FINDING DARK PATTERNS IN CASUAL MOBILE GAMES USING HEURISTIC EVALUATION - UNDERGRADUATE THESIS PAPER - FACULTY OF SCIENCE AND COMPUTER SCIENCE ...
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FINDING DARK PATTERNS IN CASUAL MOBILE
GAMES USING HEURISTIC EVALUATION
UNDERGRADUATE THESIS PAPER
By:
Refal Pradama Dahlan
105216029
FACULTY OF SCIENCE AND COMPUTER SCIENCE
DEPARTMENT OF COMPUTER SCIENCE
UNIVERSITAS PERTAMINA
JULI 2020
FINDING DARK PATTERNS IN CASUAL MOBILE
GAMES USING HEURISTIC EVALUATION
UNDERGRADUATE THESIS PAPER
By:
Refal Pradama Dahlan
105216029
FACULTY OF SCIENCE AND COMPUTER SCIENCE
DEPARTMENT OF COMPUTER SCIENCE
UNIVERSITAS PERTAMINA
JULI 2020
LEMBAR PENGESAHAN
Finding Dark Patterns in Casual Mobile Games
Judul Tugas Akhir :
Using Heuristic Evaluation
Nama Mahasiswa : Refal Pradama Dahlan
Nomor Induk Mahasiswa : 105216029
Program Studi : Computer Science
Fakultas : Science and Computer Science
Tanggal Lulus Sidang Tugas Akhir : 6 Juli 2020
Jakarta, 16 Juli 2020
MENGESAHKAN
Pembimbing I : Nama : Meredita Susanty, M.Sc.
NIP : 116020
MENGETAHUI,
Ketua Program Studi
Muhamad Koyimatu, Ph.D.
NIP. 116108
ABSTRACT
Refal Pradama Dahlan. 105216029. Finding Dark Patterns in Casual Mobile Games Using
Heuristic Evaluation.
The popularity and monetary success of casual games are owed to many factors, but some of
them that are not entirely honest and ethical. One of them comes in the form of the imple-
mentation of dark pattern, a design pattern that negatively affects the experience of playing the
game they’re implemented in. This research unveils the dark patterns most commonly used
in popular and profitable casual mobile games, using heuristic evaluation conducted by three
undergraduate student evaluators that have sufficient domain knowledge and experience of the
topic at hand. Three of those dark patterns are: (1) Pay to Skip: When the game sells various
game elements that allow the players to skip some of the its core challenge, (2) Grinding: When
the game forces the players to sit through repetitive mechanics to make progress in the game,
and (3) Playing by Appointment; When the game forces the player to play it during a specific
time period, through the use of rewards or punishments. The findings of this research should act
as a counter-guideline on developing a casual game, as well as and provide the basis on future
discussions on ethical game design.
Keywords: Dark pattern, Game design, Human computer interaction
Universitas Pertamina - iPREFACE
All praises to Allah, Lord of the worlds, The Entirely Merciful, the Especially Merciful, for
His gracious mercy in enabling me to finish this undergraduate thesis. This research entitled
”Finding Dark Patterns in Casual Mobile Games Using Heuristic Evaluation” is submitted as
a part of the requirements in completing the Bachelor’s (S-1) Degree at the Department of
Computer Science, Universitas Pertamina.
Plenty of individuals have generously contributed their time and effort to the production of this
research report. First of all the author would like to express his sincere and deepest gratitude
to his sole advisor, Meredita Susanty, M.Sc., who have provided valuable insights, corrections,
suggestions, and many other form of support that extend far beyond what is expected of a stu-
dent’s research advisor, as well as for her lectures a few semesters back on the topic of this
research. Additionally, the author would like to express his gratitude to his research progress
examiner, Intan Oktafiani, S.Kom., and Rangga Ganzar Noegraha, Ph.D., as well as Hani Ra-
madhan, M.Kom., M.Sc., who have provided additional insight to the improvement of this
research report, and by extension to all of the lecturers of the Computer Science Department for
the knowledge and wisdom that they have passed on to him.
The author also gives his gratitude to all of the research partipants for their cooperation and
commitment throughout the whole duration of the research. The author would also like to
express his massive gratitude and affection to all of his classmates of the Computer Science’s
Class of 2016 — that he doesn’t have it in his heart to only name a select few — for their
supportive attitude and for painting such a wholesome and positive color in all of the classes
and off-campus activities that they went through together.
Finally, the author would like to express his eternal gratitude and love to his parents, sisters, and
grandparents, for their prayers, emotional support and constant encouragement to keep pushing
through when the going gets tough.
This undergraduate thesis, imperfect as it is, is hopefully able to provide positive contributions
to the topic at hand, and to bring educational value to any of those that reads it.
