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Bibliometric Analysis: The Main Steps: History
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Subjects: Computer Science, Information Systems
Contributor: Ioannis Passas

Bibliometric analysis is a systematic study carried out on scientific literature for the identification of patterns, trends, and impact within a certain field. Major steps include data collection from relevant databases, data cleaning and refining, and subjecting data to various bibliometric methods—an ensuing step in the generation of meaningful information. Bibliometric analysis is an increasingly popular and thorough technique for examining and assessing massive amounts of scientific data, which is being used more and more in research. This entry thoroughly introduces bibliometric methodology, emphasizing its numerous methodologies. It also provides a set of reliable, step-by-step instructions for confidently performing bibliometric analysis. Furthermore, we investigate the suitable use of bibliometric analysis as an alternative to systematic literature reviews. This entry aims to be a useful tool for learning about the methods and approaches that may be used to perform research studies that use bibliometric analysis, particularly in the fields of academic study.

  • bibliometric analysis
  • literature review
  • meta-analysis
  • performance analysis
  • science mapping
Bibliometrics has become a trend in academic research in recent years [1][2][3][4][5]. However, many young colleagues still lack the skills to conduct a start-to-end bibliometric analysis. Conversely, bibliometrics in research is not just a passing trend. First, the term bibliometrics was introduced in the nineteen thirties by the Belgian documentalist Otlet [6] and was re-invented and made popular by Pritchard in 1969 [7]. In that same year, Nalimov proposed the term scientometrics [8][9]. Although in those years there were some differences between the two fields, nowadays both terms, bibliometrics and scientometrics, are synonyms [7][9][10][11][12][13].
Bibliometrics reflects its applicability in handling vast amounts of scientific data and its significant contribution to research impact. Numerous variables, including the development, accessibility, and availability of bibliometric tools like R and VOSviewer and scientific databases like Google Scholar, Scopus, and Web of Science, are responsible for this popularity. The cross-disciplinary influence of the bibliometric methodology from data science to operational research has also played a significant role in its widespread adoption [14].
Academics employ bibliometric analysis for many other purposes, including uncovering emerging trends in article and journal performance, collaboration patterns, and research constituents and exploring the intellectual construction of a given domain within the existing literature [15][16][17][18]. The data central to bibliometric analysis is often extensive, for instance, hundreds, if not thousands of pieces, and objective, like the number of citations and publications, occurrences of keywords, and topics. At the same time, its meanings often rely on subjective (for instance, thematic analysis) and objective (for example, performance analysis) evaluations derived via well-informed methods and processes. Through rigorous efforts to make sense of vast unstructured data, bibliometrics, or similarly, scientometrics, helps map and understand cumulative scientific knowledge and evolutionary subtleties of well-established domains. Thus, well-conducted bibliometric research may provide strong groundwork in all fields.
Although bibliometric analysis has many advantages, it is still a relatively new tool in research, and its full potential remains untapped. The results from bibliometric studies provide a fragmented knowledge of an area by relying on a small collection of data and approaches [16][19][20]. Notably, academics looking for a thorough yet readable resource on the approach and its application have a major hurdle as there is no authorized reference for bibliometric analysis. Although there are reputable manuals for systematic literature reviews [21][22], they fall short in their coverage of the bibliometric analysis technique.
This entry aims to accomplish two things: first, present a thorough review of bibliometric techniques, and second, present the main steps and instructions for carrying out a bibliometric analysis. This entry introduces bibliometric analysis for academics across all fields, including its principles, methods, processes, supporting details, and explanations. This entry makes significant contributions, as seen in Table 1 below.
Table 1. Main steps for bibliometric analysis.
Step Description Tools/Software Expected Outcome
1. Define Research Objectives Clearly outline the objectives of the bibliometric analysis. N/A Clear research questions and objectives.
2. Literature Search and Data Collection Collect relevant literature from Web of Science, Scopus, and Google Scholar databases or collect raw data (e.g., from no database) and build your own database. EndNote, Zotero, Mendeley A comprehensive dataset of relevant publications.
3. Data Cleaning and Preprocessing Clean and preprocess the data to ensure accuracy (e.g., removing duplicates and correcting author names). R, Python, Excel or LibreOffice A refined and accurate dataset ready for analysis.
4. Selection of Bibliometric Techniques Choose appropriate bibliometric techniques based on research objectives (e.g., co-citation analysis, co-word analysis, bibliographic coupling). VOSviewer, CiteSpace Identification of suitable analysis techniques.
5. Data Analysis Conduct the analysis using chosen techniques. R, Python, VOSviewer, CiteSpace Insights and patterns in the literature.
6. Visualization Visualize the results to aid interpretation and presentation. VOSviewer, CiteSpace, Bibliometrix Graphs, maps, and other visual representations of data.
7. Interpretation and Reporting Interpret the results and prepare a report detailing the findings and their implications. MS Word, LaTeX A comprehensive report with insights and recommendations.

