Travel Behaviour and Society 7 (2017) 12–25

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Travel Behaviour and Society
journal homepage: www.elsevier.com/locate/tbs

Modelling social norms: Case study of students’ car purchase intentions
Prawira Fajarindra Belgiawan a, Jan-Dirk Schmöcker b,⇑, Maya Abou-Zeid c, Joan Walker d, Satoshi Fujii eaKyoto University, Department of Urban Management, C1-2-437, Kyoto Daigaku-Katsura, Nishikyo-ku, Kyoto 615-8540, Japan
Kyoto University, Department of Urban Management, C1-2-436, Kyoto Daigaku-Katsura, Nishikyo-ku, Kyoto 615-8540, Japanc
American University of Beirut, Department of Civil and Environmental Engineering, P.O. Box 11-0236/FEA-CEE, Room 2M3, Riad El-Solh/Beirut 1107 2020, LebanondUniversity of California, Berkeley, Department of Civil and Environmental Engineering, 111 McLaughlin Hall, CA 94720-1720, USAe
Kyoto University, Department of Urban Management, C1-2-432, Kyoto Daigaku-Katsura, Nishikyo-ku, Kyoto 615-8540, Japanba r t i c l e

i n f oArticle history:
Received 2 May 2016
Received in revised form 16 November 2016
Accepted 21 November 2016

Keywords:
Social norms
Car purchase intention
Ordered hybrid choice modela b s t r a c t
In this paper we aim to quantify the influence of social norms on car ownership intention by estimatingordered hybrid choice models (OHCM) with car purchase intention as dependent variable. Our sample
consists of 1229 university students from three developed and four developing countries. We constructsubjective social norms (SSN) by interacting the perceived expectation to buy a car with motivation tocomply with the expectation. Four approaches to incorporate social norms into OHCM are presented
while controlling for other explanatory variables such as attitudes and socio-demographics. From thefour estimated models we find that social norms significantly correlate with car purchase intention.
Though differently defined in the four models, we find similar parameter estimates in all models, whichleads us to conclude that the effect is fairly robust even with simplified definitions. From a model withperson-group specific observed SSN, we further find though that group specific influences can differ significantly and that parents and university peers significantly influence car purchase intention.
Ó 2016 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved.

1. Introduction
Decisions of all kinds, including car purchase decisions, areinfluenced by a multitude of factors. Among others, it is wellknown that the perceived usefulness of an option is often influenced by not only one’s perception but also the perception thatthe decision maker supposes others to have of this option. This
has led to a large literature on the role of ‘‘expectation of others”
on decision-making. Asch (1951) already concludes that ‘‘a substantial minority yielded, modifying their judgment in accordancewith the majority” indicating the role the general wider social network has on one’s decisions.
Similarly, to explain behavior related decision-making, the theory of reasoned action (TRA) (Ajzen and Fishbein, 1980; Fishbein
and Ajzen, 1975), the subsequent theory of planned behavior(TPB) (Ajzen, 1985, 2012; Ajzen and Driver, 1991) and the latest
Reasoned Action Approach (RAA) (Fishbein and Ajzen, 2010) weredeveloped. These theories posit that the immediate antecedent of
behavior (action/decision) is behavioural intentions (intention/⇑ Corresponding author.
E-mail addresses: belgiawan@trans.kuciv.kyoto-u.ac.jp (P.F. Belgiawan),schmoecker@trans.kuciv.kyoto-u.ac.jp (J.-D. Schmöcker), ma202@aub.edu.lb(M. Abou-Zeid), joanwalker@berkeley.edu (J. Walker), fujii@trans.kuciv.kyoto-u.ac.
jp (S. Fujii).

motivation), which in turn have several determinants that include‘‘subjective norm.” The strength of subjective norm refers to anindividual’s perceptions of how others expect him/her to behave
regarding the behavior in question as well as the individual’s motivation to comply with the expectations of those important others.
The importance of ‘‘norms” has been reported as instrumental
for a wide range of repeated behaviours in research related tohealth and environmental friendly behavior. In the field of health,
we can refer to work on college students drinking (DeJong et al.,2006; Neighbors et al., 2007) and on smoking behavior (Nyborg
and Rege, 2003). Several researchers have successfully investigatedand explained the effect of norms on environmental friendly
behavior such as Goldstein et al. (2008) on hotel towel re-use,
Schultz et al. (2007) on house energy reduction, and some on thecase of littering and recycling (Cialdini et al., 1990; Harland et al.,1999). Rivis and Sheeran (2003) review several studies that incorporate the influence of norms on several behavioural intentions
such as healthy eating, smoking, and drug use. In transportation
research, we can also find the positive influence of norms on transportation behavioural intentions as can be seen from a growing
body of literature that is reviewed in the next section.
Since there are several ways of defining social norms related
factors for transportation modelling, the question remains, which
is a better way for modelling social norms as a determinant oftransportation behavioural intentions? How many person

http://dx.doi.org/10.1016/j.tbs.2016.11.003
2214-367X/Ó 2016 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved.

P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25

reference groups should we incorporate in order to find the best
representation of social norms? These are the key questions motivating this paper. We aim to quantify the influence of social norms
on transportation behavioural intentions, particularly car purchaseintentions of students with different model formulations. Though
there is a significant body of literature illustrating the effect ofnorms, our literature review will show that findings are not coherent and that there is no agreement on the best modelling approachfor social norms.
We further emphasize that in this paper we discuss desire for‘‘cars in general” though recent literature has been often on attitudes towards alternative fuel vehicles (AFVs). Our choice is conscious as we believe that car ownership itself, independent of
whether it is an AFV or not, remains an important policy topic.
We explore social norms in more detail by developing orderedhybrid choice models (OHCM) with car purchase intention as
dependent variable. Our results are limited to a specific population
subgroup, undergraduate students. However, besides the estimation results, we believe the more important contribution of thispaper is a methodological discussion on how social norms might
be estimated and modelled.
The structure of this paper is as follows: Section 2 discusses previous research on the role of social norms in transport planning
related literature. In Section 3, we discuss different forms of how
we formulate our choice models incorporating social norms. In Section 4, we describe how the data were collected and include somedescriptive analysis of the data. Section 5 then explains the carpurchase intention model and in Section 6 we discuss our findings,
conclusions and the implications for transport modelling.

2. Literature review
2.1. Social norms and transportation decisions
Norming effects have been described in various studies withdifferent terms such as mass effects, herd behavior, peer effects,
fashion or conformity (Abou-Zeid et al., 2013). Though the terms
are partly used interchangeably, the former terms mostly include
a notion of dynamics and are associated with modelling the spreadof behavior. Instead social norms is the term mostly used in social
psychology to explain the behavior of an individual which is also
our focus here. For a more detailed review on the psychologicalfoundations, with a focus on implications for mobility decisions,
we refer the reader again to Abou-Zeid et al. (2013). In the remainder of this section, we focus on empirical evidence for the importance of the influence of others for transportation related decisions.
Thøgersen (2006) explores the correlation between mode
choice for different trip purposes and subjective social norms. Subjective social norms (SSN) were constructed by asking the respondents about their agreement with a five-point Likert scalestatement ‘‘I believe that most of my acquaintances expect that I
take the bus or train to work and shopping if the choice is between
bus or train and my own car.” The results show that subjective
social norms have a strong correlation with commuting behavior.
Closely related, according to Jakobsson et al. (2000), expectations
about others’ intentions were found to be one determinant of car
use reduction. Muñoz et al. (2016) propose a number of cycling
indicators based on TPB for their bicycle commuting logistic
regression model. They construct a ‘‘subjective norms” factor byasking 654 respondents from one city in Spain about the approvalof certain specific groups on bicycle commuting as well as theimportance of those specific groups on the decision to commuteby bicycle. They conclude that subjective norms influence bicycle
commuting decision.

