ETH Library

Analysis of car type preferencesamong students based onseemingly unrelated regression
Conference Paper
Author(s):Belgiawan, Prawira Fajarindra; Schmöcker, Jan-Dirk; Abou-Zeid, Maya; Fujii, Satoshi
Publication date:2017-01
Permanent link:
https://doi.org/10.3929/ethz-b-000122905
Rights / license:
In Copyright - Non-Commercial Use PermittedThis page was generated automatically upon download from the ETH Zurich Research Collection.
For more information, please consult the Terms of use.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45

ANALYSIS OF CAR TYPE PREFERENCES AMONG STUDENTS BASED ON
SEEMINGLY UNRELATED REGRESSION

Prawira Fajarindra Belgiawan
Postdoctoral Researcher, Department of Urban Management, Kyoto University, Japan
andInstitute for Transport Planning and System (IVT), ETH Zürich, Switzerland
HIL, F51.1, Stefano-Franscini-Platz 5 8093 Zürich Switzerland
Tel: +41-44-633-3325; Fax: +41-44-633-1057; Email: fajarindra.belgiawan@ivt.baug.ethz.ch
Jan-Dirk Schmöcker, Corresponding Author
Associate Professor, Department of Urban Management, Kyoto University, Japan
C1-2-436, Kyoto Daigaku-Katsura, Nishikyo-ku, Kyoto, Japan.615-8540
Tel: +81-75-383-7491, Fax: +81-75-383-3236; Email: schmoecker@trans.kuciv.kyoto-u.ac.jp
Maya Abou-Zeid
Associate Professor, Department of Civil and Environmental Engineering, American Universityof Beirut, Lebanon
P. O. Box 11-0236 / FEA-CEE, Bechtel Building, Room 527 Riad El-Solh / Beirut 1107 2020,
LebanonTel: +961-1-350000 Ext. 3431, Fax: +961-1-744462, E-mail: ma202@aub.edu.lb
Satoshi Fujii
Professor, Department of Urban Management, Kyoto University, Japan
C1-2-432, Kyoto Daigaku-Katsura, Nishikyo-ku, Kyoto, Japan.615-8540
Tel: +81-75-383-3238, Fax: +81-75-383-3236; Email: fujii@trans.kuciv.kyoto-u.ac.jp
Word count: 5,836 words text + 5 tables/figures x 250 words (each) = 7,086 words

Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

2

ABSTRACT
Vehicle type purchase intentions are interesting from a marketing point of view, as car
manufacturers are competing to attract potential buyers. Furthermore, from the point of view oftransport planners as well as the government there are environmental concerns. This paper focuses
on students’ intention for purchasing different types of vehicles. We obtain a sample of 1,229
students from seven countries in order to understand also cultural differences in preferences. Ourdependent variables are intention to buy different types of car categories, including hybrid andelectric cars. We aim to explain intentions with three categories of explanatory variables:
psychological aspects, socio-demographics and site specific dummy variables. To account forcorrelation between our dependent variables, we use seemingly unrelated regression (SUR). We
find that attitudinal factors are significant, particularly for the intention to buy moreenvironmentally friendly types of vehicles and discuss policy implications. We observe
specifically the importance of the symbolic-affective value of a car to explain car-type preferences
as well as the importance of awareness to negative effects cars might have on the environment andsociety. We suggest these findings can be used to promote more environmentally friendly cars.
Keywords: car purchase intention, vehicle type choice, seemingly unrelated regression, alternativefuel vehicle

Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

3

1. INTRODUCTION
Even customers with a limited budget have nowadays the choice among a large range of vehicles.
In Japan, for example, so-called small box cars, though arguably not very fashionable from anaesthetic point of view, have become more popular due to the space they offer and since they canbe bought as “light car” (in Japanese: kei-jidosha) with reduced tax. Furthermore, hybrid cars havebecome common in several countries. Also electric cars are slowly becoming an alternative option.
At the same time fuel-consuming large SUVs (equipped for journeys that they might never make)remain to be seen driven in city centers.
This choice range motivates this paper. Car manufacturers are obviously not necessarily
interested in selling the most environmentally friendly cars but instead rather want to maximize
profit which shifts the responsibility back to policy makers and fuel, tax schemes as well as otherregulations. The discussion on problems created by excessive car usage does not require much
expansion in this article. Air and noise pollution are obviously worsened by vehicles with larger
engines. SUVs further are seen as a safety risk in areas with many pedestrians, especially forchildren. These are all reasons for transport planners to aim to influence a population to using
smaller and more environmentally friendly cars and to promote less-polluting alternative fuelvehicles (AFV).
To understand the future car market, we aim to understand specifically the car purchaseintentions of younger students, including those from (rapidly) developing countries, which are an
increasingly important market. Studies from Indonesia (1) or China (2) all show that young people
there have nowadays strong intentions to enter the car market as first time buyers. This is partly incontrast with the peak car trend in developed countries (3; 4).
We suggest that our analysis has several implications leading to policy suggestions.
Suppose that many students in developing countries become keen to buy hybrid/electric cars, we
can then possibly reduce some air pollution effects resulting from this growing car ownership.
Moreover, understanding the vehicle type desire tells about the status of the car, e.g. a desire for
sports car might indicate that cars are seen as “fun”, whereas choice of luxury type vehicle might
indicate that the status symbol aspect of the car is important. Therefore, we suggest that analyzing
car type desire is also an important index for vehicle desire in general. Finally, the intention topurchase hybrid and electric vehicles can be seen as a reflection of environmental attitudes ingeneral. All these reasons motivate us to study vehicle type desire not for marketing but fortransport policy purposes.
Given this motivation, the structure of the reminder of this paper is as follows: Section 2
discusses previous research on vehicle type preferences and modelling. Section 3 describes how
we collected the data. In Section 4 we explore the factors that potentially determine car ownershipand provide descriptive statistics. In Section 5, we then develop our regression approach that aims
to take into account correlation in our dependent variables. Finally, in Section 6 we discuss our
findings and conclude by deriving implications for transportation planning.
2. LITERATURE REVIEW
Previous literature on factors explaining vehicle type choice can be firstly categorized into whether
an aggregate or disaggregate modeling approach is used. Aggregate models use an accumulationof household decisions at different geographic scales as dependent variable to explain car
ownership whereas disaggregate models use individual or households as the basic unit of analysis.
A first attempt to study household car type intention, a logit model with car characteristics
and socio-economic variables as explanatory variables, is performed by Lave and Train (5). They
distinguish 10 categories: subsubcompact, subcompact-A, subcompact-B, compact-A, compact-B,

Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

4

intermediate, standard-A, standard-B, and sports and luxury cars. The main findings are that larger
households are more likely choosing subcompact cars. The authors further fit their model byinteracting vehicle attributes with socio-economic characteristics. Following Lave and Train (5),there are several studies that use similar dependent and explanatory variables such as Manski and
Sherman (6), Hocherman et al. (7), Berkovec and Rust (8), Mannering and Winston (9),Brownstone et al. (10), and Mannering et al. (11).
Differently from the above studies, Choo and Mokhtarian (12) explore the role of attitudes,
personality, lifestyle and mobility factors as explanatory variables for vehicle choice decisions
along with demographic factors using logit models. Vehicle types are grouped into nine categories
based on size and functionality. The data used in this study is from a 1998 mail-out/mail-backsurvey of 1,904 residents in the San Francisco Bay Area. They conclude that models of vehicle
type choice can be substantially more powerful with the inclusion of travel attitudes, personality,
lifestyle, and mobility factors.
While previous studies discuss about car types mainly based on size, recent literature
instead focuses more on customer willingness to purchase alternative fuel vehicles (AFV). One
example is the study by Ziegler (13) which is based on data from 598 potential car buyers inGermany. Ziegler distinguished 7 types of cars (gasoline, diesel, hybrid, gas, biofuel, hydrogen,and electric). The study aims to explain preferences for these types by considering purchase price,
motor power, fuel costs per 100 km, CO2 emissions, and service station availability. The main
findings are that hydrogen vehicles are more likely purchased among men, young people, andenvironmentally aware people.
Partly in line with the aforementioned study by Choo and Mokhtarian (12) and following
the Theory of Planned Behavior (14; 15) as well as Thøgersen’s (16) extended norm taxonomy,
attitudes, norms (subjective norms, integrated norms, introjected norms), and perceived behavioral
control (PBC) can also be incorporated in vehicle type choice decision modeling. An example ofthis is the study in a Norwegian context by Nayum et al. (17). They study the choice among diesel
cars, five-door estate car with standard petrol engine, station wagon with diesel engine and SUVs
with psychological factors as independent variables. They conclude that psychological factors suchas social norms, integrated norms, attitudes and PBC explained 63.2% of the variance in car typechoice intention.
There are also two studies about car type choices focusing on AFV using Dutch data. Kim
et al. (18) incorporate social attributes (social influence variables) besides other psychologicalfactors and vehicle characteristics aspects to model the decision to purchase electric cars (EC) or
non-electric cars. They conclude that people strongly consider opinions about EC in their social
network for purchase intentions. Hoen and Koetse (19) study again preferences among various
types of AFV. The respondents are 1,903 car owners of both new and secondhand cars (petrol,
diesel or LPG). These car owners were asked whether they were planning to replace their current
car with a new or secondhand car in the next 3-4 years. These respondents were given eight choicetasks where in each choice task there were three options of cars from the following eight types ofcars, three types for current technology (petrol; diesel; LPG) and five AFVs (hybrid; plug-in
hybrid; fuel-cell; electric; flexifuel). Their results show that whenever current technology cars
showed up in the choice task, these were preferred over AFVs.
Finally, Tanaka et al. (20) compare respondents’ willingness to pay for AFV in the US andJapan. They investigate the choice among nine types of vehicles size and 3 motor types (electric;
hybrid electric; gasoline). They investigate the choice based on scenarios regarding AFV prices
and factors such as fuel costs and charging station availability. They found that US respondents
are more sensitive to fuel cost reductions and station availability than Japanese respondents. In a

Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

5

base case scenario, conventional gasoline vehicles still dominate both in the US and Japan. In an
innovation scenario, there is a high penetration of alternative fuel vehicles both in the US andJapan.
In conclusion, while there are many studies discussing several types of cars including types
of fuel (fossil fuel and AFV) and factors influencing the decision to buy that particular type of car,none we have seen so far asked respondents to scale their intention to buy different types of cars.
Intention to buy a different type of car might not be mutually exclusive or exhaustive; therefore,
in this paper we asked our respondents to rate their intention to buy seven different types of carssuch as sports cars, luxury cars, and electric cars. Since some car types share some attributes,
correlation to answers are likely and seemlingly unrelated regression (SUR) appears to beappropriate.
SUR is a modelling method that estimates two or more regressions where the error term
between regressions are correlated for a given individual but are uncorrelated across individuals(21). It is a collection of two or more regression relations that can be analyzed separately while
each has its own dependent variable and potentially different sets of exogenous explanatory
variables. In transportation research SUR has been used to e.g. examine the effects of fuel priceon people’s travel time expenditure for different kinds of activities (22).
3. DATA AND DESCRIPTIVE ANALYSIS
3.1. Respondents
Our sample is the same as the one studied in Belgiawan et al. (23) where we investigate carpurchase desire per se. All respondents are undergraduate students from a wide variety of
disciplines. The data were collected between January-June 2013 in seven different countries. The
sites are chosen to cover a wide range of countries (and partly due to previously establishedresearch connections).
Four of the sites are from Asia. Indonesia is included as a fast developing country with
rapidly increasing motorization among younger people. Taiwan is chosen as a more developed
Asian country in which currently the motorbike is the dominating mode among younger people.
China, particularly Shanghai, is included as a city where the desire to own a car has lately been
rapidly increasing especially among younger people (2). Japan is included as a more developed
country in which car ownership has been increasing until lately. Beirut, Lebanon, a city in which
the car is the dominating mode among all generations is further included. As examples from
“Western 1st world countries,” this paper includes Utrecht, The Netherlands and Berkeley, U.S.A.,
two cities with very different mobility patterns and spatial organization.
In all countries, the survey was translated into the local language with the exception of
Lebanon where the survey was conducted in English, which is the language of instruction at theAmerican University of Beirut (AUB). All responses were gathered via a web-based survey,
although the methods to recruit respondents differed in each country. In Indonesia, surveying
agencies recruited respondents in person on the campuses of the University of Indonesia in Jakarta
and the Bandung Institute of Technology. In Japan, the recruitment was via emails sent toengineering departments in several universities.
In China, the recruitment was via email and through an internet forum in Shanghai with asmall incentive in the form of a mobile phone voucher for those who complete the survey. Since
most of the respondents come from outside Shanghai, so in the subsequent analysis we use China
instead. In Berkeley, recruitment was handled by the Experimental Social Science laboratory, andeach respondent received a financial incentive for participating. In Beirut, the recruitment wasBelgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

26
27
28
29
30
31
32
33
34
35
36

6

done via emails sent to approximately one third of AUB students (chosen randomly). In Utrecht,
recruitment was done via an announcement in a general student newsletter. In Taiwan, recruitment
was done via an announcement in a popular Bulletin Board System (Ptt.cc). No financial incentives
were used other than in Shanghai and Berkeley.
In total 2,272 undergraduate and graduate students accessed the survey website, of which
1,806 completed the survey. For better cross-site comparability, in this paper only the data fromthe undergraduate students are reported. Further data cleaning is performed, ignoring incomplete
surveys and responses that were completed in fewer than eight minutes, which seems a lower limit
to answer all of the survey questions in a serious manner. This results in a sample size of 1,229
used for the analysis below. For more detail about the surveying methods and contexts of our
specific sites, we refer the reader to Belgiawan et al. (23).
3.2.Dependent Variables
We firstly ask respondents how likely they are in general to purchase a car. We then measure ourdependent variables of main interest by asking students this question “Now realistically, andassuming you will buy a car in the future, please rate how likely you are to buy following type ofcar for your next car.” The response rate is on a 7-point Likert scale (very unlikely – very likely)
and the type of cars is as follows: Small, Mid-size, Large, Luxury, Sports, Hybrid and Electric.
The descriptive statistics of our dependent variables can be seen in Table 1. The mean value
for general car purchase intention for all samples is higher than the intention to buy specific typesof cars which appears to be reasonable. However, if we look at specific sites we can see a few
exceptions. For example, respondents answer in Taiwan that they are more likely to purchase asmall car then a car in general.
TABLE 1 Dependent Variables Mean Value
Variables(number of observations)
Car purchase intention (general)

All (μ (σ))(1229)
5.15 (1.62)

Utr
(84)
4.55

Jpn
(142)
4.62

Brkly
(226)
4.92

Twn
(139)
4.99

Idn
(200)
5.24

Chn
(167)
5.42

Brt
(271)
5.65

Small Car

4.58 (1.87)

4.77

4.32

4.49

5.17

4.96

5.48

3.58

Mid-Size Car

5.06 (1.39)

4.98

5.20

5.17

4.91

5.54

4.53

4.96

Large Car

3.52 (1.71)

3.23

3.46

2.94

3.25

5.15

2.71

3.58

Luxury Car

3.65 (1.84)

2.87

3.46

3.14

3.17

4.78

3.16

4.14

Sports Car

3.59 (1.94)

2.38

2.87

3.06

3.20

4.67

3.17

4.46

Hybrid Car

4.52 (1.62)

4.08

4.74

4.67

4.81

5.02

4.24

4.06

Electric Car

4.19 (1.68)

3.94

4.39

4.09

4.05

4.83

4.15

3.86

For small cars, the mean value for China and Taiwan is quite high compared to other sites,
while for Mid-size car the mean values observed for Indonesia, Japan and Berkeley are quite high.
Interestingly the average for large car is quite small in most countries except for Indonesia. Luxury
and sports cars are less desired among students from developed countries which is in line withresearch showing the changing image of cars and arguments that the peak car discussion can bepartially explained with the car losing some of its status symbol value. Instead, the mean values ofhybrid and electric cars are quite high compared to luxury and sports cars.
In order to control for differences in the overall desire to obtain a car in the subsequent
analysis, we are not using the mean value as in Table 1 but instead the relative value of car intention.
The relative value is calculated by subtracting from each intention variable the average of theBelgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5

7

intention for the seven types of cars as denoted in Eq. 1 where 𝑦𝑖𝑗 denotes the intention of person
i to purchase a vehicle of type j. A positive (negative) value for 𝑦̃𝑖𝑗 denotes hence that the person
is more (less) likely to purchase a specific car type than his/her average desire to purchase any of
the seven types.

