Loughborough University
Browse

Buy, lease, or share? Consumer preferences for innovative business models in the market for electric vehicles

Download (697.11 kB)
journal contribution
posted on 2021-02-04, 09:02 authored by Youlin Huang, Lixian Qian, Didier Soopramanien, David Tyfield
Although business models are critical to the successful market penetration and diffusion of sustainable innovations, little is known about consumer preferences for adopting electric vehicles (EVs) under innovative business models. Drawing on existing conceptualisations of business models, we study consumers’ preferences for three innovative EV business models: (i) battery-leasing, (ii) EV-leasing, and (iii) Business-to-Customer (B2C) EV-sharing, in addition to the conventional EV-buying model. By conducting a nationwide stated preference (SP) experiment in China, we show that consumers perceive battery-leasing and EV-buying models to be close substitutes, while EV-leasing and EV-sharing models are perceived as independent. Important monetary attributes are the operational cost saving in the battery-leasing model and the leasing cost in the EV-leasing model. Critical service and policy attributes include home charging capability, vehicle licensing policy, and the density of battery-swapping stations for the battery-leasing model. We also find that female consumers, those who are well-educated, and those who have a pro-EV attitude are most likely to adopt EVs in innovative business models. Our work has significant value for companies and government in terms of better designing and supporting the innovative business models for EV adoption.

Funding

National Natural Science Foundation of China (Grant No. 71573213, 71973107 and 71804149)

History

School

  • Business and Economics

Department

  • Business

Published in

Technological Forecasting and Social Change

Volume

166

Publisher

Elsevier

Version

  • AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Technological Forecasting and Social Change and the definitive published version is available at https://doi.org/10.1016/j.techfore.2021.120639.

Acceptance date

2021-01-21

Publication date

2021-02-06

Copyright date

2021

ISSN

0040-1625

Language

  • en

Depositor

Dr Didier Soopramanien. Deposit date: 2 February 2021

Article number

120639

Usage metrics

    Loughborough Publications

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC