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This article discusses the mobility intentions of adolescents in Tirana, Albania-one of the least studied areas of Central and Eastern Europe. The main research question-explored through Structural Equation Modelling (SEM)-is whether now, nearly three decades after the demise of state socialism, cars are still considered as a necessity and/or a status symbol among adolescents, who never experienced socialism and its extreme restrictions on car ownership and use. Although Tirana is a very compact city with work, services, and social contacts typically within walking distance, the findings indicate that most adolescents in Tirana, including those who do not particularly like cars and driving, intend to purchase cars and drive in the future. Cars remain a strong status symbol. This does do not bode well for transport sustainability. If unchecked, adolescents' intentions might directly translate into car-dependent travel behavior in the future.
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Cars as a status symbol:
Youth attitudes toward sustainable transport in a post-socialist city
1) Dr Elona Pojani
University of Tirana, Tirana, Albania elonapojani@feut.edu.al
2) Dr Veronique Van Acker
LISER, Esch-sur-Alzette, Luxemburg, Veronique.VanAcker@liser.lu
3) Dr Dorina Pojani
The University of Queensland, Brisbane, Australia d.pojani@uq.edu.au
Acknowledgment
This is an Authors’ Original Manuscript of an article whose final and definitive form, the
Version of Record, has been published in Transportation Research Part F, 2018,
available online at: https://www.sciencedirect.com/science/article/pii/S1369847817302930
Abstract
This article discusses the mobility intentions of adolescents in Tirana, Albania one of the least
studied areas of Central and Eastern Europe. The main research question - explored through
Structural Equation Modelling (SEM) - is whether now, nearly three decades after the demise
of state socialism, cars are still considered as a necessity and/or a status symbol among
adolescents, who never experienced socialism and its extreme restrictions on car ownership and
use. Although Tirana is a very compact city with work, services, and social contacts typically
within walking distance, the findings indicate that most adolescents in Tirana, including those
who do not particularly like cars and driving, intend to purchase cars and drive in the future.
Cars remain a strong status symbol. This does do not bode well for transport sustainability. If
unchecked, adolescents’ intentions might directly translate into car-dependent travel behavior
in the future.
Keywords
mobility intentions; adolescents; Structural Equation Modelling (SEM); Tirana, Albania;
sustainable transport;
Note
Accompanying figures and tables are at the end of this manuscript.
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1. Introduction
In Western contexts, there is evidence that Millennials (the generation born after 1980),
especially men, are less interested in owning and driving cars than their parents, and more
attracted to alternative modes of transport (Kuhnimhof et al. 2012). However, the opposite
seems to be true in parts on Central and Eastern Europe (CEE). Here, after the demise of
socialism in 1990, private car ownership and use increased sharply (Pucher and Buehler 2005).
Cars were purchased not only to fulfill mobility needs but also to signify freedom and a higher
socio-economic status in the new, market-driven, competitive milieu (Pucher and Buehler 2005;
Stead and Pojani 2017).
Now, nearly three decades on, are cars still considered as a necessity and/or a status symbol in
the region? In particular, are youth, who never experienced socialism and its extreme
restrictions on car ownership and use, as car-oriented as earlier cohorts? Are environmental and
mobility attitudes and beliefs among Eastern European youth converging with the attitudes and
beliefs of their Western counterparts? Or are they still shaped by the socialist and post-socialist
legacy of the region? The foregoing questions prompted this research study.
The way in which pro-environmental behavior in adolescents is established is particularly
important in terms of sustainability outcomes because the origins of behavioral beliefs are in
childhood (UN 1992). This article discusses the mobility aspirations of university students in
Tirana, Albania – one of the least studied areas of CEE. This in an interesting case because car
ownership and use was entirely prohibited until 1990s. Now, the city is very compact with
work, services, and social contacts typically within walking distance, but it is also flooded with
car traffic.
The first part of the article presents the conceptual framework, which guided the study design
and analysis. This conceptual framework is loosely based on the Theory of Planned Behavior
(Ajzen 1991). The case study context is described next. The third part of the article provides an
overview of the data collected for this study (374 surveys of first-year university students) and
the method of analysis (Structural Equation Modeling). The reminder of the article deals with
the findings, concluding with policy implications.
2. Conceptual framework
2.1 Theory of Planned Behavior
The authors’ understanding of the role of motivation or intention in predicting behavior derives
from the Theory of Planned Behavior (TPB, Figure 1), a well-tested theoretical model
developed by Ajzen (1991). According to this theory, the likelihood of a particular behavior
being performed in specific contexts (e.g., commuting to school or work by a certain travel
mode) is highly dependent on an individual’s intention to perform the behavior. In this study,
the authors focus specifically on intentions rather than the behavior that results from those
intentions (or motivations). In turn, the intention to perform a behavior can be accurately
predicted by three independent concepts: (a) beliefs about the likely consequences of the
behavior (behavioral beliefs), (b) beliefs about the expectations of others (normative beliefs),
and (c) beliefs about the presence of factors that may further or hinder the performance of the
behavior (control beliefs). As a general rule, the stronger the intention to engage in a behavior,
the more likely is its performance. Therefore, to predict future travel patterns and behaviors
correctly, it is important to understand the travel intentions of youth. But the behavior is only
performed if an individual has actual control over the behavior in addition to having the right
motivation or intention. Actual control depends on an individual’s ability to decide at will
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whether to perform or not the behavior and on his/her opportunity and resources - such as,
money, skills, cooperation of others, etc. (Ajzen 1991).
Clearly, the TPB views human social behavior as reasoned although people’s beliefs might
be unfounded or biased. However, habits, moral principles, personal norms, self-identity, and
lifestyles are also likely to be a motivation for humans to perform certain behaviors (Aarts et
al. 1998; Hunecke et al. 2001). Habits, in particular, may play an important role during
routinized, semi-automatic actions, such as the travel mode choice for the daily commute (Aarts
et al. 1998; de Bruijn et al. 2009). If habits are strong, the relationship between intention and
behavior in the TPB is weak, and vice versa. A few empirical studies have found that a trade-
off between habits and intentions exists in travel behavior (see Verplanken et al. 1998; Bamberg
et al. 2003). Also, individuals with a certain self-image, psychological makeup, personal norm,
morality, or lifestyle, e.g., those who see themselves as “environmentalists” or “urbane,” might
be more likely to use public transport or non-motorized transport for their commute (Hunecke
et al. 2001; Anable 2005; Scheiner and Holz-Rau 2007; Van Acker et al. 2011). Obviously, the
influence of factors such as habits, moral principles, personal norms, self-identity, and lifestyles
on travel modes is complex and may defy measurement. It is still being debated in the academic
community. TPB keeps being refined for application to travel and mobility studies (Haustein
and Hunecke 2007).
2.2 Mobility intentions of adolescents
The TPB has been applied to the following summary of the knowledge to date on the mobility
intentions of adolescents. Based on TPB and the findings from this literature review, the authors
constructed a preliminary conceptual model for this study, which is graphically represented in
Figure 2. This model, and the complex relationships therein, were then empirically tested
employing Structural Equation Modelling (SEM).
A few notes and caveats on the literature review:
The literature specific to mobility intentions and the fundamental beliefs of adolescents and
youth is very limited. That is why studies focusing on preteens (and their caretakers) have
been included.
Behavioral and normative beliefs have been discussed in conjunction with one another
because in the case of young and dependent people it is difficult to separate individual
attitudes from family norms. Parents’ beliefs have a large influence on their children’s
(Grønhøj and Thøgersen 2012). Conversely, children and youth with particular
environmental motivations have been shown to influence their parents’ decision about travel
modes (Panter et al. 2008).
