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Assessments of social factors
responsible for adoption of
electric vehicles in India:
a case study
Abhijeet K. Digalwar and Arpit Rastogi
Department of Mechanical Engineering, Birla Institute of Technology and Science,
Pilani, India
Abstract
Purpose –Environmental crisis and energy security concerns forced researchers, environmentalists and
industrialists to look for a cleaner mode of transportation. Rigorous efforts have been made to make electric
vehicles (EVs) feasible for commercial use. However, despite of many efforts by the Government of India, the
rate of adoption of EVs in India has not been up to the mark. To bridge this gap, present study understands
the social acceptability and sustainability of EVs and identifies the social factors, builds inferences from the
results obtained and helps in orienting the manufacturers and decision makers towards faster adoption of the
EVs.
Design/methodology/approach –The social factors responsible for the growth of EVs in India are
identified by literature survey. A questionnaire has been developed for understanding the customer’s
perception towards EVs. The results of the survey are analysed using the tools on descriptive statistics,
structural equation model using Statistical Package for the Social Sciences and hypothesis testing and the
results are validated.
Findings –The results of the study are based on three hypotheses. The findings show that although the
financial and the infrastructure factors have positive impact on rate of adoption of EVs in India; the vehicle
performance factors have a negative impact on EVs adoption, implying that the respondents of the survey
who feel that the vehicle performance factors are the most imperative have a more passive mind-set towards
the EVs adoption.
Research limitations/implications –The research work is based on the survey conducted on the pilot
region of the national capital region of the country where the majority of the respondents of the survey are
conventional fossil fuel vehicles (CFFV) owners. A more accurate analysis on the social factors affecting
deployment of EVs in the Indian market can be done if the population of the survey consists of equal share of
CFFV and EV owners from all across the nation.
Practical implications –This study will help researchers get a better understanding of the reasons for
slow adoption rate of EVs in India. This paper sheds light upon the social factors responsible for the same.
The Government of India can use the results of this study to understand the factors responsible for non-
adoption and the recommendations for its further work on “Faster Adoption and Manufacturing of (Hybrid)
and Electric Vehicles”India scheme.
Social implications –Results of the study identifies the factors that slow down the adoption rate of
EVs in India. The paper suggested potential solutions for the same. Successful implementation in terms of
policies and technological advancements can propel India to the top in EV market. Switching to EVs
brings about a radical change in the social life of the people and can improve the social status and lifestyle
of the people.
The authors would like to acknowledge the reviewers for their constructive and helpful comments.
This work is part of a project “Empirical investigation and analysis of factors for sustainable growth
of electric vehicles manufacturing in India”, funded by IMPRESS-ICSSR, New Delhi India.
Adoption of
electric
vehicles in
India
Received 5 June2021
Revised 23 December2021
24 January 2022
Accepted 15 March2022
International Journal of Energy
Sector Management
© Emerald Publishing Limited
1750-6220
DOI 10.1108/IJESM-06-2021-0009
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1750-6220.htm
Originality/value –Existing research has not focussed much on the social aspects of EVs in India. The
present work is solely the result of the strategic thinking, planning, work and implementation by the authors.
Keywords Electric vehicles, Social factors, Descriptive statistics, Structural equation model,
Hypothesis testing, CO
2
emission, Decision-making, Transport, Surveys, Policy,
Environmental damages, Regression, Mail questionnaires, Fossil fuel, Interviews, Online surveys
Paper type Research paper
1. Introduction
India, the 7th largest commercial vehicle manufacturer in the world, has huge potential for
flourishing in the electric vehicles (EVs) market. The global EV industry is in its growth
phase. It can provide employment to many people in the EVs manufacturing sector,
charging infrastructure sector, after-sales service sector or even scrap management sector.
Implementing the adoption of EVs in major cities of India such as New Delhi can bring
down the harmful effects of pollution from extensive use of conventional fossil fuel vehicles
(CFFV) and make significant improvement in the air quality of the region. Choosing EV over
CFFV has its own social benefits as well. It improves the social lifestyle and outlook
altogether. Thus, focussing the attention of the automotive industry into EVs can bring
about economic, social and environmental benefits to the citizens.
