=Paper=
{{Paper
|id=Vol-2490/paper10
|storemode=property
|title=Analyzing Psychological Goals in Gamified Applications Using i*
|pdfUrl=https://ceur-ws.org/Vol-2490/paper10.pdf
|volume=Vol-2490
|authors=Luiz Ferreira,Marcello Bax,Eric Yu
|dblpUrl=https://dblp.org/rec/conf/istar/FerreiraBY19
}}
==Analyzing Psychological Goals in Gamified Applications Using i*==
https://ceur-ws.org/Vol-2490/paper10.pdf
Analyzing psychological goals in gamified
applications using i*
Luiz Gustavo da Fonseca Ferreira1 , Marcello Peixoto Bax1 , and Eric Yu2
1
Universidade Federal de Minas Gerais, Brazil
2
University of Toronto, Canada
Abstract. This paper presents a model-based approach to guide the
design of gamified applications. The models enable a structured repre-
sentation and analysis of the social and intentional dimensions of the
actors involved in the organizational context of the gamified system.
The proposed notation (called GStar) makes use of the agent- and goal-
oriented features of the i* modeling framework. The main contribution of
this work is to bring the social and intentional aspects, which are usually
treated in an ad hoc manner in the design of gamified systems, to the
core of a modeling approach. This approach supports the analysis of the
consequences of the inclusion of each gamification element and facilitates
the evaluation by the designer of different alternatives, contributing to a
project more suitable to the organizational context and more capable of
fulfilling the established goals.
Keywords: gamification · social modeling · i* framework.
1 Introduction
There are many definitions for Gamification in literature. The most widely used
presents Gamification as the "use of game design elements in non-game contexts"
[1]. This definition implies that Gamification is not about building complete
games, but rather about the utilization of some parts (game design elements).
Another definition goes beyond and includes the goal of gamification: “Gami-
fication refers to a product improvement process through the development of
gamified experiences aiming at supporting value creation for users.” [2]
The fact that it deals with human motivation makes the project of gamified
applications a complex task, since it implies the need to understand the user’s
psychological perspective. The Self-Determination Theory (SDT) indicates, for
example, that details such as the meaning of a reward - whether it is perceived
or not as a symbol of competence - have a significant role in the reward’s ability
to motivate a person [3]. Another critical challenge of gamification is related to
the fact that its interventions can affect directly or indirectly the behavior of
the users. It is often not trivial to evaluate the effects and possible side effects
of implementing a gamification element. Taking an example of an organizational
environment with a strong collaborative culture, the implementation of a leader-
board can create excessive competition between people that can undermine the
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
2 Ferreira, L. Bax, M., and Yu, E.
group’s social cohesion and collaboration. Besides, the source of gamification in-
novation (games) are complex, multifaceted, and therefore, their characteristics
are challenging to transfer to other contexts. [4]
In this context, this work aims at proposing a modeling method to help ana-
lysts and designers to deal with the complexity, especially from a psychological
and social point of view, of designing a gamification application. This method
is based on the use of GStar, an extension proposed by this work to the agent-
oriented and goal-oriented framework i* [8].
The research methodology used to guide the development of the proposed
extension was the Design Science Research. Successive suggestion / develop-
ment / evaluation iterations were carried out, first to explore the possibilities of
modeling gamified applications with an agent/goal oriented modeling approach
(framework i*), and then to extend this approach to better suit the context of
gamification.
2 GStar: extending the i* framework
The i* modeling language is flexible and satisfactorily represents the dependen-
cies, intentions, and potential conflicts between actors in an organization. It has
been extended to include concepts from different domains, including security,
software development, and autonomic computing systems [9] [10]. In the case of
gamification, it is possible, through some extensions, to improve readability and
increase the framework’s ability to represent the design of gamified systems.
Gamification can be understood as a way of influencing the user’s motivation
to manifest certain desirable behaviors through the satisfaction of psychological
drivers. These drivers are associated with the satisfaction of basic psychological
needs that are the precursors of human motivation [5]. Based on this under-
standing, this work proposes three new concepts from the gamification domain:
target behaviors, gamification elements, and psychological drivers.
