lOMoARcPSD| 36991220
A CASE STUDY ON THE INFLUENCE OF PERCEIVED RISKS ON
CONSUMERS’ ATTITUDES TOWARD PURCHASING AND BUYING
ELECTRIC VEHICLES (EVS) IN HANOI
Nguyen Thi Xuan Hoa1, Nguyen Quang Tung, Vu Hoang Anh, Le Minh Hoang, Tran Khanh Linh, Nguyen Minh Quan Abstract
The case study aims at investigating the effects of perceived risks on customer’s attitude towards purchasing and using
EVs in Hanoi City. The study methodology is based on quantitative approach to collect the primary data by using Google
forms. The questionnaire of this study was developed and adapted based on previous studies. Forty eight (48) customers
answered the questionnaire form. After collecting the data from the responses, 48 samples were analysed. SPSS was
used to conduct regression tests. And find out which elements in the perceived risks of customers have an effect on the
customer’s attitude towards purchasing and using EVs. Based on the results, this study also further discusses the factors
that effect on each specific elements. Finally, this study also indicated some drawbacks that the researchers cannot
fulfilled regarding the credibility of the study.
Keywords: Customer behaviour, customer attitude, electric vehicle, perceived risk model 1. INTRODUCTION
Electric vehicles offer the promise of reduced environmental externalities relative to their gasoline counterparts (Stephen
P Holland, et al., 2015) . According to a study of 2015, approximately 60% of carbon pollution is caused by passenger
vehicles, and that EVs represent a worthwhile means to lower such carbon emissions (Mauricio Featherman, et al., 2021)
. As a result, although EVs were invented many decades ago, the trend of using EVs has just begun in recent years because
of not only the new experience they bring to the traditional users but also environmental-friendly benefits - the most
important reason. According to the “Global Electric Vehicle Market Outlook 2018”, which was released by Global
Automotive & Transportation Team (2018), global EV sales are expected to climb from 1.2 million in 2017 to 2 million
in 2019, and it is expected that EVs users would reach to the number of 25 million in 2025 (Hua Wang, et al., 2019) . In
Quebec, Canada, they calculated that if EV consumes 20 kWh/100 km, it would only emit 6.9 gCO2/km, which is 25
times less than carbon vehicles (Pierre Laffont, et al., 2022). Consequently, it is easy to see that if EVs are used in the
same amount as carbon vehicles, the carbon emissions nowadays would be estimated 25 times lower, contribute a huge
impact in improving air quality. EVs also reduce much less noise compared with traditional vehicles, so that noise
pollution would no longer a big problem. Therefore, in terms of green productivity, most people in this clean environment
would be more productive. Green effect of EVs might also impulse green manufacturing and green processing, leading
to big improvement in general green productivity.
EV’s potential is enormous, unfortunately, it still remains significant risks that affect directly on customer’s attitude
toward this type of vehicle. Higher accident risk due to the absence of the engine sound, EV maintenance difficulties,
price fluctuation, people disagreement, long charging time (Bessenbach and Wallrapp, 2013), those risks are the most
popular risks that customer concern about. As a result, in 1992, Michell V.W created perceived risk theory, which divided
EV risks into five types of risks: physical risk, functional risk, financial risk, social risk, and time risk. In 2020, Malek
Amajali, base on the perceived risk theory of Michell, released the perceived risk model which link the perceived risk
theory with customer’s attitude, help we see a detail picture in how and why some customers are still hesitating to own EV.
Through those risks, customer will have various concerns toward EV. Therefore, this research’s goal is to explore and
analyse the various dimensions of customer attitudes towards electric vehicle risks. Furthermore, by understanding these
attitudes, it is easier to pave the way for strategies and interventions that address consumer concerns and contribute to the
widespread adoption of electric vehicles.