13

Bamberg et al. (2007) investigate if there is empirical evidence
for the assumption that social norms do influence intentions indirectly via their impact on attitude, perceived behavioural control
and personal norms. Their research obtains the social norms construct by asking respondents in two German cities about the extent
to which people who are important to them think they should use
public transport instead of car. They find that in both data setssocial norm is strongly associated with personal norm, attitude,
and perceived behavioural control but has no direct association
with intention. Partially following on from Bamberg et al. (2007),
Zhang et al. (2015) utilize the same approach in Shanghai context
and find that SSN might have stronger effects on one’s intention in
the Asian context compared to Western context. Dharmowijoyo
et al. (2015) investigate if subjective norms influence the intention
to use motorized vehicle/public transport/non-motorized transport for out-of-home activity in the context of Bandung, Indonesia
and find positive significant effect.
Also for the usage/ownership of alternative fuel vehicle (AFV),
norming effects appear to be important. Jansson (2011) points
out that there is a significant difference in perceived personaland social norms between adopters and non-adopters of AFV in
Sweden. Personal and social norms were constructed in his paper
by using principal component analysis (PCA) using indicators thatemphasize on reducing oil/petrol usage and the use of AFV. The
research found that the prevailing norms are to use fossil fueledvehicles. Ozaki and Sevastyanova (2011) analyse the Prius market
share in the U.K. They used a sample of 1263 individuals who had
purchased a Prius in the 24 months prior to January 2009. They
construct two social norm factors based on PCA results. The firstone is social orientation while the other norms factor is identity
which is constructed from comments in open comments questions
that are related to compliance with norms of the social group andthe expression of self. Both of the above studies suggest that social
norms are important for AFV purchase decisions, though they do
not conduct a regression analysis in order to understand the relative importance of norms.
Moons and De Pelsmacker (2012) use intention to use an electric car as dependent variable in the regression model with a sample of 1199 individuals from Belgium. In their regression analysis
they include subjective norms of peers and of media. These wereconstructed by asking several questions related to peers’ expectation and media influence related to electric car. They found thatthose two norms variables significantly influence electric car intention. They do not though distinguish person groups, the role of perceived expectations and the importance of the group for the
respondents which we aim to explore in this paper.
In conclusion, there appears to be some evidence that social
norms are an important determinant of mode choice and consequently vehicle ownership choice. What the above studies havethough not discussed is how best to implement their social norms
construct into a ‘‘standard” random utility choice model (RUM)
framework where one can better control for a wide range ofsocio-demographics and attitudinal factors (Kim et al., 2014). Someof their studies relied on structural equation modelling which
explains correlation but arguably is less suitable for choice prediction. Achieving the formulation of such a RUM framework (Hybrid
Choice Model) with different social norm constructs is precisely
the objective of the present study. As a background to this, the following section continues this literature review by pointing out different prevailing norms and discusses how they have beenmeasured.
2.2. Types and measurement of norms
Schwartz (1977) and Schwartz and Howard (1982) distinguishnorms into personal norms and social norms. Personal norms are14

P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25

defined as self-expectation of specific actions in a particular situation, experienced as a feeling of moral obligation while social
norms are defined as norms based on group expectations. Similarly, Cialdini et al. (1990) distinguish two types of norms: descriptive and injunctive norms. Descriptive norms refer to the common
behavior of others whereas injunctive norms are defined as expectations on oneself, which will hence include the willingness to conform to the expectation of others.
According to Thøgersen (2006), types of injunctive norms canagain be distinguished according to how internalized they are
which leads to the definition of personal norms and subjective
social norms. In terms of Thøgersen’s categorization, norms as discussed in this paper refer to the most externalized part of the
injunctive norms: subjective social norms. Subjective social norms
can be defined as norms based on group expectations (what otherpeople think a person should do). Thøgersen (2006) mentions that
subjective social norms also strongly correlated with descriptive
norms, which is a reason why they are not distinguished at firstin TPB (Ajzen, 1991).
In TPB subjective norms are defined as the perceived socialpressure to perform or not to perform the behavior in question(Ajzen, 1991, p. 188). TPB includes attitudes towards behavior,subjective norms and perceived behavioural control as factors
influencing the intention towards a behavior. Ajzen (1991) distinguishes direct and indirect measures of subjective norms (SN).
Direct measures, also known as Global SN, are assessed by using
two Likert scales with respect to behavior: ‘‘Most people who areimportant to me approve/disapprove of my engaging in this activity” (approve-disapprove) and; ‘‘Most people who are important in
my life think I should engage in this activity” (likely-unlikely).
Then a global measure of SN is obtained by averaging responses
to the two scales.
Indirect measures are derived by multiplying normative belief
and motivation to comply. Normative beliefs concern theapproval or disapproval of important referent groups of an individual performing a certain behavior. For example: ‘‘Most persons who are important to me think I should/should not[description of behavior.]” Responses are often measured on aseven-point Likert scale, with scores from +3 (should, extremely)to 3 (should not, extremely). Motivation to comply is a measure of how likely a person wants to do what other persongroups think in terms of whether he/she should perform a certain behavior. Responses are often measured on a seven-point
Likert scale, with scores from not at all to very much. Then,
responses to each scale would be multiplied and summed if
multiple specific referents were included. According to Ajzen(1991), empirical investigations have shown that the best correspondence between measurements of subjective norms and Global SN is usually obtained with bipolar scoring of normative
belief and unipolar scoring of motivation to comply.
In RAA (Fishbein and Ajzen, 2010), norms are distinguished into
injunctive and descriptive. Injunctive norms are similarly measured
to the way subjective norms are measured in TPB. Descriptive
norms instead are measured by asking respondents whether they
believe that persons important to the respondent perform thebehavior or not. In response to RAA, the newest TPB (Ajzen,2012) also incorporates descriptive norms. Interestingly, Ajzen uses
both Subjective Injunctive Norms and Subjective Descriptive Norms as
separate determinants of intention (de Leeuw et al., 2015).
Most of the empirical studies on social norms and travel behavior discussed earlier have used direct measures of social norms.
Fishbein and Ajzen (2010) note though that the measurement of
normative beliefs and hence indirect measures of social norms
are important for understanding the potential of behaviouralchanges. This is one motivation why we employ an indirect measure in this paper.