61~
yij  yij   j 17 yij
7

7
8
9

The results are depicted in Figure 1. We observe country specific differences now even clearer.

10
11
12
13
14
15
16
17
18
19
20
21
22
23

(1)

FIGURE 1 Car type desire relative mean value.
In Utrecht there is a big gap in intention to buy large, luxury and sports car with sports car
being the least favorite. Similarly in Japan sports car is the least favorable and there is a big gap
with luxury and large car. In Berkeley, China, and Beirut, large car is the least favorite though inBerkeley the gap between large car and the other less favorite car is not quite big. In Taiwan theleast favorite choice is luxury car but there seems to be no big gap between the three least favorite
options. In Indonesia the least favorite car is sports car but large car is more favorite car which isquite different from the other sites. Interestingly in all developing countries, except for China,
electric cars are less favorable possibly because of the lack of (trust in) charging infrastructure;that is charging stations as well as supply of energy in some areas.
If we look at more favorable car, in most of the sites, the most favorite car is mid-size car,with few exceptions in Taiwan and China who favor small car. In Utrecht two of the most favorite

Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22

8

cars are mid-size and small, but hybrid and electric are also more favored. In Japan and Berkeley,
we see a similar pattern in that mid-size and hybrid are the most favorite. Beirut is the only sitewhere sports car is the most favorite after mid-size.
In general, for almost all countries, the intention to buy large, luxury and sports car are
below the relative mean value, while especially small and mid-size vehicles have positive values.
Interestingly hybrid vehicles get generally positive values, showing their wider acceptance. In
developed countries further desire for electric cars is similar to that for convential motor types. In
contrast to (19) this possibly suggests that their acceptance is increasing especially among young
people. In Section 4 we will try to explain these different preferences.
3.3. Determinants of Vehicle Choice Intention
Attitudes towards Cars
The students are given a range of questions regarding their attitudes/perceptions toward cars andpublic transportation (PT). Each question is posed on a seven-point Likert scale with verballydefined endpoints (strongly disagree – strongly agree). The questions are based on surveys used inprevious research in particular the studies by Steg (24), Van and Fujii (25), and Belgiawan et al.
(1).
There are 20 attitudinal variables toward cars obtained from the survey. To understandbetter “emotional attachment” of students to cars, the attitudinal questions are analysed andgrouped with PCA. The results of this PCA can be seen in Table 2.
TABLE 2 Rotated Factor Loadings on Attitudes Toward Cars
Variables

Symbolic
Affective

Independence

Negative
Aspects

Social/env.
Care

Cars allow to distinguish oneself from others(22.3%)a
0.78 b

(16.1%)
0.01(9.9%)
0.19(9.4%)
0.08

Cars are trendy

0.75

0.14

0.00

-0.09

Cars bring prestige

0.74

0.00

0.22

0.08

Cars are cool

0.70

0.18

-0.06

0.22

Cars allow to express oneself

0.70

-0.02

-0.02

0.06

Cars are fun to have

0.66

0.27

-0.14

0.07

Cars are convenient

0.05

0.80

-0.09

0.12

Cars give freedom to travel anytime

0.05

0.79

-0.11

0.02

Cars help one to save time when making a trip

0.08

0.75

-0.05

-0.01

Cars are useful to pick up or drop off others

0.22

0.54

0.17

0.05

Cars are expensive to own and maintain

-0.13

0.29

0.71

0.09

Cars are disturbing one's neighborhood

0.03

-0.16

0.67

-0.15

Cars are giving an arrogant impression

0.33

-0.26

0.60

-0.10

Cars allow one to travel safely

0.14

0.09

0.00

0.81

Cars are environmentally friendly

0.08

0.02

-0.12

0.78

a

Numbers inside brackets are total variance explained by each factor.
Bold means that the variable in the row is grouped to the respective column factor

b

23
24
25
26

Based on varimax rotation, four factors with eigen values larger than one could be
extracted. These factors are consistent with findings in the literature: Symbolic Affective(explaining 22.3% of the variance) (1; 24; 25), Independence (16.1% of the variance) (1; 24),

Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

9

Negative Aspects of car (9.9% of the variance) (2), and Social/Env. Care (9.4% of the variance).
Finally, the mean value of each attitudinal factor can be seen in Table 3.
Expectation of others
To measure the person group specific expectations of others to buy a car (em), the respondents wereasked “To what extent does each of the following groups m {1. Your parents, 2. Your partner, 3.
Your family members and relatives, 4. Your close friends, 5. Your classmates, friends and peers atuniversity, 6. People in your neighborhood and 7. People in your province/state} expect you to buy
a car within the next 10 years?” Responses to this group of questions were measured on a 7-point
Likert scale ranging from -3 (they strongly expect me not to buy a car) to 0 (they have no
expectation) as middle point and +3 (they strongly expect me to buy a car) as the other end point.
Then, responses to each scale are summed up as shown in Eq. 2. This is similar to the bipolarmeasurement of normative belief in TPB (14). The descriptive statistics of total expectation ( ê )for each country can be seen in Table 3.
716eˆ   em

(2)

m 1

17
18
19
20
21
22
23

24
25
26
27

Other explanatory variables
The students are asked about their average income, age, gender, as well as their current living
situation: whether they live by themselves, with their family or friends or whether they live in adormitory or apartment1.
TABLE 3 Explanatory Variables Mean Value
Variables(number of observations)Symbolic Affective

All (μ (σ))(1229)
4.41 (1.43)

