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International Journal of Sustainable Transportation
ISSN: 1556-8318 (Print) 1556-8334 (Online) Journal homepage: http://www.tandfonline.com/loi/ujst20
Can we promote sustainable travel behavior
through mobile apps? Evaluation and review of
evidence
Varsolo Sunio & Jan-Dirk Schmöcker
To cite this article: Varsolo Sunio & Jan-Dirk Schmöcker (2017) Can we promote sustainable
travel behavior through mobile apps? Evaluation and review of evidence, International Journal of
Sustainable Transportation, 11:8, 553-566, DOI: 10.1080/15568318.2017.1300716
To link to this article: http://dx.doi.org/10.1080/15568318.2017.1300716
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Can we promote sustainable travel behavior through mobile apps? Evaluation and
review of evidence
Varsolo Sunio and Jan-Dirk Schm€
ocker
Department of Urban Management, Kyoto University, Kyoto, Japan
ARTICLE HISTORY
Received 27 April 2016
Revised 15 February 2017
Accepted 24 February 2017
ABSTRACT
Mobile phone applications to monitor and influence one’s behavior are numerous. Most developed appear
to be health applications, but in the past decade, “persuasive technology”has also been leveraged and
applied to promote sustainable travel behavior. We discuss the health applications and review and
evaluate existing behavior change support systems (BCSS) designed to promote sustainable travel
behavior. We extract the persuasive features embedded in these systems and evaluate their persuasive
potential by using the persuasive systems design (PSD) model that has been used to evaluate BCSSs in the
health domain. Our evaluation reveals that some features crucial for successful travel behavior change,
such as tunneling, rehearsal, and social facilitation, are missing. Furthermore, we assess studies conducted
to evaluate the effectiveness of these BCSSs in changing behavior and find indications that effect sizes are
mostly small though methodologically robust studies are largely missing; hence, no definitive conclusion
yet can be derived. Based on these findings as well as literature related to public health where BCSSs
appear to be further developed, we then derive three important suggestions on research needs and
applications for further development of BCSSs in the transport policy realm.
KEYWORDS
Behavior change support
system; persuasive
technology; smartphone;
sustainability; travel behavior
1. Introduction
There has been a burgeoning interest in using technology to
deliver interventions to change behavior ever since Fogg (2002)
introduced his pioneering work on “persuasive technology”.
This is defined as technology designed to change attitudes and
behavior of users through persuasion (Fogg, 2002). Recently,
Oinas-Kukkonen (2010,2013) coined the concept of behavior
change support system (BCSS), which builds on this tradition
of persuasive technology. Oinas-Kukkonen (2010)defines
BCSS as an “information system designed to form, alter, or
reinforce attitudes, behaviors or an act of complying without
using deception, coercion or inducements.”In the design of the
information system, a web- or mobile-based platform is often
used. Here, smartphones as a medium of intervention are par-
ticularly noteworthy and promising. With their widespread
adoption and pervasive use in society, smartphones can be lev-
eraged to deliver large-scale and cost-effective behavior-change
interventions (Lathia et al. 2013). Indeed, as Fogg and Eckles
(2007) asserted, the future platform for persuasion and behav-
ior change is mobile.
BCSSs have been implemented in domains such as health-
care/well-being (Lane et al., 2011), energy use conservation
(Weiss, Staake, Mattern, & Fleisch, 2011), education (Mintz
and Aagaard, 2012; Mintz, Branch, March, & Lerman, 2012),
and travel (Froehlich et al., 2009), to name only a few. In the
healthcare/well-being domain, extensive reviews have already
been conducted. These studies include evaluations on the per-
suasiveness of the design features of the health/well-being
BCSSs (Langrial, Lehto, Oinas-Kukkonen, Harjumaa, &
Karppinen, 2012; Lehto & Oinas-Kukkonen, 2010; Lehto &
Oinas-Kukkonen, 2011); content analysis on the extent of
inclusion of health behavior theory constructs (Cowan et al.,
2013); characterizations of behavior change techniques
implemented (Conroy, Yang, & Maher, 2014; Yang, Maher, &
Conroy, 2015); and assessment of their effectiveness (Wang
et al., 2014). Although a number of reviews like these have been
done in the healthcare/wellness domain, we observe that, in the
domain of travel behavior, no review of this kind has been
carried out yet.
Moreover, in recent years, Fogg’s(2002) framework has also
been applied to the topic of environmental sustainability (DiS-
alvo, Sengers, & Brynjarsd
ottir, 2010). A broad range of envi-
ronmental sustainability issues are being addressed or tackled,
such as energy consumption, water and fuel use, indoor air
quality, and transportation (Brynjarsdottir et al., 2012). The
primary aim is to promote environmental sustainability
through persuasive technology.
In this paper, our interest is on persuasive technology, in
particular BCSSs, designed to promote “sustainable travel”or
“sustainable mobility.”Technology—or in particular, informa-
tion and communication technology (ICT)—can promote sus-
tainable urban mobility in various ways by changing travel
demand, travel patterns, and urban forms (see, for example,
Cohen-Blankshtain & Rotem-Mindali, 2016), but here, we only
consider ICT’s potential effects on travel behavior. Moreover,
we use “travel”and “mobility”interchangeably. Definitions of
sustainability vary in literature, but it is generally considered
as encompassing environmental, economic, and social
CONTACT Jan-Dirk Schm€
ocker schmoecker@trans.kuciv.kyoto-u.ac.jp Department of Urban Management, Kyoto University, Kyoto 615-8540, Japan.
© 2017 Taylor & Francis Group, LLC
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
2017, VOL. 11, NO. 8, 553–566
http://dx.doi.org/10.1080/15568318.2017.1300716
dimensions. In this paper, we regard sustainability in its narrow
environmental sense of simply minimizing the amount of car
travel or carbon emissions. Unsustainability of travel is thus
usually equated with car use.
To the best of our knowledge, work on sustainable travel
BCSSs began with Ubigreen Transportation Display (Froehlich
et al., 2009). Its purpose is to increase awareness about the
user’s sustainable travel behavior. Each time the user takes
greener alternatives, Ubigreen senses this and feeds it back as
ambient changes to the background graphics of the user’s
phone. Later on, other sustainable travel BCSSs were developed:
Peacox (Schrammel, Busch, & Tscheligi, 2012), Quantified
Traveler (Jariyasunant et al., 2015), and MatkaHupi (Jylh€
a,
Nurmi, Sir
en, Hemminki, & Jacucci, 2013).
Traditionally sustainable travel behavior has been promoted
through various voluntary behavior change programs (VTBC)
without extensive support systems. Examples include so-called
“Travel Feedback Programs”(TFP) in Japan (Fujii and
Taniguchi, 2005). In a typical TFP, participants of the program
are given feedback based on their reported travel behavior with
the aim of modifying their behavior without coercion. TFPs
come in several types, differentiated by the location, techniques,
procedures, and communication media used in their imple-
mentation (Fujii and Taniguchi, 2006). With respect to com-
munication media, TFPs have relied so far only on traditional
technologies, namely face-to-face communication, regular mail,
telephone, and email (Fujii and Taniguchi, 2006). This severely
limits the potential of classical TFPs for scaling up (Jariyasun-
ant et al., 2015). Nonetheless, as argued earlier, a new platform
for persuasion has emerged recently—the mobile platform—
which TFPs, or VTBC programs in general, appear to not yet
have taken full advantage of. The mobile platform can broaden
the applicability of VTBC programs while maintaining their
effectiveness (Meloni & di Teulada, 2015). For instance, Quan-
tified Traveler (Jariyasunant et al., 2015), mentioned earlier, is
a mobile-based computational TFP.
Besides their potential in promoting sustainable behavior
change, many of these systems also function as automated trip
diaries, capable of generating travel surveys at large scale.
Smartphones are equipped with sensors that can be used to col-
lect trip data from individuals, often with minimal respondent
burden. These data can then be processed to derive detailed
information about mobility patterns (Rojas, Sadeghvaziri, &
Jin, 2016; Sadeghvaziri, Rojas IV, & Jin, 2016; Patterson and
Fitzsimmons, 2016) and urban activities (Demissie, Correia, &
Bento, 2015).
In this paper, we aim to extract and evaluate the persuasive
elements embedded in travel BCSSs by applying the Persuasive
Systems Design (PSD) model (Oinas-Kukkonen and Harjumaa,
2009; explained below). We are motivated by the need to iden-
tify important persuasive design elements in these BCSSs.
Davies (2012) observed that in non-technology-based VTBC
programs, “little regard has been paid to the actual design of
VTBC campaigns and their individual elements…However,
there is also a need to understand and theorize the design of
campaigns, both singularly and collectively, as part of overall
policy to change travel behavior on a wide scale.”Hence, it is
necessary to evaluate the design of the VTBC program itself, in
addition to its impact on changing behavior. Finally, we
conclude with some propositions and suggestions to advance
the field.
2. Methods
2.1 Eligibility criteria
There are clearly a large number of studies aiming to change
the travel behavior through various information feedback
(Ampt, 2003; Fujii and Taniguchi, 2005; Richter, Friman, &
G€
arling, 2011). We limited our review by the following criteria:
A study was included if (1) it aimed to change travel behavior
by influencing the traveler’s trip, mode, departure time, or
route choices; (2) the intervention used a mobile/smartphone
application and/or website, and (3) it is published in journals
or conferences. Studies that use smartphones as mobile sensors
to automatically detect trip information or construct travel dia-
ries, but do not (explicitly) aim to change behavior, are
excluded. Moreover, those which use smartphones in designing
behavioral modification interventions but have no correspond-
ing publication are likewise excluded. For example, “in-Time,”
“Wiewohin,”“Kongressnavigator,”and “EcoWalk,”which are
cited in Busch, Schrammel, Flu, Kruijff, and Tscheligi (2012),
are not included in this review because we could not identify
sufficient materials describing these.
