Me Versus Them: Exploring the Perception of Susceptibility to Persuasion in Oneself and Others in Online Gambling

Abstract

Perceived persuasiveness, an individual’s acknowledgement of the system’s influence on the self, may affect users’ response to persuasion attempts. Existing research mainly focused on systems where persuasion supports a desired behaviour, e.g., a healthy lifestyle. Studies have also considered how people perceive persuasion in themselves but not in others. In this paper, we conducted an online survey and explored users’ perception of susceptibility to persuasion in themselves and in others, taking online gambling as an example domain. We further examined how player attributes may influence their perception of susceptibility. A total of 250 participants (age range 18–75, 127 female) completed the online survey. Findings showed that susceptibility to the different persuasive design techniques differed significantly, with participants reporting the highest susceptibility to in-game rewards. Females were significantly more likely than males to report higher susceptibility to all of the persuasive design categories, and problem gamblers had higher susceptibility scores for all the persuasive design categories compared to other groups. There was a discrepancy between self-reported susceptibility scores and susceptibility scores that participants assigned to others. For each persuasive design category, participants assigned higher susceptibility scores to others compared to themselves. Moreover, the difference between self-reported susceptibility and susceptibility scores assigned to others was significantly higher for males and for all the persuasive design categories, non-problem gamblers exhibited a much greater discrepancy between the two compared to other groups. More research is required to determine whether the perception of susceptibility to persuasive design techniques is related to other individual or domain-specific factors.KeywordsPerceived PersuasivenessPersuasive Design TechniquesOnline Gambling

Full text

Me versus them: exploring the perception of
susceptibility to persuasion in oneself and others
in online gambling
Deniz Cemiloglu1( ), Emily Arden-Close1, Sarah E. Hodge1, Nan Jiang1
and Raian Ali2
1 Faculty of Science and Technology, Bournemouth University, U.K.
{dcemiloglu,eardenclose,shodge,njiang}@bournemouth.ac.uk
2 College of Science and Engineering, Hamad Bin Khalifa University, Qatar
raali2@hbku.edu.qa
Abstract. Perceived persuasiveness, an individual's acknowledgement of the system's influence
on the self, may affect users' response to persuasion attempts. Existing research mainly focused
on systems where persuasion supports a desired behaviour, e.g., a healthy lifestyle. Studies
have also considered how people perceive persuasion in themselves but not in others. In this
paper, we conducted an online survey and explored users' perception of susceptibility to per-
suasion in themselves and in others, taking online gambling as an example domain. We further
examined how player attributes may influence their perception of susceptibility. A total of 250
participants (age range 18 – 75, 127 female) completed the online survey. Findings showed that
susceptibility to the different persuasive design techniques differed significantly, with partici-
pants reporting the highest susceptibility to in-game rewards. Females were significantly more
likely than males to report higher susceptibility to all of the persuasive design categories, and
problem gamblers had higher susceptibility scores for all the persuasive design categories com-
pared to other groups. There was a discrepancy between self-reported susceptibility scores and
susceptibility scores that participants assigned to others. For each persuasive design category,
participants assigned higher susceptibility scores to others compared to themselves. Moreover,
the difference between self-reported susceptibility and susceptibility scores assigned to others
was significantly higher for males and for all the persuasive design categories, non-problem
gamblers exhibited a much greater discrepancy between the two compared to other groups.
More research is required to determine whether the perception of susceptibility to persuasive
design techniques is related to other individual or domain-specific factors.
Keywords: Perceived Persuasiveness, Persuasive Design Techniques, Online
Gambling.
1 Introduction
Persuasive systems are defined as "computerised software or information systems
designed to reinforce, change or shape attitudes or behaviours or both without using
coercion or deception" [1, p.486]. Persuasive systems are typically grouped into two
categories: behavioural change support systems (BCSS), in which users utilise tech-
nology to modify their behaviour or attitude to attain a pre-defined goal [2], and sys-
tems that utilise technology to persuade users [3]. Typical examples of BCSS applica-
tions are those that promote positive behaviours such as physical activity, personal
well-being, and energy savings [4-6]. The second category includes online platforms