Jakarta, 14 June 2020
Refal Pradama Dahlan
Universitas Pertamina - iiTABLE OF CONTENTS
ABSTRACT i
PREFACE i
TABLE OF CONTENTS iii
LIST OF TABLES v
LIST OF FIGURES vi
CHAPTER I INTRODUCTION 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Thesis Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Problem Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.4 Potential Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.5 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.6 Research Timetable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
CHAPTER II LITERATURE REVIEW 3
2.1 Casual Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Dark Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.3 Usability and Heuristic Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.4 Severity Rating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
CHAPTER III METHODOLOGY 8
3.1 Research Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Evaluation Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Heuristic Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.4 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
CHAPTER IV RESULTS AND DISCUSSION 14
4.1 Chosen Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Universitas Pertamina - iii4.2 Chosen Evaluators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.3 Evaluation Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.3.1 Grinding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3.2 Playing by Appointment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.3.3 Pay to Skip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3.4 Pre-Delivered Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.3.5 Monetized Rivalries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.3.6 Social Pyramid Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3.7 Impersonation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.4 Result Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
CHAPTER V CONCLUSION AND SUGGESTIONS 23
REFERENCES 25
Heuristic Evaluation Result 27
Consent Form 47
Heuristic Evaluation Brief 48
Universitas Pertamina - ivLIST OF TABLES
Tabel 1.1 Research Timetable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Tabel 2.1 Summary of Nielsen and Molich’s four experiments after aggregation . . . . 5
Tabel 2.2 Summary of Nielsen’s experiments after aggregation . . . . . . . . . . . . . 6
Tabel 2.3 Factors considered in a given severity rating . . . . . . . . . . . . . . . . . . 7
Tabel 3.1 Heuristic Evaluation Form . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Tabel 3.2 Descriptions of the Severity Ratings/Score . . . . . . . . . . . . . . . . . . . 12
Tabel 4.1 Final ranking of the games after assigning ranking score . . . . . . . . . . . 14
Tabel 4.2 Severity ratings of each dark patterns . . . . . . . . . . . . . . . . . . . . . . 16
Tabel 4.3 Commonness of each dark patterns . . . . . . . . . . . . . . . . . . . . . . . 17
Tabel 4.4 Result Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Universitas Pertamina - vLIST OF FIGURES
Gambar 3.1 Data Gathering and Analysis Flowchart . . . . . . . . . . . . . . . . . . . . 8
Gambar 3.2 Steps taken to filter the games to be evaluated; (a) The Categories tab on the
Games menu, (b) The Casual game menu, (c) The Top Charts section in the
Casual game menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Gambar 3.3 Steps taken to select the games to be evaluated; (a) Scoring the games from
each chart, (b) Listing games that are overlapped by two or more charts, (c)
Tallying the scores from step (a) from the games selected from step (b) . . . 9
Gambar 4.1 Top-ranked games in the Casual genre in Google Play Store; (a) Top Free
chart, (b) Top Grossing chart, (c) Trending chart . . . . . . . . . . . . . . . 14
Universitas Pertamina - viCHAPTER I
INTRODUCTION
1.1 Background
There are many factors that contribute to the spread and ubiquitousness of casual games on the
general public. Kultima proposed a framework that listed four casual game design values; accept-
ability, accessibility, simplicity, and flexibility, all of which explains how they can attract people that
don’t normally play games to try them out [1]. A research conducted by Harrigan et al. in 2010 has
compared some of casual games’ gameplay elements and mechanics to that of a slot machine, such as
attractive visual and auditory feedbacks, competitive elements, illusory rewards, etc., and how they
can be utilized to keep players engaged on playing the game [2].
However, the success of casual games aren’t always owed to positive and wholesome factors like
above. Some factors can be considered morally ambiguous at best, and legally questionable at worst.
One of these factors come in the form of dark pattern, a term coined by Harry Brignull, describing
a user experience (UX) design patterns that are used as tricks to exploit and manipulate users —
or in this case, players— into doing something they’re not fully consenting to, and/or to generally
worsen the user experience, sometimes done with malicious intents in order to benefit the owner of
the application or website [3, 4].
In the past, there have been a couple extensive academic research on dark pattern. The first
one was conducted by Mathur et al., analyzing ˜11K shopping websites and discovered 1,818 dark
patterns on them [5]. The other one was conducted by Zagal et al., listing and categorizing a set of
dark patterns typically found on video games in general [4].
This research is going to expand on the findings from [4], by focusing on the usage of dark
patterns in casual games, discover which ones are most commonly seen in the most popular and
profitable casual games on the mobile market, what form they’re often implemented in, and how they
impact the players and their playing experience (the user experience of playing a game).
1.2 Thesis Statement
What type of dark patterns are most commonly used in popular and profitable casual mobile
games, and what is the assumed impact on player experience in those instances?
1.3 Problem Scope
As of the writing of this research, Android still holds overwhelming domination in the smartphone
operating system (OS) market [6, 7], thus the games analyzed will be games that are available on the
Play Store, the official app store for Android OS.
There are two metrics that determine the games that are going to be chosen, popularity and profit.
”Popularity” refers to the Downloads count in the Play Store, regardless of the game rating. ”Profit”
refers to how much players are spending real money in the game. And thus five games are going to
be chosen from the ”Top Free”, ”Trending”, and ”Top Grossing” charts in the ”Casual” genre in the
Google Play Store.
Universitas Pertamina - 1This research is going to focus solely on the dark patterns found on those games. Other previously
mentioned game elements that contribute to their success such as fun gameplay mechanic, striking
visuals, catchy music, or anything that is not considered unethical and manipulative does not count as
dark pattern, and thus is not going to be discussed in this research.
1.4 Potential Outcomes
Shed light on the dark patterns that game developers often adhere to when developing a casual
mobile game for the masses, and, if possible, reveal more dark patterns that previous researches might
have missed, and document them for future use.
1.5 Contributions
This research aspires to inspire an understanding and rationale on how dark patterns are used
in casual mobile games, as well as to provide a reference to be used in future discussions on dark
patterns, e.g. the formation of new laws regarding ethical game designs, implementation of new
application/game marketplace policies, etc.
1.6 Research Timetable
Table 1.1. Research Timetable
Activities W1 W2 W3 W4 W5 W6 W7 W8
Game Installation
Appointing Evaluators
Evaluation Preparation
Heuristic Evaluation
Analysis
Universitas Pertamina - 2CHAPTER II
LITERATURE REVIEW
2.1 Casual Games
In a 2013 study, Chiapello pointed out the many contradictions and discussions between experts
and researches as to what can be constituted as a casual game [8]. The discussion is still ongoing
as of the writing of this research and thus no clear definition has been agreed on so far, but for the
sake of brevity, a 2009 study by Kultima is going to be used as a basis on this topic, as it provides a
clear-cut framework that gives contextual understanding of the nature of the games that are going to
be discussed in this research [1]. Kultima proposed the framework in the form of four design values
of casual games, namely:
1. Acceptability. Casual games have to adhere to widely accepted social contracts and as such
should strive to appeal to as broad range of audience as possible, by avoiding offensive topics,
endorsing positive values, encouraging constructive behaviors, etc.
2. Accessibility. Casual games need to be able to be accessed and played by as many people
as possible despite various personal limitations and/or disabilities, by lowering the mental and
physical requirements to play, simplifying the rules and mechanics, etc.
3. Simplicity. Casual games need to be simple and easy to play as to lower the barrier of entry
to the game, by cutting down complex mechanics and rules, simplifying controls and interface,
automating some activities such as saving, etc.
4. Flexibility. Casual games need to be able to adapt to players and their real life situation, by
allowing them to switch focus to other activities with little in-game repercussions, allowing
them to tweak some game functions so that they can define their own objectives, etc.
