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The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.

ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.

ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset.

This is a novel application of the PageRank algorithm.

This paper examines the diffusion of Jerry Hausman's econometric ideas using citation counts, citing authors, and source journals of his most referenced citers. Bibliographic information and citation counts of references to econometrics papers were retrieved from Thomson Reuters Web of Science and analyzed to determine the various ways in which Hausman's ideas have spread in econometrics and related disciplines. Econometric growth analysis (Gompertz and logistic functions) is used to measure the diffusion of his contributions. This analysis reveals that the diffusion of Hausman's ideas has been pervasive over time and disciplines. For example, his seminal 1978 paper continues to be strongly cited along exponential growth with total cites mainly in econometrics and other fields such as administrative management, human resources, and psychology. Some of the more recent papers have a growth pattern that resembles that of the 1978 paper. This leads us to conclude that Hausman's econometric contributions will continue to diffuse in years to come. It was also found that five journals have published the bulk of the top cited papers that list Hausman as a reference, namely, Econometrica, Journal of Econometrics, Review of Economic Studies, Academy of Management Journal, and the Journal of Economic Literature. “Specification tests in econometrics” is Hausman's dominant contribution in this citation analysis. We found no previous research on the econometric modeling of citation counts as done in this paper. Thus, we expect to stimulate methodological improvements in future work.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

This paper aims to explore the characteristics of knowledge diffusion in library and information science (LIS) to reveal the impact of knowledge in LIS on other disciplines and the disciplinary status of LIS.

Taking the 573 highly cited papers (HCP) of LIS during the years 2000–2019 in Web of Science and 85,638 papers citing them from non-LIS disciplines as the analysis object, this paper analysed the disciplines to which the citing papers belonged regarding the Biglan model, and the topics and their characteristics of the citing disciplines using latent Dirichlet allocation topic clustering.

The results showed that the knowledge in LIS was exported to multiple disciplines and topics. (1) Citations from other disciplines were overall increasing, and the main citing disciplines, mainly from applied science disciplines, were medicine, computer science, management, economics, education, sociology, psychology, journalism and communication, earth science, engineering, biology, political science, chemistry and agronomy. However, those disciplines had fewer citations to LIS during for the years from 2000 to 2004, with rapid growth in the next three time periods. (2) The citing papers had various topics and showed an increasing trend in quantity. Moreover, topics of different disciplines from 2000 to 2019 had various characteristics.

From the perspective of discipline and topic, this study analyses papers citing the HCP of LIS from non-LIS disciplines, revealing the impact of knowledge in LIS on other disciplines.

This paper seeks to investigate the use of engineering conference papers by examining where the papers presented at the Stapp Car Crash Conference from 1980‐2005 were subsequently cited. Also, to examine whether the references were made to the conference paper or to the individual, the SAE Technical Paper, the second publication method. It also aims to study whether the citations were made to the lead author or secondary authors.

The individual papers were searched in Science Citation Index on the Web of Science platform using the “Cited author” search and the appropriate work(s) were selected using the “Cited work” information. The number of citations to each paper was recorded and the bibliographic information from the citing articles for the “top cited” articles were retrieved and examined to determine the subject focus of the citing journals.

Almost two‐thirds of the papers from the Stapp Car Crash Conference were cited at least once, mirroring that of engineering journal articles. These citations appear in journals representing a wide variety of disciplines besides engineering including journals covering “clinical neurology”, “orthopedics”, “rehabilitation” and “surgery”. The majority of citations (92 per cent) were found to be to the lead author and approximately 85 per cent were cited as papers within the conference proceedings with the remaining papers cited as individual technical papers.

The results on the impact and use of engineering conference papers, especially the results in terms of author and material type, will provide valuable insight into this literature, providing librarians and researchers with a better understanding of their use in the advancement of research and scholarship.

The purpose of this paper is to reveal the distributional characteristics and evolutional patterns in source periodicals, topics, authors, funding, and institutes of research papers in Chinese Agricultural Economics so as to understand the current situations and developmental tendency of Chinese agricultural economics research over the past decade.

Using the citation analysis method, this paper analyzed the distributional characteristics and evolution of source periodicals, fields, authors and topics of 2,203 highly cited journal papers from the database of China National Knowledge Infrastructure (CNKI) and 189 cited journal papers from database of Social Sciences Citation Index (SSCI) in agricultural economics first-authored by Chinese scholars from 2006 to 2015.

First, over the past decade, agricultural economics research in China has seen a rapid development. Specially, 103 scholars and 42 institutes have played key roles in the development, and 12 Chinese periodicals and 3 international journals have been the most influential outlets. Second, the coverage of the topics in Chinese agricultural economics research is broad and has expanded over the past decade. The rural land issue has been the most popular topic, while the issues regarding rural institutional arrangements and industrialization in rural areas have been explored extensively. However, issues in other fields, such as agricultural markets and trade, rural labor, food safety, etc. have to be further studied. Third, the improvements of economic theory and quantitative analytic techniques, the supports from research funding, and an increase in the collaboration between Chinese scholars and those from other countries have made great contribution to the rapid development of Chinese agricultural economics research over the past decade.

