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The present urban transportation system, mostly tailored for cars, has long shown its limitations. In many urban areas, public transportation and soft mobility would be able to effectively satisfy many travel needs. However, they tend to be neglected, due to a deep-rooted car dependency. How can we encourage people to make sustainable mobility choices, reducing car use and the related CO_2 emissions and energy consumption? Taking advantage of the wide availability of smartphone devices, we designed GoEco!, a smartphone application exploiting automatic mobility tracking, eco-feedback, social comparison and gamification elements to persuade individual modal change. We tested the effectiveness of GoEco! in two regions of Switzerland (Cantons Ticino and Zurich), in a large-scale, one year long randomized controlled trial. Notwithstanding a large drop-out rate experienced throughout the experiment, GoEco! was observed to produce a statistically significant impact (a decrease in CO_2 emissions and energy consumption per kilometer) for systematic routes in highly car-dependent urban areas, such as the Canton Ticino. In Zurich, instead, where high quality public transport is already available, no statistically significant effects were found. In this paper we present the GoEco! experiment and discuss its results and the lessons learnt, highlighting practical difficulties in performing randomized controlled trials in the field of mobility and providing recommendations for future research.
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A Large Scale, App-Based Behaviour Change
Experiment Persuading Sustainable Mobility
Patterns: Methods, Results and Lessons Learnt
Francesca Cellina 1,* , Dominik Bucher 2, Francesca Mangili 3, José Veiga Simão 1,
Roman Rudel 1and Martin Raubal 2
1Insitute for Applied Sustainability to the Built Environment, SUPSI, Via Trevano,
6952 Canobbio, Switzerland; (J.V.S.); (R.R.)
2Institute of Cartography and Geoinformation, ETH Zurich, Stefano-Franscini-Platz 5,
8093 Zürich, Switzerland; (D.B.); (M.R.)
3Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI, Galleria 2, Via Cantonale 2c,
6928 Manno, Switzerland;
*Correspondence:; Tel.: +41-058-666-6321
Received: 4 April 2019; Accepted: 6 May 2019; Published: 10 May 2019
The present urban transportation system, mostly tailored for cars, has long shown its
limitations. In many urban areas, public transportation and soft mobility would be able to effectively
satisfy many travel needs. However, they tend to be neglected, due to a deep-rooted car dependency.
How can we encourage people to make sustainable mobility choices, reducing car use and the related
emissions and energy consumption? Taking advantage of the wide availability of smartphone
devices, we designed GoEco!, a smartphone application exploiting automatic mobility tracking,
eco-feedback, social comparison and gamification elements to persuade individual modal change.
We tested the effectiveness of GoEco! in two regions of Switzerland (Cantons Ticino and Zurich),
in a large-scale, one year long randomized controlled trial. Notwithstanding a large drop-out rate
experienced throughout the experiment, GoEco! was observed to produce a statistically significant
impact (a decrease in CO
emissions and energy consumption per kilometer) for systematic routes
in highly car-dependent urban areas, such as the Canton Ticino. In Zurich, instead, where high
quality public transport is already available, no statistically significant effects were found. In this
paper we present the GoEco! experiment and discuss its results and the lessons learnt, highlighting
practical difficulties in performing randomized controlled trials in the field of mobility and providing
recommendations for future research.
Keywords: persuasion; randomized controlled trial; smartphones; mobility patterns
1. Introduction
The present urban transportation system, mostly tailored for cars, has long shown its
limitations [
]. Cities seek to improve mobility alternatives to cars and promote a mix of transport
modes, to counteract local and global problems associated with traffic and energy-intensive lifestyles [
For this purpose, they usually target the development of new infrastructures producing tangible
changes in the urban environment, such as bike lanes, the improvement of public transport offers [
or legislative interventions, or the reduction of attractiveness and opportunities for car travel, such as
road closures, road pricing or monetary incentives for non-car transport [
]. However, only relying
on such structural or regulatory tools is often insufficient to break car-dependent habits, promote
inter-modal mobility choices and produce tangible reductions in car use at the community level:
Sustainability 2019,11, 2674; doi:10.3390/su11092674
Sustainability 2019,11, 2674 2 of 23
in many urban areas public transportation, soft mobility networks, or vehicle-sharing systems still
tend to be neglected due to a deep-rooted car dependency [57].
To promote adoption of more sustainable mobility patterns, soft policy measures [
] were
suggested as complementary tools to strengthen traditional urban mobility planning and management
tools. Adopting a cognitive-motivational approach [
], they usually exploit insights from both
psychology and behaviour disciplines [
], with the aim of stimulating individuals to voluntarily
change their mobility patterns (Voluntary Travel Behaviour Changes programmes, VTBC [
]). They
provide feedback, opportunities for social comparison, and economic incentives, targeting a change in
affects, beliefs and attitudes towards individual mobility choices [3].
The growing diffusion of Information and Communication Technologies (ICTs) in the
transportation field [
] opened up new possibilities for exploring cognitive-motivational tools and
assessing their effectiveness. A whole body of research under the “captology” framework (i.e., the study
of Computers as Persuasive Technologies by Fogg
) studies how to effectively couple traditional
offline persuasive techniques, with ICT tools. In this framework, smartphone apps were identified as
ideal tools, since they allow for bi-directional, frequent, and cheap interaction with individual app
users, thus potentially being able to provide them with the right element to support their behaviour
change process, at the right moment. Persuasive apps in the field of mobility started being developed
following progress in automatic tracking capability by exploiting the GPS and other sensors embedded
in smartphones [
]. For an in-depth discussion, one can refer to [
]. Frequently, such
persuasive apps adopt a gamification approach, which is usually defined as the use of game elements in
non-gaming contexts [
]. Typical gamification mechanics and elements are competition, cooperation,
assignments, quests, goals, points, levels, badges and leaderboards [
]. More specifically, they
rely on techniques for effective persuasion such as those identified by
Froehlich [28]
: provision of
feedback on consequences of individual mobility choices (such as the impact on energy consumption
or CO
emissions), invitation to define personal goals for change, engage in challenges, and compare
performance within virtual communities composed of several app users.
Even though such persuasive, gamified apps are frequently popping up, their development is
still a young discipline. In particular, similarly to soft policy measures and VTBC interventions, they
were frequently seen to lack grounds in a proper behaviour change theory [
], which was said
to limit both their behaviour change effectiveness and the capability to gain in-depth understanding of
the effects produced by their use. Moreover, so far only a few field studies were run to assess their
behaviour change effectiveness, and most of them were criticized due to the small sample of app
users (understandably, sometimes heavily biased towards volunteer university students), the short
period of app testing time and the consequent focus on short-term effects only, and, similarly to
other VTBC interventions, the lack of proper randomization techniques, or even the lack of a control
group, which precluded performing randomized controlled trials (RCTs), the “gold standard for
interventions” [3,11,23,24,31,32]. Overall, this situation implies that:
results could not be generalized to the whole society,
long-term behavior change could not be measured, and
potential changes in their mobility behavior could not unequivocally be attributed to the app
alone, since they might also be due to other external factors.
Against this backdrop, we developed and field tested GoEco!, a mobile app targeting a reduction
in individual car use and the related energy consumption and CO
emissions, by promoting the
use of public transport and soft mobility. Well aware of the above limitations affecting persuasive
interventions in the mobility domain, we aimed at exploring the feasibility of performing a large scale,
app-based field experiment, quantitatively assessing the app’s effectiveness by measuring the change
in key mobility variables.
For this purpose, we designed and managed a one year long randomized controlled trial,
involving voluntary individuals living in two different urban contexts across Switzerland. In this paper
we introduce the GoEco! persuasive app and provide details about the methodology we followed in the
Sustainability 2019,11, 2674 3 of 23
field experiment aimed at assessing its effectiveness (Section 2). In Section 3we present the results of
the experiment and in Section 4we discuss our results under the light of practical limitations that still
affect internal and external validity of randomized controlled trials in the mobility field. In Section 5
we conclude by reflecting on the lessons learnt and on the main open challenges to be addressed in
future research.
2. Materials and Methods
In this section we present the persuasive app GoEco!, state our research hypotheses and describe
how we designed and managed the GoEco! experiment.
