Electric vehicle is a new way of transportation having no air, noise pollution and an environmental friendly way to commute. India being a major market for electric vehicle and government plan to be an electric vehicle country by 2030 is a major ambitious plan and to achieve this this study has been conducted to check the acceptability of people towards electric vehicle and its effect on automobile industry. In this study we tried to find the people opinion and their awareness about the electric vehicle, reaction to some shortcomings of electric vehicle and will people accept it wholeheartedly. Government of India launched FAME scheme to increase the adoption of electric vehicle among masses. Almost all the vehicle producer in the world have at least one electric vehicle in their product portfolio and around the globe the acceptance of electric people is rapidly growing. Several policies in favor of electric mobility has been rolled out and its immediate effect are positive. However it’s an ambitious and long journey with a comprehensive policy plan, it can be achieved. Policies need to be introduce to discourage the further adoption of gasoline vehicle and new schemes need to be implemented to aggravate the adoption of electric vehicle. This study used 9 independent factors pertaining to characteristics of electric cars and developed a regression model for determining the buying behavior of customer. The analysis was done using R software. The study found that mobility and recharging characteristics were found to be most significant factors while RTO norms was considered to be the least significant characteristic affecting the buying decision of electric cars. The model developed from our study was 88% accurate and hence can be used for predicting the buying behavior of customer. This study is of prime importance to the companies who wanted to launch electric cars in India.

1. http://www.iaeme.com/IJMET/index.asp 391 editor@iaeme.com
International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 05, May 2019, pp. 391-398, Article ID: IJMET_10_05_041
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=5
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
CUSTOMER BUYING INTENTION TOWARDS
ELECTRIC VEHICLE IN INDIA
Dr. Bharti Motwani
Associate Prof. BIMM, Pune, India
Abhishek Patil
Student, BIMM Pune, India
ABSTRACT
Electric vehicle is a new way of transportation having no air, noise pollution and
an environmental friendly way to commute. India being a major market for electric
vehicle and government plan to be an electric vehicle country by 2030 is a major
ambitious plan and to achieve this this study has been conducted to check the
acceptability of people towards electric vehicle and its effect on automobile industry.
In this study we tried to find the people opinion and their awareness about the electric
vehicle, reaction to some shortcomings of electric vehicle and will people accept it
wholeheartedly. Government of India launched FAME scheme to increase the
adoption of electric vehicle among masses.
Almost all the vehicle producer in the world have at least one electric vehicle in
their product portfolio and around the globe the acceptance of electric people is
rapidly growing. Several policies in favor of electric mobility has been rolled out and
its immediate effect are positive. However it’s an ambitious and long journey with a
comprehensive policy plan, it can be achieved. Policies need to be introduce to
discourage the further adoption of gasoline vehicle and new schemes need to be
implemented to aggravate the adoption of electric vehicle.
This study used 9 independent factors pertaining to characteristics of electric cars
and developed a regression model for determining the buying behavior of customer.
The analysis was done using R software. The study found that mobility and recharging
characteristics were found to be most significant factors while RTO norms was
considered to be the least significant characteristic affecting the buying decision of
electric cars. The model developed from our study was 88% accurate and hence can
be used for predicting the buying behavior of customer. This study is of prime
importance to the companies who wanted to launch electric cars in India.
Key words: Electric Vehicle future, Electric vehicle Acceptance, Factors influencing
EV adoption, Gov. attitude towards Electric vehicle
Cite this Article: Dr. Bharti Motwani and Abhishek Patil, Customer Buying Intention
towards Electric Vehicle in India, International Journal of Mechanical Engineering
and Technology 10(5), 2019, pp. 391-398.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=5

2. Dr. Bharti Motwani and Abhishek Patil
http://www.iaeme.com/IJMET/index.asp 392 editor@iaeme.com
1. INTRODUCTION
An electric vehicle, unlike a conventional vehicle, is quite flexible. This is because of the less
number of moving parts that are important in working of a conventional vehicle. In an electric
vehicle, the number of moving parts is limited to one, the motor. It can be run by different
control mechanism.
In recent times, electric vehicles are on massive rise, there are many reasons behind it.