2.2 Search method
We conducted our literature search in several databases using
keywords such as “smartphone,”“travel,”“behavior,”“change,”
“persuasive,”and “intervention.”We searched articles pub-
lished between January 2005 and June 2015. We screened the
articles based on their titles and abstracts, and those that
seemed to meet the inclusion criteria were selected for further
examination. After we verified that they indeed met the neces-
sary criteria, they were then included in the final list. A snow-
ball review was then conducted, in which selected articles that
emerged from the literature search were screened and checked
for potential eligible studies. In case when some papers
obtained through this search method are only a part of a bigger
study, supplementary papers are also obtained.
2.3 Evaluation method
2.3.1 Persuasiveness
Several frameworks have been introduced to evaluate persua-
siveness of design. The BCSSs included in the study were evalu-
ated based on the framework introduced by Oinas-Kukkonen
and Harjumaa (2009), known as the PSD model. The PSD rep-
resents an extensive conceptualization for technology-based
persuasion and the most sophisticated evaluation method avail-
able (Lehto & Oinas-Kukkonen, 2011). In general, PSD can be
used in a variety of settings: from evaluating the software design
specifications to assessing the literature in some problem
domain (Oinas-Kukkonen, 2013).
The PSD model is a conceptualization for designing, devel-
oping, and evaluating BCSSs. In evaluating BCSSs using PSD,
we first analyze the persuasion context and then the persuasive
system features. Persuasion context analysis includes
554 V. SUNIO AND J.-D. SCHM
€
OCKER
identifying the intent (Who is the persuader? What type of
change does the persuader target?), the event (use, user and
technology contexts), and the strategy (message and route).
Next, we analyze the persuasive system features. In the PSD
model, there are four categories for the features: primary task
support, dialogue support, system credibility support, and
social support. Primary task support helps the user achieve or
carry out the primary task or target behavior by means of seven
principles, namely reduction, tunneling, tailoring, personaliza-
tion, self-monitoring, simulation, and rehearsal. Dialogue sup-
port enables user-system interaction to keep the user active and
motivated in using the system. Praise, rewards, reminders, sug-
gestion, similarity, liking, and social role are seven principles of
dialogue support. System credibility support aims to make the
system more believable and thereby more persuasive. It
includes trustworthiness, expertise, surface credibility, real
world feel, authority, third-party endorsements, and verifiabil-
ity. Social support leverages social influence to motivate users
and employs social learning, social comparison, normative
influence, social facilitation, cooperation, competition, and
recognition (Oinas-Kukkonen and Harjumaa, 2009; Oinas-
Kukkonen, 2010; Oinas-Kukkonen, 2013).
Two methodologies can be used to carry out BCSS evalua-
tion according to the PSD model. The first, and the most rigor-
ous, is to have two or more research scientists independently
carry out feature-by-feature evaluation of the applications using
the persuasive features specified by the PSD model. After the
independent review is made, the research scientists meet to dis-
cuss findings and to resolve disparities in their evaluations. In
Langrial et al. (2012), four scientists carry out independent
evaluations, while in Lehto and Kukkonen (2010) and Kelders
et al. (2012), only two perform independent reviews. The sec-
ond, which is less rigorous, consists of one research scientist
preparing a comprehensive evaluation, which is then verified
and commented by one or more research scientists. In Lehto
and Kukkonen (2011), this is the methodology employed: one
research scientist did the coding, which was then checked by
the second scientist.
Moreover, feature-by-feature evaluation can be done by sim-
ply coding descriptions based on published literature without
using the applications (Lehto and Kukkonen, 2011; Kelders
et al., 2012) or by actually using the applications and perform-
ing representative tasks (Lehto and Kukkonen, 2010), or both
(Langrial et al., 2012).
In our study, four research scientists performed the PSD
evaluation. We first developed a common coding framework
from the seminal paper on PSD by Kukkonen and Harjumaa
(2009), and papers that use the PSD model to evaluate applica-
tions (i.e. Lehto and Kukkonen, 2010; Lehto and Kukkonen,
2011; Kelders et al., 2012; Langrial et al., 2012). This coding
framework (see Table 1) was used by the first author to prepare
a comprehensive feature-by-feature evaluation of the nine
BCSSs as described in the published reports. The resulting
entries were then put in tabular form, and independently
reviewed and commented by the second author and two other
research scientists. Any disparity was then resolved by all the
four evaluators through a rigorous discussion. It was agreed
that persuasive features would only be reported if all four eval-
uators reached a consensus. Our evaluation is limited only to
published reports, since many of the applications are not down-
loadable from the online stores, and if ever they are, they can-
not be used in Japan. Lastly, in our evaluation, as in Kelders
et al. (2012), we omitted system credibility support because the
published studies do not sufficiently report these principles,
making any evaluation difficult to carry out in a manner that is
as objective as possible.
Finally, we note that evaluating persuasiveness of design
using PSD is based on “interpretive categorization”(Lehto and
Kukkonen, 2011). In many articles cited in this review, their
authors did not state explicitly the persuasive features; in few
others, their authors clearly stated these features even though
they did not necessarily use the same terminologies in the PSD.
Hence, the authors of the present review, in extracting and cat-
egorizing persuasive features, had to use their subjective judg-
ment—and herein lies potential bias.
2.3.2 Effectiveness
We then characterize the evaluation studies conducted to assess
the effectiveness of the identified BCSSs. In our characteriza-
tion, we adapt parts of the framework used by Graham-Rowe,
Skippon, Gardner, and Abraham (2011) to assess the efficacy of
77 car-use reduction interventions. In this framework, evalua-
tion studies are characterized in terms of intervention strategy
(structural, psychological), methodological quality of study
design (high, low), and measure type (distance, mode change,
trips/frequency, time/duration). Structural interventions
involve modification of the structures surrounding travel
behavior through physical and/or legislative measures (e.g.
road pricing and bus priority lanes). Psychological interven-
tions are designed to modify perceptions, beliefs, and attitudes
(Graham-Rowe et al., 2011). Since we are interested only in
BCSSs, we only consider psychological intervention strategies.
Three study designs are considered to be of high methodolog-
ical quality: experimental, quasi-experimental, and cohort-ana-
lytic (with control). In experimental designs (e.g., randomized
controlled trials), individuals are allocated randomly to either
intervention or control group. In quasi-experimental designs,
matched but not randomized control groups are used. In the
cohort-analytic (with control) method, groups exposed and
unexposed to an intervention are compared. In this method, the
investigator does not control the intervention exposure but only
“observes”it (observational study design). These three designs
are considered to be of superior quality because they provide
strongest control of confounding variables.
Low-quality study designs include case-controlled/cross-sec-
tional, and cohort-uncontrolled designs. In case-control or
cross-sectional between-group evaluation, two groups, without
pre-intervention measures, are compared post intervention.
One group is exposed to the intervention, and the other is not.
In cohort-uncontrolled, before- and after-intervention meas-
ures are recorded, but there is no control group for comparison.
In the original framework by Graham-Rowe et al. (2011),
medium and medium/low study designs are also cited, but for
our purposes, we deem it unnecessary to include them here
(Table 2).
The outcome measures are categorized as: (1) distance trav-
eled, (2) number of car trips or frequency or car use, (3) time
spent in a car, and (4) measures of modal shift away from car
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 555
Table 1. Coding Protocol used for PSD Evaluation of nine BCSSs (developed from Kukkonen and Harjumaa, 2009; Kelders et al., 2012; Lehto and Kukkonen, 2011; Langrial et al., 2012).
Persuasion strategy Description Coded as element included when the BCSS: Example implementation
Reduction A system that reduces complex behavior into simple tasks
helps users perform the target behavior, and it may
increase the benefit/cost ratio of a behavior.
Specifically divides the target behavior into
small, simple steps.
A support system for weight management
includes a diary for recording daily calorie
intake, thereby dividing the target behavior
(reducing calorie intake) into small, simple
steps of which one is recording calorie intake.
Tunneling Using the system to guide users through a process or
experience provides opportunities to persuade along
the way.
Delivers content in a step-by-step format with a
predefined order.
A support system for the prevention of depression
that delivers the content in sequential lessons
that can only be accessed when the previous
lesson is completed.
Tailoring Information provided by the system will be more
persuasive if it is tailored to the potential needs,
interests, personality, usage context, or other factors
relevant to a user group.
Provides content that is adapted to factors
relevant to a user group, or when feedback is
provided based on information filled out by a
participant.
A support system for personal training provides
different information content for different user
groups, e.g. beginners and professional.
Personalization A system that offers personalized content or services has a
greater capability for persuasion.
Provides content that is adapted to one user (ie,
the name of the user is mentioned and/or the
user can adapt a part of the intervention)
A support system personalizes service and content
based on user-inputs and other known
variables e.g., name, gender, age, location,
language.
Self-monitoring A system that keeps track of one’s own performance or
status supports the user in achieving goals.
Provides the ability to track and view the user’s
behavior, performance, or status
A support system for smoking cessation allows
participants to review their smoking by
sending immediate feedback forms and copies
of the personalized assessments to their email
accounts.
Simulation Systems that provide simulations can persuade by
enabling users to observe immediately the link
between cause and effect.
Provides the ability to observe the cause-and-
effect relationship of relevant behavior.
A support system for smoking cessation includes
an interactive smoker’s risk tool that simulates
changes in the risk of death due to smoking
based on the smoker’s history and time of
quitting.
Rehearsal A system providing means with which to rehearse a
behavior can enable people to change their attitudes or
behavior in the real world.
Provides the ability and stimulation to rehearse a
behavior or to rehearse the content of the
intervention.
A support system that includes a flying simulator
that helps flight pilots practice for severe
weather conditions.
Praise By offering praise, a system can make users more open to
persuasion.
Offers praise to the participant on any occasion. A support system that aims to promote healthy
nutritional habits compliments participants
when they have eaten two pieces of fruit for
5 days.