Most previous studies have examined control beliefs and actual control, with a focus on the
home-school commute. As such, they have assessed the roles of individual constraints (e.g.,
gender, age, and family income), natural environment constraints (e.g., topography and
weather), and built environment constraints (e.g., urban form, urban size, home-school
distance, and transport infrastructure and facilities) on travel mode choices. Nearly all
studies on adolescents are set in developed, Western countries (see Sigurdardottir et al.
2013).
The role of habits, moral principles, personal norms, self-identity, and lifestyles is not
discussed in the following review because it is theoretically less clear, as mentioned.
2.2.1 Behavioral and normative beliefs
A few researchers have sought to determine the environmental beliefs of preteens and teenagers
in relation to travel choices. The findings vary by country. A summary of the finding is provided
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below, together with an explanation of how the constructs and relationship identified in prior
studies have been incorporated in the preliminary model for this study (Figure 2).
In the Low Countries, a study of Dutch preteens revealed that most children were aware of
sustainable transport issues and understood the negative effects of pollution and congestion.
Nevertheless, nearly all the study participants felt that the car would play an important role in
their transport as adults. While the greatest influence on travel behavior came from parents,
differing messages from parents and teachers appeared to cause conflict in beliefs (Kopnina
2011). In Figure 2, this is relationship is represented by an arrow pointing from the
interpersonal domain to the intrapersonal domain. A study of preteens in Flanders, Belgium,
found that cycling and driving were the favorite commute modes while negative attitudes
toward public transport prevailed. This suggests a relationship between attitudes, intentions,
and behavior, which in Figure 2 is represented by an arrow pointing from mobility attitudes to
mobility intentions. Participants who did use buses or trains evaluated public transport more
positively, which suggests that familiarity with a mode mitigates the effect of stereotyping.
Again, parents were an important influence on the travel beliefs and behaviors of their children
(Zwerts et al. 2010).
In Scandinavia, two Danish studies of adolescents’ attitudes toward sustainable transport and
commute modes found that a very high percentage of participants planned on learning how to
drive and owning a car. However, more than one quarter also intended to cycle to work in the
future. Positive travel experience by bicycle was associated with a greater intention to commute
by bicycle in adulthood. In Figure 2, this relationship is represented by an arrow pointing from
mobility habits to mobility intentions. Pro public transport and bicycle beliefs were heavily
influenced by the dominating norms within the family and in particular by how strongly they
were manifested in their parents’ beliefs and behaviors - i.e., parents’ car ownership choices,
environmental attitudes, and usual travel modes (Grønhøj and Thøgersen 2012; Sigurdardottir
et al. 2013). Again, this relationship is represented in Figure 2 by the arrows pointing from the
interpersonal domain to the intrapersonal domain. A study set in Sweden showed that family
car-ownership influenced attitudes about the importance of the car for adolescents, as both those
in car-free and car-owning families endorsed their own situation. Contrary to expectations, car-
owning and car-free parents did not differ in their attitudes toward adolescents’ independent
use of public transport. However, parents’ beliefs made a difference as their children
experienced more independent mobility if they had family support to do so (Sandqvist 2002).
In the U.K., an early study of preteens’ beliefs found that they had only vague knowledge or
understanding of the effect of transport (e.g., car pollution) on health. However, those
participants who believed that transport severely affects health showed a strong tendency to
also accept, and even suggest, car use restrictions (Boyes and Stanisstreet 1998). This suggests
a relationship between mobility attitudes, environmental personal norms, and the willingness
to accept car limitations, represented through arrows in Figure 2. Three more recent studies,
which examined perceptions and attitudes of preteens and teenagers toward transport modes,
found that participants were “car cultured.” Even teenagers had only a weak to average
understanding of the negative effects of overreliance on car transportation on health, safety,
wellbeing, and climate change. The younger age groups liked active transport activities such as
cycling and walking as they provided opportunities for socializing. However, even preteens had
already formed some negative stereotypes about alternative transportation. Also, many of the
participants made cultural associations with the car, perceiving its benefits in terms of identity,
self-image, and social recognition - at the expense of their environmental values. These pro-car
attitudes were reinforced from multiple sources of influence, cars being part of mainstream
travel culture in the U.K. and incorporated in major social institutions. This suggested that, a
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high proportion of youth participating in these studies will themselves own or want a car when
they are older (Baslington 2009; Kingham and Donohoe 2002; Line et al. 2010).
2.2.2 Control beliefs and actual control
Notwithstanding the importance of behavioral and normative beliefs, in the case of preteens
and adolescents control beliefs are dominant predictors of travel choices, and have been studied
more in depth, as noted, especially in relation to the home-school commute and in Western
European and North American settings. Generally, it is difficult to compare studies on the
school commute due to the heterogeneity of sample sizes, family composition, school systems,
and cultural and environmental characteristics, all of which have been examined as potential
predictors of active school travel. The main findings of the more comprehensive studies and
reviews are summarized below (Panter et al. 2008; Faulkner et al. 2009; Sirard and Slater 2008;
Wong et al. 2011; Lubans et al. 2009; Chillón et al. 2011).
While the reduction in preteens and adolescents’ walking and cycling is related to the increase
in car ownership, parents’ changing attitudes are also influenced by the feelings that the outside
world is a hostile, dangerous place where children are likely to be hit by cars or harmed by
adults – the “stranger-danger” concept (Zwerts et al. 2010). Road injuries are a reality and the
quality of public outdoor spaces, especially in the poorer sections of big cities, has declined.
Air, noise, and visual pollution (i.e., traffic and parking), as well as poor urban design, with
unwalkable spaces, are widespread outside pedestrianized historic city centers. Other factors,
unrelated to the built and natural environment, include increased incomes, increased female
participation in the workforce, cultural shifts from free play to organized activities, and
increased use of personal electronic home entertainment. In some countries, school choice
policies that allow students to attend any school within their city, rather than the nearest to
home, have contributed to growing car use for school related travel (Mackett 2012).
As for actual control, factors which correlate with active travel to school include: short home-
school distances; lower socio-economic status; minority status, urban character of residence
neighborhood; a densely populated school neighborhood; caretakers’ schedules; caretakers’
beliefs related to safety and security; small school size; male gender; and availability of
pedestrian infrastructure and route safety. The effect of age is unclear. Some studies show lower
levels of active commuting to school for children than adolescents - due, for example, to the
increased use of cycling in adolescents. Others show higher levels of active commuting to
school for children than adolescents - due, for example, to the increase of driving among
adolescents (Panter et al. 2008; Faulkner et al. 2009; Sirard and Slater 2008; Wong et al. 2011;
Lubans et al. 2009; Chillón et al. 2011). Note that Figure 2 does not refer to control beliefs and
actual control factors. However, in the subsequent analysis every relationship hypothesized in
Figure 2 was controlled for socio-economic and demographic factors such as age, gender, and
car ownership.
A handful of studies set in developing countries report mixed findings. A study based in Cyprus
found low rates of active school travel among Greek adolescents (20%) compared to other
European countries (almost 50% in Britain and as high as 80% in the Netherlands and Belgium)
despite Cyprus’ lower per capita GDP. This suggests that variables unrelated to income may
play a central role in travel behavior (Loucaides et al. 2010). A study based in the Philippines
also found relatively low rates of active school travel (40% overall and lower for female
students) which was surprising given the country’s low income levels. Adolescents in wealthier
and car owning households were more car-dependent, as expected. Participation in school or
after-school sport or exercise, or current employment, could not explain differences in
commuting patterns (Tudor-Locke 2003). In Albania and China (both post-socialist countries
with densely populated cities and short home-school distances), very high rates of walking to
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school were reported for preteens and adolescents, including those from car-owning families.
In these cases, variables such as gender, family composition, familiarity with the city, or
exposure to fearmongering news did not correlate with travel modes (Pojani and Boussauw
2014; Shi 2006).
