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3This is the unformatted version of: Dong, Z., Schmöcker, J.-D., Fujii, S. and Yang, X. (2015).
Social norms and public transport usage: Empirical study from Shanghai. Transportation.
Available from: DOI 10.1007/s11116-015-9625-y

4Social norms and public transport usage:

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Empirical study from Shanghai

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Dong Zhanga, Jan-Dirk Schmöcker*b, Satoshi Fujiib, Xiaoguang Yanga

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aSchool of Traffic and Transportation Engineering, Tongji University, Room 502, Shanghai,

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bDepartment of Urban Management, Kyoto University, C1-2-432, Nishikyo-ku, Kyoto 615-8540,

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Abstract

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Chinese cities need to consider various TDM measures to cure adverse influences fromexceeding private car ownership and use. We therefore explore the potential role of norms andawareness of problems caused by excessive car traffic for car purchase and mode choice decisions.
With a sample of 465 responses collected via an online survey in Shanghai, our study suggests thatthe perception of “everyone around me is using public transport” (descriptive norms) is negatively
correlated with car ownership. More importantly, we find that expectation of important others(subjective social norms) and internalized responsibility to use greener transport (personal norms
activated by social problem awareness) stimulate the intention to use public transport and increase
actual public transport usage. We further find that income does not influence public transport
usage intentions or the actual modal choice directly but only via car ownership as mediating factor.
We discuss that our findings support regulations to limit private car ownership and use but also toutilize “soft policy measures” in order to achieve a voluntary shift towards more sustainable travel
behavior in Shanghai.

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Keywords

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Public transport usage; Car Ownership; Norms; Social problem awareness; China

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Introduction

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To encourage public transportation usage and to abate the conflict between mobility needs and thewell-documented negative effects of exceeding private car usage, Shanghai has conductedmeasures both at the supply and the demand ends. Since 1993 a metro system is in operation

201804, P.R.China

Japan

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which until 2012 has extended to 468 km with 13 lines and 289 stations. From 2001 to 2010,
investments into the metro system have been averaging about USD1.7 billion per year (Peng et al,2012). In 2010 alone, Shanghai municipal government spent 630 million USDon surface transit(Wu, 2011). Car ownership and usage reduction measures are conducted at the same time.
Shanghai was the first Chinese city to adopt a policy of limiting overall car ownership by using a
bid-auction mechanism for local vehicle registration as early as 1994. The lowest knock down
price in April 2014 has exceeded USD1,1000, according to an announcement from the officiallyassigned auction corporation (The Shanghai International Commodity Auction Co., Ltd, 2014),
even higher than prices of some Chinese brand private cars. Vehicles without local plate areprohibited to enter the downtown area during peak hours. At the same time, the monthly parking
costs in Shanghai’s CBD and other sub-centers averaged about USD300 in 2011, ranking 47 out of
the top 50 cities worldwide, according to a report by Colliers International (2011).

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Despite these measures being declared a great success (Shanghai Municipal Transport andPort Authority, 2013) implementation of further policies that encourage public transportation
usage in Shanghai (and other Chinese cities) are required given the continuous growth in car
ownership and usage. It appears that these “hard” measures, including improving public transport(PT) through infrastructure construction as well as car ownership and usage pricing policies, arenot sufficient (Stopher,2004). Instead one might have to look “deeper” at individuals’ motivations
to use cars and public transport. Arguably the changing perception of the car not any longer as
status symbol but as a polluter has been one reason for recent “peak car” developments and thesuccess of soft transport policy measures in some Western countries (Kuhnimhof et al., 2012;
Goodwin, 2012). With “soft” we mean policies that are based on some form of communication(instead of pricing or building infrastructure) and that appeal to population groups to voluntarily
consider alternatives to private car usage (Bamberg et al., 2011). Various soft transport policies
such as mobility management initiatives in Japan or travel awareness and public transport
advertising campaigns in London have by now been successfully developed and assessed (Fujii &Taniguchi, 2006, Cairns et al., 2008). The measures resulted in both road traffic reduction andimpressive cost-benefit performance (Cairns et al., 2008). Theoretically, these communication
measures generally consider attitudes, values and norms as fundamental factors that determine
mobility patterns (Bamberg et al., 2011). With very few exceptions such socio-psychologicalfactors of mode choice have not been investigated within a Chinese context yet. The underlying
motivation for this study is therefore to understand whether “soft” transport policy measures could
also be an option in Shanghai, and if yes, how such voluntary travel behavior change towards
public transportation could be promoted.

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We start with a brief review on how different psychological constructs are introduced andapplied in various travel behavior modeling contexts. Then, partly following existing literature, we
propose our model framework that focuses on the role of norming effects. Next, we describe hownecessary data are collected through a survey, followed by a detailed report on the estimation
results, their implications, and a comparison with findings from a study with German data. Finally,
we summarize our findings and give comments on future work as well as practical policy
implementation.

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PT Usage and Importance of norms

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The impact of individual socio-demographics, time or monetary cost on mode choice has been
well documented in literature. Further, hybrid models incorporating latent instrumental values
such as convenience, accessibility, and comfort are generally found superior in predicting public
transport mode shares (Morikawa,1989; Walker, 2001; Yáñez, Raveau, & Ortúzar, 2010). In
particular, individual related symbolic/affective values like autonomy, excitement, status andprivacy exert influences on travel mode alternative evaluation (Anable, & Gatersleben, 2005;
Hunecke, Haustein, Böhler & Grischkat, 2010), and thus could be applied into TDM oriented
traveler or trip segmentation. Similarly to car use there might be though further attitudinal factorsthat encourage people to use public transport such as pro-environmental attitudes, health reasonsin the form that one is aware that excessive car usage might lead to lack of exercise, or deeper held
values that might be summarized with a perception that “using public transport is the right thing todo” for the benefit to oneself and the surrounding society (Heath & Gifford, 2002).