2
that utilise persuasive interfaces to boost user engagement, such as social networks,
gaming, and online gambling platforms.
Typically, the effectiveness of persuasive systems is measured in terms of their
persuasiveness, which refers to a system's persuasive capacity to induce behaviour
change [7]. Perceived persuasiveness, "the individual's subjective evaluation of the
system and its impact on the self" [8, p.5], was suggested to be a significant factor in
determining users' engagement with persuasive systems [8, 9]. Various studies have
been conducted to evaluate users' self-reported susceptibility to persuasive design
techniques and how culture, age, and gender may influence their susceptibility levels
[9-13]. Current research, however, mostly centres on behavioural change support
systems, in which users employ technology intentionally to alter their behaviour to
achieve a pre-defined objective. To our knowledge, little to no research has been con-
ducted on perceived susceptibility to persuasive design techniques in which persua-
sive design is not administered by the users but rather employed by the technology
provider to persuade users to engage with online platforms, whether for legitimate or
for questionable reasons.
Persuasive interfaces intended to maximise user engagement may induce or accel-
erate psychological and cognitive mechanisms related to addictive behaviour [14].
Thus, users' perception of susceptibility to persuasive design techniques may influ-
ence how they interact with potentially addictive platforms. Those who engage in
addictive behaviours have the tendency to resort to denial (i.e., being assured that
there is no problem to be fixed) [15] or to illusory superiority cognitive bias (i.e.,
having an inflated sense of own skills relative to others) to resolve discomfort they
experience from having conflicting beliefs and actions [16]. Moreover, according to
protection motivation theory [17], an individual's self-protective behaviours in the
face of a threat are shaped by their threat appraisal (i.e., the perceived severity of the
threat, the perceived probability of the threat harming the individual, the perceived
reward linked to threat) and their coping appraisal (i.e., response efficacy, self-
efficacy, and the response costs). Accordingly, the development and maintenance of
addiction or addiction-type behaviour for the user may relate to incorrect beliefs about
the dangers associated with the behaviour or underestimating the probability of dan-
gers happening to them even when they know about the related risks [18]. Individuals
who engage with online platforms in an addictive manner may be more prone to un-
derestimating their susceptibility to persuasive design techniques compared to others,
and player characteristics may also influence this. It is argued that those who attempt
to quit an undesired behaviour would strive to mentally separate themselves from that
behaviour's stereotypical characterisation [19]. However, when individuals engage in
downward social comparison to defend their self-esteem and mood (i.e., comparing
themselves to others who they perceive are doing worse than them) [20], such dis-
tancing may be hindered. This can, in return, further reinforce the undesired behav-
iour [21].
With the growth of the online gambling industry, persuasive interfaces have be-
come a crucial component of the gambling experience. For example, online gambling
platforms reward players with casino bonuses, offer rehearsal options with demo
games and ease gambling with auto-spin functions. While debates exist on whether

3
social networks or online gaming platforms may cause addiction, gambling disorder is
recognised by DSM-5 [22] as a disorder. People with gambling disorder may face
serious consequences that can compromise their health, relationships, and finances
[23]. Studies show that erroneous beliefs (i.e., perceived skill, biased recall, supersti-
tion, incorrect perceptions of randomness) are a risk factor in online gambling and
may contribute to the increased prevalence of gambling disorder [24, 25]. Prior re-
search has indicated that people with gambling disorder tend to have more erroneous
beliefs regarding gambling than social gamblers. Also, it was reported that males have
more cognitive distortions than females when assessed by Gambling Related Cogni-
tions Scale (GRCS) [26], and higher levels of cognitive distortion was observed to be
a strong predictor of gambling disorder [27].
In this paper, we explore users' perception of susceptibility to persuasive design
techniques in themselves and in others through an online survey. We take online
gambling as an example domain where persuasion might lead users to the undesired
behaviour of problem gambling. We restrict the scope of the research to persuasive
design techniques used in games of pure chance, such as roulette, and exclude games,
such as sports betting and poker in which player engagement can be influenced by
knowledge and experience [28]. We further analyse how factors such as gender and
addiction level may influence the perception of susceptibility to persuasive design
techniques in oneself and in others.
In this study, we address the following research questions within the context of
online gambling platforms.
RQ1: How susceptible do players believe they are to persuasive design techniques?
RQ2: What are the effects of gender and gambling addiction level on players' self-
reported susceptibility to persuasive design techniques?
RQ3: Is there a difference between how susceptible people think they are to persua-
sive design techniques and how susceptible they think others are?
RQ4: What are the effects of gender and addiction level on the mismatch between
self-reported susceptibility to persuasive design techniques and susceptibility assigned
to others?
The remainder of the paper is organised as follows. In Section 2, we summarise the
research methodology, and in Section 3, we report the study results. In Section 4, we
discuss the findings and the limitations of our study.
2 Method
2.1 Participants
A total of 250 participants (age range 18 – 75, 123 male) were recruited to the online
survey through ProlificTM (www.prolific.co), an established online research partici-
pant recruitment platform. Participants who regularly bet online on slot or roulette
games in the past 12 months were recruited. Additional eligibility requirements were
being aged over 18 years, fluency in English, and residing in the U.K.