1 Associate Director of BK Fintech, School of Economics and Management, Hanoi University of Science and Technology, Hanoi, Vietnam [Type here] lOMoARcPSD| 36991220
2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT 2.1 Literature review
There has been many case studies being conducted with the aim to addressed the variables that affect customer behaviour
and intention to purchase and use EVs. In the case study of Huaxiong Jiang et al., 2023, the results provided insights into
the factors influencing individual’s willingness to adopt EVs that is: access to green spaces, high rise buildings, parking
availability, loan accessibility, commute time and housing ownership,… To access the customer buying intention toward
electric vehicles in India, Bharti Motwani et al., 2019 identified 9 importations factors related to the study that is: mobility,
recharging, tax benefit, subsidy, interior attributes, oil dependency, electricity demand and RTO norms. Their 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 decisions of electric vehicles. Another study by Ona Egbue
et al., 2012, in which addressed the barriers to widespread adoption of electric vehicles, concluded that attitudes,
knowledge and perceptions related to EVs differ across gender, age, and education groups; emphasizes the need to address
socio-technical barriers facing EVs, certain measures need to be taken to increase the market share of EVs and influence
the public appreciation for non-financial benefits of adopting EVs. And Marlise Westerhof et al., 2023 presents a dataset
concerning a transnational survey about knowledge and attitude towards electric vehicles across European consumers
with an aim to understand the public knowledge and perceptions of EVs, and to identify potential misconceptions regarding EVs.
To further addressed the determinants effects to customer behavior and intention, Indra Gunawan et al., 2022 conducted
an integrated model analysis that focus on analysing the determinants of customer intentions to use electric vehicles in
Indonesia. By integrating TPB (Theory of Planned Behavior) model (Ajzen, I, 1991, 1993), UTAUT2 (Unified Theory
of Acceptance and Use of Technology 2) model (Venkatesh et al., 2003) and perceived risk theory model (Mitchell,
1992), the results of their study indicate that the predictors for intention to use electric vehicles, including Attitude Toward
Use (ATU), Subjective Norm (SN), and Perceived Behavioral Control (PBC), can assist prediction of interest in using
electric vehicles in Indonesia and the effects of perceived risks on attitude towards the use of electric vehicles (Indra
Gunawan et al., 2022), with a negative effect on the attitude toward the use of electric vehicles on functional risk and financial risk factors.
This literature review implies that a considerable number of researchers have analysed the factors influencing the purchase
of EVs (Zulfiqar Ali Lashari et al., 2021). While many studies have been conducted to addressed the factors’ as well as
determinants’ effects on consumer attitudes on purchasing and using EVs around the world, the same kind of study in
Vietnam has been limited. So, the aim of this study is to using the perceived risk theory model by Mitchell, 1992 as a
theory based to create a questionnaire survey and set up the hypotheses to addressed the effects of the perceived risks of
consumer on purchasing and using EVs (Malek Mohammad) in Vietnam and in Hanoi City especially.
2.2 Theories, Models and Hypotheses
Based on the study model of Malek Mohammad et al., 2020, the five dimensions of the Perceived Risk Theory is used to
addressed the effects on consumer attitudes, with the five risks dimensions consists of physical risk, financial risk,
functional risk, social risk and time risk.