2.1. The GoEco! App
The persuasive app GoEco! is anchored in the Transtheoretical model for behaviour change [
according to which behaviour change occurs through several cognitive-motivational processes,
that make individuals progress from the stage of pre-contemplation of change to the stage of maintenance
of change. Consistently, it is endowed with a set of features and components specifically designed to
support individuals in each stage of behaviour change. Since the app is largely presented in another
work [
], here we limit ourselves to introduce its key features, following the overview provided in
Table 1, and to show some screenshots (Figure 1).
Table 1.
Features and components of the persuasive app GoEco!, with respect to stages and processes
of change identified by the Transtheoretical model [33].
Stages of Change Processes of Change GoEco! Components/Features
Consciousness raising
Increase awareness for causes, consequences
and cues about a behavior
Feedback on each travelled route
Baseline mobility patterns
Cognitive and affective assessment of one’s
self-image, with and without a particularly
unhealthy habit
Alternatives for systematic routes
Overall potentials for change
The belief that one can change and
commitment to act on such a belief
Goal setting
Learning of more sustainable behaviours, that
can substitute the less sustainable ones
Weekly report
Action and
Contingency management
Provide consequences (rewards) for taking
steps in a particular direction
Trophies and Badges
Leaderboard/Hall of Fame
Helping relationship
Social support (care, trust, openness,
acceptance and general support) for
new behaviour
Notification system to stimulate
action maintenance
In-person events outside the app
The app users are supposed to start from the pre-contemplation stage of behaviour change, at which
they have no intention to change their mobility behaviour. By exploiting basic activity tracking features
provided by the commercial app Moves [
] (discontinued since July 2018), GoEco! tracks all the
travelled routes and, by means of on-purpose developed algorithms [
], detects the transport mode
used, asking users for a manual validation of the detected transport modes (either confirmation
or correction). All the travelled routes are automatically collected by the app, with no manual
activities requested by the users, apart for the validation of the transport mode. If needed, users
can temporarily stop the Moves tracking tool, and they can provide the validations at their own
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convenience, for example in the evening or in the weekends, going back to all the routes travelled in
the previous week. Soon after validation, the users are provided with feedback about the individual
routes they have travelled (kilometers and travel time, as well as CO
emissions produced and
energy consumed, estimated based on the Mobitool consumption and emissions factors available for
Switzerland [
], that depend on the transport mode used and on the amount of kilometers travelled,
independently on the vehicle’s occupancy rate). To get more realistic estimates of impact, users are
also allowed to set the average fuel consumption value of their car, expressed in fuel liters per 100 km,
which is then used by GoEco! to customize the Mobitool estimates. Once per week, they are also
given a summary of their mobility patterns and impacts (average weekly kilometers and travelling
time, percentage use of transport modes). After four weeks of app use, they are also provided with
information about their “baseline mobility patterns”, namely how they travel on average and the
related impact on energy consumption and CO2emissions.
Figure 1.
A selection of screenshots of the GoEco! app: (
) List of tracked routes; (
) Validation of
the transport mode; (
) Goal setting; (
) Weekly summary of mobility patterns and progress towards
goal achievement; (
) Hall of fame and leaderboard. Screenshots (
) are also offered by the GoEco!
Tracker app.
This information is supposed to raise their consciousness towards the need for a different mobility
behaviour. At this stage, GoEco! supports them by suggesting them alternatives for the routes they
regularly travel (“systematic” home-to-home loops) and by calculating their overall “potential mobility
patterns”, namely the mobility patterns they could have, by always opting for the lowest carbon
emitting alternative compatible with their needs. For a detailed description of how we perform
this activity, please refer to [
]. Basically, the assessment of someone’s potential is based on the
comparison to optimal travel behavior, where the optimisation criterion is, in this case, the amount
of CO
emissions: for every travelled route, and specifically for every starting and final point, we
search for all available itineraries and transport mode alternatives and select the one with the lowest
emissions. If the selected alternative does not provide a relevant improvement compared to the
original route, no suggestion is made.
Once they get accustomed to this piece of information, individuals start contemplating change.
Even though ideas and methods for automatically determining the time at which this happens
(from passively recorded mobility data) exist [
], they were not advanced enough to be used in
GoEco. Therefore, GoEco! assumes that people start contemplating change after they have been
supplied with such a mobility feedback. At this stage, GoEco! invites them to set their personal goal for
change, by choosing one of the five pre-set goal options (“reduce car use”, “increase slow mobility”,
“increase public transport”, “reduce energy consumption”, and “reduce CO
emissions”), and setting a
target by choosing a value between their baseline and potential mobility patterns. Progress towards
this individual goal is therefore the main motivational factor that stimulates individuals to prepare
to action. On a weekly basis, GoEco! provides users with an update on the progress towards their
goal, showing them its level of achievement on a simplified bar chart (the height of the bar being
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proportional to the percentage of goal achievement) and providing them with a congratulation or
encouragement message, depending on their performance.
To support users during action,GoEco! invites them to engage in individual challenges, providing
users with practical suggestions on what should be done to progress towards one’s goal, such as for
example “I will not use the car during peak hours for five days out of the next seven days”, “This week
I will travel by slow mobility all my short routes”, or “During the weekend I will take the train for trips
longer than 100 km”. Users are free to join or ignore challenges. The idea is that by joining a challenge,
they are supported to replace their automatically performed mobility choices (habits) and turning them
into different choices, explicitly controlled by intention. The completion of challenges is automatically
assessed against a set of rules, so that users who achieve a challenge are automatically rewarded with
a trophy. Additionally, users are rewarded with surprise badges, when specific sustainable mobility
choices are detected by the system, such as using the bicycle every day for at least five consecutive days
or travelling long trips by train. This also has a guidance effect: receiving an unexpected reward for
spontaneously performed actions makes users aware of positive actions they perform and stimulates
them to repeat them in the future. Moreover, badges reinforce commitment and rekindle user interest.
Finally, action is further stimulated by the possibility of comparing individual performance with the
other app users, seen as members of the same GoEco! community. Typically, gamified environments
exploit a leaderboard, though in GoEco! this is not straightforward, since there is no point-based
reward system. As such, personal goals for change are put at the center of the motivational mechanics,
basing the comparisons between members of the GoEco! community on each individual’s level of
achievement, combined with the number of challenges she completed and the number of badges she
obtained. The leaderboard is updated every week, when the top-3 members of the week are also
rewarded with visibility in the “Hall of fame” section. Regardless of the complexity of the goals,
users can still be listed in the Hall of fame if they achieved their goals. The system does not judge
goal complexity, which depends on the users’ initial mobility patterns, on their potential for change,
on external, personal constraints and on their level of engagement. Progression towards an own goal is
therefore the key motivational factor, and it gives a measure of success both at the individual level and
in the competition with the other participants. Users are free to progress at their own pace and in their
own direction, while being stimulated by GoEco! to achieve their personal goal for change. Also, note
that monetary or tangible performance-based rewards are not included among the GoEco! motivational
factors, since its aim is mostly to stimulate mobility behaviour change as a personal, intrinsic choice of
app users, instead of buying it in exchange for money or other tangible goods, which has been found
to only have temporary effects [39].
To support users in the maintenance of the new behaviour they started to put into practice, GoEco!
strengthens individual commitment by means of a notification system, that daily reminds users to
check and validate their routes, notifies them about updates of the weekly statistics, congratulates
them whenever they achieve good results (goal achievement, challenge completion, attribution of
badges, visibility in the Hall of fame) and encourages them to try again the next weeks if they fail their
goals or challenges. Furthermore, in-person events outside the app are put in place to create a feeling
of belonging to a community.
2.2. Research Hypotheses
The effectiveness of the GoEco! app is field-tested in a randomized controlled trial
(experiment) [4042]
involving voluntary citizens across Switzerland, in two contexts largely differing
in the urban settlement type and in the available mobility options: the City of Zurich (German-speaking
part of Switzerland), a dense urban area, characterized by high levels of accessibility to public transport
and infrastructures for slow mobility, and the Canton Ticino (Italian-speaking part of Switzerland),
a region characterized by urban sprawl, where effective alternatives to individual cars are often missing.
