Big data analytics in Bibliometric investigations: unraveling consumer intentions for electric vehicles from 2010 to 2023

Yiran Zhao; Jun Wang

doi:10.1117/12.3033110

5 July 2024 Big data analytics in Bibliometric investigations: unraveling consumer intentions for electric vehicles from 2010 to 2023

Yiran Zhao, Jun Wang

Author Affiliations +

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131843X (2024) https://doi.org/10.1117/12.3033110
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

This study aims to dig deeper into the evolution of consumers' willingness to purchase electric vehicles (EVs) between 2010 and 2023, along with the research hotspots and cutting-edge trends within this domain. We employed big data analytics and text mining techniques to achieve this objective, utilizing two bibliometric analysis software tools: CiteSpace and VOSviewer. These tools enabled us to thoroughly analyze and visualize the extensive dataset indexed by the Web of Science Core Collection within the specified timeframe. Through big data analytics, we excavated how various factors, including technological advancements, policy environments, infrastructure developments, and consumer psychology, have collectively influenced consumers' acceptance of EVs. Simultaneously, text-mining techniques facilitated the extraction of critical insights from the vast literature, further elucidating the underlying motivations behind consumers' willingness to purchase EVs. This study transcends the analytical framework of traditional bibliometrics and provides a fresh perspective and profound understanding of EV purchase intention research through integrating big data and text mining techniques.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yiran Zhao and Jun Wang "Big data analytics in Bibliometric investigations: unraveling consumer intentions for electric vehicles from 2010 to 2023", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131843X (5 July 2024); https://doi.org/10.1117/12.3033110

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KEYWORDS

Analytical research

Analytics

Sustainability

Statistical analysis

Mining

Transportation

Data mining

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