The most prominent one is their contribution in reducing the pollution. In 2009, the
transportation sector was accountable for 25% of the greenhouse gases produced by energy
related sectors. [1]
2. LITERATURE REVIEW
Government‟s ambitious plan of having all electric vehicle on road by 2030. While this
transformative poses challenges along with opportunities[2].Electric mobility is widely seen
today as a way to improve air quality and meet environment necessities, but hardly is it
integrated in a comprehensive vision for smarter cities. Electric vehicle continue to be
associated with traditional purchase and use models, and are still generally considered as just
cars: innovative uses and services relating to batteries, or to integration with smart buildings,
are ignored, or at least not given proper thought. [3] Impeccable and profound planning and
patience in execution strategy are the needs of electric vehicle market introduction for positive
outcome. Governments should give emphasison creating an atmosphere positive toward EV
acceptance. International coordination/collaboration lowers the cost for government to
introduce EVs and demonstrates to the world its environmental concern and commitment
towards this new way of transportation.
The overall impact of electric vehicle ultimately benefits the people. Electric vehicles are
97% cleaner compared to gasoline powered vehicles It does not produce nay tailpipe
emissions that can place particulate matter into the air. The Particulate matter, (carcinogens)
when released into the atmosphere by gasoline powered vehicles, “can aggravate health
condition of asthma patients, and also irritate respiratory systems.
.In a world where environmental protection and energy conservation are growing
concerns, the development of electric vehicle technology has taken on an accelerated pace to
fulfill these needs. Concerning the environment, EVs can provide emission-free urban
transportation. From the energy aspect, Electric vehilce can offer a secure, comprehensive and
balanced energy option that is efficient and environmentally friendly, such as the utilization of
various kinds of the renewable energies. Furthermore, EVs will have the potential to have a
great impact on energy, environment and transportation as well as hitech promotion, new
industry creation and economic development.[6]
India, the world‟s third-largest energy consumer after the United states and Republic of
China, is working towards building a green and sustainable economy and plans to achieve 175
gigawatt (GW) of renewable energy capacity by the year 2022 as part of its mission and
commitments under the global climate change accord. Of this, 100GW is to come from solar.
“This industry (EV) is starting to uplift itself and it‟s still a small percentage of the overall
vehicle market but it‟s starting to reach an point where it can have a very strong and profound
impact globally,” Such a shift to renewable energy makes good sense for India which paid
Rs4.16 trillion to buy 202.85 million tonnes of crude oil in 2015-16.Electric vehicles‟ impact
on the environment has been considered many times without any serious steps taken in that
direction. Our economic model is original because it hopes to examine rational consumers‟
choice between switching to electric vehicles (EVs) and purchasing all other goods. We were
able to explore the environmental impact of adoption electric vehicle , which will supplement

3. Customer Buying Intention towards Electric Vehicle in India
http://www.iaeme.com/IJMET/index.asp 393 editor@iaeme.com
our argument for whether or not pushing for consumers to go for electric vehicle is a good
choice or not.[7]
Currently there are basically two types of present in the market:
1.All-electric vehicles (AEVs) 2.Plug-in hybrid electric vehicles (PHEVs). Main component
of an electric vehicle are battery, motor and an electric engine.The total impact of the electric
vehicle ultimately is beneficial for people. Compared to gasoline vehicles, electric vehicles
are considered to be 90% cleaner, does not produce any tailpipe emissions that can place
particulate matter into the air. Particulate matter, which is harmful for health, when released
into the atmosphere by gas-powered vehicles, “can increase asthma conditions, as well as
irritate respiratory system [8].Like any new technology, electric vehicles create a variety of
potent economic development opportunities. While the electric vehicle market is in theinitial
phase of development, it has potential to reshape the industry[9].In a world where
environmental and energy are growing concerns, the development of electric vehicle
technology has taken on an accelerated pace to fulfill these needs. Concerning the
environment, EVs can provide emission-free urban transportation. From the energy aspect,
EVs can offer a secure, comprehensive and balanced energy option that is efficient and
environmentally friendly, such as the utilization of various kinds of the renewable energies.
Furthermore, EVs will have the potential to have a great impact on energy, environment and
transportation as well as hitech promotion, new industry creation and economic development
[10].
