A SEM–Neural Network Approach to Predict Customers’ Intention to Purchase Battery Electric Vehicles in China’s Zhejiang Province

Yueling Xu, Wenyu Zhang, Haijun Bao, Shuai Zhang and Ying Xiang
Additional contact information
Yueling Xu: China Institute of Regulation Research, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Wenyu Zhang: School of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Haijun Bao: China Institute of Regulation Research, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Shuai Zhang: School of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Ying Xiang: School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou 310018, China

Sustainability, 2019, vol. 11, issue 11, 1-19

Abstract: As part of the increasing efforts toward the prevention and control of motor vehicle pollution, the Chinese government has practiced a range of policies to stimulate the purchase and use of battery electric vehicles (BEVs). Zhejiang Province, a key province in China, has proactively implemented and monitored an environmental protection plan. This study aims to contribute toward streamlining marketing and planning activities to introduce strategic policies that stimulate the purchase and use of BEVs. This study considers the nature of human behavior by extending the theory of planned behavior model to identify its predictors, as well as its non-linear relationship with customers’ purchase intention. To better understand the predictors, a substantial literature review was given to validate the hypotheses. A quantitative study using 382 surveys completed by customers in Zhejiang Province was conducted by integrating a structural equation model (SEM) and a neural network (NN). The initial analysis results from the SEM revealed five factors that have impacted the customers’ purchase intention of BEVs. In the second phase, the normalized importance among those five significant predictors was ranked using the NN. The findings have provided theoretical implications to scholars and academics, and managerial implications to enterprises, and are also helpful for decision makers to implement appropriate policies to promote the purchase intention of BEVs, thereby improving the air quality.

Keywords: battery electric vehicles; purchase intention; structural equation model; neural network; theory of planned behavior (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/11/3164/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/11/3164/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:11:p:3164-:d:237384

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().