4
2.2 Questionnaire Design
The questionnaire was designed using QualtricsTM (https://www.qualtrics.com), a
web-based survey platform. The data was collected as part of a larger study. Full de-
tails of the survey and procedure are reported in [29].
In the first part of the questionnaire, participants were asked about their gambling
experience (e.g., the number of online gambling accounts and time spent on gambling
sites per week). The 9-item Problem Gambling Severity Index (PGSI) was used to
assess problem gambling severity [30, 31]. The scale includes items related to gam-
bling behaviour and experienced adverse consequences due to gambling on a 4-point
scale: 0 never; 1 = sometimes; 2 = most of the time; 3 = almost always. The standard
cut-points are 0 = non-problem gambler; 1-2 = low-risk gambler; 3-7 = moderate-risk
gambler; and 8 and more = problem gambler. The PGSI has high internal consistency
and test-retest reliability rate and is widely employed in gambling research [32-34].
For our sample, Cronbach's Alpha was 0.93, indicating excellent internal consistency.
The second part of the questionnaire presented participants with 13 persuasive de-
sign techniques used in online gambling platforms through explanation cards. Before
developing the explanation cards, we conducted a literature review to identify the
persuasive design techniques utilised in online platforms. The analysis was guided by
criteria set by the Persuasive Systems Design model (PSD) [1] and informed by
Cialdini's work on principles of persuasion [35] and McCormack and Griffiths's work
on structural and situational characteristics of internet gambling [36]. The main per-
suasive design techniques employed in online gambling platforms were identified by
analysing seven gambling websites from six different operators with the biggest mar-
ket share in the U.K. online gambling and betting market [37]. Freely accessible in-
formation on the website's homepage, casino page, slot page, roulette page, game
information sections and promotion page were analysed. Due to membership limita-
tions, we examined the game interface of one of seven online gambling platforms. We
note here that most gambling operator sites rely on similar techniques developed and
offered by the same major online gambling service providers. This analysis also guid-
ed the development of the illustrative material for the study. The 13 persuasive design
techniques included in the study are categorised in Table 1.
Table 1. Persuasive design techniques presented in the study.
Persuasive Design Technique
Definition in The Context of Online Gambling
Primary Task Support
Reduction
Persuades players to have continuous/uninterrupted interaction with the
game by reducing the effort and actions needed to gamble.
Self-Monitoring
Persuades players to interact with the game by providing the ability to
track and evaluate gambling performance.
Rehearsal
Persuades players to interact with games by providing the ability to
gamble without having to experience it in a real-world setting (i.e.,
without betting real money).
Dialogue Support
Praise
Persuades players to interact with games by expressing approval or
admiration via words, images, symbols, and sounds.
In-game Rewards
Persuades players to gamble by giving something in return when the
players perform a target behaviour set by the gambling platform.

