Beyond Limitations of Current Behaviour Change
Apps for Sustainable Mobility: Insights from
a User-Centered Design and Evaluation Process
Francesca Cellina 1,*ID , Dominik Bucher 2ID , José Veiga Simão 1, Roman Rudel 1and
Martin Raubal 2ID
1Insitute for Applied Sustainability to the Built Environment, SUPSI, Via Trevano,
6952 Canobbio, Switzerland; firstname.lastname@example.org (J.V.S.); email@example.com (R.R.)
2Institute of Cartography and Geoinformation, ETH Zurich, Stefano-Franscini-Platz 5,
8093 Zürich, Switzerland; firstname.lastname@example.org (D.B.); email@example.com (M.R.)
*Correspondence: firstname.lastname@example.org; Tel.: +41-058-666-6261
Received: 22 March 2019; Accepted: 11 April 2019; Published: 16 April 2019
How can we encourage people to make sustainable mobility choices, reducing car
dependency and the related CO
emissions and energy consumption? Taking advantage of the wide
availability of smartphone devices, we designed GoEco!, an application (app) exploiting automatic
mobility tracking, eco-feedback, social comparison and gamiﬁcation elements to persuade individuals
to change their mode of transport. The app features and contents are grounded in the transtheoretical
model for behaviour change and were designed to avoid over-reliance on “one-size-ﬁts-all”, simplistic
point-based systems. The GoEco! app was designed in a user centred approach and was ﬁeld-tested
in Switzerland in a three-month experiment involving around 150 voluntary users. In this paper, we
present the app’s features and comment on their evaluation from the perspective of the ﬁeld-testers.
The insights we collected through an online questionnaire and individual interviews allowed
us to develop recommendations for similar persuasive apps and to identify open challenges for
the future. In particular, we recommend to endow such apps with multi-modal travel planning
components and features evoking the feeling of belonging to a community, that provide support and
Keywords: persuasion; eco-feedback; gamiﬁcation; app; mobility patterns; user-centered design
To counteract local and global problems associated with the deep-rooted car dependency [
and lock-in in the “automobility system” [
] currently affecting most urban areas, cities seek to improve
mobility alternatives to cars and promote the use of a mix of transport modes, facilitating the break
of car-dependent habits [
] and encouraging a higher uptake of public transport and soft mobility
transport modes. Leveraging cognitive-motivational aspects [
], voluntary travel behaviour change
] were introduced, targeting a change in individual mobility choices [
]. The growing
diffusion of Information and Communication Technologies (ICTs) in the transportation ﬁeld [
and the parallel emergence of smart [
] and responsive [
] city programmes offer opportunities to
design novel cognitive-motivational tools supporting individuals in the adoption of more sustainable
mobility patterns. Particularly, under the inﬂuence of the captology framework (the study of Computers
As Persuasive Technologies, CAPT) [
], a fast growing body of literature is exploring the potential of
coupling persuasion techniques with ICTs, and smartphone apps were identiﬁed as very promising
tools, due to their capability to provide users with real-time and bi-directional interaction possibilities.
Sustainability 2019,11, 2281; doi:10.3390/su11082281 www.mdpi.com/journal/sustainability
Sustainability 2019,11, 2281 2 of 26
1.1. Persuasion for Behaviour Change by Smartphone Apps
Smartphone apps persuading users to engage in more sustainable behaviour were at first developed
to promote electricity and water savings, accompanying the roll-out of smart meter devices to measure
real-time electricity and water consumption (among others, see [
]). Such apps provide users
with feedback on consequences of their choices (usually, in terms of energy consumption and CO
emissions), invite them to define personal goals for change, engage in challenges and compare their
performances with other users, often also exploiting the users’ social network relationships [
Frequently, such apps also exploit gamification, which is usually defined as “the use of game elements
in non-gaming contexts” ([
], p. 1). Typical gamification mechanics and elements are competition,
cooperation, assignments, quests, goals, points, levels, badges, and leaderboards .
Persuasive apps within the electricity and water domains are fed by consumption data provided by
ﬁxed metering infrastructures. Automatic monitoring of individual mobility patterns, instead, requires
a new, dedicated, and ﬂexible tracking system, able to follow individuals along their movements.
In the last decade, pilot projects aimed at automatic mobility data tracking through smartphone apps,
GPS devices and sensors embedded in smartphones were developed [
], and, thanks to the fast
progress in the quality of automatic mobility tracking, since then several persuasive apps promoting
mobility behaviour change have emerged [49–54].
1.2. Recommendations for Effective Persuasive Apps
Persuasive apps can be classified under the concept of Behaviour Change Support Systems (BCSS),
introduced by Oinas-Kukkonen ([
], p. 1225) to refer to “information systems designed to form, alter or
reinforce attitudes, behaviours or an act of complying without using deception, coercion or inducements”.
Even though persuasive, gamified app-based BCSSs are frequently popping up, their development is
still a young endeavour. At a general level, Froehlich and
Anagnostopoulou et al. [56,57]
number of recommendations for persuasive, gamified systems, which can be summarised as follows:
Provide information: Information should refer to available transport alternatives tailored to the
individual’s needs, interests or living context. It should be speciﬁcally related to the individual’s
behaviour and be as timely as possible (close to the triggering cause, in both space and time), thus
being easier to understand and remember.
Provide goal setting opportunities: Allowing individuals to select their own goals and targets for
change can have powerful effects, since, when selected targets are really challenging for the
individual, they create a self-competitive context leading the individual to strive for personal
progress and mastery (intrinsic motivation for change ).
Provide feedback: Since individuals require a baseline to assess their performance and progress
over time, providing feedback is complementary to and essential for goal setting activities.
Provide rewards (incentives) or punishment (disincentives): These can be either tangible or intangible,
expressed in monetary terms or in physical units, and need to be strictly related to the individual’s
performance. Rewards of good performances can reinforce individual motivation to adopt a
certain behaviour, while punishment of poor performances can stimulate strengthening individual
efforts. Punishment has however to be handled carefully, since it might quickly demotivate .
Provide occasions for social comparison: Offer individuals the opportunity to compare their choices
and performances against other people or groups they perceive as similar to themselves, such as
members of the same community. This generates both peer pressure and a desire for imitation.
Detailed practical suggestions are also provided by the framework for persuasive systems
design (PSD) [
] and further practical guidance is offered by the comprehensive, theory-linked
taxonomy of twenty-six behaviour change techniques developed by Abraham and Michie [
favour effectiveness, standardisation and replicability of behaviour change interventions.
Sustainability 2019,11, 2281 3 of 26
1.3. Limitations of Current Persuasive Apps
Reviews of key characteristics and limitations of current persuasive apps have already been
performed, also with speciﬁc reference to apps in the mobility sector [
], and several aspects
were identiﬁed to critically affect their overall behaviour change impact.
Above all, similar to voluntary travel behaviour change interventions and soft mobility policies
in general, persuasive apps were frequently seen to lack grounds in a proper behaviour change theory,
which is supposed to reduce their practical behaviour change effectiveness [
]. The same
scholars cast doubts on the claims of effectiveness by their proponents, since they found that most
assessments of such app-based interventions were lacking scientiﬁc rigour, due to limited adoption of
experimental design (randomised controlled trials), the “gold standard” for interventions.
Further, common approaches to the provision of feedback, rewards and goals were questioned.
Most of the existing persuasive apps in the mobility ﬁeld, in fact, rely on a point system, usually
allowing for points to be redeemed for real-life goods and services. Points are attributed whenever the
detected mobility choices of a user are coherent with a given set of rules, generally in proportion to
the number of kilometres travelled with soft transport modes (walking and cycling) and/or public
transport. This implies that going for a bicycle ride during leisure time, while continuing to use
the car for work commutes, would reward users with points: as remarked by Froehlich [
point systems might encourage people to take more trips simply to earn more points, paradoxically
ending up worsening their mobility impact, instead of improving it. In addition, other scholars
remarked that using the same point-attribution rules for all users fails to acknowledge that there is
no “one-size-ﬁts-all” solution [
] and that the persuasive effectiveness of a given tool is strictly
dependent on individual baselines, viable alternatives, daily needs and constraints, besides individual
attitudes and perceptions. Finally, the dominant point-based approach was criticised for its inherent
technology patronising and elitist vision [
], i.e., designers of persuasive systems apparently know
what is always good and right, while ordinary people do not.
