Key factors influencing consumers’ willingness to

purchase electric vehicles in China

Ning Wang1, Yafei Liu

2

School of Automotive Studies, Tongji University, Shanghai, wangning@tongji.edu.cn

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.

[10] Deloitte Consulting LLP, 2010. Gaining Traction:

A Customer View of Electric Vehicle Mass Adoption

in the U.S. Automotive Market.

<http://www.deloitte.com.br/publicacoes/2007/MFG.

Gaining_Traction_customer_view_of_electric_vehicle

_mass_adoption.pdf>.

[11] Caperello, Nicolette D., and Kenneth S. Kurani.

“Households’ stories of their encounters with a plug-in

hybrid electric vehicle.” Environment and

behavior44.4 (2012): 493-508.

[12] Krupa, Joseph S., et al. “Analysis of a consumer

survey on plug-in hybrid electric

vehicles.” Transportation Research Part A: Policy and

Practice 64 (2014): 14-31.

[13] Browne, David, Margaret O'Mahony, and Brian

Caulfield. “How should barriers to alternative fuels

and vehicles be classified and potential policies to

promote innovative technologies be

evaluated?.” Journal of Cleaner Production 35 (2012):

140-151.

[14] Silvester, Sacha, et al. “Exploring design

scenarios for large-scale implementation of electric

vehicles; the Amsterdam Airport Schiphol case.”

Journal of Cleaner Production 48 (2013): 211-219.

[15] Gallagher, Kelly Sims, and Erich Muehlegger.

“Giving green to get green? Incentives and consumer

adoption of hybrid vehicle technology.” Journal of

Environmental Economics and Management 61.1

(2011): 1-15.

[16] Lane, Ben, and Stephen Potter. “The adoption of

cleaner vehicles in the UK: exploring the consumer

attitude–action gap.” Journal of Cleaner

Production15.11 (2007): 1085-1092.

[17] Potoglou, Dimitris, and Pavlos S. Kanaroglou.

“Household demand and willingness to pay for clean

vehicles.” Transportation Research Part D: Transport

and Environment 12.4 (2007): 264-274.

[18] Turrentine, Thomas S., and Kenneth S. Kurani.

“Car buyers and fuel economy?.” Energy Policy 35.2

(2007): 1213-1223.

[19] Graham-Rowe, Ella, et al. “Mainstream

consumers driving plug-in battery-electric and plug-in

hybrid electric cars: A qualitative analysis of responses

and evaluations.” Transportation Research Part A:

Policy and Practice 46.1 (2012): 140-153.

[20] Heffner, Reid R., Kenneth S. Kurani, and Thomas

S. Turrentine. “Symbolism in California’s early

market for hybrid electric vehicles.” Transportation

Research Part D: Transport and Environment 12.6

(2007): 396-413.

[21] Zhang, Yong, Yifeng Yu, and Bai Zou.

“Analyzing public awareness and acceptance of

alternative fuel vehicles in China: The case of EV.”

Energy Policy 39.11 (2011): 7015-7024.

[22] Axsen, Jonn, Dean C. Mountain, and Mark

Jaccard. “Combining stated and revealed choice

research to simulate the neighbor effect: The case of

hybrid-electric vehicles.” Resource and Energy

Economics 31.3 (2009): 221-238.

[23] DeVellis R F.Scale development: Theory and

applications[M].Sage Publications, 2011.

[24] Lean, Ooh Kim, et al. “Factors influencing

intention to use e-government services among citizens

in Malaysia.” International Journal of Information

Management 29.6 (2009): 458-475.

Authors

Ning Wang, Ph.D. in

Management, is the associate

professor of the School of

Automotive Studies in Tongji

University, mainly researching

in consumer behavior,

marketing strategy, policy and

demonstration of EVs.

Yafei Liu, a postgraduate

student of the School of

Automotive Studies in Tongji

University, majoring in

automobile product

management and marketing.