3. Case study context
Tirana, the case study setting, is a compact city of about 800,000 inhabitants, without separation
of commercial and residential uses. Tirana’s density (approximately 14,500 inhabitants/sq. km)
is high. Its overall population and size are small compared to other European capitals. Since the
demise of the socialist regime in 1990, Tirana has become a primate city within Albania. It has
almost one third of the national population and most of the national wealth. The car ownership
rate (55%) is lower than in Western Europe but more than twice the national rate of car
ownership. The city has retained vibrancy and a social mix and the urban economy has
improved at a fast pace. This is a direct result of the densification, the fine-grained land use
pattern, and the economic transformation. On the other hand, the public sector obtains only
meager revenues due to the fact that much of the income within the country is earned through
remittances and informal channels and is therefore not taxable (Pojani 2010). The following
discussion of transport and mobility is primary based on Pojani (2011).
During socialism, private car ownership was prohibited. However, unlike other socialist
capitals, Tirana did not create a decent public transport system. During that era, the existing
(inadequate) bus system was heavily used. Extensive walking was also common, and bicycling
was popular, although as a privilege of adults, since few families could afford to purchase bikes
for their children. After the transition, when restrictions were removed, car ownership
skyrocketed. Now, more than half of the city’s households own a car, as noted. The middle and
high income portions of the population are increasingly car-dependent. Not only were cars
purchased to fulfill (perceived) mobility needs, but were also seen as symbols of freedom from
past deprivation and social status (though evidence on the latter has been anecdotal until now).
Very dense and chaotic traffic and unruly driving are the norm.
The shift from public transportation and non-motorized modes towards private cars has led to
a host of problems including enormous health and environmental damage. The capital was not
designed to accommodate cars and is choked with traffic much of the day. The urban population
endures high levels of air and noise pollution. Pedestrian traffic, which is still massive, must
navigate under unpleasant conditions, including constantly having to dodge heavy traffic and
cars parked on the sidewalks. Despite the mild weather and flat topography, for all but a few
inhabitants bicycling is no longer considered an option, due to the heavy traffic. In addition,
there has been an alarming increase in obesity rates, including childhood obesity, and a decrease
in physical activity, which may be related to the ever greater car dependence and the conversion
of neighborhood parks into building lots and parking lots.
Recently, steps have been taken to deal with urban traffic and transport issues. Curb ramps for
the handicapped and speed bumps are being installed, a few streets in the center have been
pedestrianized, and a few bicycle lanes and a low-tech bikesharing scheme have been
introduced. However, low budgets, corruption, and an extremely politicized planning
environment dominated by political parties are barriers to carrying out sustainable urban
transport planning.
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4. Method and analysis
4.1 Survey and sample characteristics
This study is based on a survey of 374 first-year university students, who were enrolled in the
three largest universities in Tirana (two public and one private). For two reasons, university
rather than high school students were surveyed: (1) university graduates will be part of the
country’s elite i.e., that portion of the population which will be in a position to own cars, set
trends, and influence public opinion; (2) due to ethical and safety concerns, it is practically very
difficult for researchers to access and survey high schools students in Tirana. Participants in
this study were all 18 or 19.
Surveys were conducted in 2014. In 2014, a total of 10,857 first-year students were enrolled in
the three universities. For this population size, a sample size of at least 371 is needed for an
accurate study with a 95% confidence level and a 5% confidence interval. Questionnaires were
distributed among the three universities proportionally to the number of students enrolled.
Participants were selected in the following manner: first the faculties from within the three
universities were selected, and then a random number of courses was selected among each of
those faculties. Paper-based questionnaires were distributed to 400 students in those courses
immediately before a class session, and the completed questionnaires were collected upon
completion about 15 minutes later. In total, 382 completed questionnaires were collected,
slightly above the required sample of 371.
After a Missing Value Analysis in SPSS, a sample of 374 respondents was retained. Six cases
(or ‘respondents) with missing values on more than half of the variables were deleted. Two
additional cases were deleted because extreme values were reported for the home-school
commuting distance variable (more than 100 km). Descriptive statistics for this final sample (N
= 374) are summarized in Table 1. A few comments on the sample characteristics are noted
below:
Women are overrepresented with three quarters of the sample being female. In Tirana, more
than half but less than three quarters of the student body is female.
The average distance between home and university is 28.3 km (with a standard deviation of
17.39). Only a small portion of the home-university commute trips are short while a third
are rather long (30km or more). In light of this, it is remarkable that so many respondents
walk to university on a regular basis. On the other hand, cycling is almost non-existent,
despite some public sector efforts to provide cycling infrastructure in the city. Public buses
are commonly used to commute. Car use is low. This is unsurprising given the rather low
level of car ownership and/or availability. In the sample, 1 in 5 households is carless, and
most car-owning households have only one car which is shared by several drivers in the
household.
About 40% of the parents of the surveyed students are university graduates. This is a high
level compared to a national average of less than 15%. Most respondents report to have an
average household income (about $560/month in Tirana). These characteristics are as
expected. A strong correlation between parents’ and children’s education level is typical in
other countries too, and university graduates are less likely to live in poverty (Ermisch and
Pronzato 2010; Serafino and Tonkin 2014).
The questionnaires included 17 questions consisting of statements. Respondents had to agree
or disagree with each statement on a 4-point Likert scale (1=strongly disagree / 4=strongly
agree) with an additional ‘I do not know’ option. This additional option was treated as a neutral
option in the Likert scale. The questionnaire used in a similar study set in Denmark
(Sigurdardottir et al. 2013) was used as a starting point; however, questions were adapted to
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Albania’s local context. Questions measured, among other things, participants’ lifestyle
orientations and aspirations, personal norms about the environment, social norms about the
environment and mobility, mobility attitudes, and future mobility intentions.
4.2 Structural Equation Modeling (SEM)
The survey data were analyzed through Structural Equation Modelling (SEM). SEM is a
relatively recent, multivariate statistical technique which uncovers structural relationships. It
combines confirmatory factor analysis and regression analysis. It is commonly used by social
scientists to examine complex situations, which involve both latent constructs e.g., attitudes
or beliefs – and directly measured variables. These variables are formalized as a series of related
regression equations. SEM was preferred for this study because it estimates the multiple and
interrelated dependence in a single analysis. As a confirmatory method, SEM is guided by a
priori theories, which are then empirically tested. That is why the authors constructed a
preliminary conceptual model for this study (Fig. 2), based on the findings from the literature
review. For more detail on SEM, see Raykov and Marcoulides (2000), Byrne (2001), and Kline
(2005).
The SEM analysis of this study proceeded according to the following steps:
a) A measurement model was estimated in a first stage of the analysis to verify whether
the observed variables (i.e., the questionnaire items) correctly measure the underlying
constructs (i.e., latent variables or constructs) in the preliminary conceptual model.