Against this background, it was recommended to rethink the currently dominant point-based
rewards system, with the aim of giving app users as much freedom and customisation as
. For instance, users should be allowed to freely choose their own goal and target
for change, by independently deciding if and how much they would like to change. Then, feedback
and rewards should explicitly be connected to progress regarding the target they autonomously set
1.4. Research Objectives and Contents of the Paper
While the above limitations currently affecting persuasive apps have already been identiﬁed,
how to effectively overcome them is still an open research question. Moreover, additional research
should be performed with the aim of directly exploring the perspective of app users to identify further
limitations and shortcomings, as well as recommendations for improvement that could inform future
development of persuasive apps.
We took on these research challenges within the Swiss-based GoEco! project, aimed at developing
a smartphone app that persuades users to reduce their car use and transition to low carbon and energy
efﬁcient transport modes (walking, cycling and public transport). Following the above literature,
in GoEco! our speciﬁc research objective was twofold:
designing the GoEco! persuasive app so as to overcome the above identified limitations, particularly
the lack of grounding in a behaviour change theory and the over-reliance on “one-size-fits-all”
point-based reward systems; and
testing it in real-life settings and identifying additional shortcomings and recommendations for
future works, from the perspective of its direct users.
In this paper, we present the methodology we followed to achieve these objectives (Section 2)
and the results we obtained, in terms of the GoEco! persuasive app contents and features (Section 3).
Sustainability 2019,11, 2281 4 of 26
Then, we discuss the evaluation of the GoEco! app effectiveness according to real-life, volunteer users,
who experimented it in a three-month ﬁeld test run in two Swiss regions (Section 4). We conclude
by summarising key suggestions and recommendations for future apps aimed at persuading more
sustainable mobility patterns in similar, wealthy countries, and commenting on remaining open
research challenges (Section 5).
The methodology we followed to achieve the above research objectives is sketched in Figure 1: first,
we designed the app’s persuasive contents and features (Phase 1), then we field tested it in a three-month
real-life intervention (randomised controlled trial, Phase 2), and, finally, we evaluated its contents and
features (Phase 3). Throughout these stages, we always adopted a user-centred perspective.
Figure 1. The methodology followed to design and evaluate the GoEco! app.
2.1. User Centered Design
As a ﬁrst step, we selected a relevant behaviour change theory, to which we could anchor the
app’s features and components. Our understanding of behaviour change processes was framed in
the transtheoretical model [
]: individual behaviour change does not happen as a single event,
instead it occurs through a number of processes that lead the individual to progress along a set
of cognitive and motivational stages, from pre-contemplation to maintenance of change. Therefore,
persuading an individual to change her behaviour actually requires different processes, depending
on the speciﬁc stage she is in—and a persuasive app such as GoEco! should be capable of providing
each user with the most appropriate set of features and components to facilitate the activation of
each speciﬁc process. To design such app features and components, we opted for a user centred
design approach [
]. Following the authors of [
], we developed a “user persona” proﬁle
for each stage of the behaviour change process towards the reduction of car use. Such personas
were ﬁctitious user characters, designed by explicitly referring to individuals we had observed and
interacted with during a previous app-based mobility tracking intervention run in the Italian-speaking
part of Switzerland [
]. We then developed “persona stories” [
], namely descriptions of when the
above personas would interact with the GoEco! app, why, and how. In developing such stories, we
referred to the motivational techniques suggested by Prochaska and Velicer [
] for each behaviour
change stage, by the framework for persuasive systems design [
] (as later revised by Sunio and
) and by the taxonomy for behaviour change interventions [
]. In parallel, we also
designed the app user interface elements.
Sustainability 2019,11, 2281 5 of 26
As soon as a sufﬁciently accurate app paper prototype was available, it was tested through
in-depth interviews with four possible app users, each of whom was in a different stage of the
behaviour change process. Interviewees were voluntary-recruited colleagues, not engaged in our
speciﬁc research ﬁeld to reduce self-selection bias, and attributed to a speciﬁc behaviour change stage
according to a screening question by Bamberg [
] (people in the action and maintenance stages were
considered equivalent, since they are affected by similar processes for change).
2.2. Field Test
Based on feedback from these interviews, the app’s components and features were reworked
and updated, and then the paper prototype was turned into a software prototype, on which testing
and bug ﬁxing activities were performed. The resulting GoEco! app was subsequently ﬁeld-tested in a
three-month randomised controlled trial, run between October 2016 and January 2017 in two Swiss
cantons (Ticino and Zurich, in the Italian- and German-speaking parts of Switzerland, respectively) [
The experiment involved 212 self-selected individuals, who voluntarily answered a public call to
participate in the GoEco! ﬁeld test. Two thirds of them (145 individuals, 69 in Ticino and 76 in Zurich)
were invited to test the GoEco! app (treatment group), while one third of them (67 individuals, 34 in
Ticino and 33 in Zurich) was only passively tracked to gather counterfactual mobility data (control
group). The call was spread through a press-conference with local communication media (radio,
television and newspapers), paid and regular communications on social media channels, and an
in-person event directly targeting interested citizens. Throughout the trial, a number of drop-outs
occurred, so that the number of active testers of the GoEco! app decreased from 145 to 47 (27 in Ticino
and 20 in Zurich). Such self-selection recruitment processes are typically affected by a volunteer
selection bias [
], which in turn might affect the possibility to generalise the project results. It is in
fact well-known that opt-in frameworks such as the one adopted in GoEco! tend to raise the interest of
already motivated subgroups of the general population, thus tending to engage individuals with higher
than average environmental awareness and pro-environmental attitude. Nonetheless, in interventions
such as GoEco!, which require active and conscious engagement with a smartphone app, no obligations
can be put into force and no opt-out strategies can be implemented. Therefore, a self-selection of
participants can barely be avoided and a related bias has to be taken into account. Similar tendencies
might also have occurred throughout the app ﬁeld test, with higher drop-out rates by individuals with
lower environmental awareness and attitude. A questionnaire targeting all initial project participants
attributed to the treatment group (145 individuals) showed that the two major reasons for leaving the
project were related to the need for conﬁrmation of the mobility data automatically tracked (validation,
see Section 3.1) and technical problems precluding, from time to time, the app from fully working.
2.3. User-Centred Evaluation
The evaluation of the experiment from the participants’ perspective was performed by a mixed
quali-quantitative survey, consisting of an online questionnaire and individual interviews.
Six months after the trial (between June and July 2017), a questionnaire was administered to the
project participants to explore how they evaluated the app’s persuasive features and components.
The questionnaire was created with the Google Forms platform and its link was sent to all project
participants via email. No obligations to answer were set, instead incentives were made available,
through a ﬁnal draw among respondents, offering 20 prizes of the value of 50 Swiss francs each
(vouchers for Swiss retailers, public transport tickets, or charity donations). The questionnaire consisted
of nine parts, for an estimated 15 minutes answering time and a total of 32 multiple-choice questions,
11 of which were mandatory to complete the questionnaire. Overall, 45 users responded to the
questionnaire (though only 21 of them answered all questions, including non-mandatory ones),
and the collected data were analysed through descriptive statistics techniques, via the IBM SPSS
Sustainability 2019,11, 2281 6 of 26
Moreover, ten months after the end of the ﬁeld experiment (December 2017), 20 respondents of the
questionnaire were randomly selected for an additional in-person, semi-structured interview, aimed at
in-depth discussion and collection of bottom-up recommendations for future similar persuasive apps.
Users accepting to join the interview were rewarded with a 50 Swiss francs voucher, and in the end
19 interviews were performed (11 in Ticino and 8 in Zurich). The interviews were held in the ofﬁcial
language of each canton (Italian in Ticino and German in Zurich). They were recorded and detailed
reports were produced in their original language, while no full transcription of the recorded material
was performed. The reports were then analysed in a “grounded theory inspired approach” [82,83].