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Yet psychological factors determining car /PT usage go beyond the above mentioned intra
personal ones (e.g., intention, symbolic value, etc.). In more recent literature, inter personal factorsor (social) norm effects have attracted much attention from scholars across disciplines (Bamberg
et al., 2011; Abou-Zeid et al, 2013). Without any generally acknowledged definition, social norms
describes the behavioral influence of others. Social norms refers to rules and standards that areunderstood by members of a group and that guide and/or constrain behavior without the force ofthe legal system but through interaction with others (Cialdini & Trost, 1998). For example, one of
the frequently referred contexts where social norms are important is public littering, i.e., one may
or may not litter publicly due a perception whether littering is in line with the behavior as well as
the expectation of others.

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As in the example the terms perception and expectation indicate, social norms can be further
segmented into different categories, accounting for different prescription and internalization
degrees. Thøgersen has proposed an extended taxonomy categorizing norms first into descriptive
and injunctive (Thøgersen, 2006). Descriptive norms (DN) refer to the perception on what isnormal, i.e., what most people do (Cialdini, Reno, & Kallgren, 1990; Fishbein & Ajzen,
2011;Thøgersen, 2006). Compared to this, injunctive norms describe rules or beliefs as to what
constitutes morally approved and disapproved conduct, that is, what should be done. Within
injunctive norms, further differentiations can be made. Schwartz (1977) deﬁnes personal norms(PN) as self-expectation of speciﬁc action in a particular situation, experienced as a feeling of
moral obligation, while subjective social norms (SSN) are enforced by expectations of externallyadministered rewards and/or sanctions (Thøgersen, 2006). We do notice different social norms
taxonomies are parallelly employed in the literature (Matthies, & Klöckner, 2012), however, we
prefer the one by Thøgersen due to its clear hierarchical concept definition (Cialdini, Reno, &Kallgren, 1990; Thøgersen, 2006).

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There is by now a significant body of literature in environmental psychology demonstrating
the importance of norming effects for behavioral decisions. Nordlund and Garvill (2003) for
instance conclude that personal norms mediate the influence of values and problem awareness on
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willingness to reduce car usage. Similar empirical findings on subjective social norms could befound in Bamberg et al (2003). Their analysis on travel behavior of people who recently move to
Stuttgart, Germany, suggests that neither past behavior nor direct habit are a good measure to
predict future behavior. Instead attitude, perceived behavioral control and subjective norms are themain contributors for travel mode changes. An extensive review emphasizing on how descriptive
norms influence mobility decision can be found in Abou-Zeid et al (2013). They review that there
is ample evidence that for decisions such as whether to jay-walk, to illegally park bicycles, tocommute by bicycle or to purchase cars norming effects are important. They conclude therefore
that proper consideration of these norming effects can lead to more efficient design of (soft)transport policy measures.

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Literature with China applications

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To the best of our knowledge most of the studies to explain car ownership and PT usage
respectively among the Chinese do not yet take into account norming effects nor attitudinal factorsthat have been found important to explain the effectiveness of soft transport policy measures.
There are a few notable exceptions though:

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Zhu et al (2012) has studied impacts of attitudes, social norms and aspiration for car
ownership among Chinese university students. They suggest that socio-psychological aspects are
the most influential determinant, having almost twice the effects than instrumental values on
private car ownership. Thus they propose that measures aimed at changing attitudes towards cars
as well as social norms should have a fundamental inﬂuence on car purchases and private car
usage. Zhao et al (2012) conduct a web based survey among private car users in Shanghai
regarding their PT usage intention and actual PT travel frequency with corresponding attitudinal
constructs. They examine the validity of the Theory of Planned Behavior (TPB) in explaining car
owners’ PT usage intention with a multiple regression model. The significant influence of attitude,
subjective norms as well as descriptive norms has been confirmed, though perceived behavioral
control was found to be insignificant. The work by Lai and Chen (2011) focuses on factors
affecting PT usage intention. Using survey data from Kaohsiung Mass Rapid Transit, they discuss
the importance of “public transit involvement”. They find that a person’s feelings about therelevance or importance of public transport as well as service quality, perceived value andsatisfaction all have significant positive effects on usage intention. Finally, as part of aninternational comparison, Belgiawan et al (2014) also collected some data from undergraduate
students in Shanghai on car ownership motivations. They find that the perception of the car as a(negative) status symbol, the independence it creates as well as social norms all effect ownershipmotivations.

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While the four studies analyze how attitudes, socio-demographics, and partially also social
norms, affect car ownership and car or PT usage, they do not focus on identifying usage intentions
and choice between modes which is the objective of this paper. Further, an aim of the present
study is a systematic examination on the different norm categories discussed in the previous4

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section and their specific influence on usage intentions.

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Hypotheses

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Besides the above quoted literature, a key reference for our study is the work by Bamberg et al(2007), on which we base parts of our hypotheses and methodology. With independent survey datafrom two German metropolitan areas, Bochum/Dortmund and Frankfurt, they first find empiricalevidence in both samples that personal norm (PN) influences travel behavior but is mediated by
the intent construct. They further demonstrate that social norms influence public transport usage
intention via attitudes, perceived behavior control (PBC) and PN, while the feeling of guilt serves
as a second determinant for PN. All the findings hold for both samples but with significant
different path coefficients, motivating them to make a detailed comparison on influence of social
contexts. In the current study, we partially replicate the Bamberg et al work with Chinese datawith an emphasis on the norming effects. To derive policy suggestions we further aim to explain
factors that create the perception of injunctive norms by individuals.

