Key factors influencing consumers’ willingness to purchase electric vehicles in China Ning Wang 1 , Yafei Liu 2 School of Automotive Studies, Tongji University, Shanghai, [email protected]Abstract Electric vehicles are considered the automobile technology in the future. However, consumers are not willing to purchase electric vehicles in China. Thus, it is necessary to analyze key factors influencing the willingness to purchase electric vehicles. This paper applies the chi-square test and a binary logistic regression model based on the questionnaires of 1057 Chinese online consumers. The results indicate that the group who are willing to adopt electric vehicles embraces the characteristics of high income, EV as second vehicle, interests in new things and environmental sensitivity. Moreover, consumers are more likely to purchase electric vehicles when they perceive less risks such as short driving range and long charging time, as well as more social values obtained from adopting electric vehicles. The charging infrastructure is also an influence on consumers’ preferences. Finally, policy recommendations that encourage the purchase of electric vehicles and the construction of charging infrastructure are provided for the Chinese market. Keyword : Electric vehicles, Logistic regression, Willingness to purchase 1 Introduction Since the year of 2009, China has become the world’ s largest car market by sales. It is forecasted that the sales volume would rise to 30 million by 2020 and the growth would last for a long time. The growing number of cars will lead to the increasing oil demand and greenhouse gas emission, which will pose a great challenge for the development of the social economy and environment. Electric vehicles are considered as an effective technological innovation to reduce energy use and greenhouse gas emission, which has raised great attention among the government and car manufacturers. In China, the electric vehicle technologies are being promoted as securing the future of mobility. In 2012, the Chinese government issued the ‘‘Planning for the Development of the Energy-saving and New Energy Automobile Industry (2012-2020)’’, in which the electric vehicle has been chosen as the main strategic orientation to promote new energy vehicle technologies and thus develop Chinese automobile industry. A series of policies to promote electric vehicle industrialization and commercialization have been introduced in recent years, including pilot demonstration projects (Ministry of Science and Technology (MOST), 2009), production standards (Ministry of Industry and Information Technology (MIIT),
12
Embed
Key factors influencing consumers’ willingness to · “Directory of New Energy Vehicle Models Exempted From Purchase Tax” will enjoy the policy of purchase tax exemption. In
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Key factors influencing consumers’ willingness to
purchase electric vehicles in China
Ning Wang1, Yafei Liu
2
School of Automotive Studies, Tongji University, Shanghai, [email protected]
Abstract
Electric vehicles are considered the automobile technology in the future. However, consumers are
not willing to purchase electric vehicles in China. Thus, it is necessary to analyze key factors
influencing the willingness to purchase electric vehicles. This paper applies the chi-square test and
a binary logistic regression model based on the questionnaires of 1057 Chinese online consumers.
The results indicate that the group who are willing to adopt electric vehicles embraces the
characteristics of high income, EV as second vehicle, interests in new things and environmental
sensitivity. Moreover, consumers are more likely to purchase electric vehicles when they perceive
less risks such as short driving range and long charging time, as well as more social values
obtained from adopting electric vehicles. The charging infrastructure is also an influence on
consumers’ preferences. Finally, policy recommendations that encourage the purchase of electric
vehicles and the construction of charging infrastructure are provided for the Chinese market.
Keyword : Electric vehicles, Logistic regression, Willingness to purchase
1 Introduction
Since the year of 2009, China has become
the world’s largest car market by sales. It is
forecasted that the sales volume would rise to 30
million by 2020 and the growth would last for a
long time. The growing number of cars will lead
to the increasing oil demand and greenhouse gas
emission, which will pose a great challenge for
the development of the social economy and
environment.
Electric vehicles are considered as an
effective technological innovation to reduce
energy use and greenhouse gas emission, which
has raised great attention among the government
and car manufacturers. In China, the electric
vehicle technologies are being promoted as
securing the future of mobility. In 2012, the
Chinese government issued the ‘‘Planning for the
Development of the Energy-saving and New
Energy Automobile Industry (2012-2020)’’, in
which the electric vehicle has been chosen as the
main strategic orientation to promote new energy
vehicle technologies and thus develop Chinese
automobile industry. A series of policies to
promote electric vehicle industrialization and
commercialization have been introduced in
recent years, including pilot demonstration
projects (Ministry of Science and Technology
(MOST), 2009), production standards (Ministry
of Industry and Information Technology (MIIT),
2009) and purchase subsidies (NDRC and MOF,
2010 and 2013). In 2009 , the Chinese
government initiated the “Ten Cities, Thousand
Vehicles” program for new energy vehicles in 13
cities. From the year of 2009 to December 2012,
the first three-year demonstration operation of 25
cities, initiated as the “Ten Cities, Thousand
Vehicles” program, has been finished. The
research and summary report of these 25 cities
indicated that the quantity of the electric vehicles
in all the demonstration cities is 27,400, which is
only 26% of the whole deployment goals. And
these vehicles are mostly applied in the public
sector. The market share of the private sector is
relatively small.