To avoid biases, two researchers independently analysed the reports and coded categories in
English, following the constant comparative method suggested by [
]: when semantically similar
category labels were identiﬁed, they were compared and discussed, and the most appropriate one was
selected. Once the list of categories was deﬁnitively established (in English), the interview reports were
read again and every occurrence of a category was registered in a matrix, together with a quotation
of the direct words of the participants, if they were especially clarifying. These quotations were ﬁrst
recorded in the original language and then translated into English, with the aim of keeping the original
meaning of the words of the interviewee as much as possible.
Finally, a cross-analysis of the elements emerging from questionnaire and interviews was
performed, which allowed us to reciprocally enrich and sustain the insights they individually provided.
A quantitative assessment of the effectiveness of the GoEco! app in producing tangible behaviour
change in terms of individual mobility patterns and the related impact on energy consumption and
emissions was performed as well, through analysis of the mobility data directly tracked by the
app in the GoEco! randomised controlled trial (comparison before/after the intervention, with respect
to a similar control group).
3. Results: An App Supporting Behaviour Change Step by Step
In this section, we summarise the results of the app design activities performed in Phase 1,
by presenting the GoEco! app’s components and features. Since they were developed with the aim of
fostering progress from a speciﬁc stage of the behaviour change process to the next one, we introduce
them from the perspective of each behaviour change stage: pre-contemplation (of change),contemplation,
preparation,action,maintenance, and termination. To facilitate comparison with other (app-based) BCSSs,
we also refer to the framework for persuasive systems design [
] and the taxonomy for behaviour
change interventions [
]: Table 1shows an overview of all GoEco! features and components, allowing
to frame them in terms of both the theoretical background and the persuasive principles and techniques
Features and components of the GoEco! app, with respect to the stages and processes of change
identiﬁed by the transtheoretical model [
], the techniques for behaviour change interventions by
Abraham and Michie , and the framework for persuasive systems design [59,62].
Stage of Change Processes of Change
Increase awareness for
and cues about a
Feedback on each
travelled route and
1. Provide general
2. Provide information
13. Provide feedback on
Cognitive and affective
assessment of one’s
self-image, with and
without a particularly
8. Provide instructions
9. Model or demonstrate
Sustainability 2019,11, 2281 7 of 26
Table 1. Cont.
Stage of Change Processes of Change
The belief that one can
change and commitment
to act on such a belief
4. Prompt intention
10. Prompt speciﬁc goal
Action and Maintenance
Learning of more
that can substitute the
less sustainable ones
Challenges 7. Set graded tasks
8. Provide instructions
Weekly report of
13. Provide feedback on
(rewards) for taking
steps in a particular
Trophies and Badges
13. Provide contingent
Hall of Fame
opportunities for social
Social support (care,
acceptance and general
support) for new
to stimulate action
6. Provide general
17. Prompt practice
outside the app
6. Provide general
3.1. Pre-Contemplation Stage—Getting Feedback on Individual Baselines
App users in the pre-contemplation stage have no motivation for reducing car use and do not intend
to take action to change their daily mobility patterns. This might be due to insufﬁcient information
about their possibilities for change or to a lack of trust in their ability to change. To support users
towards change, at this step, Prochaska and Velicer
suggested implementing consciousness raising.
In GoEco!, this is performed by increasing users’ awareness about consequences and practical mobility
options available to them: the app provides users with an automatic feedback on each route they
travel, in terms of distance, time, energy consumption, and CO2emissions (self-monitoring).
To provide such feedback, GoEco! tracks individuals’ mobility data. For this purpose, it exploits
the Application Programming Interface (API) of the commercial, free ﬁtness tracker app Moves [
(discontinued since July 2018), that records users’ positions, segments travelled paths into “routes”
and “activities”, and automatically determines whether they are walking, running, cycling, or taking
another mode of “transport”. Building on this information, new algorithms developed speciﬁcally
for GoEco! further classify the generic “transport” activities identiﬁed by Moves, so that also bus,
train and tram mode activities are automatically detected. To produce the needed ﬁne-grained
distinction between these different transport modes, a classiﬁer based on a naïve Bayes algorithm [
was built, which takes into account several route characteristics, such as travel speed, acceleration,
or spatiotemporal dependencies between visited points and the public transport network (stops and
lines). The whole algorithm is presented in detail in [
]. As Moves data are updated at unknown
points in time, GoEco! is not capable of providing real-time feedback. Instead, users are encouraged to
interact with it once a day, with the purpose of checking and validating the automatically detected
Sustainability 2019,11, 2281 8 of 26
transport mode for every activity tracked on the previous day (Figure 2a,b). While performing this
validation, they receive the feedback on both transport-related indicators and related impacts.
The impacts are expressed in terms of primary energy consumption (kWh) and CO
emissions and are based on the Mobitool consumption and emission factors [
], which depend on
the mode of transport, refer to a single kilometre travelled in Switzerland and take into account
the consumption and emissions of the full life-cycle, by considering an average vehicle occupancy.
The speciﬁc values are presented in [
]. For electricity-fuelled transport modes, such as trains, these
factors heavily depend on the involved power generation systems (e.g., power from renewable sources
leads to much fewer CO
emissions than power generated by fossil-fuel power plants). As such, these
factors and the resulting energy consumption and CO
emission values are speciﬁc to Switzerland.
To apply the framework to other countries, their power generation systems have to be analysed and
taken into account accordingly.
Regarding cars, the Mobitool values reﬂect the powertrain composition of the Swiss vehicle
ﬂeet of passenger cars. To get more meaningful feedback, GoEco! app users can enter the average
fuel consumption of their car (expressed in fuel litres per 100 km), otherwise GoEco! automatically
considers the Mobitool default values. Electric cars are not automatically identiﬁed by the system,
since GoEco! has no elements to distinguish them from internal combustion engine cars; however,
whenever anyone uses an electric car, they can manually select it when validating the transport mode,
therefore GoEco! can account for the related energy consumption and CO2emission factors.
Equally, the systems is not capable of automatically detecting the number of users travelling
in the same car, therefore ridesharing or car-pooling routes cannot be accounted for by the app.
Acknowledging these limitations, therefore, the feedback on one’s own mobility impact is meant as a
reference, for comparison with the other transport modes.
A selection of screens of the GoEco! app: (
) list of tracked activities; (
) manual validation of
the transport mode; and (c) goal setting.
After four weeks of use of the GoEco! app, namely when a sufﬁcient amount of diverse mobility
data are gathered, GoEco! provides users with a summary of their personal “baseline mobility patterns”.
These baselines are expressed on a weekly basis and summarise the average kilometres travelled,
the time spent travelling, and the use of each transport mode in percent (aggregated into the following
categories: car, public transport, bicycle, walking, other). A richer and more detailed individual
Sustainability 2019,11, 2281 9 of 26
report is also made available outside the app and sent via email as a PDF document (see Figure 3a).
By offering the users a glimpse of their mobility patterns as a whole, the feedback on such baselines
allows reducing the complexity of processing each single route (reduction).
3.2. Contemplation Stage—Receiving Information on Individual Potentials For Change
The above feedback is expected to increase users’ awareness about pros and cons of changing
their behaviour, thus leading them to develop the intention to change in the near future. When this
happens, they enter the contemplation stage. To avoid users remaining at this stage, stuck in “chronic
contemplation” of elements in favour and against a change in their behaviour, GoEco! stimulates them
with an additional feedback on the available low-carbon alternatives for any systematic route they
travel and on their overall “potential for change”—a summary of how they could move, by always
choosing the available alternative route with the lowest CO2emissions (simulation, tailoring).
Leading users to perform a cognitive assessment of their self image, under both their “baseline”
car-dependent mobility patterns and their “potential” mobility patterns (that are more sustainable),
this feedback is expected to stimulate a self-reevaluation process, which, according to the transtheoretical
model, would in turn prompt progress to the preparation stage.