5
Reminders
Persuades players to interact with the gambling platform by reminding
them about gambling.
Social Support
Social Norms
Persuades players to interact with the gambling platform by showing
how the majority acts.
Social Facilitation
Persuades players to interact with the gambling platform by showing
how other players are engaging in the same activity simultaneously.
Competition
Persuades players to gamble by stimulating players to compete against
themselves or each other.
System Credibility Support
Authority
Persuades players to interact with the gambling platform by promoting
statements or norms of authority figures.
Other
Scarcity
Persuades players to interact with the gambling platform by emphasis-
ing rarity and exclusivity or by underlining possible losses of missing
such an advantage.
In-game Control Elements
Persuade players to gamble by stimulating their perceived control over
betting outcomes.
Near Misses
Persuade people to gamble by implying that the win is missed margin-
ally by just a symbol and is around the corner.
The Persuasion Knowledge Model [38] was utilised as the primary reference mod-
el for determining the content of the explanation cards to demonstrate each technique.
The explanation cards contained information about the persuader's intention, tactic
and the psychological mediators associated with the persuasive technique (i.e., infor-
mation on why the technique is persuasive). The cards also provided information on
the risks of interacting with the persuasive technique, which was adopted from the
informed consent theory [39]. Prior research was utilised to derive conclusions about
how persuasive design techniques may encourage problem gambling [14]. One exam-
ple of a persuasive design technique explanation card is shown in the Appendix. In
the study, the face validity of the explanation cards was considered. The explanation
cards' completeness, validity, and clarity were evaluated by two responsible gambling
officials, four academics, and one ex-problem gambler.
2.3 Data Collection
Bournemouth University Research Ethics Committee approved the study, and the data
was collected during the first two weeks of December 2021. Participants were invited
to participate in an online survey that explored the impact of persuasive design tech-
niques used in online gambling platforms on player engagement. The link to the
anonymous survey was provided to those who met the inclusion criteria. Before
commencing the questionnaire, participants were required to read the participant in-
formation sheet and provide informed consent. Participants were informed that they
could opt out of the study at any time. After answering questions about their gambling
experience, participants were instructed to read each explanation card carefully and
answer questions for each persuasive design technique. On a 5-point Likert scale (1 =
extremely unlikely, and 5 = extremely likely), participants were asked how much they
thought they could be influenced by the persuasive design technique and how much
they thought the same persuasive design technique could influence others. To lessen
the effects of fatigue and habituation, the 13 persuasive design technique explanation
cards were shown in a random order [40]. Eligible participants were compensated for
their participation.

6
2.4 Data Analysis
Data was analysed using SPSS version 28. Non-parametric tests were used as the data
was not normally distributed. Friedman test was used to explore the differences in
susceptibility to different persuasive design techniques. Pairwise comparisons were
performed with a Bonferroni correction for multiple comparisons. Mann-Whitney's U,
Kruskal-Wallis H and Wilcoxon signed-rank tests were used on ordinal data to ana-
lyse group differences [41].
3 Results
3.1 Participant Demographics
Table 2 summarises participant characteristics.
Table 2. Participant characteristics.
N
250
Education (%)
Age: M(SD)
36 (10.4)
Compulsory school education completed
14.8
Age: Range
18 – 75
Vocational training
6
Gender: Males (%)
123 (49.2)
College
23.6
Females (%)
125 (50)
University degree
40.4
Gambling Days Per Week: M(SD)
2.8 (1.9)
Postgraduate qualification (e.g., MSc, PhD)
15.2
Number of Online Gambling Accounts (%)
Employment (%)
1 account
9.6
Full-time employment
62.4
2 accounts
23.6
Part-time employment
14.4
3 accounts
23.2
Self-employed
6
4 accounts
7.2
Unemployed
2.8
5 accounts
5.6
On sick leave
1.6
6 or more accounts
30.8
Student
5.6
Problem Gambling Severity Index (%)
Retired
0.4
Non-problem gambler
17.6
Homemaker
6
Low-risk gambler
25.6
Other
0.8
Moderate-risk gambler
29.2
Problem gambler
27.6
3.2 RQ1: Players' Self-reported Susceptibility to Persuasive Design
Categories and Techniques
Participants were asked how much they thought they could be influenced by persua-
sive design techniques with a 5-point Likert scale (1 = extremely unlikely, and 5 =
extremely likely). The overall self-reported mean susceptibility scores for the persua-
sive design categories and standard deviations are displayed in Figure 1. A Friedman
test showed that susceptibility to persuasive design categories differed significantly
between categories, χ2(4) = 305, p < .001. Significance was set at p = 0.005 using a
Bonferroni correction as we conducted multiple tests. Post hoc analysis revealed that
susceptibility to the dialogue support category (M:3.8, SD:0.8) was significantly
higher than susceptibility to the other persuasive design categories, and susceptibility