In future electric cars will most likely carry lithium-ion phosphate (LiFePO4) batteries
that are now becoming popular in other countries. The LiFePO4 batteries are rechargeable
and powerful and are being used in electric bikes and scooters [11].Electric vehicle
transportation is best suited for developing nations like India which already has a potential EV
market for two wheel, three wheel vehicles and buses.The purpose of this research is to find
out electric vehicle future in India and its acceptance among masses.There is no doubt that
auto industry can build Electric Vehicles, but the key question is, will consumers buy
them.Electric vehicle costs similar as compared tosame model of a conventional vehicle.If an
electric vehicle price is high, what value will the consumer obtain for the extra cost Rate at
which electric vehicles penetrate the market. Electricvehicle sales be evenly distributed across
the country, or disproportionately located in certain urban areas. Consumers look to purchase
new vehicles which provide better attributes as compared to their existing vehicles. For
electric vehicles to be competitive, they must be able to perform at the same level, possess the
same attributes as the conventional Vehicles that they are replacing.China at present is the
largest market for electric vehicles, about 650,000 electric vehicles on road, representing
about a 3rd
of the world‟s total. India is joining China in setting aggressive EV targets. India is
thinking about even more radical action, with plans to electrify all vehicles in the country by
2030. Which would mean India needs to sell 10 million electric Vehicles in 2030.Various
experts believe that demand for electric Vehicles will further increase in near future. Electric
vehicle may constitute almost a 3rd
of new-Vehicle sales by the end of the next decade.
Electric mobility is widely seen today as a way to improve air quality and meet climate goals.
3. RESEARCH METHODOLOGY
A self structured questionnaire was used for primary data collection. Non probability
judgmental sampling method was used for the collection of primary data. The data was
collected from 345 respondents and 10 variables related to electric car were considered for
this study. The underlying assumptions of multiple regression analysis were also met before
the analysis, the results are as follows.

4. Dr. Bharti Motwani and Abhishek Patil
http://www.iaeme.com/IJMET/index.asp 394 editor@iaeme.com
Normality of Variables
Regression assumes that variables should have normal distributions. Non-normally
distributed variables (highly skewed or kurtotic variables, or variables with substantial
outliers) can distort relationships and significance tests. The skewness and kurtosis value of
all the variables in our study were found to be lying inside the range, hence this assumption
was fulfilled
Linearity
Standard multiple regression can only accurately estimate the relationship between dependent
and independent variables if the relationships are linear in nature. A significant correlation
between the variables confirms the linearity. In the present study the correlation between the
variables were found to be significant at the .05 level which indicates the existence of linear
relationship between the dependent and independent variables.
Independence of Errors
The Durbin-Watson value informs about whether the assumption independence of errors is
defensible i.e there is no autocorrelation of error terms. This study found D-W to be close to
2. Hence, this assumption was fulfilled.
Homoscedasticity
Homoscedasticity means that the variance of errors is the same across all levels of the
independent variables. When the variance of errors differs at different values of the
independent variables, heteroscedasticity is indicated. The Breusch-Pagan test fits a linear
regression model to the residuals of a linear regression model and rejects if too much of the
variance is explained by the additional explanatory variables. This test is applied in R using
ncvTest() function. A non significant value of p shows that this assumption is also fulfilled
(Annexure 1).
Multicollinearity
Multicollinearity occurs when you have two or more independent variables that are highly
correlated with each other. This leads to problems with understanding which independent
variable contributes to the variance explained in the dependent variable, as well as technical
issues in calculating a multiple regression model. An inspection of correlation coefficients and
Tolerance/VIF values help in detecting Multicollinearity.
If the square root of vif function for all the variables under study is less than 2, then this
assumption if fulfilled. In our study, the function (sqrt(vif))>2, is showing false for all the
variables. Thus, this assumption is fulfilled and no multicollinearity is seen in the data
(Annexure 1).
Since all the assumptions of regression are fulfilled, hence our data is now appropriate for
applying regression analysis.
