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ASSESSING CUSTOMER TRUST, CUSTOMER
SATISFACTION AND CUSTOMER LOYALTY: A CASE OF
ELECTRIC VEHICLES IN THE UK
CHAPTER 4 - DATA FINDINGS & ANALYSIS (6000 WORDS)
4.1 Introduction This chapter of the research focuses on discussing the results based on the survey and
interpreting the experimental results in statistical methods. Further, this chapter discusses the
key findings of the primary research for understanding the customer trust level, satisfaction,
and customer loyalty in EVs of the UK. This research chapter established the correlation
between the variables using SPSS-based statistical analysis to interpret the results more
appropriately. Furthermore, the triangulation method has been used in this chapter to compare
the experimental results with previous literature for answering the research questions. 4.2 Results 4.2.1 Demographic analysis Objective 1: To investigate the role of demographics in the purchase and use of electric
vehicles
Figure 4.2.1.1: Age group (Source: Self-created)
From the above figure 4.2.1.1, the age group of the survey participants is identified, to which
40.86% of the participants belong to the 25-34 years old age group, 25.81% belong to the 18-
24 years old age group, and 21.51% participants belong from 35-44 years old age group.
However, for this primary data collection, primarily young people are targeted, as in the UK,
most young customers have been found to use the EV for different factors. Thus, the age
group insights portray that most EV users belong to the young age group, which means these
age groups have a high level of trust in EVs and their useability compared to elderly people.
Figure 4.2.1.2: Educational background (Source: Self-created)
From the above figure 4.2.1.2, the participants' educational backgrounds are identified, of
which 37.63% and 33.33% belong to master’s degree and bachelor's degree education,
respectively. However, for this research, other educational backgrounds, including doctoral
degrees, college or high school students, and vocational course students, are taken to collect
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data. This research data was collected by taking the most highly educated participants to get
more specific data and transparent information about EV use and their perception of it. Figure 4.2.1.3: Income level of participants (Source: Self-created)
From the above figure 4.2.1.3, research participants come from different financial
backgrounds for the variation of annual income. The above figure shows that 31.18% of
participants have a yearly income of Less than £20,000; 18.28% have an income of £40000-
£60000. In addition, 12.90% have an income of more than £80000, and 11.63% have an
annual income of £20000-£40000. Additionally, of 92 participants, nearly 12.90% prefer not
to state their annual income for this research on EVs. Therefore, the demographic analysis of
this research indicates the research participants belong to upper-middle-class to upper-class
families based on their income to understand the perceptions, loyalty, and trust factors related
to EV purchases.
Figure 4.1.2.4: Residence of the participants (Source: Self-created)
From the above figure 4.1.2.4, the survey participants come from different regions of the UK,
of which 26.88% are from London, 17.20% are from the Northwest UK, and 13.98% are
from the East region of the UK. In addition, the participants come from the different areas,
including the Midlands, the South region of the UK, Wales, and Northern Ireland. Therefore,
they indicated that for data collection, unbiasedly, participants are selected to get specific
information about EV use and their perception of it.
Figure 4.1.2.5: Occupational background of participants (Source: Self-created)
From the above figure 4.1.2.5, the occupational background of the survey participants has
been identified, of which 45.16% of participants are students, 121.83% are self-employed,
and 9.68% are associated with education/medical services. Besides, other occupational
backgrounds, including professional qualifications, senior management professionals, retired
persons, clerical staff, technical staff, etc. Therefore, the research participants from different
occupation groups have been selected for this research. 4.2.2 Descriptive analysis Objective 2: To investigate the consumption patterns related to the use of electric
vehicles
How long have you owned an electric vehicle (EV)?
