Customer Perception and Purchase Behaviour on
Electric Vehicles in India- A Study on Selected Cities
Ranu Yadav1*,
Dr. Ajay Kumar Mandil2
1 Research Scholar,
Jiwaji University, Gwalior, M.P. India
Email id- ouriginal.sku@gmail.com
2 Professor, Department
of Commerce, ML.B. Arts and Commerce Govt. College, Gwalior M.P. India
Abstract: This study examines customer
perception and purchase behaviour towards EVs in selected Indian cities, with a
focus on understanding the factors influencing adoption. A
descriptive study approach was used to gather primary data from 547
participants, ranging in age from 24 to 40, using a structured questionnaire.
Secondary sources, such journals and papers, were also consulted. Descriptional
statistics, t-tests, analysis of variance, chi-square tests, and regression
analysis were some of the instruments employed by statisticians to investigate
the hypotheses. Despite customers' favourable impressions of electric vehicles—especially
when it comes to their potential good impact on the environment,
sustainability, and technical progress—the results show that this impression
does not transfer into actual purchases. When it comes to making a purchase,
factors like pricing, charging infrastructure, government subsidies, and brand
recognition are still quite important. The study highlights the gap between awareness
and actual adoption, offering insights for policymakers and manufacturers to strengthen
strategies that encourage wider acceptance of electric vehicles in India.
Keywords: Customer Perception, EVs, Purchase Behaviour, India, Regression Analysis.
1. INTRODUCTION
Vehicles
that use electric motors and are propelled by batteries, fuel cells, or external
charging systems are known as electric vehicles (EVs). They are capable of running
on their own electricity, whether that's from charging stations, gasoline conversion
devices, or solar panels. Electric propulsion is being investigated for application
in several forms of transportation, not limited to automobiles. owing to their less
noise and easier mechanics, electric cars (EVs) were an early competitor to gasoline
vehicles. However, in the end, internal combustion engines (ICEs) were more popular
owing to their longer range and better infrastructure for refueling. With the 3rd
largest road network in the world, 60% of India's population uses roads for
transportation, which is a major cause of air pollution and climate change [1].
Improvements
in battery technology, stronger government restrictions, and the need to decrease
air pollution are propelling the rising demand for electric vehicles [2]. Electric
cars provide a potential alternative to traditional vehicles in terms of their impact
on the environment because to their reduced reliance on fossil fuels, cheaper running
costs, and reduced number of moving parts. They contribute to solving problems like
pollution, climate change, and resource depletion while producing almost no emissions
from their tailpipes [3][4].
There
is a pressing demand for environmentally friendly modes of transportation in India
due to the country's growing fuel costs and heavy traffic. To hasten the adoption
of electric vehicles, improve fuel security, and encourage environmentally friendly
transportation, the government has launched programs including the NEMMP- 2020 [5][6].
Because attitudes, knowledge, and perceived advantages greatly impact the readiness
to transition from conventional cars, it is vital to understand customer perception
and buying behavior in order to promote adoption [7][8]. Strategies for greater
EV adoption may be informed by this research, which focuses on customer perception
and buying behavior in selected Indian cities.
2. OBJECTIVES
·
To find out the customer perception towards electronic vehicles.
·
To find out the purchase behaviour towards electronic vehicles.
3. HYPOTHESIS
·
HO1: There is no significant effect of customer perception on
electric vehicles.
·
HA1: There is a significant effect of customer perception on electric
vehicles.
·
HO2: There is no significant effect of purchase behaviour on electric
vehicles.
·
HA2: There is a significant effect of purchase behaviour on electric
vehicles.
4. METHODOLOGY
4.1 Research Design
The study defines and analyses electric
vehicle buyer attitude and behaviour using a descriptive research technique. The
study's predetermined goals and hypotheses make the descriptive research design
appropriate. The study is well-organized and pre-planned, using pre-established
techniques and samples for data collection and analysis. Primary observations and
theoretical knowledge from the body of existing literature form the basis of the
investigation, which is then followed by data collecting and hypothesis testing.
4.2 Data
Collection
·
Primary
Data: Using a standardised questionnaire, primary data was gathered from
respondents in designated cities. The poll was out to collect data on people's
attitudes and purchasing habits about electric automobiles. Online surveys were
conducted using Google Forms.