Rewards Systems that reward target behaviors may have great
persuasive powers.
Offers some kind of reward when the participant
performs a target behavior relating to the use
or goal of the intervention.
A support system for the treatment of social
phobia gives points to participants when they
engage in exposure exercises.
Reminders If a system reminds users of their target behavior, the users
will more likely achieve their goals.
Provides reminders about the use of the
intervention or the performance of target
behavior
A support system to support self-management
among patients with rheumatic arthritis sends
an automatic email message to remind the
participant that the new lesson may begin.
Suggestion Systems offering fitting suggestions will have greater
persuasive powers.
Provides a suggestion to help the participants
reach the target behavior.
A support system for weight management
provides low-calorie recipes.
Similarity People are more readily persuaded through systems that
remind them of themselves in some meaningful way.
Is designed to look familiar and designed
especially for the participant.
A support system for the treatment of panic
disorder in teenage girls explains the exercises
through a teenage girl with panic problems.
Liking A system that is visually attractive for its users is likely to
be more persuasive.
Is visually designed to be attractive to the
participants.
A support system that aims at encouraging
children to take care of their pets properly has
pictures of cute animals.
556 V. SUNIO AND J.-D. SCHM
€
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Social role If a system adopts a social role, users will more likely use it
for persuasive purposes.
Acts as if it has a social role (eg, a coach,
instructor, or buddy).
A support system to support self-management
among patients with migraine incorporated an
avatar to guide the participant through the
intervention.
Social learning A person will be more motivated to perform a target
behavior if (s)he can use a system to observe others
performing the behavior.
Provides the opportunity and stimulates
participants to see others using the
intervention or performing the target
behavior.
A support system for weight management
provides the option, and stresses the
importance, of posting physical activity self-
monitoring data on the discussion board and
commenting on the performance of others.
Social comparison System users will have a greater motivation to perform the
target behavior if they can compare their performance
with the performance of others.
Provides the opportunity for participants to
compare their behavior to the target
behavior of other participants and stimulates
them to do this.
A support system for drug abuse prevention for
teenagers automatically compares the
response of the participant to other users of
the intervention.
Normative influence A system can leverage normative influence or peer
pressure to increase the likelihood that a person will
adopt a target behavior
Provides normative information on the target
behavior or the usage of the intervention
A support system to promote self-management
among patients with COPD provides feedback
on the level of physical activity of the
participant by comparing it to the physical
activity of well-managed COPD patients
Social facilitation System users are more likely to perform target behavior if
they discern via the system that others are performing
the behavior along with them.
Provides the opportunity to see whether there
are other participants using the intervention.
A support system that provides opportunity to
contact others using the same intervention
(e.g. discussion group, peer-to-peer forums,
chat rooms).
Cooperation A system can motivate users to adopt a target attitude or
behavior by leveraging human beings’natural drive to
cooperate.
Stimulates participants to cooperate to achieve a
target behavior.
A support system for the promotion of physical
activity stimulates participants to form groups
and to achieve the group goal of a certain
number of steps each week.
Competition A system can motivate users to adopt a target attitude or
behavior by leveraging human beings’natural drive to
compete.
Stimulates participants to compete with each
other to achieve a target behavior.
A support system for diabetes management
among children includes a leaderboard in
which the children who enter blood glucose
levels at the right times receive the highest
place.
Recognition By offering public recognition for an individual or group, a
system can increase the likelihood that a person/group
will adopt a target behavior.
Prominently shows (former) participants who
adopted the target behavior.
A support system for treatment of anxiety includes
a testimonial page where successful users of
the intervention tell their story.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 557
use or away from single car occupancy. Section 3.3 presents the
results of our assessment of the efficacy evaluation studies of
the BCSSs using this categorization.
3. Results
3.1 Overview of BCSSs
Our search method yielded nine unique BCSSs that met our
inclusion criteria, and these BCSSs are listed and described
in Table 3. We note that these BCSSs differ quite signifi-
cantly in the size of the consortium involved and hence
probably also in the amount of resources available for their
development. Five of these nine BCSSs (Peacox, SuperHub,
Tripzoom, MatkaHupi, and I-Tour) are part of larger proj-
ects, sponsored by for example the European Commission.
In addition, Personal Environmental Impact Report (PEIR)
is a collaboration between academics and industry. The
remaining three BCSSs (IPET, Ubigreen, and QT) appear to
be smaller scale academic projects. Moreover, all nine
BCSSs are from the developed countries in Europe (Italy,
Finland, and Austria) and the United States only, and none
from developing countries.
3.2 Persuasiveness evaluation using PSD
3.2.1 Persuasion context
The persuaders behind the BCSSs (intent) were either consortia
of public and private partners, or stand-alone universities. All
the BCSSs target some behavioral and attitudinal changes
(change type). The event contains the use, user, and technology
contexts. Some BCSSs explicitly identify the use scenarios (use
context); the target users (user context), however, are not
directly mentioned, but only assumed by the authors to be the
population at large.
In so far as message is concerned, we observe that all the
BCSSs use carbon emissions as one of the attributes for feed-
back/feedforward information to induce sustainable mobility
behavior. We can observe the increasing popularity of utilizing
carbon emission information also from other transport litera-
ture. While there is no conclusive evidence yet regarding the
effect of carbon information on behavior change of a popula-
tion at large (Avineri and Waygood, 2013), three experiments
conducted by Gaker, Vautin, Vij, and Walker (2011) among
UC Berkeley undergraduates suggest that presenting carbon
impact of transport alternatives can influence transport deci-
sions such as mode choice, and therefore, can potentially be
used to promote sustainable transport behavior. In addition,
other attributes are also used in BCSSs: time, cost, calories,
environmental impact, and exposure. Time and cost are
Table 2. Framework for characterizing the effectiveness evaluation study (adapted
from Graham-Rowe et al., 2011).
Intervention strategy Methodological quality Outcome measure
Psychological High: Distance
Structural Experimental, quasi-experimental,
and cohort-analytic
(with control)
Mode change
Trips/frequency
Time/duration
Low:
Case-controlled/cross-sectional
and cohort-uncontrolled
Table 3. Overview of BCSSs reviewed/evaluated in this paper.
Name of BCSS,
implementation country
and main reference Description of main functionality and objective
IPET, Italy (Meloni, Sanjust,
Delogu, & Sottile, 2014)
IPET is an Individual Persuasive Eco-Travel Technology. IPET is not, strictly speaking, a BCSS but a technological platform for
large-scale implementation of voluntary travel behavior change program. The BCSS (or the mobile application) is called
Activity Locator. The system collects relevant trip information; converts the data into an activity-travel diary; and provides,
through mail or a website, a personalized travel plan in place of car.
MatkaHupi, Finland
(Jylh€
a et al., 2013)
MatkaHupi automatically tracks the carbon emissions of the transportation modes and uses this information to recommend
the traveler a set of challenges, such as “Reduce this week’s CO2 by 10%”or “Walk 3 km.”
QT, United States,
(Jariyasunant et al., 2015)
QT (Quantified Traveler) is a computational travel feedback system that aims to change mode or trip choice, without need for
traditional travel counselors. The system automatically collects trip data, converts them into a travel diary, and gives
quantitative feedback (time and money spent, calories burned and CO
2
emitted) to the traveler.
Peacox, Austria
(Schrammel et al., 2013)
PEACOX, or Persuasive Advisor for CO2-reducing cross-model trip planning, is a multimodal navigation smartphone
application that aims to help users travel with lower carbon impact. Suggestions can be “We estimated that you could walk
29% of your car –and 41% of your PT trips to save more CO2”or “Improve your Rank by reducing your CO2 Consumption.
We recommend walking instead of using your car/PT for short trips.”
Tripzoom, Netherlands
(Broll et al., 2012)
Tripzoom is a mobile application that constructs individual mobility profiles and patterns from mobile sensing. Based on these
data, it then encourages the users to change their behavior by offering appropriate incentives and providing feedback
from the community.
SuperHub, Italy
(Carreras et al., 2012)
SUPERHUB (SUstainable and PERsuasive Human Users moBility in future cities) is a mobile application and open-source
platform that aims to raise in citizens a personal awareness of the carbon impact of their daily mobility, thus fostering a
more environmental-friendly behavior. As an open-source platform, it collects, mines, and aggregates data from a variety
of mobility sources/providers, then builds eco-friendly and suitable multimodal journey plans for citizens.
I-Tour, Italy (Magliocchetti,
Gielow, De Vigili, Conti, &
De Amicis, 2011)
I-Tour, or intelligent Transport system for Optimized Urban Trips, is a personal mobility assistant that aims to promote
sustainable travel choices. To encourage use of public transport, the system supports routing across a multimodal transport
network.
PEIR, United States
(Mun et al., 2009)
PEIR, the Personal Environmental Impact Report, is an application that automatically calculates and provides estimates of one’s
environmental impact (carbon and sensitive site impact) and exposure (smog and fast food exposure).
Ubigreen, United States
(Froehlich et al., 2009)
Ubigreen Transportation Display uses iconic feedback (tree or polar bear) and ambient changes in the graphics of the user’s
mobile phones to provide awareness about green mobility behavior. Rewards can be earned by taking sustainable
transportation.
558 V. SUNIO AND J.-D. SCHM
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associated with the utility of transport modes. Calories burned
are increasingly being included to promote active transporta-
tion as a means to combat the global problem of obesity. The
rest of the attributes are BCSS-specific.
The route of aiming to influence users is both direct and
indirect depending on the BCSS: Some explicitly and directly
ask the users to reduce carbon emissions (direct route), whereas
others indirectly do this by simply providing carbon feedback,
without explicitly asking the users to reduce their footprint
(indirect route).
3.2.2 Persuasive features
The distinctive persuasive features assimilated in the nine
selected mobility BCSSs are described next. We begin with
BCSSs that contain most of the persuasive features: Peacox,
SuperHub, IPET, and Tripzoom.