Confirmatory Factor Analysis (CFA) was used for this purpose. The preliminary
conceptual framework consisted of several latent variables (or constructs) (i.e.,
lifestyle orientation”, personal norm about the environment”, “willingness to accept
mobility measures”, “mobility attitudes”, and “future mobility intentions”). While the
questionnaire included numerous statements, Table 2 summarizes only those statements
which were identified as significant indicators of the constructs. Before starting the
CFA, an Exploratory Factor Analysis (EFA) was first performed in SPSS to verify that
in fact the latent variables in our preliminary conceptual framework are indeed the
factors emerging. Also, Cronbach’s alpha was calculated for each latent variable to
confirm that the questionnaire items possibly belong to the same dimension. The EFA
and Cronbach’s alpha provided input for the CFA in Mplus. A CFA was then performed
for each category of latent variables separately. The identified latent variables are
“trustworthy” if properties of unidimensionality, reliability, and discriminant validity
are fulfilled. Unidimensionality means that a set of indicators is associated with a single
latent variable. It is present when the factor loadings of each indicator are significant
and the respective standardized coefficients are larger than 0.5. Reliability refers to the
degree of confirmation between an indicator and its latent variable. It can be evaluated
by the composite reliability (> 0.6) and the variance extracted (> 0.5). Discriminant
validity is fulfilled when two latent variables of the same category are not strongly
correlated with one another (Wijnen et al. 2002). These three properties were fulfilled
when each category of latent variables was estimated separately. At this point, a CFA
was repeated for all latent variables together (Table 3) and the three properties were
checked once again. Discriminant validity was fulfilled as correlations between latent
variables of the same category never exceeded 0.4. Fulfilling the other two properties
(unidimensionality and reliability) was not as easy. A few standardized coefficients
were slightly lower than 0.5 (indicating a lack of unidimensionality) and the variance
extracted was not always higher than the 0.5 (indicating a lack of reliability). Composite
reliability was fulfilled for all latent variables except for negative attitude towards
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bicycling”. Therefore, it was decided to delete this latent variable from the estimation
of the structural model in a second stage of the analysis.1
b) A structural model was then estimated to examine how the latent variables identified
in the measurement model are related to one another. Through a series of simultaneous
regression equations, the coefficients that best match the model-implied covariance
matrix to the empirically-based covariance matrix were identified.2 One structural
model was estimated that explains the future mobility intentions (car use, bus use, and
bicycle use) - similar to the preliminary conceptual model structure shown in Figure 2.
However, for reasons of clarity, results are presented graphically in a series of three
illustrations for each future mobility intention separately. Figures 3 to 5 therefore
present the unstandardized coefficients of the structural model of car use, bicycle use,
and bus use respectively. The results were also controlled for socio-economic and
demographic differences among adolescents. Tables 4 to 6 distinguish between direct,
indirect, and total effects on each type of future mobility intention (car use, bicycle use,
and bus use). Section 5 of this paper describes the findings from the structural model
more into detail.
c) The quality of each measurement and structural model fit is assessed. Several authors
suggest the following cut-off values for an adequate model fit: χ²/df < 2.0; CFI and TLI
> 0.90; RMSEA < 0.05; and WRMR < 1.00 (Bollen 1989; Hu and Bentler 1999; Kline
2005; Yu 2002).3 The model fit of the present measurement and structural model is not
perfect, but it is reasonably good.
d) Model results are interpreted based on the researchers’ knowledge of the context and/or
phenomenon under study in this case, youth attitudes toward sustainable transport in
Tirana.
The findings from the structural model are discussed in detail below.
5. Findings
5.1 Future car use intentions
The structural model of future car use intentions is illustrated in Figure 3. As seen, stronger car
use intentions are directly associated with the social influence of family, and personal attitudes
towards cars. If an adolescent’s social network favors driving, then his/her future intentions are
also more positively oriented towards cars. As expected, positive attitudes towards cars have
the same effect; in fact, they are the strongest predictor of future car use intentions.
Rather surprisingly, negative attitudes towards cars also have a positive effect on car use
intentions though much weaker than the effect of positive attitudes towards cars (see the
standardized coefficients in parentheses in Table 4). Even adolescents who do not like cars or
driving intend to drive in the future, or at least learn how to drive. This is because most
respondents are eager to drive.
Figure 6 shows that the overwhelming majority of respondents agrees” or “strongly agrees”
with statements on future car use intentions much more so than when asked about their future
intentions to cycle or use public transportation. In the sample, 52% of the respondents indicate
a strong intention to buy a car upon joining the workforce, 54% have learned or are very willing
to learn how to drive, and 41% are confident that they are, or will become, good drivers.
Positive and negative attitudes towards cars are influenced by personal norms about the
environment. If adolescents are concerned about the environment, they are more likely to have
a negative attitude towards cars. The influence of the family is important in shaping this norm.
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Personal norms about the environment are generally stronger among adolescents from families
that make an effort to act in an environmentally-friendly way.
A materialistic ideological orientation has a direct effect on a positive attitude towards cars and
an indirect but strong positive effect on future car use intentions. On the other hand, an
egalitarian ideology is not immediately connected to a negative attitude towards cars. The direct
relationship is statistically not significant but an indirect effect exists which is mediated through
personal norms about the environment.
In terms of demographic and socio-economic factors, only gender, income, and car ownership
have a significant effect on some latent variables. Men are less likely to follow an egalitarian
ideology, households owning more cars tend to have weaker personal norms about the
environment, and low-income groups tend to have a more negative attitude towards cars and
driving (e.g., cars are considered expensive). Car ownership positively influences the attitudes
toward car use. Due to the interactions between the various latent variables, indirect effects of
gender, income, and car ownership on future car use intentions are observed as well.
Interestingly, travel habits do not have a significant direct effect on future car use intentions.
They only have an indirect effect through the interaction with attitudes toward car use. Of
particular interest is the habit to commute to university as a car passenger.
5.2 Future bicycle use intentions
The structural model of future bicycle use intentions is illustrated in Figure 4. Stronger
intentions to cycle in the future are directly associated with a variety of intrapersonal latent
variables (i.e., a materialistic lifestyle, the willingness to accept transportation measures, and
personal attitudes toward cycling). Unlike future car use intentions, social influences of friends
and/or family do not have a significant direct effect on future bicycle use intentions. Personal
attitudes toward cycling are the most crucial predictor in this case (see Table 5). A positive
attitude toward cycling is likely to support future bicycle use intentions. (Note that the influence
of a negative attitude towards cycling could not be tested because the reliability of this latent
variable was not satisfactory.)
The relationships between positive attitudes towards cycling and lifestyle orientations and
personal norms about the environment are similar to those found in the structural model of
future car use intentions. A positive attitude towards cycling is directly influenced by a
materialistic outlook and personal pro-environment norms.
However, unlike future car use intentions, positive attitudes toward cycling have a significant
influence on the willingness to accept a variety of transportation measures. This considerably
complicates the structural model of future bicycle use intentions. A positive attitude is likely to
be associated with a higher level of acceptance of transportation measures such as pedestrian
areas, bicycle lanes, or bus lanes, which limit car use and favor alternative transport. Other
attitudes towards car use and bus use also have a significant influence on a willingness to accept
transportation measures.
Latent variables such as lifestyle/ideological orientations, personal norms about the
environment, and attitudes towards car use and cycling have indirect effects on future bicycle
use intentions. In contrast to the structural model of future car use intentions, some of these
indirect effects are strong (see Table 5).
Cycling habits have a significant influence on future bicycle use intentions. Tirana adolescents
have not had an opportunity to develop strong cycling habits yet. (Note the rather low share of
bicycling in the home-university commute, reported in Table 1). Nonetheless, results are
11
promising given that occasional bicycle use might encourage future bicycle use intentions.
Moreover, occasional bicycle use also strengthens a positive attitude towards bicycle use.
Gender does not have a significant direct effect on future bicycle use intentions, but it does have
a significant indirect effect, with women being more inclined toward cycling. By contrast, prior
(qualitative) research in the Balkans has revealed that women are more averse to cycling than
men (Pojani et al. 2017). This discrepancy might be due to the fact that women are
overrepresented in this study, and this might mask significant differences with men. The
indirect effect mainly owes to the interaction between an egalitarian lifestyle and the
willingness to accept transportation measures favoring car alternatives. An egalitarian lifestyle
is positively associated with acceptance of measures to curtail driving, and men are less likely
to endorse an egalitarian lifestyle. The indirect effects of income and car ownership can be
explained in a similar way.