7
to system credibility support (M:2.6, SD:1.2) was significantly lower than susceptibil-
ity to the other persuasive design categories.
Figure 1. Mean score for susceptibility to each persuasive design category
As shown in Figure 2, when examined individually, out of the 13 persuasive design
techniques presented in the study, participants mainly reported susceptibility to in-
game rewards (M:4.2, SD:0.9), reminders (M:3.9, SD:1.0) and near misses (M:3.4,
SD:1.3). In contrast, participants reported the lowest susceptibility to social norms
(M:2.9, SD:1.2), competition (M:2.9, SD:1.3) and authority (M:2.6, SD:1.2). A
Friedman test revealed that susceptibility to persuasive design techniques differed
significantly by technique, χ2(12) = 528, p < .001. Significance was set at p = 0.0009
using a Bonferroni correction as we conducted multiple tests. Susceptibility to in-
game rewards was significantly higher than other persuasive design techniques except
reminders. Susceptibility to authority was significantly lower than other persuasive
design techniques except self-monitoring, social norms, and competition.
Figure 2. Mean score for susceptibility to each persuasive design technique

8
3.3 RQ2: Effect of Gender and Addiction Level on Players' Self-reported
Susceptibility
3.3.1 Gender Effect
Mann-Whitney U-test was used to determine gender differences in self-reported play-
er susceptibility to persuasive design categories presented in the study. The compari-
son between females and males is shown in Table 3. Females were significantly more
likely than males to report higher susceptibility to all of the persuasive design catego-
ries.
Table 3. Gender differences concerning susceptibility to each persuasive design category.
Gender
Females
Males
Mann-
Whitney's
U
Z
P
Mean
Rank
Sum of
Ranks
Mean
Rank
Sum of
Ranks
Primary Task Support
138.0
17245.5
110.8
13630.5
6004.5
-3.0
0.003
Dialogue Support
136.4
17048.5
112.4
13827.5
6201.5
-2.7
0.008
Social Support
140.7
17584.0
108.1
13292.0
5666.0
-3.6
< .001
System Credibility Support
135.1
16891.5
113.7
13984.5
6358.5
-2.4
0.015
Other
139.6
17452.0
109.1
13424.0
5798.0
-3.4
< .001
When examined individually, Mann-Whitney U-test revealed that females were
significantly more likely than males to report susceptibility to self-monitoring (p =
< .001), praise (p = < .001), social norms (p = < .001), social facilitation (p = 0.004 ),
competition (p = 0.01 ), authority (p = 0.01), scarcity (p = 0.006), in-game control
elements (p = 0.01 ), and near miss technique (p = < .001).
3.3.2 Problem Gambling Severity Effect
The Kruskal-Wallis H test was used to determine differences in self-reported player
susceptibility to persuasive design categories by problem gambling severity. Pairwise
comparisons were performed using Dunn's procedure [42]. Significance was set at p =
0.008 using the Bonferroni correction for multiple tests. As indicated in Table 4, post
hoc analyses revealed statistically significant differences between PGSI groups with
regard to susceptibility to persuasive design categories. Problem gamblers had higher
susceptibility mean ranks for all the persuasive design categories.
Table 4. Problem gambling severity difference concerning susceptibility to
each persuasive design technique.
Persuasive Design Category
Mean Ranks
Dunn's Pairwise Comparison
(adj. p-value)
Primary Task Support
A. Non-problem gambler
115.3
B. Low-risk gambler
103.2
B-D
0.004
C. Moderate-risk gambler
131.7
D. Problem gambler
146.1
Kruskal-Wallis H Test
χ2 (3) = 13.3, p = 0.004
Dialogue Support