2.2.1 Consumer Attitudes
According to Malek Mohammad et al., 2020, attitude is considered as the amount of positive and negative feelings one
has towards an object and how these factors are influenced by the antecedents to shape the people’s attitudes. Therefore,
by meaning consumer attitudes, in this study, it indicates Hanoi City residents’ feelings toward using and purchasing EVs. 2.2.2 Physical risk
Mitchell (1992, p.27) defined perceived physical risk as: “The risk that the performance of the service will results in
health hazard to the consumer”. In the field of this study physical means that customers anticipate any physical or heath
risk that may be caused be the use of a new innovation (Klerck and Sweeney, 2007), more specifically in this study, is
the innovation of EVs compared to conventional vehicles with customers in Hanoi City. 2.2.3 Financial risk
Mitchell (1992, p. 27) defined financial risk as: “The product being not worth the price paid”. Therefore, financial risk in
this study can be understood as customer’s fear of customers that they may pay more than the value of EVs (Malek
Mohammad et al., 2020 ) . And in the context of this study, it can link to price of purchasing EVs or using in Hanoi being expensive. [Type here] lOMoARcPSD| 36991220 2.2.4 Functional risk
Functional risk is an indicator of uncertainty of the performance of EVs ( Malek Mohammad et al., 2020 ). Customers
are concerned that EVs as an innovation working efficiently, which won’t obtain the consumers’ confidence (Wiedemann
et al., 2013). Therefore, they fear the failure of this product and suffer from feelings of remorse and dissatisfaction later
on. Therefore, consumers hesitate to buy these products for fear of poor performance. (Malek Mohammad et al., 2020 ) . 2.2.5 Social risk
Social risk suggests that the purchase or use of a product may reduce the buyer's relationship with his/her family, friends,
colleagues or co-workers ( Malek Mohammad et al., 2020 ) . Therefore, they may hesitate to buy the EVs in the fear of
worsen the relationships with family, acquaintances, colleagues. 2.2.6 Time risk
Michell (1992, p.27) defined time risk as “the risk that the consumer will waste time, lose convenience or waste effort in
getting a service redone”. When talking about EVs, consumers are afraid of the need for a lot of time to deal with and
recognize this type of cars, as well as to be aware of different specifications ( Malek Mohammad et al., 2020 ) . Therefore,
the time risk factor may create customer dissatisfaction with EVs ( Malek Mohammad et al., 2020 ) . 2.2.7 Study model
Based on the discussion above and the study of Malek Mohammad et al., 2020, the study model is shown in Figure 1 as below.
Fig. 1. The study model 3. METHODOLOGY 3.1 Study area
The study chooses Hanoi as an ideal study area for examining the factors influencing customer behavior on using electric
vehicles (EVs). Firstly, Hanoi is facing the problem of air pollution and traffic congestion. Air pollution in Hanoi ranks
third in the world due to car and motorbike exhaust fumes (Hue, N, 2023). It harms directly human health and the
environment. To reduce air pollution, it is necessary to change living habits as using electric vehicles instead of using
gasoline vehicles. Secondly, many people still lack knowledge and skills about protecting the environment and electric
vehicles, which affects the habits and demand for using EVs (Vero, 2023). Thirdly, the Vietnamese government has
implemented and introduced a few policies to promote the use of electric vehicles in Hanoi (Prime Minister of Vietnam,
2014) such as tax incentives, infrastructure investment, propaganda and education, … that can change people’s electric
vehicles usage habits. Finally, Hanoi is one of the biggest cities in the country with a large population size and economic
importance so, the application of policies and the study’s findings in here can be highlighted practical implications of
them. By selecting Hanoi as the study area, this research aims to provide the overview of customer behaviour on using EVs in Hanoi. [Type here] lOMoARcPSD| 36991220
3.2 Questionnaire design
Based on the available study (4), the researcher found that there are five main factors affecting attitudes and intentions of
customer about buying EVs and using EVs, including: physical risks, financial risks, functional risks, social risks, and
time risks. In this research, the researcher created three sections of a questionnaire. Section one includes five questions
concerning the respondents' age, gender, employment, and attitude toward electric vehicles. Sixteen questions about the
five factors the research revealed above are included in Section two. Three questions regarding how customers behave
when using EVs are included in Section three. Based on Bessenbach and Wallrapp (2013), the items that are being
improvised and adjusted suitable for the purpose of this study are shown in Table 1. The researcher used quantitative
research methods, built a set of questions based on the above factors, created an online survey form to collect data. To
measure items of variables, the questionnaire of this study used a five-point Likert scale from 1-5: (1) Strongly disagree,
(2) Disagree, (3) Undecided, (4) Agree, (5) Strongly agree and build a model to evaluate the variables that affect decisions
of customer to buy electric vehicles.
Table 1. Variables and Their questions The variables The questions Source Profile’ respondents - What do you do? Author’s own - What is your gender? opinion. - What is your age? - Have you ever used EVs?
