Transportation
DOI 10.1007/s11116-014-9548-z

Car ownership motivations among undergraduate
students in China, Indonesia, Japan, Lebanon,
Netherlands, Taiwan, and USA
Prawira Fajarindra Belgiawan • Jan-Dirk Schmöcker •Maya Abou-Zeid • Joan Walker • Tzu-Chang Lee • Dick F. Ettema •Satoshi Fujii

 Springer Science+Business Media New York 2014

Abstract ‘‘Peak car’’ and related discussions suggest that especially younger people (agecohort until 30) have less desire to drive and purchase cars. This might though only be true
for a limited range of developed countries. This study aims to understand the role ofpersonal background and the country context influencing future car ownership decisions ofyounger people in seven countries (China, Indonesia, Japan, Lebanon, Netherlands, Taiwan, and USA). The main foci of this research are undergraduate students where it isexpected that their current attitudes and habits will influence their travel behavior after they
graduate and obtain a job. A web survey asked students about their attitudes towards car
and public transportation, social norms, their socio-demographic situations, current
mobility patterns and the intention to own a car after graduation. We conducted a
descriptive analysis as well as correlation analysis of the survey data focusing on
explaining intentions to own a car in the future. We find that there is a significant difference between developing and developed countries; students in developed countries haveless desire to purchase cars. Expectations of others appears an important determinant ofpurchase intentions whereas income and the symbolic affective meaning of the car are lesscorrelated with intentions.

P. F. Belgiawan (&)  J.-D. Schmöcker  S. Fujii
Department of Urban Management, Kyoto University, Kyoto Daigaku-Katsura, Nishikyo-ku,
Kyoto 615-8540, Japane-mail: belgiawan@trans.kuciv.kyoto-u.ac.jp
M. Abou-Zeid
Department of Civil and Environmental Engineering, American University of Beirut, Beirut, Lebanon
J. Walker
Department of Civil and Environmental Engineering and the Center for Global Metropolitan Studies,
University of California, Berkeley, USA
T.-C. Lee
Department of Urban Planning, National Cheng Kung University, Tainan, Taiwan
D. F. Ettema
Faculty of Geosciences, Utrecht University, Utrecht, Netherlands

123Transportation

Keywords Car ownership motivations  Developed versus developing countries 
Attitudes towards cars  Social norms

Introduction
Car ownership levels are increasing rapidly in many developing countries due to risingincome levels. In Asia it is not only that former non-vehicle owners can afford to buy cars
but also the ‘‘upgrade’’ from motorcycles to cars that causes various traffic and environmental problems. For example, in Indonesia and China this trend towards more and larger
vehicles appears to persist despite the lower average speeds of cars compared to motorcycles in the already congested cities and despite the observable environmental side effects(Belgiawan et al. 2012; Zhu et al. 2012). Contrary to this is the discussion on ‘‘peak car’’ indeveloped countries. Peak car is a hypothesis that travel by passenger vehicles has notgrown much recently in a number of the highest income economies, and has even declined,
where more income no longer translates into more car travel when income is very high(International Transport Forum 2011). A number of recent studies report reduced car usageof younger people relative to older generations (Kuhnimhof et al. 2013; Van Der Waard
et al. 2013). Some cite lifestyle changes as an explanation (Institute for Mobility Research2013), such as increases in part-time rather than full-time work, living with parents longer,
and delaying having children. Understanding car use trends and their underlying factors
may be useful for deriving policy measures to reduce the usage of cars.
The aim of this paper is to provide further insight on the reasons for these contrary
trends. We do, however, not analyze the trends themselves by time-series or age-cohort
data; instead, our focus is on young students in seven different countries and their motivations to purchase cars after graduation. We emphasize the role of personal backgroundand the country context, including prevailing social norms influencing mobility decisionsof younger people. We have done this by conducting a survey among undergraduate
students from seven different countries. The sites are chosen to cover a wide range ofcountries (and partly due to previously established research 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. Shanghai is included as a city where the desire to own a car has lately been rapidly
increasing especially among younger people (Zhu et al. 2012). Japan is included as a moredeveloped country in which car ownership has been increasing until lately. We further include
Beirut, Lebanon, a city in which the car is the dominating mode among all generations. As
examples from ‘‘Western 1st world countries,’’ we include Utrecht, the Netherlands andBerkeley, USA, two cities with very different mobility patterns and spatial organisation. By
conducting the survey in such diverse countries, we aim to make suggestions regarding thecauses of differences among the desire to purchase cars among young people.
The structure of this paper is as follows: The next section discusses previous research on
car ownership forecasting and the motivation of individuals to buy cars. We then focus on
the mobility context in the surveyed countries. The following section then explains ourdata collection approach and survey instrument. The ‘‘Peak car indices and mobility patterns in our sample’’ section discusses car use and intention reported in the survey. The
following section describes variables that may explain the intention to buy a car and in123Transportation‘‘Intention to buy a car in the future’’ we discuss our findings, before we discuss conclusions and implications for transportation planning in the final section.

