IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i12p4269-d835824.html
   My bibliography  Save this article

Policy Instruments for the Improvement of Customers’ Willingness to Purchase Electric Vehicles: A Case Study in Iran

Author

Listed:
  • Elham Allahmoradi

    (School of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran)

  • Saeed Mirzamohammadi

    (School of Industrial Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran)

  • Ali Bonyadi Naeini

    (School of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran 13114-16846, Iran)

  • Ali Maleki

    (Sharif Policy Research Institute (SPRI), Sharif University of Technology, Azadi Street, Tehran 14588-89694, Iran)

  • Saleh Mobayen

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu 64002, Taiwan)

  • Paweł Skruch

    (Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland)

Abstract

Given the various advantages of electric vehicles compared to conventional gasoline vehicles in terms of energy efficiency and environmental pollution (among others), this paper studies the factors affecting customers’ willingness to purchase electric vehicles. An integrated discrete choice and agent-based approach is applied to model the customers’ choice for the valuation of electric vehicles based on the internal reference price. The agent-based model evaluates customers’ preferences for a number of personal and vehicle attributes, according to which vehicle they chose. Data from 376 respondents are collected to estimate a random-parameter logit model where customers are asked to reveal their preferences about five attributes of electric vehicles, including travel range, top speed, charge cost, government incentives, and price. The role of social networks of customers and their threshold purchase price is also examined in the agent-based model. The scenario simulation results indicate that the allocation of government incentives for electric vehicles, decreasing electric vehicle/non-electric vehicle price gap, expanding electric vehicle travel range, increasing gasoline prices, and enhancing electric vehicle top speed stimulate electric vehicle market shares, respectively.