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Figure 4.2.2.1: Time of owned EVs (Source: Self-created)
The above Figure 4.2.2.1 shows the participants' time of owning the EVs. The above figure
shows that among the participants of the survey, 30.77% have owned EVs for less than 6
months, and 27.47% have owned the EV for nearly 6 months to 1 year. In addition, the figure
demonstrates that 17.56% and 10.99% of participants claimed that they have owned the EV
for 1-2 years and more than 2 years, respectively. However, in this survey, nearly 13.19% of
participants mentioned that they did not own any EVs. Therefore, from the response to the
survey, it can be identified that most of the owners of EVs are newly joined; the critical
reason for including this participant is to identify the consumption pattern of EVs in the UK.
Furthermore, the above data shows that UK people's consumption of EVs is of two types:
wholly owned and not owned EVs. Table 4.2.2.1: Descriptive statistics for the time of EVs' own
(Source: Self-created) From the above Table 4.2.2.1, the Mean value and Standard deviation (SD) values are 2.48
and 1.377, respectively. Further, as this data's SD value is relatively high, data is spread
around the Mean value. How frequently do you use your EV for daily commuting? Figure 4.2.2.2: Frequency of EV use in commuting (Source: Self-created)
From the above Figure 4.2.2.2, the frequency of using the EV among the UK people has been
identified by analysing the response of the research participants. The above figure shows that
34.83% of participants use EVs several times a week, and 28.09% use EVs daily. Further,
16.85% of participants use the EV once a week, and 20.22% of participants use the EV
rarely. The above figure disclosed that in the UK, EV users mostly use EVs several times a
week or daily, indicating relatively high frequency in the usage of the EV in the UK. Another
interpretation of this frequency of EV use can illustrate that popularity. Trust level and
loyalty towards the EVs are increased among the UK participants.
Table 4.2.2.2: Descriptive statistics of EV use frequency (Source: Self-created) From the above table 4.2.2.2, descriptive statistics analysis of Ev use frequency has been
identified, on which the Mean and SD values are 2.29 and 1.089, respectively. As in this
table, the Mean value is higher than the SD value, data is moderately spread around the
Mean, and distribution is not perfectly symmetrical compared to normal distribution. Thus, it
indicates the EV use frequency is moderate to high in the UK, and it is expected to increase Objective 3: To understand why consumers buy electric vehicles
What factors have heavily influenced your decision to purchase an EV?
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Figure 4.2.2.3: Factors influencing customers to buy EVs (Source: Self-created)
From the above Figure 4.2.2.3, several factors have influenced people's purchasing decisions
for EVs. The above figure shows that 21 participants mentioned environmental concerns, 20
mentioned cost-saving fuel, and 7 mentioned that government initiatives for using EVs to
protect the environment are the key influencing factors on their EV purchasing decision. In
addition, 5 participants mentioned that charging infrastructure availability influences the
purchasing decisions of EVs, as the availability of charging stations can increase the purchase
intention of EVs to its customers. However, for many participants, more factors influence
their purchasing decisions; notably, nearly 9 participants are influenced by environmental
concerns and cost-saving fuel for their purchasing decisions. Besides, for 3 participants,, the
environmental concern and high availability of charging stations combined influence the EV
purchasing decisions.
Table 4.2.2.3: Descriptive statistics for the Influence of EV purchasing-related decisions (Source: Self-created)
Table 4.2.2.3 indicates that the Mean value is 7.48 and the SD value is 9.70, which shows
here SD value is greater than the mean. Thus, as per the result, the data is situated on the
large sphere shaped around the mean, and high variability has been identified from the
distribution. Therefore, in EV purchasing decisions, the influencing factors and their
variability chance are high, which indicates the charge of those factors is also possible. How often do you find yourself needing to charge the EV before comfortably resuming your
journey?