·
Secondary Data: Secondary data on consumer perceptions and
purchasing patterns of electric cars were gathered from both public and unpublished
sources, including journals, research papers, articles, reports, and internet resources.
4.3 Sample Design
The research used a non-probability sampling
approach known as a convenience sampling strategy. Their
availability and eagerness to take part in the survey were the deciding factors
in their selection as respondents. Customers between the ages of 24 and 40 were
the only ones included in the survey because of budget and time restrictions. The
sample size of the study was 547 participants.
4.4 Research Instrument
The
study tool used was a structured questionnaire. To guarantee participant comprehension
and correct answers, the questionnaire was created in a straightforward and uncomplicated
manner. The questions were designed to focus on broad areas of consumer attitudes
and purchasing patterns regarding electric automobiles.
4.5 Analysis Tools
In order to analyse the data according to the
study's requirements, descriptive statistics tools and advanced research
statistical techniques were used.
1. Chi-Square Test
Using
the following formula, the test was used to see if there was a connection between
the two methods of classifying respondents:
The statistical metric used to determine
whether there is a significant discrepancy between the predicted and actual
frequencies is X², which stands for chi-square value. The values that would be
predicted in the absence of any relationship between the variables are denoted
by E (predicted Frequency), while O (Observed Frequency) denotes the actual
values that were acquired from the data. To get the final Chi-square statistic,
the computed values across all categories are added together, as shown by the
symbol ∑ (summation).
2. Analysis of Variance
We used
ANOVA to analyze three age groups' means simultaneously, rather than just two. The
purpose of this was to compare a parameter across three age groups of children.
The test statistic used in analysis of
variance to ascertain whether there are notable disparities among the means of
the groups is F, which stands for analysis of variance coefficient. With MST,
we can see how much of the observed variation is attributable to the treatment
or independent variable, and with MSE, we can see how much of the observed
variation is attributable to random error or other unexplained variables.
3. Regression Analysis
“To
evaluate the effect of several independent variables on a single dependent variable,
log-linear and multiple regression analysis were used. The algebraic version of
the regression equations is given by:
Linear Regression Model
Log–Linear Regression
Model
Here, Y represents the dependent variable of the
study, while x₁ to xₙ denote the independent variables included in the
model. b₁ to bₙ are the regression coefficients associated with each
independent variable, indicating the magnitude and direction of their influence
on Y. The term a refers to the constant or intercept of the regression equation,
and µ represents the random error term capturing the effect of unobserved factors.
The linear functional form was adopted for the study considering a higher value
of R², the economic significance of the explanatory variables, and the logical relevance
of the selected factors.”
5. RESULTS
Table 1: The demographic profile of the respondent
|
Variables |
Category |
Frequency |
(%) |
Cumulative (%) |
|
Age group |
18-24 |
85 |
15.5 |
15.5 |
|
25-34 |
58 |
10.6 |
26.1 |
|
|
35-44 |
83 |
15.2 |
41.3 |
|
|
45-54 |
93 |
17.0 |
58.3 |
|
|
55-64 |
70 |
12.8 |
71.1 |
|
|
65 or older |
77 |
14.1 |
85.2 |
|
|
Under 18 |
81 |
14.8 |
100.0 |
|
|
Gender |
Female |
150 |
27.4 |
27.4 |
|
Male |
143 |
26.1 |
53.6 |
|
|
Non-binary |
127 |
23.2 |
76.8 |
|
|
Prefer not
to say |
127 |
23.2 |
100.0 |
|
|
Education |
Bachelor's