2.2 Dark Pattern
As briefly explained in the previous section, Dark Pattern is a set of design patterns that are
constructed with the intent of manipulating the users of a certain application or website [5, 3]. Dark
patterns in video games [4] consists of three different categories based on the effects that they inflict
on the players:
1. Temporal Dark Patterns
Dark patterns that revolve around manipulating players’ time commitment when playing the
game.
(a) Grinding: Forcing players to perform repetitive tasks in order to make progress in the
game.
(b) Playing by Appointment: Incentivizing or even forcing players to play the game during
a specific range of time.
Universitas Pertamina - 32. Monetary Dark Patterns
Dark patterns that involves requiring players to make a financial investment in the game.
(a) Pay to Skip: Allowing or even encouraging players to spend money to make progress in
the game easier and faster.
(b) Pre-Delivered Content: Requiring players to pay to unlock certain content despite it
being already included in the game (i.e. in the installation disc or the downloaded files).
(c) Monetized Rivalries: Allowing or even encouraging players to spend money to gain a
competitive edge against other players.
3. Social Capital-Based Dark Patterns
Dark patterns that involves social or human capital interactions.
(a) Social Pyramid Schemes: Encouraging or sometimes forcing players to invite people to
join them in the game to gain in-game incentives or make progress.
(b) Impersonation: Showing representations (impersonations) of people related to the play-
ers, to make the game more relatable, leading to a negative impact on their social relations.
On a side note, one of the business model that casual mobile game developers use is freemium
[9, 10]. In this model, the games are distributed and are downloadable for free, but they offer certain
features and perks to the player at a price, for a premium experience, hence the name, freemium,
which is a portmanteau of free and premium.
This particular business model is closely related to the dark patterns in the monetary dark patterns
category. Adopting a freemium business model doesn’t automatically mean that the game implements
dark patterns in this category, so long as the premium experience that it offers is not gameplay-related,
or in other words, it’s only cosmetic in nature. It’s only when the game clearly favors the premium
players in terms of playing experience can it be accused of employing a monetary dark pattern, as
will be explored later in the next few chapters.
2.3 Usability and Heuristic Evaluation
ISO 9241-11 defines usability as ”the extent to which a product can be used by specified users
to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use”
[11]. Similarly, Nielsen defines usability as the efficiency, user satisfaction, memorability, ease of
recovery from errors, and learnability in a given user interface [12].
Nielsen and Molich stated that there are four methods to evaluate the usability of a user interface;
formally, which involves the use of some analysis technique, automatically, which requires a proce-
dural approach assisted with computer, empirically, which involves experiments with test users, and
heuristically, which involves evaluators and a set of usability principles (heuristics) as the basis of the
evaluation [13]. The latter is often referred to as heuristic evaluation.
While it has never been formally noted that dark patterns can be discovered with methods associ-
ated with usability, based on the explanations above, it can be generally understood that dark patterns
is essentially a design pattern that aims to go against widely known usability principles, or as Zagal
et al. put it, an ”anti-pattern”. Based on this association, this research is thus going to approach dark
patterns the way one normally would approach conventional usability principles.
Universitas Pertamina - 4As mentioned above, heuristic evaluation involves a set of evaluators (more on this later) evalu-
ating a user interface in regards to a given heuristics, i.e. how much it conform or deviate from them
[13]. Zagal et al.’s aforementioned list of dark patterns is going to be the heuristics in this research,
thus those are the design patterns that the evaluators are going to focus on during the evaluation pro-
cess. Heuristic evaluation is often described as the method that is more relatively inexpensive, fast,
and easy to do compared to other usability evaluation methods [13, 14], which is why it’s chosen as
the method for this research.
In most research papers and academic journals, heuristic evaluation is specified as the usability
evaluation method that utilizes experts or ”usability specialists” as the participant [15, 12, 16]. How-
ever, there are a couple studies that challenge this particular element of heuristic evaluation, which
are going to be the basis to more clearly define the level of competence or expertise that is expected
of the evaluators, and how many of them should be involved in the activity.
The first one is by Nielsen and Molich, in which they conducted four experiments with three
different evaluator groups [13]. The evaluators are specifically selected to be non-usability experts,
such as computer science students who took a user interface design class, and industrial computer
professionals.
The result shows that despite the large number of evaluators, even in the best case scenario, each
individual evaluator can find on average only half of all of the existing usability problems in the
system. However, the result improve significantly once the findings of the evaluators are aggregated,
shown in Table 2.1.
Table 2.1. Summary of Nielsen and Molich’s four experiments after aggregation
Usability Problems Found After Aggregating Evaluators
Experiments
1 Evaluator 2 Evaluators 3 Evaluators 4 Evaluators 5 Evaluators
Teledata 51% 71% 81% 90% 97%
Mantel 38% 52% 60% 70% 83%
Savings 26% 41% 50% 63% 78%
Transport 20% 33% 42% 55% 71%
Similarly, in a different research, Nielsen evaluated a telephone operated interface with three
different evaluator groups [14];
1. ”Novice” evaluators; people who are acquintated with computers, but not usability and its
principles.
2. ”Regular” usability specialists; people with graduate degree (formal education) in usability,
and/or industrial usability practicioners.
3. ”Double” usability specialists; usability specialist with domain expertise of the user interface
being evaluated.
As expected, individuals within the ”novice” group performs the worst of all three, and while
there’s a significant difference between individuals in the ”regular” and ”double” groups (the latter
performs, again, as expected, better than the former), both are only capable to find around half of the
total usability problems that exist in the user interface.
Universitas Pertamina - 5However, consistent with the previous study, once the the findings of individual evaluators are
aggregated, the result shows a significant improvement.
Table 2.2. Summary of Nielsen’s experiments after aggregation
Before Aggregation After Aggregation
Evaluator Group
Avg. Avg.
No. of Evaluator No. of Evaluator
Problems Found Problems Found
”Novice” evaluators 1 22% 14 >75%
”Regular” specialists 1 41% 3-5 74-87%
”Double” specialists 1 60% 2-3 81-90%
Based on these findings, a general conclusion can be drawn that a proper heuristic evaluation
would require the following in regards to the evaluators involved:
1. A formal education in user interface design or human-computer interaction in general, and
are given proper briefing in the heuristic evaluation process. A domain expertise in the user
interface that is evaluated is a major plus.