For indirect measures, Fishbein and Ajzen (2010) note that
there is considerable empirical evidence that salient normative
belief can predict subjective norms; however, there is far less compelling evidence that motivation to comply contributes to the prediction of subjective norms. Ajzen and Driver (1991) demonstratethat motivation to comply can have different effects on behavior
depending on the reference group. For example, the correlation
between behavior and subjective norms with motivation to comply
with friends is smaller than that without motivation to comply. An
opposite effect is found regarding the effect of motivation to comply
with siblings. Therefore, in this paper we construct models with andwithout motivation to comply.
To quantify the effect of norms toward intention there are several methods used in the past. Some studies constructed the indirect norm factor(s) by averaging several variables and used that in
the regression analysis. For example in transportation research
Dharmowijoyo et al. (2015) and Moons and De Pelsmacker(2012) have done so and for other research disciplines we can refer
to Astrom and Rise (2001), Conner et al. (1996), and McMillan andConner (2003). Other studies instead constructed their norm factor
by principal component analysis or factor analysis, e.g. Jansson(2011), Muñoz et al. (2016), and Ozaki and Sevastyanova (2011).
Rivis and Sheeran (2003) used meta-analysis to measure the effectof descriptive norm and intention on several behavioural intentions such as healthy eating, smoking, and drug use. They refer
to 14 previous studies all of which used either averaging or PCA
analysis to construct norms.
In transportation research, there are further few studies thatsimultaneously construct norms and regress these with intentions
in a latent choice framework. Examples are Bamberg et al. (2007),
Jakobsson et al. (2000) and Zhang et al. (2015). To the best of ourknowledge no study though uses a hybrid choice model framework
as proposed in Section 3.
2.3. Reference groups for subjective norms (modal normative belief)
An important model specification decision is which referencegroups are defined. In a Fishbein and Ajzen (1975) study on choice
between different brands of products (coffee, detergents, potato
chips, and gasoline), motivation to comply with five referents(mother, friends, husband, consumer reports, and advertising) ismeasured. They find that the perceived expectations of women’shusbands are of particular importance. In a research study about
leisure activities, Ajzen and Driver (1991) mention five referents(friends, parents, boyfriends/girlfriend, brothers/sisters, and otherfamily members) in which respondents have to rate their approvalor disapproval of their engaging in a given leisure activity. In the
study of cigarette behavior, Primack et al. (2007) use three referents (parents, friends, and most people at my age) to measure subjective norms. For the case of subjective norm for marijuana, Zhao
et al. (2006) distinguish two types of referents, authority approval(parents, teacher, and grandparents) and peer approval (closefriends, boy/girlfriends, and people of the same age). In travel
behavior related research, De Pelsmacker and Janssens (2007) utilise five referents (my best friend, my children/parents, my partner,
most people that are important to me, and my passenger) whereas
Muñoz et al. (2016) utilise three person groups (my family, myfriends, and my co-workers/classmates) for constructing subjective
norms.
In conclusion, there is no general consensus on which referents
are the most appropriate to use. A useful distinction though is
between an inner and outer circle of reference groups also in linewith Kahn and Antonucci (1980). Therefore, in this paper we define
an inner circle as persons that are commonly viewed as important
support providers and recipients such as parents; partner; family
members and relatives; close friends. The outer circle instead

P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25

consists of peers; neighbours; and other people in the respondent’s province/state.
With this background on the different definitions of norms andreference groups, in the following we discuss how we measuresubjective norms and how we incorporate subjective norms in a
hybrid choice model.
3. Ordered hybrid choice model
For this paper, the case study is focused on undergraduate students’ car purchase intentions. We measure the main dependent
variable, students’ stated intentions to buy a car in the future (next10 years), on a 7-point Likert scale (very unlikely–very likely). We
note that this variable is similar to ‘‘intention” in the Theory ofPlanned Behavior (TPB) since we also use attitudes and norms as
explanatory variables. We define our dependent variables as ‘‘carsin general” and not a specific type of car. We believe that people
often choose first to own a car or not, then decide on a different
car type. In the car ownership modelling research this is in fact
the usual assumption, see for example Xu et al. (2015). We
acknowledge that with the increasing spread of alternative fuelvehicles there might be some change and that some people might
only consider buying AFVs but in general we would argue that thetwo-step decision approach is still often true.
To control for socio-demographic characteristics, we asked students about their average personal income, their gender and age.
We also asked students about their car ownership and car use patterns. In this section, we describe the measurement of social norms
and the other explanatory variables and present several orderedhybrid choice (OHCM) formulations which combine latent variable
modelling with discrete choice models (Ben-Akiva et al., 2002a;
Walker and Ben-Akiva, 2002).
3.1. Respondents
This sub-section is based on Belgiawan et al. (2014) where weuse the same sample. In that paper we provide descriptive statistics and discuss car ownership developments in the countries fromwhich the samples are drawn.
All respondents are undergraduate students from a wide variety
of disciplines. The data were collected between January and June2013 in seven different countries. The sites are chosen to cover awide 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 thedominating 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 (Zhu et al.,2012). Japan is included as a more developed country in which car
ownership has been increasing until lately. Beirut, Lebanon, a cityin which the car is the dominating mode among all generations isfurther 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 spatialorganization.
In all countries, the survey was translated into the local languagewith 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 eachcountry. In Indonesia, surveying agencies recruited respondents inperson on the campuses of the University of Indonesia in Jakarta
and the Bandung Institute of Technology. In Japan, the recruitment

15

was via emails sent to engineering departments in several
universities.
In China, the recruitment was via email and through an internet
forum in Shanghai with a small incentive in the form of a mobile
phone voucher for those who complete the survey. Since most of
the respondents come from outside Shanghai, in the subsequent
analysis we use China instead. In Berkeley, recruitment was handled by the Experimental Social Science laboratory, and each
respondent received a financial incentive for participating. In Beirut, the recruitment was 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 2272 undergraduate and graduate students accessed thesurvey website, of which 1806 completed the survey. For bettercross-site comparability, in this paper only the data from theundergraduate students are reported. Further data cleaning is performed, ignoring incomplete surveys and responses that werecompleted 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 1229 used for the analysis below. For
more detail about the surveying methods and contexts of our
specific sites, please refer to Belgiawan et al. (2014).

3.2. Subjective social norms
To measure person group specific expectation of others to buy acar (ei ), the respondents were asked ‘‘To what extent does eachof the following groups i (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 neighbourhood and 7. People in your province/state) expect you to buy a carwithin the next 10 years?” Responses to this group of questions
were measured on a 7-point Likert scale ranging from 3 (theystrongly expect me not to buy a car) to 0 (they have no expectation) as middle point and 3 (they strongly expect me to buy acar) as the other end point. As discussed, this is similar to the bipolar measurement of normative belief in TPB.
Motivation to comply (mi ) is measured by asking respondents
how important the same seven groups are to their intention
regarding buying a car in the future. The responses are again measured on a 7-point Likert scale, this time ranging from 0 (not at allimportant) to 6 (very much important).
In line with the previously discussed literature, we interact
expectation with motivation to comply. Our rationale is that the
expectations of a certain group only become salient if that groupis important to the respondent. For example, if parents have asmall positive expectation that their child should buy a car, thisweak expectation might in fact be a very important determinant
due to the overall influence of parents on their child. Therefore,
new variables ni are constructed as shown in Eq. (1) where i = 1,
. . . , 7 denotes the person groups that are shown in Table 3 and that
we hypothesize to have different influence on the respondent. We
remind that the definition of this variable is similar to what
Thøgersen (2006) describes as subjective social norms. ni can takevalues from 18 (strong expectation and motivation to comply
for not buying a car) to +18 (strong expectation and motivation
to comply for buying a car).

ni ¼ ei  mi

ð1ÞSince in the TPB literature subjective norms are defined withand without consideration of motivation to comply, we will in16

P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25

our model test both the summation of expectations only (Eq. (2))
as well as the sum of the subjective social norms over all referencegroups (Eq. (3)).