- What type of transportation do you usually use to move? Physical risks
- Difficulty finding a charging station is the reason I don't use it Bessenbach
- No noise from the engine makes my traffic unsafe, which is the and Wallrapp
reason I don't buy an electric car. (2013)
- The risk of fire and explosion from electric vehicle batteries is the reason I don't use them Financial risks
- The expensive cost of repairs and replacement parts affects my Bessenbach decision to buy a car and Wallrapp
- The high price of the car is the reason why I did not decide to buy (2013) it
- The high price of using the service makes me not choose Functional risks
- The experience of driving an electric car is not as good as a Bessenbach
gasoline car is the reason I did not choose it and Wallrapp
- The distance that can be traveled on a short charge makes me not (2013) buy an electric car
- I don't have confidence that the performance of electric cars will
be better than gasoline cars in the long run, which is the reason
why I don't buy an electric car. Social risks
- My friends around me who don't use electric cars influenced my Bessenbach decision and Wallrapp
- The service attitude of public transportation services is the (2013) reason why I do not use it
- There are not many centers providing electric vehicle
maintenance and repair services that influence my decision to buy a car
- The feedback from the media is the reason why I don't use electric cars Time risks
- The long waiting time for the car to fully charge makes me not Bessenbach buy an electric car and Wallrapp
- Long waiting time is the reason why I don't use it (2013)
- Taking a lot of time to master using an electric vehicle is the
reason why I don't buy an electric vehicle Customer behavior
- In the future you will use electric vehicles to replace current Bessenbach vehicles and Wallrapp
- You will use electric transportation services on a daily basis - (2013) You like electric cars [Type here] lOMoARcPSD| 36991220
3.3 Case study procedure
The study determined that the using of electric vehicles will be the trend in the world in general and Vietnam in particular
due to the environmental and energy issues. Therefore, the researcher decided to investigate the risk variables influencing
consumers' attitude in purchasing and using EVs in Hanoi. The research team then identified five main risk factors
affecting consumer’s attitude for electric vehicles in Hanoi: Physical Risk, Financial Risk, Functional Risk, Social Risk
and Time Risk and created a questionnaire based on them. The researcher began the experimental survey through an
online survey by filling out a Google form and received 48 samples. To conduct the required test, SPSS software was
used to identify data, process data. This study used regression analysis research method to produce results, then based on
results analysed which factors influenced consumers' decisions to purchase and use electric vehicles and drew conclusions.
Fig.2 Case study procedure
4. RESULTS AND DISCUSSION
4.1 Results of the study
The respondents’ age in this study were broken into 4 parts: 18-25, 25-35, 35-55, 55 years and more. The respondents
were mostly from 18 to 25, account for 83.7%. The analysis results showed that the majority (86.3%) has been using
electric vehicles and mainly using personal transportation vehicles (72.5%), followed by all of vehicles (11.8%) and
public transportation services/private transportation services at the same rate of 7.8%.
These questions were modified to be used in Hanoi context to describe some factors related to using EVs in Hanoi. The
answer to each question in each factor is given in 5-point Likert scale (1-Strongly Disagree, 2-Disagree, 3-Neutral,
4Agree, and 5-Strongly Agree).