In India, the EV is still in its infancy stage. Understanding the importance of EVs, the
Government of India is working on implementing various policies and incentives on the EVs
(Digalwar et al., 2021). India has launched Faster Adoption and Manufacturing of (Hybrid)
and Electric Vehicles (FAME) India scheme under which, both the customers as well as the
manufacturers are provided with uniform demand incentives @ INR10,000/- per kWh forall
e-vehicles except e-buses and @ INR 20,000/- per kWh for all e-buses (Department of Heavy
Industries, 2019). Currently, India has around 7,50,000 units EVs deployed on the roads and
the number is continuously increasing (Gupta, 2019).
Development of EVs and the charging infrastructure should go hand-in-hand. One
cannot grow without the other. Therefore, it is equally important to invest in EVs charging
infrastructure as well. In January 2020, the Department of Heavy Industries approved 2636
EV charging stations in 62 cities across 24 states and union territories under the second
phase of FAME India program (Ministry of Heavy Industries and Public Enterprises, 2020).
The potential of EVs to reduce the dependency on non-renewable fossil fuels is what
drives the nations across the world to switch to it. However, if the electricity which is
powering the EVs is in turn generated by burning coal, the efforts to reduce the pollution
levels go in vain (Saraswat and Digalwar, 2021). At present, 63% of India’s total energy
comes from Coal and other fossil fuels, and the share of renewable sources is limited to 17%
(International Energy Agency, 2020). Therefore, it is equally important to make sure that
there is a fair share of renewable sources of energy that backsup the EV industry.
While the countries such as Norway, China, USA, Japan, etc. have been successful in
adopting the EVs, India despite its continuous efforts is still struggling to deploy EVs on its
roads. So, it is necessary to understand how the customers perceive the EVs and its social
factors. Hence, the study works on exploring the following questions:
Q1. What are the social factors to widespread adoption of EVs?
Q2. Are these social factors significantly affecting the public adoption of EVs?
Q3. Which social factors have a positive effect and which factors have negative effects
on purchase intentions of the people?
IJESM
Air pollutants, including particulate matter (PM), sulphur dioxide (SO
2
), nitrogen oxides
(NO
x
), carbon monoxide (CO), and ozone (O
3
) often exceed the National Ambient Air Quality
Standards in Indian cities (Vidhi and Shrivastava, 2018;Rahman, 2020). Delhi, the capital
city of India, has been selected as the research site for the study. Delhi (area 1484 sq. km) has
the worst air quality in the world with presence of PM10 particular matter –292 micrograms
PM per cubic meter (as per Statista Infographic Newsletter), while the acceptable safe limit
is 60 per cubic meter. As per the data from the Transportation department of Delhi
Government, there are 109.86 lakh vehicles registered in Delhi by 2018. Pollution-related
death toll is more than 10,000 per year. These factors make Delhi a suitable research site for
the pilot study.
2. Literature review
Thorough efforts have been made by researchers worldwide for identifying the barriers to
the adoption of EVs in the automotive market. The factors could be technological, economic,
political, environmental or even social. While the technological, economic and environmental
aspects have been explored in the past, the social factors need to be studied in-depth to gain
competitive advantage in the global market.
Axsen et al. (2013) worked on customer’s perception in the UK and discussed the impact
of social influences on these perceptions. After identifying these social factors, they
categorized them into translation, reflexivity and diffusion. Digalwar and Giridhar (2015)
have implemented interpretive structural modelling approach for prioritizing factors which
are barriers to deployment of EVs and found that lack of awareness and passive
government commitment are the greatest barriers and hence need to be tackled first. The
other barriers such as industrial growth, supplier’s availability, customer’s management
and battery technology have high dependencies and will automatically be eliminated if the
other barriers are sorted out.