2. A group of 3-5 persons evaluating the same user interface, to which their findings would later
be aggregated.
2.4 Severity Rating
A grading system called severity rating [17] is used to rate a usability problem. This grading
system is suggested as a basis to allocate resources to fix the problems. The more severe the problem,
the more resources assign to fix it.
The rating goes from 0 to 4, and is a combination of three factors, described in Table 2.3.
Table 2.3. Factors considered in a given severity rating
Factor Description
Frequency How often does the usability problem appears?
Impact How hard is it to overcome?
A combination of the above; how hard is it to overcome,
Persistence
even after encountering it several times?
In [4] it is noted that it is common to combine all the factors above in a single rating, that is, an
implicit consideration of them has been made when an evaluator gives a severity rating.
Nielsen also mentioned that the ratings alone aren’t enough. The evaluators would also need to
describe the problems they found in reasonable details, and provide the necessary attachments to that
description, e.g. screenshots and notes on exactly when and where the problem occurs.
Lastly, a severity rating from a single evaluator is hardly enough to accurately judge the accuracy
of said rating, Nielsen considered that only the mean of a set of ratings given by at least three evalu-
ators can be considered satisfactory. This consideration is consistent with the previous conclusion of
Universitas Pertamina - 6how many evaluators are needed in a heuristic evaluation, and thus, any discrepancies in the usability
problems found by each evaluator should be covered if this mean of severity ratings is combined with
the aggregated problem descriptions that they wrote.
Universitas Pertamina - 7CHAPTER III
METHODOLOGY
This research will be conducted in four phases; (1) research preparation, (2) evaluation prepara-
tion, (3) heuristic evaluation, and finally (4) data analysis, as illustrated in Figure 3.1.
1. Research Preparation: Preparing the games that are going to be evaluated and appointing the
suitable evaluators.
2. Evaluation Preparation: Preparing the brief for the evaluation process and the heuristic eval-
uation form.
3. Heuristic Evaluation: The heuristic evaluation process itself.
4. Data Analysis: Compiling the evaluation data and analyzing the result.
Figure 3.1. Data Gathering and Analysis Flowchart
3.1 Research Preparation
The first thing that needs to be prepared are the games that are going to be evaluated in this re-
search. Upon opening the Google Play Store, the following steps are taken to pick the appropriate
games; (1) Go to the ’Games’ menu, (2) Pick ’Casual” on the ’Categories’ tab, (3) Go to the ’Top
Charts’ section, (4-6) Pick the top ranked games from the highlighted tabs; ’Top Free’, ’Top Gross-
ing’, and ’Trending’. These steps are illustrated in Figure 3.2.
Universitas Pertamina - 8Figure 3.2. Steps taken to filter the games to be evaluated; (a) The Categories tab on the Games
menu, (b) The Casual game menu, (c) The Top Charts section in the Casual game menu
Following that, up to ten games from each top charts are listed and are given scores that are
inversely proportional to their rankings (e.g. 1st ranking gets 10 points, 2nd ranking gets 9 points,
3rd ranking gets 8 points, and so on). The three lists are then compared, and the five games that both
appear in at least two charts and have the highest scores are the ones that are going to be used in this
research. These steps are illustrated in Figure 3.3.
Figure 3.3. Steps taken to select the games to be evaluated; (a) Scoring the games from each chart,
(b) Listing games that are overlapped by two or more charts, (c) Tallying the scores from step (a)
from the games selected from step (b)
Universitas Pertamina - 9Once the games are picked, the next step is appointing the evaluators. Five evaluators are going
to be involved in this research, and should fulfill the requirements described in Section 2.3. While
there arent any particularly sensitive data involved in this research, it is ethically important to require
the evaluators to sign informed consent form and give appropriate informations regarding the nature
of the experiment; from the details of the activities that they’re going to go through, to the treatment
of the data that they’re providing [18].
3.2 Evaluation Preparation
Before the actual evaluation process are conducted, the evaluators are first briefed the following:
• A recap on HCI and heuristic evaluation. While the evaluators are expected to already have
at least a basic understanding in this subject, a short recap could greatly help them recall their
knowledge on the concept of usability, usability goals, usability evaluation, etc. and make sure
that everyone is on the same page during the evaluation process.
• The definition of dark patterns and some notable examples. Given the qualifications, the evalu-
ators should likely have encountered plenty dark patterns in some of the application or website
that they use everyday, as well as the games that they’ve played. However, they might not
know that they’re a design pattern that are malicious in nature or even realize that they were
manipulated by one. An explanation with use cases could help solidify their understanding of
these design patterns and paint the picture of what exactly they’re going to look for during the
evaluation.
• Evaluation procedure. After having a literary understanding of the topics at hand, the evaluators
are finally briefed of the technicalities of the activities that they’re going to be conducting.
During the evaluation process, the evaluators are instructed to fill out a form to record their find-
ings (details of the evaluation process itself is explained in the next step), presented in Table 3.1. It’s
important to keep in mind, however, that the list of dark patterns used in this form was gleaned from
video games in general, and since this research focuses on a specific genre, some dark patterns listed
here might not appear at all, and the ones that do might take a different form from what [4] have
originally defined them to be.
Table 3.1. Heuristic Evaluation Form
Evaluator’s Initials:
Game Name:
Publisher:
Evaluation Date:
Predefined Dark Patterns
1. Temporal Dark Patterns
Dark Pattern Name Score Notes
Grinding
A.
Forcing players to perfrom repetitive tasks in
order to make progress in the game.
Universitas Pertamina - 10Playing by Appointment
B.
Incentivizing or even forcing players to play
the game during a specific range of time.
2. Monetary Dark Patterns
Dark Pattern Name Score Notes
Pay to Skip
A.
Allowing or even encouraging players to
spend money to make progress in the game
easier and faster.
Pre-Delivered Content
B.
Requiring players to pay to unlock certain
content despite it being already included in
the game (i.e. in the installation disc or the
downloaded files).
Monetized Rivalry
C.
Allowing or even encouraging players to
spend money to gain competitive edge against
other player.
3. Social Capital-Based Dark Patterns
Dark Pattern Name Score Notes
Social Pyramid Schemes
A.
Encouraging or sometimes forcing players to
invite people to join them in the game to game
in-game incentives or make progress.