Literature review
Generally GDP is considered as a prime indicator of nationwide car ownership levels(Tanner 1978; Kahn and Willumsen 1986; Sillaparcharn 2007). Tanner (1978) proposed an
equation for car ownership per person that includes, besides GDP, income per person,
population density, growth of population over 10 years, the population proportions under
15 and over 64, and the percentage of self-employed people. Together these factors‘‘explained’’ 89 % of the variation in car ownership among countries. However, as Tanner
himself noted, these studies have some limitation, in that there is no reason to expect
relations of this kind to apply unchanged over long time periods and in particular whensaturation is being approached (Tanner 1978).
Other studies on car ownership have been carried out at a disaggregate level to identify
factors that affect car ownership decisions of individuals, though more often the focus has
been on vehicle type choice (Mannering and Winston 1985; Manski and Sherman 1980).
The models developed in these studies generally consider vehicle attributes (e.g., operating
cost, capital cost, and fuel efficiency), household characteristics, and principal driver
characteristics as explanatory determinants.
Relatively few studies consider psychological determinants, namely attitudes and social
norms (e.g., as emphasized in Ajzen 1991). An exception is Steg (2005) who suggests thatpeople do not only drive their cars because it is necessary to do so, but also because they
love driving. Her results show that the ‘‘status value,’’ also referred to as ‘‘symbolic/affective’’ value of a car, is the most important factor for mode choice decisions followed
by instrumental factors (speed, flexibility and convenience) and independence factors.
Van and Fujii (2011) studied attitudes towards private car usage, but not purchase,across six Asian countries and found that attitudinal variables had significant effects on the
behavioral intention to commute by car only in Japan, China and Vietnam but not inIndonesia, Thailand and Philippines. They proposed three dimensions of attitudes towards
car: symbolic/affective, instrumental, and social orderliness. The first two factors are inline with Steg (2005) and the latter comprises beliefs such as environmental friendliness,safety, altruism, quietness, etc.
Besides the above studies, our survey is influenced by another stream of literature thatsuggests that ‘‘norming effect’’ or ‘‘influence of others’’ significantly influences (mobility)
decisions. Cialdini et al. (1990) distinguish two types of norms: descriptive and injunctive
norms. Descriptive norms refer to the common behavior of others (e.g., the majority
choices) whereas injunctive norms refer to one’s perceptions of the expectations of others
regarding the behavior in question. Studies on the effect of the ‘‘influence of others’’ onvehicle ownership have been mainly discussing ‘‘social network structure’’ and/or controlling the extent of the mass effect (through surveys/experiments) instead of attempting
to measure norms (Gaker et al. 2010; Goetzke and Weinberger 2011; Rasouli and Timmermans 2013). Generally, these studies find that the influence of others is significant. For
the case of hybrid or electric cars, Oliver and Lee (2010) compare intentions to purchasehybrid cars in South Korea versus USA and find that social factors are possibly more123

Transportationinfluential than environmental factors in both cultures. Abou-Zeid et al. (2013) provide a
review of the impact of descriptive norms on a wide range of mobility decisions and also
conclude that the norming effect cannot be ignored and rather should be used positively byplanners.
In conclusion, we believe the literature suggests that the role of norms and attitudes isimportant in explaining car ownership motivations and that there are some trends of
reduced car use and car ownership desire among younger people in developed countries.
However, it is not clear whether the same is true in developing countries. These findings
motivate our study.

Mobility context in the surveyed countries
Before describing our survey in more detail, this section provides an overview on themobility context in the seven countries in which the students were surveyed. Table 1 lists
some key factors that describe the motorization level and costs associated with cars in the
countries in which we surveyed. The first three columns consist of the three developed
countries in our sample, Taiwan is described by International Monetary Fund (IMF) as anadvanced economic country, and the latter three countries are according to IMF emerging
or developing economies (IMF 2013).
In the developed countries, such as the Netherlands, population growth has been minor
in the last two decades (UN 2013) while car ownership has increased from 294 cars per
1,000 people in 1990 (Statistics Netherlands 2013) to 406 cars per 1,000 people in 2010
(World Bank 2013). In Japan, the population has been fairly stable over the last decades
and is in fact declining since 2010, while car ownership has increased from 286 cars per
1,000 people in 1990 to 458 cars per 1,000 people in 2010 (Oak Ridge National Library2013). In the USA, the population increased from 254 million in 1990 to 312 million in
2010; however, in contrast to the former two countries, car ownership has declined from564 cars per 1,000 people in 1990 to 413 cars per 1,000 people in 2010 (Oak Ridge
National Library 2013).
Taiwan, with a total population of 23 million people in 2010, also experienced rapid
population growth over the past two decades and is one of the densest countries in theworld. The passenger car ownership rate in Taiwan at the end of 2010 was 251 cars per
1,000 people, growing from 108 cars per 1,000 people in 1990 (Directorate-General of
Budget, Accounting and Statistics, Taiwan, DGBAS 2013).
Indonesia, China, and Lebanon similarly experienced rapid population growth over the
past 20 years as well as a rapid increase in car ownership. In Indonesia the number ofprivate cars increased from 7 to 37 cars per 1,000 people in the same time period (OakRidge National Library 2013). In China the car ownership rate increased from 2 to 25 cars
per 1,000 people between 1990 and 2010 (Oak Ridge National Library 2013) with muchhigher rates in the large metropolitan areas. In Lebanon, estimates of car ownership range
from one car for every three persons to one car for every two persons (Ministry ofEnvironment 2011, 2013).
The cost of owning a car and using a car is also shown. The most expensive purchaseprice is in the Netherlands followed by Indonesia and China with the cheapest in Lebanon(Numbeo Doo 2013). The expensive price of cars in conjunction with purchasing power
per capita, partly explains lower car ownership rates especially in Indonesia, even considering low gasoline prices.