She et al. (2017) done a similar study in Tianjin, China and found that most of the people
have identified the importance of EVs and agreed upon their promotion, while on the other
hand few people who still have low interests towards EVs, have a “wait and watch attitude”
because of various concerns associated with EVs such as safety, reliability and range. The
people of China feel that the policies need to be extended and more subsidies should be given
for an increase in the adoption rate. Westin et al. (2018) have worked on adoption of EVs in
Sweden and found that people have a positive attitude towards EVs due to ecological
motives. This attitude provides the customer with opportunities to show-off his/her concern
towards the environment. Their results mention that social influence plays a very crucial
role on EVs adoption. (Park et al., 2018) has also analysed such social factors and found that
the drivers were quite satisfied with the driving experience of EVs. The purchase price and
battery replacement cost, however, pose a big barrier to them. Moving a step ahead, Zheng
et al. (2018) established a trade-off between production decisions and government subsidies
from the perspectiveof maximal social welfare. It concludes that government policies should
be made keeping in mind both the customers and manufacturers.
On the basis of thorough literature, 14 factors and 3 main factors are developed.
Following section discussed it in detail.
2.1 Financial factors
EVs market and the corresponding infrastructure market go hand-in-hand. One cannot
grow without the other. Original equipment manufacturers will not take initiative to build
charging stations if there is no scope for EVs and vice versa. Due to this reason, EV market
is still in its infancy in India. As a result, the production cost of EV becomes high, which
Adoption of
electric
vehicles in
India
automatically results in a high purchase price for EV itself. As India does not have enough
resources for the manufacturing of the lithium-ion batteries, which are imported from the
North Asian countries, leading to high cost of the batteries due to the transportation charges
and import related taxes. Thus, battery cost indirectly becomes a financial factor that
influences the adoption of EVs in the automotive market (Bennett et al.,2016). Although
purchase cost of EV is comparatively higher than that of a CFFV, the lower fuel and
maintenance costs of EVs make it the better choice in the long run (Schmidt et al.,2015).
2.2 Vehicle performance factors
The biggest problem with the EVs is its range. There is a trade-off between the seating
capacity and the range. If the seating capacity is increased, the range decreases. A typical
four-wheeler EV with seating capacity of four persons has an average range of 130
kilometres which is enough for the daily driving purpose of a customer. However, this may
not be able to fulfil the requirement of long-distance tours and trek drivers (Kumar et al.,
2015). Also, it takes around 10 h to charge this battery with a normal 220 volts AC power. In
such cases battery swapping may be considered as a viable solution, as it takes a couple of
minutes to swap exhausted batteries with charged ones (Saxena et al., 2014). Safety of EVs is
also a very sensitive factor, as it has been seen that the battery explodes upon crash of
vehicle. The top speed and the acceleration of the EVs are also directly dependent on the
battery. Due to this an old or partially charged battery cannot be used for high-speed
purposes. The end-of-life phase of the battery also poses a serious problem of its disposal.
Few important factors to be considered in case of EVs are reliability, performance
consistency and trustworthiness.
2.3 Infrastructure factors
For the successful deployment of the EVs, charging infrastructure needs to be available
throughout the nation. It is required to promote the charging infrastructure equally with the
EVs; it cannot be deployed one after the other and must be established together. These
charging stations are of two types –fast charging grid system and slow speed charging
system. The fast-charging stations need to be located at highways and the city roads. The
slow chargers can be installed at home or workplaces because there is not much constraint
about the charging time. To resolve consumer concerns about charging, the construction of
charging infrastructure should be the top priority of India (Yang et al., 2018).
2.4 Policy incentives
Various innovative policies have been adopted by various countries for attracting the
customers and increasing the adoption rate of EVs. Some common policies are purchase
subsidy, vehicle tax benefits and dedicated lanes for EVs, etc. In India, to begin with the cap
on incentives for buses is kept at 40% of the cost of vehicles and for all other categories it is
fixed at 20% (Department of Heavy Industries, 2019). The other policies, under government
consideration are discounted toll tariff, subsidized charging fee, etc. The government is
continuously allocating funds to establish charging infrastructure and lower down the
purchase price of EVs through the FAME India scheme. The study implies that the central
government’s incentives EVs affects consumer buying willingness; however, state strategic
support and charging point placement are crucial elements to the widespread adoption of
EVs(Wang et al.,2017).