^e ¼

7
X

ei

ð2Þ

7
X
ni

ð3Þ

i¼1

^¼
n

i¼1

We realize that not all students have complete seven referencegroups, i.e. some may have no siblings or some may have no partners anymore. Thus, the summation implies that all else being
equal, respondents who experience social pressure from more person groups hold stronger subjective norms.
3.3. Further explanatory variables
Table 1 summarises the explanatory variables, their coding aswell as their measurement. To control for general attitudes towardcars, we pose 10 statements related to ‘‘cars in general” to which
respondents have to indicate their level of agreement (7-point Likert scale with verbally defined endpoints ‘‘strongly disagree” and‘‘strongly agree”). The statements are as follows: Cars are cool; Cars
allow to express oneself; Cars are trendy; Cars bring prestige; Cars
allow to distinguish oneself from others; Cars are fun to have; Cars
are convenient; Cars allow one to travel anytime; Cars allow one to
be independent; Cars allow one to travel anywhere.
These questions were used to construct two factors referred toas symbolic affective and independence. They are based on a numberof previous studies where it has been found that attitudinal factors
can explain car purchase intentions (Belgiawan et al., 2016; Steg,
2005; Van and Fujii, 2011). Symbolic affective was constructed withthe first six attitudinal statements above (indicators) and independence with the last four.
Other explanatory variables include monthly income and adummy variable for those respondents who do not provide income
information (equal to 1 if income is missing and 0 otherwise). We
also have collected information on family income as students’ carpurchases as well as purchase intentions might be dependent on
the parent’s financial situation. Since the average family income
variable significantly correlates with personal income of the student though, we decided to omit it to minimize multicollinearity.
Monthly income is specified as a continuous variable. Each income
category of the respective country is transformed into US$ by using
the purchasing power parity conversion factor published by theWorld Bank (2014). We also control for car usage. We define regular car use as using the car at least twice a week.
Given the correlation results and various model tests, we fit amodel with two latent variables: symbolic affective and independence, and their indicator variables as well as three other observed
variables: regular car use, income, and income dummy for missing
income observations. Income is transformed from ordinal categories into continuous measurement based on the midpoint of
the income range associated with each of the seven levels. To distinguish among the seven sites, we add site-specific dummy variables into the model.
The following section discusses that depending on the type ofnorm formulation the norm factor can be treated as latent variable
or as observed variable such as total expectation (^e), and total sub^ ).
jective social norms (nrepresented as rectangles; solid arrows represent structural relationships while dashed arrows represent measurement relationships. In this model, similar to TPB, subjective norms is the sumof all referent groups’ expectations.
The systematic utility function for the model with total expectations is as follows (the index for an individual is not shown forsimplicity):

V ¼ ðbas zas þ baf zaf þ b^e ^e þ brc xrc þ bw xw þ bwd xwd þ bsa xsa
þ bsb xsb þ bsc xsc þ bsd xsd þ bse xse þ bsf xsf Þð4Þ

where b are parameters to be estimated. The total utility in thesense used here refers to the latent response underlying theobserved response to the car purchase intention question. It is
assumed that all latent variables are normally distributed across
the population, i.e. zas  N 0; r2as and zaf  N 0; r2af and are uncor1
related with each other. The r2 terms are the variances of the latentvariables which are to be estimated. Further, it is assumed that theerror term of the latent response (or utility as in Fig. 1) underlying
the dependent variable car purchase intention is independently
and identically distributed with: e  Logisticð0; p2 =3Þ.
The utility is the sum of the systematic utility and the error
term as shown in (Eq. (5)), and the measurement model for car
purchase intention (likelihood to buy a car) is given by (Eq. (6))
where the s parameters are thresholds to be estimated.

U ¼V þe

ð5Þ

8
1 ðv ery unlikelyÞ
>
>
>
>
>
2 ðunlikelyÞ
>
>
>
>
>
>
< 3 ðsomewhat unlikelyÞ
y ¼ 4 ðundecidedÞ
>>> 5 ðsomewhat likelyÞ
>
>
>
>
>
>
6 ðlikelyÞ
>>:7 ðv ery likelyÞ

9if  1 6 U 6 s1 >>>>if s1 6 U 6 s2 >>>>>if s2 6 U 6 s3 >>=if s3 6 U 6 s4>>>
if s4 6 U 6 s5 >>>>>if s5 6 U 6 s6 >>>;if s6 6 U 6 1

ð6Þ

The psychometric indicators for latent variables zas (Symbolic
Affective) and zaf (Independence) are treated as continuous variables and modelled as follows:

I r ¼ kr zas þ trwith r ¼ 1; . . . ; 6

ð7Þ

I r ¼ kr zaf þ trwith r ¼ 7; . . . ; 10

ð8Þ



where tr  N 0; r2tr ; r ¼ 1; . . . ; 10, where the variances r2tr are tobe estimated. It is assumed that the error terms in (Eqs. (7) and(8)) are uncorrelated. The identification of latent variables is
ensured by fixing k1 ¼ 1 (for latent variable Symbolic Affective);and k8 ¼ 1 (for latent variable Independence).
The joint probability of the choice and psychometric indicators
is given by:Pðy; IÞ ¼

R1 R1
1

1

PðyjX; e^; zas ; zaf Þg 1 ðI 1 jzas Þ . . .

g 6 ðI 6 jzas Þg 7 ðI 7 jzaf Þ . . . g 10 ðI 10 jzaf Þf as ðzas Þf af ðzaf Þdzas dzaf
ð9Þ^; zas ; zaf Þ is an ordinal logit model. That is, the probawhere PðyjX; ebility that y takes level m is given as follows:Pðy ¼ mÞ ¼ Fðsm  VÞ  Fðsm1  VÞ

ð10Þ

where F is the cumulative distribution function of the logistic distribution, g are the probability density functions for the indicators I
and f the probability density functions of the latent constructs.

3.4. Model formulation with expectation as in TPB
The model framework can be seen in Fig. 1. In the figure, latentvariables are represented as ellipses and observed variables are

1
In line with most of the literature on hybrid choice models we assume that thelatent constructs are orthogonal/uncorrelated with each other (e.g. Ben-Akiva et al.,
2002b; Bolduc and Alvarez-Daziano, 2010).

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P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25
Table 1
Measurement of observed variables, latent variables and indicators.
Variables
Latent variables
zas
Attitudes-Symbolic
affective (Cronbachs’
alpha = 0.83)

Indicators

Measurement

I1
I2
I3
I4
I5

1 = strongly disagree
2 = disagree
3 = somewhat disagree
4 = neutral
5 = somewhat agree
6 = agree
7 = strongly agree

I6

Cars are coolCars allow to express oneself
Cars are trendy
Cars bring prestige
Cars allow to distinguish oneself
from others
Cars are fun to havezaf

Attitudes-Independence(freedom) (Cronbachs’
alpha = 0.67)

I7
I8
I9
I10

Cars are convenient
Cars allow one to travel anytime
Cars allow one to be independent
Cars allow one to travel anywherezn

Subjective social norms –SSN (Cronbachs’
alpha = 0.83)

I11
I12
I13
I14
I15
I16
I17

Parents
Partner
Family members and relatives
Close friends
Peers at university
People in neighbourhood
People in province/state18 = The person group has a strong expectation that the respondent
will not buy a car and the group is extremely important; . . . ; 0 = No
expectation or no influence; . . . ; 18 = strong expectation and strong
influence on purchase motivation

Observed variables
xrc
Regular car use

1: use car at least twice a week; 0: otherwise

Income (wages)

xwd
xsa
xsb
xsc
xsd
xse
xsf
xsg

1: no answer; 0: otherwise
1: respondent is from this site; 0: otherwise^

Income dummy
Utrecht (site a) dummy
Japan (site b) dummy
Berkeley (site c) dummy
Taiwan (site d) dummyIndonesia (site e) dummy
China (site f) dummy
Beirut (site g) dummy
Total expectation of others to buy cars^

Total SSN

126. . .; 0; . . .126enMonthly income is specified as a continuous variable.
Each category of each country is transformed into US$ by
using the purchasing power parity (ppp) conversion
factor published by the World Bank (2014). Then the
middle point of each category is taken as representativeof that category. The right column shows the categories
given to students and the midpoint conversion for
Berkeley