123

413c

127,353b

337.0b286c

458c

16,615b400.1b294 (Statistics
Netherlands
2013)

406 (Statistics
Netherlands
2013)

Population 2010 (inthousands)

Population density per sq km
in 2010

Number of cars per 1,000
people in 1990

Number of cars per 1,000
people in 2010

51

25

NA

Car (%)

Public transportation (%)

Motorcycle (%)

19

16

45
5

1

81

(Berkeley, CA;
Metropolitan
Transportation
Commission 2006)(Utrecht;
Statistics
Netherlands
2013)

(MLIT
2007)

Mode choice pattern(reference)

1.05f

1.61f

1.95f

Fuel price/l (diesel) 2011

0.97

2.00

2.33

Fuel price/l (super grade
gasoline) 2011f

20,000ef

31,693e

312,247b

254,507b

51,703af

Purchase price

22,613e564c

122,249b

14,890b

Population 1990 (inthousands)

Car ownership and use costs (US$)

32.4b35,855a41,527aGDP (purchasing power
parity) per capita in 2012
(thousand US$)51,703a46,706a46,010a

GDP per capita in 2012
(thousand US$)

USA

Japan

Netherlands

Variables

Table 1 Context/statistical data for the seven surveyed countries

48

15

16

33

8

(Indonesia Central
Agency of
Statistics 2013)(DGBAS
2013)

23

0.47f

0.47

f

26,908e37c

7c126.4b240,676b

178,663b

4,923a3,593aIndonesia

NA

1.18

f

20,422e251(DGBAS
2013)

108(DGBAS
2013)

639.5b23,146b

20,232b38,356a20,335a

Taiwan1

25

Negligible

29

71(Beirut;
Nakkash(1999)(Shanghai; Urban
Construction andCommunications
Commission 2010)
20

0.94f

1.11f

19,000e*300–500d*300d

417.4b

4,341b2,703b

15,587a10,310a

Lebanon

1.28f

1.37

f

24,640e25c2c141.7b

1,359,821b

1,165,429b

9,055a6,071aChina

Transportation

123

123

Ministry of Environment (2011, 2013)

Numbeo Doo (2013)

World Bank (2013)

d

e

f

UN (2012)

Oak Ridge National Laboratory (2013)c

IMF (2013)ba

Other (primarily non-motorized, %)Variables

Table 1 continued

24

Netherlands
20

Japan
13

USA
14

Taiwan44

Indonesia
54

China
NA

LebanonTransportation

Transportation

Finally, the mode choice patterns are presented for the seven surveyed sites. For Japan,
Taiwan, and Indonesia, since the survey was conducted nationwide we present also
nationwide data. In Japan the dominant mode share is car at 45 % (Ministry of Land,
Infrastructure and Transport Japan, MLIT 2007). In Taiwan motorcycle dominates themodal share at 48 % (it has the highest motorbike ownership in the world). In Indonesia
public transportation (PT) and other modes (primarily non-motorized) dominate the modal
share (Indonesia Central Agency of Statistics 2013).
In the other four countries (the Netherlands, USA, China, and Lebanon), our survey
only focused on specific cities/regions and we hence show mode choice patterns forthese specific areas. Seventy-one percent of trips in the Greater Beirut Area are madeby private car (Nakkash 1999). The modal shares in Berkeley (Metropolitan Transportation Commission 2006) and Utrecht (Statistics Netherlands 2013) are dominatedby cars. In Shanghai (Urban Construction and Communications Commission 2010)
other modes obtain a share of 54 %, where a large percentage of this is walk. Thesestatistics provide a high level picture of the varying transportation conditions andculture at the different sites, and now we move on to the survey results to examine car
use with a survey that provides more control over the comparability of results acrossthe sites.

Data collection method
Respondents
All respondents are undergraduate students from a wide variety of disciplines. The data
were collected between January and June 2013. 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 the American 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 Indonesian University in Jakarta and the Bandung Institute of Technology. In Japan,
the recruitment was via email sent to engineering 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 thesurvey. In Berkeley, recruitment was handled by the Experimental Social Science Laboratory, and each respondent received financial incentive for participating. In Lebanon,
the recruitment was done via email sent to approximately one third of AUB students(chosen randomly). In the Netherlands, 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 inShanghai 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, we only report inthis paper on the data from the undergraduate students. We further performed data
cleaning, ignoring incomplete surveys and responses that were completed in fewer than
8 min which seems a lower limit to answer all of our survey questions consciously. This
results in a sample size of 1,229 used for the analysis below.