degree |
65 |
11.9 |
11.9 |
|
Doctoral degree |
91 |
16.6 |
28.5 |
|
|
High school
graduate |
63 |
11.5 |
40.0 |
|
|
Less than high
school |
76 |
13.9 |
53.9 |
|
|
Master's degree |
86 |
15.7 |
69.7 |
|
|
Other |
89 |
16.3 |
85.9 |
|
|
Some college |
77 |
14.1 |
100.0 |
|
|
Employment
Status |
Employed part-time |
67 |
15.2 |
28.0 |
|
Employed full-time |
86 |
12.7 |
15.7 |
|
|
Other |
71 |
13.0 |
41.0 |
|
|
Retired |
79 |
14.4 |
55.4 |
|
|
Self-employed |
84 |
15.4 |
70.7 |
|
|
Student |
78 |
14.3 |
85.0 |
|
|
Unemployed |
82 |
15.0 |
100.0 |
|
|
Annual Household
Income |
$100,000 or
more |
94 |
17.2 |
17.2 |
|
$20,000-$39,999 |
79 |
14.4 |
31.6 |
|
|
$40,000-$59,999 |
81 |
14.8 |
46.4 |
|
|
$60,000-$79,999 |
107 |
19.6 |
66.0 |
|
|
$80,000-$99,999 |
102 |
18.6 |
84.6 |
|
|
Less than $20,000 |
84 |
15.4 |
100.0 |
|
|
Marital Status |
Divorced |
100 |
18.3 |
18.3 |
|
In a relationship |
97 |
17.7 |
36.0 |
|
|
Married |
87 |
15.9 |
51.9 |
|
|
Prefer not
to say |
85 |
15.5 |
67.5 |
|
|
Single |
89 |
16.3 |
83.7 |
|
|
Widowed |
89 |
16.3 |
100.0 |
|
|
Current Living
Situation |
Living alone |
107 |
19.6 |
19.6 |
|
Living with
parents |
98 |
17.9 |
37.5 |
|
|
Living with
roommates |
118 |
21.6 |
59.0 |
|
|
Living with
spouse/partner |
114 |
20.8 |
79.9 |
|
|
Other |
110 |
20.1 |
100.0 |
|
|
Occupation |
Clerical/Administrative |
83 |
15.2 |
15.2 |
|
Other |
77 |
14.1 |
29.3 |
|
|
Professional/Managerial |
82 |
15.0 |
44.2 |
|
|
Retired |
64 |
11.7 |
55.9 |
|
|
Service worker |
69 |
12.6 |
68.6 |
|
|
Skilled worker/Technician |
95 |
17.4 |
85.9 |
|
|
Student |
77 |
14.1 |
100.0 |
|
|
Place of Residence |
Rural |
185 |
33.8 |
33.8 |
|
Suburban |
182 |
33.3 |
67.1 |
|
|
Urban |
180 |
32.9 |
100.0 |
|
|
Numbers of
Vehicles Own |
None |
124 |
22.7 |
22.7 |
|
One |
133 |
24.3 |
47.0 |
|
|
Three or more |
137 |
25.0 |
72.0 |
|
|
Two |
153 |
28.0 |
100.0 |
The table provides a summary of the
respondent demographics, including age, gender, education, income, profession,
and residence. This diverse profile offers a representative sample,
which is essential for researching customer attitudes and behavior regarding electric
vehicles.
Table 2: Customer Perception towards Electric
Vehicles
|
Statement |
Response Category |
Frequency |
(%) |
|
Electric
vehicles are better for the environment than traditional vehicles |
Strongly
Agree |
98 |
17.9 |
|
Agree |
114 |
20.8 |
|
|
Neutral |
127 |
23.2 |
|
|
Disagree |
108 |
19.7 |
|
|
Strongly
Disagree |
100 |
18.3 |
|
|
Electric
vehicles are a sustainable solution for future transportation |
Strongly
Agree |
98 |
17.9 |
|
Agree |
123 |
22.5 |
|
|
Neutral |
121 |
22.1 |
|
|
Disagree |
107 |
19.6 |
|
|
Strongly
Disagree |
98 |
17.9 |
|
|
Electric
vehicles offer the same or better performance than conventional vehicles |
Strongly
Agree |
96 |
17.6 |
|
Agree |
127 |
23.2 |
|
|
Neutral |
125 |
22.9 |
|
|
Disagree |
99 |
18.1 |
|
|
Strongly
Disagree |
100 |
18.3 |
|
|
Electric
vehicles help in reducing air pollution and improving air quality |
Strongly
Agree |
98 |
17.9 |
|
Agree |
110 |
20.1 |
|
|
Neutral |
124 |
22.7 |
|
|
Disagree |
113 |
20.7 |
|
|
Strongly Disagree |
102 |
18.6 |
|
|
Electric
vehicles are technologically advanced and innovative |
Strongly
Agree |
94 |
17.2 |
|
Agree |
92 |
16.8 |
|
|
Neutral |
124 |
22.7 |
|
|
Disagree |
132 |
24.1 |
|
|
Strongly
Disagree |
105 |
19.2 |
|
|
Electric
vehicles are a good long-term investment |
Strongly
Agree |
95 |
17.4 |
|
Agree |
104 |
19.0 |
|
|
Neutral |
126 |
23.0 |
|
|
Disagree |
116 |
21.2 |
|
|
Strongly
Disagree |
106 |
19.4 |
Source: Primary data, SPSS output (N = 547)
Figure 1: Customer Perception towards Electric
Vehicles
The
table shows how people think electric cars are doing in terms of environmental impact,
performance, sustainability, and technical progress. The findings show that customers
have a divided but overall favorable impression.