The basic principle for the representation of the new concepts is the adherence
to the original view of the i* modeling language. The i* framework is goal-
oriented. The three new concepts are represented by intentional elements already
included in i*, being differentiated from the original ones by symbols attached to
the visual elements. This allows the use of analysis techniques developed in the
context of i* without the need for adaptations and facilitates the use of modeling
tools originally developed for i *. Figure 1 shows the proposed representation for
the new concepts:
Fig. 1. Gamification concepts notation
– Target behaviors: behaviors that the designer wants to incentivize through
the implementation of the gamified experience.
Analyzing psychological goals in gamified applications using i* 3
– Gamification elements: game design mechanisms selected by the designer
to create the gamified experience. They are represented through a game
related goal, from player’s point of view. For example, points can be repre-
sented by “ Earn points ”, and leaderboards by “ Improve on the leaderboard”.
– Psychological drivers: The main drivers related to gamification are com-
petition, status, achievement, self-expression, and altruism.
3 Beehive design analysis
For illustration, we consider the design of the Beehive system, a private social
network implemented inside IBM reported in [6]. The purpose of the platform
is to foster social interactions among employees, promoting a channel that facil-
itates the creation and maintenance of personal and professionals relationships.
The network provides a profile page where users can share photos and lists. Users
can connect with other users, building their social network, and also comment
on any profile, photo, or list on the site. Three gamification elements were imple-
mented in beehive: points, leaderboards, and levels. Users were randomized into
two groups, control and experimental. Users in the control group used a version
of the site without gamification, i.e., they could not see any information on how
to earn points or any other information related to the scoring system. Users in
the experimental group used the gamified version; they were able to visualize
their score and that of other users, including those of the control group. [6]
Users’ engagement in the experimental group increased after the deployment
of the gamification experience, but the result was not sustainable. In the second
week after the implementation of gamification, the activity reduced significantly,
and three weeks later the engagement measured by the average amount of shared
content per user was already at the same levels as the control group. The authors
of the study believe that the reasons behind the decrease on engagement in the
experimental group after the first week of implantation of gamification were
related to the fact that some users, after achieving an intermediate level, were
satisfied and stopped contributing [6]. The following section presents an analysis
of the Beehive design using the model-based approach presented by this work
and other possible explanations for the results obtained by the implementation
of the gamification experience in the Beehive system. The Beehive design was
analyzed following a simple process: for each gamification element implemented
the following steps were followed: (1) Analysis question(s), (2) Modeling and (3)
Analysis and conclusion.
3.1 Points
The points system analysis was guided by two analysis questions: (1) What are
the implications of the point system?, and (2) Assuming that users are engaged
in the competition, and behave in a way that maximizes their chances to "win"
the game. How would those users behave considering the designed point system?
If users are engaged in the competition, they will behave in a way that raises their
4 Ferreira, L. Bax, M., and Yu, E.
chances to win, i.e., they will seek to increase their score, and thus improving
their positions in the leaderboards. As shown in Figure 2, there are three ways
to earn points in Beehive: commenting (15 points), posting a list (10 points),
or sharing a photo (5 points). In a competitive environment, engaged users will
naturally prioritize activities that require less effort and reward with more points.
As shown in Figure 2, in Beehive, the activity that demands the least effort is
also the one that rewards with the highest number of points, which is Comment
(this is highlighted through the dashed circles). Another important aspect easily
seen in the model of Figure 2 is that, since comments are posted on photos and
lists, they depend on them being previously created to exist.
Fig. 2. Earning points in Beehive
Gamified experiences that provide the driver competition can provide a sense
of competence to its users, which is one of the basic needs postulated by the
Self-Determination Theory (SDT) [5]. Also according to the SDT (Cognitive
Evaluation sub-theory), people’s perception about the meaning of the origin of
this competence (in the case of competition) is essential to define the effects
on motivation [7]. Attributing the highest amount of points to the activity that
demands less effort compromises the meaning of the competition because com-
petitive users will tend to excessively perform that activity. If there is a way to
take advantage of the system to earn points more easily (for example by overdo-
ing simple comments like "cool!" and "awesome!"), users will quickly realize it,
and the result is the decline of interest in competition as it ceases to be able to
provide any sense of genuine competence. Another implication of this distortion
Analyzing psychological goals in gamified applications using i* 5
is the possibility of promoting imbalances in the types of content created by the
users, since users may prioritize sharing comments over any other content. This
may negatively affect the network since comments depend on previously created
lists and photos to exist. Therefore, if there are fewer lists and photos, there will
be fewer comments as well, and fewer reasons for users to re-visit the site.
