Citation Context Analysis Research Articles

ABSTRACTThis paper conducts a systematic, data‐driven analysis of H. Igor Ansoff's enduring impact on strategic management scholarship. Despite Ansoff's recognition as the “father of strategic management” and the continued relevance of his frameworks—including the Ansoff matrix, weak signals analysis, and strategic thrust—a comprehensive understanding of his influence remains incomplete. While previous research has documented the adoption of his concepts, few studies have empirically examined how his theories have influenced subsequent literature or been critiqued over time. We address these shortcomings through topic modeling, citation context analysis, and historiography applied to Web of Science publications citing Ansoff's Corporate Strategy (1965–2024). Our methodology identifies which concepts are most frequently referenced, whether citations are substantial or peripheral, and how the literature developed over time. This empirical approach reveals thematic developments in Ansoff‐related research, highlights underexplored theoretical elements, and demonstrates how his frameworks continue to shape contemporary strategic thinking. The findings contribute to understanding how classical strategic management theories maintain relevance in today's business landscape.

Although the significance of academic research in policymaking is increasing, empirical examination of elite researchers' contributions to policy and the related knowledge disparities, especially in communication, is still insufficient. This study examines the policy impact of International Communication Association (ICA) Fellows, evaluating data from 288 Fellows (August 2024) using the Overton database, categorized by type of policy institution (government, intergovernmental organizations, Think tanks). Findings indicate a substantial U.S. institutional predominance (69%) among highly prominent Fellows. Overall policy influence correlated positively with IGO influence (r=.22, p<.05) but not with government or Think tank influence; negative correlations (p<.01) between different institution types suggest differentiated influence pathways. A t-test showed U.S.-affiliated scholars had significantly higher influence on government policy reports than non-U.S. counterparts [t(127)=2.36, p=.020], empirically highlighting U.S.-centrism and potential knowledge imbalance in policy citations. This research offers foundational data for science-policy interface studies by segmenting elite scholars' policy influence. Limitations, including absent citation context analysis and non-U.S. sample constraints, call for future research.

Compared to feature engineering, deep learning approaches for citation context analysis have yet fully leveraged the myriad of design options for modeling in-text citation, citation sentence, and citation context. In fact, no single modeling option universally excels on all citation function classes or annotation schemes, which implies the untapped potential for synergizing diverse modeling approaches to further elevate the performance of citation context analysis. Motivated by this insight, the current paper undertook a systematic exploration of ensemble methods for citation context analysis. To achieve a better diverse set of base classifiers, I delved into three sources of classifier diversity, incorporated five diversity measures, and introduced two novel diversity re-ranking methods. Then, I conducted a comprehensive examination of both voting and stacking approaches for constructing classifier ensembles. I also proposed a novel weighting method that considers each individual classifier’s performance, resulting in superior voting outcomes. While being simple, voting approaches faced significant challenges in determining the optimal number of base classifiers for combination. Several strategies have been proposed to address this limitation, including meta-classification on base classifiers and utilising deeper ensemble architectures. The latter involved hierarchical voting on a filtered set of meta-classifiers and stacked meta-classification. All proposed methods demonstrate state-of-the-art results on, with the best performances achieving more than 5 and 4% improvements on the 11-class and 6-class schemes of citation function classification and by 3% on important citation screening. The promising empirical results validated the potential of the proposed ensembling approaches for citation context analysis.

This systematic review evaluates the accuracy of citations to a landmark study on premature mortality among autistic people.