Peacox has seven primary task support, six dialogue support
and six social support features. It is a multimodal navigation
smartphone application that guides users through a step-by-
step process of route searching (tunneling) and aims to help
them travel with lower carbon impact. It is a route choice plan-
ner (rehearsal), which is able to provide prognosis of emitted
carbon for each trip option (simulation). Routes that help users
save more emissions are highlighted (suggestion, reduction).
Peacox also gives other tailored and personalized recommenda-
tions (tailoring and personalization) such as “We estimated
that you could walk 29% of your car—and 41% of your PT trips
to save more CO2”or “Improve your Rank by reducing your
CO2 Consumption. We recommend walking instead of using
your car/PT for short trips.”Peacox calls this “shift potential.”
Feedbacks on user’s performance through detailed CO2 and
PM10 statistics or a tree showing overall CO2 status are also
provided (self-monitoring). Users who saved carbon emissions
or completed challenges are praised or given positive feedback
(praise) and badges (rewards). They are also regularly reminded
to save carbon emissions (reminder). Users also have their own
personal accounts (similarity), with attractive features (liking).
Peacox allows comparison of one’s performance with others
(social comparison), especially with one’s in-group (normative
influence), and viewing the details of others’trips (social learn-
ing). It also calls for the best to win (competition) or for every-
one to cooperate (cooperation). Finally, it displays statistics
leader board (recognition).
SuperHub also includes many support features: seven pri-
mary task support, six dialogue support, and five social support.
It seriously profiles the users in order to “tailor, customize and
rank mobility offers”(reduction) so that the solutions are close
to the users’needs (tailoring). It also allows users to self-moni-
tor their progress through personalized statistics and charts
(personalization) or track their behavior change status or past
trips (self-monitoring). Moreover, it supports a multimodal
multicriteria journey planner (rehearsal) and allows calculation
of CO2-footprints using various transport modes (simulation).
Once the user is close to achieving his goals (or has achieved
them), s/he is congratulated (praise) or given incentives includ-
ing discounts or concessions on travel or within local establish-
ments (reward). He is also prompted to set and review his
individual, mobility-related behavioral goals (reminder and sug-
gestion). SuperHub continuously updates itself over time of
user’s preferences in terms of mobility options (similarity). One
unique feature of SuperHub is its persuasive games, which offer
the users opportunities to learn more about sustainability
through games (tunneling). Its interface is also visually
appealing (liking). Users can also compare their scores with
their friends or others in the community (social comparison,
normative influence, competition, recognition) and share trip
plans (social learning).
IPET is embedded with seven primary task support, six dia-
logue support, and four social support features. Under primary
task support, IPET guides the users through a process (tunnel-
ing), consisting of three steps: data collection of car users’actual
behavior, construction of activity-travel diaries, and provision
of a personalized travel plan (PTP). The PTP is a detailed plan
that can be used to rehearse the target sustainable behavior
(rehearsal). It also “simplifies the complex process of consider-
ing different alternatives of transport (reduction)”by identify-
ing a prospective sustainable travel behavior that is “highly
customized and based on the individual’s particular needs and
characteristics (tailoring).”Moreover, the PTP makes immedi-
ate comparison of the unsustainable car and the proposed sus-
tainable alternative modes using four measures: travel time,
cost, distance traveled, and calories burned or carbon emitted
(simulation). Users can also “monitor their own behavior (self-
monitoring) which allows them to view their movements and
feedback quantities”in a personal website (personalization).
Under dialogue support, IPET first identifies an alternative
transport solution (suggestion) and then monitors and com-
pares the actual behavior with the suggested behavior. If users
follow the sustainable advise, they will be congratulated (praise)
and given badges (rewards). If they continue to use car for their
trips, they will receive regret messages, and hence will be con-
tinuously reminded to try traveling sustainably (reminder). In
designing the messages and PTP, “persuasive graphics are
used”in order to attract the participants (liking). All the infor-
mation is displayed on a dedicated personal webpage (similar-
ity). Finally, under social support, IPET automatically
calculates the scores, so that participants can see the results of
their performance and those of others (social learning), com-
pare their scores (social comparison) and their rank with others
(competition). The names of top scorers can be seen by other
users (public recognition).
Tripzoom embeds four primary task support, six dialogue
support, and five social support features. Tripzoom proposes a
set of challenges to users (e.g. Take the bike to work, Go for a
walk during your lunch break), which dare them to improve
their regular travel behavior (reduction, suggestion, tailoring).
Their performance is then translated in terms of saved money,
CO2, health and collected points, which can serve as feedback
(self-monitoring). TripZoom has a “Me”tab, where detailed
information about the user’s mobility profile, including visited
places, trails, or statistics is provided (personalization and
similarity). Users who master challenges are congratulated
(praise) or given incentives (rewards). There are also illustra-
tions—which can be either positive or negative—that can
remind them of their goals (reminder). Tripzoom also exploits
several social support features. Through the “Community
Tab,”one can compare his performance with that of others
(social comparison). Aggregated data and comparison with
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 559
averages can also give clues about community norms (norma-
tive influence). In the “Friends Tab,”one can share their travel
behavior with friends or obtain concrete and more detailed
mobility behavior of others (social learning) or view ranking
among friends (a source of competition and public
recognition).
The next three BCSSs we describe have strong support in
other features, but have very weak social support: I-Tour, Mat-
kaHupi, and Ubigreen.
I-Tour has six primary task support and three dialogue
support features but only one social support feature. I-Tour
is a personal multimodal travel assistant. It guides users
through a route selection search process (tunneling)using
various visualization modes, which can be selected to iden-
tify the best route (reduction). The visualization mode is a
visually attractive circular graph-like structure (liking)
showing routes to final destination (rehearsal)thatis
dynamically adjusted depending on user preferences and
other contextual information (tailoring, personalization and
similarity), such as time, distance, cost, or emission gener-
ated to reach the final destination (simulation). A recom-
mended route option (providing fastest, most sustainable,
shortest, or cheapest solution) is highlighted, while less
favorableoptionsarenot(suggestion). With I-Tour, one can
also see if others are traveling by the same leg of a particu-
lar route (social facilitation).
MatkaHupi contains five primary task support, five dialogue
support, and no social support features. By means of automatic
sensing of behavior and trips taken by the user, MatkaHupi tar-
gets behavior change through challenges that are tailored
according to individual behavior (tailoring). The challenges are
simple and direct (reduction), such as “walk 3 km, cycle 3k and
tram 3 km.”Through an appealing visual feedback (liking), the
users can track their progress toward their goals/challenges
(self-monitoring). Moreover, users can also view their trip his-
tory (personalization and similarity). After each detected trip,
MatkaHupi checks if the same trip can be made faster and with
lesser emission (simulation), and if so, proposes an alternative
route plan for the future (suggestion). It also works as a journey
planner for public transportation (rehearsal). After the user
completes the challenges, he is congratulated (praise) and
awarded a badge and some points (reward).
Ubigreen Transportation Display has two primary task sup-
port and five dialogue support features but assimilates no social
support feature. It is a mobile phone application that provides
iconic feedback about one’s green transportation behavior (self-
monitoring). The icon can either be a polar bear or tree, and
the user can select which of the two he can use for his ambient
display (personalization). Ubigreen tracks transport behavior of
the user either through automatic sensing or self-report (simi-
larity), and each time the user rides a green transportation such
as bus or train, this green mode is emphasized with its corre-
sponding benefits (saving money, getting exercise, etc.), thus
serving as a suggestion for his next transport mode (suggestion).
Moreover, the user also earns some small graphical rewards,
which culminates in a complete tree or bear (praise and
reward). Since the ambient wallpaper represents a critical area
of persuasion, care is taken to ensure it is visually attractive
(liking).
The last two BCSSs, in addition to the just-described Ubi-
green, have weak primary task support: PEIR and Quantified
Traveler.
In PEIR, only two primary task support (but four dialogue
support and five social support) features are present. Personal-
ized estimates of environmental impact (carbon and sensitive
site) and exposure (smog and fast food) are given as feedback
(self-monitoring). The feedback can be viewed from a visually
appealing interactive map (liking) and is broken down weekly
or daily, as well as via a time browser, through a personal web-
site (personalization and similarity). If one’s impact and expo-
sure are low relative to friends, green icons of trees appear;
conversely, if they are high, smoky and smoggy icons appear
(praise). These icons can also serve as reminders to travel sus-
tainably (reminder). Users can also see the performance of
others (social learning), and their ranking relative to their
friends, encouraging competition (social comparison, competi-
tion and recognition). Aggregate statistics from friends can also
provide information about norms (normative influence).
Quantified Traveler (QT) assimilates the least number of
features: two primary task support, one dialogue support, and
three social support features. QT gathers data from users and
automatically transforms such raw data into trip diaries
and footprints (time, money, calories, and CO2 emitted), which
is then presented as personalized feedback to the user (self-
monitoring) in his own personal website (personalization and
similarity). QT also exploits social influences: QT users can
view the average performances of other users (social learning),
enabling peer comparisons (social comparison). The average
statistics of these peer groups (SF Bay area, US average, Berke-
ley students) can provide clues to norms (normative influence).
3.2.3 Discussion
Table 4 summarizes the presence of support features in the nine
evaluated BCSSs.
In Table 4, under the primary task support category, we
observe that personalization (found in all applications), and
self-monitoring (in eight applications) appear to be the most
commonly utilized techniques. Personalization is easily imple-
mented because of the capability of all the BCSSs to automati-
cally sense travel footprints and present them in the personal
accounts of the users. Self-monitoring enables tracking of
behavior or performance with respect to certain measures (e.g.
carbon emission, cost, calories burned, time spent, distance
traveled per mode, etc.), aggregated in a variety of ways, such
as by day, week, or month. Only I-Tour does not support self-
monitoring, as this is unnecessary, because this BCSS only
promotes sustainable multimodal routing options without
checking or monitoring the user’s actual behavior.