Data of the Swiss Mobility and Transport Census 2015 [
] show that in Ticino car use is definitely
higher than in Zurich (the daily average percentage of kilometers travelled by car and motorbike is
Sustainability 2019,11, 2674 6 of 23
equal to 74% in Ticino and 57% in Zurich), while in Ticino on average less kilometers are travelled
than in Zurich (29.17 km/day in Ticino and 34.94 km/day in Zurich).
The use of the persuasive app GoEco! is expected to help people substituting their car uses
with lower CO
emission transport modes (modal change), and consequently to help reducing travel
impacts in terms of CO
emissions and energy consumption. In particular, this is expected to occur for
“systematic loops”, namely the home-to-home loop routes that are frequently travelled.
We speculate that changing one’s own habitual routes is easier than changing occasional ones,
because origins and destinations are well-known and convenient alternatives can more easily be found,
and then beneficially implemented over time, without any need for additional effort in the search for
alternatives. Therefore, the GoEco! experiment is designed to test the following null-hypotheses.
Hypothesis 1
After treating individuals with the use of the GoEco! app, (i) average CO
emissions per
kilometer and average energy consumption per kilometer are lower than before the intervention, and (ii) the
after/before difference between these variables is higher than the after/before difference between the same variables,
calculated in the same period for a comparable group of individuals, that are not treated with the GoEco! app.
Hypothesis 2
The same conditions also hold true when only considering a subset of frequently travelled
routes, namely those automatically classified as “systematic loops” by GoEco!’s algorithms.
2.3. Design of the Experiment
Both these hypotheses are separately tested in Ticino and in Zurich, in order to detect possible
different outcomes in the two regions. In each region, the experimental design envisions a treatment
and a control group: the former groups are treated with the use of the GoEco! app, while the latter
do not receive the persuasive treatment. However, also the control groups are requested to use an
app, named GoEco! Tracker, developed on purpose within the project as well, and aimed at simply
collecting mobility data, without any persuasive goal. The GoEco! Tracker app is a simplified version
of the full GoEco! app, that only performs automatic tracking of individual mobility data. For this
purpose, it exploits the same tracking component of GoEco! (algorithms, user interface, look and feel,
etc.), but without offering any eco-feedback to the users. Similar to GoEco!, however, it requires users
to perform a manual validation of each detected route, in order to confirm or correct the detected
transport mode. Therefore, its users are still requested to interact with the app on a daily basis.
Overall, as shown in Figure 2, the experiment is designed around three mobility tracking periods:
period A (March–April 2016) aims at collecting pre-intervention, baseline mobility data, through
the GoEco! Tracker app;
period B (October 2016–January 2017) aims at collecting “persuaded” mobility data while using
the GoEco! app for the treatment groups and at collecting counterfactual mobility data for the
control groups, through the GoEco! Tracker app;
period C (March–April 2017) aims at collecting post-intervention mobility data, again through the
GoEco! Tracker app.
Periods A and C take place in the same months of the year, with a difference of one year, so that
our research hypotheses can be tested by directly comparing the mobility data collected in these
periods, without any data post-processing activity aimed at removing seasonal macro effects. They
last for six weeks each, a duration chosen as a trade-off between the need for collecting as much and as
diverse data as possible, and the need for limiting attrition as much as possible. Period B, instead, lasts
longer (twelve weeks), since for a behavioural intervention to produce a long-lasting change, more
time is needed. Since the assessment of the intervention is made by comparing mobility data collected
between period C and period A, the specific time of the year chosen for period B is a less critical
choice; it is set from October to January to avoid overlapping with the Summer holidays, during which
management of the intervention is expected to be more complex and less effective.
Sustainability 2019,11, 2674 7 of 23
Besides the quantitative data automatically collected by the GoEco!/GoEco! Tracker apps, to get
additional insights on our research hypotheses, a post-experiment online questionnaire delivered to
all project participants is also performed (June–July 2017), followed by individual semi-structured
interviews conducted one year after the intervention with a subsample of 20 participants, randomly
selected (November–December 2017). To conclude, a cross-analysis of the elements emerging from the
quantitative data tracked by the app, the questionnaire, and the interviews is performed, to reciprocally
enrich and sustain the insights they individually provide.
Figure 2.
Timeline of the GoEco! experiment, showing the three tracking periods and the final
questionnaire and interviews. All activities are performed in parallel in Ticino and Zurich.
2.4. The Experimental Sample
The research goal to perform a large-scale field experiment is quantified in a target sample of
overall 600 individuals, equally distributed between Ticino and Zurich regions—a much larger sample
than previous interventions. They are are voluntary citizens, performing their usual everyday activities
and trying to satisfy their mobility needs. They have to be a sufficiently large and varied group of
individuals, to smooth out biases due to self-selection and allow generalization of the results of the
intervention to the target segment of population more familiar with everyday use of smart device
applications, namely individuals living in urban areas aged between 25 and 44 [
]. In order to
reproduce the conditions of a “real-life” use (outside of the field test) of the GoEco! app as much as
possible, we decided against paying people for their participation in the experiment, as well as against
introducing any enforcement for app use.
To raise the interest by this target group, a three-month public recruitment campaign is set
(December 2015–March 2016), exploiting a variety of communication channels available at the two
experimental sites: press conferences, participation in local radio and television programmes, articles
in newspapers and magazines, posts in social networks (Facebook and Twitter) and paid advertising
on Facebook. Even though prizes for the participants remaining active until the end of the field
experiment are envisioned (see Section 2.6), the campaign does not explicitly focus on them, in order
to address intrinsic motivational factors as much as possible. Our general aim is in fact to stimulate
mobility behavior change as a personal, intrinsic choice of app users, instead of buying it in exchange
for money or other tangible goods, which has been found to only have temporary effects [39].
2.5. Randomization and Assignment to Treatment and Control Groups
During period A, no differentiation is made within the sample, and all recruited participants are
asked to use the GoEco! Tracker app, in order to identify their baseline mobility patterns. The split
in the treatment and control groups is performed at the end of period A, in order to only account
for those participants that collect a sufficient amount of data to estimate reliable baseline mobility
patterns—namely, to avoid that poor data quality and/or drop-outs could unbalance size and
composition of the groups and preclude effectiveness of the experiment.
Thus, at the end of period A, the full set of routes collected by GoEco! Tracker for each participant
is processed, in order to decide whether they are allowed to enter period B. Period A data is processed
based on the following rules:
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consider only days with at least one validated route (
“active days”; no difference is made
between working and non-working days);
consider only weeks with at least four “active days” (“active weeks”);
if the participant has at least three active weeks, at least fifty routes, and at least validated 80% of
the routes (
“active participant”), she can enter period B and has to be randomly attributed to
treatment or control group; otherwise, she has to be excluded from the experiment.
Once the full sample of participants allowed to enter period B is identified, they are randomly
assigned to the treatment and control groups. To avoid that further drop-outs during periods B and C
affect the possibility to get statistically significant results, two thirds of the participants are randomly
assigned to the treatment groups and one third to the control groups—namely, while respecting
random assignment principles, we create a larger group of people using the persuasive GoEco! app,
than the group of control people being just tracked with the GoEco! Tracker app.
Finally, in Tracking period C both groups are again asked to use the GoEco! Tracker app.
2.6. Retention of the Experimental Sample over Time
Since regular validation of the mobility data collected by GoEco! Tracker and GoEco! are essential
(lacking them, no data is available to test our research hypotheses), to keep the sample of participants
engaged over time and limit attrition, we set up an incentivizing system based on tangible prizes:
jackpot: in the three tracking periods, every week a participant is randomly chosen and rewarded
with a 50 CHF voucher if she has confirmed the transport mode (validation) for all her routes
registered by the GoEco! app;
quizzes: three monthly quizzes are run during tracking period B, each one offering two vouchers
of the value of 100 CHF each;
random draw: a final random draw at the end of tracking period C offers larger prizes, such as a
folding bicycle, a tablet, a smartphone or vouchers for walking holidays.