123Transportation

Questionnaire design
The main objective of the paper is to analyze differences among university students in theseven countries in terms of their stated intentions to buy a car in the future (next 10 years),
which was measured on a seven-point Likert scale (very unlikely – very likely). In addition,
we asked questions about a wide range of possible determinants of car purchase intentions,
including questions regarding socio-demographic characteristics, current transportation
patterns, attitudes and perceptions, and norms, each of which is expanded on below.
Socio-demographic characteristics
We asked students regarding average income (personal and family), age, gender as well as
their current living situation, i.e., whether they live by themselves, with their family or
friends and, whether they live in a dormitory or apartment.
Current transportation patterns
We asked about current transportation patterns including car, bicycle, and motorcycle
ownership and how much they use the car for various trip purposes.
Attitudes/perceptions
We asked 15 questions about the students’ attitudes/perceptions toward cars and also PT.
Each question was posed on a seven-point Likert scale with verbally defined endpoints(strongly disagree–strongly agree). The questions are based on surveys used in previous
research in particular those studies by Steg (2005) and Van and Fujii (2011). The questions
are listed in Table 4. In the table the questions are grouped by their factor analysis resultsthat are largely in agreement with the results of the aforementioned studies.
Social norms
We asked a range of questions regarding the influence of others on the students’ carpurchase intentions. To understand ‘‘descriptive norms’’ we asked respondents about thepercentages of family members, close friends, peers, people in their neighborhood, andpeople in their province/state that have cars. The response categories were: less than 25,
25–50, 50–75, and more than 75 %. To measure the perceived ‘‘expectation of others to
buy a car’’, respondents were asked ‘‘To what extent does each of the following groups (1.
Your parents, 2. Your partner, 3. Your family members and relatives, 4. Your close friends,
5. Your classmates, friends and peers at university, 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 seven-point Likert scale ranging
from ‘‘they strongly expect me not to buy a car’’ to ‘‘they have no expectation’’ as middle
point and ‘‘they strongly expect me to buy a car’’ as the other end point.
We further measured the ‘‘Strength of Influence of others to buy a car’’ by asking
respondents how important the same seven groups are to their decision regarding buying a
car in the future. Finally, we aimed to measure subjective social norms or ‘‘Perceived
pressure to buy a car’’ by asking respondents to rate their level of agreement with thefollowing statements on a seven-point Likert scale (strongly disagree – strongly agree): ‘‘Ifeel that there is social pressure to have a car here’’, ‘‘Transport modes other than car

123Transportation(walking, bike and public transport) are looked down upon’’, and ‘‘The majority of people
think that having a car is the right thing to do.’’Peak car indices and mobility patterns in our sample
Our aim is to explain variables of interest for the peak car discussion with the available setsof possible explanatory variables. Table 2 describes the past, current, and potential future
car ownership and car usage situations of students as reported in the survey.
In all sites more than 90 % of students grew up with a car in the household except forIndonesia and Shanghai, illustrating how prevalent the car has been for most students not
only in the most developed countries but also Taiwan and Beirut. We then have a numberof indices that describe the current mobility pattern. We define a car owner as a student
who owns a car personally or who has regular access to his/her family car. In all cases, themajority of these ‘‘car owning’’ students (70–90 %) are using family cars. The exception is
Beirut where one-third of ‘‘car-owning’’ students have their own car. Interesting to note isthat the difference between current student car owners and those who grew up with a car ismuch more significant in developed countries (average car user and owner below 30 %)compared to developing countries (average car user and owner above 50 %). As expected,
the percentage of car users (using a car at least 2 days per week) is slightly higher than thepercentage of car owners for all countries.
Noteworthy in Beirut and Indonesia are the smaller differences between the percentage
of students who grew up with a car at home and the percentage using a car now regularly,
whereas in the ‘‘1st world’’ samples from Japan, Berkeley, and Utrecht the difference isvery significant. The difference in Taiwan might be explained partly by the reliance on
motorcycles.
In general, the current car ownership and use as well as driving license statistics are in linewith our expectations. Students in Utrecht are frequent bicycle users, Beirut students heavily
rely on private cars, and Taiwanese students heavily rely on motorbikes. Utrecht figures canalso be understood from the fact that many students live independently in the city of Utrecht
and use their bikes to commute to the university. Those who live with their parents andcommute longer distances often use their transit pass that is provided to all students.
Finally, we report in Table 2 future car ownership intentions. We show the responses to ourseven point scale and further convert here in addition the answers into a binary scale byinterpreting answers from 1 to 4 as ‘‘have no intention’’ and 5 to 7 as ‘‘have intention’’. (In thefollowing tables we return to the seven-point scale.) More than 60 % of students in all countries
have an intention to buy a car in the future. As this is the dependent variable of interest, we haveordered this table as well as all other tables in the paper based on this auto intention response.
The order of intention to own a car sorts our countries precisely into developed versus
developing countries with Taiwan being in between the two groups. Students in Utrecht, Japan,
and Berkeley have the lowest car purchase intentions; and students in Indonesia, Shanghai, andBeirut have the highest. This is consistent with the peak auto hypothesis of the developed world
as well as increasing auto dependency in the developing world.

Potential determinants of car ownership intention
In this section we present descriptive statistics for the three categories of possible determinants discussed above: socio-demographic characteristics, attitudes/perceptions, and123

Transportation
Table 2 Car ownership, use, and intention: past, present, and future
Variables (number of observations)

Utr
(84)

Jpn
(142)

Brkly
(226)

Twn
(139)

Idn
(200)

Sgh
(167)

Brt
(271)

96.4

95.1

97.3

90.6

76.5

30.5

97.8

Past
Grew up with car (% yes)

Present: ownership and transportation pattern
Car user (%)

27.4

26.1

48.2

41.0

64.5

28.7

89.7

Car user and owner (%)

21.4

23.9

31.4

38.8

56.5

26.3

85.6

Car user and non-owner (%)

6.0

2.2

16.8

2.7

8.0

2.4

4.1

Non-car user (%)

72.6

73.9

51.8

59.0

35.5

71.3

10.3

Motorcycle owner (%)

0

15.5

0.9

68.3

51.5

2.4

4.1

Motorcycle or car owner (%)