5.3 Future bus use intentions
The structural model of future bus use intentions is illustrated in Figure 5. It is as complex as
the structural model of future bicycle use intentions. Stronger bus use intentions are directly
associated with the social influence of friends and personal attitudes towards bus use. If an
adolescent’s social network favors driving, then this might discourage his/her intention to use
other modes of transportation, such as buses.
While social influence is important, it is not the main predictor of future bus use intentions. A
positive attitude toward bus use is much more important, and clearly encourages future bus use
intentions (see Table 6). Negative attitudes toward bus use are not significant in shaping future
intentions to use buses. While bus vehicles may be considered as dirty and bus commutes may
be viewed as stressful, such negative attitudes do not preclude the development of future bus
use intentions.
In comparison to the previous two structural models, the relationships between this positive
attitudes toward bus use and lifestyle orientations and personal norms about the environment
are slightly different. A positive attitude towards buses is still significantly associated with a
materialistic outlook, but it is no longer associated with personal pro-environment norms.
Similar to future bike use intentions, personal attitudes toward bus use significantly influence
the willingness to accept a variety of transportation measures. Rather surprisingly, the effect of
a positive attitude toward bus use is negative - but only significant at a significance level of
90%. More importantly, a negative attitude toward bus use is positively associated with a
willingness to accept transportation measures favoring car alternatives.
Bus use habits are also important. They have both a direct effect on future bus use intentions
and an indirect effect mediated by a positive attitude toward buses and the willingness to accept
transportation measures. Adolescents who already commute to university by bus on a regular
basis are more likely to develop a positive attitude towards this mode and to accept
transportation measures limiting car traffic and favoring car alternatives.
Of all demographic and socio-economic factors, only income has a significant effect on future
bus use intentions, albeit in an indirect way. This indirect effect is due to the interaction between
a negative attitude toward car use and the willingness to accept transportation measures
favoring car alternatives. As notes, low-income groups are more likely to have a negative
attitude towards cars (e.g., cars considered to be expensive). This negative attitude is, in turn,
positively associated with a higher level of acceptance of transportation measures - which
explains the positive indirect effect of a low income.
12
Combined with the high modal share of buses in the school commute, these findings point to
low local expectations in terms of public transport quality and a near universal acceptance of
this mode. This contrasts with many other developing countries in which public transport is
viewed as a mode of last resort (see Stead and Pojani 2017). Small public investments would
make bus travel safer, cleaner, more dignified, and more reliable.
6. Discussion and conclusion
This study has provided detailed insights into the determinants of future mobility intentions of
university students (18-19 years old) in Tirana. These include future car use intentions, bicycle
use intentions, and bus use intentions. A better understanding of the intentions of this future
“young elite” can help planners not only to predict the travel behavior of future generations, but
also to identify policy elements and approaches to promote sustainable transitions in travel
patterns.
A main finding is university students in this sample, including those who do not particularly
like cars and driving, intend to purchase cars and drive in the future. Pro-car attitudes prevail
revealing that cars remain a strong status symbol. This finding is particularly discouraging
because women were overrepresented in this study sample. Car orientation might be even
higher among men given prior findings that in Tirana women are much less car oriented than
men (see Pojani et al. 2017).
This situation does do not bode well for transport sustainability (see Ashmore et al. 2017). Past
studies have shown that it is difficult to contain car use if car ownership is unrestricted (see
Pojani et al. 2017). At the time of the last mobility survey in 2009, only 21% of people
commuted by car in Tirana (City of Tirana 2009). If unchecked, adolescents’ intentions might
translate into actual future behavior, which is much more car-dependent than in the past. As
university graduates (i.e., part of the country’s elite), their behavior would likely influence
public opinion and persuade others to shift or aspire to driving. Of course, some people might
only use cars for weekend outings rather than on a daily basis. But Tirana is already so
congested that even a small increase in car traffic would be problematic.
A positive attitude toward a travel mode is consistently the main predictor of future mobility
intentions regarding that mode. Creating and maintaining a positive attitude towards cycling
and public transportation is therefore necessary to prevent adolescents from embracing driving
as their primary mode. Awareness-raising campaigns are often suggested as a preferred measure
in this respect. This study shows that, campaigns which highlight the negative consequences of
car use for the purpose of creating negative attitudes towards cars might not achieve the desired
effect because negative attitudes are not necessarily associated with weaker intentions to use
cars in the future.
Framing active and public transport in a positive light might be more productive. Planners can
approach discussions about bicycle and bus riding by first mentioning things that are important
to popular audiences, such as children, fitness, fun, convenience, financial savings, clear air,
and livability (see Kahan 2012; Pojani et al. 2017). At the same time, negative beliefs with
regards to cycling and public transportation – e.g., cycling is strenuous, bad weather precludes
cycling, buses are poor people’s mode, wayfinding to bus stops is complicated, and the like -
must be overcome through education and planning interventions.
One effective way to change beliefs in favor of sustainable transport is by offering people the
opportunity to gain (positive) experience with bicycle and bus riding for example, by
promoting the recently implemented bikesharing system and offering free public transportation
to university students. However, positive experiences alone are not sufficient. Long-term
13
behavioral change can be achieved only where the built environment is conducive to a newly
experienced behavior (i.e., where individuals have actual and perceived control over the
environment, in TPB terminology). This means that an extensive network of segregated bus
and bicycle lanes must be introduced. This study indicates that further investments in bicycle
lanes and traffic calming along popular cycling routes are key in influencing future bicycle use
intentions, while easy access to the bus networks is an important indicator of future bus use
intentions.
In addition to personal attitudes, experiences, and habits, general lifestyle and ideological
orientations are crucial in shaping mobility intentions. This study shows that a materialistic
outlook fosters a preference for cars and driving, whereas an egalitarian outlook leads
adolescents to prefer bicycles. The social influences of both family members and friends are
also important predictors of future transport intentions. Future car use intentions are stronger
among adolescents if their families expect them to learn how to drive, and future bus use
intentions are weaker if their friends also plan on driving or already do so. These findings
indicate that travel behavior is not only a personal, but also a socio-cultural issue, and as such,
it is much more difficult to challenge.
This interplay between a conducive environment and promotion efforts and experiential
campaigns targeting individuals and the society at large might be successful in preventing car
use habits from forming among adolescents. Breaking already formed car habits is very
important too. Although only a small share of adolescents commute by car on a regular basis,
this study indicates that as soon as they come to rely on cars for their mobility needs, their future
car use intentions solidify. But habits have a significant effect on cycling too although only a
small share of adolescents cycle occasionally. Helping young people form cycling habits early
on (possibly from childhood) is crucial in orienting their future mobility intentions toward
sustainable transport modes.
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Notes
1 To improve properties of unidimensionality and reliability of the “negative attitude towards cycling”, the authors
attempted to exclude some questionnaire items, starting with those with low standardized coefficients. However,
this did not result in any significant improvement. Consequently, all questionnaire items were retained.
2 A standard estimation technique is Maximum Likelihood (ML) which assumes a multivariate normal distribution
of all endogenous variables in the model (Bentler and Dudgeon 1996; Kline 2005). However, in this study ML
could not be used because the observed variables are categorical and, therefore, not normally distributed. To model
categorical variables, the authors used the software package M-plus, which employs an alternative estimator,
Means and Variance Adjusted Weighted Least Squares (WLSMV) instead of ML. WLSMV generates robust
standard errors without extensive computations or enormous sample sizes. In addition, a robust chi-square test
statistic (χ²-statistic) can be produced in M-plus (Muthén 1983; Satorra 1992; Yu and Bentler 2000).