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Our primary hypotheses are depicted in Fig. 1. Household private car ownership, public
transport usage intention and actual usage frequency are selected as behavioral indicators.
Generally, we are assuming both public transport usage intention and private car ownership are
directly influenced by the same norms, but in opposing directions. Besides a direct effect, theinfluence of norms on public transport usage intentions should be mediated by private carownership. Further in line with the empirical evidence by Bamberg et al (2007), norms do not
influence actual public transport usage directly, but are mediated by intentions and private carownership. We therefore hypothesize, as one might expect, that owning a private car reduces
intentions to use public transport as well as the actual usage. The in Fig. 1 hypothesized effects ofthe demographics gender, age, income and education on mobility decisions are in line with a large
set of literature, we agree though that some of the relationships are discussable and possiblycontext dependent. Higher education might in some cases only result in less public transport usage
intentions due to (uncontrolled) correlation with income and car ownership. For example
Schwanen et al (2001) studied leisure trips of older people and found that higher education can
associate with more public transport usage.

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To design and implement the aforementioned soft transport policies, it is necessary toconceptualize paths between norm constructs and behavior as in the norms activation model(NAM) by Schwartz (1977). NAM suggests that personal norms only influence behavior when
two preconditions are met. First, individuals must be aware of potential consequences caused by
the targeted actions for themselves and others. Second, they must feel a degree of personal
responsibility for these actions and their potential consequences (Brown, 2007). Inherited from
NAM, we introduce social problem awareness (SPA) as an activator of personal norms. Besides
the well documented air pollution problem in Chinese cities, we investigate whether awareness(fear) of future possible fuel shortages, road congestion, traffic accident, health/obesity issues andsocial exclusion issues caused by excessive private car usage are perceived as problems. There issignificant media coverage and public concerns on the pollution caused by car traffic and theresulting adverse health effects so that we hypothesize that those being more concerned about
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pollution would also have a stronger perception that car usage should be reduced. Similarly,
obesity and a lack of exercise are issues nowadays more often perceived in connection withexcessive car usage. Further, the rapid expansion of Shanghai and land-use changes that havecome along with the increased car usage have also led to accessibility problems for some
population groups.

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In Fig. 1, latent variables are shown as ellipses while observed, manifest ones are inrectangles. The solid and dashed lines indicate hypothesized positive and negative association
respectively. To reduce the number of arrows and crossings, we enclose the three norms constructs
as well as the socio-demographic variables (i.e., gender, age, household monthly income category,and education level) with a sub box. For sake of brevity and clarity, we omit the manifest variables
for the latent constructs, except for SPA, in the graphic description here and the following.
SPA1(Air Pollution)
SPA2(Energy Crisis)

SPA3(Traffic Safety)
SPA4(Road Congestion)

Social Problem
Awareness

Personal Norm

SPA5
(Social Exclusion)

Subjective
Social NormIntention to Use Public
Transport

Actual Public
Transport Usage

SPA6(Individual Health)
Descriptive
Norm

Car Ownership

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Gender(Male)

Age(Older)

Income(Higher)

Education(Higher)

Fig.1 Hypothesized model structure

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Data collection and description

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A questionnaire was designed to collect the necessary information. We adopted an internet based
survey and disseminated the link via a local bulletin board system and other social media in
Shanghai. Compared to a paper based survey, this is both more cost and time effective and allows
us to reach a wide spread sample. We note though a natural limitation of respondent bias by using
this web based approach as younger people are more likely to access these bulletin board systems.

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We paid each validated respondent an incentive of 10 CNY (about 1.6 USD) in form of amobile phone voucher. Within two days we collected 551 responses, among which 489 werecompleted. 24 respondents were excluded as they refused to provide any household monthly
income information. This resulted in 465 responses that are the basis for the subsequent data
analysis. The descriptive analysis in this section and the subsequent structural modeling was
carried out with R version 3.0.2.
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Among all the validated responses, 25.8% were from female respondents, and 61.1% were
distributed in the age range between 25 and 35 whereas according to the latest official statistics, inShanghai, only 22.7% of the whole population ages between 18 and 34 (Wang & Ma, 2013). Thisbias in our sample should be due to the fact that young men are more active in sharing theiropinions in online virtual communities. Income ranges were provided in the survey, after testing
linear and loglinear structures we found that grouping answers into three categories provides
better model fits and statistically significant parameters. The three groups were: Low(0-5,000CNY/ 815 USD, 20.9%), Medium (5,000-11,000 CNY/1,794 USD, 46.5%), and High(Above 11,000 CNY/ 1,794 USD, 32.7%). Since the household income distribution for Shanghai
was not publicly available, we calculated the average household monthly income of the year 2012by multiplying the monthly income per capita and average household size, resulting in 8,355 CNY(1,343 USD) (Wang & Ma, 2013) which fell into the Medium income category. 90% of the
respondents report holding a bachelor’s degree and 47.3% own a car (Table 1). The private carownership rate for the whole city is 64.5 cars per 1,000 population (Wang & Ma, 2013). It should
be noted that this only includes private cars with locally registered number plates. If non-local
registered private cars are also considered simultaneously, it increases to roughly 100 cars per
1,000 population (Wang & Ma, 2013). Nevertheless, the age, education and the car ownershipdata show that, compared to the general Shanghai population, our sample is clearly skewed
towards the younger and better educated who are also more likely to own cars. Though we control
for these socio demographics in our subsequent models these biases should be kept in mind whengeneralizing our results to a wider population.

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About 54% of the respondents used public transport more than 10 times per week whichmight imply that they use it for commuting which appears reasonable given the car ownership rateof our sample as well as the average daily trip rate for the whole population in Shanghai, i.e., 2.23
trips per person and day, according to the Comprehensive Traffic Survey Report in 2009 (Lu,2010).