For the private sector, the central
government and local governments have
introduced many policies to actively promote the
development of electric vehicles, including the
purchase subsidies, infrastructure subsidies and
non-monetary incentives. When purchasing new
energy vehicles, consumers can obtain subsidies
from both the state government and the local
government. Taking Beijing for an example, if
consumers in Beijing purchase a new energy
passenger car whose electric driving range is not
less than 250 km in the year of 2014, they can
get 114,000 yuan from the state and local
government subsidies. At the same time ,
consumers who buy vehicle models in the
“Directory of New Energy Vehicle Models
Exempted From Purchase Tax” will enjoy the
policy of purchase tax exemption. In Shanghai,
consumers can obtain the free license plate
provided by the municipal government when
purchasing electric vehicles. In Wuhan, electric
vehicle drivers can enjoy the non-monetary
incentives such as road tolls exemptions and free
public charging in designated charging places.
Some Chinese car manufacturers have already
launched their EV models and made mass
production plans, such as the BYD E6, BAIC
E150, JAC iev4, Zotye 5008EV, Roewe E50 and
Shanghai GM Springo EV. The charging
infrastructure operators such as the State Grid
and China Southern Power Grid have engaged in
the construction of charging stations. However,
the electric vehicle market in the private sector
has not been effectively developed. Compared
with the traditional auto industry, the electric
vehicle industry has no competitiveness. The
electric vehicles have characteristics of high
purchase cost, inadequate charging infrastructure
and long charging time, which make consumers
unwilling to purchase.
Consumers’ willingness to purchase electric
vehicles is the basis of purchase behavior, which
can be used to predict the behavior of consumers.
Consumers’ willingness to purchase electric
vehicles can be affected by many factors. This
paper investigates the online potential consumers
of electric vehicles, analyze the main factors that
influence the willingness of consumers to
purchase and provide references for government
policies and marketing strategies. This paper is
divided into 5 sections, the first being this
introduction. The second section presents the
factors affecting consumers’ willingness to
purchase electric vehicles through literature
review and expert interviews. The third section
provides the methodology. The fourth and fifth
parts present the research results and
conclusions.
2 Research Model
Researches on factors affecting consumers’
willingness to purchase electric vehicles are
relatively mature. Through analyzing these
researches, we can summarize that the factors
affecting consumers’ willingness to purchase
electric vehicles include the internal factors and
external factors.
2.1 Internal factors of consumers
(1) Demographics
Demographic variables including gender,
age, education, occupation, income and other
personal information are essential characteristics
of consumers. Demographics is a decisive factor
affecting consumers’ willingness to purchase.
Carley [1] investigated the American drivers of
their willingness to purchase plug-in hybrid
electric vehicles, and the results showed that well
educated drivers are more willing to purchase.
Knez [2] found that older consumers are more
likely to purchase new energy vehicles based on
the research of 700 Slovenian consumers. Patrick
[3] made a survey of German electric vehicle
consumers and found that the most likely group
of electric vehicle buyers are middle-aged men
with technical professions living in rural or
suburban multi-person households. The
demographic variables in this paper include:
gender, education level, marital status, age,
occupation, overseas educational experience and
annual household income.
(2) Perceived Risks
Perceived risks can reversely influence
consumers’ purchase willingness. For electric
vehicles, consumers lack the appropriate product
knowledge, and thus will perceived risks.
Consumers will be less likely to purchase electric
vehicles when they perceive more risks. Oliver
and Rosen [4] presented that consumers’
acceptance of new products is influenced by their
perceived risks based on an investigation of
hybrid vehicle owners. In this paper, the
perceived risks of consumers include: the short
battery life, the unreliable quality and the short
driving range.