To estimate the “potential” mobility patterns, GoEco! processes the four-week data already used to
determine the “baseline” mobility patterns through additional algorithms that assess the feasibility of
replacing the transport mode of any travelled route with low carbon ones [
]. The resulting “potential
mobility patterns” are summarised in the GoEco! app and also in an external PDF report sent via email
(see Figure 3b), which also provides specific information about viable,
efficient alternatives for every
automatically identified “systematic loop”, namely for every home-to-home frequently travelled route.
A user’s baseline (
) and potential (
) mobility patterns, as provided in the PDF reports sent
Sustainability 2019,11, 2281 10 of 26
3.3. Preparation Stage—Setting Individual Goals For Change
Preparation is the crucial stage during which individuals develop plans for action, with the
intention of putting them into practice in the near future. To enhance self-liberation (the belief that one
can change and the commitment to act on such a belief), GoEco! stimulates app users to set their own
goals for change. Users are offered a choice among ﬁve goals, that are closely related to each other
and explicitly address the concept of change with respect to the GoEco! baseline mobility patterns:
Reduce car use,Increase slow mobility,Increase public transport (in percentages of the weekly kilometres
travelled), Reduce energy consumption, and Reduce CO2emissions.
Once they have chosen their favourite goal, users are invited to set the related target, by selecting
a value between their baseline mobility patterns and their potential for change through a slider
component, as shown in Figure 2c. The choice of such a target indicates the amount of change they
would like to achieve and is therefore related to the amount of effort they plan to invest in trying to
reduce their car-dependency. This choice is on purpose fully left to the users (personalisation), to avoid
any one-size-ﬁts-all, patronising or super-imposed solution. In fact, not only depending on the places
where they live and work, individuals have different access to car alternatives, but also, depending on
their family needs, may have different mobility requirements, which affect the practical feasibility of
the potential for change estimated by GoEco!: in some cases even small targets for change might imply
a signiﬁcant reorganisation of one’s individual and family routines and habits.
Finally, both the goal and the target value can be changed over time, thus allowing users to start
with relatively easy targets and to later increase difﬁculty (reduction), or to simplify their target, in case
they started with one that was too challenging. Users are therefore free to progress at their own pace
and in their own direction, while being stimulated by GoEco! to achieve their personal goal for change.
3.4. Action and Maintenance Stages—Practicing New Behaviours and Being Individually and
Having committed themselves to their own goal, to enter the action stage, users need practical
support and suggestions on how to perform the change (counter-conditioning, namely learning more
sustainable behaviour with respect to their current one). Then, they need to be rewarded for the efforts
they have performed in trying to put the new behaviour into practice (contingency management) and to
be shown that the new behaviour enjoys a favourable opinion at the social level (helping relationship
and social support).
As long as individuals practice with the implementation of the new behaviour, they enter the
maintenance stage, during which the need for external support decreases, they are less tempted by
relapse and more conﬁdent that they can maintain their change. To avoid relapse, which is always
possible and would lead individuals back to an earlier stage (in the worst case to pre-contemplation),
the same techniques as in the action stage can be put into practice—however, they will be needed less
frequently. The behaviour change process concludes when individuals reach the termination stage,
namely they have no temptation and the new behaviour is regularly put into practice. In such a
condition, the GoEco! app would no longer be needed and users could conﬁdently stop using it, having
managed to improve their mobility patterns.
3.4.1. Practicing New Behaviours and Getting Individual Rewards
To help individuals to achieve their goal step-by-step (reduction), GoEco! offers them to join
individual challenges that prompt them to put speciﬁc, more sustainable mobility choices into practice,
such as I will not use the car during peak hours for three days out of the next seven days,I will not use either
cars or planes for the whole weekend or this week I will travel all my short routes by slow mobility (Figure 4a).
By providing practical suggestions, challenges are particularly helpful in supporting individuals to
concretely step out of their mobility habits. These hands-on challenges help “unfreezing” speciﬁc
mobility choices that individuals were performing without any premeditation or deliberative reasoning
(automatic mental processes) while in the pre-contemplation stage, turning them into controlled mental
Sustainability 2019,11, 2281 11 of 26
processes, moved instead by intention, cognitive effort and awareness [
]. Users are free to join
the challenge(s) they prefer or ignore them. If they join one, GoEco! automatically assesses their
performances, rewarding them with a bronze trophy if they achieve it. Successfully repeating the
same challenge over time allows levelling up (rehearsal): the duration of the challenge increases from
one week to longer periods and its achievement is rewarded with higher-level trophies (silver, gold
and platinum). Another way to level up is to get engaged in similar, but more difﬁcult, challenges.
For example, after achieving the Three days without car in peak hours challenge, they could engage in the
Five days without car in peak hours challenge (tunnelling). Repetition is essential, since it helps users to
stay with their new mobility patterns and make them habitual.
GoEco! also provides users with surprise rewards (badges) if it detects they have spontaneously
performed sustainable mobility choices, such as “using the bicycle every day for at least five consecutive
days” or “travelling long trips by train”. Receiving an unexpected reward for such spontaneous actions
makes users aware of the good choices they have performed without any deliberate reasoning (activation
of a cognitive dissonance between their usual behaviour and their attitude [
]), thus stimulating them to
repeat the action in the future. Moreover, since they are unexpected, badges rekindle users’ commitment
to implement their action plan towards achievement of their goal. Note that GoEco! trophies and badges
are only virtual and no points to be redeemed for real-life prizes are available. This is because the
overarching aim of GoEco! is 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 .
Overall, in the action stage individuals need to consciously act to change their daily patterns.
To further support conscious action, GoEco! also provides users with a weekly notiﬁcation praising
them about progress towards their own goal (self-monitoring), which is visualised by a simpliﬁed bar
chart, where the height of the bar is proportional to the percentage of the goal having been achieved in
the past seven days (see Figure 4b). Further notiﬁcation elements are daily reminders to check and
validate the tracked routes, a weekly notiﬁcation on the update of individual and collective comparison
statistics, congratulation messages whenever users achieve good results (goal achievement, challenge
conclusion, attribution of badges, visibility in the Hall of Fame), and encouragement messages to try
again in the following weeks in case of failure of challenges or goals.
3.4.2. Getting Social Reward and Support
A notiﬁcation system congratulating for achieved results can substitute for an in-person counsellor,
enhancing the perception of social support. However, virtual communities might be less effective
in motivating to change, especially in the long term, if no direct and in-person interactions among
the users are possible. To anticipate a critical decrease of interest of users and to prevent them from
stopping to use the app before they permanently change their mobility patterns, in the action and
maintenance stages, virtual activities are backed up with in-person events outside the app, open to the
whole GoEco! community and their circle of family and friends. On a monthly basis, in fact, users are
invited to join recreational events related to sustainable mobility topics, such as visits to exhibitions,
slow mobility and public transport treasure hunts across the city or lazy bicycle rides in natural areas.
During such events, the app users get to know each other, share their experiences and tips to overcome
difﬁculties in achieving challenges and, in general, support and motivate each other towards their
personal goals for change (social learning and facilitation).
To reinforce the users’ motivation to adopt a different behaviour, GoEco! also builds on individual
competitive attitudes, offering weekly comparisons with peer members of the same community (social
comparison, competition), which is usually assessed as a powerful approach to increase motivation for
]. Typically, in gamiﬁed contexts this is performed by ranking users in a leaderboard, based
on the number of points they earned. However, to overcome the limitations about points introduced
in Section 1.3, and especially due to the difﬁculty of ensuring a fair distribution of points, if one wants
to respect all heterogeneous individual circumstances and stay transparent, in GoEco!, we explicitly
Sustainability 2019,11, 2281 12 of 26
opted for avoiding a point-based reward system, putting instead goals at the centre of the process
of change. Thus, comparison between members of the GoEco! community is based on the level of
achievement of one’s own goal, combined with the number of completed challenges (trophies) and the
number of obtained badges. On a weekly basis, these indicators are computed, the GoEco! leaderboard
is also updated, and the top-3 users of the week are posted in the “Hall of Fame section” (recognition)
(Figure 4c). To provide users with comparisons, which are valuable and meaningful, comparison
in the leaderboard can also be made against a subset of users who are pursuing the same type of
goal for change or whose homes have the same level of accessibility to public transport, based on a
classiﬁcation available for all Switzerland  (similarity).