21.4

33.8

31.9

79.9

78

27.5

86

Driving license (% have)

71.4

60.6

80.5

77.7

74

27.5

80.1

Mileage per week (average km)

62.8

111.5

56.5

76.4

69.4

71.2

77.3

Bicycle (% have)

98.8

88

27.9

66.2

51

69.5

28

Car (%)

2.4

6.3

4.9

5.8

28.0

5.4

60.9

Motorcycle (%)

0

7.7

0.4

41.0

24.0

0.0

1.1

Public transportation (%)

42.8

27.4

17.2

33.7

33.0

59.3

17.4

Bicycle (%)

51.2

52.8

7.1

13.7

1.0

7.8

0.7

Walk (%)

3.6

5.8

70.4

5.8

14.0

27.5

19.9

Mode of commuting

Future: purchase intentions (how likely are you to buy a car within the next 10 years?)% have intention

61.9

62.7

65.9

66.9

67.5

77.2

83.8

Very likely

9.5

17.6

21.7

14.4

18.5

26.9

38.0

Likely

10.7

18.3

25.7

15.8

36.5

31.1

28.0

Somewhat likely

41.7

26.8

18.6

36.7

12.5

19.2

17.7

% have no intention

38.1

37.3

34.1

33.1

32.5

22.8

16.2

Undecided

15.5

11.3

10.2

24.5

20.5

10.8

5.2

Somewhat unlikely

11.9

7.0

9.7

5

8.0

6.6

4.1

Unlikely

7.1

9.9

9.7

3.6

3.0

2.4

3.3

Very unlikely

3.6

9.2

4.4

NA

1.0

3.0

3.7

Bold represents that the total percentage of students’ response from ‘‘very likely to somewhat likely’’ andthe total percentage of students’ response from ‘‘undecided to very unlikely’’norms. In the next section, we examine how these categories of variables, plus current
transportation patterns, are correlated with the intention to buy a car.
Table 3 summarizes the socio-demographic characteristics of each site. In terms ofliving arrangement, the majority of students in Indonesia and Beirut and a substantial
proportion in Japan and Taiwan live with their families whereas a significantly lower
proportion of students live with their families in Shanghai, Berkeley, and Utrecht. The
average age of students in the sample is between 19 and 21. In terms of gender, theJapanese, Taiwanese, and Shanghai samples are unbalanced, which is a result of therecruitment methods.
Average personal monthly income (converted into US$) is, as expected, lowest among
Indonesian students while the students with the highest income are those from Utrecht
followed by Berkeley and Beirut students. Considering average family income, we findthat families of Berkeley students are the wealthiest followed by families of students in123Transportation
Table 3 Socio-demographic characteristics in the seven surveyed sites
Variables (number ofobservations)

Utr
(84)

Jpn
(142)

Brkly
(226)

Twn
(139)

Idn
(200)

Sgh
(167)

Brt
(271)

Living alone (%)

6.0

55.6

4.4

13.7

24.0

2.4

5.5

Living with family (%)

23.8

41.5

14.2

45.3

58.0

12.0

77.1

Living with friends (%)

63.1

2.1

75.2

36.0

17.6

80.8

10.7

Other (%)

7.1

0.8

6.2

5.0

0.4

4.8

5.7

Dormitory (%)

22.6

7.7

5.8

36.0

6.5

89.2

11.8

Apartment (%)

41.7

54.9

68.1

34.5

3.0

4.8

50.2

Socio-demographic characteristics
Living arrangement

Dwelling unitaOther (%)

35.7

37.4

26.1

29.5

90.5

6.0

38.0

Average age

21.6

20.0

20.3

21.7

20.5

21.1

19.7

Gender split (% male)

54.8

77.5

50.4

72.6

51.5

59.9

45.4

Average personal income (US$)a

792

307

620

295

86

226

535

Average family income (US$)a

5,981

6,471

7,395

3,369

824

1,119

6,435

Based on income group range measured with local currency; ANOVA significant for all variables

Japan and Beirut and then in Utrecht. Taiwanese and Shanghai families have a significantly
lower income, and Indonesia the lowest.
To better understand ‘‘emotional attachment’’ of students to cars, we analyze our attitudinal questions and group these with a principal component analysis (PCA). In Table 4
we report (in parentheses) the loading of the items in the factors in which they load highest.
Based on varimax rotation, four factors with eigen values larger than one could be
extracted. Table 4 shows the mean values of the responses to the attitude/perception
statements loading on the different factors (as well as norm statements in the lower part of
the table). These factors are consistent with findings in the literature: symbolic affective(explaining 22.3 % of the variance; Steg 2005; Van and Fujii 2011), independent (16.1 %of the variance; Steg 2005), negative aspects of car (9.9 % of the variance; Zhu et al.
2012), and social orderliness (9.4 % of the variance; Van and Fujii 2011). The differencein the single questions as well as the country specific factors is statistically significant
according to ANOVA analysis.
For symbolic affective, the lowest ratings are those of Utrecht and Japan, which indicates that the car is not perceived as bringing social status in these locations compared tothe other countries. For independent, the highest overall rating comes from Berkeley
followed by Utrecht, Beirut, and Shanghai. Indonesia and Taiwan have the lowest rating,
except the variable ‘‘cars are useful to pick up or drop off others’’. This might link to theirintensive use of motorcycles, which offer more convenience, freedom, and saving of travel
time than cars do.
In terms of negative aspects, the rating of the ‘‘cars give an arrogant impression’’ ishighest in Beirut and Indonesia, the two countries in our sample in which, arguably,
students most need to rely on cars for daily activities. An explanation might be envy among
those who cannot afford to buy a car as well as the ‘‘misuse’’ of the car as a status symbolof a few among the students who purchase large, expensive cars. In terms of social
orderliness, students in all countries tend to disagree that the car is environmental friendly,