Table 2. Demographic Factors Demographic factor Code Job R01 Student (7.8%) University student Employed Others (74.5%) (11.8%) (5.9%) Gender R02 Male (56.9%) --- --- Female (43.1%) [Type here] lOMoARcPSD| 36991220 Age R03 18-25 (82.4%) 25-35 (2%) 35-55 (11.8%) More than 55 (3.9%) Experience using EVs R04 Yes (86.3%) No (13.7%) --- ---
Type of transport usually R05 Personal Public Private All of use transportation transportation transportation above vehicles services (7.8%) services (7.8%) (11.8%) (72.5%)
Table 3. Response Rates Related to 6 Factors Code 1-Strongly 2-Disagree 3-Neutral 4-Agree Disagree 5Strongly Agree Physical risks Difficult when finding a charging station is the reason I don't use it R06 2% 15.7% 13.7% 31.4% 37.3% No noise from the engine makes my traffic unsafe, which is the reason I don't buy an electric car. R07 19.6% 29.4% 27.5% 11.8% 11.8% The risk of fire and explosion from electric vehicle batteries is the reason I don't use them R08 9.8% 23.5% 23.5% 27.5% 15.7% Financial risks The expensive cost of repairs and replacement parts affects my decision to buy a car R09 11.8% 21.6% 29.4% 19.6% 17.6% The high price of the car is the reason why I did not decide to buy it R10 13.7% 27.5% 31.4% 9.8% 17.6% The high price of using the service makes me not choose R11 11.8% 27.5% 37.3% 13.7% 9.8% Functional risks The experience of driving an electric car is not as good as a gasoline car is the reason I did not choose it R12 27.5% 19.6% 31.4% 11.8% 9.8% The distance that can be travelled on a short charge makes me not buy an electric car R13 3.9% 17.6% 25.5% 21.6% 31.4% [Type here] lOMoARcPSD| 36991220 I don't have confidence that the performance of electric cars will be better than gasoline cars in the long run, which is the reason why I don't buy an electric car. R14 9.8% 19.6% 27.5% 21.6% 21.6% Social risks My friends around me who don't use electric cars influenced my decision R15 25.5% 25.5% 31.4% 9.8% 7.8% The service attitude of public transportation services is the reason why I do not use it R16 3.9% 35.3% 37.3% 7.8% 15.7% There are not many centres providing electric vehicle maintenance and repair services that influence my decision to buy a car R17 2% 13.7% 37.3% 27.5% 19.6% The feedback from the media is the reason why I don't use electric cars R18 11.8% 29.4% 35.3% 7.8% 15.7% Time risks The long waiting time for the car to fully charge makes me not buy an electric car R19 3.9% 21.6% 19.6% 33.3% 21.6% Long waiting time is the reason why I don't use it R20 5.9% 17.6% 37.3% 25.5% 13.7% Taking a lot of time to master using an electric vehicle is the reason why I don't buy an electric vehicle R21 33.3% 25.5% 19.6% 9.8% 11.8% Customer behavior In the future you will use electric vehicles to replace current vehicles R22 7.8% 15.7% 37.3% 17.6% 21.6% You will use electric transportation services on a daily basis R23 5.9% 17.6% 41.2% 19.6% 15.7% You like electric cars R24 5.9% 13.7% 41.2% 23.5% 15.7%
The survey is divided into 6 sections which contains 24 questions. However, 14 out of 24 have p value > 0.05, which
contribute to disqualify the model in general, so that they need to be eliminated.
The analysis results are shown in Table 4. There are 5 out of 10 factors, which are R06, R12, R14, R21, R16, supported
by the p value (.043, .006, .002, .010, .033) < 0.05, have influenced on decision in using EVs in the future. At first, finding
charging station (R06) increasing by 1% leads to decreasing 0.297% in customer’s decision in using EVs. Secondly,
0.453% decreasing in decision in using EVs results from 1% increasing in experience of driving EVs (R12). [Type here] lOMoARcPSD| 36991220
Next factor that leads to 0.451% increasing in decision in using EVs is 1% increasing in performance in long run (R14).
Another reason that decreases 0.404% in decision in using EVs is 1% increasing in time to master using EVs (R21).