Figure 4.2.2.4: Frequency of charging the EV for resuming the journey (Source: Self-created)
From the above figure 4.2.2.4, the charging needs of the EV and frequency of use have been
identified in the context of EV. The above figure shows that 34.83% of participants
mentioned they had changed their EVs rarely, and 32.58% of participants mentioned that the
charging need of their EV is on an occasional basis. In addition, 10.11% of participants
mentioned that they had rarely changed their EVs. These data point out that the charging need
of WEVs is relatively low, which means the batteries of these EVs are durable and
sustainable for long-term usage. Therefore, EVs need less charging for their durable battery,
and their sustainability features are attractive points for their users. However, 6.74% of
participants mentioned the charging need is often, and 15.73% mentioned that they have
charged their EVs very often, almost always. Thus, the discussion indicates that in the UK
context, the charging needs of EVs are quite low. However, few participants claimed regular
charging needs, which indicates EVs are slightly more sustainable for charging. Table 4.2.2.4: Descriptive statistics for charging needs of EV
(Source: Self-created)
Table 4.2.2.4 indicates that the Mean value is 2.83, and the SD value is 1.199, which
indicates the SD value is lower than the mean. Thus, it indicates that data is close to the
mean, and less variability has been found in the data; in other words, it can be identified that
data is tightly clustered towards the mean. Therefore, the charging needs of EVs is slightly
low in the EVs, and that indicates sustainability. Objective 4: To investigate which brand of electric vehicles consumers prefer
Which of the following is your favourite EV brand?
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Figure 4.2.2.5: Favourite EV brand (Source: Self-created)
From the above figure 4.2.2.5, the favourite brands of EVs, as per the survey participants,
have been identified in this research. The above figure shows that 34.44% of participants'
favourite EV brand is BMW, and 32.22% mentioned Tesla as their favourite EV brand. In
addition, 22.22% of participants mentioned Mercedes as their favourite EV brand, whereas
5.56% and 4.44% said Nissan and Kia were their favourite EV brands. However, 1.11% of
the total participants mentioned other EV brands like Ford as their favourite EV brands.
Therefore, it can be identified that in the UK, people primarily use all the modern EV-
producing brands, mainly the reputed automotive companies. However, Tesla and BMW are
identified as favourites for most EV users.
Table 4.2.2.5: Descriptive statistics of Favourite EV brand (Source: Self-created)
From the above table, the Mean and SD values are 2.19 and 1.150, respectively. The mean
value is greater than the SD value, indicating data is moderately spread around the Mean, and
distribution is not symmetrical. Thus, it has indicated that various EV manufacturing brands
are favourites among UK customers, and this varies from person to person. Objective 5: To investigate customer trust, customer satisfaction and customer loyalty
associated with the purchase of electric vehicles.
Customer Satisfaction Table 4.2.2.6: Descriptive statistics for customer satisfaction (Source: Self-created)
From the above table 4.2.2.6, the Descriptive statistics results for customer satisfaction have
been identified. The above table shows that for these 5 sub-variables, Mean values are 4.81,
5.03, 5.12, 5.03, and 5.15, respectively. In addition, SD values are 2.099, 1.968, 2.027, 2.053,
and 1.898, which shows the SD values are lower than the mean values. Thus, it indicates that
data is clustered around the mean. Therefore, customer satisfaction depends on several
factors, including the quality driving experience of the EV, and if the EVs satisfy customers'
expectations, it can increase the satisfaction rate.
Brand Trust
Table 4.2.2.7: Descriptive statistics for brand trust (Source: Self-created)
From the above table 4.2.2.7, descriptive statistics for brand trust have been identified, on
which the Mean values are 4.81, 4.87, 4.91, 4.91 and 5.07 respectively. Further, the SD
values of the brand trust sub-variables are 2.149, 1.961, 1.981, 1.987, and 2.022. The
descriptive statistics show that SD values are lowered compared to Mean values, indicating
the data is clustered towards the Mean. Therefore, the data implies that brand trust in EV
brands has increased among UK consumers, and several factors are behind brand trust. The
critical factors for brand trust are when the brands prioritise customer satisfaction and
consistently deliver the promises as a brand. Brand Loyalty
Table 4.2.2.8: Descriptive statistics for brand loyalty (Source: Self-created)
From the above table 4.2.2.8, Descriptive analysis for brand loyalty has been identified, in
that table, the Mean values for all sub-variables are 4.47, 4.51, 4.57, 4.85 and 4.89
respectively. The above table shows that SD values are 2.284, 2.079, 2.191, 2.043, and 2.024
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respectively. However, here the SD value is lower than the Mean, which indicates data is
clustered to the Mean, meaning data is closer to mean values. Thus, the results indicate that
brand loyalty of EV brands in the UK depends on customer satisfaction rate and trust among
brands. Further, survey results and descriptive statistics analysis mentioned that customers
are loyal to their brand of EV and sometimes recommend others for their favourite brand as
loyal customers. 4.2.3 Regression analysis Correlation statistics Table 4.2.3.1: Correlation test for customer satisfaction (Source: Self-created)
From the above table 4.2.3.1, correlation statistical analysis for customer satisfaction
demonstrates that all the values are positive, which means variables are positively correlated.