3 The χ²-statistic is a commonly used model fit index which measures the discrepancy between the empirically-
based and the model-based covariance matrices. However, χ² values increase with sample size. Therefore, models
based on large sample sizes might be rejected even when only small differences exist between the empirically-
based and model-based covariance matrices. To resolve this issue, the standard χ²-statistic was transformed into a
dozen alternative model fit indices (for a detailed discussion, see Van Acker and Witlox 2010).
Tables
Table 1. Sample characteristics.
Personal characteristics
Gender
75.1% female; 24.9% male
Home-university travel distance
9.6% 0-5km; 12.6% 6-10km; 19.8% 11-20km; 25.1% 20-30km; 32.9% +30km
Commuting on foot
8.6% never; 17.4% occasionally; 42.0% often; 32.1% always
Commuting by bicycle
78.3% never; 18.2% occasionally; 2.7% often; 0.8 % always
Commuting by bus
17.4% never; 23.5% occasionally; 32.69% often; 26.5% always
Commuting by car (passenger)
20.3% never; 50.5% occasionally; 24.1% often; 5.1% always
Commuting by car (driver)
70.6% never; 19.0% occasionally; 2.9% often; 4.5% always
Household characteristics
Number of cars in household
27.5% no car; 57.5% one car; 15.0% two cars
Car availability
70.9% more drivers than cars; 27,6% equal; 1,4% less drivers than cars
Education of mother
38.8% mother has a university degree
Education of father
40.9% father has a university degree
Income
9.9% below average; 78.3% average; 11.8% above average
Table 2. Latent variables of the conceptual framework and its indicators based on questionnaire items
Latent
factor
name
Latent factor
Indicator
name
Indicator description
LsEq Lifestyle oriented
towards equality
LifeEnv
I want to protect the environment
LifeEq
I want to live in a fair and just society
LifeVol
I want to help others, volunteer for charity
LsMat
towards
LifeRich
I want to be rich
LifeFun
I want a fun life, full of partying
LifeAdv
I want a life of travel, adventure and risk
PersEnv
about the
EnvIam
I am an environmentalist
EnvWorry
I am worried about pollution in Tirana
EnvInform
I am informed about Tirana’s environmental problems
CarPos Positive attitude
towards car use
CoWeekend
Cars are necessary for the weekend
CarFreedom
Cars offer freedom
CoLux
I like luxury cars
CoHol
Cars are necessary to go on holidays
DrivLike
I like driving
CarLikeGen
I like cars in general
CarNeg Negative attitude
towards car use
CarAnnoy
Car traffic is annoying
CarExp
Cars are expensive
CarPol
Cars pollute the city
BikePos Positive attitude
towards cycling
BikeLike
I like to ride a bike (or I would like to learn it)
BikeEff
Riding a bicycle is more efficient in Tirana
BikeFreedom
A bike offers freedom
BikeFit
Cycling makes you fit
BikeNeg Negative attitude
towards cycling
BikeDang
Bicycling is dangerous
BikeWeath
Bicycles cannot be used in bad weather (rain, cold, heat)
BikeSport
Bicycle are for sporty types
BusPos Positive attitude
towards bus use
BusLike
I like buses
BusEff
Buses are more efficient than other modes
BusEasy
Taking a bus is the easiest thing to me
BusFreedom
Buses offer freedom
BusNeg Negative attitude
towards bus use
BusStress
Riding a bus is stressful
BusPeople
In buses people push or harass you
BusDirty
Buses are dirty
LimCar
accept car
LimParking
Parking fees should be introduced in Tirana
LimLic
The minimum age for a driver’s license should increase to 20
LimPol
Old polluting cars should be fined
LimCarAlt
Willingness to
accept measures
favoring car
alternatives
LimPed
Tirana should have pedestrian areas
LimBikeLane
Tirana should have bike lanes on major streets
LimBusLane
Tirana should have bus lanes on major streets
LimSpeed
Those who break speed limits must be punished
LimFines
Traffic fines should be severe
LimTraffic
Car traffic needs to be limited in Tirana
CarInten Future car use
intentions
CoInten
When I get a job, I will buy my own car
ExpDriver
I think I am or I will be a good driver
ExpDriving
I have learned or I would like to learn to drive
BusInten Future bus use
intentions
BusQual
I would use a bus more frequently if the quality was higher
BusRoute
I would use a bus more frequently if there is a route by my house
BusExp
I would use a bus more frequently if it was for free
BikeInten
BikeLane
I would cycle more frequently if there were segregated bike lanes
BikeTraf
I would cycle more frequently if car traffic was not so heavy
Table 3. Measurement models.
Variable
Estimate
Est. / S.E.
Std. Est.
Composite
reliability
Variance
extracted
Cronbach’s
alpha
Lifestyle orientation towards equality
0.796
0.577
0.552
LifeEnv
1.000
0.000
0.793
LifeEq
1.145
9.190
0.908
LifeVol
0.663
6.967
0.526
Lifestyle orientation towards materialism
0.685
0.432
0.578
LifeRich
1.000
0.000
0.761
LifeFun
0.945
9.034
0.719
LifeAdv
0.585
6.456
0.446
Personal norm about the environment
0.660
0.440
0.526
EnvIam
1.000
0.000
0.393
EnvWorry
2.532
4.566
0.995
EnvInform
1.064
4.028
0.418
Positive attitude towards car use
0.808
0.414
0.720
CoWeekend
1.000
0.000
0.683
CarFreedom
0.774
9.796
0.528
CoLux
0.936
13.504
0.639
CoHol
0.955
12.475
0.652
DrivLike
1.011
12.701
0.690
CarLikeGen
0.959
12.345
0.655
Negative attitude towards car use
0.693
0.432
0.502
CarAnnoy
1.000
0.000
0.732
CarExp
0.769
6.083
0.563
CarPol
0.910
6.534
0.666
Positive attitude towards cycling
0.679
0.352
0.602
BikeLike
1.000
0.000
0.738
BikeEff
0.756
10.523
0.558
BikeFreedom
0.617
7.701
0.455
BikeFit
0.796
9.235
0.588
Negative attitude towards cycling
0.370
0.185
0.380
BikeDang
1.000
0.000
0.208
BikeWeath
1.906
2.326
0.396
BikeSport
2.864
2.132
0.595
Positive attitude towards bus use
0.783
0.477
0.662
BusLike
1.000
0.000
0.691
BusEff
1.143
11.773
0.789
BusEasy
0.998
11.807
0.689
BusFreedom
0.838
9.235
0.578
Negative attitude towards bus use
0.683
0.419
0.582
BusStress
1.000
0.000
0.644
BusPeople
1.079
7.059
0.695
BusDirty
0.930
6.574
0.599
Willingness to accept car limitations
0.716
0.459
0.602
LimParking
1.000
0.000
0.627
LimLic
0.995
8.167
0.625
LimPol
1.228
8.186
0.770
Willingness to accept measures favoring car alternatives
0.830
0.452
0.723
LimPed
1.000
0.000
0.711
LimBikeLane
1.104
12.479
0.785
LimBusLane
0.787
10.591
0.560
LimSpeed
0.938
12.591
0.668
LimFines
0.874
9.991
0.622
LimTraffic
0.934
11.999
0.664
Future car use intentions
0.632
0.364
0.545
CoInt
1.000
0.000
0.616
ExpDriver
0.996
9.088
0.614
ExpDriving
0.941
9.025
0.580
Future bus intentions
0.797
0.568
0.732
BusQual
1.000
0.000
0.701
BusRoute
1.149
13.432
0.806
BusExp
1.070
12.563
0.750
Future bicycling intentions
0.901
0.820
0.832
BikeLane
1.000
0.000
0.967
BikeTraf
8.69
17.233
0.840
While some latent variables should be interpreted carefully, the overall model fit is reasonably good:
χ² = 401.479 with df 191 and p-value 0.000; CFI = 0.87; TLI = 0.89; RMSEA = 0.054; WRMR = 1.177
Table 4. Direct, indirect, and total effects on future car use intentions.