9
A. Non-problem gambler
104.4
A-D
0.005
B. Low-risk gambler
103.1
B-D
< 0.001
C. Moderate-risk gambler
136.3
D. Problem gambler
148.3
Kruskal-Wallis H Test
χ2 (3) = 18.8, p = < 0.001
Social Support
A. Non-problem gambler
112.8
A-D
0.005
B. Low-risk gambler
93.9
B-D
< 0.001
C. Moderate-risk gambler
128.9
D. Problem gambler
159.3
Kruskal-Wallis H Test
χ2 (3) = 29.2, p = < 0.001
System Credibility Support
A. Non-problem gambler
98.7
A-D
< 0.001
B. Low-risk gambler
98.8
B-D
< 0.001
C. Moderate-risk gambler
134.1
D. Problem gambler
158.2
Kruskal-Wallis H Test
χ2 (3) = 31.8, p = < 0.001
Other
A. Non-problem gambler
102.6
A-D
0.001
B. Low-risk gambler
108.5
B-D
0.001
C. Moderate-risk gambler
124.6
D. Problem gambler
156.8
Kruskal-Wallis H Test
χ2 (3) = 21.2, p = < 0.001
Non-problem gambler (n:44), Low-risk gambler (n:64), Moderate-risk gambler (n:73), Problem
gambler (69).
The test statistic is adjusted for ties.
When examined individually, problem gamblers had the highest susceptibility
mean ranks across all persuasive design techniques except rehearsal and reminder.
Moderate-risk gamblers had the highest susceptibility mean ranks for rehearsal and
reminder techniques.
3.4 RQ3: Self-reported Susceptibility vs Susceptibility Assigned to Others
Participants were asked how much they thought they could be influenced by the per-
suasive design technique and how much they thought the same persuasive design
technique could influence others with a 5-point Likert scale (1 = extremely unlikely,
and 5 = extremely likely). A Wilcoxon signed-rank test was conducted to compare
participants' self-reported susceptibility to persuasive design categories and how they
perceive susceptibility in other players. As shown in Table 5, for all persuasive design
categories, there was a statistically significant difference between the self-reported
susceptibility scores and the susceptibility scores they assigned to others. For each
persuasive design category, most participants assigned higher susceptibility scores to
others compared to themselves. Thus, participants assigned greater susceptibility to
persuasive design categories in other players.
Table 5. Self-reported susceptibility versus perceived susceptibility of others
to persuasive design categories.
Persuasive Design Category
N
Mean Ranks
Sum of Ranks
Z
P
Primary Task Support
Negative Ranks
12a
28
336.5
-10.614x
< .001
Positive Ranks
152b
86.8
13193.5
Ties
86c

10
Total
250
Dialogue Support
Negative Ranks
44d
48.6
2140.5
-10.366x
< .001
Positive Ranks
170e
122.7
20864.5
Ties
36f
Total
250
Social Support
Negative Ranks
19g
41.3
785
-11.125x
< .001
Positive Ranks
175h
103.6
18130
Ties
56i
Total
250
System Credibility Support
Negative Ranks
6j
42
252
-10.409x
< .001
Positive Ranks
144k
76.9
11073
Ties
100l
Total
250
Other
Negative Ranks
29m
53.4
1549
-10.334x
< .001
Positive Ranks
169n
107.4
18152
Ties
52o
Total
250
a. Primary Task_Others < Primary_Task_Me
b. Primary_Task_Others > Primary_Task_Me
c. Primary_Task_Others = Primary_Task_Me
d. Dialogue_Support_Others < Dialogue_Support_Me
e. Dialogue_Support_Others > Dialogue_Support_Me
f. Dialogue_Support_Others = Dialogue_Support_Me
g. Social_Support_Others < Social_Support_Me
h. Social_Support_Others > Social_Support_Me
i. Social_Support_Others = Social_Support_Me
j. System Credibility_Support_Other < Credibility_Support_Me
k.System Credibility_Support_Other > Credibility_Support_Me
l. System Credibility_Support_Other = Credibility_Support_Me
m. Other_Others < Others_Me
n. Other_Others > Others_Me
o. Other_Others = Others_Me
x. Based on negative ranks
3.5 RQ4: Effect of Gender and Addiction Level on the Mismatch Between
Self-reported Susceptibility and Susceptibility Assigned to Others
3.5.1 Gender Effect
Mann-Whitney U-test was used to determine the gender effect concerning the dif-
ference between the self-reported susceptibility scores and susceptibility scores as-
signed to others. As shown in Table 6, the difference between self-reported suscepti-
bility and susceptibility scores assigned to others was significantly different between
males and females for three of the categories.
Table 6. Gender differences regarding the mismatch between susceptibility scores
(me versus others).
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Primary Task Support
7203.0
14829.0
-0.9
0.379

