IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v195y2024ics0301421524003811.html
   My bibliography  Save this article

The barriers, determinants, and willingness-to-pay in electric motorcycle conversion (EMC) adoption

Author

Listed:
  • Rizki, Muhamad
  • Rossolov, Oleksandr
  • Susilo, Yusak O.

Abstract

The rise in motorcycle use in Southeast Asian countries like Indonesia has caused environmental issues and transitioning from fossil-fuel to electric motorcycles (EM) will reduce emissions and improve air quality. This study aims to investigate the barriers, determinants, and willingness-to-pay in electric motorcycle conversion (EMC). In a choice experiment, data from motorcycle users in Bali, Indonesia, was collected and analysed in this study using K-modes cluster analysis and the Mixed-Logit Model. The study identified different barriers to EMC among motorcyclist groups: mix-motor commuters and hardcore oldies concerned with financial challenges, all-day riders with mature motorcycles face a lack of information on EMC costs and procedures, and higher-power enthusiasts and newbies with light motorcycles concerned with daily travel disruptions during the conversion process. This study also found waiting and conversion time to play a role in EM adoption and travellers who use older motorcycles are the most likely to adopt EMC. Lower-income individuals tend to be more inclined towards EMC and younger demographics lean towards internal combustion engine (ICE) motorcycles. Moreover, the study indicates that EMC reduces the adoption of ICE motorcycles more than conventional EM. A 50% increase in conversion time lowers EMC adoption probability by 5.2%pts. and increases new ICE motorcycle adoption by 3%pts. and new EM adoption by 2.2%pts. Additionally, motorcyclists are more willing to invest in EMC if it means reducing conversion time/charging costs, particularly for older motorcycles. This study offers several policy recommendations for accelerating EMC adoption in Indonesia.