In addition, from the above table, it has been identified that Sig. (p-value) of the sub-variables
are 0.000, which indicates all the values are statistically significant (p<0.05). The strongest
positive correlation between CS2 and CS4, which is 0.9287, at a significant level of 0.000,
has been found. Therefore, the quality of EVs and the fulfilment of customers' expectations
are strongly correlated with each other and indicate these two factors are critical for the
satisfaction of EV customers.
Table 4.2.3.2: Correlation test for brand trust (Source: Self-created)
The above Figure 4.3.2.2 shows that for brand trust correlation, statistical analysis shows that
variables are positively correlated with each other, as no negative values have been noticed in
the table. From the above table, BT2 and BT4 have a strong correlation timothy pearson
correlation value of 0.922 at a significant level of 0.000. Additionally, the table identified that
for all the variables, the p-value is 0.000 (p< 0.05), which means values are statistically
significant. Therefore, customer satisfaction prioritisation and consistency in maintaining
promise for customers are strongly correlated, and these two factors are also identified as key
terms to influence brand trust.
Table 4.2.3.3: Correlation test for brand loyalty (Source: Self-created)
From the above table 4.2.3.3, correlation test results for brand loyalty have been identified,
which shows all the sub variables related to brand loyalty are positively correlated with each
other, as values sign are positive. In addition, the SigThe value (p-value) of the sub-variables
is 0.000 (p<0.05), which indicates the values are statistically significant. Besides, in this
brand loyalty variable, the BL1 and BL4 have the strongest correlation with a value of 0.889,
at a significant level of 0.00. This value implies that customers' favourite EV brands
influence the recommendation of EV brands, which means loyal customers recommend other
EVs based on their experience. Furthermore, brand loyalty depends entirely on customer
perception; strong perceptions of customers can influence customers to study loyalty for the
brand. Hypothesis testing (H1): Electric Vehicle purchase interest is positively correlated with the economic strength of
the Customers.
H0: Electric Vehicle purchase interest is negatively correlated with the economic strength of
the Customers.
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Table 4.2.3.4: Hypothesis Testing (Source: Self-created)
From the above table 4.2.3.4, the hypothesis testing has been identified, on which the
"Levene's Test" shows the P-value is 0.814 (p>0.05), which means for this data set equal
variance is assumed, which groups of the variables are indicates homogeneity of variance. In
addition, the t-test value is 0.814 in df 37. Further, the significant (2-tailed) value is 0.421
(p>0.05), which means the t-test is statistically not significant, and this data failed to reject
the null hypothesis as the p-value is greater than 0.05. (
H2): Customer satisfaction is positively correlated to the Convenience of Electric Vehicle
purchase.