For vouchers, users are allowed to choose between Swiss retailers, public transport tickets,
or charity donations. Since gathering correct mobility data is essential for the quantitative assessment
of our research hypotheses, such prizes are only attributed if participants have validated all their
routes and are only distributed at the end of the tracking period when they are issued. Please note
that such prizes are introduced with the only aim of maintaining the users’ interest over time, namely
to guarantee collection of a sufficiently large and reliable amount of data. Since their attribution is
not related to any specific transport mode choice by the users, and only depends on trip-validation
in the app, they are not expected to produce any influence on the users’ mobility behaviour. Neither
the potential folding bicycle prize offered by the final random draw can influence the users’ mobility
patterns and introduce a bias in the experiment, since the draw takes place after the end of the three
tracking periods.
To invoke the feeling of belonging to a community and in anticipation of a critical decrease of
commitment by the sample of participants during the intervention (period B), two in-person events
outside the GoEco! app are also organized (one in Ticino and one in Zurich) for the treatment groups.
Participants are invited to join recreational events providing practical examples of more sustainable
mobility practices (visits to exhibitions and lazy bicycle rides across the city). During such events,
participants are expected to get to know each other, share their experiences and tips to overcome
difficulties in achieving challenges and, in general, support each other, fostering a long-lasting change
into the new mobility behaviour.
Apart from these meetings, no other in-person interaction with the experimental sample is
performed throughout the GoEco! intervention, and all activities are developed online, through the
project website, videos and presentations, a newsletter and email communications. An help-line phone
number is also provided, for any technical problem with the app use.
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2.7. Overall Effectiveness of the Intervention—Test of Hypothesis H1
The test of our research hypotheses is made by comparing baseline data (period A) with data
collected during period C, namely after the intervention has been discontinued, in order to account
for its persistence over time. From the start of period B to the end of period C, further drop-outs are
possible, and participants could reduce the frequency of their interactions with the apps, thus ending
up collecting poor quality data, not sufficient to summarize their mobility patterns with the needed
accuracy. Therefore, in line with the requirements concerning data collected during period A, to test
hypothesis H1 we only consider the sample of participants who have at least three active weeks of
data collection per period (A–C), collected at least fifty routes per period, and validated at least 80% of
them. Then, we only account for validated routes, ignoring all data about non-validated routes.
By referring to the resulting set of data, we obtain a measure of the average ecological impact
of each individual in each period, by calculating the average value over all routes travelled by
each participant, respectively, in periods A (pre-intervention) and C (post-intervention), of the
following variables:
CO2emissions per km [gCO2/km];
average energy consumption per km [kWh/km].
Finally, the reduction of the CO
emissions and the related energy consumption of each individual
between the two periods A and C are retained as two measures of the ecological improvement of travel
behaviours. Paired comparison statistical hypothesis tests are performed to verify the existence of
a significant difference between the CO
produced (or the energy consumed) in period A and that
produced (consumed) in period C. For each variable, two separate tests are performed, one for each of
the two regions Zurich and Ticino.
2.8. Effect of the Intervention on Systematic Routes—Test of Hypothesis H2
Hypothesis H2 only refers to systematic routes travelled by participants, namely those that they
travel more frequently, automatically identified by GoEco! post-processing algorithms [36].
For this purpose, we first look for systematic loops each individual travelled both in period A
and in period C. If a given systematic loop is found for a specific individual in both tracking periods,
the related mobility data enters the test of hypothesis H2; otherwise, the related data is not considered
in this analysis. Considering only systematic loops allows loosening requirements about the minimal
number of active weeks and the minimal number of collected routes: if the same systematic loop is
found both in period A and in period C, it does not really matter how many other routes the individual
travelled in the same week, or for how many weeks she was active with the mobility tracking app:
such a loop can be used in the test of hypothesis H2. Therefore, the only condition we keep on collected
data is to consider validated routes and exclude all the others. As a consequence, hypothesis H2 is
assessed on a different sample of participants with respect to the one used for hypothesis H1.
To test H2 we consider the same variables as for H1, i.e., CO
emissions and energy consumption
per kilometer. Two measures of each individual’s ecological impact, one for each variable, are obtained
in two steps: first, the two variables of interest are averaged over all repetitions of each systematic loop,
to characterize the loop ecological impacts; then, for each participant, the loop average impacts are
averaged over all her systematic loops. This way, the impacts of different systematic loops traveled by
the same participant are not treated as independent. This choice is based on the following consideration:
although each loop requires an independent decision about how to travel it, some characteristics of
the participant (e.g., owning a season ticket for public transport) may represent a common factor
influencing all her choices; this implies that modal choices made by participants about how to travel
each loop may not be independent. The ecological impact of systematic travels before (period A) and
after (period C) intervention is compared via a paired statistical hypothesis test for the difference
between the means of two populations. To assess a significant improvement of the travel behaviour of
the treated population, only individuals from the intervention group are considered in this analysis.
Sustainability 2019,11, 2674 10 of 23
In order to ascribe the observed improvement to the GoEco! app, the improvements of the treatment
group are compared to those obtained by the control group. For this purpose, classical statistical tests
for independent samples of the difference between two population means are performed. For each
variable, two separate tests are performed, one for each of the two regions, Zurich and Ticino.
3. Results
The campaign to recruit voluntary participants for the GoEco! experiment took place between
December 2015 and March 2016. At its conclusion, we obtained a total of 599 applications (277 in Ticino
and 322 in Zurich), plus another 35 outside the study areas, who had to be rejected from the beginning.
Of these 599 applicants, 26 owned neither an iOS nor an Android smartphone (the only two operating
systems supported by the GoEco! apps), therefore they had to be rejected. 573 participants entered
tracking period A, aimed at collecting their baseline mobility data using the GoEco! Tracker app.
Out of the initial 573 accepted participants at the start of period A, only 212 fulfilled the
requirements presented in Section 2.5 and were assessed as “active participants”, thus eligible to
enter period B. In fact, even though applying to the project was a totally voluntary decision, 138
accepted participants did not even install the GoEco! Tracker app, while 223 of them had not collected
enough data to provide us with three active weeks of data. The sample of 212 active participants
was then randomly assigned to the treatment and control groups (69 treatment in Ticino, 34 control
in Ticino; 76 treatment in Zurich and 33 control in Zurich). Starting from these 212 participants who
entered period B (the actual intervention period), however, further attrition occurred during both the
intervention and the final period C.
Please note that in our app-based interventions, abandonment did not occur by participants
officially stepping out of the experiment (we just received 28 explicit notifications by participants
who requested to leave the project): it occurred when they either totally stopped using the GoEco!
app(s), or when reduced their frequency of interaction with it, though keeping some level of activity.
In both cases, they ended up not collecting enough data to fulfill the requirements indicated in
Sections 2.7 and 2.8, namely to summarize their mobility patterns with the needed accuracy to test
hypothesis H1 and H2. Lacking these data, the widely recommended “intention to treat” analysis [
(namely, testing hypotheses on the data collected by the full samples of randomly assigned treatment
and control groups, no matter their level of activity within the experiment) could not be performed,
and we were compelled to exclude non active participants from the test of our hypotheses.
3.1. Overall Effectiveness of the Intervention—Test of Hypothesis H1
After processing the collected data according to the rules summarized in Section 2.7, the overall
number of participants for which data of sufficient quality to test hypothesis H1 was available (sample
of active participants) decreased to just 52: 21 in the treatment and 10 in the control group in Ticino,
and 13 in the treatment and 8 in the control group in Zurich.
Notwithstanding such a large decrease in the sample of participants, the relative size of the
treatment with respect to the control groups remained roughly the same (two thirds treatment and
one third control). Under the assumption that drop-outs occurred randomly [
], and having verified
that there was no significant difference in the baseline (period A) distribution of the CO
and energy consumption per km between the active participants of control and treatment groups,
we thus tested our research hypothesis H1 on the final sample of 52 active participants, instead of the
initial sample of 212 participants who were randomly assigned to the treatment and control groups.