From an exhaustive literature survey, Table 1 below shows the frequency analysis of the
identified social factors.
IJESM
The factors mentioned in Table 2 provide a clear understanding of the public perception and
these would be tested by the following hypotheses:
H1. The financial factors have a significantly negative impact on the EV adoption.
H2. The vehicle performance factors have a significantly negative impact on the EV
adoption.
H3. The infrastructural factors play a significantly negative impact on the EV adoption.
It has also been searched if the personal characteristics and the policy incentives play any
driving force for thepublic perceptions regarding EV adoption.
Table 1.
Frequency analysis
of identified social
factors
S. No. Citation F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14
1She et al. (2017)
2Digalwar and Giridhar (2015)
3Zheng et al. (2018)
4Park et al. (2018)
5Axsen et al. (2013)
6Kushnir and Sandén (2012)
7Westin et al. (2018)
8 Shalender (2018)
9Bennett et al. (2016)
10 Oliveira et al. (2015)
11 Schmidt et al. (2015)
12 Shang and Shi (2013)
13 Kimble and Wang (2013)
14 Pilkington and Dyerson (2006)
15 Naor et al. (2015)
16 Bennett and Vijaygopal (2018)
17 Raymond Byrne and Polonsky (2001)
18 Kumar et al. (2015)
19 Saxena et al. (2014)
20 Berkeley et al. (2017)
21 Yong and Park (2017)
22 Soltani-Sobh et al. (2017)
23 Zhang et al. (2017)
24 Steinhilber et al. (2013)
25 Egbue and Long (2012)
26 Budde Christensen et al. (2012)
27 Wikström et al. (2015)
28 Rezvani et al. (2015)
29 Madina et al. (2016)
30 Sierzchula (2014)
31 Haddadian et al. (2015)
32 Hardman et al. (2016)
33 Morrissey et al. (2016)
34 Jakobsson et al. (2016)
35 Wikström et al. (2016)
36 Contestabile et al. (2017)
37 Quak et al. (2016)
38 Zhang et al. (2018)
39 Margaritis et al. (2016)
Adoption of
electric
vehicles in
India
3. Methodology
3.1 Questionnaire survey
A questionnaire survey was done in Delhi for finding out the customer perception
regarding EV adoption and the relevant social factors associated with it. The
questionnaire consisted of four parts. Part-1 contained the demographic information of
the respondents such as their age, sex, education, income, car ownership, etc. Part-2 was
about the customer’s attitude towards various factors and had to be answered on a five-
point “Likert scale”. The significance of the scale was: 1 (least important) to 5 (most
important) and was added into the survey for a clear understanding of respondents.
Part-3 was about the government incentives and policies regarding EVs. This aimed to
understand the customer’s attitude regarding the driving powers of government
incentives and policies. Here also the responses were recorded on a five-point “Likert
scale”with significance: 1 (least inspiring) to 5 (most inspiring). Part-4 was about the
customer’s willingness for the EV. Three questions were asked: if the customers were
ready to buy an EV, ready to recommend EV to others and ready to have more EVs in
the market. The responses were collected on the same five-point “Likert scale”having the
significance as: 1 (not at all willing) to 5 (most willing).
The survey was conducted in November 2020 through an open Google Survey Form for
the people of Delhi. Due to pandemic situation, it was difficult to collect the data manually
and by face-to-face discussion. The responses collection continued till 90 days, and a total of
632 responses were collected. These responses were then filtered by criterion:
The respondent must be an adult with more than 18 years of age.
He/she must have lived for more than 2 years in Delhi.
Table 2.