Finally, 0.279% increasing in decision in using EVs leads 1% increasing in service attitude (R16)
In conclusion, the relationship between these supported factors can be illustrated in the equation (1):
DUEt = -0.297R06i – 0.453R12i + 0.451R14i – 0.404R21i + 0.279R16i (1)
Table 4. Regression Result Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta T value Significance (Constant) 4.497 .803 5.601 .000 Age (R03) .169 .142 .160 1.192 .241 Cost of repairs and .326 .161 .345 2.024 .050 replacement parts (R09) Price of using service (R11) -.337 .194 -.320 -1.733 .091 Finding charging station -.326 .156 -.297 -2.093 .043 (R06) Experience of driving EVs -.430 .148 -.453 -2.906 .006 (R12) Performance in long run .424 .130 .451 3.268 .002 (R14) Time to master using EVs -.352 .130 -.404 -2.713 .010 (R21) Friends don’t use EVs (R15) .226 .145 .232 1.560 .127 Service attitude (R16) .302 .137 .279 2.211 .033 Not many maintenance -.280 .155 -.237 -1.805 .079 centers (R17)
4.2 Discussion of the results
The main objective of the current study is to investigate the effects of perceived risks on customer’s attitude towards
purchasing and using EVs in Hanoi City. Through regression testing, the results show the effect of each element on
customer’s attitude in purchasing and buying EVs.
When comes to the physical risks, most respondents (68.7%) showed their agree to strongly agree attitude with the fact
that difficulty finding a charging station is the reason they don't use it. There is not too much different between number
of people that stay disagree and neutral in this question, 8 and 7 respondents which take 15.7% and 13.7% respectively.
Only 2% strongly disagrees with that fact mentioned above. 29.4% respondents disagree when asking no noise from the
engine makes my traffic unsafe, which is the reason I don't buy an electric car; followed by neutral (27.5%), strongly
disagree (19.6%), and both agree and strongly agree (11.8%). Percentage of people think that “The risk of fire and
explosion from electric vehicle batteries is the reason I don't use them” spread pretty close from disagree to agree at the
rate of 23.5%, 23.5% and 27.5% correspondingly.
Asking about financial risks, especially the reason why they don’t use or buy EVs, 29.4% of respondents are neutral when
answering the risk of fire and explosion from electric vehicle batteries. The democracy among disagrees (21.6%), agree
(19.6%) and strongly agree (17.6%) is not clear, compared with strongly disagree (11.8%). The reason of high price does
not discriminate the range between 5 points transparently. However, high price of using the service does. 37.3% people
is neutral about that, followed by disagree (27.5%) but strongly disagree, agree and strongly disagree is even less than 15% each.
The survey highlights concerns regarding driving experience, travel distance on a short charge, and long-term
performance. A significant percentage of 27.5% feels that the driving experience of electric cars does not match that of
gasoline cars. Additionally, apprehensions about the distance covered on a short charge (25.5%) and doubts about the
long-term performance (27.5%) present functional barriers to electric car adoption.
Social influences play a crucial role in shaping individual choices regarding electric cars. 25.5% of respondents indicated
that their friends who do not use electric cars influenced their decision. Moreover, concerns related to public transportation [Type here] lOMoARcPSD| 36991220
services, such as service attitude (35.3%) and the limited availability of electric vehicle maintenance and repair services
(37.3%), contribute to the social risks associated with electric car adoption.
The survey identifies time-related factors that hinder the adoption of electric cars. Concerns about the long waiting time
for a full charge (at the rate of 21.6%) and the time required to master using an electric vehicle (at the rate of 33.3%) are
significant barriers. The time constraints associated with charging and learning to use the vehicle are critical
considerations that impact consumer choices.
Understanding customer behavior is essential for predicting future trends in electric vehicle adoption. A notable
percentage (37.3%) of respondents express their intent to use electric vehicles as replacements for current vehicles.
Additionally, a significant portion of 41.2% envisions using electric transportation services daily. These findings suggest
a potential shift towards greater acceptance and integration of electric vehicles into daily life. 5. CONCLUSION
This study examined the effect of perceived risk on consumer’s attitude toward EVs in Hanoi. After forty eight (48)
respondents returned the forms, forty eight dataset were analysed by regression tests using SPSS. And findings of this
study indicated that the finding charging station (physical risk), experience of driving EVs (functional risk), performance
in long run of EVs (functional risk), time to master using EVs (time risk) and service attitude of EVs service providers
(social risk) are the factors that influence the consumer’s attitude toward purchasing and using EVs. 6. REFERENCES
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