Such a conﬁguration implies that, regardless of the speciﬁc effort each user needs to devote to
achieve her goal for change, every week any user could be shown in the “Hall of fame”, depending
on how close she gets to her target and on how many trophies and badges she collected. The system
does not judge the level of difﬁculty associated with the goal and target chosen by the user, or the
level of difﬁculty of the obtained trophies (which depend on the user’s initial mobility patterns, on her
potential for change, on external, personal constraints, and on her level of engagement with the app),
and assumes that all users have genuinely set the most proper goals and targets for themselves.
A selection of screens of the GoEco! app: (
) list of available challenges; (
) weekly summary
feedback on individual performances; and (c) Hall of Fame (top users in the weekly leaderboard).
4. Discussion: User Evaluation of GoEco! App’s Persuasive Features and Components
The GoEco! app was designed with the explicit aim of overcoming the limitations already
identiﬁed in the literature. However, how did users evaluate its components and features and which
suggestions would they provide for future similar apps? To answer this question, we collected material
from a three-month ﬁeld experiment that involved the 47 active testers in the two Swiss regions of
Zurich and Ticino, through a survey composed of an online questionnaire and individual interviews.
Answering the questionnaire was not mandatory for all questions, therefore the sample of
respondents varies from a minimum of 21 to a maximum of 45 users, depending on the question.
Basic socio-economic characteristics of questionnaire respondents and interviewees are shown in
Table 2: medium to high income individuals, mostly males, with a balanced age distribution between
25 and 54 years old. According to their answers to the questionnaire, reported in Table 3, they have a
medium to high pro-environmental attitude, and also share the feeling of a personal responsibility
to control pollution and climate change. Since no direct comparison is possible with corresponding
ﬁgures for the average population, we cannot state whether these ﬁgures indicate the presence of
Sustainability 2019,11, 2281 13 of 26
a self-selection bias. However, even in the case it occurs, this does not disallow investigating the
user’s opinions and viewpoints with respect to the app’s features and contents. In the following, when
appropriate, we directly report elements collected during the interviews, by stating the numerical
identiﬁcation code attributed to the given interviewee (where T stands for Ticino and Z for Zurich).
Socio-economic characteristics of questionnaire respondents and interviewees. Since answering
was not mandatory for all questions, some respondents only partially answered the questionnaire.
Answer n= 45
Answer n= 21
Gender Female 17 8 5
Male 28 13 14
Age 20–24 2 2 1
(years old) 25–34 13 8 5
35–44 13 7 5
45–54 14 4 6
55–64 3 0 2
Income Between 3000 and 4500 2 0 1
(CHF/month) Between 4500 and 6000 8 4 2
Between 6000 and 9000 10 6 5
Between 9000 and 12,000 8 4 3
More than 12,000 10 3 4
I prefer not to say it 7 4 4
Location Ticino 27 13 11
Zurich 18 8 8
Pro-environmental attitude of the questionnaire respondents (45 respondents out of 47 active
testers). A seven-point Likert score was used, where 1 = “Totally disagree” and 7 = “Totally agree”.
Climate change is a problem for society 6.47 (0.84)
Saving energy helps to limit climate change 6.31 (0.90)
The quality of our environment will improve if we use less energy 6.16 (1.35)
I feel responsible for pollution and climate change: it is not just a matter of
governments and industries
I try to use the car as little as possible 5.56 (1.39)
To start with, users were asked to assess their overall experience with the app, in terms of its
usability, the design of the user interface, the time effort needed to interact with it, and the overall
usefulness and pleasure of the experience it delivered. Semantic differential questions were used and
the answers were organised on a seven-point Likert scale. As Table 4shows, above average evaluations
were attributed to all the considered evaluation criteria. The least appreciated criterion, however, was
the one related to the expenditure of time needed to interact with the app. In fact, during interviews,
most users complained about the requested validation of the transport modes: “there was too much
manual work, that needed a lot of time” (Z3); “validation was boring, if you did it every day. But if you forgot
validating your trips for a week, and then you tried to validate all them at once... it became annoying!”(T11)
Therefore, while users acknowledged the importance of validating the transport modes, they also
asked for improved automatic detection capabilities (better segmentation of trips and identiﬁcation of
the transport mode): “people should not be forced to validate all their trips” (Z7);“because in some cases trips
were—wrongly—highly segmented” (T1); and “at least for walking and car trips, detection of the transport
mode should be fully autonomous” (T4).
Sustainability 2019,11, 2281 14 of 26
Another general comment that emerged from the interviews highlighted an initial barrier to
effective use of the app, due to the richness of its features and components: “the app offered quite a lot
of features, which were also related to another: you had to fully understand all of them and also the way they
were related, which was not always straightforward. To avoid too much burden on the user, you might release
new features over time. For example, ﬁrst you enable goal setting; then, after a few days of app use, you enable
challenges as well, and so on” (T11). Namely, users suggested to include an on-boarding phase for each
feature, providing the user with enough time to get acquainted with it, before adding more complexity
with a new feature.
Table 4. General assessment of the GoEco! app, according to the ﬁnal questionnaire.
Total (n= 45)
1 2 3 4 5 6 7 M SD
Difﬁcult to install 0 0 1 3 4 15 22 Easy to install 6.20 1.01
Difﬁcult to use 1 0 4 5 6 18 11 Easy to use 5.51 1.41
Unattractive in design 1 3 5 8 12 13 3 Attractive in design 4.73 1.45
Time-consuming 4 6 6 4 8 15 2 Time-efﬁcient 4.31 1.83
Uninformative 1 2 1 9 14 12 6 Informative 5.07 1.37
Useless 1 1 0 2 12 18 11 Useful 5.69 1.24
Boring 1 2 5 1 14 14 8 Interesting 5.20 1.50
Not fulﬁlling my expectations 1 1 3 6 14 15 5 Fulﬁlling my expectations 5.13 1.33
For a general screening of the GoEco! features, we then investigated their capability to stimulate
app users to adopt more sustainable mobility patterns: as shown in Table 5, those related to the
provision of feedback were assessed as the most effective ones, while the possibility to compare
individual performances against other users was assessed as the least effective one. In the next sections,
we explore these evaluations in more detail, with respect to the key recommendations for effective
persuasion, as summarised in Section 1.2.
Assessment of GoEco! features, with respect to their effectiveness in stimulating more
sustainable mobility patterns. The assessment was based on a seven-point Likert scale, where 1 = “Not
at all effective” and 7 = “Extremely effective”.
Total (n= 25)
1 2 3 4 5 6 7 M SD
Getting statistics on my mobility footprint (weekly
energy consumption and CO2emissions)
0 0 2 4 6 8 5 5.40 1.23
Getting statistics on my mobility patterns (weekly
kilometers, transport modes, travelling time)
1 0 0 5 8 8 3 5.20 1.29
Potential for change (alternatives) 0 1 2 9 5 5 2 4.71 1.27
Setting a personal goal for change 3 0 1 7 7 4 3 4.56 1.71
Engaging in challenges against myself 3 2 3 3 5 4 5 4.48 2.02
Being part of a community 2 2 3 5 6 5 2 4.36 1.57
Receiving unexpected badges 2 3 2 5 7 3 3 4.32 1.77
Comparing my performances with other app users
2 4 4 5 3 5 2 4.04 1.81
4.1. Providing Information
Either directly in the app or in the separate PDF report, GoEco! provided individuals with
information on their baseline mobility patterns, on overall individual potential for change and on
speciﬁc alternatives for systematic loops. As answers to the questionnaire show (see Table 6), getting to
know one’s potential for change was assessed as very interesting (M = 5.93, SD = 1.12, on a seven-point
Likert scale), although this was not always sufﬁcient to activate a critical reﬂection on one’s mobility
patterns (M = 4.80, SD = 1.58), and even less to persuade to try the suggested alternatives out (
M = 3.51
Sustainability 2019,11, 2281 15 of 26
SD = 1.94
) or start to regularly use them (M = 3.11, SD = 2.07). According to some interviewees,
the lack of action following the information on the potential for change was due to practical difﬁculties
in implementing the suggested public transport or bicycle alternatives (“how could I satisfy all my
family requirements, accompanying the kids and also carrying weights?”, T2). Other interviewees, however,
remarked that the lack of action could have been overcome by a more effective notiﬁcation system:
“often those are excuses, rather than real impeding factors” (T4).“For me, there were not enough and timely
notiﬁcations to keep me going” (Z1). Particularly, notiﬁcations reminding users about possible alternatives
for a given route, soon after that route was detected, would have been appreciated as useful triggers
to support app users in at least trying the alternatives out. Even more useful from the users’ point
of view would have been the possibility to ask GoEco! for information on available alternatives for
speciﬁc routes: “GoEco! was lacking in providing me with practical support when I was looking for alternatives
to car use: I do not need to know the alternatives for a trip I have already taken! I need to know the available
alternatives before I start a new trip” (T4). Namely, users asked for a multi-modal travel planning system,
such as, for example, the one already offered by the Peacox persuasive app [
]. Endowing the app
with such a component would also increase the frequency of user interactions with the app (“whenever
I need to reach a different destination than usual, I would open the app”, T4), thus also addressing one of the
key problems affecting the Action and Maintenance stages, that is the risk of abandoning the app early,
with consequent relapse to past mobility patterns.
