Skip to main content

Measuring and Modelling Electric Vehicle Adoption of Indian Consumers

Transportation in Developing Economies volume 8, Article number: 6 (2022) Cite this article

Abstract

Advancements in energy-efficient sustainable mobility technologies like electric vehicles are considered as one of the essential responses to minimizing the greenhouse gas emissions from the transportation sector. In spite of financial and non-financial incentives being provided to both consumers and manufacturers in India, the acceptance and adoption of electric vehicles remains very low. Based on the extended unified theory of acceptance and use of technology (UTAUT) model, this study uses structural equation modelling to analyze the effects of eight factors viz. environmental enthusiasm, technological enthusiasm, anxiety (or perceived risk), social image, social influence, perceived benefits, performance expectancy and facilitating conditions on the consumers’ intention to adopt electric vehicles. The study is analytically examined based on the data obtained from 675 students in Bengaluru, India. Results indicate that environmental enthusiasm, technological enthusiasm, social image, social influence, perceived benefits, and performance expectancy are positively related to adoption intention whereas facilitating conditions and anxiety have negative influence on a consumer’s intention to adopt an electric vehicle. This study adds to the limited literature on this subject in the context of developing economies. In practice, the results from this study can be useful to planners and policy makers to improve the adoption rate of electric vehicles.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price includes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2

References

  1. Kahn Ribeiro S, Kobayashi S, Hata H et al (2007) Transport and its infrastructure. In: Climate change 2007: Mitigation. Cambridge University Press, Cambridge

  2. U.S. EIA (2017) International Energy Outlook 2016

  3. IEA (2009) World Energy Outlook 2009. IEA, Paris

    Google Scholar 

  4. IEA (2020) Tracking Transport 2020. IEA, Paris

    Google Scholar 

  5. Lefevre B, Enriquez A (2014) Transport Sector Key to Closing the World’s Emissions Gap|World Resources Institute. https://www.wri.org/insights/transport-sector-key-closing-worlds-emissions-gap. Accessed 6 May 2021

  6. MoRTH (2020) Ministry of road transport and highways annual report 2020–2021

  7. Wu T, Zhao H, Ou X (2014) Vehicle ownership analysis based on GDP per capita in China: 1963–2050. Sustainability (Switzerland) 6:4877–4899. https://doi.org/10.3390/su6084877

    Article  Google Scholar 

  8. Lu H, Ma H, Sun Z, Wang J (2017) Analysis and prediction on vehicle ownership based on an improved stochastic gompertz diffusion process. J Adv Transp. https://doi.org/10.1155/2017/4013875

    Article  Google Scholar 

  9. IEA (2020) World energy balances: overview. IEA, Paris

    Google Scholar 

  10. IQ Air (2019) World Air Quality Report 2019

  11. Eckstein D, Kunzel V, Schafer L (2021) Global Climate Risk Index 2021

  12. UN environment (2018) Emissions Gap Report 2018. Kenya

  13. Bakker S, Jacob Trip J (2013) Policy options to support the adoption of electric vehicles in the urban environment. Transp Res Part D Transp Environ 25:18–23. https://doi.org/10.1016/j.trd.2013.07.005

    Article  Google Scholar 

  14. Sierzchula W (2014) Factors influencing fleet manager adoption of electric vehicles. Transp Res Part D Transp Environ 31:126–134. https://doi.org/10.1016/j.trd.2014.05.022

    Article  Google Scholar 

  15. Romm J (2006) The car and fuel of the future. Energy Policy 34:2609–2614. https://doi.org/10.1016/j.enpol.2005.06.025

    Article  Google Scholar 

  16. Lieven T, Mühlmeier S, Henkel S, Waller JF (2011) Who will buy electric cars? An empirical study in Germany. Transp Res Part D: Transp Environ 16:236–243. https://doi.org/10.1016/j.trd.2010.12.001

    Article  Google Scholar 

  17. Ajzen I, Fishbein M (1980) Understanding attitudes and predicting social behavior. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  18. Egbue O, Long S (2012) Barriers to widespread adoption of electric vehicles: an analysis of consumer attitudes and perceptions. Energy Policy 48:717–729. https://doi.org/10.1016/j.enpol.2012.06.009

