A Study on Persuasive Applications for Electric Energy Saving
Un Hee Schiefelbein
1
, William B. Pereira
1
, Renan L. Souza
1
, João C. D. Lima
1
, Alencar Machado
1
,
Eduardo C. Stabel
2
and Cristiano C. da Rocha
3
1
Informatics Graduation Program, Federal University of Santa Maria, Santa Maria, RS, Brazil
2
Polytechnic School, Federal University of Santa Maria, RS, Brazil
3
Center of Informatics and Automation of the State of Santa Catarina (CIASC), Florianópolis, SC, Brazil
Keywords: Persuasive Computing, Persuasive Technology, Electrical Energy Saving and Mobile.
Abstract: The growing development of persuasive technologies has led to the creation of systems that help society in a
variety of sectors, one in the electric power sector, where applications seek to persuade users to change
behavior and save electricity. In this sense, this article seeks to present concepts and techniques of
persuasion applied in systems with this objective and to present a prototype of an application that seeks to
show the user a prediction about the consumption of electric energy without him acquiring intelligent
sensors, only with data that he has easy access.
1 INTRODUCTION
Decades ago the scenario of persuasive technologies
was very different from what is seen today. The
internet could not be accessed from anywhere and
computers were designed primarily for information
manipulation, calculations, storage and retrieval of
data, however, according to the advances in
technology, computers have migrated to people's
daily lives, and have become more persuasive by
design (Fogg, 2003). Today, persuasive technologies
are almost ubiquitous, because of access to the
internet anywhere, computers and systems are taking
on the role of persuaders, which motivate and
influence users which facilitates behavior change
(Fogg, 2003; Oinas-Kukkonen and Harjumaa, 2009).
This change in human behavior can take place in a
variety of areas, such as health-related e-health
applications, social networking sites, netflix
integrated tv applications which sugest programs
and even applications that will monitor and will
suggest savings.
Studies on energy-saving systems using
persuasive technologies have gradually increased,
some of which suggest that persuasive technology, if
applied in an environment where people are not
consciously aware, may have an influence on their
attitudes, and could (under certain conditions) be
comparable to the influence where he would have
focused attention (Ham, Midden and Beute, 2009).
This approach becomes feasible to induce behavioral
changes through systems that provide, for example,
real-time feedback to users (Chen, 2012). In this
sense, this work seeks to present the definitions of
the area of persuasive technology as well as to
emphasize some principles of persuasion. From the
study of each technique was created the first version
of an application that presents an estimate on the
electric energy bill.
The article is divided as follows: section 2, the
essential concepts for the understanding of the work
are presented. In section 3, a study of related
articles. In section 4 the prototype of the application
is presented and finally in section 5, the final
considerations and the future works are presented.
2 CONCEPTUAL REFERENCE
2.1 Persuasion and Persuasive
Technology
Cialdini (2001) defines persuasion as the ability to
induce beliefs and values in other people, thus
influencing their thoughts or actions. But for
persuasion to be applied, according to the author, it
is necessary to define specific strategies, with the
190
Schiefelbein, U., Pereira, W., Souza, R., Lima, J., Machado, A., Stabel, E. and Rocha, C.
A Study on Persuasive Applications for Electric Energy Saving.
DOI: 10.5220/0006788701900197
In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 190-197
ISBN: 978-989-758-298-1
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

focus of inducing people to adopt different
behaviors.
These strategies of persuasion are commonly
used by professionals in the field of marketing and
web design and are divided into six main strategies
according to Cialdini (2007): 1) Reciprocity: giving
and receiving, the persuaded person gets the feeling
of debt with the persuader 2) Commitment: people
face pressures to behave in a manner consistent with
their commitment; 3) Social Approval: seeking other
people's approval opinions; 4) Affection: people are
more influenced by those who have more affinity; 5)
Authority: people with more authority have greater
potential to influence; 6) Shortage: everything
becomes more valuable when it is rare or limited.