4.2. Providing Goal Setting Opportunities
As indicated in Section 1.2, the provision of goal setting opportunities is acknowledged as an
essential feature to trigger a change in individual behaviour. However, the average assessment of the
goal setting feature was equal to M = 4.56, SD = 1.71 (Table 5). Additionally, as reported in Table 6,
users stated they were only slightly motivated by the opportunity to set a goal (M = 4.42, SD = 1.56,
on a seven-point scale) and they were even less eager to know whether, at the end of the week, they
had achieved their goal or not (M = 4.38, SD = 2.02). Goals were in fact judged to “quickly get boring”
(Z2), since they were still seen as quite generic and impersonal: “it reminds me of those eating apps, where
you always have to type what you eat: they are also rather boring” (Z2).“Instead, the app should have asked
something like: ‘Your friends drive by car so much, and use the bicycle so often. Would you like to do something
similar?’ And thus, tailor goals to people” (Z5). (Note that the idea of directly referring to the behaviour
of other app users, which evokes a sense of belonging to a community, is discussed in Section 4.6.)
More variety was indeed offered in GoEco! by challenges, which were designed to provide users
with a number of practical suggestions towards achievement of their goals for change. As shown in
Table 5, however, challenges were poorly assessed as well (M = 4.48, SD = 2.02). Particularly, users
reported that challenges provided them with limited support towards achievement of their individual
goal (M = 4.13, SD = 1.90) and played a limited role in activating a critical reﬂection on their own
mobility patterns (M = 4.38, SD = 2.08) (Table 6). Furthermore, even though challenges were not
assessed as particularly boring or incompatible with individual constraints on mobility (respectively,
M = 2.83, SD = 1.31 and M = 3.54, SD = 1.87), users tended not to replicate the new behaviour suggested
by a challenge, once it had ended (M = 3.88, SD = 1.75).
Again, interviewees lamented challenges were still too repetitive: “for a short time, a challenge is
okay. In the long run, it gets boring, especially as it does not change and it is not personalised” (Z3). To avoid
that boredom leads individuals to abandon the challenge and forget about it, they suggested to replace
the current static, time-based notiﬁcation system (one notiﬁcation in the middle of the challenge and
one at the conclusion of the challenge), with a dynamic one, based on user performance: “people should
receive notiﬁcations depending on their progress towards challenge achievement. Particularly, you could send a
notiﬁcation when only little is missing to complete a challenge, such as: you only need to do another 500 m to
achieve your challenge” (T6).
Also, some users suggested to explicitly present the potential beneﬁts individuals could get by
completing a challenge: “there is a difference between saying ‘do that, it’s a challenge’ and ‘do that, you save
Sustainability 2019,11, 2281 16 of 26
ten minutes’. These challenges are too general” (Z2). Finally, many users noted they would have expected
“more timely and personalised challenges” (Z8):“since I couldn’t increase my bicycle use anymore, I shouldn’t
have been shown bicycle-related challenges” (Z3) and “I would like to be able to start my own challenges” (Z8).
Assessment of GoEco! features, based on a seven-point Likert scale, where 1 = “Strongly
disagree” and 7 = “Strongly agree”. The number of answers depends on the question.
1 2 3 4 5 6 7 M SD
Baseline and potential mobility patterns
I was interested in knowing about my potential for change
0 0 1 5 8 13 8 5.93 1.12
The report stimulated me to critically reﬂect on my
1 3 5 8 13 6 8 4.80 1.58
I tried out the alternatives suggested by the reports 11 4 5 8 7 6 2 3.51 1.94
I’m now regularly using some alternatives suggested by
17 2 6 8 4 3 4 3.11 2.07
The meaning of the goal for change was clear to me 1 1 3 2 5 4 8 5.21 1.79
I was stimulated to change my mobility patterns in order
to achieve my goal
1 2 3 7 3 7 1 4.42 1.56
I was eager to know if, at the end of the week, I had
achieved my goal
3 3 2 2 5 6 3 4.38 2.02
Challenges helped me to achieve my goal for change 4 1 3 4 6 4 2 4.13 1.90
Challenges made me critically reﬂect on my mobility
4 2 2 1 6 6 3 4.38 2.08
Challenges were boring: they did not stimulate me at all 4 6 7 5 1 1 0 2.83 1.31
Challenges were not compatible with the constraints
affecting my mobility choices
4 5 2 5 5 1 2 3.54 1.87
After engaging in a challenge, I kept replicating the
mobility patterns suggested by the challenge
4 1 4 5 6 3 1 3.88 1.75
Hall of Fame
The way the ranking in the Hall of fame was computed
was clear to me
3 3 4 6 2 3 1 3.64 1.73
I checked my ranking in the Hall of fame every week,
to see my progress compared to the other participants
7 4 2 2 3 3 2 3.30 2.16
Apparently, no signiﬁcant changes occurred in the
rankings, so I stopped regularly checking the Hall of fame
2 3 1 8 3 4 0 3.90 1.58
I was stimulated to modify my mobility patterns, in order
to achieve a top ranking in the Hall of fame
8 3 4 3 1 2 2 3.00 2.05
4.3. Providing Feedback
Notwithstanding the positive assessment of features providing feedback in Table 5, many
interviewees noted the eco-feedback information was not fully meaningful to them, since they were not
used to quantities such as tons of CO
or kilowatt-hours. “It’s true I could still check my progress over time,
by comparing one week to the other, though I would have preferred to intuitively guess the very meaning of the
numbers shown by GoEco: what do 10 kilograms of CO2really mean?” (T4). To make those numbers more
intuitive and immediately understandable, interviewees suggested to evoke their meaning through
metaphors and comparisons with other well known variables. For instance, “you could back
with gasoline litres” (T10) or “relate tons of CO
with other activities we are used to perform at home—for
example, what about providing us with the amount of corresponding washing machine cycles?” (T1) Others
suggested to directly avoid the provision of numerical feedback (“people do not like to see numbers”
(Z5), and “replace it with a graphical visualisation of the size of the impact” (T1), such as, for example,
the UbiGreen app [
]. Finally, some interviewees suggested to focus on the avoided impact, instead
of the actually produced one, such as, for example, “thanks to your use of the bicycle today, xxx litres
of oil have not been consumed” (T5) or “the CO
emissions you saved are equal to yyy trees being planted”
Sustainability 2019,11, 2281 17 of 26
(T10). However, we would be careful in embracing this approach, since it stands in disagreement with
the basic behavioural economics principle about loss aversion: behaviour change is better stimulated
if people perceive the dis-utility of having lost something, instead of the utility of having gained
]. Actually, from this point of view, providing users with feedback in terms of the
distance to their own potential for change would have probably been more effective.