Utr
(84)
3.71

Jpn
(142)
3.73

Brkly
(226)
4.66

Twn
(139)
4.49

Idn
(200)
4.70

Chn
(167)
4.29

Brt
(271)
4.52

Independent

5.77 (1.17)

5.92

5.83

6.16

5.63

5.03

5.86

5.86

Negative Aspect

4.23 (1.32)

4.32

4.41

4.07

4.37

4.27

4.14

4.49

Social Environmental Care

3.92 (1.39)

3.71

4.12

3.80

4.61

4.24

4.03

3.41

Total Expectation (𝑒̂ )4.71 (7.42)

-3.93

5.58

5.48

5.22

4.70

4.60

6.11

Regular user car owner

0.46 (0.50)

0.21

0.24

0.31

0.39

0.57

0.26

0.86

Regular user non car owner

0.07 (0.25)

0.06

0.02

0.17

0.02

0.08

0.02

0.04

Gender split (% male)

0.57 (0.50)

0.55

0.77

0.50

0.73

0.52

0.60

0.45

-3

Av. personal income (10 US$)

0.52 (0.61)

0.70

0.36

0.62

0.56

0.32

0.40

0.97

Missing Income Dummy

0.12 (0.33)

0.00

0.11

0.07

0.03

0.33

0.03

0.16

The income categories are adjusted according to the typical student income in therespective countries. We added a “missing income dummy” for those not providing income
information. We also control for car usage. We define regular car user as using car at least twice a1Note that we also asked students about their current type of car. However, since there are too many missing
observations, we omit that variable from our analysis. We also have family/household income; since that variable
significantly correlates with personal income, we decided to omit it to minimize multicollinearity.

Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

29

30
31
32
33
34
35
36
37
38
39
40
41
42

10

week and distinguishing them by the ownership of car. To distinguish among the seven sites, we
add site-specific dummy variables into the model with Berkeley as reference variable.
Table 3 also shows the descriptive statistics of the socio-demographic aspects regarding
car usage. The average age of students in the sample is between 19-21 and 84% of our respondents
grew up with car. That means even though their age is very close to the minimum age to drive acar, for most of the respondents it is familiar experience to have access to cars. In terms of gender,
the Japanese, Taiwanese, and Chinese samples are unbalanced, which is a result of the recruitment
methods. Monthly income is specified as a continuous variable. Each category of respective
country is transformed into US$ by using the purchasing power parity conversion factor publishedby the World Bank (26). Average personal monthly income is, as expected, lowest among
Indonesian students while the students with the highest income are those from Beirut followed by
Utrecht and Berkeley students. Finally, regarding car usage, car owners are more dominant thannon-car owners especially in Indonesia and Beirut.
4. CAR TYPE INTENTION
In this section we describe our modelling method. The SUR method that we present here is anadjustment of the Zellner (21) method. Detailed discussion on SUR can be found in Srivastava andGiles (27). Our SUR model is a system of 7 equations. It consists of 𝑗=1,…,7 linear regression
equations for 𝑖=1,…,N individuals. The 𝑗th equation for individual 𝑖 is:𝑦𝑖𝑗 = 𝑿𝑇 𝑖𝑗 𝜷𝑗 + 𝜀𝑖𝑗 , 𝑗 = 1,2, … ,7(3)

where 𝑦𝑖𝑗 is the buying intention for car type 𝑗 for individual 𝑖, 𝑿𝑖𝑗 is a vector of the K explanatory(exogenous) variables for individual 𝑖, 𝜷𝑗 is the parameter vector of size K for each car type j and𝜀𝑖𝑗 is an error term of the jth equation for individual 𝑖. Note that in our case all explanatory variables
are identical for the seven car types so that in fact 𝑋𝑖𝑗𝑘 reduces to 𝑋𝑖𝑘 (for a variable k). Summingup over all individuals i the system can also be written as:
 y1   X 1
y   0 2       y7   0

0
X2

0

 0  β 1    1 
 0  β 2   2            X 7  β 7   7 (4)

We assume that there is no correlation of error terms across individuals as in Eq. 5 but thatfor a given individual the errors are correlated across equations as in Eq. 6. Furthermore, we
assume that for each individual the mean error terms for all car types are zero as in Eq. 7.
𝐸(𝜀𝑖𝑗 𝜀𝑖 ′ 𝑗 |𝑿) = 0 𝑤ℎ𝑒𝑟𝑒 𝑖 ≠ 𝑖 ′(5)𝐸(𝜀𝑖𝑗 𝜀𝑖𝑗′ |𝑿) = 𝜎𝑗𝑗′ , 𝑎𝑛𝑑 𝜎𝑗𝑗′ ≠ 0 𝑤ℎ𝑒𝑟𝑒 𝑗 ≠ 𝑗 ′(6)𝐸(𝜀𝑖𝑗 |𝑿) = 0(7)

Thus, the covariance matrix 𝛀 of all error terms is:Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46

𝐸(𝜀𝜀 ′ ) = 𝛀 = 𝚺 ⊗ 𝑰𝑁

11(8)

where 𝚺 is the error covariance matrix with elements 𝜎𝑗𝑗′ , ⊗ is the Kronecker product, 𝑰𝑁 is an
identity matrix of dimension N where N represents the number of observations in each of the sevenequations. The optimal estimation of SUR is by using generalized least squares (GLS) estimation(28). The SUR estimators and their covariance can be obtained by Eqs. 9 and 10.
̂ −𝟏 𝑿)−1 𝑿𝛀̂ −𝟏 𝑦𝛽̂ = (𝑿𝑇 𝛀(9)̂ [𝛽̂ ] = (𝑿𝑇 𝛀̂ −𝟏 𝑿)−1
𝐶𝑂𝑉