As the field data clearly showed that the distribution of the variables of interest is not Gaussian,
our hypotheses were tested using non-parametric tests, i.e., the Wilcoxon signed-rank test was used to
compare the emissions and consummations of each individual in periods A and C (dependent samples),
whereas the Wilcoxon rank-sum test was used to compare the improvements in the treatment and
control groups (independent samples). The results of the Wilcoxon signed-rank test for H1 are shown
in Table 2: comparing values before (period A) and after (period C) the intervention, a reduction in
Sustainability 2019,11, 2674 11 of 23
average CO
emissions per kilometer is observed in Ticino, while in Zurich the average CO
per kilometer are observed to increase. For the average energy consumption per kilometer, instead,
negligible differences are found, in both regions. None of these differences, however, are statistically
significant. Therefore, due to the the small size of the final sample of participants, the large variability
of the recorded routes, and the relatively short duration of periods A and C, there is no statistical
evidence that the use of the GoEco! app produced any effect on the overall mobility patterns.
Table 2. Test of hypothesis H1: effect of GoEco! on the treatment groups (nTicino = 21, nZurich = 13).
CO2Emissions per km Energy Consumption per km
(one side Wilcoxon signed-rank test)
Ticino 0.12 0.12
Zurich 0.19 0.19
Average difference between periods
C and A (XCXA)
Ticino 12.03 gCO2/km 0.05 kWh/km
Zurich 5.96 gCO2/km 0.02 kWh/km
* sig. at 0.05 level (this condition is never met in the above table).
3.2. Effect of the Intervention on Systematic Routes—Test of Hypothesis H2
Following the procedure summarized in Section 2.8, the number of participants for whom at least
one common systematic loop was found between period A and period C was instead 45: in Ticino, 15 in
the treatment and 7 in the control group, and in Zurich 14 in the treatment an 9 in the control group.
Again, we noticed that the relative sizes of treatment with respect to control groups remained
roughly the same (two thirds treatment and one third control). Under the assumption that drop-outs
occurred at random and having verified that there was no significant difference in the baseline
(period A) distribution of CO
emissions and energy consumption per kilometer between the active
participants of the control and treatment groups, we tested our research hypothesis H2 on this sample
of 45 participants. As already mentioned, for this purpose, considering that data are observably non
Gaussian, we used the non-parametric Wilcoxon signed-rank test.
The results of test of hypothesis H2 are summarized in Table 3: comparing the variables before
(period A) and after (period C) the intervention, a statistically significant reduction in CO
and energy consumption per kilometer is found in Ticino. In Zurich, instead, no statistically significant
after/before difference is found for any of the considered variables. Even though not statistically
significant, the differences found show a slight tendency in the desired direction (a decrease in energy
consumption and CO2emissions per kilometer).
Table 3.
Test of hypothesis H2: effect of GoEco! on systematic loops of the treatment group members
(nTicino = 15, nZurich = 14).
CO2Emissions per km Energy Consumption per km
(one side Wilcoxon signed-rank test)
Ticino 0.023 * 0.018 *
Zurich 0.342 0.458
Average difference between periods
C and A (XCXA)
Ticino 23.931 * gCO2/km 0.107 * kWh/km
Zurich 7.776 gCO2/km 0.047 kWh/km
* sig. at 0.05 level.
To check whether these differences regarding systematic loops can actually be related to the use
of the GoEco! app or depend on external factors affecting treatment group members, we compare the
performance of the treatment group against the control group, both in Zurich and in Ticino. For this
purpose, a Wilcoxon rank-sum test is used (Table 4).
According to the test, from period A to period C, in Ticino there is statistical evidence of a change
in the systematic mobility behaviour of the treatment group (reduction of average energy consumption
and CO
emissions per kilometer), which instead is not observed in the control group. In the Ticino
Sustainability 2019,11, 2674 12 of 23
treatment group, in fact, considering all the systematic loops common to periods A and C, CO
emissions per km decrease on average by 23.9 gCO
/km (corresponding to 22.4% of the average
emissions per km of systematic loop in period A). On the other hand, in the Ticino control group they
increase by 9.2 gCO
/km (corresponding to the 8.6% of the average emissions per km of systematic
loop in period A): the GoEco! intervention thus results in net savings of 33.1 gCO
for every kilometer
of systematic loops travelled, corresponding to 31.0% of the average emissions per km of systematic
loop in period A.
Similarly, though with much less impact, the average energy consumption per kilometer of
systematic loops is found to decrease by 0.107 kWh/km in the treatment group, while it increased
by 0.029 kWh/km in the control group, with a net effect attributable to GoEco! equal to a decrease of
0.136 kWh/km. In both cases, the reported differences are significant at an
= 0.05 level (the respective
p-values being equal to 0.049 and 0.036).
Finally, in Zurich, no statistically significant changes in systematic mobility were observed for any
of the above variables. Therefore, we conclude that the hypothesis of a change in systematic mobility
induced by the GoEco! app (H2) can be accepted only in Ticino, whereas for Zurich, no sufficient
evidence in favor of it was collected.
Table 4.
Test of hypothesis H2: differences between effects on the treatment and control groups,
considering only systematic loops (nTicino = 22, nZurich= 23).
CO2Emissions per km Energy Consumption per km
(one side Wilcoxon rank sum test)
Ticino 0.049 * 0.036 *
Zurich 0.157 0.264
Difference between treatment and control group
(XCXA)Treatment (XCXA)Control
Ticino 33.137 * gCO2/km 0.136 * kWh/km
Zurich 1.439 gCO2/km 0.036 kWh/km
* sig. at 0.05 level.
4. Discussion
According to the above results, GoEco! is found to bring about a change in individual mobility
patterns of its active users, reducing the average CO
emissions and energy consumption per kilometer.
However, such a change is only found for systematic routes, namely the ones they frequently travel.
Also, it is only found in Ticino: in Zurich a certain increase in soft mobility is found, but it is not
statistically significant.
The fact that GoEco! is found to impact systematic routes only can be explained by considering that
it is in general easier to change habits on paths one frequently travels, partly because their constraints
are often satisfied by a wide range of mobility options (e.g., the distance to cover is small, there are
no particular requirements to carry heavy loads, etc.), and partly because once a more sustainable
(and suitable) alternative has been found, one can rely on it again and again over time. We explain the
different impacts in Ticino and Zurich by referring to the differences in the average mobility patterns
of the population in these areas: as indicated by average mobility data estimated by the Swiss Mobility
and Transport Census [
], differently than in Ticino, the use of public transportation is already very
common in Zurich: there, the tendency is to only use cars when other options are really not convenient
(or even not available). Namely, systematic routes are in general already optimized with respect to
avoiding car use, thus available room for change is quite limited, and bringing about additional change
in Zurich is more difficult.
However, several factors affect both internal and external validity of the experiment and the
overall possibility to generalize these results, suggesting to carefully handle them and highlighting the
need for further investigation along this line of research. Therefore, in addition to its behavior change
results, which might be difficult to generalize, the GoEco! experiment is valuable for the lessons we
can draw regarding the development of randomized controlled trials in the field of mobility and the
Sustainability 2019,11, 2674 13 of 23
possibility of exploiting similar persuasive apps within urban mobility policy-making. In particular,
here we discuss:
whether baseline and control data are reliable, considering they were collected through a mobility
tracking app with which the experimental sample regularly had to interact with—and, if not, how
to reduce biases;
if and how attrition bias can be reduced, thus strengthening the internal validity of the experiment;
whether the final experimental sample reflects the characteristics of the GoEco! target group,
or instead is polarized by already “converted” public transport and soft mobility users—and, if so,
if this can be avoided.
4.1. Mobility Tracking: Does It Influence Individual Mobility Patterns?
All participants of the GoEco! experiment, both in the treatment and control groups, are engaged
in three mobility tracking periods, which require installing an app (either GoEco! or GoEco! Tracker,
depending on the group and on the period) and interacting with it on a daily basis, in order to validate
the automatically detected transport mode, for each recorded route.
Therefore, all GoEco! participants are somehow aware they are being monitored, and also receive
a daily indirect feedback on their mobility patterns. This is in contrast with design requirements for
randomized controlled trials, that, for internal validity purposes, state that experimental data collection
should be as unobtrusive as possible [
], so that the collected data reflects how participants
would have ordinarily behaved. Thus, the question arises whether data collected through the GoEco!
Tracker app can be regarded as reliable, unbiased representations of pre-intervention baseline mobility
patterns (period A, all groups), of post-intervention mobility patterns (period C, all groups), and also
of control mobility patterns (period B, control groups).