Factors type,
grouping and their
explanations
Factor Type Alias Factor Explanation
Financial Factors F1 Purchase Cost Purchase price of the EV without
subsidy
F2 Battery Cost Cost of a new battery once its life
ends
F3 Unawareness about maintenance The routine servicing cost of the
vehicle
F4 Unawareness about fuel cost Electricity cost for charging the
batteries
Vehicle Performance
Factors
F5 Driving Range Longest distance covered per full
charge
F6 Refuelling time Time to charge battery from zero
to full
F7 Safety Safety of the passengers during
the EV’s lifetime
F8 Reliability Trustworthy and performance
consistency
F9 Life of battery Time from purchase to disposal of
battery
F10 Vehicle power Top speed, acceleration of EV
Infrastructural
Factors
F11 Public charging infrastructure Service radius of charging station
F12 Charging infrastructure at home Charging facility at home
F13 Charging infrastructure at work Charging facility at work
F14 Charging infrastructure on highways Service range and fast charging
station on highways
IJESM
The responses were then filtered by contradictory responses for the same question in
different sections and by doing so 543 responses were finally accepted.
3.2 Structural equation model
Structural equation model (SEM) is a causal modelling tool, very common for finding the
output of various social analysis. It assesses the latent variable from the data about the
observed variables and the output model is created using the independent regression
equations. It helps provide a quantitative study about the interacting relations amongst the
variables. The factor analysis and path analysis has been done in SEM and the relationships
between various factors and customer willingness has been judged.
4. Results and discussion
As the survey was conducted at one phase, there was no need to test the variation in the
responses received from respondents. Descriptive analysis was carried out. Mean and
standard deviation were calculated for each factor to rank them. The responses were then
run over the Statistical Package for the Social Sciences (SPSS) to get the model. Later, the
hypothesis tests were performed over the results.
4.1 Descriptive analysis
Table 3 describes the mean and standard deviation of all the factors from the responses
accepted. The results show that the battery cost (F2) is the most critical factor amongst all
the social factors followed by purchase cost (F1) and public charging infrastructure (F11),
which implies that the root cause of the unwillingness to buy EVs is the associated costs and
underdeveloped charging infrastructure. It can be inferred that the adoption rate cannot be
improved until more subsidies and incentives are given to the customers on purchase of
EVs. The most significant factor for vehicle performance is the driving range which can
only be improved by intense research over battery technology.
Responses for Part-4 of the questionnaire have been analysed and presented in Table 4.It
indicates that more than 50% people are willing to purchase EVs. This could be due to the
adverse environmental conditions set into action by the use of CFFVs. The fraction of
population willing to purchase EVs are much more than the current share of EVs in Delhi,
suggesting a huge potential for the growth of EVs market in the region. Furthermore, a total
Table 3.
Descriptive analysis
of possible factors to
EVs adoption
Factor (Alias) Min. Max. Mean SD Rank
Battery Cost (F2) 1 5 4.92 0.74 1
Purchase Cost (F1) 1 5 4.88 0.59 2
Public Charging Infrastructure (F11) 1 5 4.77 0.81 3
Driving Range (F5) 1 5 4.69 0.65 4
Vehicle Power (F10) 1 5 4.61 0.74 5
Reliability (F8) 1 5 4.55 0.68 6
Safety (F7) 1 5 4.46 0.77 7
Charging infrastructure on highways (F14) 1 5 4.41 0.79 8
Life of battery (F9) 1 5 4.32 0.76 9
Refuelling time (F6) 1 5 4.26 0.7 10
Unawareness about maintenance cost (F3) 1 5 4.19 0.84 11
Unawareness about fuel cost (F4) 1 5 4.02 0.67 12
Charging infrastructure at work (F13) 1 5 3.76 0.74 13
Charging infrastructure at home (F12) 1 5 3.51 0.82 14
Adoption of
electric
vehicles in
India
of 65.10% of the population is willing to recommend EVs to others; 79.80% of the population
are of the opinion that more EVs should be there on Delhi’s roads. The unwillingness of the
people were majorly due vehicle performance factors along with few localized factors such
as road quality, traffic density, inadequate charging infrastructure, high purchase cost.
However, more than 50% respondents are willing to adopt the EVs after addressing the
above issues by the government.
4.2 Structural equation model
The path diagram (shown in Figure 1) has been made on “draw.io”from the results of the
regression fit analysis done on IBM SPSS software. The results display a good model fit
because the values of the standard errors lie within the accepted range (i.e. between 0.05 and
0.15) for the model fit. The Cronbach’s alpha values and the values of composite reliability
(CR) for each factor are also computed to check the reliability of factors, as shown in Table 5.
