IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v33y2010i2p139-155.html
   My bibliography  Save this article

Consumer learning behavior in choosing electric motorcycles

Author

Listed:
  • Yen-Ching Sung

Abstract

The purpose of this paper is to understand the effect of the learning process on consumers' choice behavior for electric motorcycles in Taiwan. The electric motorcycle is a new technological product so consumers need to gather all kinds of information -- performance, operating cost, government subsidy policy, etc. -- to reduce their uncertainty about the product. In this paper, a four-stage stated preference experiment is designed and a survey applied. At each stage, the survey gives respondents new information about the electric motorcycle. In this process, respondents gather information and update their expectation about electric motorcycles in a Bayesian manner. This paper calibrates a Bayesian learning process model to the data. The results show that respondents have a higher quality perception of the electric motorcycle than the gasoline motorcycle and there is heterogeneous learning across respondents. The manufacturers can use these to target specific consumers to promote the electric motorcycle.

Suggested Citation

  • Yen-Ching Sung, 2010. "Consumer learning behavior in choosing electric motorcycles," Transportation Planning and Technology, Taylor & Francis Journals, vol. 33(2), pages 139-155, January.
    • Handle: RePEc:taf:transp:v:33:y:2010:i:2:p:139-155
    DOI: 10.1080/03081061003643747
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081061003643747
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03081061003643747?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. Ching, Andrew T., 2010. "Consumer learning and heterogeneity: Dynamics of demand for prescription drugs after patent expiration," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 619-638, November.
    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. Mallikarjun Patil & Bandhan Bandhu Majumdar & Prasanta Kumar Sahu & Long T. Truong, 2021. "Evaluation of Prospective Users’ Choice Decision toward Electric Two-Wheelers Using a Stated Preference Survey: An Indian Perspective," Sustainability, MDPI, vol. 13(6), pages 1-22, March.
    2. Zhu, Lichao & Song, Qingbin & Sheng, Ni & Zhou, Xiu, 2019. "Exploring the determinants of consumers’ WTB and WTP for electric motorcycles using CVM method in Macau," Energy Policy, Elsevier, vol. 127(C), pages 64-72.

    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. Aamir Rafique Hashmi & Johannes Van Biesebroeck, 2016. "The Relationship between Market Structure and Innovation in Industry Equilibrium: A Case Study of the Global Automobile Industry," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 192-208, March.
    2. Tomaso Duso & Annika Herr & Moritz Suppliet, 2014. "The Welfare Impact Of Parallel Imports: A Structural Approach Applied To The German Market For Oral Anti‐Diabetics," Health Economics, John Wiley & Sons, Ltd., vol. 23(9), pages 1036-1057, September.
    3. Patrick Bajari & C. Lanier Benkard & Jonathan Levin, 2007. "Estimating Dynamic Models of Imperfect Competition," Econometrica, Econometric Society, vol. 75(5), pages 1331-1370, September.
    4. Jie Bai, 2016. "Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation," Natural Field Experiments 00540, The Field Experiments Website.
    5. Tat Y. Chan & Jia Li & Lamar Pierce, 2014. "Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth," Marketing Science, INFORMS, vol. 33(4), pages 463-484, July.
    6. Kohei Kawaguchi, 2021. "When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business," Management Science, INFORMS, vol. 67(3), pages 1670-1695, March.
    7. David Granlund, 2021. "A New Approach to Estimating State Dependence in Consumers’ Brand Choices Applied to 762 Pharmaceutical Markets," Journal of Industrial Economics, Wiley Blackwell, vol. 69(2), pages 443-483, June.
    8. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    9. Brekke, Kurt R. & Holmas, Tor Helge & Straume, Odd Rune, 2011. "Reference pricing, competition, and pharmaceutical expenditures: Theory and evidence from a natural experiment," Journal of Public Economics, Elsevier, vol. 95(7), pages 624-638.
    10. Chatterjee, Chirantan & Kubo, Kensuke & Pingali, Viswanath, 2015. "The consumer welfare implications of governmental policies and firm strategy in markets for medicines," Journal of Health Economics, Elsevier, vol. 44(C), pages 255-273.
    11. Victor Aguirregabiria, 2023. "Dynamic demand for differentiated products with fixed-effects unobserved heterogeneity," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 1-25.
    12. Johannes Van Biesebroeck & Aamir Hashmi, 2007. "Market Structure and Innovation: A Dynamic Analysis of the Global Automobile Industry," 2007 Meeting Papers 362, Society for Economic Dynamics.
    13. Kaiser, Ulrich & Mendez, Susan J. & Rønde, Thomas & Ullrich, Hannes, 2014. "Regulation of pharmaceutical prices: Evidence from a reference price reform in Denmark," Journal of Health Economics, Elsevier, vol. 36(C), pages 174-187.
    14. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2017. "Empirical Models of Learning Dynamics: A Survey of Recent Developments," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 223-257, Springer.
    15. Xu, Yan, 2017. "Essays on preference formation and home production," Other publications TiSEM b028fd7e-53ba-4ff6-97eb-4, Tilburg University, School of Economics and Management.
    16. Katharina Elisabeth Blankart & Tom Stargardt, 2020. "The impact of drug quality ratings from health technology assessments on the adoption of new drugs by physicians in Germany," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 63-82, October.
    17. Jürgen Maurer & Katherine M. Harris, 2016. "Learning to Trust Flu Shots: Quasi‐Experimental Evidence from the 2009 Swine Flu Pandemic," Health Economics, John Wiley & Sons, Ltd., vol. 25(9), pages 1148-1162, September.
    18. Pradeep Chintagunta & Renna Jiang & Ginger Jin, 2009. "Information, learning, and drug diffusion: The case of Cox-2 inhibitors," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 399-443, December.
    19. Victor Aguirregabiria & Jihye Jeon, 2020. "Firms’ Beliefs and Learning: Models, Identification, and Empirical Evidence," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(2), pages 203-235, March.
    20. Callejas, Jerónimo & Mohapatra, Debi Prasad, 2021. "Welfare effects of public procurement of medicines: Evidence from Ecuador," International Journal of Industrial Organization, Elsevier, vol. 75(C).

    More about this item

    Statistics

    Access and download statistics

    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:taf:transp:v:33:y:2010:i:2:p:139-155. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GTPT20 .

    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.