Impersonation
B.
Showing representations (impersonations) of
people related to the players, to make the
game more relatable, leading to a negative
impact on their social relations.
Evaluator’s Found Dark Patterns
Dark Pattern Name Score Notes
(Dark Pattern Name)
#
(Dark Pattern Description)
3.3 Heuristic Evaluation
After a briefing session, evaluators then proceed to evaluate the games independently, with as
little communication with one another as possible, to reduce bias in the insight gained from them
[19, 20].
Since the dark patterns are scattered across the whole game (i.e. they don’t exist exclusively in
a certain part of the interaction design), the evaluators are allowed to approach each game with no
particular instruction, but are required repeat an interaction sequence (e.g. choosing a level, playing
the level, and then returning to the main menu) at least twice; the first one is to get the general flow
of the interface, and the second one is to focus on a specific element and how they affect the bigger
picture.
Universitas Pertamina - 11Due to the simplistic nature of the games evaluated, the evaluators are only given about thirty
minutes to finish exploring and trying out the various mechanics of a single game before moving on
to the next one, with a half-an-hour extension if they need more time.
After finishing a single game, the evaluators then fill out the form shown in the previous step. The
Score column is for the severity rating for each dark pattern, with the description presented in Table
3.2 (modified from Nielsen’s to better suit the context of this research).
Table 3.2. Descriptions of the Severity Ratings/Score
Score Description
0 This dark pattern is not found in the game.
1 This dark pattern exists, but poses little to no harm to the playing experience.
2 This dark pattern exists and minorly affect the playing experience; it causes annoy-
ance to the player, but are not that big of a hurdle that they can’t overcome themselves.
3 This dark pattern exists and majorly affect the playing experience; enough to frustrate
them into considering seeking external help to play the game (e.g. looking up game
walkthroughs, using real money to get power-ups or to skip a hard level, etc.).
4 This dark pattern actively harms not only the playing experience, but also the player’s
mentality in approaching the game or ones similar to it (e.g. conditions them to rely
on in-app purchases whenever they face a difficult situation), or even quitting the
game entirely.
The Notes column is to document where, when, and how the dark pattern appears in the game,
and their opinions on how they think it might affect the players.
Finally, after a short break after filling out the form, they continue to the next game, and so on
until all the evaluators have finished evaluating all of the games that they’re tasked with.
One last thing to note regarding the result of the heuristic evaluation, is that they are inherently
subjective in nature; the insight that the evaluators produce is ultimately based on their individual
experience and worldview. This is why, as explained in the previous chapter, this research involves
qualified evaluators to make sure that the evaluation result is as close to being objective as possible,
since they’re capable of empathizing with more user perspectives than less knowledgeable or skilled
evaluators are able to do. Multiple evaluators are also included for the same reason; their numbers
make up for any usability problems that any indiviual evaluator might miss.
3.4 Analysis
After clarifying any ambiguity from the data with the evaluators and conducting further literature
studies, the data gathered is then compiled, and a detailed report is composed, consisting of:
1. A list of dark patterns (Zagal et al.’s and the new ones discovered by the evaluators, if any)
found in the games.
2. The mean of each dark pattern’s severity ratings.
3. The percentage of each dark pattern’s commonness; based on the number of evaluators that
encounter a particular dark pattern.
4. A pattern analysis on where, when and how each dark patterns appear in the games, that also
describes their general impact on the playing experience.
Universitas Pertamina - 12CHAPTER IV
RESULTS AND DISCUSSION
4.1 Chosen Games
As of 23rd February 2020, presented in Figure 4.1 are the top ranked games in the ’Casual’ genre
in Google Play Store.
Figure 4.1. Top-ranked games in the Casual genre in Google Play Store; (a) Top Free chart, (b) Top
Grossing chart, (c) Trending chart
After assigning scores to all games in each chart, presented in Table 4.1 is the overall ranking of
the games:
Table 4.1. Final ranking of the games after assigning ranking score
’Top Free’ ’Top Gross- ’Trending’ Total
Game
Score ing’ Score Score Score
Little Big Snake 8 0 7 15
Candy Crush Saga 7 7 0 14
Homescapes 5 8 0 13
Gardenscapes 3 10 0 13
Lucky Time 6 0 6 12
Township 1 6 0 7
According to the result in Table 4.1, the games that should be chosen are Little Big Snake, Candy
Universitas Pertamina - 13Crush Saga, Homescapes, Gardenscapes, and Lucky Time. However, upon further inspection, Lucky
Time can hardly be considered a game at all; it is a digital casino involving real money that’s disguised
as a casual game, and thus is dropped from the list entirely, and is replaced by Township. Below is
the final list of the games chosen for this research:
1. Little Big Snake (LBS), a player-versus-player (PVP) game in which a large number of players
control a snake and try to outgrow everyone by eating various foods scattered accross the game
arena and by blocking opposing players’ path (the game character dies when it bumps to other
players).
2. Candy Crush Saga (CCS), a ’match-3’ puzzle game [21] in which the player goes through
various levels by matching three or more same-colored candies tiled on top of variously-shaped
game board in order to clear the board using as few moves as possible.
3. Homescapes (HS) and Gardenscapes (GS), also a match-3 game, but with the added mechanic
of decorating your house and garden respectively, using the points you accumulated by playing
the main game.
4. Township, a city management simulation game in which the player is tasked with the manage-
ment and expansion of a city by constructing various buildings such as houses, factories, farms,
etc. Lewis et al. referred this type of game as a ’Ville type game, derived from its similarity by
a game named FarmVille, one of the few games that brought SNG (Social Network Game) to
the mainstream [22].
4.2 Chosen Evaluators
The five evaluators chosen are senior-year undergraduate computer science student, who have
graduated from their HCI class and have finished the student project associated with it. The evaluators
have also played a variety of video games for a total of 10,000+ hours, and have once developed and
published an indie game for a competition.
Their educational background and personal experience equip the evaluators with sufficient tech-
nological and game literacy to put them between ”regular” and ”double” specialist group according
to [14], and should be enough to find ˜80% of the dark patterns that are present in the games they’re
evaluating. The initials that they’ve consented to be addressed as in this research are AAS, AFH,
AFR, MRD, and MRW.