In addition, the estimated parameters of SEM are also shown in Table 4:
H1. The financial factors have a significant negative impact on the EV adoption.
The path coefficients shown in the path diagram (ß= 0.37, p= 0.021) do not support H1, and
hence we reject the null hypothesis. The positive effect suggests that the customers, for
Table 4.
Descriptive analysis
of public adoption to
EVs
Survey
Questionnaire
1
(Strongly
unwilling) (%)
2
(Unwilling) (%)
3
(Confused) (%)
4
(Willing) (%)
5
(Strongly
willing) (%)
Willing to purchase EV 5.68 28.39 11.61 51.22 3.10
Willing to recommend EV 1.22 5.21 28.47 49.94 15.16
Ready to have more
EVs in market 0.15 3.18 16.87 62.17 17.63
Figure 1.
Structural equation
model path diagram
IJESM
whom financial factors are more significant, show more adoption to the EVs. This can be
understood from two aspects. First, the purchase cost is exclusive of the purchase subsidy,
which shows a positive effect on public adoption. Under the FAME India scheme, a subsidy
of 20% of the ex-factory price subjected to a maximum of Rs. 1,50,000 for electric four-
wheelers has proved to be an effective solution. The public adoption of EVs will certainly
improve with increase in subsidies and incentives. Also, the overall cost of ownership of a
vehicle may decrease by around 50% if a customer switches from CFFVs to EVs. The
regression coefficient of battery cost (0.77) is highest amongst all other financial factors (as
shown in Table 5), which verifies the result of descriptive statistics. Hence, battery cost is
the most significant financial factor for the EVs, and the only solution is to increase R&D
budgets on batteries.
H2. The vehicle performance factors have a significantly negative impact on the EV
adoption.
The path coefficient (ß=0.25, p= 0.014) confirms that there is a negative correlation
between vehicle performance factors and public adoption, and thus the null hypothesis is
accepted. This means the customers who feel that vehicle performance is a significant factor
for the EVs, have a passive attitude towards EV adoption due to low performance
specifications of the EVs. Regression coefficients for all the vehicle performance factors are
positive (as shown in Table 6), which shows that all these factors are potential barriers for
the deployment of EVs in the Indian market:
H3. The infrastructural factors play a significantly negative impact on the EV adoption.
The path coefficients from the path diagram (ß= 0.32, p= 0.011) do not support the
hypothesis that there is a negative effect between the infrastructural factors and public
adoption and thus we reject the null hypothesis. The customers, for whom infrastructural
factors are significant factors, are willing to purchase the EVs. This may be because the
government has allocated an outlay of Rs. 10,000 crores to FAME India scheme in 2019, and
an ambitious target of selling 6–7 million hybrid and EVs by the year 2020 has been fixed by
the government (Ministry of Heavy Industries and Public Enterprises, 2019). To achieve
such an ambitious target, the need for sufficient vehicle charging infrastructure is also being
addressed by the government in Phase-II of FAME India scheme.
5. Conclusion
The study is based on 543 responses collected through a survey conducted in Delhi and the
data thus collected is analysed. The statistics show that despite having a positive attitude
towards the growth of the EV market, people are reluctant to switch to EVs because of
the various barriers associated with them. The SEM suggests that the respondents, for
Table 5.
Reliability of
structural equation
model constructed
variables
Factor Items Cronbach’s alpha (
a
)
Composite
reliability
Financial factors F1, F2, F3, F4 0.63 0.77
Vehicle performance factors F5, F6, F7, F8, F9, F10 0.69 0.68
Infrastructure factors F11, F12, F13, F14 0.74 0.72
Public adoption Willingness to buy.
Willingness to recommend.
Willingness to have more EVs.
0.72 0.75
Adoption of
electric
vehicles in
India
whom vehicle performance is the most significant factor, are most reluctant to EVs and are
not ready to adopt them. Current government policies include subsidies only for purchase
cost and infrastructure building while these policies lack on the grounds of safety and
reliability which can be improved by providing incentives such as insurance subsidy and
salvage value subsidy, etc. Besides the steps being taken by the government, the
manufacturers must also work on improvement of vehicle performances by allocating more
funds on research and development activities.