Fogg (2003) seeks to explain how persuasion can
be achieved through computational technologies, the
author defines persuasive technology as an
interactive computing system designed to change
people's attitudes or behaviors, but this process is
not limited to sending unilaterally a message to
influence the user, but allows the sending of useful
information that can support the development of a
habit or position of users.
According to Spagnolli, Chittaro and Gamberini
(2016), persuasive technology exploits information
that technology can offer to create a user context,
thus facilitating the adoption of a particular action or
position.
For Oinas-Kukkonen (2010) behavior change
support systems are the key to research in the area of
persuasive technology, these systems are designed to
shape, change or reinforce attitudes, behaviors or
acts without the use of deception, thus persuasion
depends on the voluntary participation of the user so
that he can be persuaded.
The present work will mainly use the definitions
of Fogg (2003) and Oinas-Kukkonen and Harjuma
(2009) on persuasion and persuasive technologies.
2.2 Fogg Behavioral Model
From the point that persuasive technology focuses
on behavior change Fogg (2009), he created a
behavior model, abbreviated by FBM, where he
states that in order to achieve a target behavior a
person needs three factors to be performed:
motivated, 2- having the ability to perform certain
behavior and 3- being activated to perform the
behavior, so-called triggers. From the model the
functional triad was elaborated.
2.3 The Functional Triad
The functional triad, elaborated by Fogg (2003), is a
conceptual framework that illustrates the different
roles that computational technologies can play in
persuading users. The functional triad shows that
technologies can function in three basic ways: as
tools, with the media, and as social actors.
According to the use of computer technology,
users will be persuaded in different ways, depending
on the elements that will be used.
1. The use of technology as a tool seeks to
increase people's ability to perform a target
behavior. The main objective is to make the
activities easier or more efficient.
2. Using technology as a medium, it can be
divided into symbolic and sensorial, being
symbolic when computer technology
transmits information about texts, graphics,
etc and being sensorial means when
providing information such as audio, video,
smells and sensations.
3. Use of technology as a social actor, through
the personification of the computer,
creating a relationship as if it were a
person.
For each of the elements of the triad, Fogg
(2003) defined some principles of persuasion, as
Table 1, which are:
Table 1: Persuasive Principles of Fogg (2003).
Tool Media Social Actor
Reduction Cause and Effect Attractiveness
Tunneling Rehearsal Similarity
Tailoring Virtual Rewards Praise
Suggestion
Simulation in Real-
World Contex
Reciprocity
Self-Monitoring Authority
Surveillance
Conditioning
Computational technology as a tool:
• Reduction: Reduce complex activities for
simple tasks.
• Tunneling: Guide the user through
sequences of events or steps, seeking to
persuade him along the way.
• Tailoring: Adapt the information that will
be presented according to the interest of the
clients and personalized information.
• Suggestion: Provide users with the right
suggestions at the right time.
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• Self-Monitoring: People can achieve their
goals or predetermined outcomes more
easily if they feel they are being monitored.
• Surveillance: Monitor a user's behavior so
that it achieves the expected result.
• Conditioning: Use positive reinforcement
to change existing behavior’s into new
habits through rewards.
Computational technology as media:
• Cause and effect: This principle allows to
observe the consequences of the real world
in a safe environment, it is possible to
follow the cause and effect in relation to the
behaviors.
• Virtual rehearsal: It consists of a simulated
environment where users can rehearse
behavior that can allow a change in the real
world, also serves to explore new behaviors
and perspectives.
• Virtual Rewards: Offering virtual rewards
for real activities.
• Simulation in Real World Context:
Simulate some behavior in the context to
which the user belongs.
Computational technology as actors:
• Attractiveness: More attractive software or
hardware will have persuasive power
greater than others.
• Similarity: Products that match user
personality tend to be more persuasive.
• Praise: Praising and rewarding users make
them closer to being persuaded.
• Reciprocity: When some computer system
does some favour to the user he usually
feels the need to reciprocate.
• Authority: The credibility that the system
passes to the user influences its behavior.
2.4 Persuasive Systems Design Model
(PSD)
Oinas-Kukkonen and Harjumaa (2009) created a
model to assist in the development and evaluation of
persuasive systems, the persuasive systems design
model (PSD) and their definitions were based on the
principles of the functional triad of Fogg (2003).
According to him, the development of persuasive
systems consists of three steps, first it is necessary to
understand the fundamental issues behind the
persuasive systems before implementing the system,
second, to analyse the context, to recognize the
intention and the events and strategies for the use of
a persuasive system and third the definition of the
real qualities of the system, for each stage the
authors defined points that must be followed.
The three-step process for the development of
persuasive systems is presented in Figure 3.
Figure 1: Steps of the development of persuasive systems.
In the first step the understanding of the key
problems of the persuasive systems is made, Oinas-
Kukkonen and Harjumaa (2009) defined seven
postulates that help in this stage of understanding the
system. These seven postulates need to be addressed
to design or evaluate persuasive systems. The first
two postulates relate to users in general, the other
two refer to persuasion strategies and the last three
refer to the real features of the system. Being them:
1. Information technology is never neutral,
people are always under someone's
influence.
2. People like to make a commitment to a
particular situation and act with
consistency.
3. Direct and indirect routes are the key to
persuasion strategies where the direct route
can be the person who carefully evaluates
the content of the message and routes
indirectly where the individual is less
thoughtful and uses simple clues or
stereotypes to evaluate the information.
4. Persuasion is often incremental, where it
gradually increases behavior.
5. Persuasion through persuasive systems
must always be open and clear.
6. Persuasive systems should be discreet to
users.
7. Persuasive systems should be useful and
easy to use.
In the second stage, the analysis of the context
of persuasion occurs, because according to the
author, without carefully analysing the context of
persuasion, it will be difficult or even impossible to
recognize inconsistencies in a user's thinking, to
discern opportune or inopportune moments to send
messages and persuade him with efficiency. This
context analysis includes recognizing the intent of
persuasion, understanding the persuasion event, and
defining and / or recognizing the strategies in use,
where:
Step 1
Understanding
the the key
points of
persuasive
systems
Step 2
Analyzing the
persuasion
context
Step 3
Design of
system qualities
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1. Intent: Recognition of the intent of
persuasion serves to determine its purpose
and to define who the persuader will be.
2. Event: Understanding the persuasion event,
seek to identify in the context of the user
particularities and useful information for
the system that will be created.
3. Strategy: The central resource to define
persuasion strategies is to analyze the
message, since persuasion depends mainly
on strategies that trigger emotions and
another central question is to consider
suitable routes to be used to reach the user
(direct or indirect route).
And in the third step, the model suggests the
analysis of the qualities of the system where 28
design principles are proposed for the content of
persuasive systems divided into 4 categories:
primary task support, dialog support, system
credibility support and social support.
In Table 2 the twenty eight design principles
divided into four categories according to Oinas-
Kukkonen and Harjumaa (2009) are presented, the
principles marked with * are similar to the principles
of Fogg (2003) which are detailed in Table 2,
therefore only the different principles of Fogg will
be described in this section.
Table 2: Persuasive Principles of Oinas-Kukkonen and
Harjumaa (2009).
Primary task Dialogue System
Credibility
Social
Reduction* Praise* Trustworthin
ess
Social
learning
Tunneling* Rewards* Expertise Social
comparison
Tailoring* Reminders Surface
credibility
Normative
influence
Personalizati
on
Suggestion* Real-world
feel*
Social
facilitation
Self-
monitoring*
Similarity* Authority Cooperation
Simulation Liking* Third-party
endorsements
Recognition
Rehearsal* Social role Verifiability Competition
Primary task, the design principles in this
category support the accomplishment of the main
tasks of the user:
• Personalization: personalized services have
greater persuasiveness.