    Article  Google Scholar 

  19. Skippon S, Garwood M (2011) Responses to battery electric vehicles: UK consumer attitudes and attributions of symbolic meaning following direct experience to reduce psychological distance. Transp Res Part D Transp Environ 16:525–531. https://doi.org/10.1016/j.trd.2011.05.005

    Article  Google Scholar 

  20. Wang S, Fan J, Zhao D et al (2016) Predicting consumers’ intention to adopt hybrid electric vehicles: using an extended version of the theory of planned behavior model. Transportation 43:123–143. https://doi.org/10.1007/s11116-014-9567-9

    Article  Google Scholar 

  21. Stern PC, Dietz T, Abel TD et al (1999) A value-belief-norm theory of support for social movements: the case of environmentalism. Hum Ecol Rev 6:81–97

    Google Scholar 

  22. Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50:179–211. https://doi.org/10.1016/0749-5978(91)90020-T

    Article  Google Scholar 

  23. Ozaki R, Sevastyanova K (2011) Going hybrid: an analysis of consumer purchase motivations. Elsevier 39:2217–2227

    Google Scholar 

  24. Carley S, Krause RM, Lane BW, Graham JD (2013) Intent to purchase a plug-in electric vehicle: a survey of early impressions in large US cites. Transp Res Part D Transp Environ 18:39–45. https://doi.org/10.1016/j.trd.2012.09.007

    Article  Google Scholar 

  25. Krupa JS, Rizzo DM, Eppstein MJ et al (2014) Analysis of a consumer survey on plug-in hybrid electric vehicles. Transp Res Part A Policy Pract 64:14–31. https://doi.org/10.1016/j.tra.2014.02.019

    Article  Google Scholar 

  26. Han L, Wang S, Zhao D, Li J (2017) The intention to adopt electric vehicles: driven by functional and non-functional values. Transp Res Part A Policy Pract 103:185–197. https://doi.org/10.1016/j.tra.2017.05.033

    Article  Google Scholar 

  27. He X, Zhan W, Hu Y (2018) Consumer purchase intention of electric vehicles in China: the roles of perception and personality. J Clean Prod 204:1060–1069. https://doi.org/10.1016/j.jclepro.2018.08.260

    Article  Google Scholar 

  28. Afroz R, Rahman A, Masud MM et al (2015) How individual values and attitude influence consumers’ purchase intention of electric vehicles—some insights from Kuala Lumpur, Malaysia. Environ Urban Asia 6:193–211. https://doi.org/10.1177/0975425315589160

    Article  Google Scholar 

  29. Sheth JN, Newman BI, Gross BL (1991) Why we buy what we buy: a theory of consumption values. J Bus Res 22:159–170. https://doi.org/10.1016/0148-2963(91)90050-8

    Article  Google Scholar 

  30. Holbrook MB, Hirschman EC (1982) The experiential aspects of consumption: consumer fantasies, feelings, and fun. J Consum Res 9:132. https://doi.org/10.1086/208906

    Article  Google Scholar 

  31. Levy M (1999) Revolutionizing the retail pricing game discount store news | course hero. Discount Store News 38(September):15

    Google Scholar 

  32. Holbrook MB (1996) Special session summary customer value C a framework for analysis and research. ACR North American Advances NA-23

  33. Zeithaml VA (1988) Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. J Mark 52:2–22. https://doi.org/10.1177/002224298805200302

    Article  Google Scholar 

  34. Gwinner KP, Gremler DD, Bitner MJ (1998) Relational benefits in services industries: the customer’s perspective. J Acad Mark Sci 26:101–114. https://doi.org/10.1177/0092070398262002

    Article  Google Scholar 

  35. Roy A (1994) Correlates of mall visit frequency. J Retail 70:139–161. https://doi.org/10.1016/0022-4359(94)90012-4

    Article  Google Scholar 

  36. Babin BJ, Darden RD, Griffin M (1994) Work and/or fun: measuring hedonic and utilitarian shopping value. J Consum Res 20:644–656

    Article  Google Scholar 

  37. Sheth JN (1983) An integrative theory of patronage preference and behavior. In: Darden WR, Lusch RF (eds) Patronage behavior and retail management. Elsevier Science Publishing Company, New York

  38. Forsythe S, Liu C, Shannon D, Gardner LC (2006) Development of a scale to measure the perceived benefits and risks of online shopping. J Interact Mark 20:55–75. https://doi.org/10.1002/dir.20061