Model Summary
The multiple regression analysis was run using R software. The buying behavioral intention
related to electric cars was considered as dependent variable where as the characteristics of
electric car were taken as predictors (independent variables). The whole data set was
partitioned into two sets, one set constituting 70% was taken for developing the model and
data analysis. The other 30% set was taken for prediction of the developed model. The results

5. Customer Buying Intention towards Electric Vehicle in India
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of regression analysis are shown in the annexure 1. The equation for buying behavior of
electric cars is:
Buying = (-1.57*mobility)+(-1.31*recharging)+(-0.444*taxbenefit)+(-
1.42*RTOnorms)+(-1.68*subsidy)+(-0.79*interiorattributes)+(-1.250*pollution)+(-
1.034*oildependence)+(-0.8494*electrcitydemand)+23.1482
The result shows that mobility and recharging were considered as most significant
attributes while RTOnorms was considered as non significant characteristic. The accuracy of the
model is 88.35%, which is excellent. The high kappa value (0.7671) also shows that the developed model is
good and can be used for further prediction related to buying of electric car.
4. CONCLUSION
Indian economy is at a mounting stage and electric cars is a new conception in India.
Therefore it is necessary to know the progress done in this genre of electric cars in India.
New technologies have made cars more user-friendly in terms of ease-of-use, appealing fuel
saving, pollution friendly and smooth navigation. Our study identified 9 important factors
related to electric cars and studied the impact of these factors on buying intention of
customers. The study found that mobility and recharging characteristics were found to be
most significant factors while RTO norms was considered to be the least significant
characteristic affecting the buying decision of electric cars. The model developed from our
study was 88% accurate and hence can be used for predicting the buying behavior of
customer. This study is of prime importance to the companies who wanted to launch electric
cars in India. Based on the results of the study, the companies should increase its efforts to do
promotion based on the significant factors of electric cars. Attempt should be made to
emphasize the usefulness of electric car by using a suitable advertising campaign by creating
web based tutorials or videos that guide the utility of electric cars.
REFERENCES
[1] Yong, J.Y.; Ramachandaramurthy, V.K.; Tan, K.M.; Mithulananthan, N. A review on the
state-of-the-art technologies of electric vehicle, its impacts and prospects. Renew. Sustain.
Energy Rev. 2015, 49, 365–385.
[2] Utpal Bhaskar (Nov 16, 2017), India’s electric vehicle drive: Challenges and
opportunities retrieved from
https://www.livemint.com/Industry/ji96zXi5dZz3L1XUSkiZxM/Indias-electric-vehicle-
drive-Challenges-and-opportunities.html
[3] https://www.weforum.org/agenda/2017/12/electric-car-range-could-be-about-to-get-a-lot-
better http://www.ieahev.org/assets/1/7/IA-HEV_Annual_Report_May_2013_3MB.pdf
[4] Electric Cars: Effect on the Environment. (1998) Retrieved January 31, 2010 retrieved
from http://library.thinkquest.org/20463/environment.html.
[5] ELECTRICAL ENGINEERING – Vol. III - Electric Vehicles - C C Chan ©Encyclopedia
of Life Support Systems (EOLSS) ELECTRIC VEHICLES
[6] The Economics and Future of Electric Powered Automobiles, Andrew Xue, Mackenzie
Strabala, Diane Lee, Paul Lou, and Anne Qiu University of Chicago BPRO 29000 -
Energy and Energy Policy December 2015
[7] Electric Cars: Effect on the Environment. (1998) Retrieved January 31, 2010 from
http://library.thinkquest.org/20463/environment.html.
[8] Paul Krutko, FM, Jay C. Moon, Jeffrey A. Finkle (2013), Analysis of the Electric Vehicle
Industry
[9] C C Chan, Electric Vehicles, Encyclopedia of Life Support Systems (EOLSS) ELECTRIC
VEHICLE, Vol. III

6. Dr. Bharti Motwani and Abhishek Patil
http://www.iaeme.com/IJMET/index.asp 396 editor@iaeme.com
[10] Future Electric Cars. (2007) Retrieved January 29, 2010 from http://www.future-
car.ghnet/future-electric-cars.html
[11] Analysis of the Electric Vehicle Industry, Paul Krutkoretrived from
https://www.iedconline.org/clientuploads/Downloads/edrp/IEDC_Electric_Vehicle_Indust
ry.pdf
[12] Recent development on electric vehicle, K.W.E. Cheng The Hong Kong Polytechnic
University
[13] https://www.livemint.com/Opinion/7RAvy8f8vdDHkFPFLYcEyI/Road-map-for-electric-
vehicles-in-India.html
[14] https://www.statista.com/statistics/270603/worldwide-number-of-hybrid-and-electric-
vehicles-since-2009/
[15] Lee, Henry, and Grant Lovellette. 2011. Will Electric Cars Transform the U.S. Market?