Table 3: Reliability Statistics (Customer Perception)
|
Cronbach's Alpha |
N of Items |
|
.893 |
52 |
The dependability of the customer impression
scale is shown by a Cronbach's Alpha score of 0.893, as seen in the table. This
shows that the measurement objects are quite reliable and consistent with one another.
Table 4: ANOVA Results for Customer Perception
towards Electric Vehicles
|
|
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
I believe that
the public perception of electric vehicles is largely positive. |
Between Groups |
4.751 |
6 |
.792 |
.385 |
.888 |
|
Within Groups |
1109.516 |
540 |
2.055 |
|
|
|
|
Total |
1114.267 |
546 |
|
|
|
|
|
I think electric
vehicles will eventually become more popular than gasoline-powered cars. |
Between Groups |
4.890 |
6 |
.815 |
.421 |
.865 |
|
Within Groups |
1046.517 |
540 |
1.938 |
|
|
|
|
Total |
1051.408 |
546 |
|
|
|
|
|
I perceive
electric vehicles as technologically advanced and innovative. |
Between Groups |
9.427 |
6 |
1.571 |
.845 |
.536 |
|
Within Groups |
1004.309 |
540 |
1.860 |
|
|
|
|
Total |
1013.737 |
546 |
|
|
|
|
The
table presents the ANOVA findings used to assess variations in customer impression.
The negligible p-values indicate a lack of substantial variance across groups, resulting
in the acceptance of the null hypothesis (H₀₁).
Table 5: Purchase Behaviour towards Electric Vehicles
|
Statement |
Response Category |
Frequency |
Percentage (%) |
|
I would
prefer an electric vehicle over a traditional gasoline-powered vehicle if the
price difference were not significant |
Strongly
Agree |
96 |
17.6 |
|
Agree |
105 |
19.2 |
|
|
Neutral |
121 |
22.1 |
|
|
Disagree |
129 |
23.6 |
|
|
Strongly
Disagree |
96 |
17.6 |
|
|
The price
of electric vehicles is a major factor in my purchase decision |
Strongly
Agree |
94 |
17.2 |
|
Agree |
125 |
22.9 |
|
|
Neutral |
116 |
21.2 |
|
|
Disagree |
117 |
21.4 |
|
|
Strongly
Disagree |
95 |
17.4 |
|
|
Availability
of charging stations influences my decision to buy an electric vehicle |
Strongly
Agree |
100 |
18.3 |
|
Agree |
125 |
22.9 |
|
|
Neutral |
124 |
22.7 |
|
|
Disagree |
101 |
18.5 |
|
|
Strongly
Disagree |
97 |
17.7 |
|
|
Government
incentives or subsidies increase my likelihood of purchasing an electric
vehicle |
Strongly
Agree |
103 |
18.8 |
|
Agree |
142 |
26.0 |
|
|
Neutral |
121 |
22.1 |
|
|
Disagree |
88 |
16.1 |
|
|
Strongly
Disagree |
93 |
17.0 |
|
|
I am willing
to pay a premium for an electric vehicle with superior performance and
features |
Strongly
Agree |
101 |
18.5 |
|
Agree |
134 |
24.5 |
|
|
Neutral |
123 |
22.5 |
|
|
Disagree |
95 |
17.4 |
|
|
Strongly
Disagree |
94 |
17.2 |
|
|
Long-term
cost savings influence my decision to purchase an electric vehicle |
Strongly
Agree |
101 |
18.5 |
|
Agree |
111 |
20.3 |
|
|
Neutral |
119 |
21.8 |
|
|
Disagree |
120 |
21.9 |
|
|
Strongly
Disagree |
96 |
17.6 |
|
|
Brand
reputation influences my decision to purchase an electric vehicle |
Strongly
Agree |
95 |
17.4 |
|
Agree |
115 |
21.0 |
|
|
Neutral |
119 |
21.8 |
|
|
Disagree |
123 |
22.5 |
|
|
Strongly
Disagree |
95 |
17.4 |
Source: Primary data, SPSS output (N = 547)
Figure 2: Purchase Behaviour towards Electric
Vehicles
The
table delineates respondents' purchasing behavior concerning price, charging infrastructure,
government incentives, and brand impact. The results indicate that several variables
jointly affect purchasing choices.