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Variables (factor loading)

4.29

3.94

…are expensive to own and maintain (0.71), …aredisturbing one’s neighborhood (0.67), …are giving anarrogant impression (0.60)…allow one to travel safely (0.81), …areenvironmentally friendly (0.78)

Negative aspects

Social orderliness

The service covers the city area well (0.85), the service
is efficient (0.80), the service is convenient (0.76)

Convenience

3.76

Subjective social norms (perceived pressure to buy a car; Cronbach’s
Alpha = 0.72)

ANOVA significant for all variable

4.41

2.85

3.70

3.39

4.63

Expectation of others to buy a car (Cronbach’s Alpha = 0.92)

Strength of influence of others to buy a car (Cronbach’s Alpha = 0.85)

4.85

2.40

4.15

3.12

3.71

4.32

5.92

3.71

Utr
(84)

2.63

Descriptive norms (perceived car usage frequency of others; Cronbach’s
Alpha = 0.72)

Social norms

The drivers drive carelessly (0.84), I feel unsafe using
public transport (0.83), the service is unreliable (0.61),
I get annoyed by long waiting times at stops (0.61)

Safety andreliability

4.11

5.76

…are convenient (0.80), …give freedom to travelanytime (0.79), …help one to save time when making
a trip (0.75), …are useful to pick up or drop off others(0.54)

Independent

Attitudes/perceptions toward public transportation (PT)

4.39

…allow to distinguish oneself from others (0.78), …aretrendy (0.75), …bring prestige (0.74), …are cool
(0.70), …allow to express oneself (0.70), …are fun tohave (0.66)

All(1,229)Symbolic affective

Attitudes/perceptions toward car. Respondents were asked whether cars …

Factors

3.52

5.01

4.80

2.48

4.08

3.17

4.12

4.41

5.83

3.73

Jpn
(142)

3.64

4.16

4.83

2.92

4.71

3.96

3.80

4.07

6.16

4.66

Brkly
(226)

3.59

4.52

4.86

2.39

4.21

4.15

4.61

4.37

5.63

4.49

Twn
(139)

Table 4 Mean values of responses to attitude/perception/norm statements loading on the different factors in the seven surveyed sites

3.75

4.85

4.75

2.64

3.24

5.25

4.24

4.27

5.03

4.70

Idn
(200)

4.02

4.64

4.66

1.92

4.52

3.64

4.03

4.14

5.86

4.29

Sgh
(167)

4.95

3.98

5.10

3.08

3.39

5.52

3.41

4.49

5.86

4.52

Brt
(271)

Transportation

Transportation

with the lowest rating given by the Beirut sample, where the transportation sector is
responsible for high levels of emissions, which are well beyond standards prescribed by theWorld Health Organization.
To complement our analysis of attitudes/perceptions towards cars, we asked students
about their attitudes/perceptions towards PT and also here conduct a PCA from which we
extract two factors that we refer to as ‘‘safety and reliability’’ and ‘‘convenience’’. Also
here we find with ANOVA analysis that the PT perceptions of both factors are distinct inall seven countries. The low quality of the PT system in Indonesia and Beirut is reflected in
the students’ ratings of the system attributes. Utrecht students report the highest ratings ofPT.
In addition to attitudes/perceptions, we investigate the role of norms. The descriptive
norm ratings are a reflection of the perceived current car ownership situation in the
countries, with higher values denoting higher perception of auto ownership. Beirut in thiscase is the highest, followed by Berkeley. Shanghai is lowest. Expectation of others is
related to the perceived status value of a car as well as the general perceived need to own acar. In Utrecht the value is understandably significantly lower than in other samples.
Slightly surprising is the high perceived expectation of others for students to buy a car inJapan and Berkeley. ‘‘Strength of influence of others’’ might be seen as a measure of‘‘independence’’. Here we find again the lowest rating for the Dutch students possiblyreflecting a ‘‘more independent Western mindset’’.
Finally, regarding subjective social norms, the high and low values in Beirut andUtrecht are understandable given the afore discussed context. Utrecht has a good transit
system and progressive transportation policies, and using the bicycle for commuting iscommon (51.2 % reported in Table 2). Noteworthy are the relatively high values inIndonesia and Shanghai. Together with previous results this might suggest that owning a
car is perceived as something one should be able to afford even if ‘‘showing the car toothers’’ (as status symbol) is not necessarily a main purpose.

