IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i9p940-d541949.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/9/940/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/9/940/
    Download Restriction: no
    ---><---

    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.
    3. Dahui Dong & Meng-Lin Chen, 2015. "Publication trends and co-citation mapping of translation studies between 2000 and 2015," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 1111-1128, November.
    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.
    6. Edmundas Kazimieras Zavadskas & Raimundas Kirvaitis & Eleonora Dagienė, 2011. "Scientific publications released in the Baltic States," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 179-190, July.
    7. repec:dau:papers:123456789/2947 is not listed on IDEAS
    8. Ping Xie, 2015. "Study of international anticancer research trends via co-word and document co-citation visualization analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 611-622, October.
    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.
    10. Howard D. White, 2015. "Co-cited author retrieval and relevance theory: examples from the humanities," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2275-2299, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chengliang Liu & Qinchang Gui, 2016. "Mapping intellectual structures and dynamics of transport geography research: a scientometric overview from 1982 to 2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 159-184, October.
    2. Qibin Chen & Guilian Fan & Wei Na & Jiming Liu & Jianguo Cui & Hongyan Li, 2019. "Past, Present, and Future of Groundwater Remediation Research: A Scientometric Analysis," IJERPH, MDPI, vol. 16(20), pages 1-17, October.
    3. Jianbo Zhao & Huailiang Liu & Shanzhuang Zhang & Yanwei Qi & Haiping Dong & Xiaojin Zhang & Weili Zhang, 2023. "Advancements in Rumor Detection Research Based on Bibliometrics and S-curve Technology Evolution Theory," SAGE Open, , vol. 13(4), pages 21582440231, December.
    4. Yaowu Sun & Yi Zhai, 2018. "Mapping the knowledge domain and the theme evolution of appropriability research between 1986 and 2016: a scientometric review," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 203-230, July.
    5. Yongming Song & Jun Hu, 2017. "Vector similarity measures of hesitant fuzzy linguistic term sets and their applications," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-13, December.
    6. Yi Peng, 2015. "Regional earthquake vulnerability assessment using a combination of MCDM methods," Annals of Operations Research, Springer, vol. 234(1), pages 95-110, November.
    7. Xingwen Chen & Li Zhu & Chao Liu & Chunhua Chen & Jun Liu & Dongxia Huo, 2023. "Workplace Diversity in the Asia-Pacific Region: A Review of Literature and Directions for Future Research," Asia Pacific Journal of Management, Springer, vol. 40(3), pages 1021-1045, September.
    8. Zheng, Guozhong & Wang, Xiao, 2020. "The comprehensive evaluation of renewable energy system schemes in tourist resorts based on VIKOR method," Energy, Elsevier, vol. 193(C).
    9. Lin, Sheng-Hau & Zhao, Xiaofeng & Wu, Jiuxing & Liang, Fachao & Li, Jia-Hsuan & Lai, Ren-Ji & Hsieh, Jing-Chzi & Tzeng, Gwo-Hshiung, 2021. "An evaluation framework for developing green infrastructure by using a new hybrid multiple attribute decision-making model for promoting environmental sustainability," Socio-Economic Planning Sciences, Elsevier, vol. 75(C).
    10. Milad Zamanifar & Seyed Mohammad Seyedhoseyni, 2017. "Recovery planning model for roadways network after natural hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(2), pages 699-716, June.
    11. Pedro Ponce & Citlaly Pérez & Aminah Robinson Fayek & Arturo Molina, 2022. "Solar Energy Implementation in Manufacturing Industry Using Multi-Criteria Decision-Making Fuzzy TOPSIS and S4 Framework," Energies, MDPI, vol. 15(23), pages 1-19, November.
    12. Mohit Jain & Gunjan Soni & Deepak Verma & Rajendra Baraiya & Bharti Ramtiyal, 2023. "Selection of Technology Acceptance Model for Adoption of Industry 4.0 Technologies in Agri-Fresh Supply Chain," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    13. Chen, Lisa Y. & Wang, Tien-Chin, 2009. "Optimizing partners' choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR," International Journal of Production Economics, Elsevier, vol. 120(1), pages 233-242, July.
    14. Wenyao Niu & Yuan Rong & Liying Yu & Lu Huang, 2022. "A Novel Hybrid Group Decision Making Approach Based on EDAS and Regret Theory under a Fermatean Cubic Fuzzy Environment," Mathematics, MDPI, vol. 10(17), pages 1-30, August.
    15. Deb, Madhujit & Debbarma, Bishop & Majumder, Arindam & Banerjee, Rahul, 2016. "Performance –emission optimization of a diesel-hydrogen dual fuel operation: A NSGA II coupled TOPSIS MADM approach," Energy, Elsevier, vol. 117(P1), pages 281-290.
    16. Kuang-Hua Hu & Wei Jianguo & Gwo-Hshiung Tzeng, 2017. "Risk Factor Assessment Improvement for China’s Cloud Computing Auditing Using a New Hybrid MADM Model," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 737-777, May.
    17. Souzanchi Kashani, Ebrahim & Roshani, Saeed, 2019. "Evolution of innovation system literature: Intellectual bases and emerging trends," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 68-80.
    18. Gallego-Losada, María-Jesús & Montero-Navarro, Antonio & García-Abajo, Elisa & Gallego-Losada, Rocío, 2023. "Digital financial inclusion. Visualizing the academic literature," Research in International Business and Finance, Elsevier, vol. 64(C).
    19. Sung-Shun Weng & Yang Liu & Yen-Ching Chuang, 2019. "Reform of Chinese Universities in the Context of Sustainable Development: Teacher Evaluation and Improvement Based on Hybrid Multiple Criteria Decision-Making Model," Sustainability, MDPI, vol. 11(19), pages 1-23, October.
    20. Fernando Rojas & Peter Wanke & Víctor Leiva & Mauricio Huerta & Carlos Martin-Barreiro, 2022. "Modeling Inventory Cost Savings and Supply Chain Success Factors: A Hybrid Robust Compromise Multi-Criteria Approach," Mathematics, MDPI, vol. 10(16), pages 1-18, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:940-:d:541949. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.