Dialogue support, design principles in this category
are provided primarily through system feedbacks to
the users:
• Reminders: If the system reminds the user
of their target behavior they are more likely
to achieve their goal.
• Social Role: if the system adopts some
social role users will use this for persuasive
purposes.
Supporting credibility, the design principles in this
category describe how to design a system to make it
more reliable and therefore more persuasive:
• Reliability: A system that is seen as reliable
will have greater power of persuasion.
• Knowledge: The system that shows
knowledge, experience and competence has
more power of persuasion.
• Credibility: People make initial
assessments of the credibility of the system
based on the first visit.
• Endorsements: Third party endorsements,
especially from well-known and respected
sources, increase perceptions about the
credibility of the system.
• Verifiability: Perceptions of credibility will
be enhanced if a system makes it easier to
verify the accuracy of website content
through external sources.
Social Support, the principles in this category
describe how to design the system so that it
motivates users by leveraging social influence.
• Social learning: A person will be more
motivated to perform a target behavior if he
or she uses a system to observe others who
perform the behavior.
• Social Comparison: System users will have
a greater motivation to perform target
behavior if they can compare their
performance to the performance of others.
• Normative Influence: A system can use
normative influence or peer pressure to
increase a person's likelihood of adopting a
target behavior.
• Social Facilitator: The system should
provide the means to discern other users
who are performing the behavior.
• Cooperation: The system should provide
means for cooperation.
• Competition: The system must provide
means to compete with other users.
• Recognition: By offering public recognition
to an individual or group, a system can
increase the likelihood of a person / group
engaging in target behavior.
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2.5 Differences between the Principles
Described in the Model of Fogg
(2003) and Oinas-Kukkonen (2009)
From the principles marked with * in Table 2 it can
be seen that ten of the twenty-eight principles of
Oinas-Kukkonen and Harjumaa (2009) are similar to
those of Fogg (2003), but in Oinas-Kukkonen (2009)
is different..
As for example, the suggestion principle, which
in Fogg's model is classified as being a tool that will
help the user to reach his goal, already for Oinas-
Kukkonen and Harjumaa he is classified in the
category of dialogue support, since the main benefit
of the suggestion is meaningful content for the user
and does not make the suggestion support the
completion of a process.
The principles related to Oinas-Kukkonen's and
Harjumaa's dialogue support resemble the social
actors of Fogg (attractiveness, resemblance and
praise) and media (the virtual rewards). Reminders
and the social role are new principles proposed by
the authors. Reciprocity is not in the frame because
according to the author it is a characteristic of a user
and not a system resource.
Design principles in the social support category
were adopted by Fogg's principles on mobility and
connectivity. The opportune and inopportune
moment and the ideas behind the quality of
information, convenience and simplicity were
addressed in the postulates in other categories.
The principles of vigilance and conditioning are
not addressed by Oinas-Kukkonen and Harjumaa,
because for authors, often people can not choose
whether they can be observed or not.
Fogg (2003) seeks in his studies mainly to define
in what ways computational technologies can act as
persuasive technologies, studying how users react
and use these technologies.
And Oinas-Kukkonen and Harjumaa (2009)
developed in their studies methods to develop
systems with persuasive requirements and methods
to evaluate these systems.
2.6 Energy Saving
The reduction of expenses with electricity
consumption has become one of the main objectives
in both the residential and industrial sectors. In the
residential sector, the spending on energy
consumption is increasing. In 2007, according to
National Electric Energy Agency, in Brazil, the
consumption of the residential sector was only
below the transport and industrial sector in the
percentage on the total energy consumption
(ANEEL, 2005). In the United States, household
energy use accounts for more than 20% of total CO2
emissions, (Dietz et al, 2009), these data have
attracted interest in the development of applications
to provide residential economy.
