Co-citation Analysis: An Overview

This article gives an overview of co-citation analysis and its applications in tracking the linkages among the intellectual works and mapping the evolutionary structure of scientific disciplines. It also focuses on the features, interface, terminology used, merits and demerits of co-citation based online database applications

Can we do better than co-citations? Bringing Citation Proximity Analysis from idea to practice in research articles recommendation

In this paper, we build on the idea of Citation Proximity Analysis (CPA), originally introduced in [1], by developing a step by step scalable approach for building CPA-based recommender systems. As part of this approach, we introduce three new proximity functions, extending the basic assumption of co-citation analysis (stating that the more often two articles are co-cited in a document, the more likely they are related) to take the distance between the co-cited documents into account. Ask- ing the question of whether CPA can outperform co-citation analysis in recommender systems, we have built a CPA based recommender system from a corpus of 368,385 full-texts articles and conducted a user survey to perform an initial evaluation. Two of our three proximity functions used within CPA outperform co-citations on our evaluation dataset

Mapping Change Management: A Co-citation Analysis

Today\u27s organizations are continually undergoing changes to make improvements in their efficiency and effectiveness. The ability of organizations to effectively implement and sustain successful change, however, has been limited, with most change initiatives failing to attain the desired success. To counter this trend, researchers across several disciplines have worked to provide practitioners better insight into how to facilitate change within their organizations. This research has resulted in many theories as to what constitutes change and how best to implement it, but it lacks a unifying theory that encompasses all aspects of change research. This effort takes a step toward a better understanding of the change management field and its nature. Using a co-citation methodology, 141 influential authors from the field of change management were identified. Their works were then categorized into identifiable sub-groups within the field and mapped, providing insight into the level of integration that has occurred within the field and across the disciplines that have explored change. Also, the extent to which the existing theories have begun to converge toward a unifying theory is observed. The purpose of this effort is to point future researchers in a direction that will lead to a unifying theory of change management. This unifying theory can then be translated into practices that will enable organizations to successfully transition through needed change initiatives

The Development of Social Simulation as Reflected in the First Ten Years of JASSS: a Citation and Co-Citation Analysis

Social simulation is often described as a multidisciplinary and fast-moving field. This can make it difficult to obtain an overview of the field both for contributing researchers and for outsiders who are interested in social simulation. The Journal for Artificial Societies and Social Simulation (JASSS) completing its tenth year provides a good opportunity to take stock of what happened over this time period. First, we use citation analysis to identify the most influential publications and to verify characteristics of social simulation such as its multidisciplinary nature. Then, we perform a co-citation analysis to visualize the intellectual structure of social simulation and its development. Overall, the analysis shows social simulation both in its early stage and during its first steps towards becoming a more differentiated discipline.Citation Analysis, Co-Citation Analysis, Lines of Research, Multidisciplinary, Science Studies, Social Simulation

Review of a proposed methodology for bibliometric and visualization analyses for organizations: application to the collaboration economy

This paper presents the bibliometric and visualization method applied to a dataset of 729 documents published in the collaborative economy research field. Four steps are described in details: (1) the delimitation of the field of study; (2) the selection of databases, keywords, and search criteria; (3) the extraction, cleaning, and formatting; and finally (4) the co-citation analysis and visualization. The method validation section shows the results obtained by applying our methodological procedure to an author network analysis as well as a source title network analysis. This study is unique which presents a co-citation analysis coupled with a network visualization applied to the rapidly growing research area of the collaborative economy as a whole and not only of the collaborative tourism and hospitality research, as has been previously. The originality of this method lies firstly in the fact that the data were extracted from two databases (Scopus and Web of Science) instead of one as is commonly done in analytic studies. Secondly, VOSviewer was our main analytical tool performing the co-citation analysis and the network visualizations

A Methodology for Profiling Literature using Co-citation Analysis

The contribution of this paper is a methodology for profiling literature in Information Systems (IS) using a powerful tool for co-citation analysis - Citespace. Co-citation analysis provide important insights into knowledge domains by identifying frequently co-cited papers, authors and journals. The methodology is applied to a dataset comprising of citation data pertaining to a leading European journal – the European Journal of Information Systems (EJIS). In this paper we outline the different steps involved in using Citespace to profile literature in IS and use the EJIS dataset as an example. We hope that the readers will employ and/or extend the given methodology to conduct similar bibliometric studies in IS and other research areas

The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis

A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks.Comment: 33 pages, 11 figures, 10 tables. To appear in the Journal of the American Society for Information Science and Technolog

Bibliometrically Mapping Team Cognition Literature: A Co-citation Analysis

Researchers investigating team cognition must source and review a challenging set of relevant, mature literature from a diverse array of academic disciplines. Such disciplines may include psychology, management, information science, military science, anthropology, and nursing science, etc. This thesis summarizes an effort to bibliometrically map team cognition literature using an author co-citation analysis methodology. The work began with a traditional literature review that identified key authors who were published in peer-reviewed journals. These authors were contacted and asked to provide their own listings of key researchers in the field, which were used in conjunction with the Social Sciences Citation Index (SSCI) to construct a co-citation matrix of authors. Using factor analysis and multi-dimensional analysis techniques, visual maps were constructed that highlight the influence of specific authors, the relationships between authors, and the branching of sub-domains in the literature over time. The overall goals of the research were to provide team cognition researchers with a tool they could use to better inform their efforts, and to provide an explicit mapping of the field in terms of where it has been, and where it may be going

Applying content-based similarity measure to author co-citation analysis

This study proposed a novel author similarity measure in author co-citation analysis (ACA). Unlike other ACA studies, we used citing sentences to reflect topical relatedness of authors. In our research, we extended traditional approaches by adopting Word2Vec, one of deep learning methods, to measure author similarity. We also conducted in-depth network analysis of author maps. The results of Word2Vec-based author map revealed more specific sub-disciplines and the important authors in perspective of topical influence than traditional approach does. Our method allows for more sophisticated analysis than the traditional ACA approach by providing a more in-depth understanding and the specific structure of a discipline