In such a situation, two complementary phenomena might in fact come into play. First, being
aware of being observed, participants might be led to alter their behaviour in order to better
comply with a resource-saving, socially desirable one, particularly trying to behave in ways they
believe researchers expect them to. These aspects were referred to as the “social desirability
effect” [
], the “experimenter demand effect” [
], “the good subject effect” [
], or, more in
general, “the Hawthorne effect” [
]. Second, daily requests for validation might act as unintended
information feedback on users’ current mobility patterns, activating awareness about them, and thus
actively starting to persuade them to opt for different transport modes, introducing a bias in the
baseline data.
This might change the above experimental results, suggesting that the persuasive effect of GoEco!
might be under-estimated: if the control group is indirectly persuaded by use of GoEco! Tracker,
an improvement of the average emissions and consumption per kilometer could have occurred also
in the control data. On the other side, the actual average emissions and consumption per kilometer
recorded in period A (baselines) for the treatment groups could be smaller than their actual value
before the GoEco! intervention. These two phenomena would imply a positive impact of the GoEco!
Tracker on the control group and a smaller impact of the GoEco! app on the treatment groups, thus
reducing the measurable treatment effect. Please note that a questionnaire-based assessment of the
experiment, asking for example to summarize one’s mobility patterns before and after the experiment,
would produce even more significant biases, therefore we never considered it. In fact, responses
would be heavily affected by the above biases as well, in addition to the general response biases and
distortions that usually affect self-reporting, such as wording of the questions, rating scales, type of
questions, etc. [52].
Acknowledging this problem, during the final interviews with a random selection of participants
of the experiment, we explicitly investigated whether this phenomenon had taken place. We asked
interviewees whether, while using GoEco! Tracker, they felt any pressure to modify their mobility
choices. Half of the interviewees confirmed this happened (partly due to the awareness of being
Sustainability 2019,11, 2674 14 of 23
included in a project fostering sustainable mobility and partly due to direct effect of feedback), even
though they were not able to quantify the magnitude of the phenomenon.
This phenomenon can barely be avoided with the currently available location tracking technology,
however. In contrast to other energy-related fields, where the effectiveness of behaviour change
interventions can be analyzed through randomized controlled trials fed by energy consumption
data collected by already in place (smart) metering networks [
], no network for automatic
and unobtrusive mobility data collection is currently available. The GPS-enabled mobility tracking
technology we opted for in GoEco! is in fact still limited, with an 80% average accuracy in
automatic route and transport mode detection [
], which calls for manual validation by app
users. Alternatively, attempts to automatically infer user routes and transport modes by exploiting the
information tracked by cellphone towers or wi-fi in urban areas have been widely explored, though
they are still unable to produce the fine-grained mobility tracking data needed by persuasive apps,
and further research in this field is needed [
]. Both these directions are worth being further explored,
since they could greatly remove those biases: in the former case, individuals would just be informed
they are going to be monitored and requested a consent, in order to guarantee compliance with ethical
data protection regulations. In the latter, they would also be requested to install a mobility tracking
app, but then they could forget about it, letting it run in the background of their phone. In both cases,
feedback-related bias would be removed, while awareness-related biases would be limited to a first
“onboarding” period, and then significantly reduced.
Therefore, while calling for further progress in automatic mobility tracking, a safe strategy to
collect less biased data could be to prolong the duration of the tracking periods (which would not
be an additional effort for the experimental sample, since tracking would automatically take place),
and then to discard data collected in the initial “onboarding” period, during which awareness-related
bias is expected to be higher.
4.2. Abandonment over Time: How Can We Limit Attrition and Keep Interest Alive?
The GoEco! experiment was designed to address the key limitations affecting app-based persuasive
interventions highlighted in Section 1, particularly the limited size of the sample, the short duration of
the intervention, and the lack of rigorous experimental procedures. For this purpose, we designed a
large-scale experiment, involving around 600 individuals over a time period of one full year. However,
a very large attrition phenomenon occurred, thus preventing us from performing the large-scale
experiment we had envisioned. Nonetheless, a comparison against several similar app-based,
persuasive interventions in the field of mobility, based on data collected by [
], shows that in
GoEco! we were definitely able to perform a longer experiment than on average, also engaging a
corresponding, if not higher than average, number of participants (Figure 3).
Figure 3.
Comparison between GoEco! (n = 52, 24 mobility tracking weeks overall), based on data
provided by [56].
Sustainability 2019,11, 2674 15 of 23
Even if we managed to guarantee a long-term duration, with several participants at least
comparable to similar app-based persuasive interventions, the problem of attrition and abandonment
over time remains and it is worth being addressed, for at least three reasons:
it might endanger the internal validity of the experiment itself: if abandonment in the control
and the treatment groups is not randomly distributed, but follows specific, and different patterns,
such groups end up not to be comparable;
if the abandonments occur so frequently in the frame of a research experiment that they lead
at least some of the participants to feel morally obliged to remain active (as they declared in
the interviews), one can expect that the use of a GoEco!-like app in real life would be flawed
by even stronger drop-out rates, which would prevent attaining tangible benefits for urban
mobility problems;
it reduces the power of the experiment, i.e., if this is indeed the case, that the intervention has
been effective.
The difficulty of retaining participants’ interest for long periods of time, thus avoiding attrition
phenomena and guaranteeing internal validity of experiments, is widely acknowledged, particularly
in attempts to promote individual behaviour change by means of voluntary interventions [
Moreover, app-based interventions specifically suffer from the so-called “app-churn” phenomenon,
which affects the use of any kind of apps: in a few weeks, users lose interest in new apps, thus
precluding to fully exploit the behaviour change potential of its motivational mechanics [58].
To reduce experimental attrition, one should for instance reduce the operational burden on
app users for both GoEco! and GoEco! Tracker apps, limiting the need for manual validation of
the transport mode, as already indicated in the previous section: pre- and post-intervention data,
as well as counterfactual data, would be automatically collected, without specific efforts by the
experimental sample.
Moreover, to reduce app churn and foster retention of interest in the app, improvements in the
current GoEco! app features could be performed, and new components could be included, with the
aim of increasing the app’s attractiveness and creating additional reasons for individuals to use it.
For this purpose, we directly asked GoEco! participants to indicate which other features they would
have liked to find in future versions of the GoEco! app. The recommendations we collected through the
final GoEco! questionnaire and interviews, reported in detail in another work [
], can be summarized
as follows:
increase the frequency of push-notifications, provided that they are made more user-specific
and personal;
make the eco-feedback more intuitive and improve its connection to the specific user’s
value system (such as, if she values money more than the environment, provide her with
monetary feedback);
offer more occasions for social interaction and add “social network”-like features;
create the need for users to access the app more frequently, by integrating a multi-modal travel
planning component.
4.3. Representativeness of the Sample: Are We “Preaching to the Converted”?
Even though the final sample of GoEco! participants is small, the tests of our research hypotheses
produced statistically significant results in Ticino. The question arises, however, whether these
results can be generalized to the whole society—namely whether the external validity of the
GoEco! experiment is guaranteed or not. This is strictly related to the key design elements of
the GoEco! experiment, and particularly the voluntary nature of the experimental sample. It is
well-known that voluntary behaviour change processes are affected by polarization that endangers
the representativeness of the sample (volunteer selection bias [
]): self-selection recruitment
strategies tend to raise interest in already motivated subgroups of the general population, frequently
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individuals with high environmental awareness and pro-environmental attitude who may even have
already adopted sustainable consumption patterns, instead of the “mainstream car driver” citizens that
the intervention is expected to target. In opt-in frameworks, such as GoEco!, pro-environmental
individuals are stimulated to join, looking for confirmations about how good their behavior is,
while mainstream citizens simply ignore the invitations. Similar tendencies might occur during
the experiment, with higher drop-out rates by individuals with lower environmental awareness and
attitude. A self-selection bias has therefore to be taken into account.
Nonetheless, when experiments are fed by individual mobility data, and pre-existing monitoring
networks are not available, no opt-out strategy can be implemented (joining the experiment requires
active and voluntary activities by the individuals, starting from installing the mobility tracking app),
and no obligations to join can be put into force. Therefore, a self-selection of participants can barely be
avoided. It is important, however, to analyze its effects and its implications.