4.3 Evaluation Result
The first data that needs to be discussed is the severity ratings of each dark patterns. The ratings
should give an estimate of how prevalent the dark patterns are in games evaluated in this research,
given that they represent how frequent, impactful, and persistent they are in hampering the playing
experience of said games, as previously explained. The result is summarized in Table 4.2.
Table 4.2. Severity ratings of each dark patterns
Evaluator
No. Dark Pattern Game Average
AAS AFH AFR MRD MRW
1. Temporal Dark Patterns
Universitas Pertamina - 14LBS 3 0 3 1 3
CCS 2 2 2 1 2
A. Grinding HS 2 2 2 1 2 1.8
GS 2 2 2 1 2
TS 3 0 2 2 1
LBS 0 2 2 0 1
CCS 1 1 3 1 0
B. Playing by Appointment HS 1 1 2 1 2 1.44
GS 1 1 2 1 2
TS 2 1 2 3 3
2. Monetary Dark Patterns
LBS 3 2 3 0 2
CCS 3 1 2 2 0
A. Pay to Skip HS 3 1 3 1 3 2.12
GS 3 1 3 1 3
TS 4 3 1 3 3
LBS 0 1 2 1 0
CCS 0 1 0 0 0
B. Pre-Delivered Content HS 0 2 0 0 0 0.44
GS 0 2 0 0 0
TS 0 2 0 0 0
LBS 3 1 0 2 3
CCS 2 2 3 0 0
C. Monetized Rivalries HS 0 1 0 0 0 0.76
GS 0 1 0 0 0
TS 0 1 0 0 0
3. Social Capital-Based Dark Patterns
LBS 2 1 1 1 0
CCS 1 1 0 0 0
A. Social Pyramid Schemes HS 1 1 0 1 0 0.72
GS 1 1 0 1 0
TS 2 1 0 2 0
LBS 2 2 0 0 0
CCS 2 1 3 0 0
B. Impersonation HS 0 0 3 0 0 0.68
GS 0 0 3 0 0
TS 0 0 0 0 0
Table 4.2 shows that at most, the evaluators only deem all of the dark patterns to be of a lower-
priority usability problem, given that the highest overall average of the severity ratings is 2.12, and
the lowest is 0.44.
The commonness of each dark pattern is presented in Table 4.3. Commonness is different from
frequency, one of the factors to consider in severity rating. Commonness refers to the percentage
of the evaluators’ encounters with a particular dark pattern, and is measured to either contrast or
complement the severity of a dark pattern.
Universitas Pertamina - 15In this research in particular, given that there are five games and five different evaluators, every
dark patterns has up to twenty-five chances of being encountered by an evaluator in any given game.
Commonness refers to the amount of encounters out of those twenty-five chances of it appearing. For
instance, a dark pattern may be considered severe if it has a severity rating average of 3.5, but it can
also be considered rare if it only appears 5 out of 25 times; a 20% percentage of commonness.
An encounter is counted as found every time any evaluator gives a severity rating between 1-4
to any given game in a particular dark pattern, and not found for every 0 severity rating, since it’s
defined as ”This dark pattern is not found in the game” in Table 3.2.
Table 4.3. Commonness of each dark patterns
Encounter (Out of. 25)
No. Dark Pattern Commonness
Found Not Found
1. Temporal Dark Patterns
A. Grinding 23 2 92%
B. Playing by Appointment 22 3 88%
2. Monetary Dark Patterns
A. Pay to Skip 23 2 92%
B. Pre-Delivered Content 7 18 28%
C. Monetized Rivalries 11 14 44%
3. Social Capital-Based Dark Patterns
A. Social Pyramid Schemes 15 10 60%
B. Impersonation 7 18 28%
However, this summary is too general in nature and may not necessarily paint an accurate picture
of how they emerge in the games that uses it, and which one tend to be used in which particular type
of game, hence each one of them would need to be inquired in more details.
4.3.1 Grinding
Grinding is one of the most common dark pattern in all of the games that are evaluated in this
research, as shown in Table 4.3. In [4] it is noted that grinding is frowned upon due to its emphasis
on the player’s time commitment on the game over their skill that are normally acquired by actively
practicing the game’s mechanics. However, such emphasis is the common nature of casual games; it
relatively requires the least technical skill to play compared to other genre, thus it makes sense that
the all of the evaluators give at least a score of 1 in this dark pattern, as shown in Table 4.2.
The evaluators commented that grinding is the gameplay in all of the games mentioned above;
the goal and mechanic stays the same no matter how much the player has progressed in the game.
However, it can also be said to be the unavoidable consquence of games of the casual genre, that is,
since the gameplay is inherently simplistic, as explained in Section 2.1, the players would naturally
grow to feel that the game is becoming repetitive and even dull the further the game progresses. And
thus, while the game’s core mechanics can be classified as grinding, it is still up to debate whether or
not this dark pattern can be considered unethical in this particular genre.
Universitas Pertamina - 164.3.2 Playing by Appointment
In all of the games evaluated in this research, the evaluators found that this dark pattern appears
in either of these three forms: daily attendance, daily challenge, and daily draw (the exact semantic
varies from game to game). Daily attendance is when the game gives the player various rewards when
they open it for the first time in a day, be it a power-up (e.g. Candy Crush Saga has Boosters that
can help the player clear up a level) or in-game currency (e.g. Coins and Tcash in Township, the
former is mainly used to construct buildings, grow crops, etc. while the latter is often used to speed
up those processes). The reward is changed every day and normally the player can only claim one that
day. There’s usually a reward with a much better quality or rarity once the player reaches a certain
milestone of attendance, which incentivizes them to open the game at least once every day.
The second form of this dark pattern is daily challenge, which as the name implies, is when the
game gives a challenge to the player in order to gain rewards in addition to the ones that they get
from daily attendance. While daily attendance incentivizes the player to simply open the game, daily
challenge incites them to actually play the game. The game usually gives multiple challenges that
they can tackle every day, and some games even continously give out new sets of challenges that
gradually gets harder to complete but also awards greater rewards. Since the challenges are usually
simple and easy enough to complete (e.g. kill x amount of enemies in a single round, gain x score
while using y character, etc.), it can be said that this the game developers’ way of ’warming up’ the
players to play for a short while, to which they may be tempted to keep playing even after they’ve
completed all of the challenges.