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Table 1 Descriptive statistics for demographics
Demographics
Sex

Age category

Monthly income

Highest Education

Car ownership
Weekly PT use frequency

Level
FemaleMale
0-20 yrs
20-25 yrs
25-30 yrs
30-35 yrs
35-40 yrs
40-45 yrs
45+ yrs
0-5,000CNY/815 USD
5,000-11,000 CNY/1,794 USD
Above 11,000 CNY/1,794 USD
No university degree
Bachelor degree
Master degree
Doctoral degreeNo
Yes
Less than 5 times
5-10 times
10-15 times
More than 15 times

N
120
345
14
60
151
133
52
29
26
97
216
152
45
367
46
7
245
220
108
106
172
79

%
25.81
74.19
3.01
12.90
32.47
28.60
11.18
6.24
5.59
20.86
46.45
32.69
9.68
78.92
9.89
1.51
52.69
47.31
23.23
22.80
36.99
16.99

Cum %
25.81
100.00
3.01
15.91
48.39
76.99
88.17
94.41
100.0020.86
67.31
100.00
9.68
88.60
98.49
100.0052.69
100.00
23.23
46.02
83.01
100.002
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Questions used for the Latent Constructs

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Measures are needed to manifest the socio-psychological orientation of the respondents. As one
objective of the current work is to partially replicate the work by Bamberg et al (2007), i.e. tocheck whether soft TDM measures derived from the socio-psychological theories apply inShanghai similar to German cities, we used the same indicator questions for our subjective social
norm (SSN) and personal norm (PN) constructs as well as for intention to use public transport(IUPT). When preparing the questionnaire, Bamberg and his colleagues organized a pre-study byasking their targeted respondents to qualitatively enumerate questions intended for latent traits
construction. The most referred items then were standardized and included in the questionnaire,and had their measuring ability tested with data from the formal survey afterwards (Bamberg &Schmidt, 2003).

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We advance the Bamberg et al (2007) study though by extending our questions used tomeasure social problem awareness (SPA) associated with car usage. As noted in the hypothesis
section, we picked up the six negative consequences frequently discussed in public media andliterature to test to what extent one perceives these as social problems caused by excessive car
usage.
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Bamberg et al also do not explicitly distinguish descriptive norms (DN). To design
appropriate questions we find two approaches in the literature, one concerning the percentage of
people around the respondent conducting specific activities (Fishbein & Ajzen, 2011) while thesecond emphasizes more on the observed activity frequency (Borsari & Carey, 2003; Sheppard,2011). We prefer the latter, as unlike taking drugs or alcohol, which where some of theapplications for the former, most citizens in Shanghai will use public transport at least very
occasionally.

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After some initial tests and to keep an appropriate length for the final questionnaire, twostatements each were included for subjective social norms (SSN), personal norms (PN), andintention to use public transport (IUPT) respectively. For each statement, respondents were asked
to rate on a 5 point Likert scale (from “1=strongly disagree” to “5=strongly agree”). Regarding
descriptive norms, the respondents were asked to report their perceived public transport usage
frequency of four different groups of people around them, with a scale from “1= Do not use at all”
to “5= Always use”. Table 2 lists the questions that were used to build these constructs as well as
SPA including their source. Judging from the mean values, we found that respondents hold apositive attitude towards the public transport service in Shanghai as their marked scores exceeded
the neutral point of the scale. The standard deviation was quite evenly distributed around 0.8
giving us sufficient variation for our subsequent analysis. Among the six social problem awarenessfactors we find that traffic accidents followed by air pollution and congestion are rated as the mostsignificant problems caused by private car usage which is fairly in line with our expectations.

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Table 2 Constructs and statements
Measures
Constructs/Statements

Λ

Item total
correlation

Alpha if
item

Mean

Std.Dev.

3.88

0.81

0.618

0.59

0.59

3.54

0.95

0.551

0.52

0.60

4.01

0.77

0.387

0.38

0.64

deletedCronbach
alphaSocial problems awareness(SPA) (Designed by the authors)
There is urgent need to deal with fine particulate matter(PM 2.5, PM10 etc.) from private car usage (SPA1).
Due to too much private car usage now, in the near
future we will encounter problems due to not enoughpetrol for cars (SPA2).
There are urgent needs to deal with traffic accidents(SPA3).
There is need to limit the amount of private car traffic inlarge towns and cities during busy times of day (SPA4).

0.66
3.79

0.96

0.480

0.46

0.62

3.58

0.89

0.498

0.49

0.61

3.81

0.89

0.436

0.44

0.63

3.49

0.88

0.955

0.77

The increase in private car usage in the past years has
made it difficult for some people to reach real needed
amenities (e.g., medical, education, government,
shopping, etc.) (SPA5).
Using private car every day causes people to become
overweight easily (SPA6).
Subjective social norms(SSN) (Bamberg et al, 2007)
People who are important to me think that I should use
public transport instead of the car for everyday routes(SSN1).
People who are important to me would support mypublic transport usage (SSN2).

-3.62

0.84

0.733

0.77

3.49

0.8

0.861

0.58

0.82

Personal norms(PN) (Bamberg et al, 2007)
Because of my own values/principles I feel anobligation to use public transport instead of private carfor everyday trips here in my city (PN1).
I feel guilt to use private car when I could use public
transport (PN2).

-3.05

0.93

0.532

0.63

0.58

Descriptive norms (DN): What’s the public transport usage frequency of people in the following category? (Designed by the authors)
My family members in Shanghai… (DN1).

3.59

0.9

--

0.59

0.82

My friends in Shanghai… (DN2).

3.57

0.83

0.710

0.76

0.74

My colleagues in Shanghai… (DN3).