(3) Personality Characteristics of Consumers
Personality characteristics of consumers
including environmental awareness, conformist
mentality and innovative personality will also
affect consumers' willingness to purchase. Kahn
[5] proposed that environmentalists are more
likely to buy hybrid vehicles than
non-environmentalists based on a survey of
hybrid vehicle consumers in Los Angeles. Axsen
[6] surveyed 508 households in California and
found that positive interest in electric vehicles
was associated with responsibility and support of
the environment and nation. Hidrue [7]
investigated 3029 consumers in America and
found that the respondents who are more likely
to buy electric vehicles have a tendency to buy
new products that come on to the market and
have made a shopping or life style change to help
the environment in the last 5 years. Tian Xu [8]
found that the Chinese consumers who are more
willing to buy electric vehicles can easily
acceptance innovative technology and have
environmental awareness.
2.2 External Factors
(1) Performance Attributes
Compared with traditional fuel vehicles,
electric vehicles have certain advantages in terms
of performance attributes, such as automatic
transmission, comfort, easy to drive,
performance, safety, reliability and quietness.
Ozaki and Sevastyanova [9] made a survey of
hybrid vehicle drivers and concluded that the
performance attributes including comfort of
driving, quietness and easy to drive are the most
important factors affecting consumers’ adoption.
A survey made by the Deloitte Consulting [10] in
America showed that the reliability of electric
vehicles is one of the most important factors that
consumers will consider. However, due to the
constraints in battery technology, the driving
range of electric vehicles is generally 100-300km,
which is too short to meet the requirements for
users to travel long distance. And the battery life
is relatively short. All these factors will reduce
the consumers’ willingness to buy EVs to some
extent.
(2) Financial Benefits
The electricity cost per hundred kilometers
for electric vehicles is about 12 yuan, which is
far lower than traditional fuel vehicles. And the
maintenance cost is relatively low because there
is no need to replace the oil filter, fuel filter and
air filter. Thus, the low usage cost of electric
vehicles is an attractive factor for consumers.
The study of Caperello and Kurani [11] indicated
that although the high purchase cost hampers the
adoption of EVs, the relatively low usage cost is
a contributing factor to promote EVs. Krupa et al.
[12] concluded that consumers pay more
attention to low energy costs than environmental
benefits with a survey of 1000 residents in the
United States, and respondents who focus on low
energy costs are more likely to buy EVs.
(3) Marketing Effectiveness
In addition to the performance attributes and
financial benefits, the marketing factors also
affect consumers' willingness to buy electric
vehicles. The marketing factors include sales
channels, after-sales service and advertising.
(4) Charging Infrastructure
The electric vehicles with short driving
range can not allow for the long-distance travel.
Thus, it is imperative to construct charging
infrastructure to eliminate the users’ “range
anxiety”. The charging infrastructure readiness is
rather important to influence the consumers’
willingness to purchase EVs. Browne et al
[13] analyzed the factors that hinder the
promotion of electric vehicles and found that the
inadequate charging infrastructure is one of the
inhibitors. When there is no charging
infrastructure available, the EV drivers will have
“range anxiety”. Therefore, the construction of
charging infrastructure will promote the
widespread EV market penetration [14].
(5) Government Policies
The government is the main driving force in
the early stage of the electric vehicle industry
development. The government policies are
developed to encourage the adoption of EVs. The
policies include monetary incentives and
non-monetary incentives. The monetary
incentives consist of the purchase incentives,
charging infrastructure incentives, purchase tax
exemptions and electricity cost subsidies. The
non-monetary incentives consist of road tolls
exemptions and free public charging. Gallagher
and Muehlegger [15] held that the tax subsidies
are more effective to encourage the consumers’
purchase than other supporting incentives. Lane
and Potter [16] found that the environmental
regulations, oil price policy, purchase subsidies
and the charging infrastructure construction will
affect the market penetration of cleaner vehicles.
Potoglou [17] applied the nested logit model
based on an online survey, and the results
showed that the purchase tax exemptions can
effectively promote the adoption of electric
vehicles, but some non-monetary policies such as
free parking and use of designated lanes are
ineffective.