Direct
Indirect
Total
Subjective influences
Lifestyle orientation towards equality
-
0.106***
(0.137)
0.106***
(0.137)
Lifestyle orientation towards materialism
-
0.592***
(0.601)
0.592***
(0.601)
Personal norm about the environment
-
0.540***
(0.253)
0.540***
(0.253)
Positive attitude towards car use
0.713***
(0.706)
N/A
0.713***
(0.706)
Negative attitude towards car use
0.172***
(0.211)
N/A
0.172***
(0.211)
Social influences
In my family we try to be environmentally-friendly, completely agree (ref.)
Completely disagree
N/A
-0.487***
(-0.069)
-0.487***
(-0.069)
Disagree
N/A
-0.123***
(-0.058)
-0.123***
(-0.058)
Nor disagree, nor agree
N/A
N/A
N/A
Agree
N/A
-0.048*
(-0.035)
-0.048*
(-0.035)
My family wants me to learn how to drive, completely agree (ref.)
Completely disagree
-0.730***
(-0.299)
N/A
-0.730***
(-0.299)
Disagree
-0.412***
(-0.246)
N/A
-0.412***
(-0.246)
Nor disagree, nor agree
-0.343***
(-0.171)
N/A
-0.348**
(-0.171)
Agree
-0.322***
(-0.224)
N/A
-0.322***
(-0.224)
Socio-demographic and economic characteristics
Gender, male
-
-0.065***
(-0.045)
-0.065***
(-0.045)
Income, average (ref.)
Below average
-
0.108*
(0.051)
0.108*
(0.051)
Above average
-
N/A
N/A
Number of cars per household
-
0.086***
(0.087)
0.086***
(0.087)
Travel habits
Commute to university as car passenger, never (ref.)
Occasionally (CP2)
-
N/A
N/A
Often (CP3)
-
N/A
N/A
Always (CP4)
-
0.197***
(0.069)
0.197***
(0.069)
Commute to university as car driver, never (ref.)
Occasionally (CD2)
-
-0.058
(-0.036)
-0.058
(-0.036)
Often (CD3)
-
N/A
N/A
Always (CD4)
-
N/A
N/A
Notes: - = effect estimated but not found to be significant, N/A = not applicable, no effect estimated
Standardized coefficients are noted in parentheses
***significant at 99%; **significant at 95%; *significant at 90%
Model fit: χ² = 445.356 with df 235 and p-value 0.000; CFI = 0.85; TLI = 0.85; RMSEA = 0.049; WRMR = 1.270
R² (car use intentions) = 70.2%
Table 5. Direct, indirect, and total effects on future bicycle use intentions.
Direct
Indirect
Total
Subjective influences
Lifestyle orientation towards equality
-
0.337***
(0.266)
0.337***
(0.266)
Lifestyle orientation towards materialism
-0.333***
(-0.207)
0.090
(0.056)
-0.243***
(-0.151)
Personal norm about the environment
-
1.508***
(0.432)
1.508***
(0.432)
Positive attitude towards car use
N/A
-0.062
(-0.038)
-0.062
(-0.038)
Negative attitude towards car use
N/A
0.191***
(0.143)
0.191***
(0.143)
Positive attitude towards bus use
N/A
-0.078*
(-0.056)
-0.078*
(-0.056)
Negative attitude towards bus use
N/A
0.264***
(0.170)
0.264***
(0.170)
Positive attitude towards cycling
0.736***
(0.568)
0.143***
(0.111)
0.880***
(0.679)
Willingness to accept car limitations
-0.535***
(-0.312)
N/A
-0.535***
(-0.312)
Willingness to accept measures favoring car alternatives
0.612***
(0.451)
N/A
0.612***
(0.451)
Social influences
In my family we try to be environmentally-friendly, completely agree (ref.)
Completely disagree
N/A
-1.361***
(-0.118)
-1.361***
(-0.118)
Disagree
N/A
-0.343***
(-0.099)
-0.343***
(-0.099)
Nor disagree, nor agree
N/A
N/A
N/A
Agree
N/A
-0.135*
(-0.060)
-0.135*
(-0.060)
Socio-demographic and economic characteristics
Gender, male
-
-0.313***
(-0.131)
-0.313***
(-0.131)
Income, average (ref.)
Below average
-
0.120*
(0.035)
0.120*
(0.035)
Above average
-
N/A
N/A
Number of cars per household
-
-0.095
(-0.059)
-0.095
(-0.059)
Travel habits
Commute to university as car passenger, never (ref.)
Occasionally (CP2)
Often (CP3)
Always (CP4)
N/A
-0.017
(-0.004)
-0.017
(-0.004)
Commute to university as car driver, never (ref.)
Occasionally (CD2)
N/A
-0.065*
(-0.025)
-0.065*
(-0.025)
Often (CD3)
N/A
N/A
N/A
Always (CD4)
N/A
N/A
N/A
Commute to university by bus, never (ref.)
Occasionally (Bus2)
N/A
0.236***
0.236***
(0.097)
(0.097)
Often (Bus3)
N/A
0.086
(0.039)
0.086
(0.039)
Always (Bus4)
N/A
0.089
(0.038)
0.089
(0.038)
Commute to university by bike, never (ref.)
Occasionally (Bike2)
0.425***
(0.159)
0.149
(0.055)
0.573***
(0.214)
Often (Bike3)
0.570*
(0.089)
N/A
0.570*
(0.089)
Always (Bike4)
-
N/A
N/A
Notes: - = effect estimated but not found to be significant, N/A = not applicable, no effect estimated
Standardized coefficients are noted in parentheses
***significant at 99%; **significant at 95%; *significant at 90%
Model fit: χ² = 445.356 with df 235 and p-value 0.000; CFI = 0.85; TLI = 0.85; RMSEA = 0.049; WRMR = 1.270
R² (bicycle use intentions) = 67.7%
Table 6. Direct, indirect, and total effects on future bus use intentions.
Direct
Indirect
Total
Subjective influences
Lifestyle orientation towards equality
-
-0.087**
(0.093)
-0.087**
(0.093)
Lifestyle orientation towards materialism
-
-0.050
(-0.042)
-0.050
(-0.042)
Personal norm about the environment
-
0.470***
(0.183)
0.470***
(0.183)
Positive attitude towards car use
N/A
0.088
(0.072)
0.088
(0.072)
Negative attitude towards car use
N/A
0.161***
(0.164)
0.161***
(0.164)
Positive attitude towards bus use
0.422***
(0.412)
-0.066*
(-0.065)
0.356***
(0.347)
Negative attitude towards bus use
-
0.222***
(0.194)
0.222***
(0.194)
Positive attitude towards cycling
N/A
0.121***
(0.127)
0.121***
(0.127)
Willingness to accept car limitations
-0.552***
(-0.436)
N/A
-0.552***
(-0.436)
Willingness to accept measures favoring car alternatives
0.516***
(0.515)
N/A
0.516***
(0.515)
Social influences
In my family we try to be environmentally-friendly, completely agree (ref.)
Completely disagree
N/A
-0.424***
(-0.050)
-0.424***
(-0.050)
Disagree
N/A
-0.107***
(-0.042)
-0.107***
(-0.042)
Nor disagree, nor agree
N/A
N/A
N/A
Agree
N/A
-0.042*
(-0.025)
-0.042*
(-0.025)
Most of my friends drive or will learn to drive, completely agree (ref.)