This entry is adapted from the peer-reviewed paper 10.3390/encyclopedia4020065

References

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  2. Donthu, N.; Kumar, S.; Pattnaik, D. Forty-five years of Journal of Business Research: A bibliometric analysis. J. Bus. Res. 2020, 109, 1–14.
  3. Donthu, N.; Kumar, S.; Pandey, N.; Lim, W.M. Research Constituents, Intellectual Structure, and Collaboration Patterns in Journal of International Marketing: An Analytical Retrospective. J. Int. Mark. 2021, 29, 1–25.
  4. Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to conduct a bibliometric analysis: An overview and guidelines. J. Bus. Res. 2021, 133, 285–296.
  5. Ellegaard, O.; Wallin, J.A. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 2015, 105, 1809–1831.
  6. Rousseau, R. Library science: Forgotten founder of bibliometrics. Nature 2014, 510, 218.
  7. Pritchard, A. Statistical bibliography or bibliometrics. J. Doc. 1969, 25, 348–349.
  8. Rousseau, R. Naukometriya, Nalimov and Mul’chenko. COLLNET J. Scientometr. Inf. Manag. 2021, 15, 213–224.
  9. Cherny, A.I.; Gilyarevsky, R.S. The impact of V.V. Nalimov on information science. Scientometrics 2001, 52, 159–163.
  10. Price, D.D.S. A general theory of bibliometric and other cumulative advantage processes. J. Am. Soc. Inf. Sci. 1976, 27, 292–306.
  11. Garfield, E. The History and Meaning of the Journal Impact Factor. JAMA 2006, 295, 90–93.
  12. Garfield, E. Citation indexes for science. Science 1955, 122, 108–111.
  13. Garfield, E. From the science of science to Scientometrics visualizing the history of science with HistCite software. J. Informetr. 2009, 3, 173–179.
  14. Doulani, A. A bibliometric analysis and science mapping of scientific publications of Alzahra University during 1986–2019. Libr. Hi Tech 2020, 39, 915–935.
  15. Verma, S.; Gustafsson, A. Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach. J. Bus. Res. 2020, 118, 253–261.
  16. Ragazou, K.; Passas, I.; Garefalakis, A.; Dimou, I. Investigating the Research Trends on Strategic Ambidexterity, Agility, and Open Innovation in SMEs: Perceptions from Bibliometric Analysis. J. Open Innov. Technol. Mark. Complex. 2022, 8, 118.
  17. Passas, I.; Ragazou, K.; Zafeiriou, E.; Garefalakis, A.; Zopounidis, C. ESG Controversies: A Quantitative and Qualitative Analysis for the Sociopolitical Determinants in EU Firms. Sustainability 2022, 14, 12879.
  18. Abramo, G.; D’Angelo, C.A. How do you define and measure research productivity? Scientometrics 2014, 101, 1129–1144.
  19. Laengle, S.; Merigó, J.M.; Modak, N.M.; Yang, J.B. Bibliometrics in operations research and management science: A university analysis. Ann. Oper. Res. 2020, 294, 769–813.
  20. Rousseau, S.; Rousseau, R. Bibliometric Techniques and Their Use in Business and Economics Research. J. Econ. Surv. 2021, 35, 1428–1451.
  21. Snyder, H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res. 2019, 104, 333–339.
  22. Torraco, R.J. Writing Integrative Literature Reviews: Guidelines and Examples. Hum. Resour. Dev. Rev. 2005, 4, 356–367.
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