Purpose This study aims to examine Erving Goffman’s contributions to management, arguing that he is an unrecognized management guru despite being widely regarded as a sociological theorist. Design/methodology/approach Using citation context analysis, this research analyzes 806 articles citing Goffman’s works across eight major management journals. This method involves coding articles from various perspectives, including the content itself, its temporal dynamics, depth and criticalness. Findings All 11 of Goffman’s books have been cited in management studies with increasing frequency and depth, supporting theories such as impression management and stigma management. Yet, only 10.8% of these articles provide empirical support, and 1.6% challenge his ideas, indicating a ritualistic reverence rather than rigorous scrutiny of his theories in management scholarship. Research limitations/implications This study excludes other high-quality journals and involves subjective judgment in coding. In addition, this study’s insights into Goffman’s selective attention and growing prominence remain speculative. Future research could broaden journal coverage, survey scholars’ citation motivations, and apply a difference-in-differences approach to identify causal factors. Social implications Goffman’s concepts of stigma, impression management and framing are frequently cited by management scholars, reflecting societal concerns for marginalized groups and a quest for authenticity, thus prompting deeper exploration of Goffman’s seminal works. Originality/value To the best of the authors’ knowledge, this is the first study to empirically analyze his impact on management, offering new insights into his influence in the field.

PurposeThis study explored the influence of Dervin’s sensemaking methodology (SMM).Design/methodology/approachCitation context analysis was used to identify the most influential SMM concepts in 948 articles citing 34 SMM-related studies by Dervin that were published between 1983 and 2017. Moreover, the bibliometric method and content analysis were incorporated to examine the disciplines and research topics influenced by the SMM-related studies and the role of cited content in SMM-related studies.FindingsThe influence of SMM is concentrated in information behavior research in the field of library and information science (LIS). The 1992 book chapter From the mind’s eye of the user was most frequently cited, followed by the first SMM study from 1983; 14 of the 18 content categories were relevant to SMM. “Sensemaking,” at the core of SMM, was the most influential cited concept, primarily cited from the 1983 SMM-related study. Although the SMM was developed as a research method, it has not been primarily applied to design research methods in other studies.Originality/valueThis study explored the interdisciplinary influence of Dervin’s SMM from several aspects and demonstrated the complex information dynamics between SMM-related works and citing articles.

Leagile is a hybrid of conventional lean and agile approaches intended to eliminate specific constraints and capacity limitations and enable agility in highly competitive business environments. Interest in incorporating lean and agile into the supply chain has grown in recent years, as shown by the growing number of scientific publications on this topic. This topic has garnered significant interest across a wide range of academic disciplines; however, a comprehensive study of the literature in this field has not yet been conducted. Therefore, the present study aims to provide a state-of-the-art summary of leagile supply chain issues that concern researchers. To do so, we use a bibliometric method, combined with citation context analysis, to evaluate data extracted from Scopus peer-reviewed articles published between 1999 and March 2023. Data analytic techniques, including citation analysis, co-citation analysis, and co-word (co-occurrence) analysis, are used. Using rigorous bibliometric and visualization tools, the results show the current status of the research problem, its impact, and suggestions for future work to address research gaps, such as a problem in an area of research that has been answered incompletely or insufficiently. The findings suggest that researchers in unexplored research areas in the ASEAN region include green supply chain strategies, supply chain risk management influenced by lean and agile strategies, and comparative analyses among ASEAN countries. These areas offer promising avenues for future research, contributing to regional competitiveness and performance enhancement.

Purely quantitative citation measures are widely used to evaluate research grants, to compare the output of researcher or to benchmark universities. The intuition that not all citations are the same, however, can be illustrated by two examples. First, studies have shown that erroneous or controversial papers have higher citation counts. Second, does a high-level citation in an introduction have the same impact as a reference to a paper that serves as a conceptual starting point? Companions to purely quantitative measures are the so-called citation context analyses which aim to obtain a better understanding of the link between citing and cited work. In this article, we propose a classification scheme for citation context analysis in the field of modelling in engineering. The categories were defined based on an extensive literature review and input from experts in the field of modelling. We propose a detailed scheme with six categories ( Perfunctory, Background Information, Comparing/Confirming, Critique/Refutation, Inspiring, Using/Expanding) and a simplified scheme with three categories ( High-level, Critical Analysis, Extending) that can be used within automatic classification approaches. The results of manually classifying 129 randomly selected citations show that 87% of citations fall into the high-level category. This study confirms that critical citations are not common in written academic discourse, even though criticism is essential for scientific progress and knowledge construction.