(10)

5. MODEL ESTIMATION
The models were estimated using system fit packages in R (28). In Table 4, we present thecorrelation of error terms between equations. We can see that those who have a preference forsmall cars are a distinctive group, with negative preference for all other car types, except midsize
cars, being positive. As for luxury and sports cars, we can see a positive correlation, possibly
indicating a group of people considering car as status symbol. Another cluster are those withpreference for AFV; these are likely to be environmentally conscious students confirming thefindings of Ziegler (13). They are especially opposed to luxury and sports cars. The highcorrelations between equations confirm that the SUR is needed instead of normal separate ordinary
least square equation.
The lower part of Table 4 shows the parameter estimates. The symbolic affective factor isnot significant for small, midsize and large cars indicating that status symbol is not a reason to buy
those types of car. In contrast, and in line with our expectations, the symbolic affective meaningof cars appears to be a reason to purchase luxury and sports cars with the expected sign.
Interestingly students who regard car as a status symbol are less likely willing to buy hybrid andelectric cars. Independence is positively significant for small (10% level) and mid-size cars
implying that these cars are mainly desired for the convenience value. We further find a negative
correlation with the desire to purchase electric vehicles, indicating possibly that the charging
process and limited distance of electric vehicles is perceived an obstacle for those valuing
convenience and independence. For all other types of cars, independence is not found to besignificant.
Students who are aware of the negative aspects of cars less likely intend to buy luxury andsports car; however, they are more likely to have an intention to buy AFV. Similarly, students who
do not perceive the social and environmental impacts of cars as important more likely intend tobuy hybrid and electric cars. Note that “Social/env. care” is measured on a reversed scale, whichmeans that those intending to buy small cars or electric cars perceive cars as not environmentallyfriendly). And those who do not consider the social and environmental aspects are more likely to
buy luxury and sports cars.
“Total expectation (social norm)” has a negative and significant (at 10% level) coefficient
only for large car indicating that the more students expect that their peers want them to buy a car
the less likely they want to buy this type of car and the opposite for sports car. In general, it appears
that perceived expectations of others cannot explain students’ intentions to buy specific types ofcar. These results are different from what we found for the case of social norms for car desire in
general (23,29). We emphasize though that the question was posed as expectation of others to buy

Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4

12

a car in general and not to buy a specific type. In further research one might ask students for
specifically trendy cars among their peers.
TABLE 4 Generalized least square model estimation of car type
Variable

Small

MidSize

Large

LuxurySportsHybridElectric

Correlation of error terms between equations
Small

0.09

Midsize

-0.35

-0.38

-0.37

-0.20

-0.11

-0.02

-0.31

-0.30

-0.24

-0.27

0.08

-0.07

-0.28

-0.30

0.35

-0.31

-0.35

-0.26

-0.31

Large
LuxurySportsHybrid0.46
Parameter estimates
1.10

1.70

-1.10

-1.23

-1.45

0.83

0.15

Symbolic affective

-0.08

0.02

0.00

0.28

0.22

-0.23

-0.20

Independent

0.10

0.11

0.00

-0.04

-0.08

0.01

-0.10

Negative aspects

-0.01

-0.04

0.02

-0.08

-0.11

0.09

0.14

Social/env. care (reverse scale)

-0.15

-0.02

0.04

0.17

0.12

-0.03

-0.12

Total expectation (social norms)

-0.02

-0.02

-0.11

0.07

0.12

0.01

-0.04

Regular car user and owner
Regular car user and non-car
ownerGender (Male)

-0.08

-0.22

0.02

0.36

0.11

-0.18

0.00

-0.52

-0.25

0.25

0.45

0.25

-0.16

-0.02

-0.45

-0.12

0.17

-0.03

0.46

-0.01

-0.01

Income

-0.38

-0.28

0.14

0.30

0.23

0.02

-0.02

Income dummy

-0.22

-0.09

0.00

0.39

0.35

-0.28

-0.16

Utrecht

0.32

-0.21

0.35

0.31

-0.12

-0.59

-0.07

Japan

-0.32

-0.27

0.36

0.56

-0.14

-0.29

0.09

Berkeley

Ref.

Ref.

Ref.

Ref.

Ref.

Ref.

Ref.