HKS Faculty Research Working Paper Series RWP11-032, John F. Kennedy School of
Government, Harvard University
[16] https://www.livemint.com/Industry/ji96zXi5dZz3L1XUSkiZxM/Indias-electric-vehicle-
drive-Challenges-and-opportunities.html
[17] https://www.livemint.com/Auto/9tIHdSJrb6BQmpQBAQrRuN/Petrol-diesel-cars-may-
be-taxed-more-to-push-electric-vehic.html.
ANNEXURE 1
> electric<-read.csv("electriccar.csv")
>#Displaying the dimensions of the file
>dim(electric)
[1] 345 10
>#Partitioning of dataset
> partition<-createDataPartition(y=electric$decision, p=0.7, list=FALSE)
> training<-electric[ partition, ]
> validate<-electric[-partition,]
>#Developing a model
> carmodel<-glm(formula = decision~.,data = training,family = binomial)
>#Assumptions check
>#Check of Homoscedasticity using ncvTest() function
>ncvTest(carmodel)
Non-constant Variance Score Test
Variance formula: ~ fitted.values
Chisquare = 0.26868 Df = 1 p = 0.6042186
>#Check of Multicollinearity Assumption using vif() function
sqrt(vif(carmodel))>2
mobility recharging taxbenefit RTO subsidy attributes pollution oildependence electricitydemand
FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

7. Customer Buying Intention towards Electric Vehicle in India
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>#Displaying the model
> summary(carmodel)
Call:
glm(formula = decision ~ ., family = binomial, data = training)
Deviance Residuals:
Min 1Q Median 3Q Max
-4.7068 -0.1702 0.0109 0.2157 1.5421
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 23.1482 3.4631 6.684 2.32e-11 ***
mobility -1.5760 0.3143 -5.015 5.31e-07 ***
recharging -1.3127 0.2924 -4.489 7.16e-06 ***
taxbenefit -0.4449 0.5093 -0.874 0.38233
RTO norms -1.4233 0.5062 -2.812 0.00493 **
subsidy -1.6820 0.5257 -3.199 0.00138 **
interiorattributes -0.7989 0.2930 -2.727 0.00640 **
pollution -1.2509 0.4438 -2.819 0.00482 **
oildependence -1.0346 0.4478 -2.310 0.02086 *
electricitydemand -0.8494 0.2751 -3.088 0.00202 **
---
Signif. codes: 0 „***‟ 0.001 „**‟ 0.01 „*‟ 0.05 „.‟ 0.1 „ ‟ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 335.334 on 241 degrees of freedom
Residual deviance: 97.937 on 232 degrees of freedom
AIC: 117.94
Number of Fisher Scoring iterations: 7
>#Prediction of the new data
> anspredict<-predict(carmodel,newdata = validate ,type = "response")
> convert<-ifelse(anspredict<0.5,0,1)
> newdf<-data.frame(predicted=convert, actual=validate$decision)
>#Use of Confusion Matrix
> result<-confusionMatrix(newdf$actual , newdf$predicted)
> result
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 45 7
1 5 46

8. Dr. Bharti Motwani and Abhishek Patil
http://www.iaeme.com/IJMET/index.asp 398 editor@iaeme.com
Accuracy : 0.8835
95% CI : (0.8053, 0.9383)
No Information Rate : 0.5146
P-Value [Acc > NIR] : 1.669e-15
Kappa : 0.7671
Mcnemar's Test P-Value : 0.7728
Sensitivity : 0.9000
Specificity : 0.8679
Pos Pred Value : 0.8654
Neg Pred Value : 0.9020
Prevalence : 0.4854
Detection Rate : 0.4369
Detection Prevalence : 0.5049
Balanced Accuracy : 0.8840
'Positive' Class : 0