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An Empirical Study on Visualizing the Intellectual Structure and Hotspots of Big Data Research from a Sustainable Perspective

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  • Feng Hu

    (School of Management, Guangdong University of Technology, Guangzhou 510520, China
    Institute of Big Data Strategic Research, Guangdong University of Technology, Guangzhou 510006, China
    Discipline of Business Analytics, The University of Sydney Business School, The University of Sydney, Camperdown, NSW 2006, Australia)

  • Wei Liu

    (School of Business, Wuyi University, Nanping 354300, China)

  • Sang-Bing Tsai

    (Zhongshan Institute, University of Electronic Science and Technology of China, Guangzhou 528400, China
    Economics and Management College, Civil Aviation University of China, Tianjin 300300, China)

  • Junbin Gao

    (Discipline of Business Analytics, The University of Sydney Business School, The University of Sydney, Camperdown, NSW 2006, Australia)

  • Ning Bin

    (School of Management, Guangdong University of Technology, Guangzhou 510520, China)

  • Quan Chen

    (Zhongshan Institute, University of Electronic Science and Technology of China, Guangzhou 528400, China)

Abstract

Big data has been extensively applied to many fields and wanted for sustainable development. However, increasingly growing publications and the dynamic nature of research fronts pose challenges to understand the current research situation and sustainable development directions of big data. In this paper, we visually conducted a bibliometric study of big data literatures from the Web of Science (WoS) between 2002 and 2016, involving 4927 effective journal articles in 1729 journals contributed by 16,404 authors from 4137 institutions. The bibliometric results reveal the current annual publications distribution, journals distribution and co-citation network, institutions distribution and collaboration network, authors distribution, collaboration network and co-citation network, and research hotspots. The results can help researchers worldwide to understand the panorama of current big data research, to find the potential research gaps, and to focus on the future sustainable development directions.

Suggested Citation

  • Feng Hu & Wei Liu & Sang-Bing Tsai & Junbin Gao & Ning Bin & Quan Chen, 2018. "An Empirical Study on Visualizing the Intellectual Structure and Hotspots of Big Data Research from a Sustainable Perspective," Sustainability, MDPI, vol. 10(3), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:667-:d:134212
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    References listed on IDEAS

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    4. Lei Wang & Mengke Yang & Zulfiqar Hussain Pathan & Shafaq Salam & Khuram Shahzad & Jianqiu Zeng, 2018. "Analysis of Influencing Factors of Big Data Adoption in Chinese Enterprises Using DANP Technique," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
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    6. Fernando Garrigós-Simón & Silvia Sanz-Blas & Yeamduan Narangajavana & Daniela Buzova, 2021. "The Nexus between Big Data and Sustainability: An Analysis of Current Trends and Developments," Sustainability, MDPI, vol. 13(12), pages 1, June.
    7. Dorota Kamrowska-Zaluska & Hanna Obracht-Prondzyńska, 2018. "The Use of Big Data in Regenerative Planning," Sustainability, MDPI, vol. 10(10), pages 1-20, October.
    8. Michael Song & Haili Zhang & Jinjin Heng, 2020. "Creating Sustainable Innovativeness through Big Data and Big Data Analytics Capability: From the Perspective of the Information Processing Theory," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    9. P. V. Thayyib & Rajesh Mamilla & Mohsin Khan & Humaira Fatima & Mohd Asim & Imran Anwar & M. K. Shamsudheen & Mohd Asif Khan, 2023. "State-of-the-Art of Artificial Intelligence and Big Data Analytics Reviews in Five Different Domains: A Bibliometric Summary," Sustainability, MDPI, vol. 15(5), pages 1-38, February.
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