Taiwan

0.71

-0.47

0.09

-0.09

-0.05

-0.07

-0.13

Indonesia

-0.50

-0.76

1.16

0.37

0.39

-0.48

-0.18

China

0.93

-0.82

-0.23

0.25

0.27

-0.51

0.11

Beirut

-1.05

-0.34

0.47

0.66

1.23

-0.64

-0.33

Sample Size

1,229

1,229

1,229

1,229

1,229

1,229

1,229

Multiple R-squared

0.17

0.05

0.10

0.17

0.21

0.11

0.08

Adjusted R-squared

0.16

0.04

0.09

0.16

0.20

0.10

0.07

Constant
Psychological aspects

Other explanatory variables

Location specific

Model Fit

Bold = p value <0.05; Italic p value<0.10

5Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

13

Students who regularly use car and own it, are more likely to have an intention to buy
luxury cars and less likely to buy midsize or hybrid cars. One might explain this with theconvenience of luxury cars. As for regular car users who do not own a car, they are more likely to
buy luxury cars and less likely want to buy small cars. Men are less likely to buy small cars, butmore likely to buy large cars and sports cars. Gender does not appear to have a significant effecton preferences for AFV and similarly for income. For income we observe further the expected
effected that those with higher income are more likely to purchase larger cars.
Considering the location specific dummy variables, we find that respondents from Taiwan
and China are more likely to purchase small cars compared to our reference location, Berkeley,
US. Instead, still compared to Berkeley, Indonesian and Beirut students are less likely to buy small
cars. For Berkeley students mid-size cars appear to be more popular than for students from othercountries. One explanation for these observations might be that in Taiwan and China smaller cars
are seen as the standard option whereas in Berkeley it is mid-size cars. Indonesian students stickout in our sample for their relative preference for large cars. Most different from other samples
appear to be the Beirut students though. In Beirut we observe a relatively strong preference forluxury cars as well as for sports cars and large cars. This indicates that in Beirut the role andsymbolic meaning of the car is (still) very different compared to other countries.
Regarding AFV car, interestingly it appears that Berkeley students are more likely to buy
hybrid cars compared to students from other sites. One might argue that this is because US students
might be more used to this technology though this argumentation does not apply to students fromJapan. This result supports findings reported in (20) though, who found that 78% of their
respondents from California (including Berkeley) have interest to change to AFV compared toJapanese respondents where 70% of respondents have an interest in AFV. For electric cars we do
not have significant country differences except for Beirut. This is possibly because students do nothave yet a strong (negative or positive) impression about electric cars.
Considering the model fit, we can explain desire for sports, luxury and small cars most,possibly because they have some clear charateristics. With some reasoning based on the above
discussion, it is not surprising that we observe the lowest model fit for mid-size and electric cars.
6. CONCLUSION
This paper focused on students’ intention for purchasing different types of vehicles. We notice that
this age group might have a tendency to change the first few cars pretty quickly. However, somestudies have found that habits developed during adolescence have significant impact on lifelong
lifestyle of individuals including influencing habits for mobility decision (30; 31; 32).
Explaining preferences will help to promote more environmentally friendly vehicles. In theliterature we find that there is no agreement on which vehicle types should be distinguished. In
this paper we distinguish five vehicle types of normal fuel i.e: small, midsize, large, luxury, andsports; and two types of alternative fuel vehicle such as hybrid and electric. We asked our
respondents to rate their intention to buy each of these seven vehicle types and employ SUR.
From our seven correlated models, we learn that psychological aspects are significant fordifferent types of cars. Symbolic affective has a positive significant effect for expensive types ofcar but a negative significant effect for more environmentally friendly cars. This result indicatesthat cars are still perceived as status symbol by some. However, when students are aware ofenvironmentally socially negative impacts of cars, they prefer to buy more environmentally AFVs.
This is an important finding to promote more environmentally friendly cars as an alternative togasoline car. By some campaign of promoting more environmental concerns, we might be able tostrengthen attitudes towards more environmentally friendly cars. In summary, the signs of theBelgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

14

coefficients of all attitudinal variables are in line with our expectations. We observe the importanceof the symbolic-affective value of a car to explain preferences as well as the importance ofawareness to negative effects the car might have on the environment and society.
Finally, as further direction of work, we emphasise that the attitudinal variables used in thispaper are not specific to the particular typs of cars; instead we asked students consciously about
attitudes towards cars in general. We did this consciously to explain how general attitudes will
influence car type preferences. However, one might also argue that there will be differences in
attitudes towards different car types, in particular fossil fuel cars and AFVs. Further studies might
hence consider asking respondents about their attitudes, and possibly perceived social norms,
towards particular types of car. Such a study will be important specifically for understanding future
market shares, whereas our study here aimed more at generally understanding factors that
influence car type desire.
ACKNOWLEDGEMENTS
The authors would like to thank Joan Walker (UC Berkeley), Dick Ettema (Utrecht University,
Netherlands), Tzu Chang Lee (National Cheng Kung University, Taiwan) and Dong Zhang (Tongji
University, China) for their help during the data collection process. The first two authors further
would like to thank Hideki Tanaka (Aomori Public University, Japan) and Phillippe Byosiere(Doshisha University, Japan) and Junghwa Kim (Kyoto University) for discussions on the topic.
The research is financially supported by JSPS project Kaken 90567801 (category “Kiban C”).
REFERENCES
1. Belgiawan, P. F., J.-D. Schmöcker, and S. Fujii. Understanding car ownership motivations
among Indonesian students. International Journal of Sustainable Transportation, Vol. 10, No.
4, 2016, pp. 295–307.
2. Zhu, C., Y. Zhu, R. Lu, R. He, and Z. Xia. Perceptions and aspirations for car ownershipamong Chinese students attending two universities in the Yangtze Delta, China. Journal ofTransport Geography, Vol. 24, 2012, pp. 315–323.
3. Kuhnimhof, T., D. Zumkeller, and B. Chlond. Who made peak car, and how? A breakdownof trends over four decades in four countries. Transport Reviews: A Transnational
Transdisciplinary Journal, Vol. 33, No. 3, 2013, pp. 325–342.
4. Van Der Waard, J., P. Jorritsma, and B. Immers. New drivers in mobility; what moves the
Dutch in 2012? Transport Reviews: A Transnational Transdisciplinary Journal, Vol. 33, No.
3, 2013, pp.345–359.
5. Lave, C. A., and K. Train. A disaggregate model of auto-type choice. Transportation ResearchPart A, Vol. 13, No. 1, 1979, pp.1–9.
6. Manski, C. F., and L. Sherman. An empirical analysis of household choice among motor
vehicles. Transportation Research Part A, Vol. 14, No. 5-6, 1980, pp. 349–366.
7. Hocherman, I., J. N. Prashker, and M. Ben-Akiva. Estimation and use of dynamic transaction
models of automobile ownership. In Transportation Research Record: Journal of theTransportation Research Board, No. 944, Transportation Research Board of the National
Academies, Washington, D.C., 1983, pp. 134–141.
8. Berkovec, J., and J. Rust. A nested logit model of automobile holdings for one vehicle
households. Transportation Research Part B, Vol. 19, No. 4, 1985, pp. 275–285.
9. Mannering, F., and C. Winston. A dynamic empirical analysis of household vehicle ownershipand utilization. The RAND Journal of Economics, Vol. 16, No. 2, 1985, pp. 215–236.
10. Brownstone, D., D. S. Bunch, and K. Train. Joint mixed logit models of stated and revealedBelgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47