Completely disagree
-
N/A
N/A
Disagree
-0.272*
(-0.115)
N/A
-0.272*
(-0.115)
Nor disagree, nor agree
-0.190*
(-0.097)
N/A
-0.190*
(-0.097)
Agree
-
N/A
N/A
Socio-demographic and economic characteristics
Gender, male
-
-0.032
(-0.018)
-0.032
(-0.018)
Income, average (ref.)
Below average
-
0.101*
(0.040)
0.101*
(0.040)
Above average
-
N/A
N/A
Number of cars per household
-
0.022
(0.019)
0.022
(0.019)
Travel habits
Commute to university as car passenger, never (ref.)
Occasionally (CP2)
N/A
N/A
N/A
Often (CP3)
N/A
N/A
N/A
Always (CP4)
N/A
0.024
0.024
(0.007)
(0.007)
Commute to university as car driver, never (ref.)
Occasionally (CD2)
N/A
-0.055*
(-0.028)
-0.055*
(-0.028)
Often (CD3)
N/A
N/A
N/A
Always (CD4)
N/A
N/A
N/A
Commute to university by bus, never (ref.)
Occasionally Bus2)
-
0.199***
(0.111)
0.199***
(0.111)
Often (Bus3)
0.278*
(0.171)
0.255***
(0.157)
0.533***
(0.328)
Always (Bus4)
0.671***
(0.389)
0.048
(0.028)
0.719***
(0.416)
Commute to university by bike, never (ref.)
Occasionally (Bike2)
N/A
-0.094*
(-0.048)
-0.094*
(-0.048)
Often (Bike3)
N/A
N/A
N/A
Always (Bike4)
N/A
N/A
N/A
Notes: - = effect estimated but not found to be significant, N/A = not applicable, no effect estimated
Standardized coefficients are noted in parentheses
***significant at 99%; **significant at 95%; *significant at 90%
Model fit: χ² = 445.356 with df 235 and p-value 0.000; CFI = 0.85; TLI = 0.85; RMSEA = 0.049; WRMR = 1.270
R² (bus use intentions) = 37.9%
Figures
Figure 1. Theory of Planned Behavior, and the interplay with habits, personal norms, moral principles,
and self-identity (based on Ajzen 1991).
Figure 2. Preliminary conceptual model of adolescents’ mobility intentions.
Note: unstandardized coefficients; ***significant at 99%; **significant at 95%; *significant at 90%.
Figure 3. Structural model of future car use intentions.
LsEq LsMat
PersEnv
CarNeg
CarInten
Inc_
below
0.631
***
CarHH
0.108
***
Driv
Fam1
Driv
Fam2
Driv
Fam3
-0.730
***
-0.412
***
Driv
Fam4
-0.343
***
-0.322
***
CarHH CD2
0.397
***
-0.340
*
CP4
0.277
***
Env
Fam1
Env
Fam2
-0.903
***
Env
Fam4
-0.228
***
-0.090
*
Sex
-0.617
***
CarHH
-0.109
***
CarPos
0.197
***
1.705
***
0.346
***
0.172
***
0.713
***
0.831
***
Note: unstandardized coefficients; ***significant at 99%; **significant at 95%; *significant at 90%.
Figure 4. Structural model of future bicycle use intentions.
LsEq LsMat
PersEnv
CarNeg
Inc_
below
0.631
***
CarHH
0.108
***
CarHH CD2
0.397
***
-0.340
*
CP4
0.277
***
Env
Fam1
Env
Fam2
-0.903
***
Env
Fam4
-0.228
***
-0.090
*
Sex
-0.617
***
CarHH
-0.109
***
CarPos BikePos BusPos BusNeg
LimCar LimCarAlt
Sex Bus3 Sex
Bike2
0.315
**
0.262
***
0.491
***
0.491
***
Bus3 Bus4 Bike2 Bus2 Bus3 Bus4
0.243
**
0.272
***
0.240
***
0.386
***
0.416
***
0.383
***
0.197
***
1.705
*** 0.346
***
1.369
***
-0.263
***
0.271
***
0.831
***
1.298
***
0.391
***
-1.390
*** 1.214
***
BikeInten
Bike2 Bike3
0.425
***
0.570
*
-1.316
***
0.311
***
0.235
***
-0.128
*
0.431
***
0.409
***
0.736
***
-0.333
***
-0.535
***
0.612
***
Note: unstandardized coefficients; ***significant at 99%; **significant at 95%; *significant at 90%.
Figure 5. Structural model of future bus use intentions.
LsEq LsMat
PersEnv
CarNeg
Inc_
below
0.631
***
CarHH
0.108
***
CarHH CD2
0.397
***
-0.340
*
CP4
0.277
***
Env
Fam1
Env
Fam2
-0.903
***
Env
Fam4
-0.228
***
-0.090
*
Sex
-0.617
***
CarHH
-0.109
***
CarPos BikePos BusPos BusNeg
LimCar LimCarAlt
Sex Bus3 Sex
Bike2
0.315
**
0.262
***
0.491
***
0.491
***
Bus3 Bus4 Bike2 Bus2 Bus3 Bus4
0.243
**
0.272
***
0.240
***
0.386
***
0.416
***
0.383
***
0.197
***
1.705
*** 0.346
***
1.369
***
-0.263
***
0.271
***
0.831
***
1.298
***
0.391
***
-1.390
*** 1.214
***
BusInten
-1.316
***
0.311
***
0.235
***
-0.128
*
0.431
*
0.409
***
-0.552
***
0.516
***
Bus3 Bus4
Driv
Friend2
Driv
Friend3
0.671
***
-0.272
*
-0.190
*
0.422
***
Figure 6. Frequency distribution of the questionnaire items on future mobility orientations.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
ExpDriver ExpDriving CoInten BikeLane BikeTraf BusQual BusRoute BusExp
strongly disagree disagree neither disagree, nor agree agree strongly agree
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This chapter reflects on the 12 case studies contained in this book, and identifies some of the key issues, trends, and policy measures which emerge from the previous chapters. Consideration is also given to the lessons that can be learned from these countries and the extent to which they may be generalizable and applicable in other contexts across the world. The chapter is structured according to the main headings used in each of the country-specific chapters.
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This study focuses on the intentions of adolescents to commute by car or bicycle as adults. The behavioral model is based on intrapersonal and interpersonal constructs from the theory of planned behavior extended to include constructs from the institutional, community and policy domains. Data from a survey among Danish adolescents is analyzed. It is found that car use intentions are related to positive car passenger experience, general interest in cars, and car ownership norms, and are negatively related to willingness to accept car restrictions and perceived lack of behavioral control. Cycling intentions are related to positive cycling experience, willingness to accept car restrictions, negative attitudes towards cars, and bicycle-oriented future vision, and are negatively related to car ownership norms. Attitudinal constructs are related to individual characteristics, such as gender, residential location, current mode choice to daily activities, and parental travel patterns.
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Relying on the theory of planned behavior (Ajzen, 1991), a longitudinal study investigated the effects of an intervention-introduction of a prepaid bus ticket-on increased bus use among college students. In this context, the logic of the proposition that past behavior is the best predictor of later behavior was also examined. The intervention was found to influence attitudes toward bus use, subjective norms, and perceptions of behavioral control and, consistent with the theory, to affect intentions and behavior in the desired direction. Furthermore, the theory afforded accurate prediction of intention and behavior both before and after the intervention. In contrast, a measure of past behavior improved prediction of travel mode prior to the intervention, but lost its predictive utility for behavior following the intervention. In a test of the proposition that the effect of past on later behavior is due to habit formation, an independent measure of habit failed to mediate the effects of past on later behavior. It is concluded that choice of travel mode is largely a reasoned decision; that this decision can be affected by interventions that produce change in attitudes, subjective norms, and perceptions of behavioral control; and that past travel choice contributes to the prediction of later behavior only if circumstances remain relatively stable.
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