3.63

0.77

0.869

0.77

0.75

People in my living community in Shanghai … (DN4).

3.66

0.72

0.747

0.73

0.76

0.8

1.173

0.8

0.82

Intention to use public transport(IUPT) (Bamberg et al, 2007)
I intend to use public transport for everyday trips(IUPT1).
My intention to use public transport for everyday trips
is: very weak (1) → very strong (5) (IUPT2).

3.76

0.85
--

3.62

0.83

0.626

0.8

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Model estimation

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Reliability of Latent Constructs

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Consistency for all the latent constructs was examined. We firstly test the reliability of our latentconstructs with Cronbach’s alpha analysis. By convention, an alpha near 0.70 is considered“adequate”, though values above 0.80 are desired (Vaske, 2008). We further report corrected
item-total correlations in Table 2, which should be larger than 0.40 (Schmitt, 1996;Vaske, 2008).
Moreover, a set of scale items can have a high Cronbach’s alpha but still be multidimensional,
when there are separate clusters of items having high inter-correlations. In this case, Cronbach andShavelson (2004) assert that Cronbach’s alpha can correctly be used for heterogeneous scales, butthat additional analysis such as confirmatory factor analysis (CFA) is required to remove
uncertainty. Goodness of fit indices include the Comparative Fit Index (CFI), Non-Normed Fit
Index (NNFI，also known as TLI), Adjusted-Goodness-of-Fit Index (AGFI), StandardizedRoot-Mean-Square-Residual (SRMR). An acceptable model fit is indicated when CFI, NFI, andAGFI are greater than 0.90, while SRMR should be less than 0.10 (Kline, 2011).

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Corrected item-total correlation and CFA fit indices are reported in Tables 2 and 3
respectively. The indices are acceptable except for DN, for which the AGFI and TLI do not meetthe 0.9 cut-off criteria. We considered dropping DN1 as Cronbach’s alpha for DN does not change
significantly and the latent construct by DN2, DN3, and DN4 could then meet the AGFI and TLI
criteria (see Table 3). However, such treatment loses information of family influence on one’s
behavior, and, as one anonymous reviewer kindly suggested, it would be interesting to examine
the difference of variously oriented descriptive norms on one’s travel decision. Thus in thefollowing analysis, we split the four DN statements into two groups, one with DN1 only and used
to formulate a Family Descriptive Norms (FDN) construct, while the second one including theother three statements used for a Non-family Descriptive Norms (NFDN) construct.

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Table 3 Reliability analysis-CFA approach
Constructs

AGFI

SRMR

CFI

TLI

Social problems awareness

0.986

0.022

1.000

1.000

Subjective social norms

1.000

0.000

0.997

1.000

Personal norms

1.000

0.000

0.991

1.000

with DN1

0.817

0.042

0.948

0.844

without DN1

1.000

0.000

1.000

1.000

Intention to use public transport

1.000

0.000

0.997

1.000

Descriptive norms

26

Correlation analysis

27
28
29

Based on the CFA analysis, Table 4 presents the correlations among the socio-demographics andlatent constructs. Our hypotheses appear well supported except for the effect of gender and PN.
Gender does not significantly correlate with any other variable even at a 0.1 significance level,
11

1
2

while the correlation between PN and CAR has the expected sign but is not statistically significant
either.

3
4

Table 4 Correlation of the socio-demographics and the five latent constructs
GenderAge

Gender1.000

Age-0.026 1.000

Income EDU

SPA

SSN

PN

NFDN FDNIUPTCAR PTU

Income -0.046 0.291* 1.000

5
6
7

EDU

-0.001 -0.043 0.214* 1.000

SPA-0.028 -0.147 -0.113 -0.047 1.000

SSN-0.022 -0.039 -0.083 -0.026 0.355 * 1.000

PN-0.054 -0.040 -0.052 0.018 0.347 * 0.567* 1.000

NFDN-0.002 0.006 -0.008 -0.057 0.144 0.134 -0.014 1.000

FDN

0.038

IUPT

-0.048 -0.123 -0.149 -0.096 0.314* 0.494* 0.418* 0.208* 0.150 1.000

CAR-0.051 0.163* 0.292* 0.144 -0.225* -0.107 -0.061 -0.193* -0.212* -0.335* 1.000

PTU

0.006 -0.208* -0.125 -0.013 0.183* 0.307* 0.242* 0.226* 0.163* 0.443* -0.315* 1.000

0.022

0.018 -0.033 0.105 0.079 -0.039 0.528* 1.000

Note: Italic p<0.1, bold p<0.05, bold* p<0.01; SPA = Social Problem Awareness; SSN= Subjective Social Norms; PN= Personal Norms; NFDN= Non-family
Descriptive Norms; FDN=Family Descriptive Norms; IUPT= Intention to Use Public Transport; CAR = Car Ownership Status; PTU=Actual Weekly Public
Transport Usage.

8

Path analysis with SEM method

9
10
11
12
13
14
15
16
17
18
19
20
21

We note that introducing the binary variable car ownership as explanatory variable introducessome additional complexity as it violates the multinormality assumption underlying commonlyused estimation software such as LISREL (Bamberg & Schmidt, 2003). This estimation problem
is tackled by using the weighted least squares means and variance adjusted (WLSMV) estimator in
Mplus and the diagonally weighted least squares (DWLS) in the R Lavaan package. For details werefer the reader to the Mplus and Lavaan manuals (Muthén & Muthén, 2010; Rosseel, 2013). Fig.
2 shows our estimated model and Table 5 the estimation of our latent constructs. Gender isdropped from the model in line with the insignificant correlations reported in Table 4. The ideathat SPA might be influenced by the socio-demographics is not supported by our data either, so thelinks are not included in Fig.2. Following index thresholds suggested in Kline (2011) our model fit
appears acceptable (χ2=409.461, df =175, p<0.001, GFI =0.973, AGFI=0.962, RMSEA=0.054;
NNFI=0.936, CFI =0.946). In the following we discuss first the influences of socio-demographics
and then focus on our norms constructs.