(6) Social Influence Values
The electric vehicle is not just a simple
means of transportation, the social values it
represents will also influence consumers’
purchasing decisions to some extent. Besides,
consumers’ purchasing decision is not an
independent decision-making behavior, it will be
restricted and influenced by the external
environment such as the reference group and the
social status. Turrentine and Kurani [18]
investigated 57 households in the United States
and indicated that the good image of
environmental protection with the usage of EVs
is an important driving factor affecting
consumers’ purchase. Graha-Rowe [19] surveyed
40 UK households of their driving experience of
EVs and found that some drivers feel good from
EV use because of the associated environmental
benefits of electric vehicles, while some drivers
feel embarrassed because of the poor
performance and appearance of EVs. Kurani [20]
found that the symbolic significance of hybrid
vehicles affects the purchase willingness of the
25 households in the United States. The
respondents hold the opinion that driving a HEV
will express their personalities such as maturity,
intelligence and distinction. Zhang [21]
investigated 299 respondents from various
driving schools in Nanjing and the results
suggested that the acceptance of purchasing EVs
is influenced by the opinion of peers. Jonn [22]
applied the discrete choice model with the RP
and SP survey and showed that the “neighbor
effect” will have an influence on the purchase
willingness of hybrid vehicles.
Based on the above analysis, the factors
affecting consumers' willingness to purchase
electric vehicles are summarized as follows:
Demographics, Personality Characteristics of
Consumers, Perceived Risks, Performance
Attributes, Financial Benefits, Marketing
Effectiveness, Charging Infrastructure,
Government Policies and Social Influence.
3 Methodology
3.1 Questionnaire Survey
In this paper, the questionnaire survey is
made to analyze the factors affecting the
consumer willingness to purchase electric
vehicles. The respondents of the survey are
private car owners, who are the potential
consumers of electric vehicles. The primary data
was obtained from an online questionnaire on
Sohu auto website. A total of 1206 questionnaires
were collected. Excluding the missing values and
outliers, 1057 copies of valid questionnaires were
selected as the data sample. The survey response
rate was 87.6%. The questionnaire was designed
based on the current literature results, which
consisted of four sections: section one covered
the question on the consumers’ understanding
levels of electric vehicles. Section two covered
the questions on the factors affecting the
consumers’ purchase willingness. All the factors
were measured by multiple items on a 5-point
“Likert” scale that ranged from 1=Strongly
Disagree to 5=Strongly Agree. In this section,
there were 35 items in all. The third section
focused on the question of the consumers’
purchase willingness. The fourth section covered
the questions on the demographic variables.
3.2 Data Analysis Methods
In this paper, SPSS Version 21.0 was
applied as a statistical analysis tool. The
statistical analysis methods consisted of
descriptive analysis, cross-table analysis,
correlation analysis and logistic regression
analysis. The descriptive and frequency statistical
analysis was conducted to observe the conditions
of the data. The cross-table analysis was applied
to get the two-dimensional or multi-dimensional
cross contingency tables and test whether there
are correlations among the variables using the
chi-square test. Then the correlation analysis was
conducted to determine whether there are
relationships between various variables and test
whether there is multicollinearity or not. Finally,
the logistic regression, which is a type of
probabilistic statistical classification model was
used to measure the relationship between the
categorical dependent variable and one or more
independent variables, which are usually
continuous, by using probability scores as the
predicted value of the dependent variable.
4 Results
4.1 Sample Descriptions
Table 1 illustrates the demographic
attributes of the respondents in this study. In the
sample, most of the respondents are male
(97.7%). In terms of age, the majority of
respondents is within the age group of 26-50
(90.5%). This group could be the target group for
electric vehicle purchase. 88.6% of the
respondents are married. In terms of education
level, most respondents have acquired high
academic degrees. The proportion of associate
and bachelor is 72.1%. The respondents have a
relatively moderate income. 66.2% of the
respondents is within the income group of
50.000-200,000. In 2013, the average disposable
income per urban resident in China is 26,955
yuan, which is lower than the annual income of
the majority of respondents. In terms of
occupation, more than 20% of the respondents
are the party and government cadres / teacher /
policeman, company managers. The ordinary
staff, technical staff and freelancers are in the
proportion between 10% and 20%.
Table 1 Demographics of the respondents
Sample
Characteristics Percentage
Sample size 1057
Gender Male 97.7%
Female 2.3%
Age
<18 2.7%
18-25 2.2%
26-30 12.9%
31-40 51.0%
41-50 26.6%
51-60 4.4%
>60 0.3%
Marital Status
Single 11.4%
Married 88.6%
Education
Level
Junior middle
school or lower 6.6%
Senior middle
school or
equivalent
15.1%
Associate 32.4%
Bachelor 39.7%
Master 5.1%
Ph.D. 1.0%
Overseas
education
experience?
