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

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  • 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
    School of Public Administration, 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)

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.

Suggested Citation

  • Yueling Xu & Wenyu Zhang & Haijun Bao & Shuai Zhang & Ying Xiang, 2019. "A SEM–Neural Network Approach to Predict Customers’ Intention to Purchase Battery Electric Vehicles in China’s Zhejiang Province," Sustainability, MDPI, Open Access Journal, vol. 11(11), pages 1-19, June.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3164-:d:237384
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    as
    1. McCarty, John A. & Shrum, L. J., 1994. "The recycling of solid wastes: Personal values, value orientations, and attitudes about recycling as antecedents of recycling behavior," Journal of Business Research, Elsevier, vol. 30(1), pages 53-62, May.
    2. Lee Cronbach, 1951. "Coefficient alpha and the internal structure of tests," Psychometrika, Springer;The Psychometric Society, vol. 16(3), pages 297-334, September.
    3. Fuad Un-Noor & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Mohammad Nurunnabi Mollah & Eklas Hossain, 2017. "A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development," Energies, MDPI, Open Access Journal, vol. 10(8), pages 1-84, August.
    4. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
    5. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    6. Samuel Brody & Himanshu Grover & Arnold Vedlitz, 2012. "Examining the willingness of Americans to alter behaviour to mitigate climate change," Climate Policy, Taylor & Francis Journals, vol. 12(1), pages 1-22, January.
    7. Peng Jing & Hao Huang & Bin Ran & Fengping Zhan & Yuji Shi, 2019. "Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China," Sustainability, MDPI, Open Access Journal, vol. 11(4), pages 1-20, February.
    8. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    9. Axsen, Jonn & Mountain, Dean C. & Jaccard, Mark, 2009. "Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles," Institute of Transportation Studies, Working Paper Series qt02n9j6cv, Institute of Transportation Studies, UC Davis.
    10. Lei Yang & Caixia Hao & Yina Chai, 2018. "Life Cycle Assessment of Commercial Delivery Trucks: Diesel, Plug-In Electric, and Battery-Swap Electric," Sustainability, MDPI, Open Access Journal, vol. 10(12), pages 1-21, December.
    11. Peng Yu & Jian Zhang & Defang Yang & Xin Lin & Tianying Xu, 2019. "The Evolution of China’s New Energy Vehicle Industry from the Perspective of a Technology–Market–Policy Framework," Sustainability, MDPI, Open Access Journal, vol. 11(6), pages 1-14, March.
    12. Zhang, Xian & Wang, Ke & Hao, Yu & Fan, Jing-Li & Wei, Yi-Ming, 2013. "The impact of government policy on preference for NEVs: The evidence from China," Energy Policy, Elsevier, vol. 61(C), pages 382-393.
    13. Axsen, Jonn & Mountain, Dean C. & Jaccard, Mark, 2009. "Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles," Resource and Energy Economics, Elsevier, vol. 31(3), pages 221-238, August.
    14. Langbroek, Joram H.M. & Franklin, Joel P. & Susilo, Yusak O., 2016. "The effect of policy incentives on electric vehicle adoption," Energy Policy, Elsevier, vol. 94(C), pages 94-103.
    15. Arie Beresteanu & Shanjun Li, 2011. "Gasoline Prices, Government Support, And The Demand For Hybrid Vehicles In The United States," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(1), pages 161-182, February.
    16. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
    17. Mau, Paulus & Eyzaguirre, Jimena & Jaccard, Mark & Collins-Dodd, Colleen & Tiedemann, Kenneth, 2008. "The 'neighbor effect': Simulating dynamics in consumer preferences for new vehicle technologies," Ecological Economics, Elsevier, vol. 68(1-2), pages 504-516, December.
    18. Egnér, Filippa & Trosvik, Lina, 2018. "Electric vehicle adoption in Sweden and the impact of local policy instruments," Energy Policy, Elsevier, vol. 121(C), pages 584-596.
    19. Sun, Baohong & Morwitz, Vicki G., 2010. "Stated intentions and purchase behavior: A unified model," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 356-366.
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    2. Leibao Zhang & Liming Sheng & Wenyu Zhang & Shuai Zhang, 2020. "Do Personal Norms Predict Citizens’ Acceptance of Green Transport Policies in China," Sustainability, MDPI, Open Access Journal, vol. 12(12), pages 1-16, June.

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