22
23

12

R-square=0.391
1.105/0.625***
Social Problem
Awareness

Personal Norm
0.262/0.324***
R-square=0.611
0.390/0.466***

Subjective
Social NormIntention to Use Public
Transport

0.802/0.483***
Actual Public
Transport Usage-0.222/-0.374***

Non-family
DescriptiveNorm-0.438/-0.277***

Car Ownership

FamilyDescriptiveNorm

R-square=0.151

R-square=0.276-0.15/-0.152**-0.136/-0.118*
0.120/0.160**-0.094/-0.127**

0.459/0.319***

Income0.157/0.302***0.367/0.258***

1
2
3

R-square=0.285

Edu

Age

Notes: For each path we show: unstandardized coefficient/ standardized coefficient, ***significant at the 0.001 level, ** significant at the 0.01 level, *significant at the 0.1 level.

4

Fig. 2 Path analysis graphic description

5
6

Table 5 Loadings for latent constructs
Construct

Manifest variables

Estimate

Std. Estimate

Std. Error

p-value

SSN

→

SSN1

1

0.838

0

NA

SSN

→

SSN2

1.002

0.886

0.055

p<0.001

PN

→

PN1

1

0.960

0

NA

PN

→

PN2

0.633

0.523

0.073

p<0.001

NFDN

→

DN2

1

0.791

0

NA

NFDN

→

DN3

0.932

0.794

0.080

p<0.001

NFDN

→

DN4

0.783

0.716

0.066

p<0.001

FDN

→

DN1

1

1

0

NA

SPA

→

SPA1(Air pollution)

1

0.532

0

NA

SPA

→

SPA2(Energy crisis)

0.846

0.389

0.108

p<0.001SPA→

SPA3(Traffic safety)

0.565

0.318

0.085

p<0.001SPA→

SPA4(Road congestion)

1.176

0.536

0.124

p<0.001SPA→

SPA5(Social exclusion)

0.925

0.450

0.121

p<0.001SPA→

SPA6(Health)

0.877

0.425

0.110

p<0.001

IUPT

→

IUPT1

1

0.790

0

NA

IUPT

→

IUPT2

1.122

0.855

0.049

p<0.001

7

13

1

Influences of socio-demographics

2
3
4
5
6
7
8
9
10
11
12
13
14
15

Older respondents or those with higher education tend to have higher income. Income theninfluences car ownership directly and indirectly PT usage, which illustrates that despite recent
investments in the public transport system in Shanghai higher income is associated with higher car
ownership, possibly due to both symbolic affective factors as well as convenience associated withcar trips. Private car ownership also mediates the effect of age on public transport usage intention
and frequency. In particular in China this is corresponding to our expectations as it takes time foryounger people to save sufficient money to afford a private car. We further find age to be directly
negatively associated with PT usage. Considering that our sample predominately consists ofpeople aged below 50 we suggest that an explanation is the more active and irregular lifestyle ofyounger Shanghainese. Trip surveys from Shanghai suggest that people aged in their 20s make on
average 2.6 trips per day whereas those aged in their 50s make on average only 2.3 trips per day(Lu, 2010). Fig.2 also reveals that education affects travel behavior indirectly via income andhousehold car ownership, i.e., a higher education first promotes one’s income, then the possibility
to own a car in her/his family, thus reduces her/his intention to use PT as well as the actual usage.

16

Influences of norms

17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

All the four norms constructs influence the individual’s intention to use PT and increase theiractual PT trip frequency. The estimated model structure suggests though that SSN, PN, NFDN and
FDN influence individual travel behavior via different paths and with different intensity. While
SSN and PN first affect one’s intention to use PT and then the actual behavior, both NFDN and
FDN effects are further mediated by the respondent’s household car ownership. Hence, the degree
to which, family members and others, use public transport (and not private car) influences the
respondents’ household car ownership status. Controlling for this mediation effect we do not find asignificant direct path from either NFDN or FDN to usage intentions. We note that the DN effectis likely to be a mixture of various factors that we cannot control for with our data, such as
similarity in lifestyle, income as well access to public transport in the neighborhood or the
workplace. In addition the path might cover some of the “learning and status effects” associatedwith trends, i.e. that one wants to assimilate the behavior to others in one’s social network. We
would expect the latter effect to be more significant for our NFDN construct and the former set offactors (lifestyle, access to PT etc.) to be more related to FDN. It is therefore interesting to observethat regarding impacts on private car ownership, we find a higher path coefficient for NFDN thanfor FDN even though the correlation information (Table 2) suggests the opposite.

33
34
35

Table 6 shows the different path strengths of independents on travel behavior anddistinguishing direct, indirect and total effects. The table illustrates that car ownership is mostly
influenced by income but also reduced by high perceived DN constructs to a comparable extent.