H0: Customer satisfaction is not correlated to the Convenience of Electric Vehicle purchase
Table 4.2.3.5: Hypothesis Testing (Source: Self-created)
From the above table 4.2.3.5, the "Levene's Test" shows the P-value is 0.019 (p<0.05), which
means the two groups taken for this independent t-test are not homogeneous, unequal
variance has been identified. For the t-test, the t-value is 1.342 at df of 35.805, and the
significant level is .188 (p>0.05), statistically insignificant, and for this, the null hypothesis
failed to be rejected. (H3): Customer trust is positively correlated with the Product quality of the EVs
H0: Customer trust is negatively correlated with the Product quality of the EVs
Table 4.2.3.6: Hypothesis Testing
(Source: Self-created)
From the above table 4.2.3.6, "Levene's Test" results have identified that the p-value for this
test is 0.698(p>0.05), indicating the two groups selected for this t-test are assumed to have
equal variance. However, the experimental results of the t-test are 0.107, and the p-value is
0.915, extremely higher than the standard value, which means the null hypothesis failed to be
rejected, and the relationship is statistically insignificant as well. (H4): Customer perception of convenience is positively correlated with brand loyalty of EVs.
H0: Customer perception of convenience is negatively correlated with brand loyalty of EVs.
Table 4.2.3.7: Hypothesis Testing (Source: Self-created)
From the above table 4.2.3.7, "Levene's Test" results show the p-value is 0.277 (p>0.05),
which shows the chosen groups are showing equal variance. However, for t-test the t-value is
1.216, in df 39 at a p-value of 0.231 (p>0.05) values are statistically insignificant. In addition,
this data failed to reject the null hypothesis. 4.3 Findings The primary data of the survey illustrates the key findings of the research, which indicates
EV use, people's perceptions, and EV-related loyalty among customers. The survey results
show that among the 92 participants, 30.77% have owned the EVs for less than six months,
and 27.47% owned the EVs for 6 months to 1 year. However, 17.58% and 10.99% of
participants owned EVs for 1-2 years and more than 2 years, respectively. Therefore, the
findings indicate the majority have used EVs recently, which means the trend of using EVs
has increased in the present context. Further, survey results find that among participants,
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34.83% are using the EV several times a week, and 28.09% use their EV daily. Further,
survey findings identified that 16.85% and 20.22% of participants use their EVs once a week
and rarely, respectively. These two data identified the consumption pattern of the EV in the
UK, which pointed out that the use of EVs is increasing rapidly, and the number of EV
purchases also increased. However, the survey disclosed several reasons influencing the purchase of EVs, of which 21
participants only mentioned environmental concerns, and 20 participants mentioned that the
saving fuel of EVs highly influenced their purchasing decisions. On the contrary, 9
participants mentioned government initiatives, 6 mentioned a range of EVs, and 5 mentioned
charging infrastructure as the key influencing factor for EV purchase. However, combined
opinion has been found from the survey, indicating that customers are strictly considered
many factors for purchasing decisions. Further, survey findings disclosed that 34.44% and
32.22% of participants' favourite Evsa brands as BMW and Tesla; on the other hand, 22.22%
of participants claimed Mercedes as their favourite brand. In addition, 5.56% say Nissan and
4.44% say Kia is their favourite EV brand. Therefore, depending on the people, the favourite
EV brand is identified. Besides, the survey results have mentioned that 34.83% and 32.58%
of participants have charged the EV batteries rarely and Occasionally, respectively. Another
10.11% of participants mentioned that they had charged very rarely, whereas survey findings
indicated that 15.73% of people charged very often, and 6.74% charged their Evs often. This
data has identified that EVs used in the UK are primarily durable, and battery life makes the
EVs sustainable for use with low power consumption. Additionally, the survey results provide brief concepts of the customer satisfaction level of
EV users, as 43.8% agreed and 14.6% participants strongly agreed with the high level of
service quality of EVs, although 18% participants disagreed here. Besides, survey results also
mentioned that 41.6% agreed and 20.2% strongly agreed that the high quality of EVs is a
crucial reason for satisfaction. On the other hand, 34.8% and 28.1% agreed and strongly
agreed that they are happy with their purchasing decisions for EVs. In a survey on customer
satisfaction, nearly 37.1% agreed, and 27% strongly agreed on their EV companies and the
product's fulfilment of the services. In the case of brand trust context, the survey findings