In the original publication of the article, Table 7 was incorrectly published. Table 7 has been corrected with this correction.

Abstract Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas.

How and why are citations between disciplines made? A citation context analysis focusing on natural sciences and social sciences and humanities

Measuring coauthors’ credit in medicine field — Based on author contribution statement and citation context analysis

Abstract Between its origin in the 1950s and its endorsement by a consensus conference in 1984, the diet–heart hypothesis was the subject of intense controversy. Paul et al. (1963) is a highly cited prospective cohort study that reported findings inconvenient for this hypothesis, reporting no association between diet and heart disease; however, many other findings were also reported. By citation context and network analysis of 343 citing papers, I show how Paul et al. was cited in the 20 years after its publication. Generally, different findings were cited by different communities focusing on different risk factors; these communities were established by either research foci title terms or via cluster membership as established via modularity maximization. The most frequently cited findings were the significant associations between heart disease and serum cholesterol (n = 85), blood pressure (n = 57), and coffee consumption (n = 54). The lack of association between diet and heart disease was cited in just 41 papers. Yet, no single empirical finding was referred to in more than 25% of the citing papers. This raises questions about the value of inferring impact from citation counts alone and raises problems for studies using such counts to measure citation bias.

Based on 898 English documents and 363 Chinese documents citing the Rising of Network Society, it studied that the knowledge contribution of citation content analysis and citation context analysis methods, and the knowledge contribution of Chinese and foreign quotations to human geography. The study found that “mobile space” is the most quoted theoretical view in domestic and foreign literature, and the proportion of domestic research is significantly higher than foreign research; the focus of domestic and foreign research focuses on the external spatial form and its transformation, while foreign research pays more attention on the internal spatial dynamics of network society and three types of knowledge contributions, reflecting the influence of “network social theory” on human geography. Among them, critical references reveal the shortcomings of “network social theory” point out the abstraction of “spatial duality” the importance of local space, and the limitations of research data, methods, and time background, which provides new enlightenment for the future application and innovation of “network social theory” in the field of human geography.

Corpus construction and mining for citation context analysis

When previous research is cited incorrectly, misinformation can infiltrate scientific discourse and undermine scholarly knowledge. One of the more damaging citation issues involves incorrectly citing article content (called quotation errors); therefore, investigating quotation accuracy is an important research endeavor. One field where quotation accuracy is needed is in the learning sciences given its impact on pedagogy. An integral article in pedagogical discussions surrounding how to teach at the college level is the meta-analysis on active learning by Freeman et al. The Freeman et al. meta-analysis compared active learning to traditional lecture in terms of its effects on student learning and has been important in national initiatives on STEM (science, technology, engineering, and mathematics) reform. Given its influence coupled with the impact quotation errors could have in scientific discourse, we used citation context analysis to analyze whether assertions in the citing text that related to the efficacy of lecture and active learning were supported by what was explicitly stated in the cited meta-analysis. Assertions were analyzed under supported, unsupported, or irrelevant for purposes of study categories. The most prevalent supported category related to active learning being more effective than lecture; the most prevalent unsupported category related to the effectiveness of specific activities/approaches other than the general approach of active learning. Overall, the percentage of supported assertions was 47.67%, and the percentage of unsupported assertions was 26.01%. Furthermore, the percentage of articles containing at least one unsupported assertion was 34.77%. Proactive measures are needed to reduce the incidence of quotation errors to ensure robust scientific integrity.

Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations. Deep analysis of citation may reveal interesting findings by utilizing deep neural network algorithms. The existing scholarly datasets are best suited for statistical approaches but lack citation context, intent, and section information. Furthermore, the datasets are too small to be used with deep learning approaches. For citation intent analysis, the datasets must have a citation context labeled with different citation intent classes. Most of the datasets either do not have labeled context sentences, or the sample is too small to be generalized. In this study, we critically investigated the available datasets for citation intent and proposed an automated citation intent technique to label the citation context with citation intent. Furthermore, we annotated ten million citation contexts with citation intent from Citation Context Dataset (C2D) dataset with the help of our proposed method. We applied Global Vectors (GloVe), Infersent, and Bidirectional Encoder Representations from Transformers (BERT) word embedding methods and compared their Precision, Recall, and F1 measures. It was found that BERT embedding performs significantly better, having an 89% Precision score. The labeled dataset, which is freely available for research purposes, will enhance the study of citation context analysis. Finally, It can be used as a benchmark dataset for finding the citation motivation and function from in-text citations.

Citation context analysis is a detailed and rigorous form of literature review that goes beyond traditional narrative and systematic reviews to better understand the impact of seminal works and influential authors. We discuss the types of questions citation context analyses can answer and provide a set of guidelines for how to effectively conduct them. Citation context analysis holds promise for enabling a more systematic assessment of how theories are used, empirically tested, and critiqued by subsequent citing authors. This has implications for both theory development and testing, and for the improvement of citation practices within the field of organizational studies and the social and physical sciences more broadly.

This paper presents a case study of long-term post-retraction citation to falsified clinical trial data (Matsuyama et al. in Chest 128(6):3817–3827, 2005. https://doi.org/10.1378/chest.128.6.3817), demonstrating problems with how the current digital library environment communicates retraction status. Eleven years after its retraction, the paper continues to be cited positively and uncritically to support a medical nutrition intervention, without mention of its 2008 retraction for falsifying data. To date no high quality clinical trials reporting on the efficacy of omega-3 fatty acids on reducing inflammatory markers have been published. Our paper uses network analysis, citation context analysis, and retraction status visibility analysis to illustrate the potential for extended propagation of misinformation over a citation network, updating and extending a case study of the first 6 years of post-retraction citation (Fulton et al. in Publications 3(1):7–26, 2015. https://doi.org/10.3390/publications3010017). The current study covers 148 direct citations from 2006 through 2019 and their 2542 second-generation citations and assesses retraction status visibility of the case study paper and its retraction notice on 12 digital platforms as of 2020. The retraction is not mentioned in 96% (107/112) of direct post-retraction citations for which we were able to conduct citation context analysis. Over 41% (44/107) of direct post-retraction citations that do not mention the retraction describe the case study paper in detail, giving a risk of diffusing misinformation from the case paper. We analyze 152 second-generation citations to the most recent 35 direct citations (2010–2019) that do not mention the retraction but do mention methods or results of the case paper, finding 23 possible diffusions of misinformation from these non-direct citations to the case paper. Link resolving errors from databases show a significant challenge in a reader reaching the retraction notice via a database search. Only 1/8 databases (and 1/9 database records) consistently resolved the retraction notice to its full-text correctly in our tests. Although limited to evaluation of a single case (N = 1), this work demonstrates how retracted research can continue to spread and how the current information environment contributes to this problem.

Scientific digital libraries speed dissemination of scientific publications, but also the propagation of invalid or unreliable knowledge. Although many papers with known validity problems are highly cited, no auditing process is currently available to determine whether a citing paper's findings fundamentally depend on invalid or unreliable knowledge. To address this, we introduce a new framework, the keystone framework, designed to identify when and how citing unreliable findings impacts a paper, using argumentation theory and citation context analysis. Through two pilot case studies, we demonstrate how the keystone framework can be applied to knowledge maintenance tasks for digital libraries, including addressing citations of a non-reproducible paper and identifying statements most needing validation in a high-impact paper. We identify roles for librarians, database maintainers, knowledgebase curators, and research software engineers in applying the framework to scientific digital libraries.