The last form of this dark pattern is daily draw, which is similar with daily attendance in regards
to its lack of requirement of having to play the game first to obtain the rewards, but presents said
rewards in a different manner. With daily attendance, the player can usually see the rewards (or at
least their level of quality or rarity) that they’re going to get for taking x days of attendance in the
game, and it’s usually presented in a calendar-like visual, likely to motivate them to maintain their
attendance get the bigger reward down the line. Daily draw, however, uses a randomized approach of
presenting the rewards. The visual varies from game to game, some uses chests or crates, some uses
spinning wheel or slot machine, but the point is that the rewards that the player can get are not set in
stone.
All three of the forms described above prods the players to make some sort of appointment with
the game. The time restriction imposed upon them is similar with the example used in [4], in the
aforementioned FarmVille. In that game, once the player plants a crop, not only do they have to wait
for it to be ripe for harvest, they also need to harvest it within a certain amount of time once it does,
otherwise it will wither and loses its value. Most crops wither after 24 hours, and this is akin to how
daily attendance/challenge/draw incites to the player to check the game every day.
However, one critical point to consider is that all of the rewards presented to the players using the
above methods are not integral to the progression of the game. While it is true that some levels in
some of the games are really hard to the point that most players would resort to using said rewards, it
is still entirely possible to beat them in a conventional way. And thus, as [4] described, the darkness
of the patterns described above are significantly reduced or even nullified, as reflected in the scores in
Table 4.2, even if it’s the second most common dark pattern, as shown in Table 4.3.
Universitas Pertamina - 174.3.3 Pay to Skip
This dark pattern is the most prominent in terms of severity and commonness in all of the games
evaluated in this research, as reflected in the scores in Table 4.2 and in Table 4.3. Similar to the
previous dark pattern, there are three ways that this pattern can take form in; power-ups, second
chance, and delay-skipping. Power-ups are usually single-use items that give various advantages to
the player. While the cheaper power-ups only provide minor conveniences to the player, the more
expensive ones grant such a significant advantage that they almost entirely nullify the challenges that
the players would normally have to face. For example, in Little Big Snake, the game progression
comes in the form of Evolution, in which the player gradually upgrade their game character, by
levelling up and purchasing said upgrades using gold coins. While both can be obtained by simply
playing the game, the latter can be bought using real money, essentially skipping half of the game’s
challenge. Another example is in all of the match-3 games, in which players can obtain power-ups by
completing levels, getting rewards from the aforementioned daily attendance/challenge/draw, or by
buying them using an in-game currency, that again, can be bought using real money. These power-ups
vary in effects, but some of them cause such a massive chain reactions in the game board that it’s not
rare for the level to be cleared immediately after using them, and on the instance that it doesn’t, the
players only have little left to do to achieve that.
The second form is by offering second life in all of the games evaluated that have a certain losing
condition (i.e. Little Big Snake; when the character bumps into a wall or another character, and all of
the match-3 games; when they fail to complete the objective within a set amount of moves). Usually,
when the player dies in a match or fails to complete a level, the game offers an opportunity to ”play-
on”, to basically reset the character state, but not the level state. While this technically doesn’t skip
the level/round, allowing the player to continue playing after they’re supposed to lose is essentially
taking away half of the challenge that the players are expected to be able to overcome on their own.
The last form is unique to Township and other ’Ville type of games. As previously explained, the
point of these games are constructing buildings and producing resources, all of which are preceded
with a proportional delay to the quality of said buildings and resources. To play ’normally’, the
player would have to pace themselves and frequently check back to the game every so often so that
their town could still be productive even when they’re away. These delays are entirely skippable,
however, by paying with in-game currency, that once again, can be purchased with real money. As
the player advances further into the game, any meaningful progress that can be made is walled with
an increasingly substantial amount of delay, and thus paying to skip becomes less of an option and
more of a necessity.
4.3.4 Pre-Delivered Content
This dark pattern is essentially non-existent in all of the games evaluated in this research as re-
flected in the scores in Table 4.2 and Table 4.3. Three evaluators found a pattern resembling the
described behavior of this dark pattern; in Little Big Snake, the player can unlock a character skin, an
alternative visual to the game character by paying with Ruby, the in-game currency that are normally
obtained by purchasing it with real money. The evaluators seem to classify this trivial element as an
additional content, but even then, since it’s purely cosmetic in nature (it gives virtually no statistical
advantage to other players), they considered it as a non-issue.
Universitas Pertamina - 18One evaluator did find an instance of this dark pattern in the games evaluated in this research,
however. They found that other than selling cosmetic elements in a game shop, some games like
Gardenscapes, Homescapes, and Township offer some form of starter pack to the player, in which
the player can unlock a multitude of power-ups and bonuses right from the get-go by paying a certain
amount of in-game-currency, which would normally require playing the game for a few hours first to
unlock.
The reason why this pattern isn’t commonly found and isn’t as severe compared to other dark
patterns is speculated to be stemmed from the Accessibility value of casual games briefly explained in
Setion 2.1. One of the equivalent design principles that Kultima listed under this design value is ”Low
price and micropayments”, which dictates that casual games tend to be designed to be accessible with
little to no payments at all required from the players. This design value is translated to the common
tactic that mobile games developers to monetize their games. Mobile games are widely perceived
to be relatively more, again, accessible, compared to its hardcore counterpart. Hardcore games also
usually have more depth in terms of game mechanic and presentation, so it makes sense that additional
contents (e.g. story campaigns, characters, maps, etc.) cost more to develop and are paywalled by
developers. Conversely, mobile games content are relatively cheaper to develop, and the notion that
they’re simply a casual, lighthearted pastime would then make it hard for developer to convince the
players to pay for additional content.
4.3.5 Monetized Rivalries
The evaluators found this dark pattern in Little Big Snake primarily, as shown in the scores in
Table 4.2. Being a PVP game, the existence of the aforementioned Evolution mechanic and the
availability to speed up said mechanic with real money strike an imbalance in the game that should
solely base the outcome of its matches on the players’ skill. While the core mechanic of the game
are not completely reliant on statistical advantages, at the end of every match a question will arise on
whether its outcome is owed to the players’ technical finesse in the game mechanic, or to the financial
investment that they have made in the game.