    Article  Google Scholar 

  39. Rezvani Z, Jansson J, Bodin J (2015) Advances in consumer electric vehicle adoption research: a review and research agenda. Transp Res Part D Transp Environ 34:122–136. https://doi.org/10.1016/j.trd.2014.10.010

    Article  Google Scholar 

  40. Heffner RR, Kurani KS, Turrentine TS (2007) Symbolism and the adoption of fuel-cell vehicles. World Electr Veh J 1:24–31. https://doi.org/10.3390/wevj1010024

    Article  Google Scholar 

  41. Heffner RR, Kurani KS, Turrentine TS (2007) Symbolism in California’s early market for hybrid electric vehicles. Transp Res Part D Transp Environ 12:396–413. https://doi.org/10.1016/j.trd.2007.04.003

    Article  Google Scholar 

  42. Bunce L, Harris M, Burgess M (2014) Charge up then charge out? Drivers’ perceptions and experiences of electric vehicles in the UK. Transp Res Part A Policy Pract 59:278–287. https://doi.org/10.1016/j.tra.2013.12.001

    Article  Google Scholar 

  43. Glerum A, Stankovikj L, Thémans M, Bierlaire M (2014) Forecasting the demand for electric vehicles: accounting for attitudes and perceptions. Transp Sci 48:483–499. https://doi.org/10.1287/trsc.2013.0487

    Article  Google Scholar 

  44. She ZY, Sun Q, Ma JJ, Xie BC (2017) What are the barriers to widespread adoption of battery electric vehicles? A survey of public perception in Tianjin, China. Transp Policy 56:29–40. https://doi.org/10.1016/j.tranpol.2017.03.001

    Article  Google Scholar 

  45. Bjerkan KY, Nørbech TE, Nordtømme ME (2016) Incentives for promoting Battery Electric Vehicle (BEV) adoption in Norway. Transp Res Part D Transp Environ 43:169–180. https://doi.org/10.1016/j.trd.2015.12.002

    Article  Google Scholar 

  46. Sierzchula W, Bakker S, Maat K, van Wee B (2014) The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy 68:183–194. https://doi.org/10.1016/j.enpol.2014.01.043

    Article  Google Scholar 

  47. Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27:425–478

    Article  Google Scholar 

  48. Martins C, Oliveira T, Popovič A (2014) Understanding the internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application. Int J Inf Manag 34:1–13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002

    Article  Google Scholar 

  49. Maillet É, Mathieu L, Sicotte C (2015) Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an Electronic Patient Record in acute care settings: an extension of the UTAUT. Int J Med Inform 84:36–47. https://doi.org/10.1016/j.ijmedinf.2014.09.004

    Article  Google Scholar 

  50. Kabra G, Ramesh A, Pervaiz A, Dash MK (2017) Understanding behavioural intention to use information technology: insights from humanitarian practitioners. Telemat Inform 34:1250–1261. https://doi.org/10.1016/j.tele.2017.05.010

    Article  Google Scholar 

  51. Khalilzadeh J, Ozturk AB, Bilgihan A (2017) Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Comput Hum Behav 70:460–474. https://doi.org/10.1016/j.chb.2017.01.001

    Article  Google Scholar 

  52. Yeung SPM (2004) Teaching approaches in geography and students’ environmental attitudes. Environmentalist 24:101–117. https://doi.org/10.1007/s10669-004-4801-1

    Article  Google Scholar 

  53. Fujii S (2006) Environmental concern, attitude toward frugality, and ease of behavior as determinants of pro-environmental behavior intentions. J Environ Psychol 26:262–268. https://doi.org/10.1016/j.jenvp.2006.09.003

    Article  Google Scholar 

  54. Pagiaslis A, Krontalis AK (2014) Green consumption behavior antecedents: environmental concern, knowledge, and beliefs. Psychol Mark 31:335–348. https://doi.org/10.1002/mar.20698

    Article  Google Scholar 

  55. Sinnappan P, Rahman AA (2011) Antecedents of green purchasing behavior among Malaysian consumers. Int Bus Manag 5:129–139. https://doi.org/10.3923/ibm.2011.129.139

    Article  Google Scholar 

  56. Rogers EM, Shoemaker FF (1971) Communication of innovations; a cross-cultural approach, 2nd edn. Free Press, New York