indicate that 40.9% agreed and 18.2% strongly agreed that EVs provide reliable and safe
transportation facilities to their customers. Further, survey results indicate that 44.9% of
participants agreed, and 14.6% strongly agreed on the EV company's ability to deliver
promises consistently. Apart from this, 21.3% strongly agreed, and nearly 36% agreed that
customers trusted their favourite EV brands. Additionally, for the brand loyalty context,
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survey results showed that 33% agreed and 19.3% strongly agreed that most participants are
loyal to their favourite EV brands. Therefore, from the findings, it has been identified that
customers' brand loyalty depends on their trust in the brand and the customer satisfaction
level of EV. 4.4 Triangulation Table
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Table 4.4: Triangulation table (Source: Self-created) From the above table 4.4.4, the triangulation of the research has been identified, where
experimental results of the survey are compared with the literature review findings to get
better outcomes. 4.5 Analysis and Discussion Theme 1: Analysis of the Role of Demographics in the Purchase of EVs Demographics have a strong influence on EV purchasing decisions. However, these
influences are based on their economic stability and occupational background. Results
showed that people from good economic backgrounds buy and use EVs. In section 4.2.1,
results have disclosed that 31.18% of EV users have an annual income of less than £20,000,
and 18.28% have an income of £40000-£60000. In addition, results also disclosed nearly
12.90% of participants have an annual income of more than £80000, indicating that the UK is
mostly economically stable and the upper-middle-class population uses EVs. On a similar
note, in section 2.3.1, Ali and Naushad (2022) mentioned that economic strength plays a
crucial role in purchasing EVs and making buying decisions. This previous literature also
indicates that professional background has a strong influence on the purchasing decision of
EVs. Similarly, in the results, the section found that the majority of the participants are
students, although people of different occupational backgrounds participated in this research,
and that indicated the EV owners come from several occupational backgrounds. The results section has identified that the young population has a much bigger effect on the
use of EVs, and from the survey, most of the EV users are young people. Section 4.2.1, of the
results, 25.81% belong to the 18-24 years ago, and 40.86% of the participants belong to the
25-34 age group, whereas only 2.15% belong to the 55-64 age group. These results indicate
that most EV users are young people, and the perception of EV usability has increased among
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younger people compared to elderly people. However, in section 2.3.1 of the literature
review, Kajanova and Bracinik (2021) observed that males belonging to the 25-60 years age
group are the significant consumers of EVs. The findings of previous literature mentioned
male domination of EV use, which is not identified from survey results. In addition, in the
literature review, Higueras-Castillo et al
. (2020) argued that EV purchase decisions depend
on demographic profiles and personal understanding of EVs' environmental benefits. This
finding is highly similar to the survey findings as it also indicates that people from stronger
economic backgrounds prefer to use EVs. Furthermore, demographic heretics strongly
influence customers' EV purchasing decisions; mainly, the economic background of the
customers influences the purchase of EVs the most. Theme 2: Analysis of the consumption pattern of EVs in the UK In the UK, EV consumption patterns have varied based on the customers and their type of use
of EVs. Results mentioned that customers have been using EVs for the past few years; in
most cases, people buy new ones for several reasons. In section 4.2.2, results showed that
30.77% of partisans owned EVs for less than six months, and 27.47% of participants owned
EVs for six months to 1 year. That indicates that most of the participant's EV users are newly
bright EVs as per survey results, and many external influences are responsible for their
purchase. However, in section 2.3.2 of the literature review, Cao et al
. (2021) argued that
customers push social and cultural initiatives to anticipate the environmental crisis to adopt
EVs. In addition, the previous literature mentioned that the consumption rate of EV use in
customers increased after solar energy and compelling EV purchase offers were introduced.
In the results, statistical analysis has mentioned data is spread from the Mean value, which
indicates that EV's new ownership is noticed chiefly among the people, and these are in the 6-
12 months range. The results pointed out that the EV pattern of use has increased recently.
On this note, Goel et al.