Intention to buy a car in the future
Given the possible explanatory factors discussed in the previous section, in this section we
aim to relate these variables to car purchase intentions. Table 5 shows the mean intentionsof car owners to purchase a new/different car and the intentions of non-car owners topurchase a car within the next 10 years. We can observe stronger intentions to buy a car
among the students in developing countries, and the difference between them and thedeveloped countries is significant (t test = 7.19; not shown in the Table), possibly partly
reflecting the higher relative price of cars when considering income and hence that thesestudents cannot yet afford cars. An additional explanation may be found in the quality of
PT in Japan and the Netherlands, especially in the urban areas where the current student
will likely reside when starting to work. We can further observe that current car owners
have a stronger intention to remain car owners than the desire of current non-owners topurchase a car. This is the case irrespective of geography though the difference is only
significant in Japan, Beirut and Berkeley.
In the lower half of Table 5, we correlate car purchase intentions, regardless of current
ownership status, with the various possible explanatory factors discussed in ‘‘Peak car
indices and mobility patterns in our sample’’ section. We note that we find various location
specific differences in terms of significance of correlation. It appears most difficult to
explain car ownership in Indonesia where we find that only regular car usage and the

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123
4.42 (1.39)-1.49

Car owner means (Sd.D) ANOVA = 0.02

Non-car owner means (Sd.D) ANOVA = 0.00

Independent t-test car owner and non-car owner

0.1
0
0.13
0.08

Subjective social norms

Average age

Average income

Average family income-1.87

Bicycle ownership (bicycle owner)

Italic p value \0.1, bold p value \0.05, bold ? * p value \0.01

1.02
NA

Motorcycle ownership (motorcycle owner)

1.02

Car ownership (car owner)

Motorcycle or car ownership (motorcycle or car owner)

1.01

Regular car use (regular user)

0.3
-0.13

Gender (male)Driving license (have)

Categorical variable (ordinal regression with one variable only)

0.12

Influence of others to buy a car

0.20

0.34*
-0.2

Descriptive norms

Expectation of others to buy a car

-0.06

-0.21

Convenience (PT)

0.05

0.53

0.22

0.47

0.56

0.73

0.24

-0.1

0.23*

-0.19

0.02

0.1

0.19

0.25*

0.05

0.26
0.05

20.32

Negative aspects (car)Safety and reliability (–) (PT)

0.09

0.34*

Social orderliness (car)

0.23*

0.08

Independence (car)

0.20

-0.07

1.05

0.80

0.75

1.04

0.28

-0.03

0.24*

0.02

0.01

0.22*

0.20

-0.09

0.55

0.99

0.37

0.31

0.58

0.55

0.26*

0.28*

0.17

0.20

0.07

0.42*

0.07
0.47*

-0.05

0.20

0.26*

0.08

0.13

0.26*

-1.0434.90 (1.10)

5.13 (1.40)

4.99 (1.23)

Twn (139)

-0.07

0.21*

0.04

-0.09

0.27*

0.24*

23.28*

0.23*

4.68 (1.86)

22.16*

5.45 (1.52)

4.92 (1.79)

Brkly (226)4.45 (1.95)

5.15 (1.52)

4.62 (1.86)

Jpn (142)

Symbolic affective (car)Correlations between variables and intention to buy a car in the future4.55 (1.47)
5.00 (1.68)

All sample means (Sd.D) ANOVA = 0.00

Utr (84)

Variables (number of observations)

Table 5 Likelihood to buy a car in the future

0.13

0.35

-0.05

0.3

0.45

1.37

0.15

0.07

20.66
0.23

0.51

-0.08

0.07

0.07

0.08

0.03

0.05

0.23*

0.08

-0.09

0.12

0.12

-0.14

0.21*

0.07

-0.3

5.40 (1.54)

5.48 (1.47)

5.42 (1.51)

Sgh (167)

-0.04

0.3

0.03

-0.06

0.09

-0.11

0.06

0.14

0.04

-0.01

0.02

0

-0.1

0.16

0.03

-0.99

5.13 (1.48)

5.33 (1.37)

5.24 (1.41)

Idn (200)

0.06

-0.55

0.60

0.68

0.98

-0.11

-0.23

0.21*

0.18

-0.01

0.12

0.35*

0.18

0.04

-0.07

0.11

0.09

-0.11

0.19*

0.21*

22.08*

5.18 (1.76)

5.74 (1.53)

5.65 (1.57)

Brt (271)

Transportation

Transportation

independence factor explain car ownership intention. Even income appears not to be agood explanatory factor for car purchase intentions in Indonesia (consistent with Zhu et al.
2012). We find that among our attitudinal constructs, the symbolic affective factor issignificant in various countries, though it is difficult to explain the observed pattern.
Students in Utrecht appear most impacted among the students of the seven study sites by
the negative car aspects (environmental impacts, costs of maintenance, arrogant image) in
their desire to purchase a car. All countries show a positive significant correlation between
independence and the intention to buy a car in the future except for Taiwan. The reason is
likely to be that Taiwanese students have long enjoyed the mobility and freedom thatmotorbikes bring to them.
Further interesting to note is that the perception of PT is only associated with carpurchase intentions in Berkeley and Taiwan. The Taiwanese students might believe that agood PT system could replace the need for a car after experiencing the successful PT in
Taipei. In other countries, the perception of PT might be too uniform to detect a correlation
with car purchase intentions.
Another significant finding is the relatively large correlations for the expectation ofothers regarding car purchase in several samples, in particular Berkeley and Taiwan. In all
samples, except Utrecht, we find in fact a stronger significance of perceived expectations
than of descriptive norms, i.e., the perceived general car ownership level. In general, we
find that norms other than descriptive norms appear less significant among the Dutch
students, probably related to a different status of car ownership as discussed previously.