11
Dialogue Support*
6201.5
13827.5
-2.7
0.008
Social Support
6942.0
14568.0
-1.3
0.182
System Credibility Support*
6396.0
14022.0
-2.4
0.016
Other*
6092.5
13718.5
-2.9
0.004
Table 7 shows the difference between the self-reported susceptibility scores and
susceptibility scores assigned to others. For dialogue support, system credibility sup-
port, and the other category, males had a significantly higher mismatch in their per-
ception than females.
Table 7. Gender differences regarding self-reported susceptibility versus
susceptibility assigned to others (Mean score).
Gender
Male
Female
Self
Others
Difference
Self
Others
Difference
Primary Task Support
2.9
3.5
0.7
3.2
3.8
0.5
Dialogue Support*
3.6
4.3
0.6
4
4.3
0.4
Social Support
2.8
3.6
0.9
3.3
4
0.7
System Credibility Support*
2.4
3.5
1.1
2.8
3.6
0.8
Other*
3.1
4
0.9
3.6
4.1
0.5
*Significant at the 0.05 level
Female (n:125), Male (n:123).
When examined individually, out of the 13 persuasive design techniques presented
in the study, the difference between self-reported susceptibility and susceptibility
scores assigned to others was significantly higher for males for praise (p = < .01),
social norms (p = 0.003), authority (p = 0.01), scarcity (p = < 0.001), and near misses
(p = 0.001).
3.5.2 Problem Gambling Severity Effect
Kruskal-Wallis H test was used to determine differences between the self-reported
susceptibility scores and susceptibility scores assigned to others by problem gambling
severity. Pairwise comparisons were performed using Dunn's procedure [42]. Signifi-
cance was set at p = 0.008 using Bonferroni correction for multiple tests. As shown in
Table 8, the difference between self-reported susceptibility and susceptibility scores
assigned to others was significantly different between PGSI groups for all persuasive
design categories.
Table 8. Problem gambling severity difference regarding the mismatch between
susceptibility scores (me versus others).
Kruskal-Wallis H
df
Asymp. Sig.
Primary Task Support*
17.6
3
0.001
Dialogue Support*
20.5
3
< 0.001
Social Support*
27.7
3
< 0.001
System Credibility Support*
14.6
3
0.002
Other*
22.8
3
< 0.001

12
Table 9 shows the difference between self-reported and susceptibility scores as-
signed to others. For all the persuasive design categories, the difference between self-
reported susceptibility and susceptibility scores assigned to others was higher for non-
problem gamblers and low-risk gamblers compared to other groups.
Table 9. Problem gambling severity difference regarding self-reported susceptibility versus
susceptibility assigned to others (Mean score).
PGSI
Non-problem gambler
Low-risk gambler
Moderate-risk gambler
Problem gambler
Self
Others
Difference
Self
Others
Difference
Self
Others
Difference
Self
Others
Difference
Primary Task Support*
2.9
3.8
0.9
2.8
3.5
0.7
3.1
3.7
0.6
3.3
3.6
0.3
Dialogue Support*
3.5
4.3
0.8
3.5
4.3
0.7
3.9
4.3
0.4
4.1
4.3
0.3
Social Support*
2.8
3.8
1.0
2.6
3.8
1.2
3.1
3.8
0.7
3.5
3.9
0.4
System Credibility
Support*
2.1
3.5
1.3
2.1
3.3
1.1
2.8
3.6
0.9
3.2
3.7
0.5
Other*
3.0
4.0
1.0
3.1
3.9
0.8
3.4
4.1
0.7
3.8
4.1
0.4
Non-problem gambler (n:44), Low-risk gambler (n:64), Moder-
ate-risk gambler (n:73), Problem gambler (69).
When examined individually, the difference between self-reported susceptibility
and susceptibility scores assigned to others was higher for non-problem gamblers for
all 13 persuasive design techniques presented in the study compared to other groups.
4 Discussion
In the present study, we explored players' perception of susceptibility to persuasive
design techniques in oneself and others by taking online gambling as an example
domain.
With respect to self-reported susceptibility to persuasive design techniques, our find-
ings showed that susceptibility to the dialogue support category was significantly
higher, with players reporting the highest susceptibility to in-game rewards. This find-
ing contradicts earlier research suggesting that reward is the least effective persuasive
design technique in the health domain after customisation [43]. Such a difference in
the findings may be attributable to domain differences since extrinsic motivation
could be more associated with gambling [44], whereas intrinsic motivation could be
more associated with having a healthy lifestyle [45]. Also, people who gamble online
may be more exposed to in-game rewards such as cash bonuses and free spins. Re-
garding gender effect, the findings suggested that females were significantly more
likely than males to demonstrate higher susceptibility to all persuasive design catego-
ries. Previous studies also reported that males and females varied greatly in their per-
suadability, with females being more susceptible to the majority of persuasive design
techniques [43, 46]. In line with previous research, the difference between genders
may be owing to the effect of conventional gender norms, in which women think they
must adopt a more submissive role (i.e., adaptive, receptive to influence) and men
think they must adopt a more dominant role (i.e., rigid, resistance to change) [47, 48].
Concerning the influence of problem gambling severity, problem gamblers reported
higher susceptibility to all the persuasive design categories compared to other PGSI