1 = US$ 0–500 ? US$ 250
2 = US$ 500–1000 ? US$ 750
3 = US$ 1000–1500 ? US$ 1250
4 = US$ 1500–2000 ? US$ 1750
5 = US$ 2000–2500 ? US$ 2250
6 = US$ 2500–3000 ? US$ 2750
7 = More than US$ 3000 ? US$ 3500xw

21. . .; 0; . . .21

3.5. Alternative formulation with total SSN

3.7. Alternative formulation with SSN as latent variables

The second model that we propose here is similar to the first^) with Totalmodel above. We replace the variable expectation (e^ ) as can be seen in Eq. (11).
SSN (nThe fourth model is similar to the other three models exceptthat the SSN variable here is a latent one. For this model, a single
latent construct SSN (zn ) is constructed by using the seven groupspecific indicators shown in Table 1. The interpretation for this formulation is that the respondent perceives one SSN as a norm factor. A further advantage of this specification is that it takes into
account the correlation among responses to the various referencegroup questions through their joint dependence on one latent construct. The formula of this model is shown in Eq. (12) below:^ þ brc xrc þ bw xw þ bwd xwd þ bsa xsa
V ¼ ðbas zas þ baf zaf þ bn^ nþ bsb xsb þ bsc xsc þ bsd xsd þ bse xse þ bsf xsf Þ

ð11ÞThe model framework can be seen in Fig. 2 below. Total SSN is
the sum of SSN across person groups. The variable enters the model
as an observed variable.

V ¼ ðbas zas þ baf zaf þ bn zn þ brc xrc þ bw xw þ bwd xwd þ bsa xsa
þ bsb xsb þ bsc xsc þ bsd xsd þ bse xse þ bsf xsf Þ

3.6. Alternative formulation with SSN as observed variables
In the above formulation one observed SSN variable is constructed by summing up the expectations from different population groups i. Alternatively, in line with our discussion on
distinguishing inner and outer person groups, we also test a model
as shown in Fig. 3 where we treat the ni for a reference group i as
an observed variable with only one observed variable for eachgroup. The decision to include only one observed variable for eachgroup is to minimize multicollinearity.

ð12Þ

where it is assumed that all latent variables are normally dis
tributed across the population, including zn  N 0; r2n , and areuncorrelated with each other. The psychometric indicators forlatent variables zn (SSN) are treated as continuous variables andmodelled as follows:

I r ¼ kr zn þ trwith r ¼ 11; . . . ; 17

ð13Þ

where tr  Nð0; r2tr Þ; r ¼ 11; . . . ; 17, where the variances r2tr are tobe estimated. It is assumed that the error terms in (Eq. (13)) are18

P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25

Fig. 1. Car intention model with total expectations as observed variable

Fig. 2. Car intention model with total SSN as observed variable.

uncorrelated. The identification of latent variables is ensured by fixing k11 ¼ 1 (for latent variable SSN).
To better understand the formulation, it can be seen in Fig. 4
below where the SSN factor is constructed by seven observed measures of SSN. The latent variable of SSN directly influences thelatent utility.

4. Descriptive analysis of students’ car purchase intentions
4.1. Aggregate survey statistics
In Table 2 we present aggregate statistics of the survey results
for the seven sites where it was conducted. In general, the respon-

P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25

19

Fig. 3. Car intention model with group specific SSN as observed variables.

dents are in their earlier twenties with an overrepresentation ofmales, especially in Japan and Taiwan, due to the sampling method
used. More than 50% of our respondents in all locations have adriving license with the exception of students in China. Note thatage, gender and driving license have no significant correlation withintention to buy a car in the future. Therefore, these variables areomitted in our subsequent models.
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. The majority of the students (more than 50%) in eachcountry are non-regular car users except for Beirut and Indonesia.
For symbolic affective, we calculated the mean value of six indicator variables (I1, . . . , I6) and report their average in the table. As
can be seen, 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 to the other locations. For independence, we utilize the same procedure for the four indicatorvariables (I7, . . . , I10). The highest overall rating comes from Berkeley followed by Utrecht, Beirut, and China. Indonesia and Taiwan
have the lowest rating. This might link to the intensive use of
motorcycles in the latter two locations, which offer more convenience, freedom, and saving of travel time than cars do.
Expectation of others is related to the perceived status value ofa car as well as the general perceived need to own a car. In Utrecht,
the value is understandably significantly lower than in other samples. Slightly surprising is the high-perceived expectation of othersfor students to buy a car in Japan and Berkeley. Motivations tocomply (mi ) might be seen as a measure of ‘‘independence.” Hereit is found again that the lowest rating is for the Dutch students
which possibly reflects a ‘‘more independent Western mind-set.”In the last row, we present the dependent variable, which is carpurchase intentions. We report the mean and standard deviationfrom responses to the 7-point Likert scale question. The order ofintention to own a car sorts the locations precisely into developed
vs. developing countries with Taiwan being in between the twogroups. Students in Utrecht, Japan, and Berkeley have the lowest
car purchase intentions; and students in Indonesia, China, and Beirut have the highest. This is consistent with the peak auto hypothesis of the developed world (Kuhnimhof et al., 2013; van der
Waard et al., 2013) as well as increasing auto dependency in thedeveloping world (Belgiawan et al., 2016; Zhu et al., 2012).
4.2. Subjective social norms
We show the result of interaction between expectation withmotivation to comply for each person group and country in Table 3.
All samples combined, it can be seen that parents are most influential followed by family members, one’s partner and close friends.
This result shows that students perceive higher expectations byparents, partners, family members, and relatives as the ‘‘inner circle of the social network.”
Outer circle consists of peers at university, people in the neighbourhood, and people in state/province which appear to have
weaker influence on the intention to buy a car. It is interesting to
see that only in the Utrecht case, all the mean values are negative
which indicates that there is rather a (weak) influence of others
motivating these students not to buy cars. In Japan and Indonesia,
the mean value of SSN parents is the highest compared to othersites. In Taiwan it appears that the influence of parents to buy a
car is the lowest among the six locations with positive values. In
Japan, Berkeley and Indonesia family members’ influence is thesecond most important while in Taiwan and China, partner is thesecond most important. In Beirut, close friends appear to be thesecond most important influence group.
In the last row of the table, we show the sum of SSN for theseven person groups for each site. We also calculate the average
from the seven indicators combined (I11, . . . , I17) and show theresult for each country as well as its correlation with the dependent variable. Looking at the latent construct SSN combined overall groups, Japanese students regard the influence of others highest. For all samples the correlation between SSN and the intention

20

P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25

Fig. 4. Car intention model with SSN as latent variable.

Table 2Aggregate survey statistics (mean and standard deviation).
Variable (number of observations)

All (l (r)) (1229)

Cor. coeff.a

Utr (84)

Jpn (142)

Brkly (226)

Twn (139)

Idn (200)

Chn (167)

Brt (271)

Age
Gender split (male)Driving license (have)
Personal income (US $/1000)b
Income dummy
Regular car user
Symbolic Affective
Independence
Expectation of othersc
Motivation to complyc
Likelihood to buy a car

20.5 (1.83)
0.57 (0.50)
0.69 (0.46)
0.52 (0.61)
0.12 (0.33)
0.53 (0.50)
4.41 (1.43)
5.57 (1.28)
0.67 (1.30)
2.22 (1/72)
5.15 (1.62)