In the same table, we notice that tunneling is the least uti-
lized feature (nD4). Tunneling means guiding the user
through a process or stages of behavior change. We revisit this
point again in our recommendation and argue that to improve
the effectiveness of BCSS, there is a need to incorporate formal
behavior change models, especially those which explicitly take
into account the temporal sequence of stages in a behavioral
change process. Tunneling can be used together with tailoring
(in the above table, tailoring is seen to be less utilized as well).
BCSSs can guide users through appropriate stages in a process
560 V. SUNIO AND J.-D. SCHM
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(tunneling) only if they are sensitive to the users’profile and
characteristics (tailoring).
Moreover, simulation and rehearsal are also less utilized
features (nD5 for both). BCSSs that support rehearsal provide
journey or trip plans to the users, while those that support sim-
ulation provide the means of observing the benefit or effect of
following such a plan in the form of, say, prognosis or equiva-
lent points. Later, we argue as a recommendation the need for
more BCSSs to include these features of simulation and
rehearsal in order to facilitate an effective behavior change.
Under the human-computer dialogue category, similarity
and liking are supported in all or most applications. Persuasive
technology places strong importance on the appeal of the
design (liking). Since the applications also have sensing capabil-
ity, it is easy for them to mimic their users in some way (simi-
larity). Rewards are implemented in the form of badges, points/
icon, discounts, or concessions given as incentives every time
the desired behavior is performed. Suggestions are recommen-
dations to try alternative modes, routes, travel plans, or pursue
mobility challenges or goals. Reminders enable the user to con-
tinue pursuing their goal or to review them. Praises are con-
gratulatory remarks as a form of approval for desired behavior.
None of the applications support a social role (i.e. it does not
support communication between users and real agents offering
advice). Lehto and Kukkonen (2011) also found that none of
web-based alcohol and smoking interventions support social
role per se.
Finally, the design principles that belong to the social sup-
port category are social facilitation, social comparison, norma-
tive influence, social learning, cooperation, competition, and
recognition. Table 4 shows that some BCSSs (I-Tour,
MatkaHupi, and Ubigreen) do not have any social support
feature at all (or only have one). Moreover, social facilitation
and cooperation (nD1 for both) are not present in almost all
applications. In our conclusions and recommendations, we also
elaborate further how BCSSs can further enable social facilita-
tion and cooperation, as they have a significant influence on
behavior change.
3.3 Assessment of efficacy evaluation studies
In the previous section, we evaluated the persuasive features of
the BCSSs to determine to what extent they assimilate the Per-
suasive Design Model. This gives us an idea of the “persuasive
potential”of the systems. While it is true that for “achieving
better outcomes from BCSSs, they should be designed by using
PSD frameworks and models”(Oinas-Kukkonen, 2010), sepa-
rate efficacy evaluation of these persuasive technologies needs
to be carried out.
One of the issues with studies on persuasive sustainability
systems (e.g. energy conservation, responsible resource con-
sumption, etc.) is that they lack user evaluation (Brynjarsdottir
et al., 2012). Many of them report system evaluation that
addresses proof-of-concept, technicality, and usability con-
cerns, but they do not evaluate intervention-induced behavior
change. A few studies have behavioral change evaluations, but
these are run on small sample sizes, short-term field study
durations, with limited evidence of lasting behavioral impact.
Of the nine mobility BCSSs reviewed in this study, seven
include system usability evaluations. However, only three (QT,
Peacox, and SuperHub) attempt to evaluate the effectiveness of
behavioral change interventions. We are aware that the devel-
opment of these mobility BCSSs is still ongoing, with design
issues as their foremost concern at the moment. We present in
Table 5 some details of the behavior change evaluation carried
out by these three studies.
The three studies are similar in many aspects. All the studies
assess changes not only in actual behavior but also in pro-envi-
ronmental shifts in attitudes and motivations. Only QT reports
a significant change in travel behavior (driving distance) as a
result of the intervention, while Peacox and SuperHub report
no significant change. Since QT’sfield study duration is short
though (3 weeks), this raises doubts regarding the long-lasting
impact of the intervention. Finally, as all employ cohort-uncon-
trolled evaluations (e.g. before-after comparison, without ade-
quate control groups), these must be considered to be of low
methodological quality (Graham-Rowe et al., 2011; Fujii,
Table 4. Presence of primary task, dialogue, and social support features in nine BCSSs.
Category Persuasive Feature PEACOX Super-Hub IPET Tripzoom I-Tour Matka-Hupi Ubi-green PEIR QT
Primary task support Personalization (n D9)
Self-monitoring (n D8)
Reduction (n D6)
Tailoring (n D5)
Simulation (n D5)
Rehearsal (n D5)
Tunneling (n D4)
Dialogue support Similarity (n D9)
Liking (n D8)
Praise (n D7)
Suggestion (n D7)
Rewards (n D6)
Reminders (n D5)
Social Role (n D0)
Social support Social learning (n D6)
Social comparison (n D6)
Norm. Influence (n D5)
Competition (n D5)
Recognition (n D5)
Social facilitation (n D1)
Cooperation (n D1)
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 561
Bamberg, Friman, & G€
arling, 2009) and threatens the validity
of any result.
It seems that there is, at this point, no possibility for a more
robust evaluation of the efficacy of persuasive sustainability sys-
tems, as many are still in their early or ongoing stages of design
development. In contrast, in the health domain, a number of
BCSS studies include effectiveness evaluations, based on suffi-
cient sample size with control groups (Wang et al., 2014).
4. Conclusion and recommendations
We considered in this review nine mobility BCSSs and exam-
ined the persuasiveness of their design features using the Per-
suasive System Design Model. We also assessed the quality of
the study conducted to evaluate the effectiveness of the BCSSs
in changing behavior.
In assessing the quality of the studies, we find that of the
three studies that include behavior change evaluations (QT,
Peacox, and SuperHub), two report no significant shift toward
sustainable travel behavior as a result of the BCSS interven-
tions. No definitive conclusion can be derived from these stud-
ies regarding the effectiveness of the BCSSs though since a
robust evaluation was not conducted in the first place. Their
study design employs only (1) a small sample size, partly
because of large dropouts and (2) uncontrolled cohorts (before-
after comparison, without any control group). Although the
foremost concern of BCSSs right now is design development,
we recommend proper evaluations of BCSSs using methodo-
logically robust study designs such as Randomized Controlled
Trial, Cluster Randomized Controlled Trial, or Controlled
Before and After studies as suggested by Arnott et al. (2014)
and Graham-Rowe et al. (2011).
In the absence of proper evaluation studies of the efficacy of
mobility BCSSs, it is instructive to look into studies on health
BCSSs, where evaluation studies appear to be more developed.
We find that, if the meta-analysis of health BCSSs is any indica-
tion, we can reasonably assume that, in general, the interven-
tion effect size of BCSSs is significant but small (Bamberg et al.,
2015). Hence, the challenge for future studies is to develop
BCSSs in the domain of travel with greater intervention effect.
In the following, based on our review, we suggest three proposi-
tions that can be pursued in future work, which address the
small effect size of BCSSs. These recommendations will be
further supported by empirical evidence from studies in the
health domain.
First, we observe that tunneling and tailoring features are
less utilized. Thus, as will be explained below, we suggest to
incorporate a stage-based behavior change model in the design
of the BCSSs. Stage-based models explicitly specify the stages in
the process of behavior change that users will go through, lead-
ing to the desired behavior. These models can be used to first
determine the users’current stage membership (tailoring) and
then to guide them to progress toward the next stages in the
process (tunneling). Second, we also notice that the simulation
and rehearsal features are underutilized. Behavior change in
mobility requires formation of an implementation intention,
which is greatly facilitated by a provision of an appropriate
travel plan (rehearsal) with its corresponding benefits (simula-
tion). Third, our persuasive design evaluation reveals that social
facilitation and cooperation are least supported. We therefore
suggest, following again findings from health BCSSs as well as
other literature, to incorporate these social support features in
the design of the mobility BCSSs. We discuss each of these rec-
ommendations further in the next sections.
4.1 Explicit reference to a behavior change theory
As pointed out by some authors, many BCSSs were developed
using techniques solely drawn from persuasive technology liter-
ature (Klein, Mogles, & Van Wissen, 2014; Arnott et al., 2014;
Bamberg et al., 2015). Too little effort is given on grounding
the BCSSs in an explicit behavior change theory. Among the
mobility BCSSs included in this paper, we note that only one
BCSS, the Quantified Traveler, is grounded explicitly on a
behavior change theory.
In the literature, this seems to be commonplace though not
only for transport applications. Klein et al. (2014) notes that
“intelligent support systems have become increasingly popular
for the use of behavior interventions in recent years, [but] those
systems are rarely based on models of behavior change.”A
notable exception to this is the eMate system, an intelligent
BCSS for therapy adherence for patients with type 2 diabetes,
HIV, and cardiovascular diseases (Klein et al., 2011,2014).
This health BCSS is based on an integrated model of human
behavior change, called Computerized Behavior Intervention
or COMBI (discussed below). Bamberg et al. (2015) is also in
Table 5. Details on evaluation of effectiveness of BCSSs.
BCSS (location)
Quality of study
design Intervention, Duration Measure type Outcomes
QT (California) Low (cohort-uncontrolled) N D135 subjects;
3 weeks
Distance
Pro-environmental attitudes
Significant: driving distance (¡), awareness
(C), attitudes (C), intention (C)
Not significant: distance of walk/bike/transit
Peacox (ITS Vienna) Low (cohort-uncontrolled) N D24 subjects;
8 weeks
Modal change
Pro-environmental attitudes
Significant: attitude toward transport
modes (C)
Not significant: environmental concern,
modal change
SuperHub
(Barcelona,
Helsinki, Milan)
Low (cohort-uncontrolled) N D471 subjects;
8 weeks
Trips
Carbon emissions
Pro-environmental attitudes
and motivation
Not significant: environmental attitudes,
behavior
Only the result of the second trial is reported since the document for the final trial is not yet published.
