Identifying Promising Technologies of Electric Vehicles from the Perspective of Market and Technical Attributes

Lijie Feng, Kehui Liu, Jinfeng Wang (), Kuo-Yi Lin, Ke Zhang and Luyao Zhang
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Lijie Feng: School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
Kehui Liu: School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
Jinfeng Wang: China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
Kuo-Yi Lin: School of Business, Guilin University of Electronic Technology, Guilin 541004, China
Ke Zhang: School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
Luyao Zhang: Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China

Energies, 2022, vol. 15, issue 20, 1-22

Abstract: The vigorous development of electric vehicles (EVs) can promote the green and low-carbon development of society and the environment. However, the research and development of EVs technology in China started late, and there are some problems such as relatively backward technology. In order to promote the decarbonization process of transportation systems, there is an urgent need for appropriate methods to identify promising technologies in the EVs field to guide the efficient development of innovation activities. This study proposes a novel approach to integrate the perspective of market and technical attributes to identify promising EVs technologies. Firstly, text mining tools are applied to extract review and technical keywords from online reviews and patents, and technical topics are summarized. Secondly, sentiment analysis is conducted to calculate user satisfaction based on online reviews, and then market demand of technical topics is obtained. Thirdly, social network centrality analysis, DEA–Malmquist model, and CRITIC method are employed to obtain technical features of technical topics based on patents. Finally, a portfolio map is constructed to analyze technical topics and identify promising EVs technologies. As the main driving force for the development and transformation of the automotive industry, the efficient identification of promising technologies in this field can provide strategic decision support for the development of EVs. This study aims to provide objective data and scientific guidance for related enterprises to carry out technological innovation activities.

Keywords: electric vehicles; promising technologies; patent analysis; sentiment analysis; DEA–Malmquist model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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