15

preferences for alternative-fuel vehicles. Transportation Research Part B, Vol. 34, No. 5,
2000, pp. 315–338.
11. Mannering, F., C. Winston, and W. Starkey. An exploratory analysis of automobile leasing by
US households. Journal of Urban Economics, Vol. 52, No. 1, 2002, pp. 154–176.
12. Choo, S., and P. L. Mokhtarian. What type of vehicle do people drive? The role of attitude
and lifestyle in influencing vehicle type choice. Transportation Research Part A, Vol. 38, No.
3, 2004, pp. 201–222.
13. Ziegler, A. Individual characteristics and stated preferences for alternative energy sources
and propulsion technologies in vehicles: A discrete choice analysis for Germany.
Transportation Research Part A, Vol. 46, No. 8, 2012, pp. 1372–1385.
14. Ajzen, I. The theory of planned behavior. Organizational Behavior and Human Decision
Processes, Vol. 50, No. 2, 1991, pp. 179–211.
15. Ajzen, I. The Theory of Planned Behavior. In Handbook of Theories of Social Psychology (P.
A. Van Lange, A. W. Kruglanski, and E. T. Higgins, eds.), SAGE Publication Inc, Thousand
Oaks, California. Vol. 1, 2012, pp. 438–459.
16. Thøgersen, J. Norms for environmentally responsible behaviour: An extended taxonomy.
Journal of Environmental Psychology, Vol. 26, No. 4, 2006, pp. 247–261.
17. Nayum, A., C. A. Klöckner, and S. Prugsamatz. Influences of car type class and carbon
dioxide emission levels on purchases of new cars: A retrospective analysis of car purchases in
Norway. Transportation Research Part A, Vol. 48, 2013, pp. 96–108.
18. Kim, J., S. Rasouli, and H. Timmermans. Expanding scope of hybrid choice models allowing
for mixture of social influences and latent attitudes: Application to intended purchase ofelectric cars. Transportation Research Part A, Vol. 69, 2014, pp. 71–85.
19. Hoen, A., and M.J. Koetse. A choice experiment on alternative fuel vehicle preferences ofprivate car owners in the Netherlands. Transportation Research Part A, Vol. 61, 2014, pp.
199–215.
20. Tanaka, M., T. Ida, K. Murakami, and L. Friedman. Consumers’ willingness to pay foralternative fuel vehicles: A comparative discrete choice analysis between the US and Japan.
Transportation Research Part A, Vol. 70, 2014, pp. 194–209.
21. Zellner, A. An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests
for Aggregation Bias. Journal of the American Statistical Association, Vol. 57, No. 298, 1962,
pp. 348–368.
22. Yang, D., and H. Timmermans. Analysis of influence of fuel price on individual activity-travel
time expenditure. Transport Policy, Vol. 30, 2013, pp. 40–55.
23. Belgiawan, P. F., J.-D. Schmöcker, M. Abou-Zeid, J. Walker, T.-C. Lee, D.F. Ettema and S.
Fujii. Car ownership motivations among undergraduate students in China, Indonesia, Japan,
Lebanon, Netherlands, Taiwan, and USA. Transportation, Vol. 41, No. 6, 2014, pp. 1227–
1244.
24. Steg, L. Car use: lust and must. Instrumental, symbolic and affective motives for car use.
Transportation Research Part A, Vol. 39, No. 2-3, 2005, pp. 147–162.
25. Van, H. T., & S. Fujii. A cross Asian country analysis in attitudes toward car and public
transport. Journal of the Eastern Asia Society for Transportation Studies, Vol. 9, 2011, pp.
411–421.
26. World Bank. Purchashing power parity (PPP) conversion factor. World development
indicators: Exchange rates and prices. http://wdi.worldbank.org/table/4.16. Accessed Oct. 10,
2014.
27. Srivastava, V. K., and D. E. Giles. Seemingly Unrelated Regression Equations Models.

Belgiawan, Schmöcker, Abou-Zeid, Fujii
1
2
3
4
5
6
7
8
9
10
11
12
13
14

16

Dekker, New York, 1987.
28. Henningsen, A., and J. D. Hamann. Systemfit: A Package for Estimating Systems of
Simultaneous Equations in R. Journal of Statistical Software, Vol. 23, No. 4, 2007, pp. 1–40.
29. Belgiawan, P. F., J.-D. Schmöcker, M. Abou-Zeid, J. Walker, and S. Fujii. Modelling social
norms: Case study of students’ car purchase intentions.Working paper, 2016.
30. Fujii, S., and T. Gärling. Development of script-based travel mode choice after forced change.
Transportation Research Part F, Vol. 6, pp. 117-124. 2003.
31. Lanzendorf, M. Mobility biographies. A new perspective for understanding travel behaviour.
Presented at the 10th international conference on travel behaviour research, Lucerne, 10-15
August 2003.
32. Millstein, S.G. and I. Litt. Adolescent health and health behaviors, S.S. Feldman, G. Elliott,
Editors. In At the threshold: The developing adolescent. Harvard University Press, Cambridge,
MA 431–456. 1990