The battery cost and purchase cost are the top concerns and are ranked 1st and 2nd by
the respondents. This clearly suggests that more efforts are needed to bring down the cost of
EVs. However, the hypothesis shows a positive relation between financial factors and public
adoption. This could be attributed to the fact that a large amount of funds are being
allocated by the government to the FAME India scheme. Meanwhile, there are customers
with a “wait-and-watch”attitude, waiting until the purchase cost for EVs comes at par with
the cost for a CFFVs and switching to EVs would be affordable. Providing subsidy on the
modification of a CFFV to EV might be a promising idea. The awareness about low
maintenance cost and ultra-low running cost must also be promoted. By exercising the
proposed measures, the customers should be convinced that the total cost of ownership of
EV would be reduced to 50% even after a higher purchase price.
The public charging infrastructure is ranked 3rd in the social factors. A dense network of
charging stations is needed for successful deployment of EVs. The SEM results suggest that
there is a positive relation between the infrastructures and public adoption, which again is
the result of large funding allocated for creation of charging infrastructure. The charging
stations located on highways must be equipped with a smart on-grid system to provide fast
charging. Operational connectivity should also be installed among the stations; so that the
driver is notified about the nearest charging station in case the vehicleneeds urgent charging.
Innovative business models such as inviting private players to install and run charging
stations must be adopted to ensure faster deploymentof EVs into the Indian market.
The study has some limitations, as the respondents of the survey are mainly CFFV users
and a few EV users, the responses may be from a prejudiced mind-set. A more extensive
Table 6.
Estimated
parameters of the
SEM
Grouping of factors ß P S.E.
Financial Factors
Purchase cost 0.45 <0.001 0.054
Battery cost 0.77 <0.001 0.074
Unawareness about maintenance cost 0.41 <0.001 0.066
Unawareness about fuel cost 0.69 <0.001 0.059
Vehicle Performance Factors
Driving range 0.62 <0.001 0.112
Refuelling time 0.33 <0.001 0.135
Safety 0.63 <0.001 0.134
Reliability 0.59 <0.001 0.094
Life of battery 0.44 <0.001 0.083
Vehicle power 0.57 <0.001 0.105
Infrastructure Factors
Public charging infrastructure 0.49 <0.001 0.096
Charging infrastructure at home 0.75 <0.001 0.069
Charging infrastructure at work 0.74 <0.001 0.087
Charging infrastructure on highways 0.58 <0.001 0.089
IJESM
research work can be done in the future by allowing the respondents to use the EVs for a
month and get a feel of it, so that a more in-depth analysis can be done. Further, study
should consider some localized factors such as the road factor, the traffic factor, anxiety due
to traffic jam factor, etc. to understand the importance of these factors from Indian point of
view.
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Further reading
Seconded European Standardazation Experts in India (2018), “Indian automobile industry”, New Delhi,
India, available at: www.sesei.eu
About the authors
Abhijeet K. Digalwar received his PhD from the BITS Pilani, India. He is currently working as an
Associate Professor at the Mechanical Engineering Department of BITS Pilani. He has over 24 years
of teaching and research experience at graduate and postgraduate levels. His areas of interest include
performance measurement systems, world class manufacturing, total quality management, lean and
sustainable/green manufacturing. He has published more than 90 papers in national and
international journals and conferences in the area of his research interest. He is a reviewer of many
prestigious national and international journals. He is a life member of the Indian Society of Technical
Education, Indian Institutions of Industrial Engineering and Fellow of Institutions of Engineers.
Also, he worked as a President for ISDSI, the regional body of DSI, USA during 2016–2018. Abhijeet
K. Digalwar is the corresponding author and can be contacted at: akd@pilani.bits-pilani.ac.in
Arpit Rastogi is a PhD scholar at the Birla Institute of Technology, Pilani, India. He received his
Master’s in Manufacturing Systems Engineering from Birla Institute of Technology, Pilani, India. He
has been a professional member of Society of Automotive Engineers, India.
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