Table 6: Chi-Square Test Results for Purchase
Behaviour Towards Electric Vehicles
|
|
Value |
df |
Asymptotic Significance
(2-sided) |
|
Likelihood
Ratio |
10.291 |
20 |
.963 |
|
Linear-by-Linear
Association |
.844 |
1 |
.358 |
|
Pearson Chi-Square |
10.154a |
20 |
.965 |
|
N of Valid
Cases |
547 |
|
|
|
a. The
predicted count is fewer than 5 in 0 cells (or 0%). A count of at least 13.58
is anticipated. |
|||
The results of the Chi-Square test, which
examines the connection between factors related to purchasing behaviour, are
shown in the table. The findings demonstrate no statistically significant
correlation, hence supporting the adoption of the null hypothesis (H₀₂).
Table 7: Independent Samples t-Test Results for
Purchase Behaviour towards Electric Vehicles
|
|
Levene's Test for Equality
of Variances |
t-test for Equality of
Means |
||||||||
|
F |
Sig. |
t |
df |
Sig. (2-tailed) |
MD |
SD |
95% Confidence Interval
of the Difference |
|||
|
Lower |
Upper |
|||||||||
|
I would prefer
an electric vehicle over a traditional gasoline-powered vehicle if the price difference
were not significant. |
Equal variances
assumed |
.655 |
.419 |
-.775 |
291 |
.439 |
-.121 |
.156 |
-.427 |
.186 |
|
Equal variances
not assumed |
|
|
-.775 |
289.525 |
.439 |
-.121 |
.156 |
-.427 |
.186 |
|
|
I would consider
purchasing an electric vehicle if it offered better fuel efficiency than traditional
vehicles. |
Equal variances
assumed |
.964 |
.327 |
-.253 |
291 |
.801 |
-.042 |
.165 |
-.367 |
.284 |
|
Equal variances
not assumed |
|
|
-.252 |
287.685 |
.801 |
-.042 |
.166 |
-.368 |
.284 |
|
|
I would be
more likely to purchase an electric vehicle if it were available in a variety
of models to suit different needs. |
Equal variances
assumed |
.204 |
.652 |
1.151 |
291 |
.250 |
.188 |
.163 |
-.133 |
.509 |
|
Equal variances
not assumed |
|
|
1.152 |
290.568 |
.250 |
.188 |
.163 |
-.133 |
.509 |
|
There is no statistically significant
difference between the group averages, since all of the p-values are more than
0.05, according to the table displaying the results of the t-test comparing the
purchase behaviours of various groups.
Table 8: Reliability Statistics (Purchase Behaviour)
|
Cronbach's Alpha |
N of Items |
|
.787 |
3 |
Here you may see the results of the
reliability study conducted on the purchasing behaviour scale. The Cronbach's
Alpha score for the scale is 0.787. It has been verified that the scale has
strong internal consistency.
There
are individual differences, but the survey shows that people generally think electric
cars are great, especially when it comes to the environmental advantages and sustainability.
Nevertheless, statistical research reveals that there is no substantial impact from
consumer perception or purchasing behavior. This suggests that elements like pricing,
infrastructure, and incentives still play a role in adoption choices. Researchers
found that people are becoming more aware of electric cars, but that this knowledge
has had little effect on people's decisions to buy one.
6. CONCLUSION
Despite an increase in consumer awareness and
positive sentiments about EVs in a few of Indian cities, the study found that
these factors alone were insufficient to significantly influence buyers'
choices. We accept the null hypothesis since we did not find any evidence of a
correlation between EV adoption and factors pertaining to consumers' opinions
or consumption habits. High initial prices, inadequate charging infrastructure,
and dependence on government subsidies continue to be barriers to adoption. So,
although everyone agrees that electric automobiles are good for the environment
and the planet, practical concerns still take precedence. The findings suggest
that in order to achieve successful large-scale adoption, manufacturers and
politicians should prioritise decreasing costs, improving infrastructure, and
bolstering incentive programs. This would help reduce the gap between
customers' perceptions and their actual purchasing decisions.
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