Yes 14.2%
No 85.8%
Occupation
Party and
government
cadres / Teacher /
Policeman
24.8%
Ordinary staff 13.4%
Business owners
/ Shareholders 3.5%
Technical staff 19.1%
Worker / Service
personnel 4.1%
Company
managers 21.1%
Freelancers 13.1%
Retirees 0.8%
Students 0.2%
Annual
Income (yuan)
<50,000 22.9%
50,000-80,000 23.8%
80,000-120,000 19.5%
120,000-150,000 11.0%
150,000-200,000 11.9%
200,000-300,000 5.7%
300,000-500,000 3.3%
>500,000 1.7%
No stable income 0.2%
4.2 Reliability analysis
Table 2 illustrates the basic statistical
characteristics of the factors affecting the
consumers’ willingness to purchase. The mean
value of these factors is within 3.20-4.12 except
the marketing effectiveness and charging
infrastructure, indicating that consumers believe
the performance attributes, financial benefits,
government policies and social influence of EVs
are performing well. Consumers perceive high
risks of EVs. The low mean value of marketing
effectiveness indicates that the marketing
strategies of OEMs are not effective. The low
mean value of charging infrastructure shows that
the charging infrastructure of EVs is inadequate
and the charging process if inconvenient. In
terms of personality characteristics of consumers,
it shows that the respondents own strong
characteristics of environmental awareness,
conformist mentality and innovative personality.
Reliability analysis refers to the fact that a
scale should consistently reflect the construct it is
measuring. Reliability is assessed by measuring
the Cronbach’s α coefficient to check the internal
consistency among the items. The acceptable
value of Cronbach’s α in reliability analysis is
above0.70 [23]. In this paper, the Cronbach’s α
for the overall scale of each factor is within
0.669-0.875, suggesting a very strong
consistency among the items for each factor.
In terms of validity, the questionnaire was
designed by reference to the existing mature
scales, which have been tested by empirical
research, and recognized by many experts of
related fields. Meanwhile, based on the existing
scales, we added some of the items with the help
of consumer interviews and expert advices. Thus,
the scale used in this paper has good validity.
Table 2 Descriptive statistics and reliability analysis
Variables
No
of
Items
Mean Standard
Deviation
Cronbach
α
Performance
Attributes 6 3.53 0.83 0.739
Marketing
Effectiveness 4 2.54 1.06 0.784
Financial
Benefits 2 4.01 1.09 0.669
Government
Policies 3 3.27 1.20 0.803
Charging
Infrastructure 2 1.97 1.24 0.833
Social
Influence 6 3.86 1.01 0.875
Perceived
Risks 3 4.12 1.11 0.866
Innovative
personality 3 3.59 1.06 0.770
Conformist
mentality 3 3.20 0.96 0.681
Environmental
awareness 3 3.70 1.04 0.766
4.3 Consumers’ willingness to purchase
EVs
The investigation of consumers’ willingness
to purchase electric vehicles shows that more
than 90% of the respondents express their
willingness to purchase electric vehicles. The
proportion of respondents willing to buy electric
vehicles as a second car (47.7%) is higher than
that of respondents willing to buy electric
vehicles to replace traditional fuel vehicles (44%)
Table 3 Consumers’ willingness to purchase EVs
Item Choice N Percentage
Y: Are you
willing to
purchase an
EV?
Unwilling 88 8.3%
Willing to purchase an EV as a second car
465 44.0%
Willing to purchase an EV
to replace traditional fuel
cars
504 47.7%
Sample 1057 100.0%
In this paper, the cross contingency tables
and chi-square test are used to analyze the
correlations between the demographic variables
and consumer willingness to purchase EVs.
In terms of gender, the proportion of men
and women willing to buy electric vehicles is the
same (91.7%). The gender variable is not
significantly different by purchase willingness by
a Chi-squared-test (χ2=1.112, df=2;
p=0.574>0.05).
With regard to the age, the respondents of
age group within 18-25 are more likely to be
unwilling to buy EVs, while the proportion of
respondents above 31 years old willing to
purchase EVs is higher. The age variable is
significantly different in purchase willingness by
a Chi-squared-test (χ2=24.392 ; df=12;
p=0.018<0.05).
In terms of occupation, the proportion of the
students unwilling to purchase EVs is the highest,
followed by the company managers. The
proportion of freelancers and retirees willing to
buy EVs is relatively high. Overall it can be
stated that the occupation is significantly
different in purchase willingness by a
Chi-squared-test (χ2=38.947 , df=16;
p=0.001<0.01).