To investigate this aspect, we consider the sample of 52 GoEco! participants that remained active
until the end of the experiment (though similar conclusions would also emerge by analysing the
sample of GoEco! participants for which baseline data are available, namely the 212 participants at the
end of period A), and analyze their baseline mobility patterns (period A) against those of the average
population in Ticino and Zurich, as they are summarized by the Swiss Mobility and Transport Census
2015 [43].
In particular, we consider the daily average kilometers travelled (29.17 km/day in Ticino and
34.94 km/day in Zurich) and the daily average percentage of kilometers travelled by private motorized
transport modes (PMT, namely car and motorbike, equal to 74% in Ticino and 57% in Zurich),
and classify each participant into one of the following categories:
soft eco: uses PMT less than average, however travels more than daily average;
hard eco: uses PMT less than average and also travels less than daily average;
soft private motorised: uses PMT more than average, however travels less than daily average;
hard private motorised: uses PMT more than average and also travels more than daily average.
The result of this classification indicates that the subsample of n = 21 active participants in Zurich
is biased towards lower use of public motorized transport (PMT, i.e., car and motorbike) than the
average population of Zurich: baseline data shows 81% of them can be classified as “eco”. Considering
the subsample of the n = 31 active participants in Ticino, instead, the percentage of “eco” individuals is
only equal to 48%. The amount of daily travelled kilometers is more evenly distributed, both in Zurich
and in Ticino, with a predominance of participants travelling more than average (Figure 4).
From this point of view, therefore, the sample of GoEco! active participants in Ticino seems to be
better comparable to the average population than the Zurich one, which suggests that external validity
is better met in Ticino than in Zurich. Together with the fact that statistical significant results were
found for test of hypothesis H2 in Ticino, this strengthens the conclusion that, in Ticino, a new policy
measure targeting the large scale use of the GoEco! app could be beneficial as a policy measure for
the transition towards more sustainable mobility. Regarding Zurich, instead, on the one hand this
can explain why no significant effects were found, when testing our H1 and H2 hypotheses: since the
majority of participants were already using the car less than average, possibilities for further reducing
car use were low, and most likely limited to an increase in soft mobility. On the other hand, this shows
that during recruitment of GoEco! project participants in Zurich we did not fully manage to raise the
interest of average citizens, and especially of our “mainstream car drivers” target individuals: our
initial expectation was to mostly address “private motorized” individuals, with the aim of turning
them into “eco” individuals.
We explain such a polarization in the group of Zurich participants by acknowledging that the
overall GoEco! approach is probably more effective in raising the interest of individuals already
characterized by higher pro-environmental awareness than average, who are more likely to have
already implemented more sustainable lifestyles, also in their everyday mobility patterns. In Zurich,
where high quality infrastructures for alternatives to car use are available, this attitude of the sample
Sustainability 2019,11, 2674 17 of 23
would then tangibly translate into more sustainable mobility patterns than average, while in Ticino an
attitude-behaviour gap [
] would appear, and, lacking valid alternatives to car, individuals would
tend to behave more similarly to the average population.
Figure 4.
Baseline mobility patterns of the n = 52 active GoEco! participants, against average mobility
patterns of the population in Zurich and Ticino, as indicated by the 2015 Swiss Mobility and Transport
Census data.
This hypothesis is corroborated by responses to the final GoEco! questionnaire, explicitly aimed
at assessing the pro-environmental attitude of the participants. In total, 48 out of the above 52 final
GoEco! active participants answered these questions, indicating a medium to high pro-environmental
Sustainability 2019,11, 2674 18 of 23
attitude (higher in Ticino than in Zurich), and also the feeling of a personal responsibility to control
pollution and climate change, both in Ticino and in Zurich (Table 5).
Table 5.
Pro-environmental attitude of the active GoEco! participants, based on the final questionnaire
(48 respondents out of 52 active participants). A 7-point Likert score is used, where 1 = “Totally disagree”
and 7 = “Totally agree”.
Ticino (n = 29) Zurich (n = 19)
M (SD) M (SD)
Climate change is a problem for society 6.59 (.87) 6.32 (.82)
Saving energy helps to limit climate change 6.31(.97) 5.90 (1.20)
The quality of our environment will improve if we use less energy 6.41 (.98) 6.32 (.82)
I feel responsible for pollution and climate change: it is not just a
matter of governments and industries 5.72 (1.64) 5.47 (1.12)
I try to use the car as little as possible 5.59 (1.32) 5.16 (1.34)
In this framework, a strategy to raise the interest of mainstream citizens might be to explicitly
include tangible and/or monetary rewards, namely to rely on extrinsic motivational factors much
more than we have done in the GoEco! experiment. Such a strategy might be useful both for future
experiments (as a way to engage a representative sample of participants) and outside them, when
using the app within voluntary behaviour change programmes. In the latter case, tangible prizes could
be exploited to start engaging people with low intrinsic motivation for change, and could gradually be
replaced by other motivational factors, as long as individuals progress through the stages of behaviour
change, under the effect of GoEco!.
A totally different approach would consist in trying to replace the voluntary, opt-in framework
by joining forces with possible other companies that are already collecting mobility data with the
needed fine-grained accuracy, and are already used by a wide and diverse amount of people. Namely,
one could create a partnership with companies offering other app-based services that already perform
location tracking, such as Google, whose app is currently automatically activated in many smartphones
as a default option, or REGA, which is a Swiss rescue agency that recently launched an app individuals
can use to send alarms in case of emergencies, transmitting their coordinates to the rescue centers in
Switzerland and abroad ( Alternatively, partnerships could be created with
any of the existing carpooling or car-sharing companies or, more generally, with companies offering
Mobility-as-a-Service solutions [
], requiring their customers to enable location tracking services.
Such a collaboration would not only simplify the location tracking, but also allow integrating their
services into the Gamification part, e.g., by rewarding the use of carpooling. Note, however, that most
of their customers are already “converted” users of public transport or innovative mobility services,
which would still provide a biased sample.
Depending on the partner company, one would either collect already processed mobility data
(such as routes and transport mode used, as provided by Google Maps), or just GPS trackpoints
(such as those provided by the REGA app), to be then post-processed in order to identify the mobility
patterns. In both cases, the collected data would only be reliable if the already indicated improvement
in automatic mobility detection has taken place, since users would provide no route validation. Users
would in fact only be requested to manually export the collected data and/or to give their consent to
access it, and in exchange for this a reward could be offered.
This way, less biased data for the control group, as well as pre- and post-intervention data for
the treatment group could be collected, by randomly assigning all users of the partner service to the
experimental groups. For the intervention itself, instead, the partner company should be willing to
offer new GoEco!-like mobility persuasive features and components, within their own app, and offer
them to all the customers of the treatment group, within an opt-in framework. Such an agreement
would be more difficult to negotiate, but it is worth exploring it. While in fact the inclusion of behaviour
change features would look odd in REGA-like apps, it would quite fit Google, which is increasingly
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providing timely and customised information regarding available mobility options. Also in the case
of such a partnership, however, limitations to validity of the experiment would emerge, particularly
in terms of internal validity and attrition, since the control on who is using the partner app would
be very limited—however possibly compensated by a definitely larger number of users. Ultimately,
further research is needed to assess overall reliability and feasibility of this approach.
5. Conclusions
In this paper, we have presented GoEco!, a soft policy measure aimed at reducing car use in urban
areas, thus providing direct benefits on transport-related energy consumption and CO
local pollution, safety, and soil sealing, to cite the main domains currently impacted by dominant
car use. Acknowledging the large potential of coupling ICTs with voluntary behaviour change
approaches, we developed the GoEco! mobile app aimed at persuading individuals to reduce their car
use and opt for already available more sustainable transport modes. Such an app performs automatic
mobility tracking, exploits information feedback elements, provides users with guidance on existing
alternatives and promotes a social comparison of their performance, within a gamified framework.