As for the non-PVP games, some evaluators pointed out an observation that’s exactly the same
with the example used in [4]; while games like Candy Crush Saga and Gardenscapes are not an
inherently head-to-head competitive game, the existence of leaderboard that compares how the player
fared against other players (that the game displays when they completed a level) may incite the player
to use the aforementioned power-ups reach a high ranking, and subsequently urge them to buy new
ones once they run out of them. This dark pattern is fairly common despite it’s severity, as shown in
Table 4.3.
4.3.6 Social Pyramid Schemes
The game FarmVille is once again used as the example to describe this dark pattern in [4]. The so-
cial pyramid aspect is enforced by the game’s difficulty to progress in the game without the help of at
least eight friends to aid the player in doing various activities in the game (tend to each other’s farms,
send mutual gifts, etc.). However, while the games evaluated in this research do offer various rewards
to the player by inviting their friends to play the game, none of the evaluators found throughout the
duration of the evaluation that any of games impose the same level of responsibility to the invitee, and
Universitas Pertamina - 19thus this dark pattern is hardly considered particularly impactful, as reflected in the scores in Table
4.2, despite it being quite common, as shown in Table 4.3.
4.3.7 Impersonation
This dark pattern is one of the lowest in terms of severity and commonness, as reflected in the
scores in Table 4.2 and in Table 4.3. Some of them that do encounter it and find it impactful to the
playing experience commented that seeing familiar faces on various leaderboards might urge them to
do better in the game.
4.4 Result Summary
Presented in Table 4.4 is the summary of the result of this research, sorted by their commonness
and severity ratings subsequently, in descending order.
Table 4.4. Result Summary
Common-
Dark Pattern Average Common Usage
ness
Takes either of these forms; (1) power-ups; some provide
such a significant advantage they might as well allow the
player to skip the level/round entirely, (2) second chance;
allowing the player to continue playing even after they’re
Pay to Skip 2.12 92% supposed to lose, essentially allowing them to skip the
challenge of beating the level/round from the ground up,
and (3) delay-skipping; unique to ’Ville type of games,
allowing the player to skip the delay that entails activities
that are integral to game progression.
The core gameplay of games in the casual genre; the sim-
plistic nature of this genre makes it inevitable for the
Grinding 1.8 92%
game mechanic to grow redundant and repetitive as the
player progresses deeper into the game.
Takes either of these forms; (1) daily attendance; a reward
system for opening the game every day, (2) daily chal-
lenge; sets of challenge that changes every day that give
Playing by proportional rewards, and (3) daily draw; a randomized
1.44 88%
Appointment reward system, usually can be taken once a day. How-
ever, the rewards obtained through these means are usu-
ally just a nice bonus and not necessary to get for game
progression.
Features that enable this dark pattern exist, but none of
Social Pyra-
0.72 60% the games evaluated in this research enforce any particu-
mid Schemes
lar responsibility to the players.
Universitas Pertamina - 20Most prominent in PVP type of games, usually takes the
form of a shop that sells character upgrades that provide
competitive edge, striking an imbalance between player
Monetized Ri-
0.76 44% skill and monetary investment during the matches. Can
valries
also appear in non-PVP games, in the form of leader-
boards that spurs the players’ competitiveness, leading
them to buy aforementioned power-ups and the likes.
In the instance that it exists in a game, seeing familiar
Impersonation 0.68 28%
faces in it might spur the player to do better in the game.
Pre-Delivered Least common and severe, since it’s against the nature of
0.44 28%
Content games in the casual genre on the mobile platform.
Universitas Pertamina - 21CHAPTER V
CONCLUSION AND SUGGESTIONS
Using heuristic evaluation, this research has documented the occurence and the prevalence of
dark patterns in casual mobile games. While the evaluation process failed to unveil a new type of
dark pattern in game design, hopefully the details in which when, where, and how a particular dark
pattern is employed could give an insight on the common behavior of a certain pattern and can be used
as a basis on future discussion on ethical game development, as well as predict future game design
trends that can potentially be used for malicious purposes. The heuristic evaluation has answered the
thesis statement stated in Section 1.2, namely:
“What type of dark patterns are most commonly used in popular and profitable casual mobile
games, and what is the assumed impact on player experience in those instances?”
To which the answer is, by picking the three most common dark patterns: (1) Pay to Skip: When
the game sells various game elements that allow the players to skip some of the its core challenge,
(2) Grinding: When the game forces the players to sit through repetitive mechanics to make progress
in the game, and (3) Playing by Appointment; When the game forces the player to play it during a
specific time period, through the use of rewards or punishments.
One thing to consider on the topic of ethical issues in this research, the factors that can determine
whether a design pattern constitutes as a dark pattern are players’ gullibility and their awareness on
manipulation [4]. Once players gain enough technological and ethical literacy to protect themselves
from the effect of dark patterns, the patterns described above cease to be a dark pattern, to simply
a bad design pattern. The unethical part of dark patterns is mainly rooted on their manipulative
aspect, on how they capitalize on players’ lack of wisdom and behavioral control. Further discussion
is needed to analyze the level of literacy that a player need to reach to develop an immunity to the
patterns discussed in this research.
One last thing to note regarding the nature of dark pattern in game design is that ’difficult’ does
not equate ’dark’. Just because a game has a hard-to-master game mechanic or a challenging level
doesn’t necessarily mean that it employs a dark pattern. Making the player feel stuck to the point that
they feel the need of an external help, for example by asking a friend to help them complete a certain
part of the game may be the sign of a bad design pattern (e.g. that it does not provide the player
adequate tutorials and in-game guide), but not exactly a dark one. It’s only when these elements are
etched into the core gameplay and the players are forced to sit through them unless they take an action
that benetits the game developers (e.g. make an in-app-purchase, invite others to also play the game,
etc.) that they’re considered a dark pattern.
Given the sample size of the games evaluated in this research and the evaluators involved, it is
recommended that future research include more evaluators or include evaluators with higher qualifica-
tions in order to produce a more accurate and insightful result, as well as widen range of the subgenre
of the games that are going to be used as the research object. Lastly, the heuristic evaluation used in
this research is only one of many usability evaluation techniques out there, and future research could
use a different approach or even combine multiple alternative methodologies if additional resources
are available.
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