Moreover, the respondents with the
education level of associate or lower are more
likely to be unwilling to purchase EVs. The
education level is also significantly different in
purchase willingness by a Chi-squared-test
(χ2=18.773,df=10; p=0.043<0.05).
Furthermore, the data shows that the
respondents with annual income below 50,000
yuan are more likely to not purchase EVs. The
respondents with annual income above 120,000
yuan are more likely to buy EVs. The annual
income is significantly different in purchase
willingness by a Chi-squared-test (χ2=61.433,
df=16; p=0.000<0.05).
To summarize, we find the correlations
between the demographic variables and
consumer willingness to purchase EVs. Except
the gender variable, the variables of age,
occupation, education level and annual income
are all significantly different by category in
willingness to purchase.
4.3 Logistic regression analysis
Before the logistic regression analysis, it is
imperative to test if there is the problem of
multicollinearity between the independent
variables. The Pearson correlation coefficient is
calculated to determine the relationships between
the variables using the SPSS software. The
Pearson correlation coefficient gives information
about the magnitude of the association, or
correlation, as well as the direction of the
relationship. Coefficient values can range from
+1 to -1, where +1 indicates a perfect positive
relationship, -1 indicates a perfect negative
relationship, and a 0 indicates no relationship
exists. If the value is near ± 1, then it is said to
be a perfect correlation. If the coefficient value
lies between ± 0.70 and ± 1, then it is said to be a
strong correlation. If the value lies between ±
0.40 and ± 0.70, then it is said to be a medium
correlation. When the value lies between + 0.20
and ± 0.40, then it is said to be a low correlation.
A correlation of less than 0.20 is considered a
slight correlation. Table 4 shows the Pearson
correlation coefficient matrix. The correlation
coefficients between variables are all lower than
0.7, which shows that there is no
multicollinearity problem as all of the variables
are within a low to medium correlation. In Table
4, PA= Performance Attributes, ME= Marketing
Effectiveness, FB=Financial Benefits, GP=
Government Policies, CI=Charging Infrastructure,
SI= Social Influence, PR= Perceived Risks, IP=
innovative personality, CM=Conformist
Mentality, EA= Environmental Awareness.
A binary logistic regression model is
proposed with ten factors affecting the
consumers’ willingness to purchase EVs. The
dependent variable is whether the respondents
would be willing to purchase EVs or not. The
independent variables of the 10 factors are in
certain correlations with each other, which means
the likelihood ratio forward stepwise method of
logistic regression should be introduced to obtain
a more scientific prediction model. This method
will leave the variables whose regression
coefficients are statistically significant in the
model and exclude the variables whose
regression coefficients are not statistically
significant from the model. The logistic
regression analysis results are shown in Table 5.
If the p-value of the variable is less than
0.01, it indicates that there is causal relationship
between the variable and consumers’ willingness
to purchase EVs. As Table 5 shows, the P-value
of the social influence, perceived risks and
charging infrastructure are all less than 0.01. The
regression coefficients of social influence and
charging infrastructure are positive, which means
that these two factors are positively correlated
with consumers’ purchase willingness. The
regression coefficient of perceived risks is
negative, which indicates that this factor is
negatively correlated with consumers’ purchase
willingness. The other variables have low
correlations with consumers’ purchase
willingness.
Table 4 Pearson correlation coefficient matrix
PA ME FB GP CI SI PR IP CM EA
PA 1
ME .319 1
FB .566 .123 1
GP .440 .454 .426 1
CI .136 .677 -.068 .284 1
SI .544 .200 .574 .429 -.019 1
PR .240 -.136 .390 .186 -.234 .408 1
IP .406 .211 .434 .329 -.007 .655 .313 1
CM .239 .251 .217 .271 .217 .254 .236 .285 1
EA .435 .242 .449 .396 .076 .638 .264 .527 .319 1
Table 5 reflects the likelihood ratio change
of consumers’ willing to unwilling of purchasing
EVs when the factors change per unit with the
other variables remaining constant. When the
value of e^b is more than 1, the respondents who
are willing to buy EVs have e^b times greater
odds than those who are not willing to buy EVs
with each unit change of one independent
variable. The following is the analysis of the
factors affecting consumers’ purchase
willingness:
(1) There is a positive statistically significant
relationship and impact between social influence
and purchase willingness.