(2021) mentioned that in developing countries, infrastructure
challenges restrict the growth of EVs, and their improvement can lead to an increase in the
adaptation rate in those countries' markets. Therefore, comparing previous literature and
survey findings, it has been identified that the EV market is slightly growing in the UK
market, which is the crucial reason EV owners are mainly new and owned for less than 6-12
months. However, the results section also identified the pattern of use of EVs in the UK, which found
that its users are using these EVs as their everyday vehicles. In section 4.2.2, 34.83% of
participants mentioned commuting the EVs several times a week, and 28.09% mentioned
using them daily. On the contrary, opposite results were also found from the results where
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16.85% say they use EVs once a week and 20.22% mention their rare use. Thus, as per
survey results in the UK, EV users are mainly of two types: one type uses EVs regularly or
frequently, and the other uses EVs rarely. On the contrary, in section 2.3.2 of the literature
review, Santos and Rembalski (2021) mentioned that EV growth in the market is possible
after the rise of the subsidiary market. Further, previous literature also indicates that the role
of government initiatives can be mentioned behind the increase in the growth and
consumption of EVs. As per results, the frequency of use of EVs is also measured through the
SPSS statistical analysis, which also discloses that EV use frequency varies based on the
customers. However, in section 2.3.2, Venegas et al
. (2021) observed promoting the EV
purchase and using critical roles played by the government authorities. Therefore, the
customers' EV use patterns are influenced by several external factors, and the frequency of
use varies from one person to another. Theme 3: Analysis of the influence of customers on buying EVs and preferring the EV
brand. The influence of EV purchasing decisions depends on different factors, and survey results
also pointed out the most favourite brands of EVs in the UK. In section 4.2.2, results show
that nearly 21 participants mentioned environmental concerns as critical factors influencing
EVs' purchasing decisions. Whereas 20 participants mentioned that only the cost-saving fuel
use in EVs influenced them most to buy them. In addition, in this section, government
initiatives, the range of EVs, and charging station availability are the key factors influencing
customers to purchase EVs. On this note, in section 2.3.3, Muratori et al
. (2021) mentioned
that the government has promoted the use of EVs, but despite the limited supply chain of
these EVs, their reach is limited to customers. Results have claimed the primary intention of
buying EVs is anticipating environmental concerns. Apart from this, it has been identified
from the implication of descriptive statistics that many factors combine to influence the
customers to purchase EVs, of which environmental factor is most common. Similarly, in
section 2.3.3 of the literature review, Halder et al.
(2020) observed that green purchase
intention increased among the customers that influence EV purchases. Thus, environmental
impact, consideration, and government influence are two key factors influencing the
purchasing behaviour toward EVs. Additionally, from the results, EVs' battery strengths and charging need data are identified,
from which the durability and sustainability of these EVs are identified, which portrays the
reason for the EV's use. Further, in section 4.2.2, it was identified that 34.83% of participants
mentioned they had rarely changed their EVs, and 32.58% of participants mentioned they had
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changed their EVs on a rare basis. These two combined percentages of EV charging needs
have identified that EV batteries are durable and sustainable, another factor influencing
customers' purchase decisions. However, in section 2.3.3, de Rubens (2019) mentioned the
price effectiveness of EVs influences customers purchasing. However, another factor influencing the EV purchase decision is the brands of EVs;
depending on the brands, the favourite brands vary from person to person, reflecting the
survey results. In addition, descriptive statistics have found that data are concentrated on the
mean and the several choices of brands for purchasing EVs. In results section 4.2.2, 34.44%
of participants liked BMW, and 32.22% preferred Tesla as their favourite EV manufacturing
company. However, 22.22% of participants mentioned Mercedes, 5.56% mentioned Nissan
and 4.44% mentioned Kia as their favourite EV manufacturing company. Therefore, as per
survey and statistical analysis results, several companies are available in the UK market for
EV manufacturers. However, in section 2.3.4, Richter (2023) has mentioned BYD, Tesla, and
BMW as the key players in the EV market, and in figure 2.3, the popularity of these
companies is adequately mentioned. Thus, several companies are currently serving EVs in the
UK market, and based on the brand's highly loyal customer base, it is expected to dominate
the UK market. Theme 4: Critical analysis of the relationship between customer trust, customer
satisfaction, and customer loyalty in the purchase of EVs
In the UK, the EV purchasing decision depends on different factors influencing customer
loyalty. In EV purchasing, the perception of customer trust and satisfaction is closely
associated with brand loyalty. The findings indicate that service quality and customer
satisfaction are associated. EVs' high level of service quality also leads to trust among the
brands. In the results, section 4.2.3, correlation statistical analysis evaluates that the service
quality of EVs and the high expectations met by customers have a positive correlation.