562 V. SUNIO AND J.-D. SCHM
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the process of developing a BCSS of this type in the mobility
domain, called “PrimaKlima Bielefeld.”
Arnott et al. (2014) suggests that developing a successful
BCSS depends not only on the creative and appropriate imple-
mentation of the behavior change techniques, but also on
explicitly grounding it on established theoretical constructs
from behavioral theories. In the health sector, a few meta-anal-
yses seem to support this conclusion (Bamberg et al., 2015).
One significant meta-analysis is by Webb, Joseph, Yardley, and
Michie (2010), which finds that “the extent of a BCSS’s theoret-
ical foundation [is] positively correlated with its effectiveness.”
An example of a theory-based BCSS demonstrated to be effec-
tive through randomized controlled trial is Happy Ending, a
digital program on nicotine withdrawal (Brendryen, Drozd, &
Kraft, 2008).
By using a formal behavior theory in the design of a support
system, we can “understand the underlying mechanisms of
behavior change and how these mechanisms can be influenced
to establish the desired behavior”(Klein et al., 2014). This is
especially important because empirical evidence increasingly
shows that “modality styles”of individuals are very much influ-
enced by deeply entrenched habits and ingrained lifestyles, and
are therefore difficult to change (Vij, Carrel, & Walker, 2013).
In fact, this may be the reason why travel behavior change pro-
grams to date have statistically significant but only small inter-
vention effects (Fujii et al., 2009;M
€
oser and Bamberg, 2008).
Hence, by carefully understanding the possible mechanisms of
behavior change, we can exploit them to increase the effective-
ness of these programs in changing behavior.
In addition, as pointed out by Brynjarsdottir et al. (2012)in
their critical review of 36 persuasive sustainability systems,
many of these systems design their persuasion around a narrow
understanding of sustainability as mere “optimization of simple
[behavioral] metrics,”and consequently, their common persua-
sion tactic is simply to “tweak behaviors with the goal of adjust-
ing actions to be more in line with benchmarks of
sustainability.”However, this may sidestep “difficult lifestyle
choices that may in fact be necessary to work toward a more
sustainable society.”By adopting a holistic model of behavior
change, we can consider multiple metrics of sustainability that
allow us to see the bigger picture.
QT, the only BCSS explicitly informed by a behavior change
theory, is based on the theory of planned behavior (TPB). One
criticism against this theory, however, is that it fails to take into
account the time dimension of behavior change. As other mod-
els such as e.g. the “Transtheoretical Model”suggest, behavior
change is a transition through a temporally ordered sequence
of different stages. Moreover, as Riley et al. (2011) point out,
static behavior change models, such as TPB, seem “inadequate
to inform mobile intervention development as these interven-
tions become more interactive and dynamic.”Recently,
Bamberg (2013) introduced a cumulative theory that incorpo-
rated the dynamic stage concept, called the Stage Model of Self-
Regulated Behavior Change Theory. Because it is a dynamic
theory, it may prove suitable in the BCSS platform. To date,
however, except for the aforementioned “PrimaKlima”that
appears to be under development, no mobility BCSS based on
this theory has been developed so far to the best of our knowl-
edge. Other models, such as the Computerized Behavior
Intervention or COMBI (Klein et al., 2011,2014), may also be
suitable to be implemented in the BCSS platform. COMBI is a
computational model of behavior change that integrates con-
structs from the most influential theories such as the Trans-
theoretical Model, Social Cognitive Theory, TPB, Attitude
Formation, Self-Regulation Theory, Relapse Prevention Model,
and Health Belief Model.
4.2 Greater integration with shared mobility services
platform
It is well known that goal initiation is not sufficient for a success-
ful goal achievement. Formation of implementation intention is
also important (Gollwitzer, 1999). This is also true in a success-
ful mobility behavior change (Fujii and Taniguchi, 2005). Imple-
mentation intention, which entails a plan for when, where, and
how to implement the target mobility behavior, mediates the
effect of behavioral intention on behavior (G€
arling and Fujii,
2002). Mobility BCSSs can facilitate the formation of implemen-
tation intention by exploiting the rehearsal feature, i.e. by pro-
viding people a trip/travel plan that they can use to rehearse the
target behavior. However, as the previous section shows, very
few mobility BCSSs have this feature: of the nine BCSSs we
examined here, only five support the rehearsal feature in the
form of PTPs (iPET), journey planner (MatkaHupi), or dynamic
trip plans (Peacox, SuperHub, and I-Tour). The rehearsal fea-
ture can be combined with the simulation feature by showing
potential outcomes of the trip plan to the users.
In contrast, in health-based BCSSs, as pointed out by the
review of Lehto and Kukkonen (2010), simulation and
rehearsal are rather common features. In these health BCSSs, a
typical example of simulation “was calculating how much calo-
ries a specific physical activity burns, or the type and duration
of exercise needed to burn the calories from, e.g., a chocolate
bar. The rehearsal feature was supported by providing workout
plans and exercise ideas to the user. As a highlight, [two BCSSs]
provided extensive video-based, customizable workout build-
ers.”In another review of mobile health applications for physi-
cal activity, Conroy et al. (2014) observed that most of them
have features that “provide instruction on how to perform the
behavior”and “model/demonstrate the behavior,”which sup-
port the formation of implementation intention. Moreover,
there have been studies in the health sector showing the
effectiveness of implementation intentions in changing
behavior (B
elanger-Gravel, Godin, & Amireault, 2013). As a
consequence, one health BCSS recently developed for cardiac
rehabilitation explicitly included “implementation intention”in
the design approach (Antypas and Wangberg, 2014).
How can mobility BCSSs support further the rehearsal
feature? Lately, due to advances in technology, both hardware
and software, multimodal integration—or the seamless connec-
tion of various modes—is becoming a reality. These technologi-
cal advances, together with the ubiquity of internet-enabled
smartphones, enable users to plan and organize their trips on a
very short notice or even en route. For instance, Peacox, as a
journey planner application, is able to provide cross-modal trip
plans to users. Such provision of trip plans is akin to making
implementation plans in the traditional VTBC programs. Tang
and Thakuriah (2012) showed that mere provision of real-time
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 563
bus information, through a system called CTA Bus Tracker, can
increase transit ridership.
An important application are the recent developments in
ridesharing, as reviewed by several authors (Agatz, Erera,
Savelsbergh, & Wang, 2012; Chan and Shaheen, 2012; Siddiqi
and Buliung, 2013; Furuhata et al., 2013). In ridesharing, travel-
ers are grouped together into common trips by car or van
(Chan and Shaheen, 2012). In recent years, as ridesharing
becomes more dynamic, i.e. real time, it is being considered as
one travel demand management strategy that can alleviate con-
gestion, while maintaining an acceptable level of service (Sid-
diqi and Buliung, 2013). In the United States, ridesharing is
increasingly being discussed as a powerful strategy to reduce
congestion, emissions, and fossil fuel dependency.
Dynamic ridesharing is being touted as a promising and
attractive alternative to private car usage because of its potential
to provide immediate access to door-to-door transportation.
Moreover, it allows users to share costs on car usage. Furuhata
et al. (2013) summarize it best: “Conceptually, ridesharing is a
system that can combine the flexibility and speed of private
cars with the reduced cost of fixed-line systems.”In other
words, dynamic real-time ridesharing combines tailored trip
and cost-sharing.
In the coming years, ridesharing is likely to take greater
modal share as it tries to include greater technology interopera-
bility and multimodal integration (Chan and Shaheen, 2012).
Interoperability here means “allowing open source data sharing
among ride-matching companies, which will enable members
to find matches across all databases.”Integration means “seam-
less connection of ridesharing with other transportation modes,
such as public transit and carsharing.”Hence, with further
technological advances, ridesharing can capture more market
share, as it positions itself as a very attractive alternative to pri-
vate car use.
In summary, a mobility BCSS is hypothesized to have a
greater intervention effect if it includes provision of a dynamic,
cross-modal trip planning tool (rehearsal), together with poten-
tial effects or outcomes (simulation feature). There is no
available evidence yet for this, but if empirical data from the
health BCSSs and the increasing adoption of ridesharing in the
recent years are any indication—a trend greatly facilitated by
the fact that ridesharing has become more dynamic and cross-
modal—we can be confident that a mobility BCSS that supports
cross-modal planning in real time may have greater chances of
causing behavior change.
4.3 Social interaction
Various studies have already shown that social information
drawn from one’s social network can be a potentially powerful
tool to trigger sustainable travel behavior change (Bamberg,
Fujii, Friman, & G€
arling, 2011; Ettema, Arentze, Timmermans,
2011; Abou-Zeid and Ben-Akiva, 2011; Axsen and Kurani,
2012; Kormos, Gifford, & Brown, 2015; Zhang, Schm€
ocker,
Fujii, & Yang, 2016). This review, in fact, shows that the major-
ity of the BCSSs have assimilated at least five of the seven social
support features. Nonetheless, we also observe that two of these
social support features—social facilitation and cooperation—
are largely missing in most applications.
Social facilitation means facilitating social interaction among
the users. In health-based BCSSs, the most common means of
social facilitation are “asynchronous peer discussion forums
and synchronous chat rooms,”for example, discussion forum,
peer-to-peer forum, chat room, and online and support com-
munity (Lehto and Kukkonen, 2011). In contrast to mobility
BCSSs, social facilitation is a widely used element in health
BCSSs (Lehto and Kukkonen, 2010; Kelders et al., 2012). None-
theless, these studies are inconclusive yet on whether social
facilitation is effective in changing behavior (for instance, better
adherence to interventions). Even though conclusive studies are
not yet available, we consider social facilitation a promising
direction to pursue as this offers social support, which is identi-
fied by Ploderer, Reitberger, Oinas-Kukkonen, and van
Gemert-Pijnen (2014) in their review as one of the key
approaches for future work of BCSSs in general. Social facilita-
tion by means of exchanges in online and offline communities
can provide social support such as “esteem support, intimacy,
companionship and validation”or “material aid as well as
informational support like advice and help in problem-solving”
(Ploderer et al., 2014).
