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Visualization Method for Decision-Making: A Case Study in Bibliometric Analysis

Author

Listed:
  • Roozbeh Haghnazar Koochaksaraei

    (Department of Computer Science, George Washington University, Washington, DC 20052, USA)

  • Frederico Gadelha Guimarães

    (Department of Electrical Engineering, Federal University of Minas Gerais, Minas Gerais 31270-901, Brazil)

  • Babak Hamidzadeh

    (Libraries, University of Maryland, College Park, MD 20742, USA)

  • Sarfaraz Hashemkhani Zolfani

    (School of Engineering, Catholic University of the North, Larrondo 1281, Coquimbo 1240000, Chile)

Abstract

Data and information visualization have drawn an increasingly wide range of interest from several academic fields and industries. Concurrently, exploring a huge set of data to support feasible decisions needs an organized method of Multi-Criteria Decision Making (MCDM). The dramatic increasing of data producing during the past decade makes visualization necessary as a presentation layer on the top of MCDM process. This study aims to propose an integrated strategy to rank the alternatives in the dataset, by combining data, MCDM methods, and visualization layers. In fact, the well designed combination of Information Visualization and MCDM provides a more user-friendly approach than the traditional methods. We investigate a case study in bibliometric analyses, which have become an important dimension and tool for evaluating the impact and performance of researchers, departments, and universities. Hence, finding the best and most reliable papers, authors, and publishers considering diverse criteria is one of the important challenges in science world. Therefore, this text is presenting a new strategy on the bibliometric dataset as a case study and it demonstrates that this strategy can be more meaningful for the end users than the current tools. Finally, the presented simulations illustrate the performance and utilization of this combination. In other words, the researchers of this study could design and implement a tool that overcomes the biggest challenges of data analyzing and ranking via a combination of MCDM and visualization methodologies that can provide a tremendous amount of insight and information from a massive dataset in an efficient way.

Suggested Citation

  • Roozbeh Haghnazar Koochaksaraei & Frederico Gadelha Guimarães & Babak Hamidzadeh & Sarfaraz Hashemkhani Zolfani, 2021. "Visualization Method for Decision-Making: A Case Study in Bibliometric Analysis," Mathematics, MDPI, vol. 9(9), pages 1-27, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:940-:d:541949
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    References listed on IDEAS

    as
    1. Jean-Charles Billaut & Denis Bouyssou & Philippe Vincke, 2010. "Should you believe in the Shanghai ranking?," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(1), pages 237-263, July.
    2. Michael Hall, C., 2011. "Publish and perish? Bibliometric analysis, journal ranking and the assessment of research quality in tourism," Tourism Management, Elsevier, vol. 32(1), pages 16-27.
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    4. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
    5. Zhigao Liu & Yimei Yin & Weidong Liu & Michael Dunford, 2015. "Visualizing the intellectual structure and evolution of innovation systems research: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 135-158, April.
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    9. Meen Chul Kim & Yongjun Zhu & Chaomei Chen, 2016. "How are they different? A quantitative domain comparison of information visualization and data visualization (2000–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 123-165, April.
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