One of the major problems of residential
consumers, according to Darby (2006), is the lack of
transparency on the expenditures of electricity
consumption, since most of the time the consumer
will only know the amount of energy consumed at
the end of the month of the light bill. The author
points out as a possible solution to this case, the
availability of direct and indirect feedback through
systems and applications on the consumption in the
residences, because from this information he can
change his behavior and as a consequence he
achieves the reduction of expenses.
Many of the users who seek energy savings are
already aware that they need to adopt a new
behavior and only need an encouragement, a trigger,
that can be delivered through ICTs - Information and
Communication Technologies (Vilarinho et al,
2016). The application of persuasive computing to
improve energy consumption habits in households is
a good case because small changes in the behavior
of energy consumption can result in substantial
impact (Winett, 2013).
In this sense, research that approaches persuasive
concepts that help users with sustainable practices
has been approached by Shevchuk and Oinas-
Kukkonen (2016), where the authors call Green IS
and Green IT, green information systems or green
information technology. Their research explores
persuasive principles used in green IT applications
designed to implement long-term changes in users'
behaviors.
In order to better understand how applications
using persuasive techniques generate changes in
human behavior and subsequently energy savings,
the next section presents works with this bias.
3 RELATED WORK
In this section of related works we searched for
articles that use persuasive techniques to obtain
behavioral changes and that subsequently caused
electric energy savings. Six articles were selected
and from them a verification of the techniques used
in each one of them was carried out and the results
were presented in a comparative table, which
appears at the end of this section.
Caption in the table:
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Article (1) refers to the article of Vilarinho et
al. (2016); Article (2) refers to the article of Zapico,
Turpeinen and Brandt (2009) ; Article (3) refers to
the article of Chen et al (2012); Article (4) refers to
the article of Sundramoorthy et al. (2011);
Article (5) refers to the article of Petersen, D.,
Steele, J., & Wilkerson, J. (2009); Article (6) refers
to the article of Casado-Mansilla et al. (2016).
Table 3: Comparison of related works.
Principles (1) (2) (3) (4) (5) (6)
Reduction
x x
Tunneling
Tailoring
Personalization
Self-monitoring
x x x x x x
Simulation
x x x x x x
Rehearsal
x x x
Praise
Rewards
x x
Reminders
x x
Suggestion
x x x x
Similarity
x
Liking
x x x x
Social role
x x
Trustworthiness
Expertise
Surface
Credibility
Real-world feel
x x x x x x
Authority
x
Third-party
endorsements
Verifiability
Social learning
x x x x
Social
comparison
x x x x x x
Normative
influence
x x x x
Social facilitation
x x x x
Cooperation
x x
Recognition
x x x x
Competition
Surveillance
x x x
Conditioning
From the analysis of Table 2, it can be seen that
all the works used the principle of self-monitoring,
simulation and social comparison. Self-monitoring
and simulation are common features in power
management systems, and this becomes clear in the
definitions of these principles, where self-
monitoring defines that users perceiving that they
are being monitored tend to change the behavior and
simulation that allows users to know consequences
of their attitudes.
The use of the principle of social comparison
shows the influence on motivation of users, because
when they can show and compare their performance
with other people they tend to change their behavior,
this has been growing in the most varied domains of
applications mainly with the use of social networks .
Other principles that have often appeared were
the principles of suggestion, appearance, social
learning, normative influence, recognition.
4 PERSUASIVE MOBILE
ENERGY SAVING
APPLICATION
Based on the principles studied on the development
of persuasive systems, this article proposes a first
version of an application focused on the
management of electric energy. Based on daily data,
inserted by the user, in relation to his perception of
the time used of the equipment that has cataloged on
the platform by the user, it is sought to project, by
means of mathematical formulas, the consumption at
the end of the month. The application uses, in its
interaction with the user, some of the principles of
persuasion that will be explained shortly.
The article sought to develop an application that
does not cause the user to spend money on the
purchase of hardware, such as sensors or specific
devices, but to have an estimated projection of your
account with all the information available on your
account.