0.00
0.02
0.02
0.11
0.04
0.22
NA
NA
NA
NA
NA

21.6
0.55
0.71
0.70
0.00
0.27
3.71
5.92
0.56
1.52
4.55

20.0
0.78
0.61
0.36
0.11
0.26
3.73
5.83
0.80
2.69
4.62

20.3
0.50
0.81
0.62
0.07
0.48
4.66
6.16
0.78
1.99
4.92

21.7
0.73
0.78
0.56
0.03
0.41
4.49
5.63
0.75
2.45
4.99

20.5
0.52
0.74
0.32
0.33
0.65
4.70
5.03
0.67
2.62
5.24

21.1
0.60
0.28
0.40
0.03
0.29
4.29
5.86
0.66
2.40
5.42

19.7
0.45
0.80
0.97
0.16
0.90
4.52
5.86
0.87
1.86
5.65

a

Correlation with the dependent variable.
Based on income group range measured with local currency.
cMean over all person groups bold: p value < 0.05.

b

to buy a car is significant. We obtain positive coefficients for all
locations except for Utrecht. This might indicate that Dutch students have a strong desire to be perceived as not following expectations. Significant correlation in the case of Japan and Taiwan
might indicate that the culture in these locations is more collective
compared to the more individualistic Dutch culture. We findthough that for China, also a more collective society, the correlation is significant only at 10% level and instead for Berkeley weobserve a surprisingly high positive correlation. Therefore, simple
explanations based on collective versus individual cultures do
not explain the effect of uncontrolled correlation between SSN
and car purchase intentions well.
The results of the correlation of SSN do not give a clear pattern
between developed and developing countries. Thus, at this point,

we might say that the impact of social norms is based on culturalissues rather than economic development. To investigate this further, we run a series of ordinal logistic regression models withcar purchase intentions as dependent variable and firstly total
expectations, ^e, and, in a second series of models, with total SSN,
^ , as independent variables. Table 4 then shows the results of subn
sequent t-tests as to whether there is a difference in parameter
estimates between two locations. Negative values indicate thatparameter estimates for locations on the vertical axis are higher.
We observe that in most cases country specific differences are
significant. We find that in Utrecht estimates are persistently lower
than in other locations, i.e. that expectations and norms are lessimportant to explain intentions. We find further some differences^ , e.g. in comparing Japanbetween parameter estimates ^e and n

21P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25
Table 3
Descriptive statistics (mean and standard deviation) of SSN.
Variables
Person group
SSN parents
SSN partner
SSN family members
SSN close friends
SSN peers at university
SSN neighbourhood
SSN province/state
Combined^)
Total SSN (nSSN (average)
Correlation with dependent variable

All (1229)

Utrecht (84)Japan (142)

Berkeley (226)

Taiwan (139)

Indonesia (200)

China (167)

Beirut (271)

5.70(7.56)
2.74(5.85)
3.08(5.31)
2.55(4.88)
1.59(3.64)
0.67(2.59)
0.64(2.55)

3.24(5.87)
2.10(5.29)
1.55(3.52)
1.51(3.99)
0.50(2.25)
0.08(0.39)
0.02(0.22)

7.75(7.24)
2.14(5.20)
5.28(6.54)
3.92(5.75)
3.63(5.15)
1.83(4.08)
2.60(4.59)

5.86(7.51)
2.25(5.11)
2.70(4.61)
2.33(4.38)
1.29(3.01)
0.71(2.29)
0.49(1.70)

4.88(7.17)
3.45(6.38)
2.84(5.02)
3.09(4.99)
1.56(3.71)
0.28(1.69)
0.53(2.18)

7.25(6.26)
3.58(5.79)
4.17(5.15)
2.51(4.15)
1.67(3.33)
0.74(2.55)
0.43(2.49)

6.57(7.03)
4.35(6.11)
3.17(4.72)
2.29(3.98)
1.39(3.14)
0.38(2.32)
0.22(1.54)

6.03(7.71)
2.98(5.71)
2.92(5.31)
3.20(5.30)
1.51(3.50)
0.61(2.62)
0.42(2.32)

16.97(24.15)
2.42
0.23⁄

9.00
(16.63
1.29
0.24⁄

27.15(26.99)
3.88
0.24⁄

15.62
(20.83)
2.23
0.37⁄

16.63
(23.98)
2.38
0.47⁄

20.33
(22.73)
2.90
0.18

18.36
(20.41)
2.62
0.17

17.66
(24.87)2.52
0.23⁄

Bold: p value < 0.05; Bold + ⁄: p value < 0.01.

and Berkeley we find that perceived expectations are less influential in Japan but that the effect of total SSN appears not to be different. One might speculate as to the reasons for suchdifferences; with the data available to us currently we do not seemto be able to come up with a consistent hypothesis though. It
would require a different (possibly qualitative) study to understand how cultural differences regarding expectations transform
into purchase intentions. In the study present here our focus
remains on which modelling approach in general appears to best
explain the influences of others on purchase intentions.
4.3. SSN person group determination
As discussed in Section 3.6 and shown in Fig. 3, in the modelwith observed SSN factors, we are looking for two groups that
could represent the ‘‘inner circle” and ‘‘outer circle” of influence.
Furthermore, as will be shown in the estimation results, we findstrong correlation between the seven SSN factors, given additional
reason to only select a reduced number if the factors are treated as
observed variables.
In line with Table 3 and after initial model tests, which we omit
for brevity, we found that SSN related to neighbours and general
people in the province are not significant and hence appear to be
not good indicators. Further, when controlling for other factors,
we observe that SSN close friends has similar, though weaker, influence than SSN peers at university, possibly because one does not
perceive as much need to compete with friends than with peers.
SSN partners have been found to be significant, though only few
undergraduates have partners so that we prefer to also drop thisgroup from our model. In conclusion, in the model with groupspecific observed SSN we decided on SSN parents and SSN peers atuniversity as representative groups since parents are being influential and in the ‘‘inner circle” of the respondents whereas peers atuniversity can be seen as an ‘‘outer circle” that still exerts significant pressure on students. Details and further discussion can befound in Belgiawan (2015).
5. Estimation results
In this section we discuss the results of the four models which
we divide into: sum of person groups, group specific, and latent
factor. All of the models are estimated using simulated maximum

likelihood estimation with 10,000 Halton draws in Python Biogeme(Bierlaire and Fetiarison, 2009). We have also tested the modelwith different number of draws as well as different starting values
to ensure convergence and stability of the results. For better interpretation, we standardize the parameter estimates for latent andobserved variables. Following Long (1997, p.128), the standardized
value, bsx is obtained as follows:

bsx ¼

rx bx
ry

ð14Þ

where rx denotes the standard deviation of variable or factor x,
while ry denotes the standard deviation of the intention to buy acar. The results of the four models can be seen in Table 5. The standardized value is reported in parenthesis. Note that the standardized value is calculated only for the statistically significant
variables. We also report the robust t-test results in the table.
Firstly looking at our control variables income and regular car
use across the models, we observe that these variables have theexpected coefficient signs and the coefficient magnitudes are similar across models. Regular car use appears to be more highly correlated with the dependent variable than income, which isunderstandable given that we aim to explain purchase intentions
not actual purchase. We also incorporate site specific dummy variables with Beirut as the reference. In the first model, four sites are
significant while Indonesia and China are not. For the other threemodels, only China is not significantly different from Beirut. All
the significant coefficients are negative indicating that comparedto Beirut, students in the other six locations are less likely to buy
a car in the future.
Based on the standardized value, for the first model, total expectations is the most influential factor (standardized value = 0.27). If
we do not compare with site-specific dummy variables, the second
most influential factor is independence followed by regular car use.
Income is the least influential factor, while symbolic affective isslightly more influential than income. For the second model, total
SSN is the most influential factor (standardized value = 0.28) while
the effects of the other explanatory variables seem similar to thosein the first model. This result indicates that the influence of expectation is almost equal to the influence of the norm which meansthat the inclusion of motivation to comply gives little power tothe prediction of intention, similar to what is discussed in RAA(Fishbein and Ajzen, 2010).