Suggested Citation

  • Elham Allahmoradi & Saeed Mirzamohammadi & Ali Bonyadi Naeini & Ali Maleki & Saleh Mobayen & Paweł Skruch, 2022. "Policy Instruments for the Improvement of Customers’ Willingness to Purchase Electric Vehicles: A Case Study in Iran," Energies, MDPI, vol. 15(12), pages 1-17, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4269-:d:835824
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/12/4269/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/12/4269/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hidrue, Michael K. & Parsons, George R. & Kempton, Willett & Gardner, Meryl P., 2011. "Willingness to pay for electric vehicles and their attributes," Resource and Energy Economics, Elsevier, vol. 33(3), pages 686-705, September.
    2. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    3. Yongyou Nie & Enci Wang & Qinxin Guo & Junyi Shen, 2018. "Examining Shanghai Consumer Preferences for Electric Vehicles and Their Attributes," Sustainability, MDPI, vol. 10(6), pages 1-16, June.
    4. Danielis, Romeo & Rotaris, Lucia & Giansoldati, Marco & Scorrano, Mariangela, 2020. "Drivers’ preferences for electric cars in Italy. Evidence from a country with limited but growing electric car uptake," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 79-94.
    5. Committee, Nobel Prize, 2017. "Richard H. Thaler: Integrating Economics with Psychology," Nobel Prize in Economics documents 2017-1, Nobel Prize Committee.
    6. Valeri, Eva & Danielis, Romeo, 2015. "Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy," Transport Policy, Elsevier, vol. 37(C), pages 44-56.
    7. Cedric De Cauwer & Joeri Van Mierlo & Thierry Coosemans, 2015. "Energy Consumption Prediction for Electric Vehicles Based on Real-World Data," Energies, MDPI, vol. 8(8), pages 1-21, August.
    8. Kalyanaram, Gurumurthy & Little, John D C, 1994. "An Empirical Analysis of Latitude of Price Acceptance in Consumer Package Goods," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(3), pages 408-418, December.
    9. Maxwell Brown, 2013. "Catching the PHEVer: Simulating Electric Vehicle Diffusion with an Agent-Based Mixed Logit Model of Vehicle Choice," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(2), pages 1-5.
    10. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    11. Katarzyna Turoń & Andrzej Kubik & Feng Chen, 2021. "When, What and How to Teach about Electric Mobility? An Innovative Teaching Concept for All Stages of Education: Lessons from Poland," Energies, MDPI, vol. 14(19), pages 1-16, October.
    12. Gurumurthy Kalyanaram & Russell S. Winer, 1995. "Empirical Generalizations from Reference Price Research," Marketing Science, INFORMS, vol. 14(3_supplem), pages 161-169.
    13. Jensen, Anders Fjendbo & Mabit, Stefan Lindhard, 2017. "The use of electric vehicles: A case study on adding an electric car to a household," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 89-99.
    14. Nykvist, Björn & Sprei, Frances & Nilsson, Måns, 2019. "Assessing the progress toward lower priced long range battery electric vehicles," Energy Policy, Elsevier, vol. 124(C), pages 144-155.
    15. Chen, Chien-fei & Zarazua de Rubens, Gerardo & Noel, Lance & Kester, Johannes & Sovacool, Benjamin K., 2020. "Assessing the socio-demographic, technical, economic and behavioral factors of Nordic electric vehicle adoption and the influence of vehicle-to-grid preferences," Renewable and Sustainable Energy Reviews, Elsevier, vol. 121(C).
    16. Onat, Nuri C. & Noori, Mehdi & Kucukvar, Murat & Zhao, Yang & Tatari, Omer & Chester, Mikhail, 2017. "Exploring the suitability of electric vehicles in the United States," Energy, Elsevier, vol. 121(C), pages 631-642.
    17. Bireswar Dutta & Hsin-Ginn Hwang, 2021. "Consumers Purchase Intentions of Green Electric Vehicles: The Influence of Consumers Technological and Environmental Considerations," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    18. Tobias Buchmann & Patrick Wolf & Stefan Fidaschek, 2021. "Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach," Energies, MDPI, vol. 14(3), pages 1-25, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lijie Feng & Kehui Liu & Jinfeng Wang & Kuo-Yi Lin & Ke Zhang & Luyao Zhang, 2022. "Identifying Promising Technologies of Electric Vehicles from the Perspective of Market and Technical Attributes," Energies, MDPI, vol. 15(20), pages 1-22, October.
    2. Hiramani Shukla & Srete Nikolovski & More Raju & Ankur Singh Rana & Pawan Kumar, 2022. "A Particle Swarm Optimization Technique Tuned TID Controller for Frequency and Voltage Regulation with Penetration of Electric Vehicles and Distributed Generations," Energies, MDPI, vol. 15(21), pages 1-32, November.
    3. Farida Shaban & Pelopidas Siskos & Christos Tjortjis, 2023. "Electromobility Prospects in Greece by 2030: A Regional Perspective on Strategic Policy Analysis," Energies, MDPI, vol. 16(16), pages 1-17, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Visaria, Anant Atul & Jensen, Anders Fjendbo & Thorhauge, Mikkel & Mabit, Stefan Eriksen, 2022. "User preferences for EV charging, pricing schemes, and charging infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 120-143.
    2. Jia, Wenjian & Chen, T. Donna, 2023. "Investigating heterogeneous preferences for plug-in electric vehicles: Policy implications from different choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    3. Reema Bera & Bhargab Maitra, 2021. "Analyzing Prospective Owners’ Choice Decision towards Plug-in Hybrid Electric Vehicles in Urban India: A Stated Preference Discrete Choice Experiment," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    4. Danielis, Romeo & Rotaris, Lucia & Giansoldati, Marco & Scorrano, Mariangela, 2020. "Drivers’ preferences for electric cars in Italy. Evidence from a country with limited but growing electric car uptake," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 79-94.
    5. Bera, Reema & Maitra, Bhargab, 2021. "Assessing consumer preferences for Plug-in Hybrid Electric Vehicle (PHEV): An Indian perspective," Research in Transportation Economics, Elsevier, vol. 90(C).
    6. Mustafa Hamurcu & Tamer Eren, 2023. "Multicriteria decision making and goal programming for determination of electric automobile aimed at sustainable green environment: a case study," Environment Systems and Decisions, Springer, vol. 43(2), pages 211-231, June.
    7. Ranjit R. Desai & Eric Hittinger & Eric Williams, 2022. "Interaction of Consumer Heterogeneity and Technological Progress in the US Electric Vehicle Market," Energies, MDPI, vol. 15(13), pages 1-25, June.
    8. Rotaris, Lucia & Giansoldati, Marco & Scorrano, Mariangela, 2021. "The slow uptake of electric cars in Italy and Slovenia. Evidence from a stated-preference survey and the role of knowledge and environmental awareness," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 1-18.
    9. Aravena, C. & Denny, E., 2021. "The impact of learning and short-term experience on preferences for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    10. Philip, Thara & Whitehead, Jake & Prato, Carlo G., 2023. "Adoption of electric vehicles in a laggard, car-dependent nation: Investigating the potential influence of V2G and broader energy benefits on adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 167(C).
    11. Agovino, Massimiliano & Ferraro, Aniello & Garofalo, Antonio, 2023. "Are green cars an optimal and efficient choice for motorists? Evidence from Italy," Transport Policy, Elsevier, vol. 141(C), pages 140-151.
    12. Fanchao Liao & Eric Molin & Bert van Wee, 2017. "Consumer preferences for electric vehicles: a literature review," Transport Reviews, Taylor & Francis Journals, vol. 37(3), pages 252-275, May.
    13. Loría, Luis Enrique & Watson, Verity & Kiso, Takahiko & Phimister, Euan, 2019. "Investigating users' preferences for Low Emission Buses: Experiences from Europe's largest hydrogen bus fleet," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    14. Nicolau, Juan L., 2011. "Differentiated price loss aversion in destination choice: The effect of tourists’ cultural interest," Tourism Management, Elsevier, vol. 32(5), pages 1186-1195.
    15. Vincenzina Caputo & Jayson L Lusk & Rodolfo M Nayga, 2020. "Am I Getting a Good Deal? Reference‐DependentDecision Making When the Reference Price Is Uncertain," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 132-153, January.
    16. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    17. Kim, Junghun & Seung, Hyunchan & Lee, Jongsu & Ahn, Joongha, 2020. "Asymmetric preference and loss aversion for electric vehicles: The reference-dependent choice model capturing different preference directions," Energy Economics, Elsevier, vol. 86(C).
    18. Zhaofu Hong & Hao Wang & Yeming Gong, 2019. "Green product design considering functional-product reference," Post-Print hal-02312293, HAL.
    19. Jia, Wenjian & Jiang, Zhiqiu & Wang, Qian & Xu, Bin & Xiao, Mei, 2023. "Preferences for zero-emission vehicle attributes: Comparing early adopters with mainstream consumers in California," Transport Policy, Elsevier, vol. 135(C), pages 21-32.
    20. VANHUELE, Marc & LAURENT, Gilles & DREZE, Xavier, 2005. "Consumers' immediate memory for prices," HEC Research Papers Series 813, HEC Paris.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4269-:d:835824. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.