subjective similarities could significantly predict the co-citation frequency. The findings re-confirm the citation analysis assumption claiming the connection between the cognitive layers of cited documents and citation measures in general and the co-citation frequency in particular.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-04-2020-0126.

The study aims to map the intellectual structure of business ethics studies by analyzing 17,246 citations of 225 papers published in Business Ethics Quarterly (BEQ) in the year between 2005 and 2014. Specifically, the purpose of the study is to describe the current state of BEQ, identify the most influential journals and works, identify the key themes of business ethics studies during 2005-2014 and, at the same time, report the changes in themes by making a comparison between two time periods – 2005-2009 and 2010-2014.

First, the study presents the information of the authors, institutions and countries that contribute to BEQ with a statistical analysis. Second, the study identifies the most cited journals and works in BEQ during 2005-2014 with a citation analysis. Third, the study identifies the key research themes in business ethics studies with a co-citation analysis. With the help of factor and social network analysis (NA), the study groups the research themes and maps their links.

First, the statistical results show that many well-known researchers from famous US institutions publishing in BEQ. Second, the citation analysis results show that quite a few journals become mature gradually in business ethics domain. Besides, most of the influential works are normative and theoretical. Third, the co-citation results indicate that “stakeholder management” and “corporate social responsibility” (CSR) are two main themes in business ethics studies in the past decade. Specifically, “stakeholder management” attracts the most research interests in both two sub-time periods. In addition, compared with the pure studies on CSR during 2005-2009, increasing researchers are keen on the theme of “political CSR under globalization” in the second five years. Meanwhile, other focus like “society, state and business ethics” earns a certain degree of attention in the time window 2005-2009. And “accountability in MNCs” and “political philosophy” are the new concerns in the year between 2010 and 2014.

The study confirms BEQ’s leadership role in business ethics domain. And, it further proves that business ethics has evolved as an independent discipline. It also helps the researchers to have a concise knowledge of the main contents and key points of business ethics research. Methodologically, co-citation analysis combined with factor and NA provides clear results and visualized figures which can be understood easily by the researchers and practitioners.

This paper focuses on the differences in domain intellectual structure discovery between author bibliographic coupling analysis (ABCA) and author co-citation analysis (ACA) considering all authors. The purpose of this study is to examine whether and in what ways these two all-author network approaches yield different results.

The sample was collected from the database of Web of Science, including all articles published in Scientometrics and Journal of Informetrics from 2011 to 2020. First, 100 representative authors were selected from each set, and ABCA matrices and ACA matrices were constructed. Second, factor analysis was carried out on the matrices, to detect the intellectual structure of scientometrics and informetrics.

The intellectual structures identified by ABCA and ACA are similar overall, but the results differ somewhat when it comes to specific structures. The ABCA is more sensitive to some highly collaborative research teams and presents a clearer picture of current intellectual structures and trends while ACA seems to have some advantages in representing the more traditional and proven research topics in the field. The combined use of ABCA and ACA allows for a more comprehensive and specific intellectual structure of research fields.

This paper compares the performance of ABCA and ACA detecting the intellectual structure of the domain from the perspective of all authors, revealing the intellectual structure of scientometrics and informetrics comprehensively.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2020-0540.

The purpose of this paper is to analyse the intellectual structure and research fronts of discipline of supply chain risk management (SCRM), in order to identify the knowledge groups in the research area to date, as well as to reveal any relationships between these subfields and the central influential trends.

By means of a bibliometric study, the 32 most co-cited articles on SCRM published in 16 top business-related academic journals are analysed using multivariate statistical techniques, i.e. multi-dimensional scaling, cluster analysis and correspondence factor analysis.

The results demonstrate a clearly identifiable structure as a result of the performed co-citation analysis. The conducted cluster analysis and factor bring forward that the research field is arranged in five different areas of interest: explaining supply chain (SC) risk phenomena, concepts, frameworks and insights of SCRM; modelling risks for SCs; inventory risks affecting supply efficiency; SC and product design methods; and SC risk mitigating strategies.

Overall, the intellectual structure of SCRM is first examined through a bibliometric approach using quantitative techniques – for improved understanding of its origins, and to identify the state of the science – and to offer suggestions for future studies that could cover current gaps. This study represents the potential to advance the SCRM literature landscape.