This paper is an original work that identifies the most influential journal papers including highly cited journal papers from CNKI and cited journal papers from SSCI, using citation frequency and standard Essential Science Indicators method. This is a contribution relative to the methods used by previous studies, which did not account for frequency of citation of a paper. Moreover, this study is based on data from two databases, CNKI and SSCI, suggesting that the coverage of sample papers is broader compared to those of previous studies.

The citation counts are an important indicator of scholarly impact. The purpose of this paper is to explore the correlation between citations of scientific articles and writing styles of abstracts in papers and capture the characteristics of highly cited papers' abstracts.

This research selected 10,000 highly cited papers and 10,000 zero-cited papers from the WOS (2008-2017) database. The Coh-Metrix 3.0 textual cohesion analysis tool was used to quantify the 108 language features of highly cited and zero-cited paper abstracts. The differences of the indicators with significant differences were analyzed from four aspects: vocabulary, sentence, syntax and readability.

The abstracts of highly cited papers contain more complex and professional words, more adjectives, adverbs, conjunctions and personal pronouns, but fewer nouns and verbs. The sentences in the abstracts of highly cited papers are more complex and the sentence length is relatively longer. The syntactic structure in abstracts of highly cited papers is relatively more complex and syntactic similarities between sentences are fewer. Highly cited papers' abstracts are less readable than zero-cited papers' abstracts.

This study analyses the differences between the abstracts of highly cited and those of zero-cited papers, reveals the common external and deep semantic features of highly cited papers in abstract writing styles, provide suggestions for researchers on abstract writing. These findings can help increase the scientific impact of articles and improve the review efficiency as well as the researchers' abstract writing skills.

As a pioneering and influential journal in the field of library and information science (LIS), the Journal of Documentation (JDoc) needs to be evaluated from a bibliometric perspective. This study aimed at conducting a bibliometric overview and visualization of the scientific output of JDoc from its inception in 1945–2018.

In this bibliometric study, 2056 papers published in JDoc were analyzed. All needed data were extracted from Scopus in 9 July 2019 in CSV format. Bibliometric analyses were done in Microsoft Excel. Visualization was done by Vosviewer software and applying techniques such as co-citation, co-authorship and co-occurrence. As a limited altmetric study, JDoc highly mentioned papers and the rate of their presence in social media were extracted from Altmetric LLP, too.

There was an increasing trend in published papers and received citations. Highly cited and most influential authors in JDoc are well-known in the field. However, the contributions of developing countries and their affiliated institutions to the journal were relatively low. This is true in case of author, country and institute co-authorship patterns. Highly frequent keywords and keyword co-occurrence patterns showed that the journal considered most topics related to LIS, including newly emerged ones. The authors and sources (generally journals) cited by JDoc are all prolific and influential ones.

The results of this study can be beneficial to JDoc editorial team for decision making on its further development as well as helpful for researchers and practitioners interesting in LIS field to have better contact with and contributions to the journal.

This paper is written as an attempt to employ the Chinese Social Science Citation Index (CSSCI) in the evaluation of Chinese humanities and social science research.

This paper uses statistics in the CSSCI (2000‐2004) to analyze the academic impact of researchers, papers and works, institutions and regions on Chinese humanities and social science research.

The authors identify 100 highly cited people, 50 highly cited papers, 50 highly cited works, 20 highly productive institutions and 20 highly cited institutions. Also provided is some regional information about Chinese humanities and social science research.

It is hoped that the CSSCI, as well as the analysis and evaluation based on it, will give researchers a better understanding of Chinese humanities and social science research.

The purpose of this paper is to systematically identify the key intellectual developments of the lean supply chain management (LSCM) field from its very first beginning by analyzing the publications that have been highly cited.

The citation classics method has been used to examine the origins and evolution of the research field. In total, 74 articles have been identified as having the highest impact on LSCM research published from its beginnings to the present day. Key aspects and links among the scientific contributions are studied by descriptive, bibliometric, network and content analysis.

Publication and citation trends, methodologies used, top journals, most influential authors, institutions and countries, collaboration patterns and evolution of the most used keywords are some of the aspects studied. Three main research areas are identified: LSCM foundations and implementation, lean-agile supply chain and performance assessment and the role of sustainability, which offers a holistic view of the field and enables the identification of potential avenues for future research susceptible to contribute to advancing the knowledge on LSCM.

This study provides a useful insight into the development of the LSCM field and can help researchers as it explores the state-of-the-art of LSCM by identifying the most relevant contributions in the area, the main research lines addressed by the scientific community in this topic and opportunities for further research on LSCM for years to come.