    Google Scholar 

  57. Turrentine TS, Kurani KS (2007) Car buyers and fuel economy? Energy Policy 35:1213–1223. https://doi.org/10.1016/j.enpol.2006.03.005

    Article  Google Scholar 

  58. Liu Y, Li H, Carlsson C (2010) Factors driving the adoption of m-learning: an empirical study. Comput Educ 55:1211–1219. https://doi.org/10.1016/j.compedu.2010.05.018

    Article  Google Scholar 

  59. Parveen F, Sulaiman A (2008) Technology complexity, personal innovativeness and intention to use wireless internet using mobile devices in Malaysia. Int Rev Bus Res Pap 4:1–20

    Google Scholar 

  60. Jansson J (2011) Consumer eco-innovation adoption: assessing attitudinal factors and perceived product characteristics. Bus Strateg Environ 20:192–210. https://doi.org/10.1002/bse.690

    Article  Google Scholar 

  61. Kim C, Mirusmonov M, Lee I (2010) An empirical examination of factors influencing the intention to use mobile payment. Comput Hum Behav 26:310–322. https://doi.org/10.1016/j.chb.2009.10.013

    Article  Google Scholar 

  62. Lewis W, Agarwal R, Sambamurthy V (2003) Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Q Manag Inf Syst 27:657–678. https://doi.org/10.2307/30036552

    Article  Google Scholar 

  63. Lu J, Liu C, Yu CS, Wang K (2008) Determinants of accepting wireless mobile data services in China. Inf Manag 45:52–64. https://doi.org/10.1016/j.im.2007.11.002

    Article  Google Scholar 

  64. Meuter ML, Bitner MJ, Ostrom AL, Brown SW (2005) Choosing among alternative service delivery modes: an investigation of customer trial of self-service technologies. J Mark 69:61–83. https://doi.org/10.1509/jmkg.69.2.61.60759

    Article  Google Scholar 

  65. Oliver J, Rosen D (2010) Applying the environmental propensity framework: a segmented approach to hybrid electric vehicle marketing strategies. J Market Theory Pract 18:377–393. https://doi.org/10.2753/MTP1069-6679180405

    Article  Google Scholar 

  66. Sweeney JC, Soutar GN (2001) Consumer perceived value: the development of a multiple item scale. J Retail 77:203–220. https://doi.org/10.1016/S0022-4359(01)00041-0

    Article  Google Scholar 

  67. Schuitema G, Anable J, Skippon S, Kinnear N (2013) The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles. Transp Res Part A Policy Pract 48:39–49. https://doi.org/10.1016/j.tra.2012.10.004

    Article  Google Scholar 

  68. Eppstein MJ, Grover DK, Marshall JS, Rizzo DM (2011) An agent-based model to study market penetration of plug-in hybrid electric vehicles. Energy Policy 39:3789–3802. https://doi.org/10.1016/j.enpol.2011.04.007

    Article  Google Scholar 

  69. Chen CF, Xu X, Frey S (2016) Who wants solar water heaters and alternative fuel vehicles? Assessing social-psychological predictors of adoption intention and policy support in China. Energy Res Soc Sci 15:1–11. https://doi.org/10.1016/j.erss.2016.02.006

    Article  Google Scholar 

  70. Jansson J, Marell A, Nordlund A (2010) Green consumer behavior: determinants of curtailment and eco-innovation adoption. J Consum Mark 27:358–370. https://doi.org/10.1108/07363761011052396

    Article  Google Scholar 

  71. Zhang X, Wang K, Hao Y et al (2013) The impact of government policy on preference for NEVs: the evidence from China. Energy Policy 61:382–393. https://doi.org/10.1016/j.enpol.2013.06.114

    Article  Google Scholar 

  72. Kang MJ, Park H (2011) Impact of experience on government policy toward acceptance of hydrogen fuel cell vehicles in Korea. Energy Policy 39:3465–3475. https://doi.org/10.1016/j.enpol.2011.03.045

    Article  Google Scholar 

  73. Verma M, Verma A, Khan M (2020) Factors influencing the adoption of electric vehicles in Bengaluru. Transp Dev Econ 6:17. https://doi.org/10.1007/s40890-020-0100-x

    Article  Google Scholar 

  74. Junquera B, Moreno B, Álvarez R (2016) Analyzing consumer attitudes towards electric vehicle purchasing intentions in Spain: technological limitations and vehicle confidence. Technol Forecast Soc Chang 109:6–14. https://doi.org/10.1016/j.techfore.2016.05.006