Besides, the correlation statistics indicate that good quality service of EVs effectively meets
customer expectations, which leads to customer satisfaction. However, in section 2.3.5, Ullah
et al
. (2018) mentioned that customer satisfaction among EV users depends on the innovation
adopted in EVs and the availability of luxurious services. Further, findings show that satisfied
customers think their purchasing decision is correct, and after getting long-term satisfaction
from an EV brand, customers want to recommend the product. This also can be linked with
customer loyalty towards EV brands. Similarly, in section 2.3.5, Spieth et al
. (2019)
mentioned value offering innovation improved customer satisfaction, although the literature
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says this can negatively impact customer trust. The most crucial steps to satisfy the customers
are innovation in the EV brands and improving the service quality. Results have disclosed that customer trust is essential to increase when a brand consistently
delivers its promises and tries to improve customer satisfaction. In this context, in section
2.3.5 of the literature review, Kwon et al
. (2020) mentioned that customer trust and loyalty
are associated with the purchasing intention of EVs. Similarly, in results, section 4.2.3,
correlation tests explored that customer satisfaction fulfilment by brands and a company’s
ability to deliver their promises consistently has the highest correlation, 0.922. The high
correlation implies that brand trust is high in the brands that consistently deliver the best
services and prioritise satisfaction needs. A positive correlation between all the sub-variables
indicates that customer trust, the performance of EVs, and self-security are associated. On the
other hand, in section 2.3.5 of the literature review, Afriani and Syah (2019) argued that
customer satisfaction and trust among the brands are adequate under a few specific
conditions. Therefore, comparing the experimental results and literature review, it has been
revealed that customers' trust increases when bread effectively satisfies them through high-
quality service and the excellent performance of EVs. Additionally, in the case of customer loyalty, findings linked these variables, customer trust
and customer satisfaction, as the critical factors for influence. From the findings section, it
has been identified that a majority of the loyal customers of EVs are willing to recommend
their favourite brands to others. Further, the results section indicates that satisfied customers
feel they have a significant loyalty to the bread of EVs. However, in section 2.3.5, Yang et al
.
(2020) mentioned that brand trust strongly influences customers' purchasing decisions for
EVs. In comparison, in results section 4.2.3, correlation test results mentioned that there is a
strong correlation between the favourite EVs and the probability of recommendation to
others, and the Pearson correlation value was 0.889. The results pointed out that customer
loyalty is essential for brands, as they recommend their favourite brands to their family and
friends. Furthermore, comparing results and previous literature has identified that customer
satisfaction has been generated through EV performance, and trust has been generated
through the constancy of EV performance. Therefore, brand trust and a high level of
satisfaction with one EV bread can create a loyal customer base, increasing the probability of
customer recommendation of one brand.
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4.6 Summary From the above discussion, it can be concluded that in the UK EVs, related customer
perception completely depends on customer satisfaction and trust level. Further, EV brands
successfully deliver consistently on delivering promises to their customers, and those brands
EVs are performing well and are able to generate a loyal customer base. In the UK market
EV purchasing decisions are slightly influenced by government initiatives, although
environmental concerns are found as the most essential factor that influences customers to
buy EVs. As per analysis, the young population and people coming from strong economic
backgrounds are the key customers of EVs in the UK. The survey results and the statistical
analysis have mentioned in the UK the brands able to generate satisfied customers, and get
automatic recommendations from customers to others to influence them to buy new EVs.
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