In contrast to this, cooperation is largely not supported, not
only in mobility BCSSs, but also in health BCSSs (Lehto and
Kukkonen, 2010; Lehto and Kukkonen, 2011; Kelders et al.,
2012) and therefore provides area for improvement. Ploderer
et al. (2014), in the same review, present some guidelines on
how cooperation can be implemented in BCSSs. They note that
many systems are designed for individual use, though some
have partially collective orientation, which facilitate interaction
between individuals. They thus suggest developing BCSSs for
collective, rather than the usual single, use. They cite as an
example the Tidy Street project (Bird and Rogers, 2010), which
focuses on the average electricity consumption of households
in the street, instead of individual households, in order to foster
cooperation and collaboration. In the domain of travel, users
can register in travel behavior change or shared-use mobility
programs in groups (e.g. as a family or co-workers) instead of
merely as single participants.
5. Summary and further research
There are numerous BCSS. Most developed appear to be
health applications, but the last ten years also saw the develop-
ment of BCSSs for travel behavior. Using the PSD, we evaluate
the persuasive potential of nine BCSSs designed to promote
sustainable travel behavior. We extract the persuasive features
embedded in these support systems and find that tunneling,
tailoring, rehearsal, simulation, social facilitation, and coopera-
tion are not widely present. In contrast, in the health domain,
these features, except cooperation, are commonly used.
Furthermore, we assess studies conducted to evaluate the
effectiveness of these BCSSs in changing travel behavior and
find indications that effect sizes are mostly small though
methodologically robust studies are largely missing; hence, no
definitive conclusion can be derived yet. We then propose
three suggestions on research needs and applications for fur-
ther development, especially addressing the small size of the
intervention effects.
564 V. SUNIO AND J.-D. SCHM
€
OCKER
In proposing that smart devices be utilized as medium of
intervention for promoting sustainable behavior change, a
caveat must be mentioned. Aside from the expected bias toward
a younger population group, there is some evidence that usage
of these devices generates a negative effect against sustainable
behavior. While studies such as Guo, Derian & Zhao (2015)
suggest that usage of such devices can enrich use of travel time
on public transport such as buses, bringing them positive util-
ity, a study by Julsrud and Denstadli (2017)finds that active
users of smart devices (“equipped travelers”) tend to bear criti-
cal (negative) attitudes to public transport. Moreover, the
amount of time spent in the usage of ICT may generate more
motorized travel, including by car (Hong and Thakuriah,
2016). This is, however, beyond the scope of our present study
and should be addressed in future research.
References
Abou-Zeid, M. & Ben-Akiva, M. (2011). The effect of social comparisons
on commute well-being. Transportation Research Part A: Policy and
Practice 45(4), 345–361.
Agatz, N., Erera, A., Savelsbergh, M., & Wang, X. (2012). Optimization for
dynamic ride-sharing: A review. European Journal of Operational
Research,223(2), 295–303.
Ampt, E. (2003). Voluntary household travel behaviour change–theory and
practice. 10th International Association of Travel Behaviour Research
Conference, Lucerne, Switzerland, August.
Antypas, K., & Wangberg, S. C. (2014). Combining users’needs with
health behavior models in designing an internet-and mobile-based
intervention for physical activity in cardiac rehabilitation. JMIR
Research Protocols,3(1), e4.
Arnott, B., Rehackova, L., Errington, L., Sniehotta, F. F., Roberts, J. R., &
Araujo-Soares, V. (2014). Efficacy of behavioural interventions for
transport behaviour change: systematic review, meta-analysis and
intervention coding. International Journal of Behavioral Nutrition and
Physical Activity,11(1), 133.
Avineri, E. & Waygood, E. O. D. (2013). Applying valence framing to enhance
the effect of information on transport-related carbon dioxide emissions.
Transportation Research Part A: Policy and Practice,48,31–38.
Axsen, J., & Kurani, K. S. (2012). Interpersonal influence within car buyers’
social networks: applying five perspectives to plug-in hybrid vehicle
drivers. Environment and Planning-Part A 44(5), 1047.
Bamberg, S. (2013). Applying the stage model of self-regulated behavioral
change in a car use reduction intervention. Journal of Environmental
Psychology,33,68–75.
Bamberg, S., Behrens, G., Bergmeyer, M., Brewitt, K., Papendick, M., Rees,
J., & Zielinski, J. (2015). Development of a theory-driven, web-based
behavioral change support system for environmentally friendly behav-
ior. In D. Evans (Ed.), Social marketing: Global perspectives, strategies
and effects on consumer behavior (pp. 109–120). New York: Nova Sci-
ence Publisher.
Bamberg, S., Fujii, S., Friman, M., & G€
arling, T. (2011). Behaviour theory
and soft transport policy measures. Transport Policy,18(1), 228–235.
Baumer, E. P., Katz, S. J., Freeman, J. E., Adams, P., Gonzales, A. L., Pollak,
J., Retelny, D., Niederdeppe, J., Olson, C. M. & Gay, G. K. (2012). Pre-
scriptive persuasion and open-ended social awareness: expanding the
design space of mobile health. Proceedings of the ACM Conference on
Computer Supported Cooperative Work, ACM, February, pp. 475–484.
B
elanger-Gravel, A., Godin, G., & Amireault, S. (2013). A meta-analytic
review of the effect of implementation intentions on physical activity.
Health Psychology Review,7(1), 23–54.
Bird, J., & Rogers, Y. (2010). The pulse of tidy street: Measuring and pub-
licly displaying domestic electricity consumption. Workshop on Energy
Awareness and Conservation Through Pervasive Applications (Perva-
sive 2010).
Brendryen, H., Drozd, F., & Kraft, P. (2008). A digital smoking cessation
program delivered through internet and cell phone without nicotine
replacement (happy ending): randomized controlled trial. Journal of
Medical Internet Research,10(5), e51.
Broll, G., Cao, H., Ebben, P., Holleis, P., Jacobs, K., Koolwaaij, J., Luther, S.
& Souville, B. (2012). Tripzoom: an app to improve your mobility behav-
ior. Proceedings of the 11th International Conference on Mobile and
Ubiquitous Multimedia, ACM, December, 57.
Brynjarsdottir, H., Ha
kansson, M., Pierce, J., Baumer, E., DiSalvo, C., &
Sengers, P. (2012). Sustainably unpersuaded: how persuasion narrows
our vision of sustainability. Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems, ACM, May, pp. 947–956.
Busch, M., Schrammel, J., Flu, M. A., Kruijff, E., & Tscheligi, M. (2012).
Persuasive Strategies Report. Vienna, Austria: CURE–Center for
Usability Research & Engineering. Retrieved from http://www.cure.at/
contact.html
Carreras, I., Gabrielli, S., Miorandi, D., Tamilin, A., Cartolano, F., Jakob,
M., & Marzorati, S. (2012). SUPERHUB: a user-centric perspective on
sustainable urban mobility. Proceedings of the 6th ACM Workshop on
Next Generation Mobile Computing for Dynamic Personalised Travel
Planning, ACM, June, pp. 9–10.
Chan, N. D., & Shaheen, S. A. (2012). Ridesharing in north America: Past,
present, and future. Transport Reviews, 32(1), 93–112.
Cohen-Blankshtain, G., & Rotem-Mindali, O. (2016). Key research themes
on ICT and sustainable urban mobility. International Journal of Sus-
tainable Transportation,10(1), 9–17.
Conroy, D. E., Yang, C. H., & Maher, J. P. (2014). Behavior change techni-
ques in top-ranked mobile apps for physical activity. American Journal
of Preventive Medicine,46(6), 649–652.
Cowan, L. T., Van Wagenen, S. A., Brown, B. A., Hedin, R. J., Seino-
Stephan, Y., Hall, P. C., & West, J. H. (2013). Apps of steel: are exercise
apps providing consumers with realistic expectations? A content
analysis of exercise apps for presence of behavior change theory.
Health Educ Behav,40(2), 133–139.
Davies, N. (2012). What are the ingredients of successful travel behavioural
change campaigns? Transport Policy, 24, 19–29.
Demissie, M. G., Correia, G., & Bento, C. (2015). Analysis of the pattern
and intensity of urban activities through aggregate cellphone usage.
Transportmetrica A: Transport Science,11(6), 502–524.
DiSalvo, C., Sengers, P., & Brynjarsd
ottir, H. (2010). Mapping the
landscape of sustainable HCI. Proceedings of the SIGCHI Confer-
ence on Human Factors in Computing Systems, April, ACM, pp.
1975–1984.
Ettema, D., Arentze, T., & Timmermans, H. (2011). Social influences on
household location, mobility and activity choice in integrated micro-
simulation models. Transportation Research Part A: Policy and Prac-
tice,45(4), 283–295.
Fogg, B. (2002). Persuasive Technology: Using computers to change what
we think and do. San Francisco, CA: Morgan Kaufmann.
Fogg, B. J. & Eckles, D. (2007). Mobile Persuasion: 20 Perspectives on the
Future of Behavior Change. Stanford Captology Media, Stanford.
Froehlich, J., Dillahunt, T., Klasnja, P., Mankoff, J., Consolvo, S., Harrison,
B., & Landay, J. A. (2009). UbiGreen: investigating a mobile tool for
tracking and supporting green transportation habits. Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems (pp.
1043–1052). ACM, April.
Fujii, S., Bamberg, S., Friman, M., & G€
arling, T. (2009). Are effects of travel
feedback programs correctly assessed?. Transportmetrica,5(1), 43–57.