The effectiveness of GoEco! was tested in two different urban contexts in Switzerland: the region of
Zurich, a dense urban area, characterized by very high quality public transport and cycling lanes, and
the region of Ticino, characterized by urban sprawl and less efficient public transport and cycling
infrastructures instead. The test engaged several voluntary citizens from both regions in a one year
long randomized controlled experiment.
The experiment started with 573 voluntary participants, but ended with data of sufficient quality
for less than 10% of the sample of participants at sign-in. Notwithstanding such a dramatic reduction in
sample size, GoEco! was found to bring about a statistically significant change in systematic individual
mobility patterns, reducing both average energy consumption and CO
emissions per kilometer of
“systematic loops”, in a context where car-dependency is deeply rooted, such as the Swiss Ticino region.
In Zurich, where individual mobility patterns are already optimized and car-dependency is lower,
no significant effects were found. Also, external validity of the experiment was better met in Ticino
than in Zurich, which leads to strengthen the conclusion that in contexts where car-dependency is
still high, such as Ticino, new policy measures targeting the large scale use of the GoEco! app could
be beneficial for the transition towards more sustainable mobility. Instead, in contexts where public
transport is already widely used, such as Zurich, persuasive interventions might be more effective
if, instead of targeting generic reductions in car use, they explicitly target the increased use of soft
transport modes. In particular, by providing bicycle and walking-friendly suggestions for alternatives
and weather-aware personalized recommendations, challenges and badges, GoEco!-like persuasive
apps could effectively complement the launch of new infrastructural measures aimed at promoting
bicycle use and/or support programmes promoting walking and cycling for public health reasons.
The GoEco! experiment provided an example of performing a randomized controlled trial in
the field of mobility, showing how current limitations in automatic mobility tracking still affect the
practical possibility to develop fully internally and externally valid experiments.
In particular, currently unresolved limitations related to the obtrusiveness of the data collection
tool, the need for voluntary enrollment in the experiment, which lead to limited representativeness
of the sample of the Zurich region, and the attrition in the experimental sample, suggest to carefully
handle these results and indicate the need for further investigations along this line of research.
In particular, future works might be aimed at quantitatively investigating if and how the potential
behaviour change is affected by initial individual mobility patterns and environmental attitudes.
Given that improved automatic transport mode detection capabilities are available, for future
interventions we recommend to enlarge the duration of the tracking periods and discard the initial
“onboarding” data, and to offer tangible prizes. Furthermore, we suggest to explore the possibility to
partner with already existing companies that offer app-based services requiring their users to enable
location tracking. Partnering with them would allow collecting data less affected by volunteer selection
Sustainability 2019,11, 2674 20 of 23
bias, for both the treatment and the control group, better meeting the requirements for external validity
of the experiment, and might allow the direct integration of strongly ICT-supported mobility concepts
such as carpooling or car-sharing. However, such an approach might lead to lower internal validity
of the experiment, due to reduced possibilities to guarantee retention over time of the sample of
participants, and therefore calls for in-depth investigation in future research.
Author Contributions:
Conceptualization, F.C., R.R., M.R.; Methodology, F.C., D.B., F.M.; Formal Analysis,
D.B., F.M., J.V.S., F.C.; Writing—Original Draft Preparation, F.C.; Writing—Review & Editing, D.B., F.M., J.V.S.;
Supervision, R.R., M.R.
This research was supported by the Swiss National Science Foundation (SNF) within NRP 71 “Managing
energy consumption” and is part of the Swiss Competence Center for Energy Research SCCER Mobility of the
Swiss Innovation Agency Innosuisse.
We thank all the researchers involved in the GoEco! project who do not figure among the
authors of this paper, as well as the supporting institution that helped us throughout the project. We are also very
grateful to all the citizens who actively engaged with us in the GoEco! field activities in Zurich and Canton Ticino.
Conflicts of Interest:
The authors declare no conflict of interest. The founding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
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We are now living in a mobile information era, which is fundamentally changing science and society. Location Based Services (LBS), which deliver information depending on the location of the (mobile) device and user, play a key role in this mobile information era. This article first reviews the ongoing evolution and research trends of the scientific field of LBS in the past years. To motivate further LBS research and stimulate collective efforts, this article then presents a series of key research challenges that are essential to advance the development of LBS, setting a research agenda for LBS to ‘positively’ shape the future of our mobile information society. These research challenges cover issues related to the core of LBS development (e.g. positioning, modelling, and communication), evaluation, and analysis of LBS-generated data, as well as social, ethical, and behavioural issues that rise as LBS enter into people’s daily lives.
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In recent years, persuasive interventions for inducing sustainable mobility behaviours have become an active research field. This review paper systematically analyses existing approaches and prototype systems as well as field studies and describes and classifies the persuasive strategies used for changing behaviours in the domain of mobility and transport. We provide a review of 44 papers on persuasive technology for sustainable transportation aiming to (i) answer important questions regarding the effectiveness of persuasive technology for changing mobility behaviours, (ii) summarize and highlight trends in the technology design, research methods, strategies and theories, (iii) uncover limitations of existing approaches and applications, and (iv) suggest directions for future research.
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With the emergence of ubiquitous movement tracking technologies, developing systems which continuously monitor or even influence the mobility behaviour of individuals in order to increase its sustainability is now possible. Currently, however, most approaches do not move beyond merely describing the status quo of the observed mobility behaviour, and require an expert to assess possible behaviour changes of individual persons. Especially today, automated methods for this assessment are needed, which is why we propose a framework for detecting behavioural anomalies of individual users by continuously mining their movement trajectory data streams. For this, a workflow is presented which integrates data preprocessing, completeness assessment, feature extraction and pattern mining, and anomaly detection. In order to demonstrate its functionality and practical value, we apply our system to a real-world, large-scale trajectory dataset collected from 139 users over 3 months.
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--- Open Access --- Mobility as a Service (MaaS) is a recent innovative transport concept, anticipated to induce significant changes in the current transport practices. However, there is ambiguity surrounding the concept; it is uncertain what are the core characteristics of MaaS and in which way they can be addressed. Further, there is a lack of an assessment framework to classify their unique characteristics in a systematic manner, even though several MaaS schemes have been implemented around the world. In this study, we define this set of attributes through a literature review, which is then used to describe selected MaaS schemes and existing applications. We also examine the potential implications of the identified core characteristics of the service on the following three areas of transport practices: travel demand modelling, a supply-side analysis, and designing business model. Finally, we propose the necessary enhancements needed to deliver such an innovative service like MaaS, by establishing the state of art in those fields.
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Information and communication technologies (ICT) have considerable importance for transport systems, as they provide access to travel information, planning tools, opportunities to share transport modes, to work at-a-distance, compare transport mode cost, make payment, improve safety and health, and to communicate travel patterns. Over the past decade, there has been massive growth in the availability of transportation ICT, in particular smartphone applications. There is considerable evidence that ICT has profoundly changed the ways in which transport systems are perceived and used, and mobilities performed; with far-reaching implications for transport mode choices and transport demand. Against this background, the paper seeks to conceptualize ICT with relevance for transport systems, and to discuss the implications for the environmental sustainability of the transport sector. Findings suggest that while some ICT innovations foster and support sustainable transport choices, others raise new and significant barriers to more sustainable transport futures.
What if every part of our everyday life was turned into a game? The implications of “gamification.” What if our whole life were turned into a game? What sounds like the premise of a science fiction novel is today becoming reality as “gamification.” As more and more organizations, practices, products, and services are infused with elements from games and play to make them more engaging, we are witnessing a veritable ludification of culture. Yet while some celebrate gamification as a possible answer to mankind's toughest challenges and others condemn it as a marketing ruse, the question remains: what are the ramifications of this “gameful world”? Can game design energize society and individuals, or will algorithmicincentive systems become our new robot overlords? In this book, more than fifty luminaries from academia and industry examine the key challenges of gamification and the ludification of culture—including Ian Bogost, John M. Carroll, Bernie DeKoven, Bill Gaver, Jane McGonigal, Frank Lantz, Jesse Schell, Kevin Slavin, McKenzie Wark, and Eric Zimmerman. They outline major disciplinary approaches, including rhetorics, economics, psychology, and aesthetics; tackle issues like exploitation or privacy; and survey main application domains such as health, education, design, sustainability, or social media.