The hypothesis that the point system may result in a disproportional in-
crease of comments is corroborated by data that shows the evolution of the
average amount of content created per user on the experimental group [6]. This
indicates that users have understood the point system and have chosen to add
more comments than other types of content that reward with fewer points. Also,
interviews conducted by the authors of [6] at the end of the experiment showed
that some participants deliberately focused on adding comments to the site be-
cause the point system rewarded more comments than other contents. All this
suggests that the decision to reward comments with more points led users to
favor comments in order to advance more quickly in the competition.
3.2 Leaderboard
According to the SDT, performance-contingent rewards, as long as they are per-
ceived as a sign of competence, and therefore having an informational character,
positively affect people’s intrinsic motivation [3]. Leaderboards are closely related
to the competition and status drivers, and when well designed, provide positive
informational feedback and project a positive image of the best-placed users.
Therefore, the visibility provided by leaderboards should be perceived as a sym-
bol of competence. This characteristic influences users engagement since most of
them welcome this visibility for its positive effects both on their self-image/self-
esteem (drivers competition and achivement) and on the image perceived by
other people (driver status).
To understand the meaning of this visibility provided by the leaderboards,
one needs to understand what it takes to reach the top in terms of performance.
And from that, try to understand how people perceive the best performers. To
guide the analysis, the following analysis question was outlined: What are the
implications of the leaderboards? To improve their positions in the leaderboards,
users need to earn game points. As shown in Figure 3, this is done when the
user actively spends time on the social network, commenting, sharing photos or
lists. Due to the triviality of these activities, the best-placed users will be users
who simply spend more time on the social network.
The fact that to climb to the top of the leaderboards is only necessary to
spend more time on the social network, together with the fact that it is possible
to "cheat" by overdoing frivolous comments to move faster in the game severely
affects the leaderboards experience. In addition, as shown in Figure 3, users who
spend more time on the network, and therefore can secure the best places in the
leaderboards, will have less time to perform their main work activities, which
conflicts with the goal of advancing their careers. This may also be perceived
by others as unprofessional behavior, affecting both the status driver and the
soft-goal of advancing the career.
6 Ferreira, L. Bax, M., and Yu, E.
Fig. 3. Beehive leaderboards implications
The analysis above suggests that the designed incentives in the Beehive gam-
ification experience fail to provide the satisfaction of the basic needs which are
precursors to human motivation. The SDT states that rewards, such as awarding
points and visibility to the best performers, without providing a sense of compe-
tence both for the individual (self-esteem) and for their peers (reputation), are
not effective in increasing people’s motivation. Perhaps the only positive aspect,
as shown in Figure 3, is that when the user is visible among the most participa-
tory users of the social network, the employee is showing himself engaged in a
project sponsored by the company’s management.
4 Conclusion
The presented approach promotes a structured evaluation of the insertion of each
gamification element, supporting the analysis of the implications of the proposed
incentives concerning the user’s intentional perspective. The Beehive social net-
work analysis example shows how the model-based approach proposed by this
work supports the identification of challenges and conflicts of interest that oth-
erwise might not be noticed by designers. The identification and consideration
of these inconsistencies already during the design phase, besides mitigating the
project risks, may lead to significant savings to the organization and the de-
velopment of a tool more adherent to the organizational context. Due to space
limitations, this paper presents only an outline of the proposed approach, a more
Analyzing psychological goals in gamified applications using i* 7
comprehensive presentation can be found in [11], including other examples and
an empirical case study.
Case studies have been carried out to illustrate and validate the usefulness of
the proposed method. Still, a more formal and comprehensive evaluation would
be an important contribution to investigate further the practical utility of the
presented approach. A possible way to approach this is through interviews with
gamification specialists. A structured evaluation by experts with experience and
practical knowledge on gamification projects would further support the practical
utility of the proposed model-based approach.
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