36
37

14

1

Table 6 Total standardized paths effects
→

CAR

IUPT

PTU

0.256Δ Δ-0.06Δ-0.212Δ Δ

0.319-0.119Δ-0.106Δ

0.082Δ-0.031Δ-0.027Δ

0.203Δ

0.098ΔSSN

0.466

0.225ΔPN

0.324

0.157Δ

AGE

AGE

INC

EDU

SPA

SSN

PN

0.302

INC
EDU

0.258

SPA

NFDN

FDN

0.625

NFDN

-0.2770.103Δ

0.092Δ

FDN-0.1180.044Δ

0.039Δ-0.374-0.333Δ Δ

CAR
IUPT

0.483

2
3
4
5

Notes: No label indicates total effects = direct effects, Δ indicates total effects = indirect effects, and Δ Δ indicates total effects = direct + indirect effects;

6
7
8
9
10
11
12
13
14
15
16
17
18
19

Our analysis suggests that 1 unit increase in SSN would cause an average 0.466 units
increase in intention to use public transport (IUPT), which is larger than the effect of PN (0.324
units) and DN (0.103 units for NFDN and 0.044 units for FDN). Increase in income and age also
results in a decrease in IUPT among our sample, though at a relatively low rate. As noted, suchnegative effects are indirect, implying that age, income and education increase per se does notnecessarily reduce travelers’ intention to use public transport. Since actual PT usage is strongly
positively correlated with IUPT, the norms positively influence PT usage while private carownership, income and age reduce PT usage. Mediated by IUPT, the coefficients for norms are
smaller than those of the “resistors” though taken together still of considerable size and cancounter the effect of the socio-demographic factors covered in our survey. Another finding thatappears important to us when comparing the standardized coefficients for the paths from norms to
IUPT and PTU is that among the four norms SSN has the most significant effect. This suggeststhat perceptions of others’ opinion are an important element in the intention and actual usage of
public transport.

20

Activation of personal norms

21
22
23
24
25
26
27
28
29
30

We further find that PN is activated by social problem awareness. As can be seen from the upper
left of Fig. 2, SPA alone explains nearly 40% variance of PN. SPA is constructed with six manifest
variables. Our estimates reveal that according to the standardized coefficients of each manifest
variable, subjects’ concern on particulate matter (SPA1) and road congestion (SPA4) are mostinfluential with nearly the same coefficients (0.532 and 0.536 respectively), followed by social
exclusion (SPA5, 0.450) and health (SPA6, 0.425). Energy issues (SPA2, 0.389) and traffic
accident (SPA3, 0.318) contribute least. The larger coefficients for air pollution and roadcongestion might be expected given the daily exposure of citizens to these issues. The significanceof perception of social exclusion might be surprising but indicates that the increase in car traffic
has led to some real issues for some communities and population groups as perceived by some.

AGE=Age category; INC= Monthly income category; EDU= Education level; SPA = Social Problem Awareness; SSN= Subjective Social Norms; PN=
Personal Norms; NFDN= Non-family Descriptive Norms; FDN=Family Descriptive Norms; CAR = Car Ownership Status; IUPT= Intention to Use Public
Transport; PTU=Actual Weekly Public Transport Usage.

15

1
2
3
4
5

Similarly, the role of SPA2 does suggest that those who are aware of peak oil discussions are moreaware of general problems caused by excessive car usage and hence do not perceive cars as muchas a status symbol. Interestingly we find a fairly low coefficient for SPA3 (Traffic safety) despite it
being rated highly as a problem (Table 2). This suggests that the risk of accidents is perceived but
does not necessarily change the perception of the car.

6

Comparison with work of Bamberg et al. (2007)

7
8
9
10
11
12
13
14
15

Finally, as noted, parts of our paper follow the Bamberg et al. (2007) approach and we can hencecompare the present findings from Shanghai with their two German case studies. Bamberg et alreport model fits with and without consideration of PT past usage for their Frankfurt case study
but only the Bochum case study reports results without habitual factors. These models without
habitual factors we use here for comparison and we pick up the common paths for both models(Table 7). We note that the signs of all paths are in line and that our estimated coefficients mostly
appear to be in range with the Bamberg et al.’s (2007) results. The exception is our larger estimate
of SPA on PN (though the correlation is also similar). We note though that our SPA constructdiffers from theirs in that we include more aspects.

16
17
18
19
20
21
22
23
24
25

In all samples we find that SSN has a stronger effect on PT usage than PN. This implies theimportance of the external social environment towards behavior. Bamberg et al argue further that
SSN is an important source of PN (Bamberg et al., 2007). Our results suggest that SSN and PN
effects in Shanghai do not differ significantly compared to a German context. Bamberg et al
discuss that social context is important to understand the impact of SSN and our results mightshow how “Westernized” Shanghai has already become. To be more general, one might
hypothesize in an Asian context a stronger effect of SSN as the influence of others on one’s
decision making might be more pronounced in more collective societies. Belgiawan et al (2014)
for example show that social norms correlate higher with car ownership intentions in Asian
countries than in the Netherlands.

26

Table7 Common paths effects for the three samples (standardized)
Estimated effects (Correlation)

Paths
IUPT→PTU
SSN→IUPTPN→IUPT
SSN→PTU
PN→PTUSPA→PN

Frankfurt

BochumShanghai

0.730 (0.75)
0.695 (0.70)
0.400 (0.82)
0.507 (0.53)
0.292 (0.60)
0.378 (0.36)

0.450 (0.48)
0.237 (0.35)
0.200 (0.29)
0.107 (0.03)
0.090 (0.16)
0.486 (0.37)

0.483 (0.443)
0.466 (0.494)
0.324 (0.418)
0.225 (0.307)
0.157 (0.242)
0.625 (0.347)

27

Summary and discussion

28
29
30

Our discussion starts by pointing out transportation problems in China and specifically Shanghai.
Private car ownership is expected to increase further in the predictable future as well as theapparent problems associated with this. Given this background and the need to design alternative
16

1
2
3

policies, in this paper we explore the structural relationship between problem awareness, norms,
and public transport usage, controlling for car ownership and public transport usage intentions.
Our focus is on the role of norms which we split into personal, social and descriptive norms.