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P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25

Table 4^ between locations towards car purchase intention.
T-test for difference in parameter estimates ^e and n
LocationsUtrecht
Japan
Berkeley
TaiwanIndonesia
China

Japan

Berkeley

TaiwanIndonesia

China

Beirut

^e

^
n

^e

^
n

^
e

^
n

^
e

^
n

^
e

^
n

^
e

^
n

4.05

4.33

5.44
2.01

5.16
1.42

5.12
2.25
0.50

5.46
2.40
1.04

3.18
0.26
2.16
2.25

3.72
0.97
2.47
3.33

3.66
0.13
2.11
2.27
0.15

3.86
0.15
1.52
2.36
2.31

4.07
0.37
-2.74
2.88
0.03
0.22

4.64
0.02
1.67
2.65
1.05
0.14

Bold: p value < 0.05; Italic: p value < 0.01.

Looking at the third model, we observe that SSN, particularly SSN
parents, is the most influential variable, followed by regular car use.
Note that in this model, SSN peers at university is the least influential
variable for car purchase intention, together with Income. Judging
also by the lower value of the influence of symbolic affective, it seems
that rather than peer pressure, family expectations are more important. In the fourth model, we see that the latent factor of SSN is themost influential (standardized value = 0.29). The effects of the otherexplanatory variables are similar to those of the first and second
model. In constructing SSN, all estimated k values are below the kvalue for parents (fixed at 1), which is in line with the third model
where we observe that parents are most influential. The k values further reflect the inner/outer circle classification with the inner circle
being more influential. The standard deviations of the measurement
errors of the indicators (r parameters) are also estimated. It is foundthat rparents is largest, suggesting that parental expectations are notperceived homogenously though.
At the bottom of the table we provide the pseudo R2 informationfor each model which was calculated using the initial
log-likelihood (L(0)) and the final likelihood for the choice-only
component of the total likelihood. The L(0) is the log-likelihood atzero for a model with thresholds only. For the comparison of theR-squared across models, the L(0) should be the same for all models
to have a consistent comparison. Therefore, we use the L(0) of thefirst model as the base value. From the result of pseudo R2, the modelwith latent SSN is not necessarily expected to perform better interms of goodness of fit of the choice (intention) model because it
optimizes the estimated parameters over both the intention indicators and the SSN indicators while the other models do not optimize
over SSN indicators. From the pseudo R2 result (Nagelkerke’s) wefind that for the three models without SSN latent factor, the variancein intention explained is around 20% while in the one with SSN latent
factor around 14% variance in intention is explained.
We expect though that the norm identified by the latent model
should be the most precise estimate since we estimate errors foreach observed variable. Therefore, SSN of the latent model is predicted to be most correlated with intention. We only find very limited effect of this though. The standardized coefficient for SSN in
our latent model is 0.29 which is similar to the effect of Total
SSN (0.28) in the second model and Total expectation (0.27) inModel 1. This suggests that the ‘‘simple” (at least computationally)
definition of norms as in the first models gives similar results tothe model with latent definition of norms.
In conclusion, we found that all the parameters for the four models seem quite stable and similar indicating that those four models
can be utilized to incorporate social norms in explaining car ownership intentions. Whether as observed variable or as latent variable,
the influence of the social norms factor remains stable.
6. Conclusions
This paper focused on the role of social norms for car purchaseintentions of young students as the literature review emphasized

the potential importance of such norms. Different possible definitions and formulations based on literature drawn from social psychology are discussed and compared, which to our knowledge has
not been done before. Furthermore, previous studies discussing theeffect of norms mostly relied on descriptive studies or modelling ofnorms within structural equation models which control for far lessexplanatory variables.
Instead, we estimate ordinal hybrid choice models (OHCM)
with four formulations of social norms. In three of them social
norms are modelled as interaction factor between expectationand motivation to comply, while one uses the sum of expectations.
Seven person groups are defined that might influence a person’spurchase intentions (parents, partner, family members and relatives, close friends, peers at university, people in one’s neighbourhood, people in the province/state).
From the four models we find that attitudinal factors (symbolic
affective and independence) and other variables (regular car use,
personal income) significantly influence car purchase intention.
Total of expectation and total of SSN also significantly influencecar purchase intention. In the model with two group specificobserved SSN, we find similarly that SSN parents and SSN peers
at university significantly influence car purchase intention. Using
the latent SSN construct, it is found that SSN significantly correlates with car purchase intention. Taking all these results together,
there is strong evidence that social norms significantly influence
car ownership motivations. Partly auto or non-auto culture as wellas collective vs. individualistic culture (consider Japanese correlation between SSN and car purchase intention reported in Table 3)
can explain expectations, but there seem to be more factors that
we cannot explain.
Given the limited sample size from each of the seven sites, we
believe that this study makes primarily a methodological contribution in showing different ways to formulate and estimate social
norms that one might choose in the future depending on the problem, data and modelling software available.
In our application, comparing the four models, we can seethat the strength of norms factors, the standardized version, isalmost equal across all models. In other words, estimating alatent SSN with group specific error terms does not appear toincrease the correlation of the resulting norms term with purchase intention significantly. If we build the theoretically lesssatisfying terms ‘‘Total expectation” or ‘‘Total SSN” by simply
summing up the perceived expectations or perceived pressure
by different influence groups, we obtain a similar result. Therefore, we might conclude that (a) latent modelling of norms does
not add much to our understanding compared to simpler model
formulations and (b) that it is sufficient to include only a few
key person groups reflecting the influence of people close to
the person (inner circle) and the wider community the respondent is embedded in (outer circle), as also supported by thehighest pseudo R2. This might be good news for future researchattempting to include norms in other studies on decisions concerning fairly large investments such as car purchase. We

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P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25
Table 5Car purchase intention models.
Variable

Sum of person groups
Expectations

Group specificSocial norms

LatentSocial norms

Social norms

Est. (stdz)

t-testEst. (stdz)

t-testEst. (stdz)

t-testEst. (stdz)

t-test0.21 (0.12)
0.24 (0.16)
NE

2.77
3.13

0.22 (0.13)
0.22 (0.15)
NE

2.92
2.96

0.24 (0.13)
0.22 (0.15)
NE

3.12
2.97

0.22 (0.13)
0.25 (0.16)
0.09 (0.29)

2.94
3.29
6.28

r Symbolic Affectiver Independence
r SSN

0.91
1.10
NE

20.36
21.09

0.91
1.10
NE

20.36
21.20

0.92
1.10
NE

20.36
21.20

0.91
1.08
5.44

20.43
19.90
22.11

Observed variables
Total expectations
Total SSN
SSN parents
SSN peers at university
Regular car use
Income (103 US$)
Income dummy

0.06 (0.27)
NENENE
0.49 (0.15)
0.30 (0.11)
0.21

6.90
NE
NE
NE
4.17
2.56
1.20

NE
0.19 (0.28)
NENE
0.49 (0.15)
0.28 (0.11)
0.19

4.15
2.46
1.11

Site-specific dummy (Beirut as reference)
Utrecht (r = 0.25)
Japan (r = 0.32)
Berkeley (r = 0.39)
Taiwan (r = 0.32)
Indonesia (r = 0.37)
China (r = 0.34)

0.53(0.08)
0.73(0.14)
0.74(0.18)
0.67(0.13)
0.25
0.13

2.28
3.26
4.02
3.60
1.35
0.65

3.30
2.27
1.55
0.72
0.36
1.70

1
1.03
0.99
1.12
0.98
1.08
1.23
1.09
1.25
0.96
0.90
1.13

Latent variables
Symbolic affective
Independence
SSN
Latent variables standard deviations