    Article  Google Scholar 

  75. Verma M, Manoj M, Verma A (2017) Analysis of aspiration for owning a car among youths in a city of a developing country, India. Transp Dev Econ. https://doi.org/10.1007/s40890-017-0037-x

    Article  Google Scholar 

  76. Hoelter JW (1983) The analysis of covariance structures: goodness-of-fit indices. Sociol Methods Res 11:325–344. https://doi.org/10.1177/0049124183011003003

    Article  Google Scholar 

  77. Garver M, Mentzer J (1999) Logistics research methods: employing structural equation modeling to test for construct validity. J Bus Logist 20:33–57

    Google Scholar 

  78. Stevens JP, New RT, London Y (2012) Applied multivariate statistics for the social sciences, 5th edn. Routledge, New York

    Book  Google Scholar 

  79. Merlin S (2018) India has 42 varsities in Asia rankings but gender gap matter of concern. https://theprint.in/india/governance/india-42-varsities-asia-rankings-widening-gender-gap-matter-concern/34805/. Accessed 1 May 2021

  80. Barbarossa C, Beckmann S (2015) A self-identity based model of electric car adoption intention: a cross-cultural comparative study. Elsevier, Amsterdam

    Google Scholar 

  81. Noppers E, Keizer K, Milovanovic M, Steg L (2019) The role of adoption norms and perceived product attributes in the adoption of Dutch electric vehicles and smart energy systems. Energy Res Soc Sci. https://doi.org/10.1016/j.erss.2019.101237

    Article  Google Scholar 

  82. Kline RB (2015) Principles and practice of structural equation modeling, 4th edn. The Guilford Press

    MATH  Google Scholar 

  83. Hair JH, Balck WC, Babin BJ, Anderson RE (2009) Multivariate data analysis: a global perspective, 7th edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  84. MacCallum RC, Browne MW, Sugawara HM (1996) Power analysis and determination of sample size for covariance structure modeling. Psychol Methods 1:130–149

    Article  Google Scholar 

  85. Hair JF, Sarstedt M, Hopkins L, Kuppelwieser VG (2014) Partial least squares structural equation modeling (PLS-SEM): an emerging tool in business research. Eur Bus Rev 26:106–121

    Article  Google Scholar 

  86. Byrne BM (2016) Structural equation modeling with AMOS: basic concepts, applications, and programming, 3rd edn. Routledge Academy, London

    Book  Google Scholar 

  87. Bentler PM, Bonett DG (1980) Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 88:588–606. https://doi.org/10.1037/0033-2909.88.3.588

    Article  Google Scholar 

  88. Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18:50. https://doi.org/10.2307/3151312

    Article  Google Scholar 

  89. Field A (2009) Discopering statistics using SPSS. Sage

    Google Scholar 

  90. Chiu CM, Wang ETG (2008) Understanding Web-based learning continuance intention: the role of subjective task value. Inf Manag 45:194–201. https://doi.org/10.1016/j.im.2008.02.003

    Article  Google Scholar 

  91. Anderson JC, Gerbing GW (1988) Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull 103:411–423

    Article  Google Scholar 

  92. Wang N, Tang L, Pan H (2018) Analysis of public acceptance of electric vehicles: an empirical study in Shanghai. Technol Forecast Soc Chang 126:284–291. https://doi.org/10.1016/j.techfore.2017.09.011

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the opportunity provided by the 6th Conference of the Transportation Research Group of India (CTRG-2021) to present the work that formed the basis of this manuscript.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Science, Bangalore, India

    Furqan A. Bhat & Ashish Verma

  2. Ramaiah Institute of Management, Bangalore, India

    Meghna Verma

Authors
  1. Furqan A. Bhat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meghna Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashish Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashish Verma.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bhat, F.A., Verma, M. & Verma, A. Measuring and Modelling Electric Vehicle Adoption of Indian Consumers. Transp. in Dev. Econ. 8, 6 (2022). https://doi.org/10.1007/s40890-021-00143-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40890-021-00143-2

Keywords

  • Electric vehicles (EV)
  • Confirmatory factor analysis (CFA)
  • Structural equation model (SEM)
  • Latent variables
  • Adoption intention
  • Perception
  • Environmental enthusiasm
  • Technology enthusiasm