Conclusions
Our objective was to provide insight on the role of personal background and country
contexts including prevailing social norms influencing mobility decisions. Our results
contribute to the peak car discussion and complement findings on lifestyle changes ofyounger generations that potentially influence the desire to own a car.
We report survey results of undergraduate students in seven different countries, asking awide range of questions, including attitudes and norms, and correlate these with statedfuture car purchase intentions. In terms of intention to buy cars, there is a significant
difference between developing and developed countries with students in developed
countries having less desire to purchase cars. While income levels partially explain purchase intension, several other factors also had positive correlations, such as symbolic
affective and independence car attitudes. On the other hand, attitudes/perceptions towards
public transit are not very correlated with auto purchase intention.
Also noteworthy is that Taiwanese students appear in some points different from ourother samples probably due to the tradition of motorcycle usage. In the same way thatcycling is an established mode in the Netherlands, this shows that the prevailing ‘‘mobility
culture’’ is an important factor when considering global trends. From our Taiwanesesample, we might also learn the importance of family bonds when predicting car mileagedevelopments in other Asian countries. Low car ownership does not necessarily translate
into low mileage in these countries due to family car sharing.
For most samples there is a strong role of expectation of others for the intention to buy a
car in the future. We do not find though a systematic difference between developing anddeveloped countries, suggesting that the effect of others on purchase decisions needs to bestudied carefully across countries. In line with this, parents’ income is a significant
determinant of car purchase intentions in a number of countries, predominantly the more123

Transportation

developed ones. The exception is Utrecht, where students in general appear to be the mostindependent and also perhaps ‘‘the most considerate’’ as they appear to take into accountnegative aspects of car ownership including environmental aspects. Compared to Asia andthe USA, Utrecht has a longer tradition of a good developed PT system as well as thediscussion on health and environmental benefits of alternative modes. This might also be
positive news as such benefits might be seen more in other countries in the future given thefocus on sustainability in many countries.
One might argue that current intentions do not necessarily reflect future car purchases,
especially given that lifestyles are likely changing after graduation. We therefore do notclaim that our findings should be directly translated into regression models that are used for
demand forecasting even for this cohort. Nevertheless, we believe that current intentions
are one important determinant explaining future purchases. In support of this argument,
literature such as Lanzendorf (2003), Simma and Axhausen (2003) or Millstein and Litt(1990) argues that travel experiences and habits (which transform into intentions) during
young age determine travel patterns during adulthood.
There are many future directions for this work. Differences in sampling methods acrossthe study sites may contribute to potential self-selection biases that we were not able tocheck for in this study. More representative samples are needed in future research,
including from different cultures. The analysis may also be extended to population segments other than students. While our current analysis is based on descriptive and correlation analysis only, future research will apply more advanced statistical modeling. Finally,
and as mentioned above, while this study analyzed the intentions to own a car in the future,
it is unclear to what extent there will be a gap between these intentions and the actual car
purchase decisions. Future analyses will therefore examine current car ownership and use
decisions, as well as examine both daily travel patterns and long distance recreational
travel to better capture the extent of changes in car use in both developed and developing
countries.
While ‘‘explorative,’’ this study made use of a detailed, individual level, cross-cultural
survey of a wide variety of drivers of auto use and provided insight into the culturaldifferences and future trends of auto ownership.
Acknowledgments The authors would like to thank Lotte International Scholarship Foundation for supporting this study through a personal scholarship to the first author of this paper as well as UC Berkeley
Global Metropolitan Studies, the National Science Foundation, and the Dean’s Office of the Faculty ofEngineering and Architecture at AUB for supporting this work. We also kindly acknowledge the help of Mr.
Zhang Dong, Tongji University with data collection in China.

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Prawira Fajarindra Belgiawan is a PhD student in the Department of Urban Management at Kyoto
University. This paper is part of his doctoral research which investigates the role of psychological factors
such as attitudes and social norms on car ownership intentions.
Jan-Dirk Schmöcker is an Associate Professor in the Department of Urban Management, Kyoto
University, Japan. His research interests cover travel behavioural issues as well as network modelling with afocus on public transport. Partly related to the present paper he also published in this journal recently a paper
titled ‘‘Dynamic Process Model of Mass Effects’’ [Vol. 41(2)].
Maya Abou-Zeid is an Assistant Professor of Civil and Environmental Engineering at the American
University of Beirut. Her research interests are in the areas of travel behavior modeling and forecasting,
driving behavior and road safety, market research, and urban transportation planning.
Joan Walker is an Associate Professor at UC Berkeley in the Department of Civil and Environmental
Engineering and Berkeley’s new Center for Global Metropolitan Studies. Her research focus is behaviouralmodeling, emphasizing methods and their application to urban issues including health, congestion, air
quality, equity, and quality of life.
Tzu-Chang Lee is an Assistant Professor at the Department of Urban Planning, National Cheng Kung
University, Taiwan, where he also received his BSc and MSc Degrees. He obtained his PhD at Imperial
College London. His research interests are mainly related to traffic modelling and simulation, land use andtransportation interaction, and transportation mode choice in urban areas.
Dick F. Ettema is an Associate Professor in the Department of Human Geography and Planning of Utrecht
University in the Netherlands. His research interests include travel decision making and behavior change,well-being implications of travel and activity patterns and the influence of the built and social environmenton physical activity and active travel.
Satoshi Fujii is a Professor of Transportation and Behavior Science in the Department of Urban
Management at Kyoto University and a Special Adviser for Public Policy to the Cabinet of Japan. He has
been engaged in applied psychological research for behavioral modification of travel behavior.

123