Step 2: Analyze the context by identifying:
Intention of persuasion: to raise awareness about
the saving of electric energy, and to promote
changes in behavior. Event: The user profile is
summarized in a parent who seeks to have a forecast
of electricity consumption without having to spend
it, this user has a smartphone connected to the
internet full time. Persuasion strategy: indirect.
Step 3: Using persuasive principles to present
information, the application uses six of them:
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Simulation, suggestion, appearance, self-monitoring,
personalization, rewards.
The application needs the following data:
information on consumption history in the previous
months (data included in the monthly bills of light);
information about the user profile; and the power of
equipment that consumes electricity (it can delete
and add new ones at any time). This quoted data is
entered only once when the application is installed.
To provide a projection requires a daily record of the
use time of each equipment, however the main data
are referring to the consumption of the electric
shower, air conditioning and electric oven, if they
exist. The time of the other equipment if the user did
not want to enter every day, will be copied the last
supply.
From this data set, it is sought to estimate the
daily consumption of electricity (in kWh), in
addition to the projection for monthly consumption
and the amount to be paid, taking into account the
tariff flag in force in that month. For this, some
formulas of physics and mathematics are used.
Below is explained how the estimated daily
consumption and the projection at the end of the
month are calculated.
According to the data insertion, we have a
number n (total) of equipment inserted in the
application. A certain index i ranges from 1 to n,
corresponding to the first, second, ... equipment.
Each day, the user enters the time of use of each
equipment in the variable time (i) (will be
transformed into hours). The formula of the energy
consumed is given by the expression power (i) x
time (i), then we have the consumption of the
equipment in the day. After calculating the
consumption of each equipment, they are all
summed up and have the consumption of the day
stored in the variable consumptionEstimate (day),
where day is the day in question. These values are
calculated, summed and stored every day.
The projection for 30 days, in this first prototype
is realized by the rule of three.
ℎ =


(). (1)
This variable is estimated to have the unit of
measure kWh (the same as the light count). In order
to calculate the value of the account (in monetary
value), it is necessary to multiply the tariff flag in
force in the month and the tariff. Figure 1 shows the
screen where the user informs the time of use of
each equipment and in Figure 2, the consumption
projection for a given month.
Figure 2: Screen of the application where the user informs
the time of use of each equipment.
Figure 3: Application screen where the projection of
consumption, comparison and notification is presented.
When presenting to the user the value of the
projection of the light account the application makes
the comparison with the value of the same month of
the previous year, if the current value is less than last
year it calculates the difference and presents a
message of congratulation. Then at the end of the
month when the actual account arrives it will
register that new value and whenever there are real
energy savings in relation to the consumption, these
values will be stored and summed. With this, the
user can track how much he has already saved
compared to the previous year.
To stimulate the provision of data on the
platform the user indicates the best time of day he
would like to receive a reminder, so at this time he
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196

will receive a notification reminding of the
importance of providing data, which will give a
more accurate estimate.
5 CONCLUSIONS
The article presented only the prototype of the
application that uses persuasive computing
principles to seek to change user behavior with the
focus on saving electricity. The main idea was to
show that only with information that is easy to
access is it possible to have a prediction of
consumption, even if it is estimated, this domain of
research allows the use of several tools to predict.
These will be developed in the future works, where
in the next version of the application the user will
not need to inform several information regarding the
energy consumption, because a neural network will
be implanted and from consumption history, number
of people in the house and average temperature of
the city will be possible to predict consumption in
the coming months, providing more assertive data
for the tests. This information will be presented so
that the user can graphically compare whether their
consumption has increased or decreased. From this
neural network it will be possible to identify
correlations between the information and identify
the consumption profile of certain groups and thus
through persuasive computation to seek changes in
behavior in order to achieve savings of electric
energy.
ACKNOWLEDGEMENTS
The authors would like to thank CAPES for partial
funding of this research and the UFSM/FATEC
through project number 041250 - 9.07.0025
(100548).
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