With other factors being constant, when the
variable of social influence increases by one unit,
the respondents who are willing to buy EVs have
2.689 times greater odds than those who are not
willing to buy EVs. The social influence includes
the social image of electric vehicles and the
opinion of consumers’ social groups. Thus, in
order to improve consumers’ willingness to
purchase EVs, it is necessary for the government
and companies to strengthen the propaganda of
the social values of electric vehicles.
(2) There is a negative statistically significant
relationship and impact between perceived risks
and purchase willingness.
With other factors being constant, when the
variable of perceived risks increases by one unit,
the respondents who are willing to buy EVs have
0.744 times greater odds than those who are not
willing to buy EVs. Consumers will be not
willing to buy EVs if they perceive more risks
related to electric vehicles. Therefore, measures
must be taken to reduce the risks that consumers
perceive.
(3) There is a positive statistically significant
relationship and impact between charging
infrastructure readiness and purchase
willingness.
When the charging infrastructure is
adequate and convenient, consumers will be
more willing to buy EVs. The government
should cooperate with social capital to promote
the construction of charging infrastructure.
5 Conclusions
In this paper, we analyze the consumers’
willingness to purchase EVs and the important
affecting factors in order to provide decision
support for the government and the car
manufacturing companies. Based on the
literature review and summary analysis, the
research model of consumers’ purchase
willingness is determined. The research variables
consist of the Demographics, Personality
Characteristics, Perceived Risks, Performance
Attributes, Financial Benefits, Marketing
Effectiveness, Charging Infrastructure,
Government Policies and Social Influence. With
the web-based survey data and logistic regression
analysis, the results of this paper have been
obtained. Finally, according to the research
findings, the suggestions have been made for the
government and companies. Although this
research has come up with some findings of the
consumers’ purchase willingness, there are still
some limitations, such as the limitation of the
study sample and the incomplete research
variables, which should be further studied in the
future work.
Table 5 logistic regression analysis results
(Forward:LR)
B Wals Sig.
Exp
(B)
Step
1
Social
influence 0.827 71.045 0.000 2.287
Constant -0.476 2.134 0.144 0.621
Step
2
Social
influence 0.805 60.930 0.000 2.237
Charging
infrastructure 0.586 16.768 0.000 1.796
Constant -1.657 14.145 0.000 0.191
Step
3
Social
influence 0.989 58.621 0.000 2.689
Perceived
risks -0.295 6.613 0.010 0.744
Charging
infrastructure 0.460 9.289 0.002 1.584
Constant -0.811 2.166 0.141 0.444
References
[1] Carley, Sanya, et al. “Intent to purchase a plug-in
electric vehicle: A survey of early impressions in large
US cites.” Transportation Research Part D: Transport
and Environment 18 (2013): 39-45.
[2] Matjaz Knez, et al. “Factors influencing the
purchasing decisions of low emission cars:A study of
Slovenia.” Transportation Research Part D: Transport
and Environment 30 (2014): 53-61.
[3] Plötz, Patrick, et al. “Who will buy electric
vehicles? Identifying early adopters in Germany.”
Transportation Research Part A: Policy and Practice
67 (2014): 96-109.
[4] Oliver, Jason D., and Deborah E. Rosen.
“Applying the environmental propensity framework: A
segmented approach to hybrid electric vehicle
marketing strategies.” The Journal of Marketing
Theory and Practice 18.4 (2010): 377-393.
[5] Kahn, Matthew E. “Do greens drive Hummers or
hybrids? Environmental ideology as a determinant of
consumer choice.” Journal of Environmental
Economics and Management 54.2 (2007): 129-145.
[6] Axsen, Jonn, and Kenneth S. Kurani. “Hybrid,
plug-in hybrid, or electric—What do car buyers want?.”
Energy Policy 61 (2013): 532-543.
[7] Hidrue, Michael K., et al. “Willingness to pay for
electric vehicles and their attributes.” Resource and
Energy Economics 33.3 (2011): 686-705.
[8] Tian Xu. The Identification of Potential
Consumers on Domestic Electric Vehiclesand the
Research of Consumer Behavior [D].Wuhan
University of Technology, 2012.
[9] Ozaki, Ritsuko, and Katerina Sevastyanova.
“Going hybrid: An analysis of consumer purchase
motivations.” Energy Policy 39.5 (2011): 2217-2227.