Using bibliometrics to support the facilitation of cross‐disciplinary communication

Williams, Christopher J.; O’Rourke, Michael; Eigenbrode, Sanford D.; O'Loughlin, Ian; Crowley, Stephen

doi:10.1002/asi.22874

Cited by 7 publications

(2 citation statements)

References 45 publications

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“…In the field of bibliometrics, there are several studies that focus on the application of small datasets. For example, Williams et al (2013) employed small datasets to characterize the LHT 39,4 spectrum of cross-disciplinary research as a way to guide academic research on crossdisciplinary research patterns and trends. Based on another relatively small sample of data, Thomas et al (2013) proposed a system for determining the existence of practical applications in emerging research fields.…”

Section: Literature Review Of Related Research 21 the Use Of Small Datasets In Bibliometrics Researchmentioning

confidence: 99%

Topic analysis of academic disciplines based on prolific and authoritative researchers

Yang

Wang

2021

LHT

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PurposeThe paper aims to explore whether topic analysis (identification of the core contents, trends and topic distribution in the target field) can be performed using a more low-cost and easily applicable method that relies on a small dataset, and how we can obtain this small dataset based on the features of the publications.Design/methodology/approachThe paper proposes a topic analysis method based on prolific and authoritative researchers (PARs). First, the authors identify PARs in a specific discipline by considering the number of publications and citations of authors. Based on the research publications of PARs (small dataset), the authors then construct a keyword co-occurrence network and perform a topic analysis. Finally, the authors compare the method with the traditional method.FindingsThe authors found that using a small dataset (only 6.47% of the complete dataset in our experiment) for topic analysis yields relatively high-quality and reliable results. The comparison analysis reveals that the proposed method is quite similar to the results of traditional large dataset analysis in terms of publication time distribution, research areas, core keywords and keyword network density.Research limitations/implicationsExpert opinions are needed in determining the parameters of PARs identification algorithm. The proposed method may neglect the publications of junior researchers and its biases should be discussed.Practical implicationsThis paper gives a practical way on how to implement disciplinary analysis based on a small dataset, and how to identify this dataset by proposing a PARs-based topic analysis method. The proposed method presents a useful view of the data based on PARs that can produce results comparable to traditional method, and thus will improve the effectiveness and cost of interdisciplinary topic analysis.Originality/valueThis paper proposes a PARs-based topic analysis method and verifies that topic analysis can be performed using a small dataset.

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Section: Literature Review Of Related Research 21 the Use Of Small Datasets In Bibliometrics Researchmentioning

confidence: 99%

Topic analysis of academic disciplines based on prolific and authoritative researchers

Yang

Wang

2021

LHT

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“…Academic librarians provide support for researchers across the disciplinary spectrum. That support includes collaboration with departmental faculty on grant projects (Brandenberg et al, 2017), and facilitating crossdisciplinary research (Williams et al, 2013;Taskin and Aydinoglu, 2015). This paper provides methods librarians can use to contribute to interdisciplinary research efforts.…”

Section: Implications For Lis Researchers and Librariansmentioning

confidence: 99%

Information flows and topic modeling in corporate governance

Kushkowski

Shrader

Anderson

et al. 2020

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PurposeMultiple disciplines such as finance, management and economics have contributed to governance research over time. However, the full intellectual structure of the governance “field” including the exchange of knowledge across disciplines and the large variety of governance topics remains to be uncovered. To appreciate the breadth of corporate governance research, it is necessary to understand the disciplinary sources from which the research stems. This manuscript focuses on the interdisciplinary underpinnings of corporate governance research.Design/methodology/approachThis paper employs bibliometric analysis to trace the evolution of corporate governance using articles included in the ISI Web of Science database between 1990 and 2015. Journals included in these categories encompass a full range of business disciplines and provide evidence of the multi-disciplinary nature of corporate governance. It also uncovers the topics treated by disciplines under the governance umbrella using a machine learning method called latent Dirichtlet allocation (LDA).FindingsCorporate governance research deals with a number of strategy-related topics. Unlike strategy topics that reside in a single discipline, corporate governance crosses disciplinary boundaries and includes contributions from accounting, finance, economics, law and management. Our analysis shows that over 80% of corporate governance articles come from outside the field of management. Our LDA solution indicates that the major topics in governance research include corporate governance theory, control of family firms, executive compensation and audit committees.Originality/valueThe results illustrate that corporate governance is far more interdisciplinary than previously thought. This is an important insight for corporate governance academics and may lead to collaborative research. More importantly, this research illustrates the usefulness of LDA for investigating interdisciplinary fields. This method is easily transferable to other interdisciplinary fields and it provides a powerful alternative to existing bibliometric methods. We suggest a number of topic areas within library and information science where this method may be applied, including collection development, support for interdisciplinary faculty and basic research into emerging interdisciplinary areas.

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Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Lorentzen

2014

Scientometrics

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Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms.

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Using bibliometrics to support the facilitation of cross‐disciplinary communication

Cited by 7 publications

References 45 publications

Topic analysis of academic disciplines based on prolific and authoritative researchers

Topic analysis of academic disciplines based on prolific and authoritative researchers

Information flows and topic modeling in corporate governance

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

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