Fujii, S. & Taniguchi, A. (2005). Reducing family car-use by providing
travel advice or requesting behavioral plans: An experimental analysis
of travel feedback programs. Transportation Research Part D: Transport
and Environment,10(5), 385–393.
Fujii, S. & Taniguchi, A. (2006). Determinants of the effectiveness of travel
feedback programs—a review of communicative mobility management
measures for changing travel behaviour in Japan. Transport Policy,13
(5), 339–348.
Furuhata, M., Dessouky, M., Ord
o~
nez, F., Brunet, M. E., Wang, X., & Koe-
nig, S. (2013). Ridesharing: The state-of-the-art and future directions.
Transportation Research Part B: Methodological,57,28–46.
Gaker, D., Vautin, D., Vij, A., & Walker, J. L. (2011). The power and value
of green in promoting sustainable transport behavior. Environmental
Research Letters,6(3), 034010.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 565
G€
arling, T. & Fujii, S. (2002). Structural equation modeling of determi-
nants of planning. Scandinavian Journal of Psychology,43(1), 1–8.
Gollwitzer, P. M. (1999). Implementation intentions: strong effects of sim-
ple plans. American Psychologist,54(7), 493.
Graham-Rowe, E., Skippon, S., Gardner, B., & Abraham, C. (2011). Can we
reduce car use and, if so, how? A review of available evidence. Trans-
portation Research Part A: Policy and Practice,45(5), 401–418.
Guo, Z., Derian, A., & Zhao, J. (2015). Smart devices and travel time use by
bus passengers in Vancouver, Canada. International Journal of Sustain-
able Transportation,9(5), 335–347.
Hong, J., & Thakuriah, P. (2016). Relationship between motorized travel
and time spent online for nonwork purposes: An examination of loca-
tion impact. International Journal of Sustainable Transportation,10(7),
617–626.
Jariyasunant, J., Abou-Zeid, M., Carrel, A., Ekambaram, V., Gaker, D., Sen-
gupta, R., & Walker, J. L. (2015). Quantified traveler: Travel feedback
meets the cloud to change behavior. Journal of Intelligent Transporta-
tion Systems,19(2), 109–124.
Julsrud, T. E. & Denstadli, J. M. (2017). Smartphones, travel time-use and
attitudes to public transport services. Insights from an explorative
study of urban dwellers in two Norwegian cities. International Journal
of Sustainable Transportation. DOI: 10.1080/15568318.2017.1292373.
Jylh€
a, A., Nurmi, P., Sir
en, M., Hemminki, S., & Jacucci, G. (2013). Matka-
hupi: a persuasive mobile application for sustainable mobility. Proceed-
ings of the ACM Conference on Pervasive and Ubiquitous Computing
Adjunct Publication, ACM, pp. 227–230.
Klein, M., Mogles, N., & Van Wissen, A. (2011). Why won’t you do what’s
good for you? Using intelligent support for behavior change. In Human
Behavior Understanding, Berlin/Heidelberg: Springer, pp. 104–115.
Klein, M., Mogles, N., & van Wissen, A. (2014). Intelligent mobile support
for therapy adherence and behavior change. Journal of Biomedical
Informatics,51, 137–151.
Kormos, C., Gifford, R., & Brown, E. (2015). The influence of descriptive
social norm information on sustainable transportation behavior: a field
experiment. Environment and Behavior,47(5), 479–501.
Lane, N. D., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., Doryab, A.,
Berke, E., Choudhury, T & Campbell, A. (2011). Bewell: A smartphone
application to monitor, model and promote wellbeing. 5th International
ICST Conference on Pervasive Computing Technologies for Health-
care, May, pp. 23–26.
Langrial, S., Lehto, T., Oinas-Kukkonen, H., Harjumaa, M. & Karppinen,
P. (2012). Native mobile applications for personal well-being: A persua-
sive systems design evaluation. PACIS, p. 93.
Lathia, N., Pejovic, V., Rachuri, K. K., Mascolo, C., Musolesi, M., & Rent-
frow, P. J. (2013). Smartphones for large-scale behavior change inter-
ventions. IEEE Pervasive Computing,12(3), 66–73.
Lehto, T. & Oinas-Kukkonen, H. (2010). Persuasive features in six weight
loss websites: A qualitative evaluation. Persuasive Technology (pp. 162–
173). Berlin/Heidelberg: Springer.
Lehto, T. & Oinas-Kukkonen, H. (2011). Persuasive features in web-based
alcohol and smoking interventions: a systematic review of the litera-
ture. Journal of Medical Internet Research,13(3), e46.
Magliocchetti, D., Gielow, M., De Vigili, F., Conti, G., & De Amicis, R. (2011).
A personal mobility assistant based on ambient intelligence to promote
sustainable travel choices. Procedia Computer Science,5,892–899.
Meloni, I., Sanjust, B., Delogu, G., & Sottile, E. (2014). Development of a
technological platform for implementing VTBC programs. Transporta-
tion Research Procedia,3, 129–138.
Meloni, I., & di Teulada, B. S. (2015). I-pet individual persuasive eco-travel
technology: A tool for VTBC program implementation. Transportation
Research Procedia,11, 422–433.
Mintz, J., & Aagaard, M. (2012). The application of persuasive technology
to educational settings. Educational Technology Research and Develop-
ment,60(3), 483–499.
Mintz, J., Branch, C., March, C., & Lerman, S. (2012). Key factors mediat-
ing the use of a mobile technology tool designed to develop social and
life skills in children with Autistic Spectrum Disorders. Computers and
Education,58(1), 53–62.
M€
oser, G., & Bamberg, S. (2008). The effectiveness of soft transport policy
measures: A critical assessment and meta-analysis of empirical evi-
dence. Journal of Environmental Psychology,28(1), 10–26.
Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M.,
Howard, E., West, R. & Boda, P. (2009). PEIR, the personal environ-
mental impact report, as a platform for participatory sensing systems
research. Proceedings of the 7th International Conference on Mobile
Systems, Applications, and Services, ACM, June, pp. 55–68.
Oinas-Kukkonen, H. (2010). Behavior change support systems: A research
model and agenda. Persuasive Technology, Berlin/Heidelberg: Springer,
pp. 4–14.
Oinas-Kukkonen, H. (2013). A foundation for the study of behavior
change support systems. Personal and Ubiquitous Computing,17(6),
1223–1235.
Oinas-Kukkonen, H., & Harjumaa, M. (2009). Persuasive systems design:
Key issues, process model, and system features. Communications of the
Association for Information Systems,24(1), 28.
Patterson, Z., & Fitzsimmons, K. (2016). DataMobile: Smartphone travel
survey experiment. Transportation Research Record: Journal of the
Transportation Research Board,2594,35–43.
Ploderer, B., Reitberger, W., Oinas-Kukkonen, H., & van Gemert-Pijnen, J.
(2014). Social interaction and reflection for behaviour change. Personal
and Ubiquitous Computing,18(7), 1667–1676.
Richter, J., Friman, M., & G€
arling, T. (2011). Soft transport policy meas-
ures: Gaps in knowledge. International Journal of Sustainable Trans-
portation,5(4), 199–215.
Riley, W. T., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S. M., &
Mermelstein, R. (2011). Health behavior models in the age of mobile
interventions: are our theories up to the task?. Translational Behavioral
Medicine,1(1), 53–71.
Rojas IV, M. B., Sadeghvaziri, E., & Jin, X. (2016). Comprehensive review
of travel behavior and mobility pattern studies that used mobile phone
data. Transportation Research Record: Journal of the Transportation
Research Board,2563,71–79.
Sadeghvaziri, E., Rojas IV, M. B., & Jin, X. (2016). Exploring the potential
of mobile phone data in travel pattern analysis. Transportation
Research Record: Journal of the Transportation Research Board,2594,
27–34.
Schrammel, J., Busch, M., & Tscheligi, M. (2013). Peacox-persuasive
advisor for CO2-reducing cross-modal trip planning. PERSUASIVE
(Adjunct Proceedings).
Siddiqi, Z. & Buliung, R. (2013). Dynamic ridesharing and information
and communications technology: past, present and future prospects.
Transportation Planning and Technology,36(6), 479–498.
Tang, L., & Thakuriah, P. V. (2012). Ridership effects of real-time bus
information system: A case study in the City of Chicago. Transporta-
tion Research Part C: Emerging Technologies,22, 146–161.
Vij, A., Carrel, A., & Walker, J. L. (2013). Incorporating the influence of
latent modal preferences on travel mode choice behavior. Transporta-
tion Research Part A: Policy and Practice,54, 164–178.
Wang, J., Wang, Y., Wei, C., Yao, N., Yuan, A., Shan, Y., & Yuan, C.
(2014). Smartphone interventions for long-term health management of
chronic diseases: an integrative review. Telemedicine and e-Health,20
(6), 570–583.
Webb, T., Joseph, J., Yardley, L., & Michie, S. (2010). Using the internet to
promote health behavior change: a systematic review and meta-analysis
of the impact of theoretical basis, use of behavior change techniques,
and mode of delivery on efficacy. Journal of Medical Internet Research,
12(1), e4.
Weiss, M., Staake, T., Mattern, F., & Fleisch, E. (2012). PowerPedia: chang-
ing energy usage with the help of a community-based smartphone
application. Personal and Ubiquitous Computing,16(6), 655–664.
Yang, C. H., Maher, J. P., & Conroy, D. E. (2015). Implementation of
behavior change techniques in mobile applications for physical activity.
American Journal of Preventive Medicine,48(4), 452–455.
Zhang, D., Schm€
ocker, J. D., Fujii, S., & Yang, X. (2016). Social norms and
public transport usage: empirical study from Shanghai. Transportation,
43(5), 869–888.
566 V. SUNIO AND J.-D. SCHM
€
OCKER