Suggested Citation

  • Rizki, Muhamad & Rossolov, Oleksandr & Susilo, Yusak O., 2024. "The barriers, determinants, and willingness-to-pay in electric motorcycle conversion (EMC) adoption," Energy Policy, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:enepol:v:195:y:2024:i:c:s0301421524003811
    DOI: 10.1016/j.enpol.2024.114361
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421524003811
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2024.114361?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Egbue, Ona & Long, Suzanna, 2012. "Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions," Energy Policy, Elsevier, vol. 48(C), pages 717-729.
    2. Chiou, Lesley & Walker, Joan L., 2007. "Masking identification of discrete choice models under simulation methods," Journal of Econometrics, Elsevier, vol. 141(2), pages 683-703, December.
    3. Csiszár, Csaba & Csonka, Bálint & Földes, Dávid & Wirth, Ervin & Lovas, Tamás, 2019. "Urban public charging station locating method for electric vehicles based on land use approach," Journal of Transport Geography, Elsevier, vol. 74(C), pages 173-180.
    4. Chorus, Caspar G. & Koetse, Mark J. & Hoen, Anco, 2013. "Consumer preferences for alternative fuel vehicles: Comparing a utility maximization and a regret minimization model," Energy Policy, Elsevier, vol. 61(C), pages 901-908.
    5. Wee, Sherilyn & Coffman, Makena & Allen, Scott, 2020. "EV driver characteristics: Evidence from Hawaii," Transport Policy, Elsevier, vol. 87(C), pages 33-40.
    6. Krueger, Rico & Bierlaire, Michel & Daziano, Ricardo A. & Rashidi, Taha H. & Bansal, Prateek, 2021. "Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity," Journal of choice modelling, Elsevier, vol. 41(C).
    7. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    8. Stephane Hess & Mark Fowler & Thomas Adler & Aniss Bahreinian, 2012. "A joint model for vehicle type and fuel type choice: evidence from a cross-nested logit study," Transportation, Springer, vol. 39(3), pages 593-625, May.
    9. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    10. 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.
    11. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    12. Choi, Siwon & Kwak, Kyuil & Yang, Soyoung & Lim, Sesil & Woo, JongRoul, 2022. "Effects of policy instruments on electric scooter adoption in Jakarta, Indonesia: A discrete choice experiment approach," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 373-384.
    13. Ozbafli, Aygul & Jenkins, Glenn P., 2016. "Estimating the willingness to pay for reliable electricity supply: A choice experiment study," Energy Economics, Elsevier, vol. 56(C), pages 443-452.
    14. Aurélie Glerum & Lidija Stankovikj & Michaël Thémans & Michel Bierlaire, 2014. "Forecasting the Demand for Electric Vehicles: Accounting for Attitudes and Perceptions," Transportation Science, INFORMS, vol. 48(4), pages 483-499, November.
    15. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
    16. Ziwen Ling & Christopher R. Cherry & Yi Wen, 2021. "Determining the Factors That Influence Electric Vehicle Adoption: A Stated Preference Survey Study in Beijing, China," Sustainability, MDPI, vol. 13(21), pages 1-22, October.
    17. Kumar, Rajeev Ranjan & Chakraborty, Abhishek & Mandal, Prasenjit, 2021. "Promoting electric vehicle adoption: Who should invest in charging infrastructure?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    18. Tri Basuki Joewono & Muhamad Rizki & Jeanly Syahputri, 2021. "Does Job Satisfaction Influence the Productivity of Ride-Sourcing Drivers? A Hierarchical Structural Equation Modelling Approach for the Case of Bandung City Ride-Sourcing Drivers," Sustainability, MDPI, vol. 13(19), pages 1-17, September.
    19. Muhamad Rizki & Jeanly Syahputri & Prawira Fajarindra Belgiawan & Muhammad Zudhy Irawan, 2021. "Electrifying Tourist Mobility in Bali, Indonesia: Setting the Target and Estimating the CO 2 Reduction Based on Stated Choice Experiment," Sustainability, MDPI, vol. 13(21), pages 1-17, October.
    20. Takumi Kato, 2020. "Differences in delivery times’ effects on purchase intentions by the purchase candidates’ sequencing in the Japanese automotive industry," Journal of Marketing Analytics, Palgrave Macmillan, vol. 8(4), pages 234-244, December.
    21. Jones, Luke R. & Cherry, Christopher R. & Vu, Tuan A. & Nguyen, Quang N., 2013. "The effect of incentives and technology on the adoption of electric motorcycles: A stated choice experiment in Vietnam," Transportation Research Part A: Policy and Practice, Elsevier, vol. 57(C), pages 1-11.
    22. Tri Joewono & Hisashi Kubota, 2007. "User satisfaction with paratransit in competition with motorization in indonesia: anticipation of future implications," Transportation, Springer, vol. 34(3), pages 337-354, May.
    23. Rossolov, Oleksandr & Susilo, Yusak O., 2024. "Are consumers ready to pay extra for crowd-shipping e-groceries and why? A hybrid choice analysis for developing economies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 187(C).
    24. Vuban Chowdhury & Suman Kumar Mitra & Sarah Hernandez, 2024. "Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study," Sustainability, MDPI, vol. 16(12), pages 1-21, June.
    25. Makena Coffman & Paul Bernstein & Sherilyn Wee, 2017. "Electric vehicles revisited: a review of factors that affect adoption," Transport Reviews, Taylor & Francis Journals, vol. 37(1), pages 79-93, January.
    26. Aditya Kothari & Manini Madireddy & Ramasubramanian Sundararajan, 2016. "Discovering patterns in traveler behaviour using segmentation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(5), pages 334-351, October.
    27. Millan, Elena & Reynolds, Jonathan, 2014. "Self-construals, symbolic and hedonic preferences, and actual purchase behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 21(4), pages 550-560.
    28. Hess, Stephane & Bierlaire, Michel & Polak, John W., 2005. "Estimation of value of travel-time savings using mixed logit models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(2-3), pages 221-236.
    29. 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).
    Full references (including those not matched with items on IDEAS)