Some users suggested they would have also appreciated other types of feedback, such as monetary,
health or time-savings feedback. The idea here is to offer all these types of feedback, letting the user
choose the ones better ﬁtting with her own system of values—an approach that was adopted by
the enCOMPASS persuasive app in the domain of energy saving in households [
]. For instance,
some users suggested to provide feedback in terms of the monetary costs per kilometre travelled
(holistically estimated using life cycle assessment, and not only direct use costs), the frequency of
gasoline refuelling needs, or the lifespan of one’s own car. “For example, you could tell people that, if they
keep using the car for short trips, they would need to replace it after, say, just seven years, instead of ten years.
This might lead them to reﬂect on their daily patterns and stimulate them to change” (T4).
In addition, interviewees suggested that such a monetary feedback could focus on the collective
level, instead of just highlighting individual beneﬁts or costs: “I would like to know whether—and how
much—my mobility choices contribute to savings of road maintenance costs. For example, something like:
highways need to be tarmacked every year, secondary roads every twenty years. With your low-car dependent
patterns, you allowed the Swiss Confederation to save xxx francs for tarmacking” (T5). Such feedback,
however, would only make sense over a long time-span, such as on a yearly basis.
Finally, the interviews also highlighted the difﬁculty of getting an overall evaluation of one’s
performance, based on the provided feedback: “whatever the unit of measurement, what is the level of the
environmental impact I am producing? I would have liked to receive some ‘red/green light’ indications, or at
least to know if I am better or worse than average” (Z1).
4.4. Providing Rewards or Punishments
According to the general GoEco! assessment shown in Table 5, intangible rewards such
as badges are judged as limited in stimulating more sustainable mobility patterns (respectively,
M = 4.32
SD = 1.77
M = 3.92
, SD = 1.82). Some interviewees suggested that rewards could be
made tangible, instead of virtual, but if so, they should be collective, instead of individual: “for example,
you could do something like the Ecosia search engine (https://www.ecosia.org): your actions contribute to planting
a number of trees. Similarly, I would like to get a badge notifying me I have contributed to saving a certain
emissions and that as a reward a number of trees will be planted somewhere in the world” (T10).
The low interest for individual, in-kind prizes reﬂects currently acknowledged behavioural economics
principles, according to which intrinsic rewards (such as recommendations, public recognition,
and praise) may have stronger and more consistent behavioural effects than monetary rewards,
which in some cases might even paradoxically end up discouraging an implementation of the new
]. Nonetheless, the low interest for tangible, individual rewards might also be
due to the fact that the majority of project participants had voluntarily applied to join the GoEo!
project: indeed, we suppose most of them already had an intrinsic motivation to actively try to
change their behaviour—namely, they were already in the contemplation stage according to the
transtheoretical model, and therefore the possibility to earn a prize only inﬂuenced them to a limited
extent. We cannot exclude, however, that less motivated individuals, such as the majority of those being
in the pre-contemplation stage, could be attracted by the availability of tangible prizes. In addition,
we are aware that such a low interest for individual tangible prizes could be due to the fact that
Switzerland, ranked second worldwide in the Human Development Index in 2018 [
], is a wealthy
country, where on average people enjoy very high standards of living. Previous research at the
international level summarised by Habibipour et al.
reports in fact that building a long-term
commitment between an ICT-based system and its users requires a proper economic reward. Therefore,
in future research activities one could exploit individual, tangible prizes, to start engaging individuals
Sustainability 2019,11, 2281 18 of 26
with low intrinsic motivation towards change (individuals in the pre-contemplation stage), and gradually
replace prizes by other motivational factors, as long as the individuals progress through the stages of
The use of punishment was not explored in GoEco!, due to the negative connotation it is usually
associated with [
]. An interviewee, however, suggested that badge-like elements could be used not
only to mark detection of particularly sustainable choices, but also to notify their absence. Namely,
she suggested to build on one’s sense of guilt—though avoiding any direct punishment or penalty:
“for example, you could remind users of something like: you have not been using the bus in quite a while.
What happened?” (T1) Such a notiﬁcation would actually be coherent with the loss aversion behavioural
economics principle mentioned above (since it implies that by not using the bus, the user has lost
something), therefore it is particularly worth being explored.
4.5. Providing Occasions for Social Comparisons
Opportunities for social comparison were provided by weekly statistics in the Hall of Fame section.
The overall effectiveness of the Hall of Fame feature was assessed as rather low, as indicated in
Table 5(M = 4.04, SD= 1.81) and in Table 6: possible doubts on how the ranking was computed (“the
computation was clear to me” M = 3.64, SD = 1.73), together with a perceived lack of dynamism
M = 3.90
SD = 1.58
), contributed to reducing the interest of users towards regularly checking it
(M = 3.30, SD = 2.16), thus largely affecting its capability to stimulate a change in individual mobility
patterns (M = 3.00, SD = 2.05).
The lack of dynamism was partially due to technical problems, which prevented the Hall of Fame
feature from working correctly for a certain period, and probably negatively affected these evaluations.
Interviewees, however, did not stress this aspect, focusing instead on how to make the comparison
metrics more meaningful to them. For instance, they suggested to rank app users based on weekly
absolute individual performances, such as “the user who saved more petrol in absolute values over the week
or the user who consumed less petrol, in absolute values, over the week” (T5). However, they also noted that
such rankings should only be made between users with reciprocally comparable proﬁles, for instance
in terms of the average commuting distance, other constraints affecting individual mobility choices,
and available alternatives to car use, instead of considering all app users: “if I am used to travel 4000 km
by car every month, and I win a car-reducing challenge, are my efforts lower, higher or the same as people
travelling 400 km by car every month, and winning the same trophy? If other people start from more favourable
conditions than mine, but we are rewarded with the same trophy, we are ranked equally in the leaderboard.
I perceive this as unfair and soon lose interest in it” (T6).
We were well aware of this risk, which also holds for the other elements used to compute the
ranking in the leaderboard, and especially for the percentage of achievement of the individual goal for
change. In fact, since GoEco! is not capable of automatically proﬁling app users based on the practical
constraints affecting their current mobility needs and potentials for change, it cannot assess the level
of difﬁculty a certain behaviour has for them. From our perspective, introducing any super-imposed
“rating of difﬁculty” would create more unfairness than the one we were trying to overcome. Therefore,
we explicitly opted for letting the users choose for themselves, in the belief they are genuinely acting
at the best of their possibilities: app users are invited to set the best goal for them and to freely join the
challenge(s) they assess as most suitable to their possibilities, if any, in a relationship of trust among
each other and with the system. And indeed, considering there are no tangible prizes, they would
have no reasons for cheating the system and opting for too easy targets and challenges on purpose.
However, this approach was not fully understood and appreciated: other ways to exploit social
interactions to favour user engagement in the behaviour change process should be sought.
4.6. From a Set of Individual Users to a Community of Users
While the above features, allowing individuals to compare themselves with the other users, were
poorly appreciated, the interviews highlighted users were highly interested in additional features
Sustainability 2019,11, 2281 19 of 26
involving social interactions. Namely, they suggested to overcome the current focus on the individual
level and to increase the feeling of belonging to a community, which was reported as pretty low
(Table 5), also due to the lack of pre-existing relationships among app users. This is also suggested
by a whole branch of research that acknowledges “low-carbon communities”, either place-based or
virtual ones, as the necessary framework to support behaviour change at the individual level (see for
For instance, users would have appreciated possibilities for direct interaction with other app users,
via an internal chat or forum, or even an external social network, to share individual performance,
success and failure. Namely, the participants would have beneﬁted from a social space to get to
know each other, as suggested by Habibipour et al.
. Then, they would have liked to create new
challenges and to invite other app users to join them: “and if I start a new challenge, I would also like to get
a (nearly real-time) feedback about how I am behaving with respect to them: am I better or worse?” (Z1)
Some interviewees also suggested users could share the routes they travel with a circle of friends.