4.16
2.47
1.05

NE
NE
0.04 (0.21)
0.05 (0.11)
0.51 (0.16)
0.30 (0.11)
0.21

4.97
2.70
4.33
2.63
1.18

NE
NE
NE
NE
0.49 (0.15)
0.29 (0.11)
0.20

0.64(0.10)
0.93(0.18)
0.75(0.18)
0.72(0.14)
0.40(0.09)
0.02

2.83
4.17
4.04
3.91
2.11
0.11

0.58(0.09)
0.90(0.18)
0.75(0.18)
0.66(0.13)
0.40
0.05

2.50
4.00
4.01
3.61
2.10
0.25

0.70(0.11)
0.94(0.18)
0.76(0.18)
0.72(0.14)
0.37(0.08)
0.06

3.08
4.16
4.03
3.89
1.91
0.27

13.44
8.12
9.64
13.28
17.73
19.39

3.35
2.32
1.75
1.55
1.92
2.42

13.76
8.07
9.64
13.29
17.77
19.35

3.30
2.27
1.55
0.71
0.37
1.71

13.43
8.08
9.63
13.30
17.74
19.38

3.66
2.63
1.91
1.08
0.01
1.35

14.90
8.08
9.64
13.28
17.72
19.31

34.05
15.28
36.03
19.67
34.92
16.87
31.65
16.30
27.22
18.35
38.28

1
1.03
0.99
1.12
0.98
1.08
1.23
1.09
1.25
0.96
0.90
1.13

34.03
15.28
36.03
19.67
34.92
16.87
31.64
16.29
27.22
18.35
38.27

1
1.03
0.99
1.12
0.98
1.08
1.23
1.09
1.24
0.96
0.90
1.13

34.01
15.29
36.05
19.67
34.92
16.87
31.65
16.29
27.25
18.36
38.26

1
1.04
0.98
1.13
0.98
1.09
1.25
1.08
1.25
0.96
0.91
1.13

34.39
14.90
35.15
19.72
35.39
16.99
31.51
16.28
27.27
18.13
38.31

7.13

Threshold

s1
s2
s3
s4
s5
s6
Measurement equations for latent variables
Symbolic affective
Cars are coolk1

r1

Cars allow to express oneself

k2Cars are trendy

k3

Cars bring prestige

k4

Cars allow to distinguish oneself from others

k5

Cars are fun to have

k6

r2
r3
r4
r5
r6

Independence
Cars are convenientk7

r7

Cars allow one to travel anytime

k8

Cars allow one to be independent

k9

Cars allow one to travel anywhere

k10

r8
r9

r10

Subjective social norms
Parentsk11

r11

Partnerk12

Family members

k13

Close friends

k14

Peers at university

k15

People in Neighbourhood

k16

People in province/state

k17

r12
r13
r14
r15
r16
r17

0.68
0.77
1
0.66
0.49
1.33
0.64
1.17
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE

15.42
26.57
11.85
9.39
36.89
14.13
35.02

0.68
0.77
1
0.66
0.49
1.33
0.64
1.17
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE

15.44
26.74
11.81
9.37
36.89
14.09
35.01

0.68
0.77
1
0.66
0.49
1.33
0.64
1.17
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE

15.44
26.74
11.81
9.38
36.89
14.10
35.02

0.70
0.77
1
0.70
0.50
1.33
0.66
1.16
1
5.53
0.68
4.69
0.79
3.40
0.78
2.77
0.56
2.24
0.27
2.20
0.23
2.26

16.67
26.61
13.77
9.47
36.59
14.43
34.53

26.84
16.20
34.83
26.48
19.63
12.22
8.56
11.51
17.95
6.87
17.68
5.91
16.98

(continued on next page)24

P.F. Belgiawan et al. / Travel Behaviour and Society 7 (2017) 12–25

Table 5 (continued)
Variable

Sum of person groups
Expectations
Est. (stdz)

Model statistics
Sample size
Estimated parameters
Halton draws number
Log-likelihood (L(0))
Log-likelihood (L(1))
McFadden R2
Cox and Snell R2Nagelkerke’s R2Group specificSocial norms

t-test1229
38
10,0002178.02
2044.96
0.061
0.195
0.200

Est. (stdz)

LatentSocial norms
t-test1229
38
10,0002178.02
2043.78
0.062
0.196
0.202

Est. (stdz)

1229
39
10,0002178.02
2044.65
0.061
0.195
0.201

Social norms
t-testEst. (stdz)

t-test1229
52
10,0002178.02
2089.28
0.041
0.134
0.138

Bold = p value < 0.05; Italic p value < 0.10; NE = Not estimated; in the parenthesis is the standardized value.

emphasize though that these conclusions require confirmation
through further studies with different samples.
We point out further that the stable standardized coefficientvalues for the respective norm related terms give us some confidence in the relative importance of social norms compared to otherfactors influencing choice. We obtain a consistent estimate for the
standardized value of the coefficient of the variable regarding
social influence of around 0.2–0.3 in all four models. This value isthe largest among all our explanatory variables in all four models.
Due to limited sample sizes we did not estimate country specific norming effects in our OHCM. From the correlation analysis weobserve that this might be worth doing though if larger samples
can be obtained. We find significant positive correlations between
norms and car purchase intentions in all samples except for
Utrecht. Dutch students appear to have a strong desire to be perceived as not following expectations, which does not seem unrealistic as it suggests that the Dutch have a strong desire to beperceived as ‘‘unique individuals”. Looking at correlation betweensocial norms and car purchase intention, it seems that the impact
of social norms on car purchase intention cannot be explained bydifferences between developed and developing countries. We also
conducted further tests with separate models for developed anddeveloping countries and did not find any differences.
Due to our limited sample, our policy recommendations remainvague though. Possibly, very tentatively we suggest that the
Utrecht case can be a good example for policy makers in the developing countries, as in Utrecht, students appear to have decoupled
perceived expectations from attitudes towards car. Our Dutch findings might though also be an example that shows that one cannotchange social norms unless the public transport system has a sufficient quality. Based on these findings, it is suggested that expectations of others and SSN should be addressed in soft policy
measures such as ‘‘mobility management”. We acknowledge that
our sample is too limited to be confident to generalize any specificrecommendations.
Finally, we acknowledge that current purchase intentions do
not necessarily reflect future car purchase, especially if studentschange their lifestyle after graduation. Therefore, this paper does
not claim that the findings should be directly translated into
regression models for demand forecasting even for this cohort.
However, it is believed that current intention might be one important determinant explaining future purchases. The study has somefurther shortcomings that one could only address though if thedata collection would be repeated. We acknowledge that the different sampling methods across the different locations might contribute to potential self-selection bias. We also acknowledge thatthe different areal coverage of samples in the sites, i.e. our Dutch
sample uses observations from Utrecht only, whereas our Japan
sample is drawn from across the country, could have had an influ-

ence on the result. Therefore, more representative samples from awider population segment including from different cultures are
preferable. Additional quantitative or qualitative surveys might
further aim to investigate the social-cultural background of
respondents in more detail. Unfortunately, current data sets do
not allow to trace the respondents in 5–10 years to see whether
they have actually made choices according to their intentions
and/or whether their attitudes and perceived norms have changed.

Acknowledgements
The authors would like to thank Dick Ettema (Utrecht University, Netherlands), Tzu Chang Lee (National Cheng Kung University,
Taiwan) and Zhang Dong (Tongji University, China) for their help
during the data collection process. The research was further supported by UC Berkeley Global Metropolitan Studies, the National
Science Foundation, and the Dean’s office of the Faculty of Engineering and Architecture at AUB.

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