    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. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    2. Martínez-Pardo, Ana & Orro, Alfonso & Garcia-Alonso, Lorena, 2020. "Analysis of port choice: A methodological proposal adjusted with public data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 178-193.
    3. Ko, Sungmin & Shin, Jungwoo, 2023. "Projection of fuel cell electric vehicle demand reflecting the feedback effects between market conditions and market share affected by spatial factors," Energy Policy, Elsevier, vol. 173(C).
    4. 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).
    5. 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.
    6. Cherchi, Elisabetta & Guevara, Cristian Angelo, 2012. "A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 321-332.
    7. 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.
    8. Alyamani, Ryan & Pappelis, Dimitrios & Kamargianni, Maria, 2024. "Modelling the determinants of electrical vehicles adoption in Riyadh, Saudi Arabia," Energy Policy, Elsevier, vol. 188(C).
    9. Juan Carlos Martín & Concepción Román & Cira Mendoza, 2018. "Determinants for sun-and-beach self-catering accommodation selection," Tourism Economics, , vol. 24(3), pages 319-336, May.
    10. Isler, Cassiano Augusto & Blumenfeld, Marcelo & Caldeira, Gabriel Pereira & Roberts, Clive, 2024. "Long-Distance railway mode choice in Brazil: Evidence from a discrete choice experiment," Research in Transportation Economics, Elsevier, vol. 104(C).
    11. Punel, Aymeric & Stathopoulos, Amanda, 2017. "Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 18-38.
    12. Chakraborty, Rahul & Chakravarty, Sujoy, 2023. "Factors affecting acceptance of electric two-wheelers in India: A discrete choice survey," Transport Policy, Elsevier, vol. 132(C), pages 27-41.
    13. Seung Jun Choi & Junfeng Jiao, 2024. "Uncovering electric vehicle ownership disparities using K-means clustering analysis: A case study of Austin, Texas," Journal of Computational Social Science, Springer, vol. 7(3), pages 2403-2456, December.
    14. Abildtrup, Jens & Garcia, Serge & Olsen, Søren Bøye & Stenger, Anne, 2013. "Spatial preference heterogeneity in forest recreation," Ecological Economics, Elsevier, vol. 92(C), pages 67-77.
    15. Sharma, Reema Bera & Majumdar, Bandhan Bandhu & Maitra, Bhargab, 2024. "Commuter and non-commuter preferences for plug-in hybrid electric vehicle: A case study of Delhi and Kolkata, India," Research in Transportation Economics, Elsevier, vol. 103(C).
    16. Haghani, Milad & Sarvi, Majid & Shahhoseini, Zahra, 2015. "Accommodating taste heterogeneity and desired substitution pattern in exit choices of pedestrian crowd evacuees using a mixed nested logit model," Journal of choice modelling, Elsevier, vol. 16(C), pages 58-68.
    17. repec:ehu:biltok:8011 is not listed on IDEAS
    18. Rico Krueger & Akshay Vij & Taha H. Rashidi, 2018. "A Dirichlet Process Mixture Model of Discrete Choice," Papers 1801.06296, arXiv.org.
    19. Poudel, Niranjan & Singleton, Patrick A., 2024. "Willingness to pay for changes in travel time and work time: A stated choice experiment of US commuters," Research in Transportation Economics, Elsevier, vol. 103(C).
    20. Marco A. Palma & Dmitry V. Vedenov & David Bessler, 2020. "The order of variables, simulation noise, and accuracy of mixed logit estimates," Empirical Economics, Springer, vol. 58(5), pages 2049-2083, May.
    21. Laura Mørch Andersen, 2013. "Obtaining reliable Likelihood Ratio tests from simulated likelihood functions," IFRO Working Paper 2013/1, University of Copenhagen, Department of Food and Resource Economics.

    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:eee:enepol:v:195:y:2024:i:c:s0301421524003811. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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.