13
groups. It has been suggested that people are more susceptible to influence when they
have conflicting goals, are under stress, cannot resist their urges and lack self-control
[49]. Because people with high problem gambling severity show the aforementioned
characteristics, their high susceptibility scores could be related to these constructs.
Also, people who are forthcoming about the problematic nature of their gambling
behaviour may also be more honest about their persuasion vulnerability, resulting in
high susceptibility scores. Another interpretation could be problem gamblers empha-
sising their high persuadability as a means of justifying their problematic relationship
with gambling.
In terms of the mismatch between self-reported susceptibility and susceptibility as-
signed to others, participants assigned higher susceptibility scores to others than to
themselves for each persuasive design category. Individuals' underestimation of their
own vulnerability to online phishing attempts is comparable with the findings of this
study [50, 51]. People may have this mismatch in perception as a result of denial and
self-deception since they may denigrate others in order to maintain their self-image
[11, 52]. Regarding gender effect, males had a significantly higher mismatch than
females for dialogue support, system credibility support, and the other category. One
explanation for this difference could be the confidence gap [53]. Research shows that
although there is no discernible qualitative difference between male and female per-
formances, males tend to exaggerate their abilities and performance, while women
tend to underestimate them [54, 55]. Lastly, findings showed that for all the persua-
sive design categories, non-problem gamblers had a significantly higher mismatch
between the self-reported susceptibility scores and susceptibility scores assigned to
others compared to other PGSI groups. This finding contradicted previous studies
which claimed that misperception of chance and probability are predictors of problem
gambling [24, 25, 27].
We identified issues that may affect the validity of the study and must be consid-
ered when evaluating the findings. The study measured the perception of susceptibil-
ity to persuasive design techniques in oneself and others by self-report. As mentioned
in [56], perceived persuasiveness may not always predict actual engagement with
persuasive design techniques. Also, due to the use of a Likert scale to measure per-
ceived persuasiveness in the current study, participants did not have the option to pick
"zero influence" when reporting susceptibility. Therefore, the study does not report an
exact score of persuasiveness. Future research could employ perceived persuasiveness
scales such as the four-item scale for measuring persuasiveness [57] or the 15 items
Persuasive Potential Questionnaire (PPQ) [58]. In addition, future studies might ex-
amine the relationship between perceived and actual susceptibility to persuasive de-
sign techniques utilised in potentially addictive technologies. The generalisability of
the findings may have been affected as only participants from the United Kingdom
were recruited. Future research needs to explore how the player's perception of sus-
ceptibility to persuasive design techniques may vary by cultural context. For example,
it has been suggested that people from collectivist cultures may be more prone to
social support techniques than those from individualist cultures because the former
places a higher importance on group membership [59]. In exploring the perception of
susceptibility to persuasive design techniques, online gambling was selected as an

14
example domain. The gambler profile may not be indicative of the broader user sus-
ceptibility to persuasive systems in other domains. Future research should examine
susceptibility to persuasive design techniques in other domains that utilise persuasive
interfaces, such as social networks or online gaming.
5 Conclusion
The findings of this study increase awareness about the perception of susceptibility to
persuasive interfaces, how player attributes may influence this, and what can be done
to mitigate this impact. It is currently unknown if erroneous beliefs regarding addic-
tion contribute to the onset of addictive behaviour or is a by-product of such activity
[55, 60]. Thus, applying social-norms interventions and correcting the perception
regarding the influence of persuasive design techniques may serve as both a preven-
tive and corrective approach in the domain of addictive technologies. Moreover, by
taking a different approach, persuasion profiling [61] might be utilised to identify
vulnerable user groups that show high susceptibility to certain persuasive design tech-
niques. By such profiling, vulnerable users could be given the opportunity to opt out
of persuasive design techniques. More research is required to investigate if the per-
ception of susceptibility to persuasive design techniques may be associated with fac-
tors other than gender and addiction level, such as cognitive capacity [9] or personali-
ty qualities [50, 62].
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Appendix
Figure 3. Example persuasive design technique explanation card

20
Tables and Figures
The detailed tables and figures used in the analysis will be provided upon request.