4
5
6
7
8
9
10
11
12
13
14
15
16
17

We find that most of our assumptions based on existing literature are confirmed by our model.
In particular income has the expected significant positive effect on car ownership while its effects
on intended and actual public transport usage are much smaller. The norm constructs explain
public transport usage to a larger degree. We discuss the importance to distinguish theinterpretation of the three norms constructs. Descriptive norms will be related to missing variables
in our survey regarding the public transport service quality in one’s neighborhood. In further work,with a more detailed survey, it would be interesting to distinguish the effect of these factors fromthe “influence of observing others”. It is therefore difficult to derive policy implications fromdescriptive norms for the time being, except that our distinction between descriptive norms
derived from family members and others suggests that indeed the perception of how non-family
members use public transport might be an important factor for car ownership decisions.
Furthermore, it is worth observing that the factors combined into descriptive norms appear to
explain car ownership as much as income suggesting the potential for a good quality public
transport service to reduce car ownership.

18
19
20
21
22
23
24
25
26

Subjective social norms and personal norms are instead rather suitable to discuss the potential
for soft transport policies. The significant effect of SSN suggests that perceived social pressure
could be used to increase the willingness to use public transport. If one could initiate an increasein public transport usage, this might turn into a larger trend as people appear to consider the
expectation of others. Our findings regarding personal norms further strengthen this argument.
The construct describes the more internalized aspects of social pressure. Convictions about the car
as status symbol and public transport as “transport for the less well-off” appear to (still) exist in
Shanghai as also observed by Zhu et al (2012) among students. Changing this perception could
have significant impacts on mode choice and car purchases.

27
28
29
30
31
32
33
34
35

We believe that our observation that PN can be explained to a significant degree by problem
awareness has further policy implications. We have noted the importance of congestion perception
and air pollution in our problem awareness construct. Taken together, and based on our causal
assumptions, our path analysis suggests that personal norms are most activated by the problems
that are also most in the news currently, namely air pollution and congestion. This might be further
evidence that mobility patterns could be influenced by letting people realize the various negative
consequences associated with car usage. If awareness of negative health, environmental as well as
economic effects associated with car usage and congestion could be further raised, one might alsofind more willingness among the citizens of Shanghai to reconsider their mobility decisions.

36
37
38
39
40

One might hence utilize the growing discussion on air pollution in China to motivate
behavioral changes. Tan (2013) reports that 26.4% of her survey respondents from Changsha, thecapital city of the Hunan province, China, would like to reduce their private car usage due to theworsening air quality. Our study confirms that such problem awareness induces personal norms
which in turn influence intentions to use public transport.

41

Referring back to our hypotheses illustrated in Fig.1, our results are based on the assumption
17

1
2
3
4
5
6
7
8
9

that the perceived personal norms are activated by social problem awareness. We suggest that
respondents who are more aware of the various negative effects of excessive car usage perceive ahigher obligation to use more public transport instead of private car. We note that the reverse
causality, i.e. justifying one’s perception and behavior by creating a set of believes on how bad car
traffic is seems less likely, especially since we do not find a strong correlation between income
and SPA (nor between income and PN). In short, a “cognitive dissonance” argument that those
who cannot afford cars project a negative image on cars appears therefore unlikely, thoughgenerally we cannot exclude the possibility that car users downplay the negative consequences of
car usage.

10
11
12
13
14
15
16

Based on these findings we therefore suggest that also in Shanghai (as well as other Chinese
cities) the implementation of soft policy measures such as mobility management, work placetransport schemes and other kinds of appeals and advertisement schemes for sustainable transportshould be more considered in addition to currently widely debated coercive ones. A perception
often held that Chinese cities first have to improve transit infrastructure before additional
measures are taken might be misleading. Rather, we believe our discussion on the three norms
constructs suggests that both hard and soft policy measures should be combined.

17
18
19
20
21
22
23

Our study clearly leaves ample room for further research. We pointed out the ambiguity ofthe descriptive norms term and suggest that aiming to solve this by a more detailed survey on theexperienced public transport quality could be worthwhile to derive additional specific policy
suggestions. Further, connected to above paragraph, it would be worth observing how social
problem awareness changes with growing environmental problems. Such kind of panel data could
further support the cause and effect assumptions made in our path analysis. Finally, it is our hopethat this study contributes to the study of specific mobility management schemes also in China.

24

Acknowledgement

25
26
27
28
29
30

The authors would like to acknowledge Dr. Teng Jing (Tongji University, China) and Miss Yu
Yixiu (now a PhD student in Texas Tech University, USA) for their helpful assistance inquestionnaire distribution and incentive assignment respectively. We also gratefully acknowledge
three anonymous reviewers who gave critical and valuable suggestions that helped improve themanuscript.

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Author Biographies

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Dong Zhang is a PhD student in the School of Traffic and Transportation Engineering at Tongji
University, China. With support from CSC funding he spent one year at Kyoto University in
2012-2013. This paper is part of his doctoral research which investigates how physical andperceptual factors can encourage individuals to use public transport in Chinese cities.
Jan-Dirk Schmöcker is an Associate Professor in the Department of Urban Management, Kyoto
University, Japan. Previously he held positions at Tokyo Institute of Technology and Imperial
College London where he also obtained his PhD. His research interests cover public transport
assignment as well as a range of travel behavioural issues. A recent focus has been on estimating
and modelling the effects of social norms.
Satoshi Fujii is a Professor of Transportation and Behavior Science in the Department of Urban
Management at Kyoto University and a Special Adviser for Public Policy to the Cabinet of Japan.
He has been engaged in applied psychological research for behavioral modiﬁcation of travel
behavior. He is currently also the director of the “Resilience Research Unit” at Kyoto University.
Xiaoguang Yang is Professor in the School of Traffic and Transportation Engineering at Tongji
University, China. His research interests focus on information augmented urban traffic systemdesign and management.

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