Allowing users to share their data with a selected circle of friends would in turn allow leveraging
the behaviour of other users. For example, the app could give visibility to exemplary users, when
they manage to address their daily mobility challenges: “in Zurich it is very hilly, rainy, cold, and people
have luggage and kids. You have to show that there are real people that indeed manage to use the bike even
though they have luggage and kids and it is raining! If you ﬁnd them, you could show their performances
to everyone, presenting them as the ‘idols’ of the community” (Z7). In addition, as already suggested in
Section 4.2, one could tailor a user’s individual goal for change, based on speciﬁc mobility patterns of
other users. Or something such as “a GoEco!-Tinder feature could even be created [Tinder is a dating app,
https://tinder.com, Ed.], matching people who like someone else’s travel style” (Z4).
Including this concept of a circle of friends, challenges could also be made more fun, such as “Who
goes furthest with an e-bike?” (Z7). Interviewees were asked whether the circle of friends they envisioned
should be better composed of pre-existing friends in real life. There was no agreement on this point:
on the one hand “if you interact with friends, the message can easily be ampliﬁed: you might be lead to discuss
about the app, and thus your mobility patterns, also when you meet your friends for a dinner or a cocktail. And if,
just like me, you have a competitive attitude, you would really do all your best to beat a friend!” (T6). However,
on the other hand “if I just talk with my circle of friends, I would not be too much stimulated to change... I
would also appreciate knowing about someone else” (T11). Moreover, “indeed, competitors should frequently
change: every week you should be able to compete against different people” (Z3). Therefore, the conclusion
would be, once again, to leave app users the choice whether interacting with just their real-life friends
or also with strangers that the app has identiﬁed as potential “matching friends”, based on a few user
proﬁling questions, asked at registration. The challenge remains, however, about how to proﬁle app
users so that their actual constraints and potential for change are properly taken into account and
suggested matching is meaningful.
Finally, the interviews allowed exploring the idea of a “team-based” approach, namely creating
teams of individuals and inviting them to collaborate in order to achieve a given common goal for
change or to compete against another team, always in terms of change with respect to baseline mobility
patterns. Even though this approach was positively assessed in the context of energy saving in
households, such as in the Social Power project [
], GoEco! interviewees did not appreciate it, due
to the difﬁculty of building balanced teams with both comparable mobility needs and accessibility
to alternatives to car and already existing real-life relationships capable of creating an enduring
commitment. However, interviewees saw a potential within corporate mobility management processes:
“you could try this approach with companies: relations between individuals would already be in place (such
as colleagues of the same ofﬁce, division, or building), and, with at least one destination in common (the
workplace), individuals would at least partially have similar mobility constraints and opportunities” (T1).
Indeed, even though with a speciﬁc focus on cycling, only involving commuting trips, and without
the support of automatic mobility tracking tools, a similar approach has long and successfully been
experimented in Switzerland, through the “Bike to Work” challenge that was ﬁrst launched in 2005
Sustainability 2019,11, 2281 20 of 26
(http://biketowork.com). In the future, an integration between GoEco! and Bike to Work could
therefore be envisioned.
In this paper, we presented an attempt to overcome the main limitations that were found to
affect behaviour change apps targeting more sustainable individual mobility patterns. Particularly,
we focused on the lack of grounding in a behaviour change theory and on the over-reliance on
one-size-ﬁts-all, point-based reward mechanics that typically occur when gamiﬁcation approaches are
exploited. To this purpose, we developed an app, named GoEco!, and opted for grounding it in the
transtheoretical model for behaviour change.
Instead of primarily relying on a standardised point-based reward system, the app’s features and
components were designed with the speciﬁc aim of assisting individual progress from one stage of
the behaviour change process to the next one, through activation of the proper process(es) of change
at each stage. Namely, speciﬁc app features were designed for each stage, from pre-contemplation
of behaviour change to maintenance of a new, more sustainable, behaviour, and, to increase their
effectiveness, a user-centred approach was adopted.
The app effectiveness was then ﬁeld-tested by 47 voluntary users in a three-month experiment
in two Swiss regions. After the experiment, we ran a survey and interviews to get insights on the
app’s features and components directly from its users. In general, the users expressed the desire for
further customisation (regarding potential for change, available alternatives, goals, challenges and
notiﬁcations), as well as further simpliﬁcation concerning the number of offered features. Based on this
experience, we therefore recommend that future persuasive apps better follow the process of behaviour
change, by releasing the app features in stages as well: at registration, a simple question such as the one
proposed by Bamberg
could allow to classify the users’ initial stage of behaviour change. Then,
users could only be offered the app features speciﬁcally designed for their stage. For example, a user
in the pre-contemplation stage should only receive feedback on each travelled route and on her baseline
mobility patterns, while a user in the action phase should have all the app features enabled (in both
cases with a proper on-boarding training period). Then, the classifying question should be periodically
repeated, and new features should be released when progress to the next stage is detected.
More speciﬁcally, this experience provides us with practical suggestions to further improve the
recommendations for effective persuasive apps that were identiﬁed in previous works:
Provide information: Improve the automatic detection capability of both travelled routes and
transport modes to reduce the need for manual validation of the transport mode by the users.
In addition, endow the app with a multi-modal travel planning system, capable of actively
supporting users in their daily travel needs, especially for non-systematic trips. Such a component
would not only provide them with additional practical information useful in their process of
change, but would also increase the frequency of their interaction with the app, thus helping them
to remain committed to their goals and challenges.
Provide goal setting opportunities: Allow for as much customisation and dynamism as possible,
in both goals and challenges, by developing a timely notiﬁcation system based on user
performances and by explicitly referring to speciﬁc mobility patterns of other app users in the
design of goals and challenges themselves.
Provide feedback: Make individual impact more intuitive and immediately understandable (for
example, by exploiting visualisation techniques instead of purely numerical values), as well as
more diversiﬁed (for instance, by offering information on health and monetary impacts, besides the
current energy and climate ones).
Provide rewards or punishments: Provide tangible rewards, both at the individual level (mainly
targeting individuals in the pre-contemplation stage, as a lure to raise their interest in the app,
and thus activate their process of change), and at the community level (to keep the interest of
those who are already in the next stages of behaviour change).
Sustainability 2019,11, 2281 21 of 26
Provide occasions for social comparison: Better exploit the power of social interactions. Instead of limiting
comparisons to behavioural aspects, which is difficult to perform in a fair way and risks not to be
trusted, let the app include features and components aimed at increasing the perception of social
support and the feeling of belonging to a community of similar people, engaged together towards
the same goal for change. Namely, move the focus from a competitive setting to helping relationships.
To this purpose, include features allowing users to share activities and performances, such as for
example comments, questions, travelled routes, average mobility patterns, challenges, and so on.
Even though these suggestions entirely originate from a process developed in Switzerland, one of
the top-three countries worldwide according to the Human Development Index, we believe they are
only marginally inﬂuenced by the Swiss high standards of living. In lower income countries, we expect
even higher interest for tangible rewards at the individual level, and suppose recommendations on
the other aspects would maintain their effectiveness. A fundamental prerequisite for the effectiveness
of a persuasive app such as GoEco!, however, is the accessibility to realistic alternatives to individual
car use, such as timely and frequent public transport and safe and widespread cycling and walking
paths. In lower income countries, where these options might be lacking, this would be a major
constraint precluding the effectiveness of similar persuasive approaches, no matter whether the above
recommendations have been put into practice.
Implementing all the above suggestions will require further research efforts in different disciplines,
from artiﬁcial intelligence for automatic transport mode detection, travel planning information and
better customisation (alternatives, potentials, notiﬁcation, etc.), to user interface design (visualisation
of the impact), and social and behavioural sciences (improvement of social support and a feeling
of community). The route for effective persuasive apps in the mobility sector is therefore still long.
We hope, however, with this work, to have identiﬁed in which direction the steps forward need to
Conceptualisation, F.C., D.B., R.R., and M.R.; Methodology, F.C. and D.B.; Formal Analysis,
F.C., D.B., and J.V.S.; Writing—Original Draft Preparation, F.C.; Writing—Review and Editing, D.B., J.V.S., M.R.,
and R.R.; and 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 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. Finally, we are very
grateful to all 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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